Cluster-based Communication Topology Selection andUAV Path Planning in Wireless Sensor Networks*

Dac-Tu Ho1, Esten Ingar Grøtli1, P. B. Sujit2, Tor Arne Johansen1, Joao Borges Sousa2

Abstract— In conventional wireless sensor networks(WSNs) to conserve energy Low Energy Adaptive Clus-tering Heirarchy (LEACH) is commonly used. Energyconversation is far more challenging for large scaledeployments. This paper deals with selection of sen-sor network communication topology and the use ofUnmanned Aerial Vehicle (UAV) for data gathering.Particle Swarm Optimzation (PSO) is proposed as anoptimization method to find the optimal clusters in orderto reduce the energy consumption, bit error rate (BER),and UAV travel time. PSO is compared to LEACHusing a simulation case and the results show that PSOoutperforms LEACH in terms of energy consumptionand BER while the UAV travel time is similar. Thenumerical results further illustrate that the performancegap between them increases with the number of clusterhead nodes. The reduced energy consumption means thatthe network life time can be significantly extended andthe lower BER will increase the amount of data receivedfrom the entrie network.

I. INTRODUCTION

Unmanned Aerial Systems (UASs) technologyhas spread widely and become popular in variousmilitary and civilian fields. Some of the popularapplications are radar localization, [1], wildfiremanagement, observing support, [2], agricultural

*The work is supported by Strategic University Program on Con-trol, Information and Communication Systems for Environmentaland Safety Critical Systems. This work was carried out during thetenure of an ERCIM ”Alain Bensoussan” Fellowship Programme.This Programme is supported by the Marie Curie Co-funding ofRegional, National and International Programmes (COFUND) ofthe European Commission.

1D. T. Ho, E. I. Grøtli and T. A. Johansen are with theCenter for Autonomous Marine Operations and Systems (AMOS),Department of Engineering Cybernetics, Norwegian University ofScience and Technology, O. S. Bragstads plass 2D, 7491 Trond-heim, Norway {dac.tu.ho, esten.ingar.grotli,tor.arne.johansen} at itk.ntnu.no

2P. B. Sujit, J. B. Sousa are with the Department of Electrical andComputer Engineering, Faculdade de Engenharia da Universidadedo Porto, Portugal {sujit, jtasso} at fe.up.pt

monitoring, border surveillance and monitoring,environmental and meteorological monitoring, andaerial photography, [3], as well as for search andrescue missions, [4]. The most noticeable benefitscompared with conventional manned vehicles arelow cost, improved safety for humans, and easydeployment. There are also many applications inwireless networks that employ Unmanned AerialVehicles (UAVs) in order to extend the range ofcommunication, [5], maximize the data communi-cation capability of the network by using vehiclesas relay nodes, [6], collect data from a wide areanetwork in remote or harsh environment [7], oraid node’s localization in mobile network, [8].The key strategic research activities of the authorsof this paper are within oil spill response, icemonitoring, ship traffic surveillance, and marinemonitoring and operations. However, many of thegeographical areas of interest are harsh, remoteand also isolated due to lack of communicationinfrastructure. Building permanent communicationinfrastructure may be considered too expensive ortoo difficult to maintain. It is easier and cheaperto set up a network of nodes on the ground or onthe sea surface, and use UAVs to gather data fromthese nodes. Extensive studies on how to gatherdata from wireless sensor nodes as surveyed in [9].In these networks, sensor nodes are condensed andusually connected to each other in clusters. Thesenodes therefore communicate with the base station(sink) via their cluster head (CH) node. Hence,it requires multi-hop data communication betweenthe nodes. That may consume a large amount ofenergy due to heavy communication and overheadmessages exchanged among them for instance forclustering, receiving and forwarding of data to thesink by the cluster heads. This problem could bereduced by the employment of mobile sinks which

