Performance Evaluation of Ant based Routing Protocol for WSNs with Heterogeneous Nodes

under Different Radio Propagation Models

Sanjay Kumar Mayank Dave Hindu College of Engineering, Sonepat, Haryana, India National Institute of Technology, Kurukshetra, India

Email: skm_09@rediffmail.com Email: mdave67@yahoo.co.in

Surender Dahiya Deenbandhu Chhotu Ram University of Science and Technology, Murthal, Sonepat, India

Email: dahiyasurender@hotmail.com

Abstract - The ever-increasing demand for multimedia surveillance and monitoring has led to the deployment of wireless sensor networks capable of capturing multimedia contents along with the scalar data. However, most of the routing protocols designed for wireless sensor networks (WSNs) are meant only for homogeneous sensor networks in which all sensor nodes have same capabilities in terms of communication, computation, energy supply, etc. This paper presents an ant-based QoS routing protocol for Heterogeneous Wireless Sensor Networks consisting of scalar and multimedia sensor nodes with diverse QoS requirements. The proposed protocol proposes different path selection criteria for control traffic and data traffic. The data traffic is further categorized into multimedia traffic and scalar traffic and QoS routes are selected according to the traffic type, thus improving network performance. Further, prior to actual deployment of such networks, an extensive evaluation of the protocols is carried out by making use of simulators. A simulator must replicate realistic conditions and one of the most difficult aspects is the radio signal propagation model. This paper shows that the non-determinism present in some radio propagation models induce randomness which may compromise the performance of many protocols and demonstrates the effectiveness of proposed protocol over AODV under different propagation models.

Keywords – Wireless Multimedia Sensor Networks; Heterogeneous Wireless Sensor Networks; ACO; QOS;

I. INTRODUCTION

The availability of inexpensive hardware devices such as CMOS cameras and microphones that are able to ubiquitously capture multimedia content from the environment has gradually shifted the paradigm from existing scalar sensor services (light, temperature, etc.) to a new world of real-time audio-visual applications . These devices form more powerful, distributed systems, conferred as wireless multimedia sensor networks (WMSNs) [1,2] that are capable of capturing video and audio streams, still images, and scalar sensor data pervasively both in real-time and non-real-time. WMSNs will

not only enhance existing sensor networks applications such as tracking objects, monitoring field and battle field surveillance, but will also lead to emergence of high-impact application such as video surveillance, tele-medicine, traffic control and so on [3].

Depending upon the applications of WMSNs, the network may consist of heterogeneous nodes wherein the nodes have moderated capabilities. Such a network is called Heterogeneous WSN (HWSN) [4]. HWSN nodes may be heterogeneous in terms of energy, wireless links, hardware or security. This helps in limiting the WSN cost as instead of using a full sensor set on a node, heterogeneous nodes may be deployed at different locations depending on the application and criticality of situation. Heterogeneous nodes in a HWSN, sense and report data at different rates with diverse QoS constraints. Although the content-rich vision-based information is undoubtedly more effective but the intrinsic features of multimedia communication simultaneously pose additional QoS challenges.

This paper proposes an ACO [5] based routing protocol for HWSNs while satisfying QoS requirements posed by multimedia traffic and scalar data. The routing protocol reacts differently to different types of packets received by a node. If control packet is received, the routing decision is taken on the basis of pheromone value only. However, if received packet is a data packet, then the forwarding decision is based on the type of data packet. As different applications pose different QoS requirements, therefore, multimedia traffic is handled differently, whereas scalar data is dealt in a different manner.

Radio propagation considerably influences the performance of wireless communication systems. The transmission path between two nodes can be a direct and unobstructed line-of-sight or a complex and strongly obstructed one, due to the presence of obstacles. Many radio propagation models have been designed to accommodate various kinds of wireless networks, environments and radio technologies. Propagation models are used to predict the propagation characteristics such as received signal power of

IEEE ANTS 2013 1569793921

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each packet. These models are very important in the planning process since they can typically predict the path loss along a link or the effective coverage area of a transmitter [6]. This paper presents the simulation behaviour of proposed protocol for three propagation models – Free Space model [7], Two Ray Ground model [8] and Shadowing model against AODV protocol. Results confirm our expectations by showing a significant performance improvement of proposed protocol over AODV.

