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i
International Journal of Scientific Research in
Computer Science, Engineering and Information
Technology
ISSN : 2456-3307
Volume 1, Issue 1, July-August-2016
International Peer Reviewed, Open Access Journal
Bimonthly Publication
Published By
Technoscience Academy
(The International Open Access Publisher)
Email: [email protected]
Website: www.technoscienceacademy.com
ii
Advisory/Editorial Board
Prof. Shardul Agravat
Information Technology, C U Shah Unuiversity, Surendranagar, Gujarat, India
Prof. Vaishali Kalaria
Information Technology, RKU, Rajkot, Gujarat, India
Prof. H. B. Jethva
Computer Enginering, L. D. College of Engineering, Ahmedabad, Gujarat, India
Prof. Bakul Panchal
Information Technology, L. D. College of Engineering, Ahmedabad, Gujarat, India
Prof. Bhavesh Prajapati
Computer Science, Government MCA College Maninagar, Ahmedabad, Gujarat, India
Dr. Syed Umar
Computer Science and Engineering, KL University, Guntur, Andhra Pradesh, India
Prof. S. Jagadeesan
Computer Science, Nandha Engineering College Erode, Tamil Nadu, India
Prof. Joshi Rahul Prakashchandra
Information Technology, Parul Institute of Engineering & Technology, Vadodara, Gujarat,
India
Dr. Aftab Alam Tyagi
Department of Mathematics, SRM University NCR Campus, Uttar Pradesh, India
Prof (Dr.) Umesh Kumar
Department of Science & Technology, Govt. Women’s Polytechnic, Ranchi, Jharkhand, India
Dr. N. Pughazendi
Computer Science and Engineering, Panimalar Engineering College Chennai, Tamilnadu,
India
Sachin Narendra Pardeshi
Department of Computer Engineering, R.C.Patel Institute of Technology, Shirpur,
Maharashtra, India
Dr. Bangole Narendra Kumar
Department of Computer Science and Systems Engineering, Sree Vidyanikethan
Engineering College, Tirupati, Andhra Pradesh, India
Dr. Dhananjaya Reddy
M.Sc.(Maths),M.Sc.(Stat), M.Phil.(Maths), M.phil. (Statistics), B.Ed., PhD Department of
Mathematics,Government Degree College, Puttur, Chittoor, Andhra Pradesh, India
iii
Dr. Ajitesh Singh Baghel
Department of Computer Science, Awadhesh Pratap Singh University, Rewa, Madhya
Pradesh, India
Prof. Sarita Dhawale
Ashoka Center for Business & Computer Studies, Ashoka Marg, Ashoka Nagar, Nashik,
Maharashtra, India
International Advisory/Editorial Board
AbdulGaniyu Abdu Yusuf
Computer Science, National Biotechnology Development Agency (NABDA), Abuja, Nigeria
Dr. M. A. Ashabrawy
Computer Science and Engineering, Prince Sattm bin Abdulaziz University, Kingdom Saudi
Arabia(KSA), Saudi Arabia
Dr. V. Balaji
Bahir Dar University, Bahir Dar, Ethiopia
Lusekelo Kibona
Department of Computer Science, Ruaha Catholic University (RUCU) , Iringa, Tanzania
Md. Amir Hossain
IBAIS University/Uttara University, Dhaka, Bangladesh
Mohammed Noaman Murad
Department of Computer Science, Cihan University Erbil, Kurdistan Region, Iraq
Prof. Dr. H. M. Srivastava
Department of Mathematics and Statistics, University of Victoria, Victoria, British Columbia,
Canada
Prof. Sundeep Singh
Mississauga, Ontario, Canada
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CONTENTS
Sr.
No Article/Paper Page No
1 A Case Study on Mobile Adhoc Network Security for Hostile
Environment Dr. D. Devi Aruna, Dr. D.Vimal Kumar
01-06
2 A Qualitative Comparison of Various Routing Protocols in WSN Kabeer Khan, Abdul Waris, Hamayun Safi
07-13
3 Detecting BOT Victim in Client Networks Abinaya. E, Balamurugan. K
14-18
4 A New Approach for Transistor-Clamped H-Bridge Multilevel Inverter
with voltage Boosting Capacity Suparna Buchke, Prof. Kaushal Pratap Sengar
19-23
5 An Improved Performance of Greedy Perimeter Stateless Routing
protocol of Vehicular Adhoc Network in Urban Realistic Scenarios Ritesh Gupta, Parimal Patel
24-29
6 A Brief Survey of Acoustic Wireless Sensor Network Mansoor Ullah, Abbas Khan, Muhammad Adil
30-34
7 A Survey on Secure Cloud Storage with Techniques Like Data
Deduplication and Convergent Key management P. Balasubhramanyam Reddy, G. Nagappan
35-39
8 Emergency Information Access using QR Code Technology in Medical
Field P. Deepika, Sushanth. B , Tarun Kumar. S. P, Vignesh. M
40-43
9 Improving Classifier Performance Using Feature Selection with
Ensemble Learning Bhavesh Patankar, Dr. Vijay Chavda
44-48
10 The Use of Wireless Sensor Networks for Forest Fire Monitoring - A
Survey Mehwish Zaheer, Rabia Riaz, Shakeeb Ahmad
49-53
10 Address Allocation Algorithm with Cooperative Communication in
MANET Parameswaran T, Dr.Palanisamy C, Logeshwari N
54-59
12 A Survey on WSN-based Forest Fire Detection Techniques Waqas Ali, Abdullah, Ishfaq-ur-rashid
60-65
13 Congestion Detection and Mitigation Protocols for Wireless Sensor
Networks Muhammad Zeeshan, Fazlullah Khan, Syed Roohullah Jan
66-71
CSEIT16111 | Received: 13 July 2016 | Accepted: 24 July 2016 | July-August 2016 [(1)1: 01-06]
International Journal of Scientific Research in Computer Science, Engineering and Information Technology
© 2016 IJSRCSEIT | Volume 1 | Issue 1 | ISSN : 2456-3307
1
A Case Study on Mobile Adhoc Network Security for Hostile Environment
Dr. D. Devi Aruna, Dr. D.Vimal Kumar
Department of Computer Science, Nehru Arts and Science College, Coimbatore, Tamil Nadu, India
ABSTRACT
A mobile adhoc network (MANET) is a peer to peer wireless network where nodes can communicate with each
other without infrastructure. Due to this nature of MANET; it is possible that there could be some malicious and
selfish nodes that try to compromise the routing protocol functionality and makes MANET vulnerable to Denial of
Service attack in military communication environments. Hence security is an important challenge while deploying
MANET. This research effort examines the case study for a Layerwise Security (LaySec) framework that provides
security for an ad-hoc network operating in a military environment. LaySec incorporates three security features
(Secure neighbor authentication and Layerwise Security techniques and multipath routing) into its framework while
maintaining network performance sufficient to operate in hostile environment. layerwise security protocol has been
implemented and simulated on Qualnet 5.0. Based on the simulation result, it is observed that the proposed approach
has shown better results in terms of Quality of Service parameters like Average packet delivery ratio, Average
throughput, Average end to end delay and Routing Overhead.
Keywords : Mobile Adhoc Network, Layer Wise Security Protocols, Denial of Service Attack.
I. INTRODUCTION
Recent years Mobile ad hoc Networks start gaining
attention from the industrial and academic research
community due to their wide deployment and inherent
nature of solving practical real world
applications[1][4].The ease of deployment without the
existing infrastructure makes ad hoc networks an
attractive choice for dynamic situations such as
military operations, disaster recovery, and so forth.
Especially, military communication environments
have been considered as one of the original
motivations for MANET, due to the need for
battlefield survivability and rapid deployment of self-
organizing mobile infrastructure. This research work
evaluates the case study for mobile adhoc network
with concentration to defend against Denial of Service
attack in MANET layers. A military case study
scenarios is introduced: the scenario modifies its
channel and physical layer settings for army military
devices in an unknown and unstable MANET military
environment system with concentration to defend
against Denial of Service attack[2].
The paper is organized in such a way that Chapter 2
discusses Review of Literature, Chapter 3 discusses
problem statement,, Chapter4 discusses proposed
method, Chapter5 discusses Experimental evaluation
and Chapter6gives the conclusion.
II. METHODS AND MATERIAL
2. Review of Literature
2.1 Denial of Service attack
This chapter briefly describes the Denial of Service
Attacks for MANET.
An attacker attempts to avoid authorized and
legitimate users from the services offered by the
network. The typical way is to flood packets to any
centralized resource present in the network so that the
resource is no longer available to nodes in the network,
as a result of which the network no longer operate in
the manner in which it is designed to operate. This
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com 2
may lead to a failure in the delivery of guaranteed
services to the end users. DoS attacks can be launched
against any layer in the network protocol stack. On the
physical and MAC layers, an adversary could employ
jamming signals which disrupt the ongoing
transmissions on the wireless channel. On the network
layer, an adversary could take part in the routing
process and exploit the routing protocol to disrupt the
normal functioning of the network. For example, an
adversary node could participate in a session but
simply drop a certain number of packets, which may
lead to degradation in the QoS being offered by the
network. On the higher layers, an adversary could
bringdown critical services such as the key
management service. For example, consider the
following: In figure1 assume a shortest path that exists
from S to X and C and X cannot hear each other, that
nodes B and C cannot hear each other, and that M is a
malicious node attempting a denial of service attack.
Suppose S wishes to communicate with X and that S
has an unexpired route to X in its route cache. S
transmits a data packet towards Xwith the source route
S --> A --> B--> M --> C --> D--> X contained in the
packet’s header. When M receives the packet, it can
alter the source route in the packet’s header, such as
deleting D from the source route. Consequently, when
C receives the altered packet, it attempts to forward
the packet toX. Since X cannot hear C,the
transmission is unsuccessful [2][3].
S ↔A↔ B↔ M ↔C↔ D↔ X
Equation 1 : Denial of Service attack
3. Problem Statement
This research investigates how to integrate security
policies of a MANET with secure neighbor
authentication that will allow the MANET to function
securely in a military environment without degrading
network performance. The specific problem to be
addressed is how to use secure neighbor authentication
of nodes in a multipath routing algorithm in MANET
protected from Denial of service attack in military
environment. Most of such performance analysis are
normally done on commercial settings. For instance,
wireless LAN technologies in the 2.4 GHz ISM
frequency band are generally assumed, offering data
rates up to 2 Mbps within the range of 250 m. This
paper is motivated by the observation that such
propagation and network models assumed by the
current ad hoc networking simulations are quite
different from real world military environments. In
fact, a few hundred MHz frequency band (i.e., VHF or
even HF) is used with very low data transmission rates
(e.g., 384 Kbps) for the military scenarios
4. Proposed Methodology
This approach aims in improving the performance in
terms of QoS characteristics as metrics. The
methodology is proposed in order to assure Layerwise
security for Mobile Ad hoc Networks. The specific
contributions are structured in six phases.
Phase I. Integration of SNAuth with SPMAODV
Phase II. SNAuth-SPMAODV with SIP for
Application and Network layer Security
Phase III. SNAuth-SPMAODV with WTLS for
Transport and Network Layer Security
Phase IV. SNAuth-SPMAODV with IPSec for
Network Layer Security
Phase V. SNAuth-SPMAODV with CCMP-AES for
Link and Network Layer Security
Phase VI. SNAuth-SPMAODV with DSSS for
Physical and Network Layer Security
Integration of SNAuth with SPMAODV SPMAODV
provides multiple paths between sender and receiver
nodes that can be used to offset the dynamic and
unpredictable configuration of ad-hoc networks. They
can also provide load balancing by spreading traffic
along multiple routes, fault-tolerance by providing
route resilience, and higher aggregate bandwidth. The
proper selection of routes using a strict-priority
multipath protocol can increase further the network
throughput. The main idea of this phase to integrate
strict priority multipath AODV with secure neighbor
authentication that facilitate neighboring nodes
exchange messages to discover and authenticate each
other. Thus this phase provides security mechanism
like message integrity, mutual authentication, and non-
repudiation; defend against Denial of Service attacks
and increase network throughput.
SNAuth-SPMAODV with SIP for Application and
Network layer Security Secure Neighbor
Authentication Strict Priority Multipath Ad hoc On-
demand Distance Vector Routing) with Session
Initiation Protocol (SIP) provides application layer and
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com 3
network layer security and it is robust against Denial
of Service attack. It reduces dependency on single
nodes and routes; it discovers multiple paths between
sender and receiver nodes and it has the advantages of
a multipath protocol without introducing extra packets
into the network offering robustness in a secured
MANET. It can be used to offset the dynamic and
unpredictable configuration of adhoc networks. They
can also provide load balancing by spreading traffic
along multiple routes, fault-tolerance by providing
route resilience, and higher aggregate bandwidth in
hostile environment [15].
SNAuth-SPMAODV with WTLS for Transport and
Network Layer Security The primary focus of this
phase is to provide transport layer security for
authentication, securing end-to-end communications
through data encryption and to provide security
services for both routing information and data message
at network layer. It also handles delay and packet loss.
The proposed model combines SNAuth-SPMAODV
Routing with Wireless Transport Layer Security
(WTLS) to defend against Denial of Service (DoS)
attack and it also provides authentication, privacy and
integrity of packets in routing, end-to-end
communications through data encryption, packet loss
and transport and network layers of MANET [14].
SNAuth-SPMAODV with WTLS is found to be a
good security solution even with its known security
problems[9].
SNAuth-SPMAODV with IPSec for Network Layer
Security Secure Neighbor Authentication Strict
Priority Multipath Ad hoc On-demand Distance
Vector Routing) with IPSec is robust against Denial of
Service attack and it also provides security services for
both routing information and data message at network
layer in MANET.The proposed method uses a hybrid
version of the IPSec protocol, which includes both AH
and ESP modes. IPSec is a protocol suit for securing
IP based communication focusing on authentication,
integrity, confidentiality and support perfect security
forward. The significant importance of the
aforementioned protocol is that it offers flexibility,
which cannot be achieved at higher or lower layer
abstractions in addition to the symmetric
cryptographic schemes [11]. These are 1000 times
faster than asymmetric cryptographic schemes, a fact
that makes IPSec appropriate to be used in handheld
resources constrained devices such as PDAs. SNAuth-
SPMAODV with CCMP-AES for Link and Network
Layer Security.
SNAuth-SPMAODV combines with CCMP-AES
model to defend against Denial of Service attack and it
provide confidentiality and authentication of packets
in both network and data link layers of MANETs[2].
The primary focus of this phase is to provide security
mechanisms applied in transmitting data frames in a
node-to node manner through the security protocol
CCMP-AES working in data link layer. It keeps data
frame from eavesdropping, interception, alteration, or
dropping from unauthorized party along the route from
the source to the destination.
SNAuth-SPMAODV with DSSS for Physical and
Network Layer Security SNAuth-SPMAODV
combines with DSSS to defend against Denial of
Service attack. The physical layer protocol in
MANETs is reliable for bit-level transmission between
network nodes and network layer is responsible to
provide security services for both routing information
and data message [10]. The proposed model combines
SNAuth-SPMAODV routing protocol and spread
spectrum technology Direct Sequence Spread
Spectrum (DSSS) to defend against signal jamming
denial-of-service attacks in physical layer and network
layer for MANET.
III. RESULTS AND DISCUSSION
A. Experimentation and Evaluation
Using the QualNet network simulator [7],
comprehensive simulations are made to evaluate the
protocol. Qualnet provides a scalable simulation
environment for multi-hop wireless ad hoc networks,
with various medium access control protocols such as
CSMA and IEEE 802.11. channel and physical layer
settings are modified to apply more realistic military
scenarios. Note that PRC 999K device is used as a
reference model. 802.11 DCF and UDP protocols are
used for MAC and a transport protocols, respectively.
Also, CBR traffic is utilized in the study. As the TCP
based application protocols such as telnet or FTP show
unstable performance in mobile wireless
communication, it cannot evaluate precise
performance of routing protocol itself. CBR
application model sends one packet per second, which
represents relatively low traffic patterns in military
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com 4
environments. Each packet size is 512 Bytes. In
military environments, operational network size is
very large as compare to conventional case. Nodes in
the simulation are assumed to move according to the
“random way point” mobility model. Pause time is
fixed to 20 seconds. The attackers are positioned
around the center of the routing mesh in all
experiments. To evaluate the performance of proposed
method by 4 measurements: Packet delivery radio,
average end-to-end delay, routing overhead and
Throughput.This simulated environment is defined by
the following parameters as shown in Table 1 and
Table 2.
Table 1: Simulation Metrics of Laysec Framework for
Military Scenario
Table 2: Physical Layer Model for Hostile
Environments
Parameters Military devices
Frequency 30-300 MHz
Propagation limits -120 dBm
Radio propagation
model
Two-Ray
Data rates 200 Kbps
Transmit power 45 dBm
Receive sensitivity -150 dBm
Reference model PRC-999K device
B. Performance Evaluation
The performance analysis of Layerwise security
framework with SNAuth-SPMAODV has been
conducted using the simulation setup for Hostile
Environment as outlined in Table 2 and 3. The
simulation scenarios consist of different network
density or size is assessed by deploying a different
number of mobile nodes over a space of 1500m x
1500m.
Average Packet Delivery Ratio (PDR)
In Figure 1, the Average Packet Delivery Ratio of
AODV, SNAuth-SPMAODV and Layerwise Security
Framework with SNAuth-SPMAODV for different
network sizes of 100 to 600 nodes are placed in a
topology area of 1500m x 1500m. Packet delivery
ratio shows how successfully a protocol performs
delivering packets from source to destination.
Figure 1 : Average Packet Delivery Ratio
Average Throughput
Figure 2 shows the network throughput is the average
rate of successful message delivery over a
communication channel.
Figure 2 : Average Throughput Ratio
0
50
100
150
200
250
300
100 200 300 400 500 600
Avg
.Pac
ket
De
liver
y R
atio
(%)
Number of Nodes
Layerwise Security Framework
SNAuth-SPMAODV
AODV with attack
02000400060008000
10000120001400016000
100 200 300 400 500 600
Av
g.T
hro
ug
hp
ut(
bit
/s)
Number of Nodes
Layerwise Security Framework
SNAuth-SPMAODV
AODVwith attack
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com 5
Average End-to-End Delay
Figure 3 shows an average end-to-end delay of AODV,
SNAuth-SPMAODV and Layerwise Security
Framework with SNAuth-SPMAODV according to
the increase of network density. Layerwise Security
Framework with SNAuth-SPMAODV exhibits the
lowest end-to-end delay most of the time. AODV has
much higher end-to-end delay than proposed method.
Layerwise Security Framework with SNAuth-
SPMAODV keeps up good performance in delay as
the network density becomes high. Layerwise Security
Framework with SNAuth-SPMAODV performs
poorly in sparse networks. (eg 200 to 300 nodes)
Figure 3 : Average End to End Delay
Routing Overhead
Figure 5 illustrates the routing overhead generated by
the proposed framework when the number of nodes is
varied. The figure shows that the generated routing
overhead in AODV, SNAuth-SPMAODV and
Layerwise Security Framework with SNAuth-
SPMAODV increases with increased number of nodes.
Layerwise Security Framework with SNAuth-
SPMAODV performs well compared to AODV and
SNAuth-SPMAODV
Figure 5 : Routing Overhead
IV.CONCLUSION
Mobile ad hoc networks (MANETs) can be applied to
many situations without the use of any existing
network infrastructure or centralized administration. In
hostile environment, there is a need for the network to
route packets through dynamically mobile nodes.
MANETs can be considered as the solution for this
highly mobile and dynamic military network.
However it is not appropriate to directly apply
conventional mobile ad hoc networks scheme to
military network, since military communication
system is different from conventional counter parts
both in device’s physical layer specification and
networking environment. Therefore consider these
particularities of military communication system
through simulation, and evaluate the performance of
Layerwise security framework on the assumed
military environment. In simulation results, the
proposed methods provide good performance with
every measurement metric in high network density
environment
V. REFERENCES
[1] Arunkumar B. R., Reddy L.C., and Hiremath
P.S., 2008, "A Survey of Mobile Ad Hoc
Network Routing Protocols" Journal of
Intelligent System Research, 8(6), 49-64.
[2] Bajaj. L., Takai.M., Ruja.R., Tang.K.,
Bagrodia.R., and Gerla.M.,1999,"GlomoSim: A
Scalable Networks Simulation Environments",
UCLA Computer Science Departments
Technical Report 900027.
0
0.2
0.4
0.6
0.8
1
1.2
100 200 300 400 500 600
Av
g.E
nd
to
En
d D
ela
y(s
)
Number of Nodes
Layerwise Security Framework
SNAuth-SPMAODV
AODV with attack
0
20000
40000
60000
80000
100 200 300 400 500 600
Rou
tin
g O
ver
hea
d(p
ack
ets)
Number of Nodes Layerwise Security Framework
SNAuth-SPMAODV
AODV with attack
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com 6
[3] Biswas K., Ali L., 2001, "Security Threats in
Mobile Ad Hoc Network" Department of
Interaction and System Design School of
Engineering, 1-39.
[4] Boomaranimalany.A., Dhulipala.S., and
Chandrasekaran R.M, 2009, "Throughput and
Delay Comparison of MANET Routing
Protocols"International Journal Open Problems
Computational Mathematics, ICSRS
Publications, 2(3), 461-468.
[5] Chenna. P and Dr. ChandraSekhar.P., 2007,
"Performance Analysis of Adhoc Network
Routing Protocols", International Symposium on
Ad Hoc and Ubiquitous Computing,
ISAUHC'06, 17, 186 – 187.
[6] Dwivedi.A.K., kushwaha.S., and Vyas O.P.,
2009,"Performance of Routing Protocols for
Mobile Ad hoc and wireless sensor networks: A
Comparative study", International Journal of
Recent Trends in Engineering, 2(4) ,101-105.
[7] Garg.N. and Mahapatra.R.P, 2009, "MANET
Security Issues". International Journal of
Computer Science and Network Security, 9(8),
241-246.
[8] Islam.S, 2006, "Implementation & Comparison
of IPSec Protocols for Secure Datab
Communication in Ad-Hoc Networks", Royal
Institute of Technology.
[9] Jang H.C., Lien Y.N., and Tsai T.C., 2009
,"Rescue Information System for Earth-quake
Disasters Based on MANET Emergency
Communication Platform" Proceedings of the
2009 International Conference on Wireless
Communications and Mobile Computing:
Connecting the World wirelessly, 623–627.
[10] Junaid.M., Dr Muid Mufti and Ilyas M.U., 2006,
"Vulnerabilities of IEEE 802.11i Wireless LAN
CCMP Protocol", In the Proceedings Of World
Academy Of Science, Engineering And
Technology, 11, 228-233.
[11] Pravin P.G., and Katkar G.G., 2010,"Mobile Ad
Hoc Networking: Imperatives and Challenges",
IJCA Special Issue on MANETs, 153–158.
[12] Reidt S., and Wolthusen S.D, 2008, "Exploiting
UAVs Capabilities in Tactical MANETS".
Proceedings of the 2nd Annual Conference of
ITA ,322–323.
[13] Salsano,, Veltri S., and Papalilo D., 2002,"SIP
Security Issues: The SIP authentication
procedure and its processing load" IEEE
Network,38-44.
[14] Taneja K., and Patel R.B., 2007, "Mobile Ad
hoc Networks: Challenges and Future"
Proceedings of National Conference on
Challenges & Opportunities in Information
Technology pp. 133-135.
[15] Vaidya.B. and Lim H., 2009 "Secure
Framework for Multipath Multimedia Streaming
Over Wireless Ad Hoc Network". Proceedings
of the 2009 IEEE Conference on Wireless
Communications & Networking
Conference,2678–2683.
[16] D.Devi Aruna and Dr.P.Subashini.,2014,"
Layerwise Security Framework with Snauth-
SPMAODV to Defend Denial of Service Attack
in Mobile Adhoc Networks for Hostile
Environment" International Journal of
Innovative Research in Science & Engineering.
[17] Qualnet Documentation, "Qualnet 5.0 Model
Library, Network Security", Available: Http://
Www.Scalablenetworks.Com/Products/Qualnet/
Downlaod
CSEIT16112 | Received: 11 July 2016 | Accepted: 22 July 2016 | July-August 2016 [(1)1: 07-13]
International Journal of Scientific Research in Computer Science, Engineering and Information Technology
© 2016 IJSRCSEIT | Volume 1 | Issue 1 | ISSN : 2456-3307
7
A Qualitative Comparison of Various Routing Protocols in WSN
Kabeer Khan, Abdul Waris, Hamayun Safi
Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan
ABSTRACT
Wireless Sensor Network (WSN) consists of a large number of small nodes with the capabilities of sensing various
types of physical and environmental conditions, data processing, and wireless communication. In Wireless Sensor
Network(WSN) the sensor nodes collects the data from its surrounding and transmit the gathered data to a particular
user, the transmission of gathered data by sensor nodes depends on the application that is used. The nodes have
limited processing power, limited transmission range and storage capabilities as well as limited energy capabilities.
In this paper we discuss the routing protocols of wireless sensor network and also discuss the classification and
comparison of routing protocols. The architecture of routing protocols categories in three main category
Hierarchical, Location-Based and data centric protocols according to some important factors and will summarize in
the way these protocols operates. Finally, we will provide a comparative study on these various protocols.
Keywords: Wireless Sensor Network, Routing Protocols, Location-based Routing, Data Centric.
I. INTRODUCTION
Wireless Sensor Network(WSN) consist of hundred to
thousand low power sensor nodes, they are deployed
in field, have capability to gather data and send to base
station for taking decision about specific region for
specific purpose. Basic component of wireless sensor
node is sensor, processer, radio transceiver, power unit.
Sensors are responsible to sense the deployed region
for capturing the data. Processer received data form
storage unit process it and transmit to nearest neighbor
which may be node are base station. Radio transceiver
has the ability to transmit and received data form
neighbors nodes
Figure 1: Components of a Sensor Node
Power unit is responsible for managing energy
consumption of the node. These various components
of a node are shown in Figure 1 above.
II. METHODS AND MATERIAL
A. Wireless Sensor Network Protocols
Wireless Sensor Network (WSN) has a wide range of
applications to industry, science, transportation, civil
infrastructure and security and many other fields, such
as some forests are very dangerous where human
approach fails, so from that areas we collect our
desired information using wireless sensor application.
In this paper we stud the routing protocol of wireless
sensor network and we categorize the routing
protocols in three basic categories on the basis of
network structures. This paper organized as follows. In
the first phase we discussed about the hierarchical
protocols, second phase is about location based and
third phase describes data centric protocols.
Routing Protocols in WSN
Wireless sensor network routing protocols are
different from traditional routing protocol [1-8]. On
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com
8
the basis of network structure routing protocols are
classified in many different categories, like data
centric, hierarchical and location-based protocols and
also compared the routing protocols.
Figure 2: classification of WSN Protocols
A. Hierarchical protocols In this paper different
hierarchical based routing protocols described.
