A Survey on Energy Efficient Data Aggregation Protocols ... · PDF file... Span, TBF, BVGF,...
Transcript of A Survey on Energy Efficient Data Aggregation Protocols ... · PDF file... Span, TBF, BVGF,...
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 10 (2016) pp 6990-7002
© Research India Publications. http://www.ripublication.com
6990
A Survey on Energy Efficient Data Aggregation Protocols for Wireless
Sensor Networks
M. Shanmukhi1 and O. B. V. Ramanaiah
2
1CSE Department, Malla Reddy College of Engineering, Hyderabad, India.
2CSE Department, JNTUH, Hyderabad, India.
Abstract
Wireless sensor networks (WSN) consist of several sensor nodes which sense different parameters of weather, soil, etc,
and send the same to sink/base station. The sensor node has
very limited battery power that is used for sensing, computing,
and communication. Elimination of redundant sensed data and
aggregation of nearest sensor values is necessary in order to
minimize the communication overhead. Hence to conserve the
battery power and thereby enhance the lifetime of a WSN, we
need energy efficient data aggregation mechanisms. Our
objective is to differentiate the comprehensive study of data
representation model in data aggregation protocols based on
energy conservation model using networks based systems. And also intends to study about the various data aggregation
network based protocols in the aspects of various factor such
as Aggregation Method, Resilience to Link Failure, Setup
Overhead, Scalability, Resilience in case of node mobility,
Energy saving method and Timing strategy.
Keywords: WSN, Data Aggregation Protocols, Battery
Conservation and communication overhead.
INTRODUCTION
A Sensor network is a collection of sensor nodes with sensing, computing and communication capabilities. Sensor nodes
have limited battery energy, besides it is not possible either to
recharge or replenish the node battery once it is deployed in
the field [1]. In a deployed WSN, the data gathering and its
radio communication to sink node (base station) consumes
more energy. Hence we need energy efficient data gathering
and processing mechanisms to enhance the network lifetime.
A sensor node is a tiny device that composed of three portions
[2]:
The data is collected from the physical environment
using the sensing subsystem.
Data manipulation and storage using the processing
subsystem.
Data transmission using wireless communication
subsystem.
In Wireless Sensor Networks, the efficient use of energy plays
a vital issue. In order to extend the lifetime of the sensor
nodes, the energy is a very scrimpy segment. The energy
sources may be of “economical” or “non-economical”. The
economical energy conservation is designated as sending/
receiving data, manipulating the queries and promoting the data and queries to its close-by nodes [3]. The non-
economical energy conservation leads to idle listening,
collision, packet loss, overhearing, control packet overhead
and over-emitting the data. The energy savvy technique is mainly subdivided into a Network subsystem and Sensing
subsystem. Here we analyzed the sensing subsystem to
increase the lifetime of the sensor nodes using the data driven
approach named, Data Aggregation [4].
Data aggregation is defined as gathering and aggregating the
sensed data to get the meaningful information. It is a
fundamental processing method to save energy and effective
way for saving the limited resources [5]. The main aim of a
data aggregation protocol is the process of gathering sensed
data and its aggregation in an energy efficient way so that the
sensor network life time is enhanced. Data aggregation eliminates the redundancy and hence reduces the size of the
data to be communicated to the sink node. The Figure 1
illustrates the working procedure of an aggregation algorithm
[6]. The data is collected by the sensor node is given to the
aggregation algorithm. The aggregated data as an output is
communicated to the sink node. Data aggregation eliminates
the redundancy and hence reduces the size of the data to be
communicated to the sink node. Data representation is an
important stage in the data aggregation.
Figure 1: Data Aggregation System [6]
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 10 (2016) pp 6990-7002
© Research India Publications. http://www.ripublication.com
6991
The paper is unionized as follows: Section 2 describes the
working of Data Aggregation. The various data aggregation
based networks are explored in Section 3. In Section 4, energy
efficient based network protocols are discussed. A new
thought is concluded in Section 5.
DATA AGGREGATION ARCHITECTURE
Figure 2: Architecture of Data Aggregation [1] [2]
The working principle of Data Aggregation is as follows [1]:
i. Select the group of nodes and separate into a cluster.
ii. The cluster should satisfy the following parameters
such as RSSI, TTL, MSRC, bandwidth, battery
consumption to represent the node in a cluster.
iii. On these above said criteria, the Cluster Head (CH)
is selected among the nodes in a cluster.
iv. The CH should be responsible for all nodes in a
cluster. It should handle the newly arrived nodes,
information exchange, information updation and
information transfer. v. The newly arrived node is chosen to be a CH, in case
of global cost is minimum.
vi. If global cost is maximum then nodes in a cluster is
chosen as CH and global cost is reestimated.
vii. In data aggregation process, the queries and its data
from user end are processed and exchanged to the
query processor.
viii. Then these data are gathered by data cube approach
and the base station acquire the aggregated data from
data cube approach.
DATA AGGREGRATION BASED NETWORKS
There are two types of networks namely, Flat Networks and
Hierarchical Networks [7].
FLAT NETWORKS
In Flat Networks, all the sensor nodes are equipped with same
battery power, and play the same role [7] [8]. In this type of
networks, data aggregation is achieved by data centric routing
[9]. In data centric routing the sink node transmits query
message to the sensor nodes via flooding or other
broadcasting techniques. The sensor nodes which have the data matching with the query message send reply back to the
sink node.
Figure 3: Flat Network [9]
HIERARCHICAL NETWORKS
The computation and communication burden is high at sink
node in flat networks. Consequently a lot of energy is
consumed. In Hierarchical Networks, special nodes in the
field perform the data aggregation and the aggregated data is
sent to the sink node. All the sensor [8] nodes send their
sensed data to the special nodes based on the query received.
The special nodes reduce the data communicated to the sink
by the use of aggregation technique. This results in the
reduction of energy consumption. Hence, energy is utilized
efficiently in Hierarchical Networks. As a result network life
time is increased [10].
Figure 4: Hierarchical Networks [10]
Table 1: Difference between Flat and Hierarchical Networks
based on the parameters [10]
Parameters Flat Networks Hierarchical
Networks
Data Aggregation
performed by the nodes along the path to sink
Performed by Leader nodes or
CHs
Overhead On the nodes in the
Communication path to the
Sink
Only on CH
Fault
Tolerance
if the sink node fails it
results in the breakdown of
the entire network
Even if CH fails the
network can still be
in operation
Latency Latency is high because the Latency is low
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 10 (2016) pp 6990-7002
© Research India Publications. http://www.ripublication.com
6992
communication to sink via
multi hop link
because
communication to
the sink through CH
Routing Optimal routing with
guaranteed overhead
Routing is simple
but may not be
optimal
Node
Heterogeneit
y
It cannot utilize node
heterogeneity for
improving energy efficiency
Node heterogeneity
can be used to
assign high energy nodes as CHs
ENERGY EFFICIENT ROUTING PROTOCOLS
Routing protocols are very important phase in supporting the
Data Aggregation process. The objective of the data
aggregation is to lessen the energy consumption. In accord to
promote the network aggregation, the sensor nodes should
track the packets based on content of the data packets and also
select the next hop [10]. Since there is no specific
infrastructure in WSNs, the sensor node should meet the
energy saving requirements. Routing protocols developed in WSN for energy efficient is tabulated based on seven
categories:
Table 2: Routing Protocols in WSN [10]
Category Representative Protocols
Location
based
protocols
MECN, SMECN, GAF, GEAR, Span, TBF,
BVGF, GeRaF
Data-centric
Protocols
SPIN, Directed Diffusion, Rumor Routing,
COUGAR, ACQUIRE, EAD, Information-
Directed Routing, Gradient Based Routing,
Energy-aware Routing, Information-Directed
Routing, Quorum-Based Information
Dissemination, Home Agent Based Information Dissemination
Hierarchical
Protocols
LEACH, PEGASIS, HEED, TEEN, APTEEN
Mobility-
based
Protocols
SEAD, TTDD, Joint Mobility and Routing,
Data MULES, Dynamic Proxy Tree-Base Data
Dissemination
Multipath-
based
Protocols
Sensor-Disjoint Multipath, Braided Multipath,
N-to-1 Multipath Discovery
QoS-based
protocols
SAR, SPEED, Energy-aware routing
LOCATION BASED PROTOCOLS
The sensor nodes are covered by their locations is known as
location based protocols. The distance between two nodes in a sensor networks is estimated for its location information.