2013 International Conference on Unmanned Aircraft Systems (ICUAS)May 28-31, 2013, Grand Hyatt Atlanta, Atlanta, GA

978-1-4799-0817-2/13/$31.00 ©2013 IEEE 59

move within the network [10]. Optimal strategiesfor the mobile sink depends on the size of the WSNand how frequently it receives status informationfrom the other nodes in network [11]. However,the mobile sink solution still acquires significantenergy consumption when the nodes usually sendinformation to the sink during its movement. Theenergy burden on each node could be furtherreduced when UAVs plays the role of the sinknode, as it flies over the network and receives datafrom as many nodes as possible, [12]. UAVs aretypically in line-of-sight (LOS) so they have betterchannel compared to surface vehicles. As with allmoving sinks, it can be controlled to improve per-formance of the whole network. When the nodesare fixed and the UAV path is defined, [13] hasintroduced a new communication protocol (MAC)for the UAV and the nodes in order to minimizenode’s energy usage and maximize network lifetime. Similarly, [14] proposed a heuristic algorithmfor the UAV to search and retrieve data from thenodes by adaptively adjusting the UAV’s path withthe assumption that the UAV can only visit theCH node of each cluster. In many cases, multipleUAVs are used and coordination is necessary tofind the optimal flight path. Path planning for theUAVs under dominant constraints such as inter-vehicle communication, collision avoidance andanti-grounding, is challenging and have been stud-ied using methods suitable for preplanning, [15],[16] and online methods suitable for replanning,[17]. In this paper we have a different approachfrom existing proposed methodologies in datagathering by UAV in wide area sensor networks.Our algorithm finds the optimal UAV path whiletaking into account energy consumption, bit errorrate of all network nodes and the UAV’s flight time.

II. RELATED WORK

The problem of optimal path planning for UAVsunder different constraints has been a study fordecades, [18]. Depending on the applications theobjective functions and optimization methods aredifferent. In this paper the objective is to mini-mize the total energy consumption by the nodes;maximize the quality of data communication viawireless channel between any node and its CH, andbetween the CHs and the UAV; and minimize the

flight time for the UAV. The target is to providea list of nodes which are then to be visited bythe UAV. The optimization runs before every UAVflight mission, and its solution provides a path forthe UAV to follow in order to complete one roundof data collection. Regarding the communicationquality for the network of WSN and a UAV,[19] showed that lower Bit Error Rate (BER) isobtained if the UAV communicates with fewersensor nodes at a time. However, the flight timewill be significantly increased if the UAV needs tofly over all the nodes in the network. This issuewas relaxed in [14] where the nodes were groupedin clusters and only the CH were visited by theUAV. The clustering technique in [14] was onlydepending on the communication ability or dis-tance, and this may be inefficient leading to poorcommunication quality or low energy efficiency.The optimal number of CHs for minimizing thetime to visit all the nodes here is similar to thestudies in covering salespersons problem (CSP). InCSP, the objective is to minimize the total travelingtime or distance for the salesperson to visit at leastone element in disjoint subsets, [20]. [14], [19] and[20] have shown that there is a relation betweenthe communication quality (BER) and the travelingdistance or time of the UAV in our network model.This can also be seen in Section V of this paper.

The energy consumption in traditonal sensornetworks is a challening issue and it relatesto cross-layer design such as network topology,clustering algorithms, transmissions method, andMAC protocol. In our previous papers, an energy-efficient MAC protocol [14] and a network codingmethod [7] have been proposed for wide area sen-sor networks. In this paper we focus on clusteringalgorithms, which generally depends on whetherthe network is centralized or decentralized, [21].Because we assume that the UAV has all theinformation of the network, a centralized cluster-ing solution is used. In conventional WSNs, LowEnergy Adaptive Clustering Hierarchy-Centralized(LEACH-C) is popular in various applicationsbecause of its efficiency and low computationalburden on nodes [22], [23]. However, in large areasensor networks, the energy conservation is farmore challenging than what in normal WSNs and abetter optimization method is necessary. Taking the

60

advantage of energy and computational capabilitiesof the UAV compared to a normal sensor node, apopulation based iterative optimization technique,Particle Swarm Optimization (PSO), is used. Theresults are compared with LEACH-C under thesame conditions. In Section III and IV we explainin detail how the LEACH-C and PSO schemes aremodified and applied for our network optimizationproblem.

III. LOW-ENERGY ADAPTIVE CLUSTERINGHIERARCHY

LEACH is a clustering algorithm aimed at con-serving energy of WSNs. It is a distributed schemewithout any central node. In LEACH, each nodecan independently decide whether to become aCH or not depending on some probabilistic func-tion, [23]. The number of CHs may vary, andthe clustering is based on the shortest Euclideandistance from a node to the cluster head. Theindependent decision of whether or not to becomea CH may lead to accelerated energy drainage forsome of the nodes in the network. LEACH-C isa centralized scheme which can improve some ofthe issues related to LEACH. In LEACH-C, theinformation about location and residual energy ofall nodes are sent to the base station (sink) at thebeginning of each UAV flight. The base stationwill then calculate the average energy level in thenetwork, and only nodes with energy above thislevel are considered for CH nodes. The base sta-tion will randomly select some candidates amongthese nodes and calculate the respective objectivefunction. This procedure is repeated for a numberof iterations, and the objective function value iscompared in order to find the most suitable CH. Inthe WSN literature it is common that the objectivefunction is based on the average distances betweenCHs and their member nodes. The selected CHnodes will then be broadcasted by the UAV toall the nodes in the network. Each node in thenetwork will then need to find the closest CHnode in order to initiate its data transmission. Inour network system, the objective function is moresophisticated and will be explained in Section IV.