Which is (LEACH, TEEN, APTEEN, PEGASIS)
energy efficient and maintain the energy of sensor
node. A hierarchical approach network is divides
in to cluster and cluster head. Cluster node
captures the data and sends the data to cluster
head. Cluster head received the data from cluster
aggregate the data and send to base station. In
hierarchical based routing protocols data send
form one node to another node and cover large
distance. This approach moves the data faster to
base station. Representative protocols of
hierarchical routing are is following:
1) LEACH
2) 2)TEEN
3) APTEEN
4) PEGASIS
1) LEACH: Low Energy Adaptive Clustering
Hierarchy (LEACH) is most popular hierarchical
routing protocols for sensor network. Leach
performs data fusion to compress the data when
data send to from cluster to base station. In this
why leach is most popular protocol to reduce the
energy consumption and enhance the lifetime of
the node. Leach protocol divide the total operation
in two phase. One is setup phase and other is
steady state phase. In set-up phase cluster head is
selected for each cluster. Cluster head is selected
from sensor nodes at the time of certain
probability. The cluster head is selected random
number between 0 and 1.the node become cluster
head for the current round if the number is less
than the threshold,
Where p is desired percentage of cluster head, r
current round, G are those node which is not selected
cluster head in 1/p round.
In steady state phase cluster head send the data to
leader node. Leader node compress, aggregate the data
and send to base station.
2) TEEN: Threshold energy efficient network (teen)
protocol a hierarchical clustering protocol. The
sensor network based on hierarchical grouping
cluster nodes from clusters and this process goes
on second level until base station is reached. In
this type of protocol cluster head send two types
of data to neighbor’s nodes. One is hard threshold
and other is soft threshold. The node transmits the
data if one of the following conditions satisfies:
a) Sense value > hard threshold
b) Sense value ~ hard threshold >= soft threshold
If hard threshold satisfy that condition if sensed value
is greater than hard threshold. This means the node
send those data which are interested and reduce the
number of transmission. In soft threshold any small
change in the sense value transmits the data to forward.
Figure 3: Architecture of TEEN protocol
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3) APTEEN: Adaptive threshold energy efficient
network protocol (APTEEN) is improve version of
teen protocol which capturing both periodic data
collections and reacting to time critical event. The
architecture of APTEEN is same like
TEEN.APTEEN support different type query.
Historical analysis of past data values.
Snapshot of the current network view.
Monitoring of an event for a period of time.
4) PEGASIS: Power Efficient Gathering in Sensor
Information System (PEGASIS), which is chain
based power efficient algorithm. In PEGASIS
only one node chose to transmit data to base
station other nodes capture the data and send the
data to neighbor node. PEGASIS chain of sensor
node every sensor node transmit data to neighbor
node and received data for neighbor node. For
example:
Figure 4 : Architecture of PEGASIS
In the above example C0 send data to C1. Node C1
combine the data of C0 and own data then transmit to
leader node.C2 is leader node which send token to C4,
node C4 transmit data to C3. C3 combine the data of
C4 with its own data and transmit to leader node.
Node C2 wait for neighbor node data if received the
data form neighbor node then combine the data and
send only one message to base station [9-28].
B. Location Based Protocol:
In sensor networks the information about the location
of nodes are very necessary. By means of their
location the sensor nodes are located in Location
Based Protocol.
The energy estimate can consumed by all the routing
protocol to calculate the distance between two
particular nodes. Location protocol can increase the
lifetime of network. In Location Based routing
protocol the position of sensor nodes are estimated to
route data in the network. Location Based routing
protocol needs some location for the sensor node these
location can be obtained from GPS (Global
Positioning System) signals, Received Radio signals
etc.
GEAR is an example of Location Based routing
protocol.
i. Geographic and Energy-Aware Routing
(GEAR): GEAR is an energy efficient routing
protocol. This protocol is used to find the location
of sensor node in the network. Localization
hardware just like GPS, GIS etc. are fitted in
nodes through this the nodes will know about their
current position, their energy as well as they will
know about their neighbors. It uses energy aware
methods geographical information for sending the
packets towards its destination. At that point
GEAR use recursive geographic forwarding to
spread the packets inside the target region.
Figure 5: Operation of GEAR Protocol
ii. Geographic Adaptive Fidelity (GAF):
It is an energy aware routing protocol. Initially GAF
was proposed for MANETS and mobile ad hoc
networks. But later it can also applied to sensor
networks. GAF is a location based routing protocol. In
GAF nodes use location information through any
system just like GPS, GIS and received radio signal
etc. to locate itself with its nearest neighbors.
Nodes consume energy while transmitting data i.e. at
sending time as well as at the receiving time. In idle
state some amount of energy is used but it is less in
comparison to the active state.
From Discovery to Active state transition:
For finding the equivalent nodes each node exchange
discovery messages. Nodes belongs to the same grid
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are equivalent. Discovery messages contains
information about the nodes i.e. node id, grid id, node
state and energy level [29-40]. After predefined time
Td the nodes enters into the active state if it doesn’t
receive any other discovery message.
Figure 6: State Operational Model
From Discovery to Sleeping state transition:
In this state if node receives any other discovery
message from another node which have higher energy
level than a node that is enter to the sleep state. At one
time only one node will be in active state the
remaining will be in sleep state. In order to keep the
routing fidelity, the sleeping neighbors will adjust
their sleeping time (Ts). If the active node expires then
another sleeping node become active.
From Active to Sleeping state transition:
Active time show that at what time a node will be in
active state. After active time (Ta), another node
which have higher energy from the rest of nodes in the
grid become active and the current one will go to sleep
state.
From Sleeping to Discovery state transition:
Before wake up a node has to complete its sleep time
and then enter to discovery state. If the node have
higher energy then it will enter to active state
otherwise re-enter into sleep state.
From Active to Discovery state transition:
When a node enters to the discovery phase after a
predefined time (Td) and rebroadcast the discovery
message for time td. If it receives a message from
another node having higher residual energy then it
enter into sleep state else re-enter into active state.
1) Trajectory Based Forwarding:
It is a method to forward packets in a dense ad hoc
network that makes it possible to route a packet over a
predefined path. The source specify the trajectory in a
packet but doesn’t explicitly the path on the hop-by-
hop basis. Based on the location information of its
neighbor a forwarding sensor makes a greedy decision
to determine the next hop that is the closest to the
trajectory fixed by the source sensor.
2) Minimum Energy Communication Network
(MECN) and Small-MECN:
Minimum Energy Communication Network (MECN)
is a Location-Based protocol. This protocol is used for
achieving minimum energy for randomly deployed ad
hoc networks. Which attempt to set up and maintain a
minimum energy network with mobile sensors. This
protocol has two phases.
In the first phase the protocol takes the position of
a two dimensional plane and constructs a sparse
graph which is also called an enclosure graph. It
consist of all the enclosures of each transmit node
in the graph. Enclose graph contains globally
optimal links in terms of energy consumptions.
The second phase finds optimal links on the
enclosure graph. It uses distributed shortest path
algorithm with power consumption as a cost
metric.
Small Minimum Energy Communication
Network (SMECN):This protocol is used to
improve MECN. In this protocol minimal graph
is regarded as with its minimum energy
property. In this protocol every sensor discovers
its immediate neighbors by broadcasting a
discovery message using some initial power that
is updated incrementally.
C. Data centric protocol
In sensor network the data centric protocol is different
from traditional in carrying data .data centric protocol
is query-based i-e the sink send queries to certain
region and wait for the required sensing data in the
sensor located region. Data is being requested through
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queries. Naming data is essential to specify properties
of data based on attributes .the most important thing it
reduce redundancy in data transmission. The base
station sends queries to the specific sensor region and
waits for the information about that filed transmitting
by the nodes. Thus is very efficient in term of energy
consumption .in data centric protocol the sensor
themselves are less important than their own data
centric protocol is divided into many categories.
1. SPIN
(Sensor Protocol for Information via Negotiation):-it
was design to improve classic flooding protocol. The
plan behind spin is to name the data using high level
descriptor or Meta data. Meta data are changed
between sensors before transmission via a technique of
data advertisement. In spin all information is
broadcasted to each node in the network user can
easily query to any node and can get the information
very soon [41-44].
2. Directed Diffusion
It develop after spin .it is a data dissemination and
aggregation protocol an application aware protocol in
which sensor node generate data and name it by
attribute value paired. The operation is shown below
Figure 7: Directed Diffusion Operation
III. RESULTS AND DISCUSSION
Comparison of Various Routing Protocols
Comparison of Various Routing Protocols
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IV.CONCLUSION
In recent year, wireless sensor network is the most
interesting field for the researcher to contribute the
main aim of the routing protocol to improve lifetime
of sensor node for the purpose to improve network
lifetime. This paper is about the classification of
routing protocols into three main categories. Such as,
data centric, hierarchical and location based. In
hierarchical node are divide in cluster and cluster head.
In data centric protocol, all data come from the nodes
to gateway. Then gateway send the data to base station,
in this way gateway is more overloading. In location-
based protocols, need the information of node to
calculate the distance between two nodes to estimate
energy consumption.
V. REFERENCES
[1] https://www.academia.edu/7579636/A_TUTORIA
L_OF_ROUTING_PROTOCOLS_IN_WIRELES
S_SENSOR_NETWORKS_
[2] M. A. Jan, P. Nanda, X. He, and R. P. Liu, “A
Lightweight Mutual Authentication Scheme for
IoT Objects,”, “Submitted”, 2016.
[3] Khan, F., & Nakagawa, K. (2013). Comparative
study of spectrum sensing techniques in cognitive
radio networks. In Computer and Information
Technology (WCCIT), 2013 World Congress on (pp. 1-8). IEEE.
[4] Khan, F., Bashir, F., & Nakagawa, K. (2012). Dual
head clustering scheme in wireless sensor
networks. In Emerging Technologies (ICET), 2012
International Conference on (pp. 1-5). IEEE.
[5] Khan, F., Kamal, S. A., & Arif, F. (2013). Fairness
improvement in long chain multihop wireless ad
hoc networks. In 2013 International Conference
on Connected Vehicles and Expo (ICCVE) (pp.
556-561). IEEE.
[6] Khan, F. (2014). Secure communication and
routing architecture in wireless sensor networks.
In 2014 IEEE 3rd Global Conference on Consumer Electronics (GCCE) (pp. 647-650).
IEEE.
[7] M. A. Jan, P. Nanda, X. He and R. P. Liu,
“PASCCC: Priority-based application-specific
congestion control clustering protocol” Computer
Networks, Vol. 74, PP-92-102, 2014.
[8] Khan, S., & Khan, F. (2015). Delay and
Throughput Improvement in Wireless Sensor and
Actor Networks. In 5th National Symposium on
Information Technology: Towards New Smart World (NSITNSW) (pp. 1-8).
[9] Khan, F., Jan, S. R., Tahir, M., Khan, S., & Ullah,
F. (2016). Survey: Dealing Non-Functional
Requirements at Architecture Level. VFAST Transactions on Software Engineering, 9(2), 7-13.
[10] Khan, F., & Nakagawa, K. (2012). Performance
Improvement in Cognitive Radio Sensor
Networks. the IEICE Japan.
[11] Khan, F., Khan, S., & Khan, S. A. (2015, October).
Performance improvement in wireless sensor and
actor networks based on actor repositioning.
In 2015 International Conference on Connected Vehicles and Expo (ICCVE) (pp. 134-139). IEEE.
[12] M. A. Jan, P. Nanda, X. He and R. P. Liu, “A
Sybil Attack Detection Scheme for a Centralized
Clustering-based Hierarchical Network” in
Trustcom/BigDataSE/ISPA, Vol.1, PP-318-325,
2015, IEEE.
[13] Jabeen, Q., Khan, F., Khan, S., & Jan, M. A.
(2016). Performance Improvement in Multihop
Wireless Mobile Adhoc Networks. the Journal
Applied, Environmental, and Biological Sciences (JAEBS), 6(4S), 82-92.
[14] Khan, F. (2014, May). Fairness and throughput
improvement in multihop wireless ad hoc
networks. In Electrical and Computer Engineering
(CCECE), 2014 IEEE 27th Canadian Conference on (pp. 1-6). IEEE.
[15] Khan, S., Khan, F., Arif, F., Q., Jan, M. A., &
Khan, S. A. (2016). Performance Improvement in
Wireless Sensor and Actor Networks. Journal of
Applied Environmental and Biological
Sciences, 6(4S), 191-200.
[16] Khan, F., & Nakagawa, K. (2012). B-8-10
Cooperative Spectrum Sensing Techniques in
Cognitive Radio Networks, 2012(2), 152.
[17] Khan, F., Jan, S. R., Tahir, M., & Khan, S. (2015,
October). Applications, limitations, and
improvements in visible light communication
systems. In2015 International Conference on
Connected Vehicles and Expo (ICCVE)(pp. 259-
262). IEEE.
[18] Jabeen, Q., Khan, F., Hayat, M. N., Khan, H., Jan,
S. R., & Ullah, F. (2016). A Survey: Embedded
Systems Supporting By Different Operating
Systems. International Journal of Scientific Research in Science, Engineering and Technology
(IJSRSET), Print ISSN, 2395-1990.
[19] Jan, S. R., Ullah, F., Ali, H., & Khan, F. (2016).
Enhanced and Effective Learning through Mobile
Learning an Insight into Students Perception of
Mobile Learning at University Level. International
Journal of Scientific Research in Science, Engineering and Technology (IJSRSET), Print
ISSN, 2395-1990.
[20] Jan, S. R., Khan, F., & Zaman, A. The perception
of students about mobile learning at University
level.
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[21] M. A. Jan, P. Nanda, X. He, and R. P. Liu, “A
Sybil Attack Detection Scheme for a Forest
Wildfire Monitoring Application,” Elsevier Future Generation Computer Systems (FGCS),
“Accepted”, 2016.
[22] Jan, S. R., Shah, S. T. U., Johar, Z. U., Shah, Y., &
Khan, F. (2016). An Innovative Approach to
Investigate Various Software Testing Techniques
and Strategies. International Journal of Scientific
Research in Science, Engineering and Technology
(IJSRSET), Print ISSN, 2395-1990.
[23] Khan, I. A., Safdar, M., Ullah, F., Jan, S. R., Khan,
F., & Shah, S. (2016). Request-Response
Interaction Model in Constrained Networks. In
International Journal of Advance Research and
Innovative Ideas in Education, Online ISSN-2395-4396
[24] Azeem, N., Ahmad, I., Jan, S. R., Tahir, M., Ullah,
F., & Khan, F. (2016). A New Robust Video
Watermarking Technique Using H. 264/AAC
Codec Luma Components Based On DCT. In International Journal of Advance Research and
Innovative Ideas in Education, Online ISSN-2395-
4396
[25] Jan, S. R., Khan, F., Ullah, F., Azim, N., & Tahir,
M. (2016). Using CoAP Protocol for Resource
Observation in IoT. International Journal of
Emerging Technology in Computer Science &
Electronics, ISSN: 0976-1353
[26] Azim, N., Majid, A., Khan, F., Jan, S. R., Tahir,
M., & Jabeen, Q. (2016). People Factors in Agile
Software Development and Project Management.
In International Journal of Emerging Technology
in Computer Science & Electronics (IJETCSE) ISSN: 0976-1353
[27] Azim, N., Majid, A., Khan, F., Tahir, M., Safdar,
M., & Jabeen, Q. (2016). Routing of Mobile Hosts
in Adhoc Networks. In International Journal of
Emerging Technology in Computer Science &
Electronics (IJETCSE) ISSN: 0976-1353.
[28] Azim, N., Khan, A., Khan, F., Majid, A., Jan, S. R.,
& Tahir, M. (2016) Offsite 2-Way Data Replication toward Improving Data Refresh
Performance. In International Journal of
Engineering Trends and Applications, ISSN: 2393
– 9516
[29] Tahir, M., Khan, F., Jan, S. R., Azim, N., Khan, I.
A., & Ullah, F. (2016) EEC: Evaluation of Energy
Consumption in Wireless Sensor Networks. . In
International Journal of Engineering Trends and Applications, ISSN: 2393 – 9516
[30] M. A. Jan, P. Nanda, M. Usman, and X. He,
“PAWN: A Payload-based mutual Authentication
scheme for Wireless Sensor Networks,”
Concurrency and Computation: Practice and Experience, “accepted”, 2016.
[31] Azim, N., Qureshi, Y., Khan, F., Tahir, M., Jan, S.
R., & Majid, A. (2016) Offsite One Way Data
Replication towards Improving Data Refresh
Performance. In International Journal of Computer Science Trends and Technology, ISSN:
2347-8578
[32] Safdar, M., Khan, I. A., Ullah, F., Khan, F., & Jan,
S. R. (2016) Comparative Study of Routing
Protocols in Mobile Adhoc Networks. In
International Journal of Computer Science Trends
and Technology, ISSN: 2347-8578
[33] Tahir, M., Khan, F., Babar, M., Arif, F., Khan, F.,
(2016) Framework for Better Reusability in
Component Based Software Engineering. In the
Journal of Applied Environmental and Biological
Sciences (JAEBS), 6(4S), 77-81.
[34] Khan, S., Babar, M., Khan, F., Arif, F., Tahir, M.
(2016). Collaboration Methodology for Integrating
Non-Functional Requirements in Architecture. In the Journal of Applied Environmental and
Biological Sciences (JAEBS), 6(4S), 63-67
[35] Jan, S.R., Ullah, F., Khan, F., Azim, N., Tahir, M.,
Khan, S., Safdar, M. (2016). Applications and
Challenges Faced by Internet of Things- A Survey.
In the International Journal of Engineering Trends and Applications, ISSN: 2393 – 9516
[36] Tahir, M., Khan, F., Jan, S.R., Khan, I.A., Azim, N.
(2016). Inter-Relationship between Energy
Efficient Routing and Secure Communication in
WSN. In International Journal of Emerging
Technology in Computer Science & Electronics (IJETCSE) ISSN: 0976-1353.
CSEIT16113 | Received: 16 July 2016 | Accepted: 24 July 2016 | July-August 2016 [(1)1: 14-18]
International Journal of Scientific Research in Computer Science, Engineering and Information Technology
© 2016 IJSRCSEIT | Volume 1 | Issue 1 | ISSN : 2456-3307
14
Detecting BOT Victim in Client Networks
Abinaya. E, Balamurugan. K
Department of Information Technology, St Peter Engineering College, Avadi, Tamil Nadu, India
ABSTRACT
In this paper we discuss my research in detecting bot victim in client networks. Botnets are collections of Internet
hosts (―bots‖) that, through malware infection, have fallen under the control of a single entity (―botmaster‖). Botnets
perform network scanning for different reasons: propagation, enumeration, penetration. One common type of
scanning, called ―horizontal scanning,‖ systematically probes the same protocol port across a given range of IP
addresses, sometimes selecting random IP addresses as targets. To infect new hosts in order to recruit them as bots,
some botnets, e.g., Conficker perform a horizontal scan continuously using self-propagating worm code that exploits
a known system vulnerability. In this project, we focus on a different type of botnet scan—one performed under the
explicit command and control of the botmaster, occurring over a well-delimited interval.
Keywords: Horizontal Scanning, Botmaster, Bots, P2P, IRC, BotGraph, DPI, Clustering
I. INTRODUCTION
Existing system contains a fundamental disadvantage
of centralized C&C servers are that they represent a
single point of failure. In order to overcome this
problem, botmasters have recently started to build
botnets with a more resilient C&C architecture, using
a peer-to-peer (P2P) structure or hybrid
P2P/centralized C&C structures. Detecting botnets is
of great importance. However, designing an effective
P2P-botnet detection system is faced with several
challenges. I am confident that this software package
can be readily used by non-programming personal
avoiding human handled chance of error.
Peer-to-peer (P2P) botnets have a random organization
and operate without a C&C server. Bot software
maintains a list of trusted computers (including other
infected machines), information drop locations and
locations where the machines can update their
malware. More advanced botnets use encryption in
order to hide communications between bots.
The purpose of decentralization is to help evade
detection and make it harder for security researchers to
access communications than is the case with a
conventional botnet topology. The lack of a command-
and-control server makes it less likely that detection of
a single bot can lead to investigators taking down the
entire network.
II. METHODS AND MATERIAL
2. Related Works
Botnets have been an active area of research for
almost a decade, starting with early generation botnets
that used IRC channels to implement centralized
Command & Control (C&C) infrastructures.
Botnets commonly scan large segments of Internet
address space, seeking hosts to either infect or
compromise, or for the purpose of network mapping
and service discovery. Analyzing and detecting these
events can improve our understanding of evolving
botnet characteristics and spreading techniques, our
ability to distinguish them from benign traffic sources,
and our ability to mitigate attacks.
Since Sality is one of the largest known botnets but
relatively undocumented in research literature, another
contribution of our study is to shed light on the
scanning behavior of this new-generation botnet.
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2.1 P2P as botnet command and control: a deeper
insight
The research community is now focusing on the
integration of peer-to-peer (P2P) concepts as
incremental improvements to distributed malicious
software networks (now generically referred to as
botnets). While much research exists in the field of
P2P in terms of protocols, scalability, and availability
of content in P2P file sharing networks, less exists
(until this last year) in terms of the shift in C&C from
central C&C using clear-text protocols, such as IRC
and HTTP, to distributed mechanisms for C&C where
the botnet becomes the C&C, and is resilient to
attempts to mitigate it. In this paper we review some
of the recent work in understanding the newest botnets
that employ P2P technology to increase their
survivability, and to conceal the identities of their
operators. We extend work done to date in explaining
some of the features of the Nugache P2P botnet, and
compare how current proposals for dealing with P2P
botnets would or would not affect a pure-P2P botnet
like Nugache. Our findings are based on a
comprehensive 2-year study of this botnet.
.
Figure 1. Structure of the Botnet
2.2 Experiences in Malware Binary DE obfuscation
Malware authors employ a myriad of evasion
techniques to impede automated reverse engineering
and static analysis sorts. The most popular
technologies include `code obfuscators' that serve to
rewrite the original binary code to an equivalent form
that provides identical functionality while defeating
signature-based detection systems. These systems
significantly complicate static analysis, making it
challenging to uncover the malware intent and the full
spectrum of embedded capabilities. While code
obfuscation techniques are commonly integrated into
contemporary commodity packers, from the
perspective of a reverse engineer, DE obfuscation is
often a necessary step that must be conducted
independently after unpacking the malware binary.
2.3 Internet Traffic Classification Using Bayesian
Analysis Techniques
Accurate traffic classification is of fundamental
importance to numerous other network activities, from
security monitoring to accounting, and from Quality of
Service to providing operators with useful forecasts
for long-term provisioning. We apply a Na¨ve Bayes
estimator to categorize traffic by application.
Uniquely, our work capitalizes on hand-classified
network data, using it as input to a supervised Na¨ve
Bayes estimator. In this paper we illustrate the high
level of accuracy achievable with the Na¨ve Bayes
estimator. We further illustrate the improved accuracy
of renewed variants of this estimator.
2.3.1 BotGraph : Large Scale Spamming Botnet
Detection
Network security applications often require analyzing
huge volumes of data to identify abnormal patterns or
activities. The emergence of cloud-computing models
opens up new opportunities to address this challenge
by leveraging the power of parallel computing. In this
paper, we design and implement a novel system called
BotGraph to detect a new type of botnet spamming
attacks targeting major Web email providers. Bot-
Graph uncovers the correlations among botnet
activities by constructing large user-user graphs and
looking for tightly connected subgraph components.
This enables us to identify stealthy botnet users that
are hard to detect when viewed in isolation.
2.3.2 BotGraph : Large Scale Spamming Botnet
Detection
Network security applications often require analyzing
huge volumes of data to identify abnormal patterns or
activities. The emergence of cloud-computing models
opens up new opportunities to address this challenge
by leveraging the power of parallel computing. In this
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16
paper, we design and implement a novel system called
BotGraph to detect a new type of botnet spamming
attacks targeting major Web email providers. Bot-
Graph uncovers the correlations among botnet
activities by constructing large user-user graphs and
looking for tightly connected subgraph components.
This enables us to identify stealthy botnet users that
are hard to detect when viewed in isolation.
2.4 Understanding Churn in Peer-to-Peer
Networks
The dynamics of peer participation, or churn, are an
inherent property of Peer-to-Peer (P2P) systems and
critical for design and evaluation. Accurately
characterizing churn re- quires precise and unbiased
information about the arrival and departure of peers,
which is challenging to acquire? Prior studies show
that peer participation is highly dynamic but with
conflicting characteristics. Therefore, churn re- mains
poorly understood, despite its significance.
.
2.5 Boosting the Scalability of Botnet Detection
Using Adaptive Traffic Sampling
Botnets pose a serious threat to the health of the
Internet. Most current network-based botnet detection
systems require deep packet inspection (DPI) to detect
bots. Because DPI is a computational costly process,
such detection systems cannot handle large volumes of
traffic typical of large enterprise and ISP networks. In
this paper we propose a system that aims to efficiently
and effectively identify a small number of suspicious
hosts that are likely bots. Their traffic can then be
forwarded to DPI-based botnet detection systems for
fine-grained inspection and accurate botnet detection.
Bot Attack
Figure 2. Bot Attack in P2P system over 2014 end and
2015 start
2.5.1 P2P Botnet Detection using Behavior
Clustering & Statistical Tests
Most recent research on botnet detection focuses on
centralized botnets and primarily relies on two
assumptions: prior knowledge of potential C&C
channels and capability of monitoring them. However,
when botnets switch to a P2P (peer-to-peer) structure
and utilize multiple protocols for C&C, the above
assumptions no longer hold. Consequently, the
detection of P2P botnets is more difficult. In this
paper, we relax the above two assumptions and focus
on C&C channel detection for P2P botnets that use
multiple protocols (randomly chosen) for C&C.
III. RESULTS AND DISCUSSION
Proposed Work
Sality is one of the largest botnets ever identified by
researchers. Its behavior represents ominous advances
in the evolution of modern malware: the use of more
sophisticated stealth scanning strategies by millions of
coordinated bots, targeting critical voice
communications infrastructure. This project offers a
detailed dissection of the botnet's scanning behavior,
including general methods to correlate, visualize, and
extrapolate botnet behavior across the global Internet.
Since bots are malicious programs used to perform
profitable malicious activities, they represent valuable
assets for the botmaster, who will intuitively try to
maximize utilization of bots. This is particularly true
for P2P bots because in order to have a functional
overlay network (the botnet), a sufficient number of
peers needs to be always online.
We need flow clustering-based analysis approach to
identify hosts that are mostly likely running P2P
applications. Approach does not rely on any transport
layer used by which can be easily violated by P2P
applications.
This project offers a detailed dissection of the botnet's
scanning behavior, including general methods to
correlate, visualize, and extrapolate botnet behavior
across the global Internet
The implementation can be done using Java, and the
following codes i.e., Coarse Grained Peer-To-Peer
Detection, File Uploading and Sending Bot Detection,
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17
Clustering and Eliminating, Detection of Attacker IP
Address
Figure 3. Project Model
3.1 Coarse Grained Peer-To-Peer Detection
This component is responsible for detecting P2P
clients by analyzing the remaining network flows after
the Traffic Filter component. For each host h within
the monitored network we identify two flow sets,
denoted as Stcp(h) and Sudp(h), which contain the
flows related to successful outgoing TCP and UDP
connection, respectively. We consider as successful
those TCP connections with a completed SYN,
SYN/ACK, ACK handshake, and those UDP (virtual)
connections for which there was at least one ―request‖
packet and a consequent response packet.
3.2 File Uploading and Sending
This module is used to upload required file from
storage device to user account and send the file into
destination account. There are many different types of
files: data files, text files, program files, directory
files, and so on. Different types of files store different
types of information.
3.3 Bot Detection
Since bots are malicious programs used to perform
profitable malicious activities, they represent valuable
assets for the botmaster, who will intuitively try to
maximize utilization of bots. This is particularly true
for P2P bots because in order to have a functional
overlay network (the botnet), a sufficient number of
peers needs to be always online. In other words, the
active time of a bot should be comparable with the
active time of the underlying compromised system.
