QuESt : a QoS-based energy efficient sensor routing protocol

WIRELESS COMMUNICATIONS AND MOBILE COMPUTINGWirel. Commun. Mob. Comput. 2009; 9:417–426Published online 13 September 2007 in Wiley InterScience(www.interscience.wiley.com) DOI: 10.1002/wcm.546

QuESt: a QoS-based energy efficient sensor routingprotocol

Navrati Saxena1, Abhishek Roy2 and Jitae Shin1,∗,†

1School of Information and Communication Engineering, Sungkyunkwan University, Suwon, Korea2WiBro System Lab, Samsung Electronics, Suwon, South Korea

Summary

Rapid penetration of smart wireless devices and enormous growth of wireless communication technologies hasalready set the stage for deployment of wireless sensor networks (WSNs). While these small sensor nodes are oftenconsidered as the future of wireless communications, they also suffer from energy constraints. On the other hand,with increasing demand for real-time services in next generation wireless networks, quality-of-service (QoS)-basedrouting has emerged as an interesting research topic. Naturally offering some QoS-guarantee in sensor networksraises significant challenges. The network needs to cope up with battery-constraints, while providing precise QoS(end-to-end delay and bandwidth requirement) guarantee. More precisely, designing such QoS-protocols, optimizingmultiple objectives, is computationally intractable. Based on the multi-objective genetic algorithm (MOGA), in thispaper we propose a QoS-based energy-efficient sensor routing (QuESt) protocol that determines application-specific,near-optimal sensory-routes demand, by optimizing multiple QoS parameters, (end-to-end delay and bandwidthrequirements) and energy consumption. Simulation results demonstrate that the proposed protocol is capable ofdiscovering a set of QoS-based, near-optimal routes, even with imprecise network information. Copyright ©2007 John Wiley & Sons, Ltd.

KEY WORDS: sensor networks; multi-objective genetic algorithm; QoS routing

1. Introduction

The concept of sensor networks lies in a set of smart,autonomous sensor nodes, equipped with heavilyintegrated sensing, processing, and communicationcapabilities and networked together in an ad hocfashion. Rapid advances in micro-electro-mechanicalsystems (MEMS) technology, proliferation of wirelesscommunications, and digital electronics have devel-oped the stage for deployment of low-cost, low-power,multi-functional sensor networks. Data acquisition by

*Correspondence to: Jitae Shin, School of Information and Communication Engineering, Sungkyunkwan University,Suwon 440746, Korea.†E-mail: [email protected]

sensing, data processing, and communication throughnode-coordination are the major tasks of these sensornetworks. In fact, a wireless sensor network (WSN)[1] is generally designed to detect events, collect andprocess data, and transmit that data to the sink (in-terested user). Unfortunately, the wireless medium andhighly compact form factors impose energy constraintson these smart networks. Replenishing this energyby battery-replacement is clearly not feasible for alarge network consisting hundreds of nodes. Moreover,wireless sensors are often deployed in an area which

Copyright © 2007 John Wiley & Sons, Ltd.

418 N. SAXENA, A. ROY AND J. SHIN

is inapproachable to humans and away from anysustained power-supply. Hence, conservation of energyto maximize the post-deployment active lifetime is awell accepted challenge in sensor networks.

On the other hand, today’s wireless communicationis gradually changing paradigm from its existing voice-alone services to a new world of real-time audio-visual applications. One primary objective behinddeployment of wireless sensor networks (WSNs) is tocapture and transmit the relevant pictures, videos, andimportant data to the interested user (through sink).However, such multimedia applications require strictquality-of-service (QoS) guarantee on bandwidth anddelay. QoS routing offers significant new challengesover the already energy-constrained sensor networks.Individual QoS parameters may be conflicting andinterdependent, thus making the problem even morechallenging. The objective of this work is to identifythe major challenge in QoS-routing over energy-constrained sensor networks and providing optimal(or near-optimal) solutions. More specifically ourcontributions are:

(1) We first prove that determining optimal routessatisfying multiple QoS parameters simultane-ously, in an energy-aware sensor network is a NP-complete problem. This motivates us to look forheuristics/approximate algorithms to obtain a near-optimal solution in computationally feasible time.