The paper is organized as follows. Section II describes the most commonly used propagation models for simulation study. In section III the proposed routing protocol is presented. The simulation results are presented and discussed in section IV and section V concludes the paper.

II. RADIO PROPAGATION MODELS

Propagation models are used in simulators to predict the received signal strength indicator of each packet received by a node. Different propagation models may be distinguished by the method used to calculate the path loss between two nodes. The path loss represents signal attenuation and is defined as the difference between the transmitted power and the received power. Therefore, a node predicts the received signal strength by computing the difference between the effective transmitted power and the calculated path loss. In this paper simulation studies have been carried out on the network simulator ns-2 [9]. In ns-2 a threshold variable which defines the minimum possible value of the Received Signal Strength Indicator (RSSI) with which a node is still able to receive a packet is created. Considering the propagation model in use, it then calculates the RSSI with which a packet was received by a node. If the value is smaller than the threshold, ns-2 considers that the packet was not received by the node. The following section presents three popular distinct propagation models available in ns-2. A. Free Space Model The free space model assumes the ideal propagation condition that there is only one clear line-of-sight path between transmitter and receiver. It defines the communication range as a perfect sphere around the transmitter. It’s represented by the following equation (1) which calculates the distance signal power in free space.

( )( ) Ld

GGPdP rtr

r 22

2

4πλ

= … (1)

Where tP is the transmitted power, )(dPr is the received power, tG is the transmitter antenna gain, rG is the receiver antenna gain, d is the separation between the transmitter and the receiver, L is the Loss factor and λ the wavelength. The free space propagation model is considered accurate to predict RSSI for satellite communication systems and microwave line-of-sight radio links [10].

B. Two Ray Ground Model The free space model in most of the cases proves to be

inaccurate as it considers a single direct path between the base station and a mobile node, which is an unrealistic assumption in a mobile radio channel [10]. The two ray ground model assumes not only a line of sight communication; instead it additionally considers interference with reflection. The received power at a distance d is computed by equation (2).

( )Ld

hhGGPdP rtrtr

r 4

22

= …(2)

When d represents the distance from transmitter, L is the system loss, tP is the transmitted signal power, tG , rG , th and rh is the antenna gains and heights of the transmitter and the receiver respectively. The two ray ground model is widely used to simulate wireless propagation in network simulations of communication protocols developed for MANETs. It is known to overestimate the receiver power for nodes that are very close to the sender. For longer ranges it delivers more useable results. So, it is more accurate in prediction at long distances than free space model, while in short distances it does not give good results. Like in free space, the communication range in the two-ray ground reflection model is an ideal circle, centered at the transmitter [10]. C. Shadowing Model

Shadowing model is more general and widely used. It uses a different approach to calculate the receiver power. The main difference is that the shadowing model does not try to compute the exact radio propagation but instead provides a model that allows experiencing the same effects that can be experienced in the real environment. As said before, radio propagation is effected by a variety of physical effects. And in most cases the knowledge to describe the environment exact enough is insufficient to compute them. So, a generalized environment is assumed e.g. outdoor downtown, outdoor landscape, office, indoor obstructed, etc. Every such environment is characterized by two properties – path loss and disturbance. The path loss describes the average signal attenuation cause by effects such as fading, reflection, multi-path interference, refraction, absorption etc. With disturbance we describe the dynamics in the environment. For example, it makes a significant difference if we measure connectivity in an office environment at night or at day when a lot of people move around in the building [11]. The shadowing model defines the receiver power as a function of the distance as given in equation (3):

( ) 10log

0 010χ

+×−×= d

dL

reP

PdP …(3) where

epL is called the path loss exponent that specifies the

degree of the path attenuation, 0P is a reference power measured at a reference distance from the transmitter, ~ N(0, 2) a normal distributed error variable that describes the disturbance of the environment. Typical values for 2σ are between 2 and 10 dB.