Here, we reviewed some of the protocols designed based on
the location-oriented routing protocols.
Li and Joseph Y. Halpern [11] proposed a method,
Subnetwork that obtains a minimum energy path between the
nodes. They computed at least one minimum energy path
between the node pairs to enhance the energy saving process.
In grid based architecture, the energy consumption is handled
by improving its communication security and cost by
Melkang Qlu et al [12]. The Access Points (AP) is also used
between the node communications. Each node possesses some
energy and this energy is harvested for further use was
proposed by Liang Liu et al [13]. In Multihop framework, the
data packets are transferred between the nodes from Cluster Header. Using the tree based deployment approach the energy
transmission ratio is kept fixed which tries to solve the NP-
complete problem [14]. When minimum energy is deployed,
the maintenance cost is increased simultaneously [15]. To
efficiently utilize the energy at low cost, a segment in data
link acquires Automatic Repeat Request (ARQ) protocol is
utilized. The concept of virtual Multiple Input Multiple
Output (MIMO) in a cross layer approach [16] can also used
to reduce overall energy consumption in per packet
transmission is modeled. The centralized algorithms are
employed to localize the energy consumption [17]. A one-to-
many protocol in dense networks is used to transfer the information between nodes using transmission power
limitation.
The broadcasting process is usually consumes a lot of energy.
It can also optimized by reducing the retransmissions rate by
maximizing the hop length. Without the neighbor information,
optimizing the broadcast networks leads to low
communication and memory overhead [18] [20] [21]. A kind
of trust management is employed between the nodes and
sensing report is maintained. The target is to reduce global
False Alarm (FA) and Missed Detection (MD). The
probabilities rate should be maintained using data fusion techniques [19] [23][24]. Anis Ouni et al framed an energy
efficient protocol using mesh networks. A linear programming
model is used to show the relationship between the throughput
and energy consumption. Thus the combination of single hop
and multi-hop routes are generated for continuous power
control [25] [26]. In terms of QOS, the Wireless Sensor
Networks in data aggregation process. The node reliability
and transmission distance should be minimized [27]. In tree
based approach, the data transmitting energy is consumed.
Applications such as Cognitive radios, the proper use of
energy efficient is important. The resource allocation
protocols are enabled to obtain a near-optimal with low-complexity channel assignment was initiated and framed by
Kandasamy Illanko et al [28]. Hamed Yousefi et al suggested
the concept of scheduling in the data aggregation techniques
[29]. The idea behind their research is to minimize the latency
time so as to schedule the process and thus in this way the
energy can be saved. He suggested a collision free schedule
with least number of time slots [30].
In a cluster approach, the resource efficiency and
dependability is vital issue in energy saving system. Xiaoyong
Li et al framed a lightweight and dependable trust system to
avoid the effect of malicious nodes. The cluster member or head feedback is avoided due to dependable trust system
which automatically improves the energy-saving [31].
Topology management protocols should be well defined in
utilizing the energy in sleep transitions of the nodes. A static
topology management protocol is efficient in real time WSN
that reduce the bound delay and route fidelity [32]. On the
contrary to static process, dynamic topology management was
initiated to balance the nodes and enhance the network
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 10 (2016) pp 6990-7002
© Research India Publications. http://www.ripublication.com
6993
lifetime by managing event driven data transmissions. The
process of synchronization between the nodes has established
in [33]. Regression analysis is used to estimate the
relationship between two variables. In Multihop architecture,
the sources of errors and data transmission rate are estimated
to find a new relationship between the nodes. The nodes in a cluster may be local or global. A k level hierarchical structure
with low energy localized clustering algorithm established
with parameters such as residual energy of nodes, the
aggregation degree and lifetime of the nodes [34].
Duty cycling is an important issue in densely connected
wireless sensor networks. The local information about the
node is gathered in the geographic oriented system. The k
covered WSNs are estimated by the Cover Sense Inform (CSI)
framework which intends to merge the sensor scheduling and
data forwarding. The high k coverage is actualized by k active
sensors to the sink node to eliminate the intruder detection and
tracking [35]. Data fidelity is also used for energy efficiency in data aggregation schemes. The Compressed Sensing (CS) is
employed to achieve the data fidelity. The diffusion wavelet
characterizes spatial correlated data. The NP complete and
integer programming model is instantiated to solve the
minimum energy compressed data aggregation problem [36].
In cluster oriented architecture, the data collection process is
efficient in handling the communication between the nodes.
The node in sleep/ awake scheduling incurs energy. It is
handled by an adaptive scheme cluster to cluster propagation
scheme to reduce communication cost and limiting prediction
cost [37]. Though there was a lot of energy saving methods that dealt with one to one communication, one to many
communications etc. In [38], the many to many
communications has been proposed using the MUSTER
routing protocol. It has ability to operate in multicast networks
using multiple sources to multiple sinks which concurrently
increase the network lifetime and balance the nodes.
Table 3: Merits and Demerits of Location based Protocols
[38]
Protocol Merits Demerits
Geographic
Adaptive Fidelity (GAF)
Best use of
performance in WSN.
Scalability is good.
Increase the
network lifetime
Restricted energy
consumption
Overhead is high.
QOS is less in data transmission.
Restricted mobility
Restricted power
management.
Geographic and
Energy Aware
Routing (GEAR)
Enhance the
network span.
Reduced energy
consumption
Restricted
versatility.
Restricted mobility
and power
management.
High overhead
QOS is poor.
Coordination of Power saving with
Routing (SPAN)
Consumes less energy.
Less overhead.
Good support for
data aggregation
Restricted versatility.
Communication
overhead is high.
Lack of QOS
process. support.
Trajectory Based
Forwarding (TBF)
Improved
Reliability
Network
management is
proper.
Network perimeter
is secured.
Overhead is high.
Bounded Voronoi Greedy Forwarding
(BVGF)
Easy to implement. High
comprehensive.
Collision in determining
Voronoi partitions.
Geographic Random
Forwarding (GeRaF)
Unsettled nodes.
Lack of creating
multi-hop overhead.
Simple to combine
with awake/ asleep
state to save energy.
Necessary to initiate
the user interaction
in each stage.
Time complexity is
high.
Minimum Energy
Communication
Network (MECN)
Energy
sustainability with
low power.
High fault tolerant
systems. Span time is
optimal.
Based on specific
application, the
fault tolerance
varies.
Small Minimum
Energy
Communication
Network (SMECN)
Incurs less energy
than the MECN.
Less maintenance
costs in supporting
source links.
Power usage is
high.
Size of message
broadcasting is
high.
DATA CENTRIC PROTOCOLS
A data centric protocol is varied from the Address centric
protocols. A sink node forwards the queries to the specific
regions and searches the data in that specific region from the sensors. Each data queries are represented by attribute based
naming strategy [39]. The research conducted on the data
centric protocols are reviewed as follows:
First of all, there should not be any redundant data
transmissions to save the energy. Harshavardhan et al [39]
framed location aided flooding scheme to efficiently utilize
the energy. He used local information about the node which is
used to discards the same packet send by a node is transmitted
in virtual grid based architecture. In dynamic networks, the
node transmission is done in a random manner. Flooding time is estimated to define the node transmission rate. These
information are bounded with high probability with the best
node mobility has framed by Andrea Clementi et al [40].
Though the node is transmitted from source to destination, an
acknowledgement from the receiver is essential to verify
whether the data packets are being forwarded to designated
destination. In [41], a link correlation is defined between the
source and destination nodes. The collective ACK is
calculated in single hop routing with low energy consumption.