IV. PARTICLE SWARM OPTIMIZATIONALGORITHM

Many applications in WSN that use Particleswarm optimization (PSO) as an efficient algo-rithm for making clusters of nodes [24], [25].The idea behind PSO is to simulate the socialbehavior of bird flocks and fish schools [26]. ThePSO algorithm is a computational method thatiteratively tries to improve a swarm of candidatesolutions or particles, based on a few basic rules.For each particle i ∈ {1, . . . , S}, where S is thenumber of particles, the particle position, xi ∈ Rn,and the particle velocity, vi ∈ Rn, is updatedaccording to

xi ← xi + vi , (1)

and

vi ← ωvi + cprp(pi − xi) + cgrg(g − xi) , (2)

respectively. Here, rp, rg ∼ U(0, 1), pi is the bestknown position of particle i, g is the best knownposition of the swarm, and ω (inertia weight), cpand cg (acceleration constants) are tuning param-eters. The fitness of a solution is calculated basedon the value of a function f : Rn → R, which isto be minimized. A particle’s best known positionis updated, that is, pi ← xi, if f(xi) < f(pi). Theswarm’s best known position is updated, that is,g ← pi, if f(pi) < f(g).

The initial positions of the particles are uni-formly distributed in the search space, i.e. xi ∼U(b, b), where b, b are the lower and upper boundsof the search space, respectively. The particlesinitial velocities are vi ∼ U(−|b− b|, |b− b|). Thealgorithm is terminated after a user specified num-ber of iterations, or some other criterion dependingon the fitness function.

PSO can be used for clustering, and for findingthe cluster centroid vectors. In [27], each particlexi is a candidate solution containing the positionvector of each of the centroids. In this paper PSOis used for clustering and cluster heads selection.Hence, we require that their positions are chosenfrom the sensor positions. Let NCH be the numberof CHs, and let N be the number of sensors inour sensor network. We are furthermore concernedwith a two dimensional problem, and the positionof each of the sensors are assumed known. Since

61

xi contains a candidate solution for the positionsof all the NCH CHs, we have that the length ofthe vector xi is n = 2NCH. To force the CH po-sitions to be chosen from the sensor positions, weintroduce the function h : R2NCH → R2NCH , whichtakes a position vector in the search space, forinstance the particle position vector xi, and returnthe position vector of the NCH sensors closest toxi. For instance, if xi = col{xi1, xi2, . . . , xiNCH},then h(xi) = col{x?1, x?2, . . . , x?NCH}, where x?i arethe solutions to

minxk∈{x1,...,xN}k∈{1,...,NCH}

∑j∈{1,...,NCH}

|xk − xij| , (3)

subject to xk 6= xl, for k 6= l and {x1, . . . , xN} isthe set of coordinates of the sensors in the sensornetwork.

The cluster heads are chosen based on a fitnessfunction,

f = α1f1 + α2f2 + α3f3 (4)

where α1, α2, α3 > 0 are scalar weightingconstants, f1 is the energy consumption of thenetwork, f2 is the transmission bit error rate, andf3 is the total travel time for the UAV. Eachterm will be throughly explained in the sectionsto follow.

Algorithm 1 summarizes the PSO algorithm.

A. Network energy consumptionThe total energy consumption for a non-cluster

head node j is given by

Ej = lEji + lctrPt0R0

+ ERXlctr , (5)

where l is the data packet length in bit, lctr is thenumber of bits in a control packet, Eji is the energyto transmit one bit from node j to the CH of thei-th cluster, Pt0 is standard TX power, ERX is theenergy consumed to receive one bit, and R0 isthe bit rate used in broadcasting messages. Theexpression for Eji is given by

Eji =Pt jiRji

=

{Pt0Rji

if Pr ji > PrminPtminRb

if Pr ji ≤ Prmin, (6)

where the transmit power is adjusted based on linkbudget estimation between node j and its CH i. Inthe first case, Pr ji = Pt0 + Gij − PLji > Prmin,