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III. ANT BASED QOS AWARE ROUTING PROTOCOL FOR

HWSNS This section describes the QoS aware routing protocol for WSNs consisting of multimedia and scalar nodes with multifarious and inherently conflicting demands using ACO. The information required to select QoS paths is facilitated by Hello Ants, Forward ants (FA) and Backward ants (BA). Hello ants are the short messages broadcast by all the nodes at every thello interval. Hello ants are used for neighbor discovery, link failure detection and pheromone diffusion. A FA is a control packet that is created by a source node to find a path from source to an assigned destination d. On its way from source to the destination node a FA stores the full array of nodes that it has visited on their way to the destination. It also carries network status information for determining QoS aware routes. On reaching the destination node each FA with tolerable delay is converted into BA and it retraces the exact path that was followed by the FA back to the source. It stores the global pheromone contribution

dg as determined at the destination node using network status

information received through FA. On its way back, it updates the routing tables based on d

g and the collected quality information about each of the links of the path. A source node s broadcasts FA , whenever, it wants to communicate to destination node d for which routing information is not available. Every intermediate node updates the residual bandwidth and route energy fields of the FA packet by taking minimum of the information stored in the packet and the value offered by the node. When generated at source node, ant contains address of source node and that of destination node and on its way to the destination node, the ant keeps record of all the intermediate nodes visited by it. An ant 1FA arriving at a node with residual bandwidth 1B is forwarded only if

21 BB ×≥ δ where 2FA is a previously received ant. δ is an acceptance factor and is used to decide whether to accept or reject a particular control packet. This is in order to reduce the routing overhead. Further, the probability of selecting node n as next node by current node i is given as [24]:

( )( ) )4(... 1,1

1

1≥

∈

,=P

diNj

dij

dnid

ni βτ

Where din is pheromone value for next node n , d

iN is the set of neighbors of i over which path to d is known, 1 is a parameter that controls exploratory behavior of the ant and is kept to 1 in current experiments. If routing information for destination d is not available at the node, the node broadcasts the Forward ant packet. The ant packet while traveling from source to destination collects status information of nodes along the route. On receiving a FA , the destination node generates BA corresponding to only those FAs that are received within a limited time interval after arrival of first FA . In current experiments this interval is 1.5

times the delay encountered by first packet. The BA stores the contribution of global information in pheromone deposition as computed by destination node.

min

min

)5( 10

eE

,BW

bRwhere

,(E),(R)

channel

ererdg

=

=

≤≤×+×=

The amount of pheromone released by BA is given by din as

follows:

1

2

)6( )(−

+ ×=

×+=

hopid

Tdg

din

ThT

T

τ

αττ

BA is unicast from destination d to source s along the same path as FA had traveled but in reverse direction. Due to local burst of traffic or any other reason there may be large variations in time estimates gathered by the ants. To take into account these large oscillations, instead of considering time estimates gathered by the ants only, the average of estimated time and time under ideal circumstances has been taken. The source node starts data forwarding on receiving the BA. Till that period, the data packets are buffered in the source node. If the source node does not receive BA for some stipulated interval, source node restarts reactive path setup phase. The deposited pheromone gets evaporated over time which allows forgetting old solutions. The proposed protocol creates multiple paths between the source and destination pair during route setup phase and data is forwarded stochastically. Multimedia traffic demands strict requirements on minimum bandwidth, energy efficiency and bounded delay. Residual bandwidth of neighboring nodes and end-to-end delay are the heuristic factors considered for selecting the next hop towards destination. For multimedia data packet, the probability d

ijP of selecting next node j is given as:

)7(... 1,,)()()(

)(≥

∈

βατ ββα

β

diNk

dikk

dik

dijj

dijd

ij tb

t)(b)(=P

Where jb and β)( dijt are heuristic evaluation factors in

considering j as next hop for destination d for multimedia data and are a measure of residual bandwidth of neighbor node j and end-to-end delay respectively. The pheromone and heuristic values are controlled by and respectively. When a node receives scalar data for which routing information is available, the node chooses next hop with probability d

ijP as:

)8(... 1,,)()(

≥

∈

βατ βα

diNk

kdik

jdijd

ij e

)(e)(=P

Where je is residual energy of neighbor node j and is heuristic evaluation factor in considering j as next hop for destination d

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for general data. Similar to eq. 10, and control pheromone and heuristic value respectively.