The node can also reference to other nodes in the time
synchronization process. Within this context, the
synchronization accuracy and flexibility has been reduced for slow flooding process [42]. Coming to the security issue,
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 10 (2016) pp 6990-7002
© Research India Publications. http://www.ripublication.com
6994
many intruders are there to spoil the systems by introducing a
new node in the system. The Open Network Foundation
(ONF) ensured to decrease the unwanted node transmission.
The Software Defined Networks (SDN) is introduced to have
a control over the control messages and incoming packets to
classify the nodes [43]. The energy maximization is done by restricting the resources
[44]. The smallest distance between the two nodes is a straight
line. These straight lines are used for framing an improved
protocol in WSN. Without the aid of geographic locations, the
straight line routing reduces the energy consumption level.
The power constrained WSN acquires two quantities namely,
broadcast capacity and information diffusion rate [45]. In
Multihop relay, the rate of broadcasting stream is estimated to
discover the asymptotic relationship between unified routing
structure and MAC schedule. The energy consumption is
directly proportional to network performance. A single path
can connect a various nodes [46]. The multipath should discover to solve the issue of control plane problem and data
plane problem so as to enhance the network performance
along with the minimized energy consumption. A key
management system was introduced to enhance the
information security process. The secret key was generated for
edge routers which generate minimal communication [47]. In
diffusion-based molecular nanonetworks, the routing
functionalities play an important. The gradient based
information is obtained between the nodes. The OR function
is utilized to serve the best routing protocols [48].
The error propagation approach is used to display the mutual information on the achievable throughput and throughput-
delay. Sometimes the transmitting nodes may not cooperate
due to interference-limited setting. This delimitation is
improved in [49]. The networks may be noisy that leads to
high power consumption. To overcome this issue, the data
delivery networks should be reliable one. This was solved in
[50]. A flexible cross layer design is maintained to enhance
the link estimation capabilities and efficient management of
neighbor tables that minimizes the power consumption.
Another aspect of energy conservation is the energy balance
routing protocols. The Forward Factor (FF) supports between
the nodes can also establish for the energy balanced mechanism. The selection of the next hop node in accord to
the link weight and predecessor nodes information, the FF
value is computed [51]. In Tree oriented architecture, a node
should be a self organized in case of embedded nodes. The
base station assigns a root node and broadcasts the messages
to all sensor nodes. Each node randomly chooses its parent
and neighbor‟s information to make a dynamic protocol [52].
The scalable nodes are formed in the hierarchical structure for
efficient use of energy. The cluster head is responsible for
network lifetime. When the cluster head fails, the node‟s
position is not properly organized which leads to overhead. This can be eliminated by selecting the CH based on the node
eligibilities [53]. A fuzzy based approach in the CH formation
can reduce the risk of node collision issue. Thus the
optimization of the routing protocols can also achieve in this
[54] [55].
Table 4: Merits and Demerits of Data-centric Protocols [55]
Protocols Merits Demerits
Flooding Easy to implement Node collision is
high.
Resource blindness.
Gossiping It avoids node
collision issue.
Node intersection is
high.
Directed
Diffusion
Data cache prevents
loops in data delivery.
Saves network
bandwidth.
Not suitable in
continuous data streams.
Rumor Routing A single path is
maintained between
source and
destination.
Node failure
handling is efficient.
Lack of handling
events.
Energy aware
routing
Use sub-optimal
paths to increase
network lifetime.
Route setup process is
complicated.
Constrained
Anisotropic Diffusion Routing
(CADR)
Minimize the
latency and bandwidth.
Dynamically adjusts
the data routes.
Node Intersection
problem is high.
COUGAR Best use of
declarative queries.
Data abstraction is
efficient.
Cluster Head must be
maintained in
dynamic.
Communication
overhead is high to
sensor nodes.
Need of
synchronization.
Active Query
forwarding in
sensor networks (ACQUIRE)
Query is very
comprehensive.
If network size is
equal to node size
then it behaves like flooding.
HIERARCHICAL ROUTING PROTOCOLS
This type of routing protocols is treated as the most energy
efficient protocols in WSN because of its higher energy
conservation, network versatility and lower data transmission
[56] [75] [76].
In Zigbee mesh networks [57], the hierarchical protocols are
used to find the shortest path for addressing scheme. This
protocol lessens the broadcast overhead and no memory
overhead to maintain the routing information. The chain based protocol minimizes the energy consumption level because
each node communicates with the next close-by neighbor and
then transmits to the base station. The data gathering process
is reduced to yield the optimal path [58]. The energy should
be dissipated to the BS to improve the network lifetime. The
sensor nodes are arranged in the clustering based schemes to
maximize the life span of the network [59]. According to the
node proximity or node degree, the CH is periodically chosen
that achieves fairly uniform cluster formation and low
message overhead [60] [61]. Route election and route
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 10 (2016) pp 6990-7002
© Research India Publications. http://www.ripublication.com
6995
distribution is an effective task in the energy efficiency issue.
A proper route should be elected for transmitting the nodes
and its information [62]. Nodes under heavy traffic might
cause energy efficiency problems. This problem exists due to
heavy burden to the CH in the data collection scenarios. The
CH size should determine effectively to eliminate the higher level of energy consumption. The data sink contains burden of
the sensory data in the data collection approach [63] [68]. The
hop distance size is estimated for CH in the data sink to
dispose the burdened sensor data. The issues such as
congestion control, node collision and resource blindness
solved in cross layer fashion. The design focused on the initial
determination of nodes in the networks. The initial
determination such as receiver based contention, initiative
based forwarding, local congestion control and distributive
cyclic operation to adopt a reliable communication [64]. In
[65], the false alarm rate in CH formation can enhance the
node scalability. The multiaxis division based approach is introduced to overcome the threshold problem and eliminate
the sink dependency. The data aggregation approach handles a
variety of queries. The Poisson Arrival Rate is estimated to
handle the heavy loads using Time Division Multiple Access
(TDMA) [66] [67].
Table 5: Merits and Demerits of Hierarchical Protocols [68]
Protocols Merits Demerits
LEACH Load balancing is
efficient.
Collision is
prevented.
Fit to small regions.
Energy is not
utilized for selection
of cluster head that leads to overhead.
Power Efficient
Gathering in
Sensor
Information
System
(PEGASIS)
Decreased overhead
in dynamic cluster
formation.
Data transmission
rate is low.
Balanced node
formation.
Not suit to time
variant topology.
Data Processing is
delayed.
Hybrid Energy
Efficient
Distributed
(HEED)
Distributed
clustering method.
CH is uniform.
Maximized energy
conservation. Support long range
communications
Inappropriate energy
consumption.
Cause overhead in
cluster head
selection. Expired soon due to
overload.
Threshold
sensitive Energy
Efficient sensor
Network
Protocol (TEEN)
Energy consumption
is reduced by the use
of thresholds.
Fit to reactive
scenes.
Periodic reports are
not generated.
Data loss.
Adaptive
Threshold
sensitive Energy
Efficient
sensor Network
protocol (
APTEEN)
Applicable to
proactive and
reactive applications.
Controlled energy
consumption
Design complexity
is large in
supporting multi-
path.
MOBILITY BASED PROTOCOLS
Based on the mobility, the cluster head are selected. The node
which possesses less mobility is selected as the cluster head
which leads to more stable clusters. These are also known
energy-unaware routing protocols. There is a chance of packet
loss in inter-cluster communication [69] [70]. In data collection process, energy hole problem is a critical
issue. The sink mobility is an efficient way to eliminate this
issue and extend the lifetime of a network by lessening the
communication overhead which is close to the sink [71] [72]
[77]. The mobile node should be of high fidelity. A
cooperative engagement should be followed in unreliable
wireless networks to effectively coordinate the
communications. The objects/ events are updated frequently
to yield the optimal path [73] [78]. The process of handling
multiple mobile sink is also an issue in data aggregation
process. The next position of the sink is selected by biased
random walk. The rendezvous point acts a threshold value. This significantly reduces the reliability, communication
overhead and flexibility [74] [79]. The beaconless routing
schemes are well defined in dynamic network topology. Each
node transfers the packets without the use of beacons interval
and neighbor information. In forward decision process, the
routing schemes are robust [80]. By encountering the RTS and
CTS message, the packet is unicasted to next hop relay which
reduces the energy consumption. The route should be
optimized in concept of discover-before-forward. The
signaling cost is reduced which directly symbolizes energy
consumption [81] [82]. Without scheduling the direction for a mobile sink ahead of
time, an information gathering convention utilizing versatile
sinks recommends that a versatile sink declare the location
data habitually all through the system. On the off chance that
sensors can anticipate the portable sink's development, the
vitality utilization would be extraordinarily diminished
furthermore, information packets handoff would be smoother.