(2) We argue that multi-objective genetic algorithm(MOGA) is one of the most efficient tools tosolve such NP-complete problems with multipleobjectives. While the basic genetic operations arebased on a guided, random search technique, ittreats the multiple QoS parameters independently,without combining them.

(3) We have applied a multi-objective genetic algo-rithmic strategy [2] to develop an efficient protocolthat determines a set of near-optimal routes,satisfying application-specific QoS-parameters, inWSNs. The protocol is called QuESt that standsfor Quality-of-Service-based Energy EfficientSensor Routing. The basic idea is to optimize QoSparameters—namely, end-to-end delay, bandwidthrequirement, while maintaining the energyconstraints—without combining them into a singlescalar objective function. With the help of suitablegenetic operators, the protocol QuESt is capable offinding near-optimal solutions within a fewiterations.

(4) Experimental results show that this protocolachieves more than 90% of the global optimal

values for all the QoS parameters under our testcases. Additionally, it exhibits lower end-to-enddelay and good throughput for wireless traffic.The lower energy consumption and delay withincreasing traffic load speaks for the superiorperformance of QuESt.

The rest of the paper is organized as follows.Section 2 reviews existing works in sensory routing,protocols, and their QoS extensions. The relevant QoS-parameters are identified and modeled in Section 3.In Section 4, we have shown that the QoS-routingproblem in WSNs with multiple QoS-parameters is aNP-complete problem. The basic concept of MOGArelevant to our context is highlighted in Section 5. Theproposed energy-aware QoS-routing algorithm andthe corresponding protocol, QuESt, are described andillustrated in Section 6. Simulation results in Section 7corroborates the fast optimization of the requiredQoS parameters and the efficiency of the protocol inreducing the packet-loss. Section 8 concludes the paperwith pointers to future work.

2. Existing Works in Sensor Routing

Given the wide number of research works in sensorrouting we give a highlight of the major works relevantto our context. A state of the art in algorithms,protocols and hardware is surveyed in Reference [1]for WSNs and multimedia applications over it. SPIN[3] is probably the first data-centric routing protocol,which considers data negotiation to reduce redundancyand save energy. Direct diffusion [4] and its extensionsprovide different variations to conserve the energy forincreasing the life-time of sensor networks. Location-based protocols, such as, MECN [5], SMECN [6],GAF [7], GEAR [8], primarily rely on the accuracy ofsensors’ location information to calculate the optimalenergy requirement. While energy consumption is onemajor issue in sensor routing, Stemm and Katz haveshown that in sensor nodes idle: receive: transmit ratiosare 1 : 1.05 : 1.4 [9]. The researches of MAC protocolsfor WSN, such as S-MAC [10] and T-MAC [11], usesleep mode to save energy.

Although QoS-routing in wireless networks isan active research area, QoS-based sensor-routinghas received relatively less attention. The energy-aware QoS-routing protocol in Reference [12] findsa least cost energy efficient path, while meetingcertain end-to-end delay constraints. Another QoS-routing protocol SPEED [13] depends on state-less

Copyright © 2007 John Wiley & Sons, Ltd. Wirel. Commun. Mob. Comput. 2009; 9:417–426

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geographical non-deterministic forwarding to providesoft real-time end-to-end QoS guarantee. A QoSsupporting scheme is developed in Reference [14]for dynamic traffic conditions by controlling datagenerating rates at individual clusters in a cluster-basedWSNs. Similarly a learning-based QoS-routing is alsoproposed in Reference [15]. However, the inaccuracyof information along the wireless links always thwartthe QoS guarantee in any wireless networks, andsensor networks are not an exception. This impactof information inaccuracy and uncertainty over QoS-routing has been investigated in References [16,17]which propose efficient heuristics to identify routes thatare most likely to accommodate the desired QoS in thepresence of uncertain network state information. Usingsuitable probabilistic models it has been shown thatuncertainty is minimal for flows with only bandwidthrequirements, but path selections become intractablewhen end-to-end delay is additionally considered.Thus, researches for considering QoS-routing withmultiple parameters mostly rely on approximationalgorithms and heuristics. MOGA is a very efficientand useful tool for solving these intractable problems.Recently, the efficiency of these MOGAs has beendemonstrated in Reference [18] by obtaining QoS-based multicast routes in computationally feasibletime. This motivates us to develop a QoS-aware energy-efficient routing protocol for WSNs.