3.4 Clustering and Eliminating
The distance between two flows is subsequently
defined as the Euclidean distance of their two
corresponding vectors. We then apply a clustering
algorithm to partition the set of flows into a number of
clusters. Each of the obtained clusters of flows, Cj (h),
represents a group of flows with similar size.
3.4 Clustering and Eliminating Bot using Coarse
grained Botnet detection technique
For each Cj (h), we consider the set of destination IP
addresses related to the flows in the clusters, and for
each of these IPs we consider its BGP prefix (using
BGP prefix announcements).
3.5 Detection of Attacker IP Address
In this module used to determine the geographical
location of website visitors based on the IP addresses
for applications such as fraud detection. We can find
the IP address of the attacker.
IV.CONCLUSION
We also identify the performance bottleneck of our
system and optimize its scalability. We presented a
novel botnet detection system that is able to identify
stealthy P2P botnets, whose malicious activities may
not be observable.
V. FUTURE ENHANCEMENT
To summarize, although our system greatly enhances
and complements the capabilities of existing P2P
botnet detection systems, it is not perfect. We should
definitely strive to develop more robust defense
techniques, where the aforementioned discussion
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com
18
outlines the potential improvements of our system.
Botnet developers are constantly improving their
development in order to produce more and more
stealthy malware for all kinds of attacks to make
profit. While various approaches have been studied or
used for botnet attacks, the risk of exploiting widely
used browser extensions and their automatic browser
extension update mechanisms for command and
control channel has not been practically investigated.
In this study, we show that it is not difficult to
construct stealthy botnet via browser extensions.
VI.REFERENCES
[1] S. Stover, D. Dittrich, J. Hernandez, and S.
Dietrich, "Analysis of the storm and nugache
trojans: P2P is here," in Proc. USENIX, vol. 32.
2007, pp. 18–27.
[2] P. Porras, H. Saidi, and V. Yegneswaran, "A
multi-perspective analysis of the storm
(peacomm) worm," Comput. Sci. Lab., SRI Int.,
Menlo Park, CA, USA, Tech. Rep., 2007.
[3] P. Porras, H. Saidi, and V. Yegneswaran.
(2009). Conficker C Analysis Online].
Available:
http://mtc.sri.com/Conficker/addendumC/index.
html
[4] G. Sinclair, C. Nunnery, and B. B. Kang, "The
waledac protocol: The how and why," in Proc.
4th Int. Conf. Malicious Unwanted Softw., Oct.
2009, pp. 69–77.
[5] R. Lemos. (2006). Bot Software Looks to
Improve Peerage Online]. Available:
http://www.securityfocus.com/news/11390
[6] Y. Zhao, Y. Xie, F. Yu, Q. Ke, and Y. Yu,
"Botgraph: Large scale spamming botnet
detection," in Proc. 6th USENIX NSDI, 2009,
pp. 1–14.
[7] G. Gu, R. Perdisci, J. Zhang, and W. Lee,
"Botminer: Clustering analysis of network
traffic for protocol- and structure-independent
botnet detection," in Proc. USENIX Security,
2008, pp. 139–154.
[8] T.-F. Yen and M. K. Reiter, "Are your hosts
trading or plotting? Telling P2P file-sharing and
bots apart," in Proc. ICDCS, Jun. 2010, pp. 241–
252.
[9] S. Nagaraja, P. Mittal, C.-Y. Hong, M. Caesar,
and N. Borisov, "BotGrep: Finding P2P bots
with structured graph analysis," in Proc.
USENIX Security, 2010, pp. 1–16.
[10] J. Zhang, X. Luo, R. Perdisci, G. Gu, W. Lee,
and N. Feamster, "Boosting the scalability of
botnet detection using adaptive traffic
sampling," in Proc. 6th ACM Symp. Inf.,
Comput.Commun. Security,
CSEIT16114 | Received: 21 July 2016 | Accepted: 29 July 2016 | July-August 2016 [(1)1: 19-23]
International Journal of Scientific Research in Computer Science, Engineering and Information Technology
© 2016 IJSRCSEIT | Volume 1 | Issue 1 | ISSN : 2456-3307
19
A New Approach for Transistor-Clamped H-Bridge Multilevel Inverter with voltage Boosting Capacity
Suparna Buchke, Prof. Kaushal Pratap Sengar
TIT, Madhya Pradesh, India
ABSTRACT
Multilevel converters offer high power capability, resulting with lower output harmonics and lower commutation
losses. Their main disadvantage is their complexity, requiring a great number of power devices and passive
components, and a rather complex control circuitry. This paper presents a new topology of the multilevel inverter
with feature like output voltage boosting capability along with capacitor voltage balancing .The proposed multilevel
inverter uses conventional transistor clamped H-bridge (TCHB) with an bidirectional switch and four auxillary
switches producing a boost output voltage . The single unit of new topology produces five-level output with output
voltage double the input DC voltage where as a single unit of conventional H-bridge produces three-level ouput
voltage similar to input DC voltage. A novel universal control scheme is used which results in balanced distribution
of power among H-bridge cells. This control scheme can also be used for the charge balance control with multiple
input DC sources in any given topology. The analysis of the output voltage harmonics is carried out and compared
with previous topology and the conventional cascaded H-bridge inverter topology. The proposed multilevel inverter
topology is modelled using matlab/simulink. From the results the proposed inverter provides more output voltage.
Keywords : Multilevel Inverter, Cascaded H-Bridge, Multicarrier Phase Width Modulation, Transistor Clamped
Inverter, Cascaded Neutral –Point Clamped Inverter.
I. INTRODUCTION
There are various application varying from medium
voltage to high voltage high power application which
requires DC to AC conversion using multilevel
inverters. The research on multilevel inverter is
ongoing further to reduce the number of switching
devices count to reduce the manufacturing cost,
capacitor voltage balancing .The inverters with number
of voltage levels equal to three or above than that are
known as the multilevel inverters. Multilevel inverters
are capable of producing high power high voltage as
the unique structure of the multilevel voltage source
inverter allows to reach high voltages with low
harmonics without the use of transformers or series
connected synchronized switching devices. As the
number of voltage levels increases , the harmonic
content of the output voltage waveform decreases. The
synthesized multilevel outputs are superior in quality
which results in reduced filter requirements [1].
There are three major multilevel voltage source
inverter topologies neutral-point lamped inverter (i.e
diode clamped) , flying capacitor (capacitor-clamped)
and cascaded H-bridge multilevel inverter . There are
also various other topologies which have been
proposed and have successfully adopted in various
industrial applications. The novel universal multi-
carrier PWM control scheme is used .This paper
mainly focuses mainly on the cascaded H-bridge
inverter topology. the cascaded multilevel inverter has
the potential to be the most reliable out of three
topologies . It has the best fault tolerance owing to its
modularity a feature that enables the inverter to
continue operate at lower power levels after cells
failure[2]. Due to the modularity of the cascaded
multilevel inverter it can be stacked easily for high
power and high voltage applications. The cascaded
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com
20
multilevel inverter mainly consists of several identical
H-bridge cells, which are cascaded, in series from the
output side. The cascaded H-bridge (CHB) may further
be classified as symmetrical if the DC bus voltage is
equal in all the series power cells and as asymmetrical
if the DC bus voltage is not same for each power cell.
The symmetrical CHB is more advantageous over the
asymmetrical CHB in terms of modularity,
maintenance and cost. In case of the asymmetrical
CHB DC bus voltage is varied in each power as per the
requirement to increase the voltage levels [2]. In case
of the symmetrical CHB the voltage, level can be
increased without varying the DC voltage with same
number of power cells. The transistor clamped
topology is popular now a days a provides provision to
increase the output levels by taking different voltage
levels from the series stacked capacitors [1]. In this
paper the new configuration of the symmetrical H-
bridge is proposed which produces a five-level output
voltage similar to conventional transistor clamped
topology Instead of three-level as in case of
conventional H-bridge. However, this new proposed
topology produces the boost output voltage in
comparison to conventional transistor clamped
topology, which also produces the five-level output but
the output voltage equal to the DC voltage.
II. METHODS AND MATERIAL
Proposed Inverter Configuration
The conventional cascaded H-bridge inverter consists
of DC voltage for each H-bridge and only four
switching devices. The value of the DC voltage in each
bridge depends whether the configuration is symmetric
or unsymmetric. Fig.1 shows the conventional H-
bridge. The general block diagram for the proposed
inverter is shown in fig.2 and the general configuration
of the proposed inverter topology is shown in fig.3
which represents a single cell which produces the five-
level output with boost output voltage. It consist of
total of 8 switches in a single cell along with an
additional bidirectional switch consisting of S11 and
S11’ which is connected between the first leg of the H-
bridge and the capacitor midpoint, enabling five output
voltage levels (+2Vdc , +Vdc , 0 , -Vdc , -2Vdc) based
on the switching combination . The switches
S21,S31,S41,S51 forms the H-bridge and the switches
Sa1,Sa2,Sa3,Sa4 are connected in the same leg which
plays a role in boosting the voltage and the input DC
voltage is connected with positive terminal between
the switches Sa1 and Sa2 and the negative terminal
between the switches Sa3 and Sa4. The capacitor
voltage divider is formed by C1 and C2.
Figure 1. Conventional cascaded H-bridge
Figure 2. General block diagram of new topology
Figure 3. Topology of five-level transistor clamped H-
bridge with boost output voltage for each cell
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com
21
Figure 4. Configuration of the proposed 1-phase
transistor clamped cascaded H-bridge inverter using
two cells
Operation of proposed inverter topology
The working of the single cell of the proposed inverter
topology is explained telling how the required five
level output is produced:
1. Maximum positive output that can be produced is
the double of the input DC voltage i.e 2Vdc which
is produced when S21 is on connecting the load
positive terminal to the load and S51 is on
connecting the load negative terminal to the Vdc
thus the total output voltage is 2Vdc. The output
voltage level Vdc is obtained when Sa1, S11, S51
and Sa2 gets turned on other switches remaining
off.
2. Maximum negative output is -2Vdc, which is
produced when switches S41 and S31 gets turned
on connecting the negative and positive terminal of
the load respectively to the input source. The
negative level –Vdc is obtained when switches
Sa1, Sa3, S11, S41 are turned on other switches
remaining off.
The look up table for the proposed inverter is given in
the figure given below.
Voltage
level
+2Vdc +Vdc 0 -Vdc -
2Vdc
Sa1 0 0 0 1 0
Sa2 0 1 0 0 0
Sa3 0 0 0 1 0
Sa4 0 1 0 0 0
S11 0 1 0 1 0
S21 1 0 1 0 0
S31 0 0 0 0 1
S41 0 0 1 1 1
S51 1 1 0 0 0
Table 1. Look up table for the proposed TCHB
Volta
ge
level
+4
V
+3
V
+2
V
+1
V
0
V
-1V -
2V
-3V -4V
Sa1 0 0 0 0 0 1 1 0 0
Sa2 0 0 1 1 0 0 0 0 0
Sa3 0 0 0 0 0 1 1 0 0
Sa4 0 0 1 1 0 0 0 0 0
S11 0 0 1 1 0 1 1 0 0
S21 1 1 0 0 1 0 0 0 0
S31 0 0 0 0 0 0 0 1 1
S41 0 0 0 0 1 1 1 1 1
S51 1 1 1 1 0 0 0 0 0
Sb1 0 0 0 0 0 0 1 1 0
Sb2 0 1 1 0 0 0 0 0 0
Sb3 0 0 0 0 0 0 1 1 0
Sb4 0 1 1 0 0 0 0 0 0
S12 0 1 0 0 0 0 1 1 0
S22 1 0 1 1 1 1 0 0 0
S32 0 0 0 0 0 0 0 0 1
S42 0 0 0 1 1 1 1 1 1
S52 1 1 1 0 0 0 0 0 0
Table 2. Lookup table for single phase proposed
transistor clamped H-bridge inverter
PWM Control Scheme
Multilevel inverter has to synthesize a staircase
waveform by using the modulation technique to have
the controlled output voltage. There is variety of
modulation techniques available. The control
technique can be classified as the pulse width
modulation, which is considered as the most efficient
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22
method. This PWM is further divided into various
PWM techniques such as single pulse PWM, space
vector PWM, multiple pulse PWM, phase
displacement control []. For this proposed topology,
we are using the multicarrier based control technique,
which can be applied, to all the topologies of the
multilevel inverter. For any given number of levels in
the output voltage the number of carrier to be used is
given as N-1.
Where N is the number of levels in the output voltage.
Simply a reference signal is taken which is a sinusoidal
signal of 50Hz frequency and this reference is
compared with the carrier signal which are the
triangular wave .The modulation index we are using in
this modulation technique is 0.95.The advantage of
this scheme is that it offers the charge balance control
in the input DC sources and voltage across the
capacitor are also balanced[4].
Table 3. Multicarrier based control scheme for the
proposed topology
Figure 4. 5-level output voltage waveform of single
phase cascaded H-bridge inverter with tw
Figure 5. 9-level output voltage waveform of single
phase proposed transistor clamped H-bridge inverter
with two bridges.
III. RESULTS AND DISCUSSION
Comparison of Proposed Topology with Cascaded
H-Bridge Topology
The purpose of research for the multilevel inverter
includes to get a quality power output with the reduced
number of switching devices, balancing of the
capacitors, reduced number of clamping diodes in
order to reduce the overall cost of the multilevel
inverter. In the proposed multilevel inverter topology,
the number of switches is more in comparison to the
conventional CHB but we get the five-level in the
output voltage, which results in reduced THD. In
addition, the input DC voltage source required is half
of the voltage source required in the conventional
CHB. much superior than the cascaded H-bridge
topology in terms of the number of level in the output
voltage, magnitude of the output voltage, total
harmonic distortion. To produce the same output
voltage the cascaded H-bridge has to use the two cells
whereas only one cell is required with the proposed
topology. Fig.3 is showing the single-phase inverter
consisting of two cells of the proposed topology each
cell is having input 100V DC voltage and the output ac
voltage is 400V each of which is producing 200V. The
total harmonic distortion produced by the proposed
inverter is 11.49% only, which is very low as
compared to the conventional cascaded H-bridge
inverter having THD of 37.64%, which is 26.15%
more than the proposed topology. In order to produce
the nine levels in the output voltage the cascaded H-
bridge requires three cells whereas the proposed
topology requires only two cells.
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23
Figure 6. THD in % for single phase cascaded H-
bridge multilevel inverter
Figure 7. THD in % for double phase cascaded H-
bridge multilevel inverter
IV.CONCLUSION
The proposed multilevel inverter topology is much
superior to the conventional cascaded H-bridge
topology in terms of the number of level in the output
voltage, magnitude of the output voltage, total
harmonic distortion (THD). To produce the same
output voltage the cascaded H-bridge has to use the
two cells whereas only one cell is required with the
proposed topology or in other words input DC voltage
sorce required in proposed topologi is half of that
required in conventional CHB.
V. REFERENCES
[1] Mahajan Sagar Bhaskar Ranjana,"MULTILEVEL
INVERTER WITH LEVEL SHIFTING SPWM
TECHNIQUE USING FEWER NUMBER OF
SWITCHES FOR SOLAR
APPLICATIONS",еISSN: 2319-1163,IJRET:
Intеrnational Journal of Resеarch in Engineеring
and Tеchnology,Volumе: 04 Issuе: 10,Oct-2015.
[2] Mr. D. Santhosh Kumar Yadav,"Analysis of
Cascadеd Multilevеl Invertеrs with Seriеs
Connеction of H-Bridgе in PV Grid",Intеrnational
Journal of Enhancеd Resеarch in Sciencе
Tеchnology & Engineеring, ISSN: 2319-7463,Vol.
4 Issuе 4, April-2015, pp: (101-106).
[3] Krishna Kumar Gupta,"Multilevеl Invertеr
Topologiеs With Reducеd Devicе Count: A
Reviеw",IEEE TRANSACTIONS ON POWER
ELECTRONICS, VOL. 31, NO. 1, JANUARY
2016.
[4] Ebrahim Babaеi,"A Singlе-Phasе Cascadеd
Multilevеl Invertеr Basеd on a New Basic Unit
With Reducеd Numbеr of Powеr Switchеs",IEEE
TRANSACTIONS ON INDUSTRIAL
ELECTRONICS, VOL. 62, NO. 2, FEBRUARY
2015.
[5] Vahid Dargahi, “A New Family of Modular
Multilevеl Convertеr Basеd on Modifiеd Flying-
Capacitor Multicеll Convertеrs",IEEE
TRANSACTIONS ON POWER ELECTRONICS,
VOL. 30, NO. 1, JANUARY 2015.
[6] Sourabh Rathorе, Mukеsh Kumar Kirar and S. K
Bhardwaj "SIMULATION OF CASCADED H-
BRIDGE MULTILEVEL"
CSEIT16115 | Received: 24 July 2016 | Accepted: 30 July 2016 | July-August 2016 [(1)1: 24-29]
International Journal of Scientific Research in Computer Science, Engineering and Information Technology
© 2016 IJSRCSEIT | Volume 1 | Issue 1 | ISSN : 2456-3307
24
An Improved Performance of Greedy Perimeter Stateless Routing protocol of Vehicular Adhoc Network in Urban Realistic Scenarios
Ritesh Gupta, Parimal Patel
Department of Computer Engineering, S. P. B Patel Engineering College, Mehsana, Gujarat, India
ABSTRACT
Vehicular Ad Hoc Networks (VANETs) or Inter-Vehicle Communication (IVC) is an extension to a popular Mobile
Ad Hoc Networks (MANETs) technology. VANET is developed to provide comfort communication between the
vehicle while driving. In VANET there is a continuous wireless data transmission occurs either between Road Side
Units (RSUs) or On Board Units (OBUs) in the vehicles. To keep the transmission smooth it required a good routing
protocol. Right from the inception of VANET technology in 2000s the work done only on basic routing protocol.
Mobility model is one of the key parameter while designing the vehicular network. In this paper the Simulation of
Urban Mobility (SUMO) and Mobility Model Generator for VANET (MOVE) are used for creating scenarios and
traffic. The real time maps are edited in JAVA open street map editor (JOSM) and the simulation is done in NS-2.
The performance is evaluated by using the two routing protocol on the basic of packet delivery ratio and end to end
delay for Urban scenarios.
Keywords : VANET (Vehicular Adhoc Network.) SUMO (Simulation of Urban Mobility), MOVE
I. INTRODUCTION
Vehicular Ad Hoc Network (VANET) is a fast
growing technology in today’s world. The
fundamental idea behind implementing VANET is to
offer information sharing, supportive driving,
providing navigation and safety to human life in fast
moving vehicles. The communication takes place
either between vehicle-to-vehicle (V2V) or between
vehicles-to infrastructure (V2I). On Board Unit
(OBU) that is fixed on vehicle is responsible for
collecting data from various sensors, which gives
condition of that vehicle. OBU send this data either to
other vehicle or to Road Side Unit (RSU). On the
other hand, RSU is a fixed infrastructure situated
along the sides of road whose work is to broadcast the
information to other vehicles. However, due to high
mobility and dynamic topology of VANET
discovering and maintaining routes is very challenging
task in VANET. To achieve an effective vehicular
communication, vehicular network must be available
all time in real time. A small delay in sending or
receiving of message may lead to devastating results.
Due to rapid changing topology, there are numerous
technical hitches in designing a Routing Protocol of
VANET.[1] Routing is the process of moving packets
from a source to a destination and Routing Protocols
are the one who decide how those packets are going to
move. Routing occurs at Layer3 (network layer) of the
OSI reference model via some logical addressing.
Routing protocols plays a key role in path discovery
so; it becomes important for routing protocol to give
effective result in real time.
In this paper, as shown in figure 1 of process flow, we
have taken the urban realistic scenario for
simulation .The real maps are taken from the open
street map for urban realistic scenario. The maps are
edited in Java Open Street Map editor (JOSM) to
remove the unwanted areas like buildings, rivers etc.
after the editing of real maps the output file is given to
the SUMO (Simulation of Urban Mobility) for
simulating the real traffic scenario of vehicular
network. The output of this SUMO is used in network
Simulator (NS-2) for the analysis of various QoS
parameters.
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Figure 1: Process flow for capturing real time
mobility model
II. METHODS AND MATERIAL
Introduction to Routing Protocols
VANET Routing protocol has significant role in
performance because of sending &receiving packets
between sources to destinations. There are number of
routing protocols has developed for wireless Adhoc
network. VANET routing protocol [1][2]basically
classified into two types: Proactive and reactive
routing protocols.
In proactive routing protocol, it maintains the route
information at all nodes and update the table
accordingly. In reactive routing protocol, it
maintaining the route information for nodes on
demand.
In this paper, the simulation and comparison is
performed on the basis of two different routing
protocols. [13] GPSR (Greedy Perimeter Stateless
Routing) & MGPSR (Modified Greedy Perimeter
Stateless Routing) protocols.
a) GPSR:
Greedy perimeter stateless routing (GPSR) is the best
known position based routing protocol for VANETs.
GPSR makes greedy forwarding decisions using only
information about a router’s immediate neighbors in
the network topology.
GPSR consists of two methods for forwarding
packets:
1. Greedy Forwarding
2. Perimeter Forwarding
Greedy Forwarding is used to send data to the
closest nodes to destination. Perimeter Forwarding
is used where Greedy Forwarding fails
1. Greedy Forwarding
Find neighbors who are the closer to the
destination
Forward the packet to the neighbor closest to the
destination
Figure 2 : Greedy Forwarding Method
Figure 3: Greedy Forwarding does not always work
2. Perimeter Forwarding
Apply the right-hand rule to traverse the edges of
a void
Pick the next anticlockwise edge
Figure 3 : Perimeter Forwarding with Void: Right-
Hand Rule
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Figure 4 : Perimeter Forwarding Pick the next
anticlockwise edge
b) MGPSR:
MGPSR is the extension to the GPSR protocol for
computing effective communication among the nodes
which substantially increases network lifetime of
nodes.
Figure 5 : Modified Greedy Perimeter Stateless Routing
(MGPSR)
III. RESULTS AND DISCUSSION
Simulation In Josm (Java Open Street Map Editor)
[17] JOSM is a desktop editing application, written in
java. It supports loading standalone GPX tracks and
GPX track from OSM database as well as loading and
editing existing nodes, ways, metadata tags and
relations from the OSM.
Figure 6 : Road Map for Vehicles of Urban Based
Scenario
The map in Figure 6 is taken from
http://openstreetmap.org, which is available free for
downloading via their export map feature.
Figure 7: Map of Urban Area – City Based Scenario Figure 8: Map of Urban Area – City Based Scenario for road in JOSM
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com
27
As shown in figure 7 & 8, the downloaded maps are
saved in “.osm” file format that can be edited in
JOSM and from “map.osm”. In figure 5 the urban area
of city based scenario contains buildings, Trees, traffic
and other unwanted streets are removed. In Figure 8
all these unwanted parts are edited in JOSM, and only
the roads are remain for the traffic simulation. So, that
the file size become small and to lessen the
unnecessary computation. We can import that file in
[16] SUMO and create traffic environment.
Simulation in Sumo (Simulation of Urban
Mobility)
To generate vehicle traffic in [16] SUMO the tools
like "net convert”, “poly converts" and
"randomTrips.py" are used.
Net convert can imports road networks from
different sources (openstretmap.org) and generates
road networks that can be used in SUMO. It will
identify the Nodes, Junctions, and Signals etc and
build the network file that is compatible with
SUMO.
Poly convert imports geometrical shapes
(polygons - buildings) from different sources &
converts them to a representation that visualized in
SUMO-GUI.
RandomTrips.Py is used to generate random
routes.
From the above steps, we get the SUMO configuration
(medical.sumo.cfg) file in which we have to give path
of both the network file and route file. The
configuration file is used to like merge the network
file and route file
Figure 9 : Road network of urban area showing the
simulation of vehicles in Sumo (Traffic Simulator for
50ms delay)
Figure 10 : Imported map from JOSM in SUMO
Figure 11 : simulation of view of Traffic in SUMO
(Traffic Simulator for 100ms delay)
Simulation & Results
The mobility model of SUMO is given to the network
simulator 2 (NS-2) for simulation. We have done the
simulation using two different routing protocols for
urban realistic scenario. The simulation is done using
different number of nodes.
Figure 12 (a): Simulation in NS2 for urban realistic
scenario for 100 nodes
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Figure 12 (b) : Simulation in NS2 for urban realistic
scenario for 120 nodes
In Figure 13, The Packet Delivery Ratio increases
with different number of nodes
Figure 13 : Comparison graph of PDR vs Number of Nodes
In Figure 14, The Packet Delivery Ratio increases with different node speed
Figure 14 : Comparison graph of PDR vs Node speed
In Figure 15, the average end to end delay decreases with different number of nodes
Figure 15 : Comparison graph of E2ED vs Number of Nodes
In Figure 16, the average end to end delay decreases with
different node speed
Figure 16 : Comparison graph of E2ED vs Node speed
IV.CONCLUSION
The simulation of urban mobility (SUMO) gives the
better mobility model after compiling in java Open
Street Map editor (JOSM) for urban realistic
scenarios. In this paper we simulate the GPSR &
MGPSR routing protocol for analyzing the packet
delivery ration and end to end delay parameters. As a
result Performance of Modified MGPSR gives better
results than GPSR for urban realistic scenario.
Modified GPSR proposed to get better performance in
all conditions and achieve better performance in high
packet delivery ratio and less end to end delay which
substantially increases network lifetime of vehicular
nodes and also increases effective communication
among the vehicles.For the future work, the
performance can be evaluated on the basis of different
Qos parameters like routing overhead throughput,
efficiency etc.
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V. REFERENCES
[1] VANET Routing Protocols: Issues and
Challenges. Surmukh Singh, Sunil Agrawal.
UIET, Punjab University Chandigarh, India.
Proceedings of 2014 RAECS UIET Punjab
University Chandigarh, 06 – 08 IEEE March,
2014.
[2] Mr. Qi-wu Wu*, Mr. Wen Wen, Mr. Qingzi Liu,
"Comparative Study of VANET Routing
Protocols", Institution of Engineering and
Technology (IET), November 2014
[3] M. Behrisch, L. Bieker, J. Erdmann, and D.
Krajzewicz, “SUMO - Simulation of Urban
MObility: An Overview,” in SIMUL 2011, The
Third International Conference on Advances in
System Simulation, 2011.
[4] Performance Evaluation of GPSR Routing
Protocol for VANETs using Bi-directional
Coupling. Dharani N.V., Shylaja B.S., Sree
Lakshmi Ele. International Journal of Computer
Networks (IJCN), Volume (7): Issue (1): 2015.
[5] Design and evaluation of GBSR-B, an
improvement of GPSR for VANETs. C. T.
Barba , L. U. Aguiar and M. A. Igartua. IEEE
LATIN AMERICA TRANSACTIONS, VOL.
11, NO. 4, JUNE 2013.
[6] Scenario Based Performance Analysis of AODV
and GPSR Routing Protocols in a VANET. Raj
Bala, C. Rama Krishna. Deptt Of Computer
Science and Engg. NITTTR,
Chandigarh ,International Conference on
Computational Intelligence & Communication
Technology ©2015 IEEE
[7] An Enhanced GPSR Routing protocol based on
the buffer length of nodes for the congestion
problem in VANETs. Computer Science and
Education. Cambridge University, UK.Tianli Hu,
Minghui Liwang, Yuliang Tang. Department of
Communication Engineering Xiamen University.