In dense oriented networks [83], a convention, called a
Scalable Energy-effective Asynchronous Spread (SEAD) is
another system for sense the data to versatile sink. The
thought is to develop a least Steiner tree for the portable sink
and assign some hubs on the tree as access points [84]. The portable sink registers itself with the nearest get to hub. At the
point when the sink moves out of scope of the entrance hub,
the route is reached out through the incorporation of new get
to hub. A stable and high recharging rate power supplies for
sensors, and effectively alleviates energy cost on data
gathering at the same time, [85] proposed a joint design of
energy replenishment and data gathering by exploiting
mobility.
Table 6: Merits and Demerits of Mobility based protocols [85] Protocols Merits Demerits
SEAD Sense the data based on the geographic locations. Self organizing protocol.
Overhead is high
Joint Mobility and Routing
Well balanced nodes. Energy sink-hide problems
Consumes more power.
Data MULES Low cost infrastructures. Less fault tolerant systems.
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 10 (2016) pp 6990-7002
© Research India Publications. http://www.ripublication.com
6996
MULTIPATH BASED PROTOCOLS
The data packets are forwarded to the sink node directly from
the sensor nodes. The sensor nodes consumes more battery, to
eliminate this action „multipath routing protocol‟ is introduced
[74] [75]. In any type of architecture, the node with maximum
energy is processed and the nodes with minimized energy yield more time which leads to heavy computational costs.
The multipath routing protocols produces less scalability and
simplicity.
A hierarchical multipath routing protocol possesses many
paths to exploit data forwarded to the sink. The candidate
parent nodes are created to the sink node based on the layered
network [86]. Some multipath routing eliminates the topology
exposure protocols. The idea is to hide the topology and does
not carry the packets information. The protocol can likewise
set up numerous hub disjoint path in a route invention
endeavor and reject the slower action before transmitting
parcels [87]. In mobile ad hoc networks, the location prediction concurrently reduces the global multipath route and
average of hop size from source to destination is reduced.
Based on the node disjoint paths, the collected location and
mobility information are utilized to reduce the control
message overhead and energy consumption [88]. Frequent
connection disappointments are brought in mobile ad hoc
systems because of hub's portability and utilization of
untrustworthy remote channels for information transmission.
In the route discovery system, the path with optimal
connection quality and path expiration time is chosen as the
essential routes. On the off chance that there is any route failures amid the information transmission through essential
way, the following accessible backup path with high
connection quality and path expiration time is chosen [89]
[93]. To eliminate the traffic of the nodes, multipath routing is
used. The load is balanced among the nodes with the use of
node disjoint paths. It is a sink initiated routing protocol. The
number of hop count and uplink neighbor‟s information,
multiple paths between the source and destination is
discovered [90]. The overhead problem is not recovered in
demand routing protocols [91] [92]. The multipath routing
extracts the benefits of AODV protocols. It discovers three
node disjoint routes from source to destination which enhances the packet delivery ratio and end to end delay. If any
route discovery fails, the backup route helps to transfer the
packets without incurring extra energy [94].
In heterogeneous WSNs, a distributed fault tolerant topology
algorithm is developed. The super nodes are generated
according to k vertex disjoint paths. It reduces the power
consumption and efficient super nodes creation [95] [100]. In
service oriented architecture, the link disjoint based multipath
routing is introduced which eliminates the packet forwarding
to the unwanted nodes. So, each node should possess the
capability of detecting reliable and related nodes [96]. The node coverage issue is solved by deploying the stationary
sensors. A weight barrier graph has been introduced to
determine the minimum number of mobile sensor nodes that
reduces the effect of overhead [97]. There is a chance of
gathering unrelated data from the mobile sensor nodes. In
[98], the authors introduced a concept of resiliency networks
in a distributed manner. A relative indexing is used to reduce
the coding coefficients to cope up with node or link failures.
The actor failure in the network may cause partition tasks into
disjoint blocks. If any node fails, it will consult with the
neighbor to define the role and actions to the network
connectivity [99] [101].
Table 7: Multipath based protocols [101]
Protocols Definition
Disjoint Path Primary preference is given to the smallest path.
If any failure occurs, it remains stagnant.
Braided Path Partially disjoint path.
Primary computation path is done.
It builds in localized manner.
QOS BASED PROTOCOLS
The Quality of Service (QoS) prerequisites varies in the
different applications in the view of delivery ratio and packet
loss. Based on the QoS requirements, the network protocol
should be designed [74] [75].
A cross layer design was introduced [102] to increase the energy consumption and network bandwidth of IEEE 802. 15.
4. In route discovery process, the location of the mobile
sensor is embedded. This information is utilized by the
network layers in later that simultaneously reduce the
transmission range and power consumption. The Cognitive
Radio Networks (CRN) in Federal Communication was also
suffering from this issue. The cognitive radio methods such as
interleave and underlay was designed to meet the Primary
User (PU) QOS requirements [103]. The pair-wise nodes are
utilized to collect the geographical information of the nodes in
data gathering approach that balance the energy consumption
and end to end delay [104] [113]. Actually, the presence of energy holes that exhibit on Primary User (PU) movement is
insufficient to empower transfer speed in the inter-
communication. It additionally needs to consider the Quality
of these gaps [105]. To minimize the interruption to the high-
transmission capacity streams, the spectrum detecting process
needs to recognize stable unmoving channels, i. e., ones that
are relied upon to stay unmoving for a broadened span of
time. Different levels of QoS requirements needed for smart
grid which symbolizes the packet delay in end to end systems
[106]. Without picking a specific program, the system should
ensure the packet delay quality in a specified range. This study seems to be quite trickier tasks in wireless NAN without
compromising the QoS. In demand routing protocols, the QoS
in terms of packet delay, packet error probability and node
outage issue are solved.
Tuning the data rate of a sensor is also a QoS requirement.
The utility concept, Nash Bargaining Solution (NBS) that
operates on co-operative game theory that saves the power
consumption and network specific parameters [107]. Sensor
hubs are defenseless to different wellsprings of failures. These
incorporate malware assaults, software corruption and
programming defilement which can decrease hubs' role and
seriously influence the most WSN operations. These attacks could prompt discriminating disadvantages, for example,
incomplete or complete hub failures that causes dangerous
impacts on the fundamental observing applications. Hubs
encountering such failures or glitch can be named
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 10 (2016) pp 6990-7002
© Research India Publications. http://www.ripublication.com
6997
contaminated and will typically neglect to perform normal
detecting and communication [108]. Consequently, they are
not able to watch the auspicious time delivery service which is
critical to look after high QoS in WSN. This is handled by
fuzzy based approach in twofold. The network with light load
possesses high sensitive and high integrity applications. And these can lead the system from congestion problem which
enhances the end to end delay. The concept of potential field
can reduce the end to end delay [109] [112]. In cluster tree
based data collection, a velocity based data collection scheme
is introduced [110]. This reduces the issues of coverage
distance, mobility, traffic delay and tree density. It works on
collecting the information from the CH and transfer to the
sink. Coming to the Resource management and Resource
allocation in terms QoS requirements, the maximum and
minimum number of channels is utilized. To overcome this
issue, the integer programming and convex optimization
method is utilized [111]. It reduces the effects of imperfect channel sensing by generating co-tier interference between the
nodes.
Table 8: QoS-based protocols [113]
Protocols Definition
Core Extraction Distributed Ad
hoc routing (CEDAR)
It‟s a medium sized routing
scheme.