Algorithm 1 - PSOfor i ∈ {1, . . . , S} do . Start initializing

xi ∼ U(b, b)pi ← xivi ∼ U(−|b− b|, |b− b|)if i = 1 then

g ← x1else if f(h(pi)) ≤ f(h(g)) then

g ← piend if

end for . End initializingk ← 1while k ≤ N Iterations do

for i ∈ {1, . . . , S} dofor d ∈ {1, . . . , n} do

rp, rg ∼ U(0, 1)vi ← ωvi+cprp(pi−xi)+cgrg(g−xi)

end forxi ← xi + viif f(h(xi)) ≤ f(h(pi)) then

pi ← xiif f(h(pi)) ≤ f(h(g)) then

g ← piend if

end ifend fork ← k + 1

end whilereturn h(g)

where PLji is the path loss beetween node j andCH i, and CH i can receive data from node jwith the standard transmission power, Pt0. In thesecond case, Pr ji = Pt0+Gij−PLji ≤ Prmin, andtransmisison power is controlled to increase nodej’s transmit power to Ptmin = PLji + Prmin −Gij

such that CH i can receive data at a basic rate, Rb,according to receiving strength of Prmin and Gij

is the total gain of antennas on node j and CH i.SNRmin is a value calculated from the ShannonHartley theorem with bandwidth B and minimalbit rate Rb. Under the assumption that the distancebetween the nodes is large compared to the heightsof their antennas, the path loss PLji between nodej and CH i can be calculated with the two-ray

62

model,

PLji = 40 log dji − 20 log hi − 20 log hj , (7)

where Gj and Gi are the gains of transmitter (nodej) and receiver antenna (CH i), hj and hi arethe heights above ground or surface of the node’santennas, and dji is the distance between them. Inwide area sensor network, the nodes are not closeto each other so it is suitable to assume that CHi will receive and forward the data of its membernodes to the UAV. Therefore, in the i-th cluster, thetotal energy consumption by the CH used for dataand control packets between it and the membernodes is

Ei =[l(ERX + EDA) + lEiu + ERXlctr

+ lctrPt0R0

](Ni − 1)

+ lETX + lEDA + lctrERX + lctrPt0R0

,

(8)

where ETX is the energy to transmit one bit.Normally ERX and ETX are the same, so (8) couldbe written in a more compact form,

Ei =lNi(ERX + EDA) + lctrNiPt0R0

+ lctrNiERX + l(Ni − 1)Eiu ,(9)

where Ni is the number of sensor nodes in cluster iincluding CH i, Eiu is the energy per bit to transmitfrom CH i to the UAV (denoted with the subscriptu). EDA is energy for data aggregation per bit .The expression for Eiu is calculated similar as in(6),

Eiu =Pt iuRiu

=

{Pt0Riu

if Pr iu > PrminPtminRb

if Pr iu ≤ Prmin ,(10)

where Pt iu is the necessary transmission power atnode i when communicating with the UAV. In thefirst case the standard value Pt0 is large enoughto maintain communication without power control,that is, Pr iu = Pt0 + Gij − PLiu > Prmin, wherePLiu is the free space (LOS) path loss betweenCH i and the UAV. The path loss for the involvedcommunication link is

PLiu = −147.55 + 20 log f + 20 log diu , (11)

where f is radio frequency in Hertz, diu is thedistance between CH i and the UAV in meter. Inthe second case of (10), Pr iu = Pt0+Giu−PLiu ≤Prmin, and power control is used such Ptmin =PLiu +Prmin −Giu where Giu is the total gain ofthe antennas at CH i and the UAV. Similar as tothe case between two nodes, Ptmin could also becalculated for this case which is between CH i andUAV.

The function f1 introduced in (4), is thereforechosen as

f1 =NCH∑i=1

Ni−1∑j=1

Eji +NCH∑i=1

Eiu . (12)

B. Bit error rate

The bit error rate (BER) usually dependson data modulation scheme and communicationchannel between transmitter and receiver. In thissensor network model, we are most concernedabout saving the nodes energy consumed by datacommunication. Therefore, M-QAM modulation issuggested to use for all the nodes. Following [28,Page 190], the average symbol error rate of thisscheme is estimated by:

P s =4

π

(1− 1√

M

)∫ π/2

0

Mγs(g, φ) dφ

− 4

π

(1− 1√

M

)2 ∫ π/4

0

Mγs(g, φ) dφ

(13)

where M is the modulation rate, and g =1.5/(M − 1). Mγs(g, φ) is a Moment GeneratingFunction (MGF) which is used in performing anal-ysis of modulation in fading with and without di-versity; and φ is the angle variable of the integrals.