The 10th International Conference on Computer
Science & Education (ICCSE 2015) July 22-
24,2015. Fizwilliam College,Cambridge
University,UK ©2015 IEEE
[8] Improved GPSR Routing Algorithm and its
Performance Analysis. Cheng Fenhua, Jin Min.
College of Software, Hunan University. Hunan
Science Vocational College Changsha China.
©2010 IEEE
[9] Performance Evaluation of Greedy Perimeter
Stateless Routing Protocol in Ad Hoc Networks.
Mohamed Adnene Zayene, Nabil Tabbane,
Refaat Elidoudi Multimedia Mobile Radio
Networks Research Unit Higher School of
communication of Tunis City of Communication
Technologies, Tunisia. 2009 Fourth International
Conference on Computer Sciences and
Convergence Technology ©2009 IEEE
[10] Research on One Kind of Improved GPSR
Algorithm. Liangli Lai, Qianping Wang, Qun
Wang School of Computer Science and
Technology China University of Mining and
Technology, CUMT Xuzhou, China. 2012
International Conference on Computer Sciences
and Electronics ©2012 IEEE
[11] An Improved GPSR Routing Strategy in VANET
Lili Hu, Zhizhong Ding, Huijing Shi Department
of Communication Engineering Hefei University
of Technology Hefei, China ©2012 IEEE.
[12] Simulated Analysis of Location and Distance
Based Routing in VANET with IEEE802.11p.
Akhtar Husaina and S.C. Sharma. Electronics and
Computer Discipline, DPT, Indian Institute of
Technology, Roorkee, India. 2015 Published by
Elsevier.
[13] Brad Karp, H.T. Kung, GPSR: Greedy Perimeter
Stateless Routing for Wireless Networks,
Retrieved March 4, 2008 from
http://www.eecs.harvard.edu/~htk/publication/20
00-mobi-karp-kung.pdf
[14] Study of various routing protocols in VANET
Nagaraj, U., Kharat, M.U., Dhamal, P.
International Journal of Computer Science and
Technology(IJCST), Volume (2): Issue (4): pp.
45–52 ©2011
[15] Vehicular communication: a survey Sourav
Kumar Bhoi, Pabitra Mohan Khilar The
Institution of Engineering and Technology(IET)
Volume (3): Issue (3): pp. 204-217 ©2014
[16] http://www.sumo.dlr.de
[17] http://openstreetmap.org
CSEIT16116 | Received: 28 July 2016 | Accepted: 4 August 2016 | July-August 2016 [(1)1: 30-34]
International Journal of Scientific Research in Computer Science, Engineering and Information Technology
© 2016 IJSRCSEIT | Volume 1 | Issue 1 | ISSN : 2456-3307
30
A Brief Survey of Acoustic Wireless Sensor Network
Mansoor Ullah, Abbas Khan, Muhammad Adil
Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan
ABSTRACT
In our earth 75% covered by water that could be rivers and ocean also. The underwater sensor network are enabling
technology and become more and more popular for monitoring Large scale of Area in oceans. Underwater sensor
Networks consist of a variable number of sensors that are deployed to perform Such as monitoring tasks over a
given area in Which The UWSNs applications like pollution monitoring, disaster prevention, Submarine detection
etc. In this paper, we discuss the internal architecture of underwater sensor. Here we Discuss architectures for two-
dimensional and three-dimensional underwater sensor network, we also discussed the application and main problem
or issue in underwater sensor network.
Keywords : Wireless Sensor Network, Acoustic, Underwater.
I. INTRODUCTION
As we Know earth Mostly covered by water. Which Is
The Most concern area and recently humans are
showing interest towards exploring it. Water is Hard to
investigate and find How’s the environment. in water
we used the Acoustic Sensors Which use the
mechanical waves. The UWSN consist of a variable
number of sensors that are deployed to perform the
monitoring tasks over a given Region. As in recent
times Many disasters Happened in the past Due To
which humans are need to greatly monitor the oceanic
environments for Different needs i.e scientific,
environmental, military, tsunamis, etc., in order to
perform these monitoring task We need to deploy
sensor nodes under water.
The UWSN Mostly operates on RF communication.
Yet, RF communication is not an Best possible
communication channel for underwater applications
because of great degree of restricted RF wave’s
propagation underwater. The Water has great
resistance propagation Power So It need HIGH
Performance antennas, bandwidth for RF. Thus, links
in underwater networks are based on acoustic wireless
communications [1] Acoustic communications are the
common physical layer technology in underwater
networks. The acoustic communication is more
reliable and Fault tolerant and bandwidth is limited.
underwater acoustic rates are between 5kb/s and
20kb/s, which is extremely slow compared to over air
RF rate(in Gb/s)[1].
II. METHODS AND MATERIAL
A. Internal architecture of underwater sensor
The internal architecture of underwater sensor is
shown in figure 1. In internal architecture the CPU-on
board controller, sensor interface HW, acoustic
modem, memory, power supply and sensor are the
principle parts in an underwater or acoustic wireless
sensor network. These parts are mostly found in each
such application of an acoustic wireless sensor
network and constitute the main body.
Figure 1. Internal architecture of underwater sensor
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31
It consists of the main controller that is interfaced with
sensor through a sensor interface circuitry. The CPU
or controller get the information from the sensor and
put it in the memory, process it and send to another
sensor through the acoustic modem. Sometimes all the
sensor component are protected by the Bottom-
mounted instrument frames that are design to permit
azimuthally omnidirectional communications, and
protect the sensor and modem from potential impact of
trawling gear[1].
In this paper, we discussed the literature survey of
underwater sensor network.
B. Literature Survey
The terrestrial sensor network and underwater sensor
network are different in many Perspectives. The
difference between terrestrial sensor network and
underwater sensor network is as follows:
Signal: In the terrestrial sensor network there are
radio signal used. But in underwater sensor
network there are acoustic signal will be used.
Power: In underwater sensor network High Energy
power required is more compare to terrestrial
sensor network because the signal will travelling
in water medium which have lots of resistance to
cover in that complex environment and high
distance among sensors.
Memory: In terrestrial sensor memory is limited
but underwater sensor may need to do some data
caching so, which require more memory.
Cost: Underwater sensors are more costly On the
other hand terrestrial sensors are not more costly.
The most important thing is use of some special
routing protocol, which can work efficiently. In this
research point of view UWSN use some special
routing protocol, which is very efficient.
Flooding based routing protocols: In the flooding
based routing protocols the node start sending too
many packets to all other node within transmission
range. There are many protocol in flooding based
family like HH-VBF(hop-by-hop vector based
forwarding protocol),DBR(depth based routing
protocol),FBR(focus beam routing protocol),HH-
DAB(hop-by hop dynamic address based routing
Protocol).
Multipath based routing protocols: In multipath based
there are more than one path are More paths available
to transmit their packets. In multipath based routing
include MPT etc.
Cluster based routing protocol: In this types of scheme
there are group of nodes .In which one is cluster head
node and cluster member node. These Protocols are
also use Data Fusion For Removing redundancy.
Cluster based include MCCP (minimum cost
clustering protocol), DUCS (distributed underwater
clustering scheme) PASCCC [2-24], etc.
C. Applications of Underwater Sensor Network
A. Fastest way for finding underwater information:
Underwater sensor is Now the most recent and
speediest method for discovering data in light of
its need and significance in a few circumstances
i.e catastrophes, marines and so on which is useful
for both the people additionally for scientists [1].
B. Disaster Prevention : The Most important is
disaster prevention characteristics of UWSN
system able to perform seismic activity which
produce tsunami warnings [1].
C. Ocean Sampling Networks: it brings refined new
automated vehicles i.e robots with advanced ocean
sea models to enhance our capacity to watch and
predict the ocean future conditions. We can
organized the sensor in various depth in ocean.so
we can sense the sea region at various depths [1]
D. Environmental Monitoring : Environment
Monitoring is a standout amongst the most
essential use of UWSN. They sense the
characteristics and properties of any object which
include pollution monitoring, Water quality and
habitant monitoring also.
E. Mine Reconnaissance : The simultaneous
operation of multiple AUVs (Autonomous
underwater vehicle) a robot with acoustic sensor
can be used to perform rapid environmental and
detect mine like object [1].
D. Underwater WSN Architecture
UWSN have diverse characterization i.e. One order
separates between static, semi-Mobile, and Mobile.
Another Type of UWSN strategy is to Divide UWSNs
into One-dimensional two-dimensional (spread sea
depths) and three-dimensional (includes depth as a
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32
measurement). UWSN can likewise be single-jump,
multi-hop, or Both i.e Mixed (single-jump individual
sensors, multi-hop clusters). Structures can be
assembled into short-term, time-critical applications,
and long haul, non-time-critical applications. RF,
optical, and acoustic wave based models are another
approach to look at the accessible UWSNs [1].
Fig. 2 demonstrates the most widely recognized
UWSN design. Each and every Device is moored at
the sea depths. They are small in size, battery worked,
and acoustic modems for transmission. . So having
acoustic modems, The Cluster heads utilize two
acoustic handsets, i.e vertical and an even Trancievers.
The group head or uw-sink to speak with the sensor
nodes [1] utilizes the flat (horizontol) tranciever:
i. Send Queries and setup information to the sensors.
This correspondence will happening between
underwater sink and Cluster head to sensors.
ii. Collect checked information, which monitored.
This correspondence will happening between
sensors to group head or sink. The information
exchange from Node to group head can be single-
Jump (every Node imparted to the Cluster head
specifically) or multi-jump. The vertical
Transceiver is utilized by the uw-sinks to transfer
information to a surface station. Vertical antenna
must be long range antenna for profound water
applications as the sea can be as deep as 10 km.
The surface station is furnished with an acoustic
Transceiver that can deal with numerous parallel
interchanges with the conveyed uw-sinks. At long
last base or surface station will send the detected
information to on-shore base station through RF
signal [1].
Figure 2. 2D architecture of underwater sensor network
Not at all like TWSNs, direct communication with sea
surface separate the cluster head from others. There it
increases the overall network lifetime and which
became energy efficient as well. Also, the group head
is possibly the most security-helpless part in UWSNs
military applications, since it is an only point of failure
node.
3D UWSN Architecture. Three dimensional
underwater systems are utilized to use to detect and
observe phenomena that cannot be easily observed by
means of sea base sensor Device, i.e, to perform
agreeable inspecting of 3D sea environment.
In 3D Architecture ,sensors In this type of Network,
the sensors are sent in the form of clusters, and are
anchored at various depths because of which
correspondence is far superior than 2D. The depth of
sensor can be managed by changing the length of wire
that interface the sensor to the anchor, by means of an
electronically controlled engine that live on sensor. [1]
3D Architecture all nodes can be straightforwardly
convey to the surface group heads sent packets to the
base. In the previous case, all nodes are of the same
type, yet correspondence may be more vitality Energy
serious than that of the cluster head approach. The
grouped methodology is to single point of failure.
Military applications are to a great degree of sensitive
due to single point failure.
E. Problem in Underwater Sensor Network
More costly devices: Underwater sensor devices
are more costly.
Hardware Protection requirement: The uAs the
devices are expensive so its require to protect
against water damage
Need High Energy for communication: In
underwater sensor communication require more
power because the data transfer will done in water
medium. It is hard to propagate the signals easily
which needs lots of energy and bandwidth [25-36].
Propagation delay: The propagation delay is
major problem in UWSN Because of water
resistance
Limited battery power: UWSNs suffer from a
sensor’s fouling and corrosion. Electronics
component the battery, tend to degrade faster
under extremely low temperatures such as the one
found in deep underwater.
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33
Bandwidth size limitation: In the underwater
sensor network bandwidth is another big problem.
Because bandwidth size is limited.
III. CONCLUSION
In this paper, we presented the underwater sensor
system. We Show the primary applications, the
importance of underwater sensor networks in recent
times, the design of UWSN sensor system, routing
protocols and principle challenges issues UWSN
system. We plan to continue our UWSN study. Also
we expect the time on to make efficient routing in
underwater sensor network.
IV.REFERENCES
[1] Survey paper on Underwater Wireless Sensor
Network Jaydip M. Kavar, Dr.K.H Wandra-
Student, CE dept. C.U.Shah College of Engineering
andTechnology,Wadhwancity,Gujarat,India1
underwater Sensor Network Applications: A
Comprehensive Survey
[2] Khan, F., & Nakagawa, K. (2013). Comparative
study of spectrum sensing techniques in cognitive
radio networks. In Computer and Information
Technology (WCCIT), 2013 World Congress on
(pp. 1-8). IEEE.
[3] Khan, F., Bashir, F., & Nakagawa, K. (2012). Dual
head clustering scheme in wireless sensor networks.
In Emerging Technologies (ICET), 2012
International Conference on (pp. 1-5). IEEE.
[4] Khan, F., Kamal, S. A., & Arif, F. (2013). Fairness
improvement in long chain multihop wireless ad
hoc networks. In 2013 International Conference on
Connected Vehicles and Expo (ICCVE) (pp. 556-
561). IEEE.
[5] Khan, F. (2014). Secure communication and routing
architecture in wireless sensor networks. In 2014
IEEE 3rd Global Conference on Consumer
Electronics (GCCE) (pp. 647-650). IEEE.
[6] M. A. Jan, P. Nanda, X. He and R. P. Liu,
“PASCCC: Priority-based application-specific
congestion control clustering protocol” Computer
Networks, Vol. 74, PP-92-102, 2014.
[7] Khan, S., & Khan, F. (2015). Delay and
Throughput Improvement in Wireless Sensor and
Actor Networks. In 5th National Symposium on
Information Technology: Towards New Smart
World (NSITNSW) (pp. 1-8).
[8] Khan, F., Jan, S. R., Tahir, M., Khan, S., & Ullah,
F. (2016). Survey: Dealing Non-Functional
Requirements at Architecture Level. VFAST
Transactions on Software Engineering, 9(2), 7-13.
[9] Khan, F., & Nakagawa, K. (2012). Performance
Improvement in Cognitive Radio Sensor Networks.
the IEICE Japan.
[10] Khan, F., Khan, S., & Khan, S. A. (2015, October).
Performance improvement in wireless sensor and
actor networks based on actor repositioning. In
2015 International Conference on Connected
Vehicles and Expo (ICCVE) (pp. 134-139). IEEE.
[11] M. A. Jan, P. Nanda, X. He and R. P. Liu, “A Sybil
Attack Detection Scheme for a Centralized
Clustering-based Hierarchical Network” in
Trustcom/BigDataSE/ISPA, Vol.1, PP-318-325,
2015, IEEE.
[12] Jabeen, Q., Khan, F., Khan, S., & Jan, M. A.
(2016). Performance Improvement in Multihop
Wireless Mobile Adhoc Networks. the Journal
Applied, Environmental, and Biological Sciences
(JAEBS), 6(4S), 82-92.
[13] Khan, F. (2014, May). Fairness and throughput
improvement in multihop wireless ad hoc networks.
In Electrical and Computer Engineering (CCECE),
2014 IEEE 27th Canadian Conference on (pp. 1-6).
IEEE.
[14] Khan, S., Khan, F., Arif, F., Q., Jan, M. A., &
Khan, S. A. (2016). Performance Improvement in
Wireless Sensor and Actor Networks. Journal of
Applied Environmental and Biological Sciences,
6(4S), 191-200.
[15] Khan, F., & Nakagawa, K. (2012). B-8-10
Cooperative Spectrum Sensing Techniques in
Cognitive Radio Networks. 電子情報通信学会ソ
サイエティ大会講演論文集, 2012(2), 152.
[16] Khan, F., Jan, S. R., Tahir, M., & Khan, S. (2015,
October). Applications, limitations, and
improvements in visible light communication
systems. In2015 International Conference on
Connected Vehicles and Expo (ICCVE)(pp. 259-
262). IEEE.
[17] Jabeen, Q., Khan, F., Hayat, M. N., Khan, H., Jan,
S. R., & Ullah, F. (2016). A Survey: Embedded
Systems Supporting By Different Operating
Systems. International Journal of Scientific
Research in Science, Engineering and Technology
(IJSRSET), Print ISSN, 2395-1990.
[18] Jan, S. R., Ullah, F., Ali, H., & Khan, F. (2016).
Enhanced and Effective Learning through Mobile
Learning an Insight into Students Perception of
Mobile Learning at University Level. International
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com
34
Journal of Scientific Research in Science,
Engineering and Technology (IJSRSET), Print
ISSN, 2395-1990.
[19] Jan, S. R., Khan, F., & Zaman, A. The perception of
students about mobile learning at University level.
[20] M. A. Jan, P. Nanda, X. He, and R. P. Liu, “A Sybil
Attack Detection Scheme for a Forest Wildfire
Monitoring Application,” Elsevier Future
Generation Computer Systems (FGCS),
“Accepted”, 2016.
[21] Jan, S. R., Shah, S. T. U., Johar, Z. U., Shah, Y., &
Khan, F. (2016). An Innovative Approach to
Investigate Various Software Testing Techniques
and Strategies. International Journal of Scientific
Research in Science, Engineering and Technology
(IJSRSET), Print ISSN, 2395-1990.
[22] Khan, I. A., Safdar, M., Ullah, F., Jan, S. R., Khan,
F., & Shah, S. (2016). Request-Response
Interaction Model in Constrained Networks. In
International Journal of Advance Research and
Innovative Ideas in Education, Online ISSN-2395-
4396
[23] Azeem, N., Ahmad, I., Jan, S. R., Tahir, M., Ullah,
F., & Khan, F. (2016). A New Robust Video
Watermarking Technique Using H. 264/AAC
Codec Luma Components Based On DCT. In
International Journal of Advance Research and
Innovative Ideas in Education, Online ISSN-2395-
4396
[24] Jan, S. R., Khan, F., Ullah, F., Azim, N., & Tahir,
M. (2016). Using CoAP Protocol for Resource
Observation in IoT. International Journal of
Emerging Technology in Computer Science &
Electronics, ISSN: 0976-1353
[25] Azim, N., Majid, A., Khan, F., Jan, S. R., Tahir, M.,
& Jabeen, Q. (2016). People Factors in Agile
Software Development and Project Management. In
International Journal of Emerging Technology in
Computer Science & Electronics (IJETCSE) ISSN:
0976-1353
[26] Azim, N., Majid, A., Khan, F., Tahir, M., Safdar,
M., & Jabeen, Q. (2016). Routing of Mobile Hosts
in Adhoc Networks. In International Journal of
Emerging Technology in Computer Science &
Electronics (IJETCSE) ISSN: 0976-1353.
[27] Azim, N., Khan, A., Khan, F., Majid, A., Jan, S. R.,
& Tahir, M. (2016) Offsite 2-Way Data Replication
toward Improving Data Refresh Performance. In
International Journal of Engineering Trends and
Applications, ISSN: 2393 – 9516
[28] Tahir, M., Khan, F., Jan, S. R., Azim, N., Khan, I.
A., & Ullah, F. (2016) EEC: Evaluation of Energy
Consumption in Wireless Sensor Networks. . In
International Journal of Engineering Trends and
Applications, ISSN: 2393 – 9516
[29] M. A. Jan, P. Nanda, M. Usman, and X. He,
“PAWN: A Payload-based mutual Authentication
scheme for Wireless Sensor Networks,”
Concurrency and Computation: Practice and
Experience, “accepted”, 2016.
[30] Azim, N., Qureshi, Y., Khan, F., Tahir, M., Jan, S.
R., & Majid, A. (2016) Offsite One Way Data
Replication towards Improving Data Refresh
Performance. In International Journal of Computer
Science Trends and Technology, ISSN: 2347-8578
[31] Safdar, M., Khan, I. A., Ullah, F., Khan, F., & Jan,
S. R. (2016) Comparative Study of Routing
Protocols in Mobile Adhoc Networks. In
International Journal of Computer Science Trends
and Technology, ISSN: 2347-8578
[32] Tahir, M., Khan, F., Babar, M., Arif, F., Khan, F.,
(2016) Framework for Better Reusability in
Component Based Software Engineering. In the
Journal of Applied Environmental and Biological
Sciences (JAEBS), 6(4S), 77-81.
[33] Khan, S., Babar, M., Khan, F., Arif, F., Tahir, M.
(2016). Collaboration Methodology for Integrating
Non-Functional Requirements in Architecture. In
the Journal of Applied Environmental and
Biological Sciences (JAEBS), 6(4S), 63-67
[34] Jan, S.R., Ullah, F., Khan, F., Azim, N., Tahir, M.,
Khan, S., Safdar, M. (2016). Applications and
Challenges Faced by Internet of Things- A Survey.
In the International Journal of Engineering Trends
and Applications, ISSN: 2393 – 951
[35] Tahir, M., Khan, F., Jan, S.R., Khan, I.A., Azim, N.
(2016). Inter-Relationship between Energy Efficient
Routing and Secure Communication in WSN. In
International Journal of Emerging Technology in
Computer Science & Electronics (IJETCSE) ISSN:
0976-1353.
[36] M. A. Jan, P. Nanda, X. He, and R. P. Liu, “A
Lightweight Mutual Authentication Scheme for IoT
Objects,”, “Submitted”, 2016.
CSEIT16117 | Received: 01 August 2016 | Accepted: 11 August 2016 | July-August 2016 [(1)1: 35-39]
International Journal of Scientific Research in Computer Science, Engineering and Information Technology
© 2016 IJSRCSEIT | Volume 1 | Issue 1 | ISSN : 2456-3307
35
A Survey on Secure Cloud Storage with Techniques Like Data Deduplication and Convergent Key management
P. Balasubhramanyam Reddy, G. Nagappan
Department of Computer Science and Engineering, Saveetha Engineering College, Thandalam, Chennai, Tamil Nadu, India
ABSTRACT
Data deduplication is a method for removing duplicate copies of data, It has been largely used in cloud storage to
reduce storage memory and upload bandwidth. It gives a challenge to do secure deduplication in cloud storage. In
encryption methods the keys can be produced but cannot manage huge number of keys. In the first attempt to
formally address the problem of achieving efficient and reliable key management in secure deduplication. The
general approach in which each user holds an independent master key for encrypting the convergent keys and
employing them to the cloud. Such a baseline key management scheme generates an enormous number of keys with
the increasing number of users and requires users to allegiance to protect the master keys. The De-key is the
process ,which creates new construction in which users do not need to manage any keys on their own but instead of
it secure distribute of the convergent key shares across multiple servers. Security analysis demonstrates that De-key
is secure in the proposed security model. Proof is that in realistic environment the De-key used in ramp secret
sharing .which can Demonstrate.
Keywords: De-Duplication, Convergent Encryption, Key Management, Auditing.
I. INTRODUCTION
The advantage of cloud storage motivates enterprises
and organizations to outsource data storage to third-
party cloud providers. One critical challenge of
today’s cloud storage services is the management of
the increasing volume of data. According to the report
of IDC, the volume of data in the will expected to
reach 50-60 trillion giga bytes in 2020. To make data
management scalable, de-duplication has been a well-
known technique to reduce storage space and upload
bandwidth in cloud storage. Instead of keeping
multiple data copies with the same content duplication
redundant data by keeping only one physical copy and
referring other redundant data to that copy. Each such
copy can be defined based on different granularities: it
may refer to either a whole file, or amore fine-grained
fixed-size or variable-size. The commercial cloud
storage services, such as Drop box, Mazy and Memo
pal, have been applying deduplication to user data to
save maintenance cost ,from the user side , data from
outside may have doubt in security and privacy
concerns. In this trust third-party cloud providers to
properly enforce confidentiality, integrity checking,
and access control mechanisms against any insider and
outsider attacks. The de-duplication is improving
storage and bandwidth efficiency, is incompatible with
traditional encryption. Especially different users to
encrypt their data with their own keys. Thus, identical
data copies of different users will lead to different
cipher texts, making de-duplication impossible
Convergent encryption provides a viable option to
enforce data confidentiality while realizing de-
duplication. It encrypts/decrypts data copy with a
convergent key, which is derived by computing the
cryptographic hash value of the content of the data
copy itself. After key generation and data encryption,
users retain the keys and send the cipher text to the
cloud.
Due to encryption is deterministic; the same data,
which already exists copies, will generate the same
convergent key and the same cipher text. This allows
the cloud to perform de-duplication on the ciphertexts.
The ciphertexts can only be decrypted by the
corresponding data owners with their convergent keys.
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36
In baseline is approach suffers two critical deployment
issues. First, it is inefficient, as it will generate an
enormous number of keys with the increasing number
of users. each user must associate an encrypted
convergent key with each block of its outsource
decrypted data copies, so as to later restore the data
copies. Although different users may share the same
data copies, they must have their own set of
convergent keys so that no other users can access their
files. As a result, the number of convergent keys being
introduced linearly scales with the number of blocks
being stored and the number of users. This key
management overhead becomes more prominent if we
exploit fine-grained block-level de-duplication.
Second, the baseline approach is unreliable, as it
requires each user to dedicatedly protect his own
master key. If the master key is accidentally lost, then
the user data cannot be recovered; if it is compromised
by attackers, then the user data will be leaked. us to
explore how to efficiently and reliably manage
enormous convergent keys, while still achieving
secure de-duplication. To this end, we propose a new
construction called De-key, which provides efficiency
and reliability guarantees for convergent key
management on both user and cloud storage sides.
II. METHODS AND MATERIAL
RELATED WORK
A. Traditional Encryption
To protect the confidentiality of outsourced data,
various cryptographic solutions have been proposed in
the literature. The idea is to builds untraditional
encryption, in which each user encrypts data with an
independent secret key. Some studies which is used to
propose the use of threshold secret sharing to maintain
the robustness of key management.
These do not consider deduplication. Using traditional
encryption, different users will simply encrypt
identical data copies with their own keys, but this will
lead to different cipher texts and hence make de-
duplication impossible.
B. Convergent Encryption
Convergent encryption ensures data privacy in de-
duplication Bellaire Formalize this primitive as
message-locked encryption, and explores its
application in space-efficient secure outsourced
storage. There are also several implementations of
convergent implementations of different convergent
encryption variants for secure de-duplication. It is
known that some commercial cloud storage providers,
such as Betas, also deploy convergent encryption.
However, as stated before, convergent encryption
leads to a significant number of convergent keys.
C. Proof of Ownership
Halevietal. propose ‘‘proofs of ownership’’ (POW)
ford duplication systems, such that a client can
efficiently prove to the cloud storage server that he/she
owns a file without uploading the file itself. Several
POW constructions based on the Merle Hash Tree are
proposed to enable client-side de-duplication, which
include the bounded leakage setting. Pietro and
Sorniotti propose another efficient POW scheme by
choosing the projection of a file onto some randomly
selected bit-positions as the file proof. Note that all the
above schemes do not consider data.
Figure 1. Impact of number of KM-CSPs n on
encoding/decoding times, where r = 2 and n - k =2.
Figure 2. Impact of confidentiality level r on the
encoding/decoding times where n=6
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com
37
Architecture
Figure 3. low block diagrams of core modules in two
different approaches. (a) Baseline approach (keeping
the hash key with an encryption scheme).(b) De-key
(keeping the hash key with (n; k, r -RSSS).
Fig. 3 presents the flow block diagrams of core
modules in the baseline approach and De-key that we
implement. In this figure, we omit the ordinary file
transfer and de-duplication modules for simplification.