3. Modeling and Analysis of QoSand Energy

We represent the input sensor network by a graphG = (V, E), where V is the set of nodes and E is theset of edges between the node-pairs, Figure 1 shows a

Fig. 1. A graph representing sensor-network.

Fig. 2. Two different routing trees.

16-node sensor network with random connectivity. Apath between a sensory-source (vs) and a destination-sink (vd) is represented by a sequence of nodesvs, v1, v2, v3, . . . , vd where vi ∈ V . There can bemultiple such paths between a source-destination pair.Figure 2 shows two possible routing trees, consideringnodes 6, 7, 12 as the set of sensory-sources and node 1as the sink (destination). The proposed QoS-routingalgorithm looks for a set of non-dominated pathsthat will satisfy the QoS parameters: (i) end-to-enddelay, (ii) total bandwidth required, and (iii) overallenergy consumed. While the wireless voice calls areoften modeled using memoryless Poisson distribution,Poisson model fails to capture the inherent long-rangedependency (LRD) of data-traffic. However, researchesalong this direction has pointed out the efficiency ofWeibullian distribution [19] in modeling the LRD dataand capturing the heavy tails with reasonable accuracy.Hence, we have used Weibullian distribution to startmodeling the delay and bandwidth.

3.1. End-to-End Delay

The delay over the individual links is assumed toobey Weibullian distribution with parameter µ. Thedelay over a particular route from a sensory-sourceto the sink is basically a sequence of such links.Hence, the probability that the delay dp, over anindividual path p of length k is less than t, is estimatedusing modified, heavy-tailed Erlangian distribution[19]. Now, assuming the worst-case scenario with allindependent paths in the routing tree, T, the probability


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that the delay (dT) of the selected tree will meet thespecific delay constraint, is obtained by taking theproduct of delays over individual paths in that tree:

Pr(dp < t) = (µ/α)kβtkβ−1e(−µt/α)β

(k − 1)!

Pr(dT < t) =∏p∈T

Pr(dp < t) (1)

where the constants α and β are respectively calledthe scale and the shape parameter. The objective of theQoS-routing protocol is to maximize this probability.

3.2. Total Bandwidth Consumption

The probability with which the bandwidth guaranteeB is satisfied for an entire routing tree T is estimatedby taking the product distribution of the individuallinks. Thus:

Prl(B) = (B/α)βe(−B/α)β

k!

PrT(B) =∏l∈T

Prl(B) (2)

We attempt to maximize this probability also.

3.3. Energy

Energy consumption in sensor node can be estimatedusing the energy used during data transmission (Et)and energy used during reception (Er). If E representsresidual energy, Einit represents the initial energy andEt and Er, respectively represents energy consumed ina sensor node during transmission and reception of datatraffic, then we have,

E = 1

Einit[Einit − (Et + Er)]

Et = (et + edrn)B

Er = erB

Pr(B) = (B/α)βe(−B/α)β

k!(3)

where et is the unit energy (energy/bit) consumedby transmitter electronics, ed represents unit energydissipated by transmit op-amp, er is unit-energyconsumed by receiver electronics and r is transmissionrange, respectively. B represents the traffic bit-rate inthe sensor nodes, which is indeed a factor of the currentbandwidth B.

4. QoS Routing Complexity in SensorNetworks

It is now clear that an optimal route from all thesensor nodes to the sink needs to satisfy all the above-mentioned QoS parameters. Hence, the optimizationproblem can be formally stated as:

OPT = max [Pr(dT < t), PrT(B), E] (4)

We will now show that this optimization problem isNP-complete by mapping this problem to ConstrainedSteiner Tree Problem [20], a well-known NP-completeproblem.