If the radio channel is between any two nodeson the ground, then the reflection portion of radiopaths on the ground is significant. For instance, thesymbol error rate of the channel between a node jand its CH i, can be calculated using (13) with theRayleigh multipath fading distribution, [28, Page189],

Mγs,ji(g, φ) =

(1 +

gγssin2 φ

)−1. (14)

Here, γs denotes the average signal to interferenceplus noise ratio (SINR) at a fixed path lossover the channel. Basically, SINR is equal to

63

S/(I + N0), where I is interference from all thesources located around the receiver node and N0

is the noise in the channel. For the communicationbetween a CH i, and the UAV u flying overit, the line-of-sight is usually dominant, so (13)can be calculated with Rician multipath fadingdistribution, [28, Page 189],

Mγs,iu(g, φ) =(1 +K) sin2 φ

(1 +K) sin2 φ+ gγs,iu

× exp

(Kgγs,iu

(1 +K) sin2 φ+ gγs,iu

).

(15)

Here, K is the Rice factor and denotes thedominance of the LOS path over the reflectionpaths portion. From our prior research on air toground communication system, the value of Kcould be varied according to the incident angle atthe node [29] . However, in the proposed model, allof the UAV and nodes are equipped with an arrayof antenna so this does not affect to the value of Kfactor. In the case where the antenna array is onlyavailable on the UAV, this effect must be taken intoaccount.

In order to evaluate bit error rate in the cases ofcommunications between node to node and nodeto UAV, Ps is converted into Pb as follows

Pb =Ps

log2M. (16)

The function f2 introduced in (4), is thereforechosen as

f2 =

∑NCH

i=1

∑Ni−1j=1 Pb,jil +

∑NCH

i=1 Pb,iul(Ni − 1)

(2N −NCH),

(17)where Pb,ji, Pb,iu is the bit error rate of the com-munication channels between node j and its CH i,and between CH i and the UAV, respectively.

C. UAV-path and travel timeThe fitness function f3 introduced in Section IV

is the total travel time for the UAV to visit allthe CHs, and then return back to the base station.The UAV velocity is assumed to be constant,and therefore the time spent is proportional tothe total distance. Since we focus on informationgathering using a fixed-wing UAV, a solution to theEuclidean Traveling Salesperson Problem (TSP)would typically not provide a feasible trajectoryfor the UAV. Instead we calculate f3 based on the

TABLE I

UAV MODEL AND CONTROLLER PARAMETERS

Parameter Value Parameter Valueρ 120 m v 40 m/sGi, Gj 10 dB Gu 10 dBhi, hj 20 cm hu 200 mXu 2.5 km Yu -5 kmXmax 5 km Ymax 5 kmPt0 33 dB f 5.8 GHzB 1 MHz Prmin -95 dBM 8 K 10 dBRb 2 Mbps R0 1 MbpsERX 50 nJ/s/bit EDA 5 nJ/s/bitETX 50 nJ/s/bit N0 -110 dBl 800 bytes lctr 200 bitsN 200 N Iterations 4000α1 100 α2 105

α3 10−3 E0 4 JSimulations 2000 NCH 10, 20, 30

solution of a Dubins TSP, which provides a pathwhich is feasible for a Dubins vehicle. A Dubins(shortest) path is continuously differentiable al-most everywhere, and consists of straight lines andcircular arcs of bounded curvature. In this paper wewill calculate f3 as the solution to the alternatingTSP algorithm, which is constructive, and a con-servative approximation to the total length can beprovided from the solution to the Euclidean TSP,[30]. The fitness function f3 is chosen as:

f3 =ETSP(P ) + 7

3

⌈NCH

2

⌉πρ

v, (18)

where ρ > 0 is the minimum turning radius, v isthe UAV speed, P is the point set of NCH CHs,and ETSP(·) is the length of the solution to theEuclidean TSP.

Notice that for a UAV in transit, it is reason-able to assume that the fuel consumption willbe proportional to the drag, which again can beassumed to be proportional to the square of thevelocity, [15]. If the UAV spends most of the timein transit at constant or almost constant speed itis reasonable to assume that the consumed energyis proportional to the traveled distance and thetraveling time.