To make full use of the multi-core feature of
contemporary processors, we assume that these
modules running in parallel on different cores in a
pipeline style. In the baseline approach, we simply
encrypt each hash key H0 with the user’s master key,
while in De-key, we generate n shares of H0.We
choose 4 KB as the default data block size. A larger
data block size (e.g., 8 KB instead of 4 KB) results in
better encoding/decoding performance due to fewer
chunks being managed, but has less storage reduction
offered by de-duplication. Which each data block,
abash key of size 32 bytes is generated using the hash
function SHA-256, which belongs to the family of
SHA-2that is now recommended by the US National
Institute of Standards and Technology (NIST). In
addition, we adopt the symmetric-key encryption
algorithm AES-256in Cipher-Block Chaining (CBC)
mode as the default encryption algorithm. Both SHA-
256 and AES-256 are implemented using the EVP
library of OpenSSL Version1.0.1e.
We implement the RSSS based on Jerasure .Regarding
to the encoding and decoding modules in Fig. 1b, the
choice of code symbol size w (in bits) deserves our
discussion here. For an erasure code, a code symbol of
size w bits refers to a basic unit of encoding and
decoding operations, both of which are performed in a
finite field. In the RSSS, we choose the erasure code
whose generator matrix is a Cauchy matrix, and thus,
w should meet the condition. However, when each
hash key is divided into pieces with a size of multiple
w, its size (i.e., 32 bytes) is often not a multiple of w.
We thus often need to pad additional zeros to fill in the
Pieces, resulting in different storage blow up ratios.
Figure 4. (a), (b)
Fig. 4a shows the storage blowups ratios versus
different values of w for (6, 4, 2)-RSSS. We see that
for some w, the storage blowups ratio can be much
higher than the theoretical value calculated by n.
However, we find that if the minimum w is chosen,
the practical storage blowup can often be closely
matched to the theoretical value. In addition, we
evaluate the corresponding encoding and decoding
times on an Intel Xeon E5530 (2.40 GHz)server with
Linux 3.2.0-23-generic OS, and the results are shown
in Fig. 2b. We find that the encoding and decoding
times increase with w. Therefore, our De-key
implementation always chooses the minimum w that
meets w.
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com
38
III. RESULTS AND DISCUSSION
In discuss of implementation details of De-key. De-
key builds on the Ramp secret sharing scheme(RSSS)
to distribute the shares of convergent keys across
multiple key servers.
A. RSSS with Pseudo Randomness
In De-key, the RSSS secret is the hash key H0 of a
data block B, where H0=hash(B) .Recall the Share
function of the (n; k; r)-RSSS embeds r random pieces
to achieve a confidentiality level of r. One challenges
that randomization conflicts with de-duplication, since
the random pieces cannot be de-duplicated with each
other. Instead of directly adopting RSSS, we here
replace these random pieces with pseudorandom
pieces in our De-key implementation.
It generates the r pseudorandom pieces as follows. Let
M=[r/(k-r)]. The first generating m additional hash
valuesasH1 = hash(B+1); H2 = hash(B+2); . . .;
Hm=hash(B+ m). We then fill in the r pieces with the
generated m additional hash values H1;H2; . . .;Hm.
These r pieces are pseudorandom because
1. H1;H2; . . .;Hm cannot be guessed by attackers
along as the corresponding data block B is
unknown; and
2. H1;H2; . . .;Hm together with H0 cannot be
deduced from each other as long as the
corresponding data block B is unknown.
The parameters n, k, and r determine the following
four factors,
Confidentiality level: It is decided by the
parameter r.
Reliability level : It depends on the parameters n
and k, and can be defined by n _ k.
Storage blow-up : It determines the key
management overhead and depends on the
parameters n, k, and r.
It can be theoretically calculated by n /k-r.
Performance: It refers to the encoding
performance and decoding performance when
using the k-of-n erasure code in the Share and
Recover functions, respectively.
Fig. 1 presents the flow block diagrams of core
modules in the baseline approach and De-key that we
implement. In this figure, we omit the ordinary file
transfer and de-duplication modules for simplification.
To make full use of the multi-core feature of
contemporary processors, we assume that these
modules running in parallel on different cores in a
pipeline style. In the baseline approach, we simply
encrypt each hash key H0 with the user’s master-key,
while in De-key, we generate n shares of H0.
The 4 KB is chosen as the default data block size. A
larger data block size results in better
encoding/decoding performance due to fewer chunks
being managed, but has less storage reduction offered
by de-duplication. For each data block, abash key of
size 32 bytes is generated using the hash.
Function SHA-256, which belongs to the family of
SHA-2that is now recommended by the US National
Institute of Standards and Technology (NIST) . In
addition, we adopt the symmetric-key encryption
algorithm AES-256in Cipher-Block Chaining (CBC)
mode as the default encryption algorithm. Both SHA-
256 and AES-256 are implemented using the EVP
library of Opens’ Version1.0.10.
The implementation of RSSS based on Jerasure
Version 1.2. Regarding to the encoding and decoding
modules in Fig. 1b, the choice of code symbol size w
(in bits) deserves our discussion here. For an erasure
code, a code symbol of size w bits refers to a basic
unit of encoding and decoding operations, both of
which are performed in a finite field GF(2w). In the (n,
k, r)-RSSS, we choose the erasure code .Theshould
meet the condition 2w > n+k . However, when each
hash key is divided into (k- r) pieces with a size of
multiple w, its size (i.e., 32 bytes) is often not a
multiple of w multiplied with (k-r) we thus often need
to pad additional zeros to fill in the (k-r) pieces,
resulting in different storage blow up ratios.
IV.CONCLUSION
The De-key is an efficient and reliable convergent key
management scheme for secure de-duplication. De-
key applies de-duplication among convergent keys and
distributes convergent key shares across multiple key
servers, while preserving semantic security of
convergent keys and confidentiality of outsourced data.
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com
39
We implement De-key using the Ramp secret sharing
scheme and demonstrate that it incurs small
encoding/decoding overhead compared to the network
transmission overhead in the regular upload/download
operations.
The audit of the file sharing and time can be recorded
and space can be utilise in various methods and make
it less expensive de-duplication can also be tried in
data warehousing although backup ,replication there
yet to we can implement this technology we can help
to make more free space and make It a low cost.
V. REFERENCES
[1] A. Shamir, "How to Share a Secret,". ACM, vol.
22,no. 11, pp. 612-613, 1979.
[2] M.W. Storer, K. Greenan, D.D.E. Long, and E.L.
Miller, "Secure Data De-duplication," in Proc.
Storages, 2008, pp. 1-10.
[3] Y. Tang, P.P. Lee, J.C. Lui, and R. Perlman, "Secure
Overlay Cloud Storage with Access Control and
Assured Deletion,"IEEE Trans. Dependable Secure
Computer., vol. 9, no. 6, pp. 903-916,Nov./Dec.
2012.
[4] G. Wallace, F. Douglis, H. Qian, P. Shilane, S.
Smaldone,M.hamness, and W. Hsu, "Characteristics
of Backup Workloads in Production Systems," in
Proc. 10th USENIX Conf. FAST,2012, pp. 1-16.
[5] Q. Wang, C. Wang, K. Ren, W. Lou, and J. Li,
"Enabling PublicAuditability and Data Dynamics for
Storage Security in Cloud Computing," IEEE Trans.
Parallel Distrib. Syst., vol. 22, no. 5,pp. 847-859,
May 2011.
[6] W. Wang, Z. Li, R. Owens, and B. Bhargava,
"Secure and Efficient Access to Outsourced Data," in
Proc. ACM CCSW,Nov. 2009, pp. 55-66.
[7] Z. Wilcox-O’Hearn and B. Warner, "Tahoe: The
Least-AuthorityFilesystem," in Proc. ACM
StorageSS, 2008, pp. 21-26
[8] A.Yun, C. Shi, and Y. Kim, "On Protecting Integrity
and Confidentiality of Cryptographic File System for
Outsourced Storage," in Proc. ACM CCSW, Nov.
2009, pp. 67-76.
[9] G.R. Blakley and C. Meadows, "Security of Ramp
Schemes," inProc. Adv. CRYPTO, vol. 196, Lecture
Notes in Computer ScienceG.R. Blakley and D.
Chaum, Eds., 1985, pp. 242-268.
[10] A.T. Clements, I. Ahmad, M. Vilayannur, and J. Li,
"DecentralizedDeduplication in San Cluster File
Systems," in Proc.USENIX ATC, 2009, p. 8.
[11] J.R. Douceur, A. Adya, W.J. Bolosky, D. Simon, and
M. Theimer,"Reclaiming Space from Duplicate Files
in a ServerlessDistributed.File System," in Proc.
ICDCS, 2002, pp. 617-624.
[12] J. Gantz and D. Reinsel, The Digital Universe in
2020: Big Data,Bigger Digital Shadows, Biggest
Growth in the Far East, Dec. 2012.
[13] R. Geambasu, T. Kohno, A. Levy, and H.M. Levy,
"Vanish:Increasing Data Privacy with Self-
Destructing Data," in Proc.`USENIX Security Symp.,
Aug. 2009, pp. 316-299.
[14] S. Halevi, D. Harnik, B. Pinkas, and A. Shulman-
Peleg,"Proofs of Ownership in Remote Storage
Systems," in Proc.ACM Conf. Comput. Commun.
Security, Y. Chen, G. Danezis,and V. Shmatikov,
Eds., 2011, pp. 491-500.
[15] D. Harnik, B. Pinkas, and A. Shulman-Peleg, "Side
Channels in Cloud Services: De-duplication in Cloud
Storage," IEEE SecurityPrivacy, vol. 8, no. 6, pp. 40-
47, Nov./Dec. 2010.
[16] S. Kamara and K. Lauter, "Cryptographic Cloud
Storage," inProc. Financial Cryptography: Workshop
Real-Life Cryptograph.Protocols Standardization,
2010, pp. 136-149.
[17] M. Li, "On the Confidentiality of Information
Dispersal Algorithmsand their Erasure Codes," in
Proc. CoRR, 2012, pp. 1-4abs/1206.4123.
[18] D. Meister and A. Brinkmann, "Multi-Level
Comparison of DataDeduplication in a Backup
Scenario," in Proc. SYSTOR, 2009,pp. 1-12
CSEIT16118 | Received: 30 July 2016 | Accepted: 04 August 2016 | July-August 2016 [(1)1: 40-43]
International Journal of Scientific Research in Computer Science, Engineering and Information Technology
© 2016 IJSRCSEIT | Volume 1 | Issue 1 | ISSN : 2456-3307
40
Emergency Information Access using QR Code Technology in Medical Field
P. Deepika , Sushanth. B , Tarun Kumar. S. P, Vignesh. M
Software Engineering, Information Technology, Easwari Engineering College, Chennai, Tamil Nadu, India
ABSTRACT
Health monitoring has become the most important factor in today’s medical era. During the time of emergency, it
would be difficult for the physicians to know the past health history of the victim to proceed with further
treatments .This project presents a health monitoring system where a person himself/herself can enter their own
health and emergency information into our servers and it can be accessed by anyone using the QR code technology
at the time of an emergency. The system is implemented in the android operating system which is the most widely
used operating system all over the world. This system helps to keep track on the individual’s health information,
henceforth giving a way for the physicians to access the information during the time of emergency. This not only
saves the life of the victim but also helps the physicians to work at ease.
Keywords : Health Monitoring, QR Code Technology, Medical Records
I. INTRODUCTION
Road safety is one issue that needs special attention as
there's one death reported every 4 minutes on the
streets of India, also, India holds the highest number of
deaths due to road accidents. Nearly 5 lakh road
accidents were reported in 2013 in which more than 1
lakh people lost their lives. A large chunk of the
victims were aged between 30 and 44 years. The
major deaths are due to the delay in the start of
treatments of patients admitted in the hospitals. This is
mainly due to the lack of previous medical
information of the patient. As they do not know the
medical information of a patient the hospitals cannot
proceed with any major treatment but just the first aid.
Our project focuses on providing the medical
information of a person at the case of emergencies.
The objective of this project is to develop a system
where a person can enter his/her medical information.
The system mainly focuses on the ability to quickly
access information in case of any emergency. The
users will be able to see the details of the person who
needs any kind of medical attention. The system
provides the information of the person, which includes
his recent medical records and also personal details
Table 1.1 Analysis of accidents that had occurred in
India.
II. METHODS AND MATERIAL
2. Existing System
The Existing Systemis used for basic hospital
management services and health care. The medical
and lab reports are shared within various departments
of the hospital and with the patients in the form of QR
codes. The existing system [3] is specific to only
Years Total
Accidents
Accidents
involving death
and personal
injury
Number of
persons
killed
2008 825561 106994 5007
2009 950 120 104212 4236
2010 1 053 346 111121 4323
2011 1 106 201 116804 4045
2012 1 228 928 131845 3835
2013 1 296 634 153552 3750
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com
41
certain hospitals, that is, the patient can only retrieve
medical records provided by a specific hospital.
This system focuses mainly on the sharing of reports
within a hospital organisation. Hence at any medical
emergencies, when a person is admitted to another
hospital, the retrieval of his previous medical records
becomes difficult.
3. Proposed System
We are creating an Android application, which uses a
login form to authenticate the user into his personal
account where he provides all the personal details and
information of his medical records. The details are
then saved in the database and a QR code is generated
which contains the required details of the user. In the
case of emergencies, the QR code can be scanned and
the details stored in the database are retrieved. This
saves the time to start the treatment of a patient
admitted at an emergency. This saves time taken to
complete all medical procedures in order to start
operating the patient. It is also a safe and secure data
storage and retrieval. By applying this method, it not
only saves the life of the victim but also helps the
physicians to work at ease.
4. QR CODE TECHNOLOGY
QR code[1], abbreviated from Quick Response Code,
is the trademark for a type of matrix barcode or two-
dimensional barcode. A QR code uses four
standardized encoding modes (numeric, alphanumeric,
byte/binary, and kanji) to efficiently store data;
extensions may also be used.A QR code consists of
black modules (square dots) arranged in a square grid
on a white background, which can be read by an
imaging device (such as a camera, scanner, etc.) and
processed using Reed–Solomon error correction until
the image can be appropriately interpreted. The
required data are then extracted from patterns that are
present in both horizontal and vertical components of
the image.
Figure 1. QR code
4.1 QR code representation
Nowadays, when smart phones equipped with
cameras are very common, conveying message via
QR code has become popular. As the aim was to
transfer data from a document to a mobile phone in
a feasible way it was a rational choice to apply
this standard to our purposes. This standard of
graphical data representation, established in 1994,
can hold even 2953 Bytes on a 177 by 177 modules
pattern. It possesses an attribute in encoding data
resistant for slight code distortions. There were set
up four error correction levels and the higher the
level, the less is storage capacity. [5] The levels L,
M, Q and H allow retrieving the whole message
when up to 7, 15, 25 and 30% respectively of the QR
image is destroyed. The priority was in getting as
much space for data as possible, not particularly in
damage resistance. That is why the level L was
acclaimed as sufficient.
5. ANDROID
Android is a software stack for mobile devices that
includes an operating system, middleware and key
applications. Android is a software platform and
operating system for mobile devices based on the
Linux operating system and developed by Google and
the Open Handset Alliance. It allows developers to
write managed code in a Java-like language that
utilizes Google-developed Java libraries, but does not
support programs developed in native code.
Android's source code is released by Google
under open source licenses, although most Android
devices ultimately ship with a combination of open
source and proprietary software, including proprietary
software developed and licensed by Google. Initially
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com
42
developed by Android, Inc., which Google backed
financially and later bought in 2005, Android was
unveiled in 2007[5] , along with the founding of
the Open Handset Alliance a consortium of hardware,
software, and telecommunication companies devoted
to advancing open standards for mobile devices.
Android is popular with technology companies which
require a ready-made, low-cost and customizable
operating system for high devices. Android's open
nature has encouraged a large community of
developers and enthusiasts to use the open-source code
as a foundation for community-driven projects, which
add new features for advanced users or bring android
to devices which were officially, released running
other operating systems[7]. The operating system's
success has made it a target for patent litigation as part
of the so-called "Smartphone" between technology
companies.
6. System Archecture
The architecture of the system is simplified and
represented in the figure b. This schematic
representation of the architecture shows the processes,
services and related activities that happen in the entire
system. This is a consolidated representation of what
happens at what point of time in which device in the
system.
6.1. Client side process:
We used android for our client side development.
Android smart phone runs with the help of android
framework, which provides environment to run the
application in mobile devices. [6] Android framework
consists of Application framework, Libraries, Android
runtime, Applications and Linux Kernel. Android
runtime provides the environment to run the
application performing all those inbuilt activities to
run the application.
6.2 .Working of rest api:
REST API provides the interface the interface for
android to connect with the server side. REST is a set
of principles describing how standards can be used to
develop web applications. Its main purpose is to
anticipate on common implementation issues and
organize the relationship between logical clients and
servers. When implementing REST over HTTP, the
logical REST client is typically a web browser and the
logical REST server is a web server.
6.3. JSON:
JSON is a lightweight data format used in place of
XML. JSON is used to store and send data through
HTTP protocol. As we use REST API to connect the
client and server, we send the JSON data through
HTTP request and response methods. It uses human
readable form to transmit data over network. We can
store JSON data in array format. In our proposed
project, we are storing the user details securely using
the JSON encrypt and decrypt method. JSON allow us
to overcome the cross domain issue.
6.4. PHP:
PHP is the web application programming language we
used for our server side development. We can simply
the mix the PHP with HTML language. Using ZEND
Framework, we developed PHP programming
language.
6.5. Database:
As we are using PHP as our server side, we need
database to store data. MySQL database is the best
database, which supports PHP programming language
well.
Figure 2. System Architecture
6.6. Overall process working:
Android is our client side system which has android
application framework, it provides environment to run
the application. Client side needs to connect with
server side, so we used REST API to provide an
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com
43
interface for android to connect with PHP using SLIM
framework. REST API offers HTTP request and
response method. JSON is used instead of XML to
transmit data from client to server side.
In server side process, PHP acts as the web application
provider and MySQL is the database we use to store
the user details. We send JSON data through HTTP
response and request method.
III. FUTUREENHANCEMENT
Our Idea can be further more enhanced by bringing in
hospitals their selves adding the information of a
patient into our servers. Similarly, the information
provided by the user can be verified by the nearby
hospitals. The medinfo profile IDs can be added to the
ID cards of major institutes and organisations.
IV.CONCLUSION
In this paper, we have presented the concept of sharing
emergency information through QR codes. The
customer has to enter all his personal and medical
information by him/herself. Consumer will be more
loyal towards the service provider. The QR code can
be scanned through any QR code scanner app across
any platforms. Hereby, we ensure that the number of
deaths due to accidents will be reduced.
V. REFERENCES
[1] Czuszynski, K., Ruminski, J,2014, "Interaction
with medical data using QR- codes", Seventh
International Conference on Human System
Interactions (HSI), pp. 101-105.
[2] Dimitris Tychalas, Athanasios Kakarountas, 2010,
"Planning and development of an electronic health
record client based on the android platform", 14th
Panhellenic Conference on Informatics, pp. 3 - 6.
[3] Hung-Ming Chen, Yong-ZanLiou, Shih-Ying
Chen, Jhuo-Syun Li, 2013, "Design of mobile
healthcare service with health records format
evaluation", IEEE 17th International Symposium
on Consumer Electronics, pp. 257 – 258.
[4] Liu Y, Yang J, and Liu M,2008, "Recognition of
QR- code with mobile phones," in Control and
Decision Conference. CCDC 2008. Chinese.
IEEE, 2008, pp. 203–206.
[5] Mohamed Amine Ben Yahmed, Mohamed Amine
Bounenni, ZeinebChelly, Amir Jlassi, 2013, "A
New Mobile Health Application for an ubiquitous
information system", 6th Joint IFIP Wireless and
Mobile Networking Conference, pp. 1 - 4.
[6] Mungyu Bae, Suk Kyu Lee, SeunghoYoo and
Hwangnam Kim, 2013,"FASE: Fast authentication
system for E-health", Fifth International
Conference on Ubiquitous and Future Networks,
pp. 648 – 649.
[7] SudhaG, GanesanR,2013,"Secure transmission
medical data for pervasive healthcare system using
android", International Conference on
Communications and Signal Processing, pp. 433 –
436.
CSEIT16119 | Received: 13 July 2016 | Accepted : 29 July 2016 | July-August 2016 [(1)1: 44-48]
International Journal of Scientific Research in Computer Science, Engineering and Information Technology
© 2016 IJSRCSEIT | Volume 1 | Issue 1 | ISSN : 2456-3307
44
Improving Classifier Performance Using Feature Selection with Ensemble Learning
Bhavesh Patankar*1, Dr. Vijay Chavda2
*1 Research Scholar, Department of Computer Science, Hemchandracharya North Gujarat University, Patan, Gujarat, India.
2NPCCSM, Kadi SarvaVishwaVidyalaya, Gandhinagar, Gujarat, India.
ABSTRACT
One of the critical task in data mining is classification. It is very much important in classification to achieve
maximum accuracy. In the field of data mining, numerous classifiers are present for the classification task. Each
classification techniques have their pros and cons. Some of the techniques work well with certain data sets while
other techniques work well with other data sets. There have been many techniques evolved for improving
classification accuracy. One of such technique is pre-processing which helps in improving quality of the data.
Another method is to combine the classifiers, which will in turn improve the classification accuracy. In this paper,
empirical study is been done on various techniques for improving classification accuracy. One of the technique is
feature selection, which will select best features from the available features in the data set. Other approach is
ensemble learning which combines many classifiers to improve the classification accuracy.
Keywords: Classification; Pre-processing; Feature Selection; Ensemble Learning;
I. INTRODUCTION
In data mining, it is evident that classification
accuracy is the critical factor for classification
techniques. Many classification techniques are been
evolved in data mining, but not every technique is
suitable for all data sets. They are various techniques
available in order to improve the classification
accuracy. Sometimes, data, which used to do
classification, is not as of required quality. Therefore,
it is good to improve the quality of the data, which
will result in improving the classification accuracy. In
data mining, pre-processing is one of the task, which
deals with the data set. It has been seen that a wide
variety of techniques are available for data pre-
processing like noise reduction, data cleaning which
includes filling missing values, feature selection,
dimensionality reduction, etc [1]. Ensemble techniques
have appeared as an influential technique for
improving the strength as well as the accuracy of both
solutions (i.e. supervised and unsupervised). In
addition, as massive amounts of data constantly
produced from different sights, it is vital to combine
different concepts for smart decision-making. In the
past few years, there have been various studies on the
problem of combining models into a single model, and
the success of ensemble techniques seen in multiple
disciplines, including anomaly detection, intrusion
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45
detection, recommendation systems and web
applications [2].
Many papers are been reviewed to figure out various
parameters to be taken into consideration in order to
improve the classification accuracy. It is good to have
pre-processing step before the classification done in
order to achieve the increasing accuracy of the
classification. The available source data set is been
converted into more qualitative data set. In some cases,
it may occur that data set can contains high
dimensions; many of the dimensions may be irrelevant
for our classification approach. Hence, it becomes
necessary to perform Feature selection to utilize the
best features for achieving the greater accuracy in
classification. Many techniques recommended
reducing noise and outliers for the improvement of
classification accuracy.
II. FEATURE SELECTION
Achieving greater accuracy is very much important in
any data mining process. An aim of feature selection is
selecting a subset of relevant features for generating
strong learning models. Camelia Vidrighin et al.[3]
have considered the wrapper approach, as a
combination of three steps: model generation, model
evaluation and model validation. They have focused
on uniting feature selection with filling the missing
values in order to improve the performance of the
learning schemes. Analysis on various approaches for
feature selection have been done and based on the
result best models have been identified which have
consistently improved the accuracy of classification.
Feature selection can be termed as combination of
search technique to find out the best features out of the
available features in the given data set. The simplest
algorithm, which minimizes the error rate, is been
considered. As seen earlier wrapper methods use
predictive model to get the relevant feature subsets.
Wrapper methods are considered computationally very
much intensive, but generally provide best feature sets
from the given data set for the given classification
model. Filter methods use proxy measure to select the
optimum feature set. Filter techniques are generally
computationally less intensive than wrapper
techniques. Hence they produced feature set which are
not tuned to specific models and so classification
accuracy from filters are generally lesser than what we
can achieve from wrapper methods.
III. ENSEMBLE LEARNING
Ensemble learning techniques are learning algorithms
that generate a set of classifiers and then classify new
data points by considering a (weighted) vote of their
estimates. The novel ensemble technique is Bayesian
averaging, however more recent techniques include
error-correcting output coding, boosting, and bagging.
Dietterich et. al. [4] have reviewed these methods and
explained why ensembles often perform better than
any single classifier. They have reviewed some
previous studies comparing ensemble methods and
some new experiments is been shown to expose the
causes that Adaboost does not overfit rapidly.
It is known that a neural network ensemble unites a
finite number of neural networks or other types of
interpreters, which are trained concurrently for a
common classification assignment. After the
experimentation, on comparing with a single neural
network, the ensemble is able to efficiently improve
the classification accuracy of the classifier. Zhao et. al.
[5] have surveyed many ensemble techniques on
different data sets to see the effect of it. And in the
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com
46
survey they have found that ensemble of neural
network always perform better than the single neuron.
Lira et. al. [6] have developed an ANN-based
automatic classifier for power system disturbance
waveforms. In the training process, actual voltage
waveforms applied and then Signals processed in two
steps that is decomposition and Principal Component
analysis (PCA) which results in reducing the input
space of the classifier to a much lower dimension.
Classification task was carried out using a
combination of six Multilayer perceptrons with
different. The result of experiment with real data
indicate that the random committee is clearly an
effective way in order to improve disturbance
classification accuracy when it compared with the
average and the separate models. Natesan et. al. [7]
have worked on secure communication between two
parties. They have proposed an Adaboost algorithm
for network intrusion detection system with single
weak classifier. The classifiers as Naive Bayes, Bayes
Net and Decision tree are been used as weak
classifiers. Experiments carried out with the help of
benchmark data set to reveal that boosting algorithm
can significantly improve weak classifiers
classification accuracy. Finally, the results were very
much effective. Base classifiers Naive Bayes and
Decision Tree have shown comparatively better
performance as a weak classifier with Adaboost.
IV. EXPERIMENTAL RESULTS AND
DISCUSSION
Experiment are been carried out using Weka. Weka
(Waikato Environment for Knowledge Analysis) is a
widespread machine-learning tool developed in JAVA
language. It is evident that it is one of the free open
source softwares available under the GNU General
Public License. Considering the experiment, it
executed on base classifier and then accuracy is
measured. Consequently, the experiment carried out
on the classifier with feature selection followed by
boosting and then the accuracy is measured. Data sets
used in the experiment is been collected from UCI
machine repository. At the end, results are been
compared and conclusion is drawn.
Following datasets from the UCI Machine Learning
Repository are been collected to initiate the
experiment.
Sr.No
Dataset Information
Dataset Instanc
es
Attribu
tes
1 Iris
150 5
2 Diabetes 768 9
3 Ionosphere 351 35
Table 1. Data set information
The experiment is been performed using Multilayer
perceptron, J48 and Naïve Bayes classifier. While
carrying out the experiment the data sets are been
chosen and not a single filter is applied on them.
Firstly experiment is performed using single base
classifier on the data set without feature selection
applied then experiment is carried out using single
base classifier with adaboost and data set with feature
selection applied on it. The experiment is been carried
out using weka 3.8.0.