Definition 1. Given a delay tolerance � and a graphG(V, E), a constrained spanning tree T is a minimum-cost tree, rooted at source node s, that spans the set ofdestination nodes D, such that for each node v in D,the delay on the path from s to v is bounded above by�. Formally if P(s, v) is the path in T from s to v,

∑e∈P(s,v)

Delay(e) < �, ∀v ∈ D,

∑e∈T

Cost(e) is minimized (5)

We will now show the mapping of our QoS-routingproblem into this constrained Steiner-tree problem. Theoverall sensor network is mapped to the graph G(V, E),the sensory-data-sink is mapped to the source node sand the set of entire sensor nodes is mapped to theset of destination nodes D. The

∑e∈P(s,v) Delay(e) is

mapped to Pr(dT < t) and∑

e∈T Cost(e) is mapped toPrT(B). Hence, the set of different constrained Steinertrees will actually provide the set of different routesbetween the sensor nodes and the sink, satisfying thedifferent QoS parameters. Table I shows this mappingof salient parameters between sensory-QoS-routingand constrained Steiner tree.

Table I. Mapping between QoS-routing in sensor and Steiner tree.

Sensory QoS-routing Steiner tree

Sensor network G(V, E)Sensory data sink Source (s)Set of sensory sources Set of destination nodes D∑

e∈P(s,v) Delay(e) Pr(dT < t)∑e∈T

Cost(e) PrT (B)Set of QoS routes Constrained Steiner tree(sensory-sources to destination)


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QuESt 421

Corollary 1. Hence we can claim that QoS routingin sensor networks with multiple QoS parameters is anNP-complete problem.

In the subsequent sections of this paper, we willfocus on obtaining a near-optimal solution of this NP-complete problem in computationally feasible time.We will use MOGA as our optimization technique.Hence, before going into the details of the optimizationstrategy, we look into the relevant concepts of geneticalgorithms required at this context.

5. Multi-Objective Genetic Algorithms:Preliminaries

A Genetic Algorithm (GA) is a guided randomsearch and optimization technique, based on the basicprinciples of evolution: survival of the fittest andinheritance [21]. To solve a problem, GAs start witha chromosomal representation of its parameter set.A set of such chromosomes or strings is termed aspopulation. The fitness/objective function is chosen insuch a way that good points in the search space possesshigh fitness values.

Many real-world problems require simultaneousoptimization of multiple objectives, which is quite dif-ferent from a single-objective optimization. However,there may not exist a single best solution with respectto all the objectives under consideration. Instead, theremight exist a set of solutions superior to the rest in theentire search space. Such a solution set is termed asPareto-optimal [2]. Since none of the solutions in thisset is absolutely better than any other, the user is giventhe freedom to choose the best possible solution thatconforms to the application specific requirements. Apareto-optimal solution is defined as follows.

Definition 2 . A point x is Pareto-optimal if for everyx either ∩i(fi(x) = fi(x∗)) or there is at least one isuch that fi(x) > fi(x∗), ∀i ∈ I (set of integers), wherefi(x) is the fitness function. In other words, x∗ is Pareto-optimal if there exists no feasible vector x which woulddecrease some criterion without causing a simultane-ous increase in at least one other criterion [2].

A set of non-dominated strings are those whichare better than others in the current population,when all the objective parameters are considered.All such non-dominated strings are included toform the initial Pareto-optimal front [2]. As MOGAiterates in every generation, the non-dominated, Pareto-optimal solutions are found and genetic operations are

Fig. 3. Niche sharing in MOGA.

performed on them to improve their fitness values. Thenon-dominated solution set quickly proceeds towardsthe global optimal and gets saturated at a near-optimalpoint. However, the procedure of obtaining non-domination between a pair of strings might result in atie (both are either dominated or non-dominated). Theconcept of Niche Sharing [22] can be used to resolvesuch ties. Given an optimization function havingseveral peaks, the goal of fitness sharing is to distributethe population over the different peaks in the searchspace, where each peak receives a fraction of the entirepopulation according to its height. One practical way toachieve such fitness-sharing is to degrade an individualstring’s fitness (fi) by dividing it by a niche count(mi) for that individual. The intuition behind the nichecount is that it is a good estimate about the crowd ofthe neighborhood of a particular individual i. Figure 3shows the form of this niche sharing between twoindividuals. It shows that a maximization along the X-axis simultaneously with minimization along the Y-axisresults in non-dominated candidate-solutions. Hence,from a Pareto-optimal point of view, neither of the twocandidate solutions are preferred. However, in orderto maintain diversity it is better to select the solutionhaving lower niche count—that is, candidate-2.