Dynamic Networks
Routing computing is
simple.
Less overhead.
Multipath Routing Protocol
(MRP)
Reactive on-demand
routing protocol.
Higher reliable nodes.
Well defined load
balancing schemes.
Ad hoc QoS on demand routing
(AQOR)
Limited flooding process.
Smallest rate of end to end
delay. Reduces communication
overhead.
CONCLUSION
Data aggregation in sensor networks has attracted a lot of
attention in the recent time. In this paper, we summarized
recent research results on data aggregation and routing in
WSN. We surveyed routing protocols by taking into account
several classification criteria, including location information,
network layering and in-network processing, data centricity,
Mobility-based, Multipath-based Protocols, network heterogeneity, and QoS requirements. The Table 9 shows the
summary of the Basic characteristics of Data Aggregation
protocols. If we consider the parameters Aggregation Method,
Resilience to Link Failure, Setup Overhead, Scalability,
Resilience in case of node mobility, Energy saving method,
and Timing strategy, the Cluster based data aggregation
protocols perform well compared with other protocols and we
can build energy efficient WSN with these protocols.
Table 9: Summary of the Basic characteristics of Data Aggregation protocols
CHARACTERISTIC
ALGORITHM TAG [118]
DIRECTED
DIFFUSION [114] PEGASIS [117] DB-MAC[119] LEACH [116] COUGAR [115]
COMB-
NEEDLE
MODEL [120]
CLUSTER BASED
COMB NEEDLE
MODEL [121]
Aggregation Method
Tree Based
Online Driven
by the sink
Tree Based Online
Driven by the sink
Chain Based
Centralized or
Distributed
Completely
distributed,
asynchronous
Cluster Based
online
distributed
Cluster Based
online distributed
synchronous
Grid Based Cluster Based
Resilience to Link
Failure Medium Medium Low Medium Low Medium Medium Low
Overhead to
setup/maintain the
Aggregation Structure
High High High Low Medium Medium High Low
Scalability Low Medium Very Low High Low Low High High
Resilience in case of
node mobility Low Medium Very Low High Low Low Medium Low
Energy saving methods Sleeping
Periods None
Rotation of the
Leader None
Rotation of the
CH, Sleeping
Periods
Local Route
repairs None
Placing high
Energy nodes
as CH
Timing Strategy Periodic per
Hop adjusted Asynchronous Periodic per Hop Asynchronous
Periodic per
Hop Periodic per Hop
Event
Driven
Periodic and
event Driven
Although these protocols promise the energy efficiency, further research is required to address issues such as quality of
service (QoS) in real-time applications. The applications
include real time military applications, event triggering in
precision agriculture etc. require energy-aware QoS routing.
This will guarantee the bandwidth of connection as well as
provides the energy efficient path.
Further possible research issue for routing protocols is to
consider the node mobility. Most of the protocols we
discussed above (except cluster-based comb-needle model)
assume all the sensor nodes and sink are stationary. Further
research is necessary in this direction to design energy efficient protocols specific to a particular application. For
example, in battle field it may be necessary for the sink to
move for detecting the events such as identification of enemy
tank, exhaustion of ammunition at a soldier, etc. The mobility
of a node requires more number of updates in terms of its
position, and propagation of that information to other nodes
may consume more energy. The battery of the nodes may
drain out fast. To handle this situation and overhead of the
mobility, new routing protocols need to be designed in an
energy constrained environment.
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 10 (2016) pp 6990-7002
© Research India Publications. http://www.ripublication.com
6998
REFERENCES
[1] Sumit Chaudhary et al, “Energy Efficient Techniques
for Data Aggregation and Collection in WSN “,
International Journal of Computer Science, Engineering and Applications, Vol. 2, No. 4, August 2012.
[2] Mousam Dagar and Shilpa Mahajan, “Data
Aggregation in Wireless Sensor Network: A
Survey”, International Journal of Information and Computation Technology, ISSN 0974-2239 Vol. 3,
No. 3, 2013, pp. 167-174.
[3] Ankit Tripathi et al, “Survey on Data Aggregation
Techniques for Wireless Sensor Networks”,
International Journal of Advanced Research in Computer and Communication Engineering, Vol. 3,
Issue 7, July 2014.
[4] Priya V. Ujawe and Simran Khiani, “Review on Data Aggregation Technique for Energy Efficiency in
Wireless Sensor Networks”, International Journal of
Emerging Technology and Advanced Engineering,
Vol. 4, Issue 7, July 2014.
[5] Sushruta Mishra and Hiren Thakkar, “Features of
WSN and Data Aggregation techniques in WSN: A
Survey”, International Journal of Engineering and
Innovative Technology (IJEIT) Volume 1, Issue 4,
April 2012.
[6] Rajashree V. Biradar et al, “classification and
comparison of routing protocols in wireless sensor networks”, Special Issue on Ubiquitous Computing
Security Systems, Vol. 4.
[7] Nandini. S. Patil and Prof. P. R. Patil, “Data
Aggregation in Wireless Sensor Network”, IEEE
International Conference on Computational
Intelligence and Computing Research, 2010.
[8] Shio Kumar Singh et al, “Routing Protocols in
Wireless Sensor Networks –A Survey”, International
Journal of Computer Science & Engineering Survey
(IJCSES) Vol. 1, No. 2, November 2010.
[9] Kiran Mariya et al, “Wireless Sensor Network: A
Review on Data Aggregation”, International Journal of Scientific & Engineering Research Volume 2,
Issue 4, April-2011.
[10] C. Intanagonwiwat, R. Govindan, and D. Estrin,
“Directed Diffusion: a Scalable and robust
communication paradigm for sensor networks”, in: Proceedings of the 6th Annual ACM/IEEE International conference on Mobile Computing and Networking (MobiCom’00), Boston, MA, August
2000.
[11] Li Erran and Joseph Y. Halpen, “A Minimum-
Energy Path-Preserving Topology-Control Algorithm”, IEEE Transactions on wireless
communications, Vol. 3, No. 3, May 2004.
[12] Melkang Qlu et al, “Balance of Security Strength and
Energy for a PMU Monitoring System in Smart
Grid”, IEEE Communication Magazine, 2012.
[13] Liang Liu et al, “Multi-Antenna Wireless Powered
Communication with Energy Beamforming”, IEEE
Transactions on communications, Vol. 62, No. 12,
December 2014.
[14] Kai Han et al, “MEGCOM: Minimum-Energy Group
COMmunication in Multihop Wireless Networks”,
IEEE Transactions on Vehicular technology, Vol. 63,
No. 4, May 2014. [15] Marco Tacca et al, “Cooperative and Reliable ARQ
Protocols for Energy Harvesting Wireless Sensor
Nodes”, IEEE Transactions on wireless
communications, Vol. 6, No. 7, July 2007.
[16] Yong Yuan et al, “Virtual MIMO-Based Cross-Layer
Design for Wireless Sensor Networks”, IEEE
Transactions on vehicular technology, Vol. 55, No.
3, May 2006.
[17] Zi-Tsan Chou et al, “Optimal Asymmetric and
Maximized Adaptive Power Management Protocols
for Clustered Ad Hoc Wireless Networks”, IEEE
transactions on parallel and distributed systems, Vol. 22, No. 12, December 2011.
[18] Melike Erol Kantarci et al, “Suresense: Sustainable
Wireless Rechargeable Sensor Networks for the
Smart Grid”, IEEE Wireless Communications, June
2012.
[19] Julien Iguchi Cartigny et al, ” Localized Minimum-
Energy Broadcasting for Wireless Multihop
Networks with Directional Antennas”, IEEE
Transactions on computers, Vol. 58, No. 1, January
2009.
[20] Arjan Durresi et al, “Optimized Broadcast Protocol for Sensor Networks”, IEEE Transactions on
computers, Vol. 54, No. 8, August 2005.
[21] Seyed Ali et al, ” Energy Efficient Collaborative
Spectrum Sensing Based on Trust Management in
Cognitive Radio Networks”, IEEE Transactions on
wireless communications, Vol. 14, No. 4, April 2015.