Accuracy of the base single classifier and base
classifier with adaboost and feature selection is
measured which is displayed in given below table.
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com
47
Classifier
Datasets
Iris Diabetes Ionosp
here
Multilayer
Perceptron 97.3
75.39 91.16
Multilayer
Perceptron with
AdaBoost and
feature selection
95.33 75.52 94.30
J48 96.00 73.82 91.45
J48 with
AdaBoost and
feature selection
94.67 73.58 94.30
Naïve Bayes 96.00 76.30 82.62
Naïve Bayes
with AdaBoost
and feature
selection
96.00 77.47 92.30
Table 2. Accuracy measures of Multilayer perceptron,
J48 and Naïve Bayes on Iris, Diabetes and Ionosphere
data set with feature selection and adaboost and
without feature selection and without adaboost.
Figure 1. Comparison of Multilayer perceptron, J48 and
Naïve Bayes with feature selection and adaboost and
without feature selection and adaboost on Iris data set.
Figure 2. Comparison of Multilayer perceptron, J48 and
Naïve Bayes with feature selection and adaboost and
without feature selection and adaboost on Diabetes data set.
Figure 3. Comparison of Multilayer perceptron, J48 and
Naïve Bayes with feature selection and adaboost and
without feature selection and adaboost on Ionosphere data
set.
V. CONCLUSION
In this paper, it is evident that classification accuracy
improved with the help of feature selection and
ensemble technique like Adaboost, which is been used
in this experiment. Here, Best First method with CFS
Subset Evaluation is been used to select the optimum
feature in order to improve the classification accuracy.
After that ensemble technique is used which combines
the multiple classifier in order to improve the
classification accuracy. Here Adaboost ensemble
technique is been used for the improvement of the
classification accuracy. From the results of the
experiment, it is clear that in most of the cases feature
93
93.5
94
94.5
95
95.5
96
96.5
97
97.5
MultilayerPerceptron
J48 Naïve Bayes
Iris Iris with Adaboost and feature selection
71
72
73
74
75
76
77
78
MultilayerPerceptron
J48 Naïve Bayes
Diabetes Diabetes with Adaboost and feature selection
75
80
85
90
95
100
MultilayerPerceptron
J48 Naïve Bayes
Ionosphere
Ionosphere with Adaboost and feature selection
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com
48
selection with ensemble technique definitely improves
the classification accuracy of the classifier. Future
work include using different feature selection
approach than what is been used in this paper. In
addition, instead of AdaBoost any other ensemble
technique can be utilize to see the result.
VI. REFERENCES
[1] Moeinzadeh, H, Nasersharif, B, Rezaee, A.,
Pazhoumand-dar, H., “Improving Classification
Accuracy Using Evolutionary Fuzzy
Transformation”, 11th Annual Conference on
Genetic and Evolutionary Computation
Conference (GECCO 2009), Montreal, Canada,
2009 (1)
[2] Han, Jiawei, Micheline Kamber, and Jian Pei.
Data mining, southeast asia edition: Concepts
and techniques. Morgan kaufmann, 2006.
[3] Bratu, Camelia Vidrighin, Tudor Muresan, and
Rodica Potolea. "Improving classification
accuracy through feature selection." Intelligent
Computer Communication and Processing, 2008.
ICCP 2008. 4th International Conference on.
IEEE, 2008.
[4] Dietterich, Thomas G. "Ensemble methods in
machine learning." International workshop on
multiple classifier systems. Springer Berlin
Heidelberg, 2000.
[5] Zhao, Ying, Jun Gao, and Xuezhi Yang. "A
survey of neural network ensembles." 2005
International Conference on Neural Networks
and Brain. Vol. 1. IEEE, 2005.
[6] Lira, Milde MS, et al. "Combining multiple
artificial neural networks using random
committee to decide upon electrical disturbance
classification." 2007 International Joint
Conference on Neural Networks. IEEE, 2007.
Nilsson,R., Statistical Feature Selection, with
Applications in Life Science, PhD Thesis,
Linkoping University, 2007.
[7] Natesan, P., P. Balasubramanie, and G.
Gowrison. "Improving the attack detection rate
in network intrusion detection using adaboost
algorithm." Journal of Computer Science 8.7
(2012): 1041.
CSEIT161110 | Received: 15 July 2016 | Accepted: 25 July 2016 | July-August 2016 [(1)1: 49-53]
International Journal of Scientific Research in Computer Science, Engineering and Information Technology
© 2016 IJSRCSEIT | Volume 1 | Issue 1 | ISSN : 2456-3307
49
The Use of Wireless Sensor Networks for Forest Fire Monitoring – A Survey
Mehwish Zaheer*, Rabia Riaz, Shakeeb Ahmad
Department of Computer science, Abdul Wali Khan University Mardan, Pakistan
ABSTRACT
Wireless sensor network consists of small sensor nodes, deployed to capture various events of interest. For example,
temperature, oxygen, humidity sensor nodes are deployed in remote, hostile and geographical areas where the
presence of human being is infeasible. These nodes are powered by small battery, to communicate with each other
for monitoring various environments. These networks have found their applications in various domains such as
forest fire monitoring, industrial monitoring, military surveillance, inventory tracking, agriculture monitoring and
health care monitoring. Forest fire is the disaster having many negative effects in social, economic and ecological
matters. Forest fire cost million dollars in damage and claim many human lives every year.
Keywords: Wireless Sensor Networks, Health Care Monitoring, AVHRR, MODIS, WSN
I. INTRODUCTION
Wireless sensor networks are spatially distributed
systems that primarily work to collect data from
physical environments. The most important
fundamental element of these networks are sensor
nodes [1-4]. These sensors nodes are small
autonomous hardware devices that are capable of
carrying out some processing, collecting sensory
information, communicating with other connected
nodes in the network and produce a measurable
response to change in a physical condition [5-7] such
as sound [8-11], temperature [12], humidity or
pressure [13-15].
Wireless sensor network introduces a wide range of
possible application such as agriculture monitoring,
forest fire monitoring and medical monitoring. Forest
fire are the unrestricted fires happening in the wide
areas [16-22] in causing significant damage to natural
and human resources.
The goal of literature and recent studies is to detect
and predict forest fire immediately and actually, in
order to minimize the loss of forests, people and wild
in the forest fire. The network of a sensor nodes are
deployed densely in a forest sensor nodes sense the
forest periodically and collect measure data
(temperature, relative humidity) and sends to the
respective cluster nodes [23]. It has been shown in the
literature that about 20% of CO2 emission in the
atmosphere is due to forest fires. There are many
causes of forest fire including lightning, human
carelessness and exposure of fuel to extreme heat and
aridity.
In this paper, we study the existing literature about
wireless sensor network that are used for forest fire
monitoring. We explain the advantages and
disadvantages of recent studies and we present our
own conclusion.
II. METHODS AND MATERIAL
A. Background of Detection System
Some of the early methods for the forest fire detection
were based on manned observation towers such as
Camera Surveillance System
Satellite Images
Satellite images have proved more efficient then
camera surveillance by two satellite the advanced very
high resolution radio meter (AVHRR) [23-26],
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com
50
launched in a 1998 and the moderate resolution
imaging spectroradiometer (MODIS) launched in 1999
have been used. Unfortunately, these satellites can
provide images of the regions of the earth every two
days and that is a long time for fire scanning; besides
the quality of satellite images can be affected by
weather conditions.
The resolution of the wireless sensor network
technology in current years has made it possible to use
this technology for early forest fire detection. These
sensors need to be self- organized and follow an
efficient algorithm, interfaced with other technologies
or networks [30-34]. A number of present literature
considered using wireless sensor network in forest fire
systems [27-29]. These techniques have their own
limitations and disadvantages that are discussed in the
next section.
B. Disadvantages of Satellite and Camera
Surveillance Systems
The accuracy and reliability of satellite-based system
are largely impacted by the weather conditions.
In satellite system the detection accuracy is not that
much accurate. In such systems, a fire can be detected
only after it has spread largely [35] So it shows that
early systems cannot provide timely detection. Camera
surveillance systems cannot be applied to large forest
areas easily and is cost effective [36, 37]. The most
critical issue in a forest fire detection system is to
provide highly rapid response in order to minimize the
scale of disaster. Camera surveillance system and
satellite images do no provide timely detection due to
long period of scan. Therefore, we need real-time fire
detection with high accuracy and reliability.
We studied that wireless sensor networks can
potentially provide such solutions.
C. Advantages of WSN over Satellite Systems:
A pair of AA batteries is used in Wireless sensor
networks that can operate for a long period to provide
a constant monitoring during the fire sensor.
Wireless sensor networks can be easily deployed and
are low cost. These networks can detect events quickly
and accurately. Sensor nodes can be deployed
anywhere even when there is no human access
possible [38-40]. There is no need to build towers or
set up complicated communication links such as
microwave and satellite.
Based on the recent studies the key issues of this
network for forest fire monitoring are:
Localization: all the previous work used a GPS or
fixed the nodes in a known place.
Coverage: the nodes deployed randomly a full
coverage almost impossible.
Network life span: For sensor nodes working on
batteries, it is impossible to go back to each node
in the forest and recharge it again.
Fire detection method: this is the heart of the
application; it should be precise and reliable.
D. Architecture of Proposed Scheme
Forest fire detection system based on wireless sensor
network consists of small sensor nodes, base station,
communication system, internet access and structure
of monitoring hardware and software system. A large
number of nodes are randomly deployed in a forest
area and construct a self-organized network to monitor
the forest fire [41]. The nodes collect the data send it
to the sink.
E. Related Work
Wireless sensor network used for forest fire detection
consist of small sensor nodes that are used to monitor
the forest environment. Sensor nodes periodically
sense the forest when some emergency situation take
place it detects that critical data (temperature,
humidity, CO2) from the region and covert it to digital
form and forward it to base station. Base station or
sink is a device having high power energy.
III. CONCLUSION
This is a survey paper in which some various studies
about wireless sensor networks in the field of forest
fire monitoring is discussed. First, this study provides
that WSN technology is a very promising green
technology for the future in detecting efficiently the
forest fires. In this paper we present the deployment
and implementation of a wireless sensor network
system for detecting forest fires. Motes in the system
periodically sense the environment and capture the
sensed data, send it to the base station. To capture
temperature and humidity in the forest in a more
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com
51
timely and precise way, we pointed out some
advantages of wireless sensor network.
The forest fire prevention is just one example of its
applications, this technology can also be used in major
areas such as intelligent transportation, environmental
detection, alarm floods, monitoring animal habitat,
monitoring health status of bridges, monitoring the
security situation under hole. Its development and
application have a profound impact on various fields
of living and produced.
IV. REFERENCES
[1] Khan, F., Bashir, F., & Nakagawa, K. (2012).
Dual Head Clustering Scheme in Wireless
Sensor Networks. in the IEEE International
Conference on Emerging Technologies (pp. 1-8).
Islamabad: IEEE Islamabad.
[2] M. A. Jan, P. Nanda, X. He, Z. Tan and R. P.
Liu, “A robust authentication scheme for
observing resources in the internet of things
environment” in 13th International Conference
on Trust, Security and Privacy in Computing
and Communications (TrustCom), pp. 205-211,
2014, IEEE.
[3] Khan, F., & Nakagawa, K. (2012). Performance
Improvement in Cognitive Radio Sensor
Networks. in the Institute of Electronics,
Information and Communication Engineers
(IEICE) , 8.
[4] M. A. Jan, P. Nanda and X. He, “Energy
Evaluation Model for an Improved Centralized
Clustering Hierarchical Algorithm in WSN,” in
Wired/Wireless Internet Communication,
Lecture Notes in Computer Science, pp. 154–
167, Springer, Berlin, Germany, 2013.
[5] Khan, F., Kamal, S. A., & Arif, F. (2013).
Fairness Improvement in long-chain Multi-hop
Wireless Adhoc Networks. International
Conference on Connected Vehicles & Expo (pp.
1-8). Las Vegas: IEEE Las Vegas, USA.
[6] M. A. Jan, P. Nanda, X. He and R. P. Liu,
“Enhancing lifetime and quality of data in
cluster-based hierarchical routing protocol for
wireless sensor network”, 2013 IEEE
International Conference on High Performance
Computing and Communications & 2013 IEEE
International Conference on Embedded and
Ubiquitous Computing (HPCC & EUC), pp.
1400-1407, 2013.
[7] Q. Jabeen, F. Khan, S. Khan and M.A Jan.
(2016). Performance Improvement in Multihop
Wireless Mobile Adhoc Networks. in the
Journal Applied, Environmental, and Biological
Sciences (JAEBS), vol. 6(4S), pp. 82-92. Print
ISSN: 2090-4274 Online ISSN: 2090-4215,
TextRoad.
[8] Khan, F., & Nakagawa, K. (2013). Comparative
Study of Spectrum Sensing Techniques in
Cognitive Radio Networks. in IEEE World
Congress on Communication and Information
Technologies (p. 8). Tunisia: IEEE Tunisia.
[9] Khan, F. (2014). Secure Communication and
Routing Architecture in Wireless Sensor
Networks. the 3rd
Global Conference on
Consumer Electronics (GCCE) (p. 4). Tokyo,
Japan: IEEE Tokyo.
[10] M. A. Jan, P. Nanda, X. He and R. P. Liu,
“PASCCC: Priority-based application-specific
congestion control clustering protocol”
Computer Networks, Vol. 74, PP-92-102, 2014.
[11] Khan, F. (2014, May). Fairness and throughput
improvement in multihop wireless ad hoc
networks. In Electrical and Computer
Engineering (CCECE), 2014 IEEE 27th
Canadian Conference on (pp. 1-6). IEEE.
[12] Mian Ahmad Jan and Muhammad Khan, “A
Survey of Cluster-based Hierarchical Routing
Protocols”, in IRACST–International Journal of
Computer Networks and Wireless
Communications (IJCNWC), Vol.3, April. 2013,
pp.138-143.
[13] Khan, S., Khan, F., & Khan, S.A.(2015). Delay
and Throughput Improvement in Wireless
Sensor and Actor Networks. 5th National
Symposium on Information Technology:
Towards New Smart World (NSITNSW) (pp. 1-
8). Riyadh: IEEE Riyad Chapter.
[14] Khan, F., Khan, S., & Khan, S. A. (2015,
October). Performance improvement in wireless
sensor and actor networks based on actor
repositioning. In 2015 International Conference
on Connected Vehicles and Expo (ICCVE) (pp.
134-139). IEEE.
[15] Khan, S., Khan, F., Jabeen. Q., Arif. F., & Jan.
M. A. (2016). Performance Improvement in
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com
52
Wireless Sensor and Actor Networks. in the
Journal Applied, Environmental, and Biological
Sciences Print ISSN: 2090-4274 Online ISSN:
2090-4215
[16] Mian Ahmad Jan and Muhammad Khan,
“Denial of Service Attacks and Their
Countermeasures in WSN”, in IRACST–
International Journal of Computer Networks and
Wireless Communications (IJCNWC), Vol.3,
April. 2013.
[17] M. A. Jan, P. Nanda, X. He and R. P. Liu, “A
Sybil Attack Detection Scheme for a Centralized
Clustering-based Hierarchical Network” in
Trustcom/BigDataSE/ISPA, Vol.1, PP-318-325,
2015, IEEE.
[18] Jabeen, Q., Khan, F., Hayat, M.N., Khan, H.,
Jan., S.R., Ullah, F., (2016) A Survey :
Embedded Systems Supporting By Different
Operating Systems in the International Journal
of Scientific Research in Science, Engineering
and Technology(IJSRSET), Print ISSN : 2395-
1990, Online ISSN : 2394-4099, Volume 2 Issue
2, pp.664-673.
[19] Syed Roohullah Jan, Syed Tauhid Ullah Shah,
Zia Ullah Johar, Yasin Shah, Khan, F., " An
Innovative Approach to Investigate Various
Software Testing Techniques and Strategies",
International Journal of Scientific Research in
Science, Engineering and
Technology(IJSRSET), Print ISSN : 2395-1990,
Online ISSN : 2394-4099, Volume 2 Issue 2,
pp.682-689, March-April 2016.
URL : http://ijsrset.com/IJSRSET1622210.php
[20] Khan, F., Jan, SR, Tahir, M., & Khan, S., (2015)
Applications, Limitations, and Improvements in
Visible Light Communication Systems” In 2015
International Conference on Connected Vehicles
and Expo (ICCVE) (pp. 259-262). IEEE.
[21] Syed Roohullah Jan, Khan, F., Muhammad
Tahir, Shahzad Khan,, (2016) “Survey: Dealing
Non-Functional Requirements At Architecture
Level”, VFAST Transactions on Software
Engineering, (Accepted 2016)
[22] M. A. Jan, “Energy-efficient routing and secure
communication in wireless sensor networks,”
Ph.D. dissertation, 2016.
[23] M. A. Jan, P. Nanda, X. He, and R. P. Liu, “A
Lightweight Mutual Authentication Scheme for
IoT Objects,” IEEE Transactions on
Dependable and Secure Computing (TDSC),
“Submitted”, 2016.
[24] M. A. Jan, P. Nanda, X. He, and R. P. Liu, “A
Sybil Attack Detection Scheme for a Forest
Wildfire Monitoring Application,” Elsevier
Future Generation Computer Systems (FGCS),
“Accepted”, 2016.
[25] Puthal, D., Nepal, S., Ranjan, R., & Chen, J.
(2015, August). DPBSV--An Efficient and
Secure Scheme for Big Sensing Data Stream.
InTrustcom/BigDataSE/ISPA, 2015 IEEE (Vol.
1, pp. 246-253). IEEE.
[26] Puthal, D., Nepal, S., Ranjan, R., & Chen, J.
(2015). A Dynamic Key Length Based
Approach for Real-Time Security Verification
of Big Sensing Data Stream. In Web
Information Systems Engineering–WISE
2015 (pp. 93-108). Springer International
Publishing.
[27] Puthal, D., Nepal, S., Ranjan, R., & Chen, J.
(2016). A dynamic prime number based efficient
security mechanism for big sensing data
streams.Journal of Computer and System
Sciences.
[28] Puthal, D., & Sahoo, B. (2012). Secure Data
Collection & Critical Data Transmission in
Mobile Sink WSN: Secure and Energy efficient
data collection technique.
[29] Puthal, D., Sahoo, B., & Sahoo, B. P. S. (2012).
Effective Machine to Machine Communications
in Smart Grid Networks. ARPN J. Syst. Softw.©
2009-2011 AJSS Journal, 2(1), 18-22.
[30] M. A. Jan, P. Nanda, M. Usman, and X. He,
“PAWN: A Payload-based mutual
Authentication scheme for Wireless Sensor
Networks,” “accepted”, 2016.
[31] M. Usman, M. A. Jan, and X. He,
“Cryptography-based Secure Data Storage and
Sharing Using HEVC and Public Clouds,”
Elsevier Information sciences, “accepted”, 2016.
[32] Jan, S. R., Khan, F., & Zaman, A. THE
PERCEPTION OF STUDENTS ABOUT
MOBILE LEARNING AT UNIVERSITY
LEVEL. NO. CONTENTS PAGE NO., 97.
[33] Khan, F., & Nakagawa, K. (2012). B-8-10
Cooperative Spectrum Sensing Techniques in
Cognitive Radio Networks. 電子情報通信学会
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com
53
ソサイエティ大会講演論文集, 2012(2), 152.
[34] Safdar, M., Khan, I. A., Ullah, F., Khan, F., &
Jan, S. R. Comparative Study of Routing
Protocols in Mobile Adhoc Networks.
[35] Shahzad Khan, Fazlullah Khan, Fahim Arif,
Qamar Jabeen, M.A Jan and S. A Khan (2016).
“Performance Improvement in Wireless Sensor
and Actor Networks”, Journal of Applied
Environmental and Biological Sciences, Vol.
6(4S), pp. 191-200, Print ISSN: 2090-4274
Online ISSN: 2090-4215, TextRoad.
[36] M. Usman, M. A. Jan, X. He and P. Nanda,
“Data Sharing in Secure Multimedia Wireless
Sensor Networks,” in 15th IEEE International
Conference on Trust, Security and Privacy in
Computing and Communications (IEEE
TrustCom-16), “accepted”, 2016.
[37] Junguo ZHANG, Wenbin LI, Ning HAN,
Jiangming KAN Forest fire detection system
based on a ZigBee wireless sensor network.
[38] Forest Fire Detection with Wireless Sensor
Networks Çağdaş Döner*, Gökhan Şimşek,
Kasım Sinan Yıldırım and Aylin Kantarcı
Computer Engineering Department, Ege
University.
[39] The 3rd International Conference on Sustainable
Energy Information Technology (SEIT 2013)
Using Wireless Sensor Networks for Reliable
Forest Fires Detection Kechar Bouabdellaha,
Houache Noureddine, Sekhri Larbi Laboratory
of Industrial Computing and Networking,
Faculty of Sciences, Oran University, PO Box
1524 El M'naouar, Algeria.
[40] Forest Fire Modeling and Early Detection using
Wireless Sensor Networks MOHAMED
HEFEEDA Simon Fraser University, Canada.
[41] IRACST – Engineering Science and Technology:
An International Journal (ESTIJ), ISSN: 2250-
3498, Vol.2, No. 2, April 2012 Wireless Sensor
Network for Forest Fire Sensing and Detection
in Tamilnadu.
CSEIT161111 | Received: 23 July 2016 | Accepted: 01 August 2016 | July-August 2016 [(1)1: 54-59]
International Journal of Scientific Research in Computer Science, Engineering and Information Technology
© 2016 IJSRCSEIT | Volume 1 | Issue 1 | ISSN : 2456-3307
54
Address Allocation Algorithm with Cooperative Communication in MANET
Parameswaran T1, Dr.Palanisamy C
2, Logeshwari N
3
1Teaching Fellow, Department of CSE, Anna University Regional Campus, Coimbatore. India
2Professor and HOD, Department of IT, Bannari Amman Institute of Technology, Sathyamangalam. India
3PG Scholar, Department of CSE, Anna University Regional Campus, Coimbatore. India
ABSTRACT
Wireless sensor network (WSN) has devices with radio transceivers that cooperate to form and continue a fully
connected network of sensor nodes. In existing systems, topology control algorithms allow each node in a wireless
multi-hop network to adjust the power. It generates the transmission and decides neighbors where it communicates
while preserving goals like connectivity or coverage. When a node drains its inadequate energy, it does not reach
neighboring nodes resulting in a disjointed network and stopping important communications. With the limited
energy, the node does not able to continue the environmental monitoring performance that is important to the
efficient operation of the system. In proposed system, MANETs controlled the provision of pre-registered or
approved nodes and it has the opportunity for pre-deployed exchange of security parameters like public keys,
session keys. Each node in a MANET moves in any direction and changes its links frequently. A low-overhead
identity based distributed dynamic address configuration scheme for secure allocation of IP addresses is used to
allow the nodes of a managed mobile ad hoc network. MANET reduces the power usage for each packet
transmission and mobile node movement. MANET also improves the security of transmission in mobile networks.
Finally, this process conduct the performance metrics are: network overhead, delay time and security level.
Keywords: MANET, low-overhead identity, Wireless Sensor Network (WSN), Topology Control algorithm, IP
Address.
I. INTRODUCTION
Wireless sensor network (WSN) has devices with
radio transceivers that cooperate to form and continue
a fully connected network of sensor nodes.A wireless
sensor network (WSN) comprises spatially distributed
autonomous sensors to examine physical or
environmental conditions to pass data through the
network to main location.
Neighbor discovery protocols (NDPs) survey is made
in [2]. Generally, protocol is divided using four
principles. They are: randomness, over half
Occupation, rotation resistant intersection, and co
prime cycles. The birthday protocols functions as
Agents of NDPs by change where the node listen
NDP used to find the future information.
The growth of wireless sensor networks was
encouraged by military applications. Wireless Sensor
Networks (WSNs) is a class of wireless ad hoc
networks where sensor nodes gather, process and
communicate data attained from the physical
environment to Base-Station (BS).
II. METHODS AND MATERIAL
2. LITERATURE SURVEY
In [1], a distributed algorithm is presented for creating
minimum weight directed spanning trees with root
node in connected directed graph. A processor
presents at each node. With weights and origins of
edges incoming to nodes, the processors follow the
algorithm and exchange messages with their neighbors
until all arborescence are built.
Cone Based Distributed topology control (CBTC)
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com
55
algorithm is designed in [3]. The algorithm fails to
consider the nodes with GPS information and it
depends on directional information. Roughly speaking
is important design of the algorithm where the node
sends with minimum power to guarantee in all cone of
quantity around.
R3E increases the packet delivery ratio when
preserving the high energy efficiency and low delivery
latency. In [4], two localized topology control
algorithms are designed for heterogeneous networks.
They are: Directed Relative Neighborhood Graph
(DRNG) and Directed Local Spanning Sub graph.
Each node builds its neighbor set by changing the
transmission power and describes the network
topology with local information.
A protocol optimized in [5] for less energy usage in
mobile wireless networks which support peer-to-peer
communications. An easy local optimization scheme is
used at all nodes to guarantee link of network and
attains the global less energy solution for stationary
networks. In [6], distributed channel access protocol
joins the channel reservation and the iterative/global
transmission power control techniques in ad hoc
networks. The designed protocol solves the
convergence problem of global power control in ad
hoc networks. The designed access criteria use the
local admission control depending on the adequate
criteria for admissibility and global power control for
balancing the SIR of the links. In [7], a minimum
spanning tree (MST)-based algorithm termed as local
minimum spanning tree (LMST) is designed for
topology control in wireless multi hop networks. In
algorithm, each node creates LMST separately and
preserves on-tree nodes that are one-hop away in final
topology.
2.1 Related Works
In [8] to develop the benefits of user cooperation in
cooperative WANETs, distributed energy-efficient
selective diversity (EESD) topology control is planned
to enhance the energy efficiency. It equally considers
the network capacity and energy consumption using
bits per Joule. EESD creates the transmission
coalitions via cooperative manner selection by
considering the cost of channel information exchange.
Game theory is briefed in [9] to solve the power
control issue in a CDMA-based distributed sensor
network. A non-cooperative game with incomplete
information is designed by Nash equilibrium. With
this equilibrium, a distributed algorithm is planned for
optimal power control and verified the system is
power stable when the nodes observe with the transmit
power thresholds.
The energy efficiency problem is addressed and
designed a comprehensive study of topology control
techniques in [10] for increasing the lifetime of battery
powered WSNs. Initially, a topology control
algorithms are designed to present insights into how
energy efficiency is attained using the design. In
addition, algorithms are derived from the energy
preservation approach that implemented and computed
using the trade-offs they provide to help the designers
in choosing a method that suits the applications.
3. ADDRESS ALLOCATION ALGORITHM IN
MANET
Nodes are within the each other’s radio range that
communicate where the nodes are not in each other’s
radio range communicate via intermediate nodes
where the packets are transmitted from source to
destination. Number of nodes is increased in the
network and the time taken to attain an IP address,
number of packet replaces in less address allocation.
The existing node in the network creates distinctive IP
addresses from its own IP address for new authorized
nodes. Mobile ad hoc networks are divided into
stateless allocation and state full allocation methods. It
is evident in many existing dynamic address allocation
methods for MANET based on DAD.
Figure 1. Architecture Diagram
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From figure 1, in initialization phase creates the node
and address the neighbor node. From the initialization
phase, it is send to the power adjustment phase. In
power adjustment phase, it checks for the network
coverage. The energy is shared through the node using
the game theory. Then, it sends for generating IP
address. After generating the address, communication
takes place through mobile. In mobile communication,
authentication verification takes place. After
authentication, nodes update the position and increase
the security of the node transmission. After that, the
performance is analysed.