6. QuESt–Routing Algorithm andProtocol Details

The underlying idea behind the algorithm is not tocombine the QoS and energy-based objective functionson an ad hoc basis to form a single scalar objectivefunction, but to tackle the problem from the perspectiveof multi-objective optimization. The motivation here


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Fig. 4. Flowchart of the MOGA-based QuESt.

is to provide the user with a set of Pareto-optimalsolutions and give him the flexibility to choose thebest possible solution from this set, depending on thespecific application requirements.

The flow of our algorithm is depicted in Figure 4. Thealgorithm takes as input a network G = (V, E), a spe-cific number of sensory source nodes vs1 , vs2 , . . . , vsn

and a single destination (sink or gateway) vd. It thenfinds a pool of possible routing paths from vd to each ofvs1 , vs2 , . . . , vsn , using a depth first search (DFS) algo-rithm. Thus an initial set of routing trees is constructed.Each of these routing trees is mapped to a stringconsisting of the sequence of nodes along the path fromvd to each of the destinations vs1 , vs2 , . . . , vsn . The endof a path from a source to a destination is markedwith −1 (sentinel). The set of all such initial stringsconstitutes the initial population of chromosomes forthe proposed MOGA. Figure 5 shows the stringrepresentations of two routing trees of Figure 2.

The fitness value of a string is computed with the helpof the above three estimated QoS parameters. The goalof the proposed algorithm now boils down to a search

Fig. 5. String representing the routing tree of Figure 2.

for different routing paths which will improve thevalues of the QoS parameters at each iteration. A certainnumber of strings (chromosomes) are picked randomlyfrom the population to generate a comparison set (C).The size, tdom, of this set gives us a good control overthe selection pressure. If a small tdom is chosen, veryfew Pareto-optimal points would be found. Instead, alarge tdom might result into a premature convergence[22]. Various existing multi-objective optimizationschemes suggest that a comparison-set that is ( 1

4 −15 )th of the population-size often provides a balancedconvergence rate [2,22]. Therefore, we choose tdom =0.20 × (population-size). From the population, twostrings are randomly selected at a time and comparedagainst each string in the comparison set. If onecandidate dominates another (i.e., better than itscompetitor considering all three QoS parameters), thenit is included in the Pareto-Optimal set (S). On the otherhand, if both the competitors are non-dominated, thena niche count is used to resolve the tie. The value ofniche count for every string present in the populationis computed as [2,22]:

mi =popsize∑j=1

Sh[dsi,sj ] (6)

where dsi,sj is the distance between the strings s1 ands2; and Sh[dsi,sj ] is the sharing function. For simplicity,a triangular sharing function [22] has been used:

Sh[dsi,sj ] ={

1 − dsi,sj

σshared ≤ σshare

0 otherwise(7)

where the niche radius (σshare) is a good estimateof minimal separation expected between the differentsolutions. Individuals within distance σshare from eachother degrade each other’s fitness, as they are in thesame niche. We introduce a new concept of adaptivesharing as follows: Depending on the fitness values ofthe particular string chosen and the population densityin the search space, the value of σshare is dynamicallyupdated in every iteration of the algorithm. Wecompute phenotypic Euclidian distance [22] betweenthe different fitness values as a good measure of dsi,sj :

dsi,sj =√

(δdelaysi,sj)2 + (δbwsi,sj

)2 + (δEsi,sj)2 (8)

where the δ’s are expressed in terms of the correspond-ing QoS parameters as follows: δdelaysi,sj

= Pr(dsi <


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QuESt 423

t) − Pr(dsj < t); δbwsi,sj= Prsi (B) − Prsj (B); and

δEsi,sj= E(si) − E(sj).

Similarly, the niche radius (σshare) is obtainedas some fraction (precisely half) of the maximumseparation possible in the population [22]:

σshare = 1

2×

√(δdelaymax

)2 + (δbwmax )2 + (δEmax )2

(9)where δdelaymax

= Prmax(dT < t) − Prmin(dT < t);δbwmax = Prmax(B) − Prmin(B); and δEmax = Emax −Emin.