V. SIMULATIONS

Extensive simulations have been conducted forboth LEACH-C and PSO schemes with differentnumber of cluster head nodes. The number of

64

sensor nodes in the network is kept constant forall simulations, with randomly generated positionsin a 5km2 area. They have all the same initialenergy E0 at the beginning of each simulation case.For each round of data collection, the optimizationalgorithms are run for N Iterations iterations. The totalnumber of simulations is 2000, or is stopped whenall the nodes in the network is drained for energy.At the beginning of each round, the result of theoptimization algorithm will provide the UAV witha list of CH nodes that it should fly to and collectdata from. This list of CHs is also broadcasted bythe UAV to all the nodes in the network. Each nodethen needs to find its best CH and prepare for datacommunication to this CH.

In these simulations, the number of CHs arevaried from 5% to 15% of the total number ofnodes in the network. In order to have the simu-lation results comparable for the two algorithms,the number of iterations needed for selecting theCHs are the same for both the LEACH-C and PSOschemes. In addition, all other parameters relatedto UAV flight, data communication and simulationconditions are the same, and are shown in Table I.It is assumed that both sensor nodes and the UAVare equipped with array antennas, and the beams ofboth the transmitter and receiver will point to eachother during their communication. This assumptionis possible if the position information of Tx andRx is known. The UAV is assumed to fly at aconstant altitude of 200m and ground speed of40m/s with a minimum turning radius of 120m.Simulations show the performances of all the termsin fitness function which include average energyconsumption in the network, average bit error rate,and traveling time for the UAV. Additional resultssuch as alive/dead node, node distribution, and thevalue of the fitness function in both LEACH-C andPSO schemes are also analyzed.

According to the results in Figure 1, 2, and3, PSO shows better performance compared toLEACH-C under the same conditions of simulationand network topology. For instance, with the samenumber of CHs, the total energy consumption byall of the nodes in the network when the UAVfollows the route provided by the PSO schemeis lower than that given by LEACH-C. It canalso be seen in Figure 2 that the number of alive

nodes remaining in the network using the PSOoptimization scheme is always larger than for theLEACH-C scheme. In addition, these three figuresshow that the higher number of CH nodes in thenetwork the lower the energy consumption as wellas the BER, which explains the higher number ofalive nodes. Figure 2 also shows that the resultwith 20 CHs when using the PSO scheme is evenbetter than with 30 CHs when using the LEACH-C scheme. This trend matches with the results inFigure 4, which shows the optimal values of the fit-ness function (4) come from these two algorithms.A dominant improvement of PSO compared toLEACH-C is shown in Figure 4. In both cases,the fitness values using with LEACH-C and PSOschemes slightly increase from the beginning, thenstart to decrease after a certain number of datacollection rounds (Figure 4). The plausible reasonis that at the moment almost all of the nodesare already dead but the number of CHs is stillunchanged; hence, the ratio of nodes that havedirect communication with the UAV is increased.This leads to the fact that the BER and the travelingtime for the UAV become lower. Because the PSOscheme can perform better in energy conservationfor the nodes, so there are still many alive nodes inthe network. Further analysis is shown in Figure 5and 6 where the positions of both dead and alivenodes are depicted. The figures show that all thenodes in the network are dead after running nearly1100 times of data collection in the LEACH-Ccase, while there are still many alive nodes in thenetwork with the PSO scheme even after running1900 rounds of simulation. The colors of the nodesare associated with the round or iteration when thenodes died due to energy drainage. In Figure 5,with LEACH-C, the nodes with the same colorsare usually located in the same areas, and theyare not as evenly distributed as the dead nodes inFigure 6 where the PSO is applied. The LEACH-Calgorithm may attempt to select many CHs fromone of these areas in the same round because theyare close to each other and therefore can reducethe values of the fitness function. The differencebetween the dead nodes distribution in LEACH-Cand PSO could be the answer to why the travelingtime for the UAV with PSO is not better thanwith the LEACH-C scheme, as shown in Figure

65

PSfrag

PSO

LEACH-C

PSO 30CH

LEACH-C 30CH

PSO 20CH

LEACH-C 20CH

PSO 10CH

LEACH-C 10CH

Number of data collection rounds (times)

Total

energy

consumed

bythenodes

(Jou

le)

0 200 400 600 800 1000 1200 1400 1600 1800 20000

100

200

300

400

500

600

700

800

Fig. 1. Total energy consumption by the nodes as a function ofthe number of data collection rounds for NCH ∈ {10, 20, 30}.