[22] Volkan Rodoplu et al, ” Minimum Energy Mobile
Wireless Networks”, IEEE journal on selected areas
in communications, Vol. 17, No. 8, August 1999.
[23] Shibo He et al, ” EMD: Energy-Efficient P2P
Message Dissemination in Delay-Tolerant Wireless
Sensor and Actor Networks”, IEEE journal on selected areas in communications/supplement, Vol.
31, No. 9, September 2013.
[24] Ming Wei Wu et al, “ARQ with Channel Gain
Monitoring”, IEEE transactions on communications,
Vol. 60, No. 11, November 2012.
[25] Hina Tabassum et al, “On the Spectral Efficiency of
Multiuser Scheduling in RF-Powered Uplink Cellular
Networks”, IEEE transactions on wireless
communications, Vol. 14, No. 7, July 2015.
[26] Anis Ouni et al, “Energy and Throughput
Optimization of Wireless Mesh Networks with Continuous Power Control”, IEEE transactions on
wireless communications, Vol. 14, No. 2, February
2015.
[27] Jun Long et al, ” Reliability Guaranteed Efficient
Data Gathering in Wireless Sensor Networks”, IEEE
special section on industrial sensor networks with
advanced data management: design and security,
2015.
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 10 (2016) pp 6990-7002
© Research India Publications. http://www.ripublication.com
6999
[28] Kandasamy Illanko et al, “Energy-Efficient
Frequency and Power Allocation for Cognitive
Radios in Television Systems”, IEEE systems
journal, 2015.
[29] Hamed Yousefi et al, “Fast Aggregation Scheduling
in Wireless Sensor Networks”, IEEE transactions on wireless communications, Vol. 14, No. 6, June 2015.
[30] Fangming Liu et al, “AppATP: An Energy
Conserving Adaptive Mobile-Cloud Transmission
Protocol”, IEEE transactions on computers, 2015.
[31] Xiaoyong Li et al, ” LDTS: A Lightweight and
Dependable Trust System for Clustered Wireless
Sensor Networks”, IEEE transactions on information
forensics and security, vol. 8, no. 6, June 2013.
[32] Lucia Lo Bello and Emanuele Toscano, “An
Adaptive Approach to Topology Management in
Large and Dense Real-Time Wireless Sensor
Networks”, IEEE transactions on industrial informatics, Vol. 5, No. 3, August 2009.
[33] Muhamad Akhlaq and Tarek R. Sheltami, ” RTSP:
An Accurate and Energy-Efficient Protocol for Clock
Synchronization in WSNs”, IEEE transactions on
instrumentation and measurement, Vol. 62, No. 3,
March 2013.
[34] Cesare Alippi et al, “An Adaptive LLC-Based and
Hierarchical Power-Aware Routing Algorithm”,
IEEE transactions on instrumentation and
measurement, Vol. 58, No. 9, September 2009.
[35] Habib M. Ammari, ” CSI: An Energy-Aware Cover-Sense-Inform Framework for k-Covered Wireless
Sensor Networks”, IEEE transactions on parallel and
distributed systems, Vol. 23, No. 4, April 2012.
[36] Liu Xiang et al, “Compressed Data Aggregation:
Energy-Efficient and High-Fidelity Data Collection”,
IEEE/ACM transactions on networking, Vol. 21, No.
6, December 2013.
[37] Hongo Jiang et al, “Prediction or Not? An Energy-
Efficient Framework for Clustering-Based Data
Collection in Wireless Sensor Networks”, IEEE
transactions on parallel and distributed systems, Vol.
22, No. 6, June 2011. [38] Luca Mottola et al, “MUSTER: Adaptive Energy-
Aware Multilink Routing in Wireless Sensor
Networks”, IEEE transactions on mobile computing,
vol. 10, no. 12, December 2011.
[39] Harshavardhan Sabbineni et al, “Location-Aided
Flooding: An Energy-Efficient Data Dissemination
Protocol for Wireless Sensor Networks”, IEEE
transactions on computers, Vol. 54, No. 1, January
2005.
[40] Andrea Clementi et al, “Opportunistic MANETs:
Mobility Can Make Up for Low Transmission Power”, IEEE/ACM transactions on networking,
Vol. 21, No. 2, April 2013.
[41] Ting Zhu et al, “Achieving Efficient Flooding by
Utilizing Link Correlation in Wireless Sensor
Networks”, IEEE/ACM transactions on networking,
Vol. 21, No. 1, February 2013.
[42] Kasim Sinan Yildrim et al, “Time Synchronization
Based on Slow-Flooding in Wireless Sensor
Networks”, IEEE transactions on parallel and
distributed systems, Vol. 25, No. 1, January 2014.
[43] Ying Dar Lin et al, “An Extended SDN Architecture
for Network Function Virtualization with a Case
Study on Intrusion Prevention”, IEEE Network
Communication, 2015. [44] Hsiang-Hung Liu et al, “On Energy-Efficient
Straight-Line Routing Protocol for Wireless Sensor
Networks”, IEEE systems journal, 2015.
[45] Rong Zheng, “Asymptotic Bounds of Information
Dissemination in Power-Constrained Wireless
Networks”, IEEE transactions on wireless
communications, Vol. 7, No. 1, January 2008.
[46] Junaid Qadir et al, ” Exploiting the power of
multiplicity: a holistic survey of network-layer
multipath”, IEEE Communications Surveys &
Tutorials, 2015.
[47] Sudip Mishra et al, “A PKI Adapted Model for Secure Information Dissemination in Industrial
Control and Automation 6LoWPANs”, IEEE special
section on industrial sensor networks with advanced
data management: design and security, 2015.
[48] Adnan Aijaz, “Opportunistic Routing in Diffusion-
Based Molecular Nanonetworks”, IEEE wireless
communications letters, Vol. 4, No. 3, June 2015.
[49] Dominique Zosso et al, “Fast Geodesic Active Fields
for Image Registration Based on Splitting and
Augmented Lagrangian Approaches”, IEEE
transactions on image processing, Vol. 23, No. 2, February 2014.
[50] Ramanan Subramanian et al, “Asymptotic
Throughput and Throughput-Delay Scaling in
Wireless Networks: The Impact of Error
Propagation”, IEEE transactions on wireless
communications, Vol. 13, No. 4, April 2014.
[51] Emilloc Ancilloti et al, “Reliable Data Delivery with
the IETF Routing Protocol for Low-Power and Lossy
Networks”, IEEE transactions on industrial
informatics, Vol. 10, No. 3, August 2014.
[52] Degan Zhang et al, “An Energy-Balanced Routing
Method Based on Forward-Aware Factor for Wireless Sensor Networks”, IEEE transactions on
industrial informatics, Vol. 10, No. 1, February 2014.
[53] Zhao Han et al, “A General Self-Organized Tree-
Based Energy-Balance Routing Protocol for Wireless
Sensor Network”, IEEE transactions on nuclear
science, Vol. 61, No. 2, April 2014.
[54] Mehdi Tarhani et al, “SEECH: Scalable Energy
Efficient Clustering Hierarchy Protocol in Wireless
Sensor Networks”, IEEE sensors journal, Vol. 14,
No. 11, November 2014.
[55] Duc Chinh Hoang et al, ” Real-Time Implementation of a Harmony Search Algorithm-Based Clustering
Protocol for Energy-Efficient Wireless Sensor
Networks”, IEEE transactions on industrial
informatics, Vol. 10, No. 1, February 2014.
[56] Alia Sabri and Khalil Al-Shqeera, “Hierarchical
Cluster-Based Routing Protocols for Wireless Sensor
Networks – A Survey”, IJCSI International Journal
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 10 (2016) pp 6990-7002
© Research India Publications. http://www.ripublication.com
7000
of Computer Science Issues, Vol. 11, Issue 1, No 2,
January 2014.