Contrary, to conventional routing protocols in which a single best path is selected by source for data forwarding, the probabilistic routing strategy leads to automatic data load spreading according to the estimated quality of the paths. The protocol continuously senses the network status and adapts data traffic as per the QoS requirements and prevailing network conditions leading to enhanced performance.

IV. SIMULATION RESULTS AND ANALYSIS

To evaluate the impact of propagation models on the performance and scalability of proposed QoS enabled routing protocol, the simulations have been conducted using 30 to 100 nodes, with intermediate values 40, 50, 60, 70, 80 and 90 nodes, where 50% of the nodes are M-sensors, deployed a 200m x 200m rectangular grid. The M-sensors are stationary, whereas S-sensors are moving with a speed of 5m/sec in the region with 50 pause time. We present the results of a set of tests with 25% of nodes acting as source nodes.

The performance results of proposed protocol and its comparison with AODV protocol in terms of packet delivery fraction, normalized routing load and average end-to-end delay for Free Space radio propagation model (FS) are shown in fig. 1 to 3, for Two Ray Ground (TRG) in fig. 4 to 6 and for Shadowing model (SM) in fig. 7 to 9 respectively. Due to effective QoS-enabled routing strategy of proposed protocol and under ideal conditions, proposed protocol has much improved performance over AODV protocol.

Figs. 1, 4 and 7 show results of packet delivery fraction for the FS, TRG and SM respectively. It can be seen from the figures that as the number of nodes and hence traffic load in the network increases packet delivery fraction decreases. Matos and Miranda identified two adaptation problems in some of the most popular routing algorithm such as AODV, DSR and DSDV [8,9]. AODV protocol selects those routes that have a lower hop count and are discovered faster. However, in doing so it tends to favour routes that include weak links and thus frequent retransmissions as a consequence of frequent link breakages. The proposed protocol determines QoS aware routes and probabilistically selects the route out of available multiple routes. The simulation results shown in the figs. substantiate the betterment of the proposed approach over AODV.

Figure 1: Packet delivery fraction vs. no. of nodes

In FS and TRG models a node located within the transmission range of another will certainly receive every packet sent. Whereas, SM does not guarantee that a node close enough to the sender will receive the message [13,14]. Due to this reason and the reason cited as above, it can be inferred that the routing overhead for SM is more than that of FS and TRG models as has been shown in figs. 2, 5 and 8. Figs. also show that the routing overhead increases as the number of nodes in the network increases. Apart from the above mentioned reasons, there are other two main reasons that contribute to the low routing overhead for proposed protocol even in dynamic scenarios. One reason is availability of good quality multiple paths, therefore, when the routes break due to node mobility, route discovery need not be initiated, resulting in reduced control overhead. The other reason is the limiting behavior of proposed protocol in forwarding control packets. Although, in AODV also, routing packet forwarding is controlled, but unavailability of multiple paths lead to the increased routing overhead in dynamic scenarios.

Figure 2: Normalized routing load vs. no. of nodes

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Figure 3: Average end-to-end delay vs.

The simulation results for end-to-end defigs. from 3, 6 and 9 for FS, TRG and SMsimilar behaviour. The performance of propbetter than that of AODV protocol. Like forproposed routing protocol has much improvedend delay than AODV routing protocol. The rto suffer maximum delay is its tendency to sefor routing. When routing information is buffers the packets and reinitiates route dWhereas in case of proposed routing protocoare determined during route discovery phase.when the node density is low, alternative pafor data routing.

Figure 4: Packet delivery fraction vs. n

no. of nodes

elay as shown in M also show the posed protocol is r delivery ration, d average end-to-reason for AODV lect shortest path not available, it discovery phase. ol multiple routes . Therefore, even aths are available

no. of nodes

Figure 5: Normalized routing Figure 6: Average end-to-end d Figure 7: Packet delivery fra

g load vs. no. of nodes

delay vs. no. of nodes

action vs. no. of nodes

5

Figure 8: Normalized routing load vs. n

Figure 9: Average end-to-end delay vs.