PSO

LEACH-C

PSO 30CH

LEACH-C 30CH

PSO 20CH

LEACH-C 20CH

PSO 10CH

LEACH-C 10CH

Number of data collection rounds (times)

Number

ofalivenodes

inthenetwork

0 200 400 600 800 1000 1200 1400 1600 1800 20000

20

40

60

80

100

120

140

160

180

200

Fig. 2. The number of nodes alive in the network as a functionof the number of data collection rounds for NCH ∈ {10, 20, 30}.

7. These results show that the UAV requires evena little longer time to collect data each roundwith the PSO scheme, but the PSO scheme alwaysprovides a better overall fitness value compared toLEACH-C. For instance, in the case of 20 CHs,fitness value given by LEACH-C scheme is morethan double the value from PSO scheme. This gapincreases when the number of CHs increases, forexample the case with 30 CHs in the network, seeFigure 4.

VI. CONCLUSIONS

In this article, we have evaluated the perfor-mances of two algorithms for optimal selection ofthe nodes to be visited by the UAV. In our objective

30CH

20CH

10CH

Number of data collection rounds (times)

Average

Bit-error-ratewithLEACH-C

(BER)

0 200 400 600 800 1000 1200

×10−3

0

1

2

3

4

5

6

7

8

9

(a) with LEACH-C

30CH

20CH

10CH

Number of data collection rounds (times)

Average

Bit-error-rate(B

ER)withPSO

0 200 400 600 800 1000 1200 1400 1600 1800 2000

×10−3

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

(b) with PSO

Fig. 3. Bit-error-rate as a function of number of data collectionrounds for NCH ∈ {10, 20, 30}.

function, the energy consumed by the network,communication quality, and travelling time for theUAV are optimized with the PSO algorithm. In thesimulations, we have given the highest priority tothe average Bit Error Rate, the lower ones are foraverage energy consumption and travelling timeof the UAV. PSO has shown that it outperformsthe LEACH-C scheme when applied to wide areasensor networks. Our simulation also shows thatthe life time of the network can be significantlyextended using PSO scheme.

REFERENCES

[1] K. Purvis, K. Astrom, and M. Khammash, “Estimationand optimal configurations for localization using cooperative

66

AVG (20CH) 30CH

20CH

10CH

Number of data collection rounds (times)

Valueof

fitnessfunctionwithLEACH-C

0 200 400 600 800 1000 1200100

200

300

400

500

600

700

800

(a) with LEACH-C

AVG (20CH)

30CH

20CH

10CH

Number of data collection rounds (times)

Valueof

fitnessfunctionwithPSO

0 200 400 600 800 1000 1200 1400 1600 1800 20000

100

200

300

400

500

600

700

(b) with PSO

Fig. 4. Fitness value f for NCH ∈ {10, 20, 30}.

uavs,” IEEE Trans. on Control System Technology, vol. 16,pp. 947–958, 2008.

[2] E. W. Frew and T. X. Brown, “Networking issues for smallunmmaned aircraft systems,” J. of Int. and Robotic Sys.,vol. 54, pp. 21–37, 2008.

[3] J. J. Ruz, O. Arevalo, G. Pajares, and J. M. De la Cruz, “Uavtrajectory planning for static and dynamic environments,” inInTech, 2009.

[4] E. F. Flushing, L. Gambardella, and G. Di Caro, “Searchand rescue using mixed swarms of heterogeneous agents:Modeling, simulation, and planning,” in Technical ReportIDSIA-05-12, 2012.

[5] T. Brown, B. Argrow, C. Dixon, S. Doshi, R. Thekkekunnel,and D. Henkel, “Ad hoc uav-ground network (augnet),” inAIAA 3rd Unmanned Unlimited Technical Conference, 2004.

[6] D. T. Ho, E. I. Grøtli, S. Shimamoto, and T. A. Johansen,

0 1000 2000 3000 4000 50000

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

200

300

400

500

600

700

800

900

1000

Fig. 5. Node distribution for NCH = 20 using LEACH-C. Thecolor represents the iteration number when each node ran out ofenergy. After approximately 1100 rounds, all nodes are dead.

0 1000 2000 3000 4000 50000

500

1000

1500

2000

2500

3000

3500

4000

4500

5000

800

900

1000

1100

1200

1300

1400

1500

1600

1700

1800

Fig. 6. Node distribution for NCH = 20 using PSO. The colorrepresent the iteration number when each node ran out of energy.After 1900 rounds there are still nodes alive (denoted by a red star).