[57] Jae Yeol Ha et al, “EHRP: Enhanced Hierarchical
Routing Protocol for Zigbee Mesh Networks”, IEEE
communications letters, Vol. 11, No. 12, December
2007. [58] Stephanie Lindsey et al, “Data Gathering Algorithms
in Sensor Networks Using Energy Metrics”, IEEE
transactions on parallel and distributed systems, Vol.
13, No. 9, September 2002.
[59] Siva D. Murugananthan et al, “A Centralized
Energy-Efficient Routing Protocol for Wireless
Sensor Networks”, IEEE Radio Communications,
March 2005.
[60] Ossama Younis and Sonia Fahmy, “HEED: A
Hybrid, Energy-Efficient, Distributed Clustering
Approach for Ad Hoc Sensor Networks”, IEEE
transactions on mobile computing, Vol. 3, No. 4, October-December 2004.
[61] Chia-Hung et al, “A Comment on “HEED: A Hybrid,
Energy-Efficient, Distributed Clustering Approach
for Ad Hoc Sensor Networks”, IEEE transactions on
mobile computing, Vol. 5, No. 10, October 2006.
[62] Franck Le et al, “Interconnecting Routing Instances”,
IEEE/ACM transactions on networking, Vol. 22, No.
2, April 2014.
[63] Dali Wei et al, “An Energy-Efficient Clustering
Solution for Wireless Sensor Networks”, IEEE
transactions on wireless communications, Vol. 10, No. 11, November 2011.
[64] Mehmet C. Vuran and Ian F. Akyildiz, “XLP: A
Cross-Layer Protocol for Efficient Communication in
Wireless Sensor Networks”, IEEE transactions on
mobile computing, Vol. 9, No. 11, November 2010.
[65] Jianzhong Li et al, “Grouping-Enhanced Resilient
Probabilistic En-Route Filtering of Injected False
Data in WSNs”, IEEE transactions on parallel and
distributed systems, Vol. 23, No. 5, May 2012.
[66] Arati Manjeshwar et al, “An Analytical Model for
Information Retrieval in Wireless Sensor Networks
Using Enhanced APTEEN Protocol”, IEEE transactions on parallel and distributed systems, Vol.
13, No. 12, December 2002.
[67] Ciamu Tang et al, “Efficient Multi-Party Digital
Signature using Adaptive Secret Sharing for Low-
Power Devices in Wireless Networks”, IEEE
transactions on wireless communications, Vol. 8, No.
2, February 2009.
[68] Viet Anh et al, “One-Bit CSI Feedback Selection
Schemes for Energy-Efficient Multiuser and
Multirelay Systems”, IEEE transactions on wireless
communications, Vol. 12, No. 3, March 2013. [69] Theofanis P. Lambrou et al, “A Survey on Routing
Techniques supporting Mobility in Sensor
Networks”, Fifth International Conference on Mobile
Ad-hoc and Sensor Networks, 2009.
[70] Megha Jain et al, “Performance of Mobility Models
with different Routing Protocols by using Simulation
Tools for WSN: A Review”, International Journal of
Advanced Research in Computer and
Communication Engineering Vol. 4, Issue 1, January
2015.
[71] Poonam Rautela et al, “Zone based routing protocol
in WSNs for varying zone size”, International
Journal of Advanced Research in Computer
Engineering & Technology Volume 1, Issue 6, August 2012.
[72] Ravinder Kaur et al, “Routing Protocols for Mobile
Wireless Sensor Networks”, International Journal of
Software and Web Sciences (IJSWS), 2013.
[73] M. Sasikumar and R. Anita, ” cluster based routing
protocol for wireless sensor networks”, International
Journal of Innovative Research in Advanced
Engineering (IJIRAE), Volume 1 Issue 9, October
2014.
[74] A. Balamurugan, “Energy Efficient Fitness Based
Routing Protocol in Wireless Sensor Networks”,
ICTACT journal on communication technology, March 2014, volume: 05, issue: 01.
[75] Eylem Ekici et al, “Mobility-Based Communication
in Wireless Sensor Networks”, IEEE
Communications Magazine, July 2006.
[76] Prerana Shrivastava and Dr. S. B. Pokle, “A Hybrid
Sink Positioning Technique for Data Gathering in
Wireless Sensor Networks”, International Journal of
Engineering and Innovative Technology (IJEIT)
Volume 1, Issue 3, March 2012.
[77] Mohammed Abo-Zahhad et al, “Mobile Sink-Based
Adaptive Immune Energy-Efficient Clustering Protocol for Improving the Lifetime and Stability
Period of Wireless Sensor Networks”, IEEE sensors
journal, Vol. 15, No. 8, August 2015.
[78] Ahmed Sobeih et al, “J-SIM: a simulation and
emulation environment for wireless sensor
networks”, IEEE Wireless Communications, August
2006.
[79] Madhumathi and D. Sivakumar, ” Enabling Energy
Efficient Sensory Data Collection Using Multiple
Mobile Sink”, China Communications, October
2014.
[80] Haibo Zhang and Hong Shen, “Energy-Efficient Beaconless Geographic Routing in Wireless Sensor
Networks”, IEEE transactions on parallel and
distributed systems, Vol. 21, No. 6, June 2010.
[81] Jinho Kim et al, “An ID/Locator Separation-Based
Mobility Management Architecture for WSNs”,
IEEE transactions on mobile computing, Vol. 13, No.
10, October 2014.
[82] Xinxin Liu et al, ” SinkTrail: A Proactive Data
Reporting Protocol for Wireless Sensor Networks”,
IEEE transactions on computers, Vol. 62, No. 1,
January 2013. [83] Jae-Wan Kim et al, “An Intelligent Agent-based
Routing Structure for Mobile Sinks in WSNs”, IEEE
Transactions on Consumer Electronics, Vol. 56, No.
4, November 2010.
[84] Mohammed Abo-Zahhad et al, “Mobile Sink-Based
Adaptive Immune Energy-Efficient Clustering
Protocol for Improving the Lifetime and Stability
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 10 (2016) pp 6990-7002
© Research India Publications. http://www.ripublication.com
7001
Period of Wireless Sensor Networks”, IEEE sensors
journal, Vol. 15, No. 8, August 2015.
[85] Miao Zhao et al, “A Framework of Joint Mobile
Energy Replenishment and Data Gathering in
Wireless Rechargeable Sensor Networks”, IEEE
transactions on mobile computing, Vol. 13, No. 12, December 2014.
[86] Ying-Hong Wang et al, “HMRP: Hierarchy-Based
Multipath Routing Protocol for Wireless Sensor
Networks”, Tamkang Journal of Science and
Engineering, Vol. 9, No 3, pp. 255-264, 2006.
[87] Yujun Zhang et al, “TOHIP: A topology-hiding
multipath routing protocol in mobile ad hoc
networks”, Elsevier, 2-14, pp: 109-122.
[88] Natarajan Meghanathan, “A node-disjoint multi-path
routing protocol based on location prediction for
mobile ad hoc networks”, International Journal of
Engineering, Science and Technology Vol. 2, No. 5, 2010, pp. 66-80.
[89] A. Monisha and K. Vijayalakshmi, “A reliable node-
disjoint multipath routing protocol for MANET”,
International Journal of Computational Engineering
Research, Vol, 03, Issue, 4, 2013.
[90] Priya Gopi, “Energy-Aware Node Disjoint Multipath
Routing Protocol for Wireless Sensor Networks”,
International Journal of Computer Trends and
Technology (IJCTT) – volume 13 number 3 – Jul
2014.
[91] Jay Kumar Jain et al, “Performance Analysis of Node-Disjoint Multipath Routing for Mobile Ad-hoc
Networks based on QoS”, International Journal of
Computer Science and Information Technologies,
Vol. 3 (5), 2012, 5000 – 5004.
[92] M. Bheemalingaiah et al, “Energy aware node
disjoints multipath routing in mobile ad hoc
network”, Journal of Theoretical and Applied
Information Technology, 2009.
[93] Rajendra Kumar Gupta, “Node Disjoint Minimum
Interference Multipath (ND-MIM) Routing Protocol
for Mobile Ad hoc Networks”, International Journal
of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 3, March
2012.