This niche sharing-based selection procedure isrepeated until all the strings of the population arechecked. The cross-over and mutation operations aresame as the basic genetic algorithms. An analysis ofthe structure of the chromosomes in Figure 5 revealsthat these genetic operations can not be performed onany arbitrary gene (network nodes), as that may resultin some illegal paths. Both the cross-over and mutationoperations can only be performed at the end of anexisting path, that is, immediately after the particularsentinel represented by−1. To give an equal probabilityto all such possible cross-over and mutation points, werandomly select one such valid point. To combine thegood strings and simultaneously preserve the effectiveones, we have considered the probability of cross-overas 0.8 and that of mutation as 0.1.

As MOGA-based routing routing scheme executes,at every iteration the genetic operations dynamicallyupdate the chromosomes (strings) and try to improvethe corresponding probabilities of the three QoSparameters. This entire process is repeated untilthe difference of fitness values between the currentPareto-optimal set and the previous one is lessthan a chosen precision, ε. Indeed, actual valuesof ε will be different for different QoS parameters.We have chosen 10−6, 10−5, and 10−3 as theprecision values for the probabilities of end-to-enddelay, bandwidth guarantee and energy consumption,respectively.

7. Simulation Experiments and Results

Simulation experiments are conducted with 802.11MAC. Initially the WSN is consisted of n = 300sensor nodes. The density of the network in takenas 0.7. The average values of bandwidth and end-to-end delay requirement are chosen as 75 Kbps and60 msec, respectively. The capacity (cl) of the networklinks is assumed to be uniformly distributed in the

Fig. 6. Pareto-optimal probabilities for QoS parameters.

interval of [90–110] Kbps. The video traffic is assumedto be encoded with International TelecommunicationUnit’s (ITU) H.264 [23] specifications with 176 × 144pixel resolution and an average 40 frames/sec. Thisarrival rate is slowly increased to notice the impacton the framework. On the other hand, best effortdata traffic are modeled as fluid queues, with heavytails and long range dependencies. The experimentsare repeated 50 times with four different networkdensities [0.5, 0.8]. Subsequently the network sizeis increased to contain n = 500 sensor nodes. Theperformance of our algorithm is compared with SPEED[13] and cluster-based QoS-routing [14] in sensornetworks.

Figure 6 explains how the Pareto-optimal frontsare developed and proceeded toward global optimain a feasible time. The novelty of our algorithm isthat it is capable of obtaining near-optimal values ofboth the QoS parameters and energy-consumption bybuilding the non-dominated fronts. From these plotwe can derive the percentage of global optimizationachieved by our algorithm for these three specificparameters. Table II demonstrates that our algorithmis capable of obtaining more than 90% of theglobal optimal values for end-to-end delay, bandwidthguarantee, and residual bandwidth utilization within100 iterations. Note that the solution set may containthose sensor routes which are not the best fromthe viewpoint of a single QoS-objective, but is non-dominated and offers a fair compromise when all three

Table II. Percentage of global-optimal solutions obtained.

End-to-end delay 93.78Bandwidth guarantee 92.55Residual energy 90.39


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Table III. Computational time.

Sensors Computational time

300 156350 169400 185450 201500 213

objectives are considered simultaneously. Table IIIshows the computational time taken by our proposedprotocol. The table points out that the computationaltime is within 4 min even for a sensor network of500 nodes.

The end-to-end delay metric is compared betweenQuESt, SPEED [13] and Cluster-QoS [14] for wirelessvideo traffic in Figure 7. It explains that end-to-end delay for wireless video traffic is lowest forQuESt. Figure 8 depicts a comparative study ofthe wireless throughput between different QoS-basedsensor routing for different traffic arrival rate. It is clear,that with traffic arrival rate more than 40 packets/sec,

Fig. 7. Comparative delay of different sensor-QoS-routings.

Fig. 8. Comparative throughput for different load.

Fig. 9. Dynamics of energy consumption.

the throughput of both SPEED [13] and Cluster-QoS[14] reduces, thereby incurring packet-loss. On theother hand, QuESt provides more steady throughput,even for pretty high packet arrivals ∼ 80 packets/sec.