3.1 MODULES
1. Nodes deployed and create source and destination
nodes
2. Node searching neighbor node for cooperative
communication
3. Game theory for minimize the energy
consumption
4. Generate IP address for each mobile node in
network
5. Updated position of mobile nodes provide secured
communication
6. Performance Analysis
3.1.1 Nodes Deployed and Create Source and
Destination Nodes
The nodes are deployed in the network with the help
of NS2. It also creates the source and destination node
with higher efficient.
3.1.2 Node Searching Neighbor Node for
Cooperative Communication
Based on the location of a node with respect to others,
some nodes end up with a larger communication
radius. By taking that all nodes initiates with the same
energy supply and make transmissions at the same
rate. Node A has the largest energy cost and the
shortest lifetime. For the cooperative communication,
Cooperative topology control algorithm is designed.
Initially, it is separated into two types. They are:
topology construction and topology maintenance.
Topology construction is the charge of initial
reduction and the topology maintenance is the charge
of maintenance of the reduced topology where the
features such as connectivity and coverage are
protected. The initial topology is employed when the
location of nodes is chance where the administrator
without the control over the design of the network.
Simultaneously, the topology is reduced and the
network starts allocation in the selected nodes by
spending the energy. Topology control is executed in
following steps to protect the desired properties like
connectivity, coverage, density.
Step 1: Change the transmission range of the nodes
Step 2: Turn off nodes from the network
Step 3: Create a communication backbone
Step 4: Clustering
Step 5: Add new nodes to the network to preserve
connectivity (Federated Wireless sensor networks)
3.1.3 Game Theory for Minimize the Energy
Consumption
Each node updates its transmission power periodically,
the algorithm functions in rounds. At staring of each
round, each node broadcasts its remaining energy. If it
not broadcasted before, the Energy Info Shared Flag
denotes it. Game theory is an ordinal potential game
looking for the optimal global potential function yield
Nash equilibrium.
3.1.4 Generate IP Address for Each Mobile Node in
Network
The network initiates from a single node and develops
as more nodes by adding one by one. These nodes are
free to move around and it joins or leaves the network
at any point of time. A node has to inform its parent
before departing the network. In case of graceless
departure, a node moves away from the network
inadvertently or even deliberately. As IPv6 provides a
large address space, it is also not that necessary for an
address to be reused.
3.1.5 Updated Position of Mobile Nodes Provide
Secured Communication
When authentication is successful, the parent node
modernizes and transmits OK message to the children.
On getting the OK message, the child node confirms
the authentication of parent. If authentication is
successful, it sends CONFIRM message and then
switches-off. On receiving CONFIRM message, the
parent node verifies the authentication of the said
children.
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57
III. RESULTS AND DISCUSSION
PERFORMANCE ANALYSIS
The performance quality is analysed for cooperative
communication using the cooperative topology control
algorithm. The metrics of parameters is given below:
Network overhead.
Security
Delay time.
4.1 Number of nodes vs. Network overhead
Network overhead is the metadata and network routing
information sent by an application that uses a portion
of the available bandwidth of a communications
protocol. The additional data creating the protocol
headers and application-specific information is
denoted as overhead. Network overhead is the ratio of
non-application bytes divided to the total number of
bytes in the message. Network overhead is measured
in terms of percentage (%).
Figure 4.1. Number of nodes vs. Network overhead
Figure 4.1 demonstrates the network overhead of
cooperative communication and dynamic address
allocation algorithm. X axis represents the no. of
nodes whereas Y axis denotes network overhead of the
cooperative topology control and dynamic address
allocation algorithm. When the no. of nodes is
increased, the performance of network overhead gets
automatically increases accordingly.
4.2 Number of nodes vs. Security
Network security involves the approval of access to
data in a network that are controlled by the network
administrator. Network security includes the large
number of computer networks both public and private
information in businesses, government agencies and
individuals. It secures the network and also protects
the operations carried out. Network security comprises
the policies implemented to avoid and
examine authorized access, misuse, alteration or denial
of a computer network and network-accessible
resources. The simple way of protecting a network
resource is by assigning a unique name and a
password. It is also measured in terms of percentage
(%).
Figure 4.2 Number of nodes vs. Security
Figure 4.2 illustrates the security of cooperative
communication and dynamic address allocation
algorithm. X axis represents the no. of nodes whereas
Y axis denotes security of the cooperative topology
control and dynamic address allocation algorithm.
When the no. of nodes is increased, the performance
of network security gets automatically increases
accordingly. In the proposed algorithm, the network
security is high.
4.3 Number of nodes vs. Delay time
Delay time is an essential design and performance
feature of the processed computer
network or telecommunications network. The delay of
a network denotes the time required for a bit of data to
travel across the network from one node or end point
to another. It is measured in terms of milliseconds
(ms). Delay changes based on the location of the exact
pair of communicating nodes. Processing delay is
defined as time taken by the routers to process the
packet header. Queuing delay is defined as time taken
by the packet for routing queues. Transmission
0
10
20
30
40
50
60
70
5 10 15 20 25
Net
work
over
hea
d
(%)
No. of Nodes
Proposed-
AAA
Existing-
CTCA
0
20
40
60
80
100
5 10 15 20 25
Sec
uri
ty (
%)
No. of Nodes
Proposed-
AAA
Existing-
CTCA
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com
58
delay is defined as time taken to push the packet's bits
onto the link. Propagation delay is defined as time
taken for a signal to reach its destination.
Figure 4.3. Number of nodes vs. Delay Time
Figure 4.3 demonstrates the delay time of cooperative
communication and dynamic address allocation
algorithm. X axis represents the no. of nodes whereas
Y axis denotes delay time of the cooperative topology
control and dynamic address allocation algorithm.
When the no. of nodes is increased, the performance
of delay time gets automatically increases accordingly.
For an effective cooperative communication, the delay
time should be as low as possible.
IV.CONCLUSION
Cooperative communication provides the
communication directly or indirectly using neighbor
nodes, the Neighbor searching technique is used for
neighbor selection. Quality of topology by CTCA
algorithm to optimal solution attained with centralized
algorithm. A distributed algorithm called Cooperative
Topology Control with Adaptation (CTCA) executes
more information or options presented at each node.
The Cooperative Topology Control with Adaptation
(CTCA) algorithm executes better than other
distributed algorithms. Game theory is implemented to
minimize the energy consumption.
V. FUTURE ENHANCEMENT
Planned to improve the security and reduce the
Network overhead, delay time in MANET, it updates
the position and provide efficient communication
between nodes in the network depending on network
coverage. Use secure address allocation algorithm for
packet transmission to reduce the energy consumption
of nodes.
VI.REFERENCES
[1] Pierre A. Humblet, "A Distributed Algorithm for
Minimum Weight Directed Spanning Trees",
IEEE Transactions on Communications,
Volume 31, Issue 6, June 2003.
[2] Wei Sun, Zheng Yang, Xinglin Zhang, and
Yunhao Liu, "ENERGY-Efficient Neighbor
Discovery in Mobile Ad Hoc and Wireless
Sensor Networks: A Survey", IEEE
Communications Surveys and Tutorials,
Volume 16, Issue 3, Third Quarter 2014.
[3] Li (Erran) Li, Joseph Y. Halpern, Paramvir
Bahl, Yi-Min Wang, and Roger Wattenhofer, "A
Cone-Based Distributed Topology-Control
Algorithm for Wireless Multi-Hop Networks",
IEEE/ACM Transactions on Networking,
Volume 13, Issue 1, February 2005.
[4] Ning Li, and Jennifer C. Hou, "Localized
Topology Control Algorithms for
Heterogeneous Wireless Networks", IEEE/ACM
Transactions on Networking, Volume 13, Issue
6, December 2005.
[5] VolkanRodoplu, and Teresa H. Meng,
"Minimum Energy Mobile Wireless Networks"
IEEE Journal on Selected Areas in
Communications, Volume 17, Issue 8, August
1999.
[6] AzrinaAbd Aziz, Y. Ahmet S. Ekercioglu, Paul
Fitzpatrick, and MiloshIvanovich, "A
Distributed Channel Access Protocol for Ad
Hoc Networks with Feedback Power Control"
IEEE Communications Surveys and Tutorials,
Volume 15, Issue 1, First Quarter 2013.
[7] Ning Li, Jennifer C. Hou, and LuiSha,"Design
and Analysis of an MST-Based Topology
Control Algorithm" IEEE Transactions on
Wireless Communications, Volume 4, Issue 3,
MAY 2005.
[8] BingyiGuo, Quansheng Guan, F. Richard Yu.,
Shengming Jiang and Victor C. M. Leung,
"Energy-Efficient Topology Control with
Selective Diversity in Cooperative Wireless Ad
Hoc Networks: A Game-Theoretic Approach"
IEEE Transactions on Wireless
Communications, Volume 13, Issue 11,
November 2014.
[9] ShamikSengupta, MainakChatterjee, and Kevin
A. Kwiat, "A Game Theoretic Framework for
Power Control in Wireless Sensor Networks"
0
10
20
30
40
50
60
70
5 10 15 20 25
Net
wo
rk o
ver
hea
d
(%)
No. of Nodes
Proposed
-AAA
Existing-
CTCA
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com
59
IEEE Transactions on Computers, Volume 59,
Issue 2, February 2010.
[10] AzrinaAbd i hmet e ercioglu Paul
Fitzpatrick, and MiloshIvanovich., "A Survey on
Distributed Topology Control Techniques for
Extending the Lifetime of Battery Powered
Wireless Sensor Networks", IEEE
Communications Surveys and Tutorials,
Volume 15, Issue 1, First Quarter 2013.
[11] C.EPerkins, E.M.Royer, and S.R.Das:"IP
Address Auto Configuration for Ad Hoc
Networks", Technical Report draft-ietf-manet-
autoconfig-00.txt, Internet Engineering Task
Force, MANET Working Group, July 2000.
[12] S.Thomson, and T.Narten:"Ipv6 Stateless Auto
Configuration", RFC 2462, December 1998.
[13] A.Misra, S.Das, A.McAulley, and
S.K.Das:"Auto configuration, Registration, and
Mobility Management for Pervasive
Computing,"IEEE Personal Communications,
Volume8, Issue 4, Auguest 2001.Pages 24-31.
[14] R.Droms,"Dynamic Host Configuration
Protocol," Network Working Group, RFC 2131,
Mar 1997.
[15] N.Vaidhya,"Weak Duplicate Address Detection
in Mobile Ad Hoc Networks," ACM
International Symposium on Mobile Ad Hoc
Networking and Computing (MobiHoc02), June
2002, pp.201-216.
[16] Jeff.Bleng,"Efficient Network Layer Addressing
for MANET s," in Proc.of International
Conference on Wireless Networks
(ICWN'020Las Vegas, USA.
[17] C.Perkins et al.,"IP Address Auto configuration
for Ad Hoc Networks,"IETF draft, 2001.
[18] J.Broch, D.Maltz, D.Johnson, Y.Hu, and
J.Jetcheva."A Performance Comparison of
Multi-Hop Wireless Ad Hoc Routing Protocols,"
Proceedings of the Fourth Annual ACM/IEEE
Inter-national Conference on Mobile Computing
and Networking, pp.85-97, October 1998.
[19] M.Mohsin and R.Prakash,"IP Address
Assignment in a Mobile Ad Hoc Network,"Proc
MILCOM, Vol.2, Oct 2002, pp.856-61.
[20] P.Patchipulusu,"Dynamic Address Allocation
Protocols for Mobile Ah Hoc Networks," M.Sc
thesis, Comp.Sci.Texas A&M Univ., 2001.
Parameswaran.T has received his B.E
degree in Electronics and
Communication Engineering from
Velalar College of Engineering and
Technology, Erode, and M.E degree in
Software Engineering from College of
Engineering Guindy, Anna University
Chennai in 2005 and 2008 respectively.
He is currently pursuing his Ph.D Anna University Chennai.
He is currently working as Teaching Fellow in the
Department of Computer Science and Engineering, Anna
University Regional Campus, Coimbatore, Tamilnadu, India.
Palanisamy.C has received his B.E
degree in Electronics and
Communication Engineering from
University of Madras, Chennai and
M.E degree (Gold Medalist) in
Communication Systems from
Thiagarajar College of Engineering,
Madurai, and Madurai Kamaraj
University in 1998 and 2000 respectively. He has received
his Ph.D from the faculty of Information and
Communication Engineering, Anna University, Chennai in
2009. He has more than 15 years of academic and research
experience and currently he holds the post of Professor and
Head of the Department of Information Technology,
Bannari Amman Institute of Technology, Sathyamangalam,
and Tamilnadu, India. He has published more than 40
research papers in various journals and conferences. He has
organized more than 15 workshops and holds 2 funded
projects. He is a lifetime member of ISTE. He Won Best
M.E Thesis Award at Thiagarajar College of Engineering,
Madurai and best paper award titled, "A Neural Network
Based Classification Model Using Fourier and Wavelet
Features ” Proceedings of the 2nd Int. Conf. on Cognition
and Recognition 2008, (ICCR 2008), Organised by P. E. S.
College of Engineering, Mandaya, Karnataka, India, pp.
664-670, 2008.His research interests include Data mining,
image processing, and mobile networks.
Logeshwari .N has received her
B.TECH degree in Information
Technology from Madras Institute of
Technology, Chrompet, and Anna
University Chennai in 2010 and
2014.She is currently pursuing her
M.E Degree in Anna University
Regional Campus Coimbatore. Her
area of Interest is networks.
CSEIT161112 | Received: 29 July 2016 | Accepted: 05 August 2016 | July-August 2016 [(1)1: 60-65]
International Journal of Scientific Research in Computer Science, Engineering and Information Technology
© 2016 IJSRCSEIT | Volume 1 | Issue 1 | ISSN : 2456-3307
60
A Survey on WSN-based Forest Fire Detection Techniques
Waqas Ali, Abdullah, Ishfaq-ur-rashid
Department of Computer Science, Abdul Wali Khan University Mardan, Pakistan
ABSTRACT
In this paper, we will present a survey on existing studies of forest fire detection system. Every year, thousands of
forest fires across the world cause disasters including thousands of hectares of forests and hundreds of houses.
Various methods are implemented in this area. We will explain in detail the advantages and disadvantages of each
method. In addition, at the end we will show the comparison between the methods that are used for forest fire
detection system.
Keywords: Forest Fire Detection System, GPS, WSN, CCD, MEMS
I. INTRODUCTION
Forest fires generally occur due to human uncontrolled
behavior in social activities and change in weather
conditions. Forest fires may result in human and
animal deaths. They are fatal threat in the world: it is
reported [1] that a total of 77,534 wildfires burned
6,790,692 acres in USA for 2004. Unfortunately,
forest fires are usually only observed when it has
already spread over a large area, making its control
difficult and even impossible at some times. Forest
fires have also a huge impact on atmosphere (30% of
carbon dioxide in the atmosphere comes from forest
fires).
Every year thousands of hectares of forests are
destroyed by fire .carbon monoxide produced from the
areas that are destroyed by fires are more than the
overall automobile traffic. There are many methods
for the detection of forest fires like satellite based
monitoring, wireless sensor networks based detection
etc. The objective is to detect the conditions that
results in forest fires. In this paper we will show the
different techniques that are implemented for the
detection of forest fires and we will briefly discuss its
advantages and disadvantages.
II. METHODS AND MATERIAL
A. Optical Sensors and Camera Surveillance
These systems are also used to detect fire in the forest.
But every technology has it pros and cons. In a
camera-based system, CCD cameras and IR detectors
are installed on top of towers. In case of fire or smoke
activity, the cameras and detectors sense this abnormal
event and report it to a control center. However, the
accuracy of such a system is highly affected by terrain,
time of day, and weather conditions such as clouds,
light reflections, and smoke from innocent industrial
or social activities. Optical sensors or camera systems
in general need to be improved in order to reduce the
number of false alarms due to various dynamic
phenomena, such as wind-tossed trees, cloud shadows,
reflections, and human activity. This kind of
technology only provides a line of sight vision; where
high trees or the hills and mountains can block the
vision; and it might be impossible to provide images
for ignition place. The performance of the camera can
be affected by weather conditions and in darkness. To
cover large area these system was developed with
minimum number of towers; each tower has to detect
smoke in range of 15–80Km, where it requires a long
delay after the ignition to produce a watchable smoke
cloud that can be detected by the camera. These
systems were tried for short distances but for large
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61
areas they are inefficient because the installation of
camera have to be manual and have to be in
appropriate position. However, these systems are very
expensive and the cost of one camera tower is in
thousand dollars.
B. Satellite Based Systems
Another alternative technology for detecting forest
fires is the use of satellites and satellite images.
Usually, satellites provide a complete image of the
earth every 1–2 days. This long scan period, however,
is not acceptable for detecting forest fires quickly.
Additionally, the smallest fire size that can be detected
by such a system is around 0.1 hectare, which also
prevents fire detection just at the time when the fire
starts, and fire localization error is about 1 km, which
is not very accurate. Two main satellites launched for
forest fire detection purposes, the advanced very high
resolution radiometer (AVHRR) [2], launched in 1998,
and the moderate resolution imaging Spector
radiometer (MODIS), launched in 1999 these satellites
are used to detect the forest fire. Unfortunately, these
satellites can provide images of the regions of the
earth every two days and that is a long time for fire
scanning; besides the quality of satellite images can be
affected by weather conditions [3]. Any existing
satellite-based observations for forest fires suffer from
severe limitations resulting in a failure in speedy and
effective control for forest areas. Some of the
limitations in an approach based on direct observation
of forest fires from geostationary (GEO) or Low Earth
Orbit (LEO) satellite are as follows: it might be
impossible to provide a full satellite coverage or even
intermittent coverage.
Geo and Leo satellites are located on orbits over
22,800 miles above the earth‟s surface. The satellite
might not be equipped with transponders, antennas,
amplification reception, regeneration, frequency
translation, and downlink transmission suited for
detection of forest fires. In fact, there may not yet be
formal allocation of the appropriate frequency and
bandwidth for forest fire detection.
C. Wireless Sensor Networks
In recent years, wireless sensor networks (WSNs)
have gained worldwide attention, particularly with the
proliferation of Micro-Electro-Mechanical system
(MEMS) technology that has facilitated the
development of smart sensors. These sensor nodes are
inexpensive and small with limited processing and
computing resources. These sensor nodes can sense
and gather information from the environment and
transmits the sensed data to the user. These sensor
nodes have limited battery power and limited memory
and are normally deployed in difficult to access
locations where humans cannot go easily. In wireless
sensor network, a radio is implemented on every
sensor node, which is used for wireless
communication between nodes and base station. In
WSNs the deployment of sensor nodes are of two
types: Random deployment and replanned deployment.
In random deployment, nodes are deployed normally
from the helicopter or plane and are used in large
wireless sensor network in which the number of nodes
is in thousands. In pre-planned deployment, nodes are
deployed in pre-planned manner and are used in small
wireless sensor network in which the number of sensor
nodes is less than the number of sensor nodes in
random deployment. Maintenance of random
deployment is difficult as compared to the
maintenance of pre-planned deployment. Wireless
sensor network have many application like
Environmental monitoring, Acoustic detection,
Seismic detection, Military surveillance, Process
monitoring etc.
Forest fire detection is the main problem faced by
number of countries all over the world. In early
detection of forest fire, camera surveillance and
satellite based monitoring were used but that was
inefficient in a number of manners. In recent years,
Wireless sensor network was used to detect the forest
fire. A number of research have considered using
wireless sensor network for wood fire system Son et
al.[4] presents in their paper a forest fire detection
system in the south Korean mountains using wireless
sensor networks. WSNs can be connected to the
internet so that the information can be used for future
risks. The developed system consists of WSNs,
middleware and web application. The protocol they
used for routing was MCF (minimum cost path
forwarding) which required a routing table for each
sensor node to find a minimum path to the base station.
Sensor nodes sense the temperature, humidity and
smoke to forward it to the base station node and then
to the gateway. The gateway is connected to the
middle ware and web application, which analyse the
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62
collected data and information on daily basis, and
looks for the likelihood of an event. Son‟s network is
more concerned with detection of the fire and decision
making than the network communication reliability.
They did not discuss the network coverage and
distribution of the sensors.
Hurting et al. [5] in their paper presents FireWxNet, a
multi-tiered portable wireless system for monitoring
weather conditions in rugged wild land fire
environments .In early stages the main aim of their
studies was to investigate the behavior of the forest
fire rather than the detection of the fire. In their
network, they used wireless sensor networks and web
cameras. Wireless sensor network for weather status
and web cameras for images of the fire. Their system
uses a tiered structure beginning with directional
radios to stretch deployment capabilities into the
wilderness far beyond current infrastructures. They
stated that for vision, they used web cameras and for
location information, they used sensor nodes with
small GPS.
Doolin and Sitar [6] proposed wireless sensor network
for wildfire monitoring in which they used
environmental sensors for sensing the temperature,
humidity, and barometric pressure. In addition, with
every sensor node a GPS device is used which is one
of the problems in this network because using a GPS
device with every sensor node will make the network
more expensive. GPS device will consume power so
they will reduce the network lifetime. The nodes will
send the sensed data to the base station. The base
station was connected to the MySQL database and
clients for alarm monitoring. The main problem in this
network is the deployment of sensor nodes are pre-
planned so deploying every sensor manually will be
impossible for large forests. Another problem is the
distance between the sensor nodes are too far so in
case of node failure the connection between sensors
and base station might be lost.
Yu et al. [7] in their paper proposed real-time forest
fire detection with wireless sensor network. To
prolong the network life time they have used neural
wireless sensor network and for routing the data they
relied on clustering algorithm in which the nodes
sends the sensed data to the cluster head and then to
the base station. They have used in-network
processing approach to reduce the communication
between the sensor and saves energy consumption.
Aslan [3] presented a framework for the use WSNs in
forest fire detection and monitoring. Their framework
incorporates the design of four main components of a
wireless sensor network: the deployment scheme, the
logical topology and architecture of the network, the
intra-cluster communication scheme, and the inter-
cluster communication scheme. They used cluster
scheme as network topology. Sensor deployment
scheme was represented as the distance between
sensors, minimum collision, and minimum number of
sensors deployed with full coverage. The
communication between nodes and clusters divided
into initialization phase, risk free phase, fire threat
phase, and progressed fire phase. Nodes enter or
change their phase according to danger rate calculation,
which depends on NFDRS (National Fire Danger
Rating System), temperature, and humidity ranges.
The aim of the intra-cluster communication scheme is
the power balancing for cluster heads.
Lloret et al. [8] in their paper presents a mesh network
of wireless sensors with internet protocol (IP) cameras
in order to detect and verify fire in rural and forest
areas in Spain. In the proposed network they suggested
that the sensor will first detect the fire and then it will
sends information to the base station. The base station
will then sends the response and will switch „on‟ the
camera closest to the event to catch real images and
avoiding false alarms. Their paper is based on testing
the performance of four IP cameras and energy
consumption. The problem in their network is that IP
cameras are not efficient in dark, foggy, and severe
weather conditions and also the transfer of captured
images that will be very huge and that will consume a
lot of energy and will occupy a lot of space. In
addition, we know in sensor network we have limited
memory and limited energy. The installation of IP
cameras should be manual and will be in appropriate
position.
Conrad et al. [9] produced a business case for the
Enhanced Forest Fire Detection System with a GPS
project in Pennsylvania. They say that every year in
Pennsylvania 2554 acres are damaged because of
forest fires, which causes economic loss and potential
loss of human life and environment. They proposed
using fire sensors and GPS devices for the detection of
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63
fire. They want to use the existing technology and
replacing the existing technology with more good ones.
When smoke was detected, the sensors will send a
signal to GPS satellite, and then the GPS satellite
would duplicate the signal to the handheld GPS device
and the central monitoring database to display the fire
location on the installed map for that area. The
problem here is that this project will require a lot of
money and also using GPS devices will reduce the
network lifetime because they will consume much
more energy. Garcia et al. [10] present a simulation
environment that can create a model for a fire by
analysing the data reported by sensor nodes and by
using some geographical information about the area.
The use of topography of the environment
distinguishes the study from some other solutions. The
estimation of the spread of a fire is sent to hand-held
devices of fire fighters to help them in fighting against
the fire in field.
III. COMPARISON AND CONCLUSION
Hefeeda et al. [11] developed a wireless sensor
network for forest fire detection based on Fire
Weather Index (FWI) system, which is one of the most
comprehensive forest fire danger rating systems in
USA. The system determines the spread risk of a fire
according to several index parameters. It collects
weather data via the sensor nodes, and the data
collected is analysed at a centre according to FWI. A
distributed algorithm is used to minimize the error
estimation for spread direction of a forest fire [12-45].
Table 1: Comparison among Various Schemes
IV. REFERENCES
[1] http://www.nifc.gov/fireinfo/2004/index.html,
“Wildland Fire Season 2004 Statistics and
Summaries,” National Interagency Coordination
Center.
[2] NOAA satellite and information service,
“Advanced Very High Resolution Radiometer
AVHRR,” 2012,
http://noaasis.noaa.gov/NOAASIS/ml/avhrr.htm
l.
[3] Y. Aslan, A framework for the use of wireless
sensor networks in the forest fire detection and
monitoring [M.S. thesis], Department of
Computer Engineering, The Institute of
Engineering and Science Bilkent University,
2010.
[4] Son, Y. Her, and K. Kim, “A Design and
Implementation of Forest-Fires Surveillance
System based on Wireless Sensor Networks for
South Korea Mountains,” International Journal
of Computer Science and Network Security, vol.
6, no. 9, pp. 124– 130, 2006.
[5] C.Hartung, R. Han, C. Seielstad, and S.
Holbrook, “FireWxNet: Amulti-tiered portable
wireless systemfor monitoring weather
conditions in wildland fire environments,” in
Proceedings of the 4th International Conference
on Mobile Systems, Applications and Services
(MobiSys ‟06), pp. 28–41, ACM, Uppsala,
Sweden, June 2006
[6] D.Doolin and N. Sitar, Wireless Sensors for
Wild Fire Monitoring, Smart Structure and
Material, San Diego, Calif, USA, 2005.
[7] L. Yu, N. Wang, and X. Meng, “Real-time
forest fire detection with wireless sensor
networks,” in Proceedings of the International
Conference on Wireless Communications,
Networking and Mobile Computing
(WCNM‟05), pp. 1214–1217, September 2005.
[8] J. Lloret, M. Garcia, D. Bri, and S. Sendra, “A
wireless sensor network deployment for rural
and forest fire detection and verification,”
Sensors, vol. 9, no. 11, pp. 8722–8747, 2009
[9] A. Conrad, Q. Liu, J. Russell, and J. Lalla,
“Enhanced Forest Fire Detection System with
GPS Pennsylvania,” 2009.
[10] E. M. Garc´ıa, M. ´ A. Serna, A. Berm´udez, and
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com
64
R. Casado, “Simulating a WSN-based wildfire
fighting support system,” in Proceedings of the
International Symposium on Parallel and
Distributed Processing with Applications
(ISPA‟08), pp.896–902, December 2008.
[11] M.Hefeeda and M. Bagheri, “Wireless sensor
networks for early detection of forest fires,” in
Proceedings of the IEEE International
Conference on Mobile Adhoc and Sensor
Systems (MASS ‟07), October 2007.