[94] Jayshree Tajne et al, “Multipath Node-Disjoint
Routing Protocol to Minimize End to End Delay and
Routing Overhead for MANETs”, International
Journal of Engineering Research and
Applications (IJERA), Vol. 3, Issue 4, Jul-Aug 2013, pp.
1691-1698.
[95] Hakki Bagci et al, “A Distributed Fault-Tolerant
Topology Control Algorithm for Heterogeneous
Wireless Sensor Networks”, IEEE transactions on parallel and distributed systems, Vol. 26, No. 4, April
2015.
[96] Shancang Li et al, “Adaptive and Secure Load-
Balancing Routing Protocol for Service-Oriented
Wireless Sensor Networks”, IEEE systems journal,
Vol. 8, No. 3, September 2014.
[97] Zhibo Wang et al, “Achieving k-Barrier Coverage in
Hybrid Directional Sensor Networks”, IEEE
transactions on mobile computing, Vol. 13, No. 7,
July 2014.
[98] Osameh M. Al-Kofahi et al, “Scalable Redundancy
for Sensors-to-Sink Communication”, IEEE/ACM
transactions on networking, Vol. 21, No. 6,
December 2013. [99] Ameer A. Abbasi et al, “Recovering from a Node
Failure in Wireless Sensor-Actor Networks with
Minimal Topology Changes”, IEEE transactions on
vehicular technology, Vol. 62, No. 1, January 2013.
[100] Hongli Xu et al, “Bandwidth-Power Aware
Cooperative Multipath Routing for Wireless
Multimedia Sensor Networks”, IEEE transactions on
wireless communications, Vol. 11, No. 4, April 2012.
[101] Ying Lin et al, “An Ant Colony Optimization
Approach for Maximizing the Lifetime of
Heterogeneous Wireless Sensor Networks”, IEEE
transactions on systems, man, and cybernetics, Vol. 42, No. 3, May 2012.
[102] Marwan Al-Jemeli et al, “An Energy Efficient Cross-
Layer Network Operation Model for IEEE 802. 15.
4-Based Mobile Wireless Sensor Networks”, IEEE
sensors journal, Vol. 15, No. 2, February 2015.
[103] Miltiades C. Filippou et al, “A Comparative
Performance Analysis of Interweave and Underlay
Multi-Antenna Cognitive Radio Networks”, IEEE
transactions on wireless communications, Vol. 14,
No. 5, May 2015.
[104] Junfeng Wang et al, ” PWDGR: Pair-Wise Directional Geographical Routing Based on Wireless
Sensor Network”, IEEE internet of things journal,
Vol. 2, No. 1, February 2015.
[105] Mohammad J. Abdel-Rahman et al, “QoS-aware
Parallel Sensing/Probing Architecture and Adaptive
Cross-layer Protocol Design for Opportunistic
Networks”, IEEE transactions on vehicular
technology, 2015.
[106] Peng-Yong Kong et al, “Wireless Neighborhood
Area Networks with QoS Support for Demand
Response in Smart Grid”, IEEE transactions on smart
grid, 2015. [107] Sudip Misra et al, “A Cooperative Bargaining
Solution for Priority-Based Data-Rate Tuning in a
Wireless Body Area Network”, IEEE transactions on
wireless communications, Vol. 14, No. 5, May 2015.
[108] Naimah Yaakob et al, “By-Passing Infected Areas in
Wireless Sensor Networks Using BPR”, IEEE
transactions on computers, Vol. 64, No. 6, June
2015.
[109] Jiao Zhang et al, “Dynamic Routing for Data
Integrity and Delay Differentiated Services in
Wireless Sensor Networks”, IEEE transactions on mobile computing, Vol. 14, No. 2, February 2015.
[110] R. Velmani and B. Kaarthick, “An Efficient Cluster-
Tree Based Data Collection Scheme for Large
Mobile Wireless Sensor Networks”, IEEE sensors
journal, Vol. 15, No. 4, April 2015.
[111] Haijun Zhang et al, “Interference-Limited Resource
Optimization in Cognitive Femtocells with Fairness
International Journal of Applied Engineering Research ISSN 0973-4562 Volume 11, Number 10 (2016) pp 6990-7002
© Research India Publications. http://www.ripublication.com
7002
and Imperfect Spectrum Sensing”, IEEE
Transactions on Vehicular Technology, 2015.
[112] Xing zhang et al, “Energy-Efficient Multimedia
Transmissions through Base Station Cooperation
over Heterogeneous Cellular Networks Exploiting
User Behavior”, IEEE Wireless Communications, August 2014.
[113] Long Cheng et al, “QoS Aware Geographic
Opportunistic Routing in Wireless Sensor
Networks”, IEEE transactions on parallel and
distributed systems, Vol. 25, No. 7, July 2014.
[114] W. Heinzelman, J. Kulik, and H Balakrishnan,
“Adaptive protocols for information dissemination in
wireless sensor networks”, in: Proceedings of the 5th
Annual ACM/IEEE international conference on Mobile Computing and Networking(MobiCom’99), Seattle WA, August 1999.
[115] D. Bramgomslu and D. Estrin, “Roumor Routing Algorithm For Sensor Networks, ” Proc. First workshop Sensor Networks and Applications (WSNA’02), Oct, 2002.
[116] S. Shakkottai, ”Asymptotics of Query strategies over
a sensor Network, ” Proceedings of IEEE INFOCOM, Mar-2004
[117] Carlos de Morais corderio and Dharma Prakash
Agrawal, “Ad Hoc and Sensor Network Theory and
Applications” 2nd edition-book.
[118] A. manjeshwar and D. Agrawal, ”TEEN: A Protocol
for Enhanced Efficiency in Wireless Sensor Networks, ” Proceedings of the 1st International Workshop on Parallel and Distributed Computing Issues in Wireless Networks and Mobile Computing,
April 2001.
[119] A. Manjeshwar and D. Agrawal, “APTEEN: A
Hybrid Protocol for Efficient Routing and
Comprehensive Information Retrieval in Wireless
Sensor Networks”, Proceedings f 2nd International Workshop on Parallel and Distributed Computing Issues in Wireless Networks and Mobile Computing,
April 2002
[120] S. Lindsey, C. S. Raghavendra, and K. Sivalingam, “Data gathering in sensor networks using the energy
delay metric”, in: Proceedings of the IPDPS Workshop on Issues in Wireless Networks and Mobile Computing, San Francisco, CA. April 2001.
[121] M. Shanmukhi, OBV. Ramanaiah, ” Cluster-based
Comb-Needle Model for Energy-efficient Data
Aggregation in Wireless Sensor Networks”, IEEE International Conference AIMOC-2015 12th-14th
February 2015.
Authors detail
Dr. O. B. V. Ramanaiah, Professor, CSE
Department, JNTU College of Engineering,
Hyderabad, India He received Ph.D. in Computer
Science from University of Hyderabad in 2005,
M.Tech(CS) from JNTU, India, and B. Tech(CSE)
from SVUCE, Tirupathi, India. His Total Teaching
Experience is 22 Years with Total Research
Experience of 13 Years. He has published research
papers in national & International Journals and
Conferences. He visited USA for paper presentation
in an IEEE Conference, ITCC 04, held during April
5-7, 2004. Currently he is providing Research
Guidance to 8 students and organized many
Refresher Courses. His research interests include
Computer Networks Distributed Systems, Mobile
Computing, Ad hoc & Sensor Networks.
Ms. M. Shanmukhi is currently pursuing her Ph.D
degree in CSE at JNTU, Hyderabad, India.
Received M.Tech (CS), and B.Tech (CSE) from
JNTU, Hyderabad, India. She has more than 11
years of teaching experience, currently working as
an Associate professor in the department of CSE,
Malla Reddy college of Engineering, Affiliated to
JNTU, Hyderabad, India. She has published
research papers in national & International
Conferences. Her main research interests include
wireless Adhoc & Sensor networks, Multimedia
communications, TCP/IP Networks and Protocols.