V. CONCLUSION

WMSNs are a promising technology fscenarios. In HWSN both multimedia as well can be deployed in a region to monitor environto detect intrusion simultaneously. However, challenges that are considerably different of win WSNs with homogeneous nodes. This pQoS enabled routing protocol using ACO multimedia and scalar nodes. The entire categorized into multimedia traffic and scaproposed protocol improves network performrouting decision on the basis of traffic type constraints for that traffic type. An effectiveHWSNs is not possible without a realistic eperformance of a number of protocols that are

no. of nodes

no. of nodes

for a number of as scalar sensors nmental data and they present the

what can be found paper proposes a

for WSNs with data traffic is

alar traffic. The mance by taking as well as QoS e deployment of estimation of the e fundamental for

WSNs expected applications. Tperformance of proposed protocol wdistinct radio propagation models shows that when the shadowing pboth the protocols show significantHowever, the performance of outperforms the standard AODV infraction, end-to-end delay and routpropagation models, which confirproposed protocol over AODV.

REFERENC [1]. E. Gurses, and O.B. Akan, “Multim

sensor networks”, Annals of Telecomm827, 2005.

[2]. S. Misra, M. Reisslein, and G. Xue, “Ain wireless sensor networks”, IEETutorials, vol. 10(4), pp. 18–39, 2008.

[3]. T. He, J.A. Stankovic, C. Lu, T.F.communication protocol for wirelessParallel Distributed Systems, vol. 16(1

[4]. Hadjidj, A. Bouabdallah, and Y. Challa Tolerant Multipath Routing Protocol foNetworks,” in Proc. of the 7th

Heterogeneous Networking for QuRobustness (Qshine), Houston, USASpringer LNICST, vol. 74, pp. 469–482

[5]. M. Dorigo, T. Sttzle, Ant Colony OptiMass, 2004.

[6]. S. M. ITOUA, “Effect of PropagatioRouting Protocols,” in the Proc. Of 2non Sensor Technologies and Applicatio

[7]. H.T. Friis,: A note on a simple tran1946.

[8]. T. Rappaport.: Wireless CommunicPrentice Hall PTR, 2001.

[9]. The Network Simulator.www.isi.edu/n [10]. D. Johnson, B., et al., The dynamic so

ad hoc networks (DSR), IETF MANET[11]. Y. Wang, X. Jia, and H.K. Lee, “An in

based on wireless local area network i6th International Symposium on SIncluding Mobile Positioning & Locati

[12]. G Di Caro, F Ducatelle, LM Gambanature inspired algorithm for routinEuropean Transactions on Telecomm455, 2005.

[13]. T. Yang, M. Ikeda, G. De Marco, L. BAODV, DSR and DSDV protocols forsensor networks”, in Proc. of Int’Workshops, ICPPW’07, 2007.

[14]. G. De Marco, M. Longo, F. Postignetworks with link asymmetrieCommunications Letters, IEEE 11(6) ,

his paper compared the with AODV protocol when are simulated. The paper

propagation model is used, t performance degradation.

the proposed protocol n terms of packet delivery ting overhead for all radio rms the superiority of the

ES media communication in wireless munications, vol. 60(7-8), pp. 799-

A survey of multimedia streaming E Communications Surveys & Abdelzaher, “A spatiotemporal sensor networks,” IEEE Trans. 0), pp. 995-1006, 2005. al. “HDMRP : An Efficient Fault or Heterogeneous Wireless Sensor

h International Conference on uality, Reliability, Security and A, November 2010, published in

2, 2011. imization, MIT Press, Cambridge,

on models on Ad Hoc Networks nd IEEE International Conference ons, pp. 752-757, 2008. smission formula. Proc. IRE 34,

ations: Principles and Practice.

nsnam/ns/ ource routing protocol for mobile T Working Group, Feb. 21, 2002. doors wireless positioning system nfrastructure”, Proceedings of the

Satellite Navigation Technology ion Services, 2003. ardella, “AntHocNet: an adaptive g in mobile ad hoc networks,”

munications, vol. 16(2), pp. 443–

Barolli, “Performance behavior of r different radio models in ad-hoc l Conf. on Parallel Processing

glione, “Connectivity of ad hoc es induced by shadowing”, pp. 495 –497, 2007.

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