[12] Khan, F., & Nakagawa, K. (2013). Comparative
study of spectrum sensing techniques in
cognitive radio networks. In Computer and
Information Technology (WCCIT), 2013 World
Congress on (pp. 1-8). IEEE.
[13] Khan, F., Bashir, F., & Nakagawa, K. (2012).
Dual head clustering scheme in wireless sensor
networks. In Emerging Technologies (ICET),
2012 International Conference on (pp. 1-5).
IEEE.
[14] Khan, F., Kamal, S. A., & Arif, F. (2013).
Fairness improvement in long chain multihop
wireless ad hoc networks. In 2013 International
Conference on Connected Vehicles and Expo
(ICCVE) (pp. 556-561). IEEE.
[15] Khan, F. (2014). Secure communication and
routing architecture in wireless sensor networks.
In 2014 IEEE 3rd Global Conference on
Consumer Electronics (GCCE) (pp. 647-650).
IEEE.
[16] M. A. Jan, P. Nanda, X. He and R. P. Liu,
“PASCCC: Priority-based application-specific
congestion control clustering protocol”
Computer Networks, Vol. 74, PP-92-102, 2014.
[17] Khan, S., & Khan, F. (2015). Delay and
Throughput Improvement in Wireless Sensor
and Actor Networks. In 5th National Symposium
on Information Technology: Towards New
Smart World (NSITNSW) (pp. 1-8).
[18] Khan, F., Jan, S. R., Tahir, M., Khan, S., &
Ullah, F. (2016). Survey: Dealing Non-
Functional Requirements at Architecture
Level. VFAST Transactions on Software
Engineering, 9(2), 7-13.
[19] Khan, F., & Nakagawa, K. (2012). Performance
Improvement in Cognitive Radio Sensor
Networks. the IEICE Japan.
[20] Khan, F., Khan, S., & Khan, S. A. (2015,
October). Performance improvement in wireless
sensor and actor networks based on actor
repositioning. In 2015 International Conference
on Connected Vehicles and Expo (ICCVE) (pp.
134-139). IEEE.
[21] M. A. Jan, P. Nanda, X. He and R. P. Liu, “A
Sybil Attack Detection Scheme for a Centralized
Clustering-based Hierarchical Network” in
Trustcom/BigDataSE/ISPA, Vol.1, PP-318-325,
2015, IEEE.
[22] Jabeen, Q., Khan, F., Khan, S., & Jan, M. A.
(2016). Performance Improvement in Multihop
Wireless Mobile Adhoc Networks. the Journal
Applied, Environmental, and Biological
Sciences (JAEBS), 6(4S), 82-92.
[23] Khan, F. (2014, May). Fairness and throughput
improvement in multihop wireless ad hoc
networks. In Electrical and Computer
Engineering (CCECE), 2014 IEEE 27th
Canadian Conference on (pp. 1-6). IEEE.
[24] Khan, S., Khan, F., Arif, F., Q., Jan, M. A., &
Khan, S. A. (2016). Performance Improvement
in Wireless Sensor and Actor Networks. Journal
of Applied Environmental and Biological
Sciences, 6(4S), 191-200.
[25] Khan, F., & Nakagawa, K. (2012). B-8-10
Cooperative Spectrum Sensing Techniques in
Cognitive Radio Networks. 電子情報通信学会
ソサイエティ大会講演論文集, 2012(2), 152.
[26] Khan, F., Jan, S. R., Tahir, M., & Khan, S.
(2015, October). Applications, limitations, and
improvements in visible light communication
systems. In2015 International Conference on
Connected Vehicles and Expo (ICCVE)(pp. 259-
262). IEEE.
[27] Jabeen, Q., Khan, F., Hayat, M. N., Khan, H.,
Jan, S. R., & Ullah, F. (2016). A Survey:
Embedded Systems Supporting By Different
Operating Systems. International Journal of
Scientific Research in Science, Engineering and
Technology (IJSRSET), Print ISSN, 2395-1990.
[28] Jan, S. R., Ullah, F., Ali, H., & Khan, F. (2016).
Enhanced and Effective Learning through
Mobile Learning an Insight into Students
Perception of Mobile Learning at University
Level. International Journal of Scientific
Research in Science, Engineering and
Technology (IJSRSET), Print ISSN, 2395-1990.
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com
65
[29] Jan, S. R., Khan, F., & Zaman, A. The
perception of students about mobile learning at
University level.
[30] M. A. Jan, P. Nanda, X. He, and R. P. Liu, “A
Sybil Attack Detection Scheme for a Forest
Wildfire Monitoring Application,” Elsevier
Future Generation Computer Systems (FGCS),
“Accepted”, 2016.
[31] Jan, S. R., Shah, S. T. U., Johar, Z. U., Shah, Y.,
& Khan, F. (2016). An Innovative Approach to
Investigate Various Software Testing
Techniques and Strategies. International
Journal of Scientific Research in Science,
Engineering and Technology (IJSRSET), Print
ISSN, 2395-1990.
[32] Khan, I. A., Safdar, M., Ullah, F., Jan, S. R.,
Khan, F., & Shah, S. (2016). Request-Response
Interaction Model in Constrained Networks. In
International Journal of Advance Research and
Innovative Ideas in Education, Online ISSN-
2395-4396
[33] Azeem, N., Ahmad, I., Jan, S. R., Tahir, M.,
Ullah, F., & Khan, F. (2016). A New Robust
Video Watermarking Technique Using H.
264/AAC Codec Luma Components Based On
DCT. In International Journal of Advance
Research and Innovative Ideas in Education,
Online ISSN-2395-4396
[34] Jan, S. R., Khan, F., Ullah, F., Azim, N., &
Tahir, M. (2016). Using CoAP Protocol for
Resource Observation in IoT. International
Journal of Emerging Technology in Computer
Science & Electronics, ISSN: 0976-1353
[35] Azim, N., Majid, A., Khan, F., Jan, S. R., Tahir,
M., & Jabeen, Q. (2016). People Factors in
Agile Software Development and Project
Management. In International Journal of
Emerging Technology in Computer Science &
Electronics (IJETCSE) ISSN: 0976-1353
[36] Azim, N., Majid, A., Khan, F., Tahir, M., Safdar,
M., & Jabeen, Q. (2016). Routing of Mobile
Hosts in Adhoc Networks. In International
Journal of Emerging Technology in Computer
Science & Electronics (IJETCSE) ISSN: 0976-
1353.
[37] Azim, N., Khan, A., Khan, F., Majid, A., Jan, S.
R., & Tahir, M. (2016) Offsite 2-Way Data
Replication toward Improving Data Refresh
Performance. In International Journal of
Engineering Trends and Applications, ISSN:
2393 – 9516
[38] Tahir, M., Khan, F., Jan, S. R., Azim, N., Khan,
I. A., & Ullah, F. (2016) EEC: Evaluation of
Energy Consumption in Wireless Sensor
Networks. . In International Journal of
Engineering Trends and Applications, ISSN:
2393 – 9516
[39] M. A. Jan, P. Nanda, M. Usman, and X. He,
“PAWN: A Payload-based mutual
Authentication scheme for Wireless Sensor
Networks,” Concurrency and Computation:
Practice and Experience, “accepted”, 2016.
[40] Azim, N., Qureshi, Y., Khan, F., Tahir, M., Jan,
S. R., & Majid, A. (2016) Offsite One Way Data
Replication towards Improving Data Refresh
Performance. In International Journal of
Computer Science Trends and Technology,
ISSN: 2347-8578
[41] Safdar, M., Khan, I. A., Ullah, F., Khan, F., &
Jan, S. R. (2016) Comparative Study of Routing
Protocols in Mobile Adhoc Networks. In
International Journal of Computer Science
Trends and Technology, ISSN: 2347-8578
[42] Tahir, M., Khan, F., Babar, M., Arif, F., Khan,
F., (2016) Framework for Better Reusability in
Component Based Software Engineering. In the
Journal of Applied Environmental and
Biological Sciences (JAEBS), 6(4S), 77-81.
[43] Khan, S., Babar, M., Khan, F., Arif, F., Tahir, M.
(2016). Collaboration Methodology for
Integrating Non-Functional Requirements in
Architecture. In the Journal of Applied
Environmental and Biological Sciences
(JAEBS), 6(4S), 63-67
[44] Jan, S.R., Ullah, F., Khan, F., Azim, N., Tahir,
M., Khan, S., Safdar, M. (2016). Applications
and Challenges Faced by Internet of Things- A
Survey. In the International Journal of
Engineering Trends and Applications, ISSN:
2393 – 9516
[45] M. A. Jan, P. Nanda, X. He, and R. P. Liu, “A
Lightweight Mutual Authentication Scheme for
IoT Objects,”, “Submitted”, 2016.
CSEIT161113 | Received: 02 August 2016 | Accepted : 07 August 2016 | July-August 2016 [(1)1: 66-71]
International Journal of Scientific Research in Computer Science, Engineering and Information Technology
© 2016 IJSRCSEIT | Volume 1 | Issue 1 | ISSN : 2456-3307
66
Congestion Detection and Mitigation Protocols for Wireless Sensor Networks
Muhammad Zeeshan, Fazlullah Khan, Syed Roohullah Jan
Deptartment of Computer Science, Abdul Wali Khan University Mardan, Pakistan
ABSTRACT
Congestion control is tremendously important area within wireless sensor networks (WSN). With the appearance of
new network applications, the non-stop increasing traffic is starting to experience unexpected situation of network
congestion. Congestion in wireless sensor network affects the nonstop flow of data, loss of information, delay and
reduces the energy of nodes due to overhead of retransmission. Therefore, congestion needs to be control in wireless
sensor network in order to extend system lifetime, improve fairness and quality of service and to attain high energy
efficiency. This paper revives different routing protocols used in wireless sensor networks to mitigate and control
congestion and to provide consistency for different applications and prolong the life of the wireless sensor network.
Keywords : WSN (Wireless sensors networks), Congestion, Congestion Control.
I. INTRODUCTION
Wireless sensor networks (WSN)[1] consists of
various wireless devices mount with various types of
sensors, shown in Fig.1 to assemble information such
as temperature, pressure, humidity, sound, vibration
and wind speed from the surroundings. Wireless
sensor network widely applied to environment such as
environmental monitoring, target tracking, habitat
monitoring, healthcare, telecommunication monitoring,
military surveillance and factory monitoring. When
the event is occurring for which the system is installed
the sensors activate and begin to send the data to the
base station.
Figure 1: Wireless Sensor network
If the event is occurring frequently or with high value
or many sensors capture the same event occurring they
will send more packets to the base station as a result
congestion starts from that point and spreads along its
links as shown in Fig 2. Data crossing that sub-
network (area of congestion) would suffers from
prolonged delays (buffer waiting) eventually leading
to timeouts (loss rate).
Figure 2 : Congestion Scenario in WSN
Initially the researchers are interested in the design of
routing scheme to enable data transfer in WSN. But
later on they realized that there must be such a
mechanism to address the situation when there is
chance of congestion or congestion has occurred. In
this paper we give an overview of the congestion
control and detection protocols/Techniques in
Wireless Sensor Networks. We have to first avoid the
congestion so that the congestion did not occur and if
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67
the congestion occurs then we will try to mitigate the
congestion using different techniques.
In Section “A” we have discussed FUSION which is
congestion detection and avoidance mechanism uses
hop by hop congestion detection like CODA [3] but
the difference is that it uses implicit notification or
congestion notification bit in the header of packet. It
also include prioritized MAC scheme to ensure
fairness i.e. unlike CSMA[2] which gives equal
chance to every node to transmit their data but in this
technique the high priority nodes which have more
data then we assign extra time to drain their buffer. In
Section “B” we have discuss the CODA (Congestion
detection and avoidance) technique which uses the
buffer overflow and channel sensing for congestion
detection. Once the congestion is detected we use the
back pressure and close loop method to inform the
other nodes that the congestion is occurred and also to
inform them to limit their sending data rate. In section
“C” we have discuss anther protocol for congestion
detection and avoidance named CCF(Congestion
control and Fairness) protocol which states that every
node is able to control the rate of its downstream
nodes This allows the root node to reduce the
generation rates of all downstream nodes. By reducing
the transmission rates of all downstream nodes when
this node's queue is full or about to become full, it
allows the queue to empty. CCF guarantees simple
fairness so that each node receives the same
throughput. In Section "D" we have discussed another
congestion control protocol which is priority based
congestion control protocol for wireless sensor
network. The node priority index is brought in to show
the importance of every one node. In Section “E” we
have discussed another congestion control protocol
named ECODA (Enhanced Congestion Detection and
Avoidance) protocol which is the superior version of
CODA which uses dual buffer thresholds to detect
congestion and a queue scheduler that arrange the
packets for transmission and to drop the packets with
low priority when the buffer is about to full or accedes
the upper limit of the buffer. On node level it uses the
back pressure method and bottle neck data sending
rate control for congestion mitigation. ECODA cares
of the high priority packets in the manner if the
congestion has occurring the flexible queue scheduler
select the low precedence packets for dropping to
defeat the congestion.
II. METHODS AND MATERIAL
A. Related Work
FUSION MECHANISIM
FUSION IS DESIGN TO PROVIDE UPSTREAM
CONGESTION CONTROL MECHANISM IT CONSISTS OF
THREE CONGESTION MITIGATION TECHNIQUES
APPLY IN DIFFERENT LAYERS, THAT IS
i. Hop-by-hop flow control
Using hop by hop flow control a sensor node present
congestion detection and congestion mitigation
congestion is discover through both queue occupancy
and channel sampling techniques. The hop by hop
flow control scheme in FUSION is similar to
backpressure scheme in CODA[9]. The only
difference in fusion is that each sensor node sets a
congestion bit in the header of every outgoing packet
instead of using backpressure messages.
ii. Rate limiting of source traffic in the traffic in
the transit sensor nodes to provide fairness.
When a sensor node overhear a packet from its parent
node (the node closer to the sink) with the congestion
bit is set, it stops forwarding data toward the sink,
Rate limiting is a defensive scheme to avoid
congestion.
iii. Prioritized MAC(Medium Access Control)
FUSION also includes a prioritized MAC scheme to
guarantee that congested nodes receive prioritized
access to the channel. In traditional CSMA mechanism
each node has the same opportunity to send the data
over the channel but in WSN the parent’s nodes that
are closed to the sink may gather more traffic and can
overflows if it does not have more chances to send out
its packets. To avoid this problem FUSION uses a
random back-off time for each node is introduced
which it related to its local congestion state so that the
congested nodes may drain its buffer faster.
CODA (Congestion Detection and Avoidance)
CODA is an energy conserving and efficient control
technique that is designed to solve congestion in the
upstream direction i.e., the sensor to sink direction.
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68
It involves three mechanism which is following:
B. Congestion Detection
Accurate and efficient congestion detection plays an
important role in the congestion control of wireless
networks. The detection method in CODA is the
receiver based congestion detection. CODA uses a
combination of the present and past channel loading
conditions, and the current buffer occupancy, to infer
accurate detection of congestion at each receiver with
low cost. Sensor networks must know the state of the
channel since the transmission medium is shared and
may be congested with traffic between other nodes in
the neighborhood. Listening to the channel to measure
local loading incurs high energy costs if performed
continually. Therefore, CODA uses a sampling
scheme that activates local channel monitoring at the
appropriate time to minimize cost while forming a
accurate estimate.
Figure 3: Congestion detection and notification in
CODA
i. Open-loop, hop-by-hop backpressure
Once congestion is detected node broadcast
backpressure message and this message circulate
towards the source as shown in Fig. 3. In CODA, a
node broadcasts backpressure mechanism as long as it
detects congestion. The node detecting congestion will
report its upstream neighbors to reduce rate of data
flow. A node that receives a backpressure message
will adjust its sending rate by Adaptive Increase
Multiplicative Decrease, the AIMD rate adjustment
technique or by dropping packets based on the local
congestion plan. When an upstream node (towards the
source) receives the backpressure message, it decides
whether it has to further propagate the backpressure
upstream based on the local network situation.
ii. Closed-loop, multisource regulation
The cost of closed loop flow control is high compared
to open loop flow control because it required feedback
signaling. CODA runs Closed-loop congestion control
mechanism on the sink to regulate multiple sources in
the case of continual congestion. Essentially when the
transmission rate of a source surpass maximum
theoretical throughput (Smax) the source informs the
sink by setting a bit in every packet that it transmits to
the sink as long as the transmission rate remains
higher than Smax. in response sink starts sending
ACKs to the source until the sink detects congestion.
When the sink detects congestion, it stops sending
ACKs until the congestion is alleviated, to implicitly
notify to drop its rate.
Disadvantages of CODA
1. Unidirectional control from sensors to sink.
2. Decreased reliability.
3. The delay and response time increases under
heavy closed loop congestion
CCF(Congestion Control and Fairness)
In this section we propose a distributed and scalable
algorithm that purges congestion within a sensor
network, and that ensures the fair delivery of packets
to a central node, or base station. We say that fairness
is achieved when equal number of packets is received
from each node. Congestion control and fairness [4]
for many-to-one routing method is a distributed and
scalable algorithm which eradicates the congestion in
a wireless sensor network. CCF provides hop-by-hop
upstream congestion control that not only eliminates
congestion but also ensure fair delivery of packets to
the base station [6] in CCF fairness is achieved
when an equal number of packets are received from
each node to the base station or sink by maintaining a
separate queue for each their preceding hop node. CCF
detects congestion based on packet service time and
control congestion based on hope-by-hope manner. In
CCF the congestion of any node can be evaluated by
the number of available child nodes (downstream
nodes) the average rate at which the packets can be
sent by it. When the congestion is experienced it
informs the downstream nodes to reduce their data rate
and vice versa by implicit notification like closed loop
hop-by-hop backpressure in CODA [5] .
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Limitations:
i. CCF uses only packet service time to detect
congestion and therefore it cannot detect either
under utilization nodes or links.
ii. CCF does not provide any reliability mechanism
i.e. preserve equal resource for each sensor node.
PCCP ( Priority Based Congestion Control Protocol):
In Priority based Congestion Control [7] node priority
index is introduced to reflect the importance of each
node. PCCP is a more rapid and more energy well-
organized congestion control algorithm than CCF.
PCCP maintains a priority index which is used as an
indicator of the importance of each node. The packet
inter arrival time and the packet service time are used
together to find out the degree of congestion.
Intelligent congestion detection (ICD)
Implicit congestion notification (ICN)
Priority based rates adjustment (PRA)
C. Intelligent congestion detection (ICD):
Not like in TCP in which congestion is detected at the
endpoints in PCCP congestion is locally detected in
the intermediary nodes, based on the mean packet
inter-arrival time (ta) which is the mean of the time
between two successively arriving packets and the
packet service time (ts) [8] which is the time between
the MAC layer and the successful broadcast of the last
bit ICD can be define as
d (i) = ts / ta
The congestion degree helps in estimating the current
congestion level at each intermediate node.
D. Implicit Congestion Notification (ICN)
PCCP uses the technique of ICN to attached
congestion information in the header of the data
packets. The congestion information is stored in the
header of the packets that are forwarded when trigged
by either of the following two events.
When the threshold is exceeded by the number of
packets forwarded by a node.
When a congestion notification is heard by a node
from its parent node.
At every node "i" PCCP attached ta, ts and overall
priority value.
E. Priority based rates adjustment (PRA)
The additive increase and multiplicative decrease
(AIMD) used in conventional transport control
protocols such as TCP is not of much help in adjusting
the transmission rate as the congestion notification bit
holding limited information. Therefore it is important
for the nodes to be notified as to precisely how much
to increase or decrease the rate the congestion degree.
The priority index and the global priority values help
in providing more information for exact rate
adjustment.
ECODA (Enhanced Congestion Detection and
Avoidance)
ECODA is an energy efficient congestion control
scheme for sensor networks. In this method, the
given method is followed.
i. Dual Buffer threshold Congestion detection:
The dual buffer thresholds and weighted buffer
difference are used to detect the congestion. The Fig
shows the details of buffer state such as “accept state
”, “filter state” and “reject state)”. The different buffer
states are pretend different channel loading which is
used to accept or reject packets in different states.
Figure 4: Dual Buffer threshold Congestion
The packet at each node has to send for buffer
monitoring and attached its weighted buffer changing
rate (WR) and weighted queue length (WQ) with
outgoing packets. The corresponding congestion level
bit in the outgoing packet header is set if a node’s
buffer occupancy exceeds a certain threshold.
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70
The ECODA [10] care of high priority packets, if the
node data is most essential among its neighbors then if
congestion take place, other nodes should lower down
their data sending rate to mitigate node’s congestion
so that the high priority packets may reach to the
station in time.
Two thresholds Qmin and Qmax are used to show
different buffer states. Different buffer states imitate
different channel loading, corresponding strategy is
adopted to accept or reject packets in different states.
ii. Flexible queue scheduler and weighted
fairness:
The Flexible Queue Scheduler is used to drop a low
priority packet rather than the high priority packet
when a high priority packet arrives if the queue in a
sensor node is nearly full and dominated with low
priority packets. At the same time, the high priority
packet may be dropped due to queue overflow with
tail-dropping.
For managing packets with different strategy, two
thresholds are used such as Qmin and Qmax .
The scheduler works based on the following steps:
a. If ( 0 ≤ N ≤ Qmin) :
All incoming packets are buffered, because queue
utilization is low.
b. If ( Qmin ≤ N ≤ Qmax ) :
Some packets with low priority are dropped or
overwritten by succeeding packets with high
priority.
c. If ( Qmax ≤ N ≤ Q ):
Some packets with high dynamic priority is
dropped or overwritten, then the expected average
buffer length increases at a rate of two variables
that can be tuned to achieve best possible system
performance.
iii. A bottleneck-node-based source sending rate
control scheme
Both transient (temporary) and persistent (continiual)
congestion handle by ECODA. It makes use of hop-
by-hop backpressure mechanism to handle transient
congestion. It uses bottleneck node based source
sending rate control and multipath load balancing to
handle persistent congestion. This mechanism does not
required explicit ACK from sink; every node
determines routing path status from sink and sender
find better path to forward data. Bottleneck node
identified and source data sending rate adjust more
accurately using this mechanism.
III. CONCLUSION
In this paper we have reviewed different wireless
sensor routing protocols / algorithm for congestion
control, reliability and packet loss. There are several
protocols for congestion control in wireless sensor
network. This paper present an overview of several
congestion control techniques like FUSION, CODA,
CCF, PCCP and ECODA. Also the comparative
analysis of congestion control techniques is presented
using several parameters like throughput, delay and
energy consumption which shows among the these
techniques Enhanced Congestion Detection and
Avoidance (ECODA) is best for congestion control as
it attain high throughput, less delay & less
consumption of energy compare to other techniques
[12-20].
Comparision Table of congestion control protocols
IV.REFERENCES
[1] Hull, B., Jamieson, K., Balakrishnan, H.:
Mitigating congestion in wireless sensor
networks. In: 2nd International Conference on
Embedded Networked Sensor Systems.
Maryland (2004)
[2] Akyildiz, W. Su, Y. Sankarasubramaniam,
and E. Cayirci, "A survey on sensor
Volume 1 | Issue 1 | 2016 | www.ijsrcseit.com
71
networks," IEEE Communications Magazine,
Vol: 40 Issue: 8, pp. 102-114, August 2002
[3] P802.11, IEEE Draft Standard forWirelessLAN
Medium Access Control (MAC) and Physical
Layer (PHY) Specifications, D2.0, July 1995.
[4] Wan C -Y, Eisenman S B, Campbell A T,
CODA: Congestion detection and avoidance in
sensor networks. In the Proceedings of the First
International Conference on Embedded
Networked Sensor Systems (SenSys’03), Los
Angeles, CA, USA, 2003,pp. 266–279
[5] V. Vijayaraja, Dr. R. Rani Hemalini,
“Congestion in Wireless Sensor Networks and
various techniques for Mitigation Congestion- A
review”. IEEE International Conference on
Computational Intelligence and computing
Research.
[6] Chien- Yin Wan, Shane B. Eiseaman, Andrew
T. Champbell, “ CODA: Congestion Detection
and Avoidance”.
[7] V. Vijayaraja, Dr. R. Rani Hemalini,
“Congestion in Wireless Sensor Networks and
various techniques for Mitigation Congestion- A
review”. IEEE International Conference on
Computational Intelligence and computing
Research.
[8] Yaghmaee, Mohammad Hossein, and Donald
Adjeroh. "A new priority based congestion
control protocol for wireless multimedia sensor
networks." In World of Wireless, Mobile and
Multimedia Networks, 2008. WoWMoM 2008.
2008 International Symposium on a, pp. 1-8.
IEEE, 2008.
[9] Yaghmaee, Mohammad Hossein, and Donald
Adjeroh. "A new priority based congestion
control protocol for wireless multimedia sensor
networks." In World of Wireless, Mobile and
Multimedia Networks, 2008. WoWMoM 2008.
2008 International Symposium on a, pp. 1-8.
IEEE, 2009.
[10] Wan ,C-Y, Eisenman, SB, Campbell, AT..
CODA: Congestion detection and avoidance in
sensor networks. In: Proceedings of
ACMSensys’03, November5–7, 2003,
LosAngeles, California, USA; 2003.
[11] Tao LQ, Yu FQ. ECODA: enhanced congestion
detection and avoidance for multiple class of
traffic in sensor networks. Transactions on
Consumer Electronics 2010.
[12] M. A. Jan, P. Nanda, X. He and R. P. Liu,
“PASCCC: Priority-based application-specific
congestion control clustering protocol”
Computer Networks, Vol. 74, PP-92-102, 2014.
[13] Khan, F., Bashir, F., & Nakagawa, K. (2012).
Dual head clustering scheme in wireless sensor
networks. In Emerging Technologies (ICET),
2012 International Conference on (pp. 1-5).
IEEE.
[14] Khan, F. (2014). Secure communication and
routing architecture in wireless sensor networks.
In 2014 IEEE 3rd Global Conference on
Consumer Electronics (GCCE) (pp. 647-650).
IEEE.
[15] Khan, F., Jan, S. R., Tahir, M., Khan, S., &
Ullah, F. (2016). Survey: Dealing Non-
Functional Requirements at Architecture Level.
VFAST Transactions on Software Engineering,
9(2), 7-13
[16] M. A. Jan, P. Nanda, X. He and R. P. Liu, “A
Sybil Attack Detection Scheme for a Centralized
Clustering-based Hierarchical Network” in
Trustcom/BigDataSE/ISPA, Vol.1, PP-318-325,
2015, IEEE.
[17] Khan, F., & Nakagawa, K. (2012). Performance
Improvement in Cognitive Radio Sensor
Networks. the IEICE Japan.
[18] Khan, F., Khan, S., & Khan, S. A. (2015,
October). Performance improvement in wireless
sensor and actor networks based on actor
repositioning. In 2015 International Conference
on Connected Vehicles and Expo (ICCVE) (pp.
134-139). IEEE.
[19] M. A. Jan, P. Nanda, X. He, and R. P. Liu, “A
Sybil Attack Detection Scheme for a Forest
Wildfire Monitoring Application,” Elsevier
Future Generation Computer Systems (FGCS),
“Accepted”, 2016.
[20] M. A. Jan, P. Nanda, M. Usman, and X. He,
“PAWN: A Payload-based mutual
Authentication scheme for Wireless Sensor
Networks,” Concurrency and Computation:
Practice and Experience, “accepted”, 2016.