Figure 9 delineates the comparative performance interms of energy consumption of sensor nodes. Resultsshow that the average energy consumption of QuEStprotocol is less than both SPEED [13] and Cluster-QoS [14]. Although the energy consumption increaseswith time, QuESt has consistent performance gainover the other-two. In order to investigate furtherinto the dynamics of sensory energy consumption,in Figure 10, we have shown the average energyconsumption in QuESt, SPEED [13] and Cluster-QoS [14], with different increasing traffic arrivalrate {20, 40, 60, 80, 100} packets/sec. While all thestrategies result in increase of energy consumptionwith increasing traffic arrival, QuESt results in lowestenergy consumption for different traffic arrival rates.This shows the efficiency of the protocol in reducingthe energy consumption to near-optimal values, whileoptimizing the delay and bandwidth as well.

Fig. 10. Energy consumption with different load.


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QuESt 425

8. Conclusion

Energy-aware routing is an active research area inbattery-constrained, WSNs. This makes the seamlesstransmission of wireless multimedia traffic over thesensor networks really challenging. It is quite naturalthat such real-time traffic should meet a numberof different and conflicting QoS-issues. Optimiz-ing a particular objective function may sacrificeoptimization of another dependent and conflictingobjective. In this paper, we have developed a QoS-based, energy-aware routing protocol in WSNs fromthe perspective of multi-objective optimizations. Theprotocol efficiently optimizes the QoS parameters andalso provides improved delay and throughput, whilereducing the overall energy consumption. Simulationresults delineates the efficiency and performance of theprotocol, even with increasing traffic load. Our futureworks lie in investigating into efficient sensory datafusion and localization schemes to integrate with thisQoS-routing framework.

Acknowledgements

We express our heartiest thanks to the anonymousreviewers for their suggestions to improve the quality ofpaper. This work was supported by the Korea ResearchFoundation Grant funded by the Korean Government(MOEHRD) KRF-2006-331-D00358.

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Authors’ Biographies

Dr Navrati Saxena is currently workingas a Research Professor in the Elec-tronics and Computer Engineering De-partment of Sungkyunkwan University,South Korea. During 2005–2006 sheworked as an Assistant Professor anddirector of research in Next GenerationNetworks in the Amity University, India.Prior to that, in 2003–2005 worked as a

visiting researcher in the Computer Science and EngineeringDepartment, University of Texas at Arlington. Navraticompleted her Ph.D. from Department of Information andTelecommunication, University of Trento, Italy. She was also


DOI: 10.1002/wcm


the recipient of Best M.S. and Best B.S. awards from AgraUniversity and Kanpur University, India, respectively. Herprime research interests involve wireless sensor networks,4G wireless and Ubiquitous/Smart Environments. She haspublished more than 20 International conference and severalInternational journal publications.

Abhishek Roy is currently workingin the WiBro (WiMAX) System Labof Telecommunications and NetworkR & D Center, Samsung Electronics,South Korea. He has about 5 yearsof experience in different aspects ofR&D in wireless telecommunication. Hereceived his M.S. degree (with BestM.S. Research award) in 2002 from

the University of Texas at Arlington (U.S.A) and B.E.degree in 2000 from Jadavpur University, Calcutta, inComputer Science and Engineering. His research interestsinclude mobility management, voice over IP (VoIP), and

performance modeling of mobile and ubiquitous systems,like, WLANs, WiMAX (WiBro), and Smart Environments.He has published over 15 International conferences and 5International journals.

Jitae Shin received the B.S. fromSeoul National University in 1986,the M.S. in Korea Advanced Instituteof Science and Technology (KAIST)in 1988, and the M.S. and Ph.D.degrees in Electrical Engineering fromUniversity of Southern California, LosAngeles, U.S.A. in 1998 and 2001,respectively. He is an Assistant Professor

in School of Information and Communication Engineeringof Sungkyunkwan University, Suwon, Korea. His recentresearch interest includes reliable and efficient multimediatransmission over next generation Internet and wirelessnetworks focusing on QoS and multimedia networkcontrol/protocol issues. He is a member of IEEE and IEICE.


DOI: 10.1002/wcm

QuESt : a QoS-based energy efficient sensor routing protocol

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