“Optimal relay path selection and cooperative communicationprotocol for a swarm of uavs,” in Proc. of the 3rd InternationalWorkshop on Wireless Networking and Control for UnmannedAutonomous Vehicles: Architectures, Protocols and Applica-tions, Anaheim, California, USA, December 3-7 2012.

[7] P. B. Sujit, D. E. Lucani, and J. B. Sousa, “Bridging coop-erative sensing and route planning of autonomous vehicles,”IEEE Journal on Selected Areas in Communications, vol. 30,pp. 912–922, 2012.

[8] J. Isaacs, S. Venkateswaran, J. Hespanha, U. Madhow, J. Bur-man, and T. Pham, “Multiple event localization in a sparseacoustic sensor network using uavs as data mules,” in The 3rdInternational Workshop on Wireless Networking & Controlfor Unmanned Autonomous Vehicles: Architectures, Protocolsand Applications, 2012.

[9] F. Wang and J. Liu, “Networked wireless sensor data collec-tion: Issues, challenges, and approaches,” IEEE COMMUNI-CATIONS SURVEYS and TUTORIALS, vol. 13, pp. 673–687,2011.

[10] L. He, J. Tao, J. Pan, and P. Xu, “Adaptive mobi uavs assisted

67

30CH

20CH

10CH

Number of data collection rounds (times)Travellingtimeper

roundof

theUAV

withLEACH-C

(s)

0 200 400 600 800 1000 1200600

700

800

900

1000

1100

1200

1300

1400

1500

(a) with LEACH-C

30CH

20CH

10CH

Number of data collection rounds (times)

Travellingtimeper

roundof

theUAV

withLECH-C

(s)

0 200 400 600 800 1000 1200 1400 1600 1800 2000600

700

800

900

1000

1100

1200

1300

1400

1500

(b) with PSO

Fig. 7. Travelling time for the UAV to finish one round of datacollection for NCH ∈ {10, 20, 30}.

data collection in wireless sensor networks,” in IEEE WirelessCommunications and Signal Processing (WCSP), 2011.

[11] W. Liang, J. Luo, and X. Xu, “Prolonging network lifetimevia a controlled mobile sink in wireless sensor networks,” inIEEE Global Telecommunication Conference (GLOBECOM),2010.

[12] L. Tong, Q. Zhao, and S. Adireddy, “Sensor networks withmobile agents,” in IEEE MILCOM, 2003.

[13] D. T. Ho and S. Shimamoto, “Highly reliable communica-tion protocol for wsn-uav system employing tdma and pfsscheme,” in Proc. of the 2nd International Workshop onWireless Networking and Control for Unmanned AutonomousVehicles: Architectures, Protocols and Applications, 2011.

[14] D. T. Ho, E. I. Grøtli, and T. A. Johansen, “Heuristic algorithmand cooperative relay for efficient energy data collectionwith a uav,” in Proc. of the IEEE International Conferenceon Computing, Management & Telecommunications, Ho ChiMinh, Vietnam, January 21-24 2013, Accepted.

[15] E. I. Grøtli and T. A. Johansen, “Path planning for UAVs

under communication constraints using SPLAT! and MILP,”Journal of Intelligent and Robotic Systems, vol. 65, no. 1-4,pp. 265–282, 2012.

[16] ——, “Path- and data transmission planning for cooperat-ing UAVs in delay tolerant network,” in Proc. of the 3rdInternational Workshop on Wireless Networking and Controlfor Unmanned Autonomous Vehicles: Architectures, Protocolsand Applications, Anaheim, California, USA, December 3-72012.

[17] A. Grancharova, E. I. Grøtli, and T. A. Johansen, “Distributedmpc-based path planning for uavs under radio communicationpath loss constraints,” in Proc. of the IFAC Conference on Em-bedded Systems, Computational Intelligence and Telematics inControl, W’uerzburg, Germany, April 3-5 2012, pp. 254–259.

[18] J. Tsitsiklis, “Efficient aalgorithm for globally optimal trajec-tories,” IEEE Trans. Autom. Contr., vol. 40, no. 9, pp. 1528–1538, 1995.

[19] Q. Zhao and L. Tong, “Quality of service specific informationretrieval for densely deployed sensor network,” in MILCOM,2003.

[20] M. Salari and Z. Naji-Azimi, “An integer programming-based local search for the covering salesman problem,” J. ofComputer & Operations Research, pp. 2594–2602, 2012.

[21] N. Shigei, H. Miyajima, H. Morishita, and M. Maeda, “Cen-tralized and distributed clustering methods for energy efficientwireless sensor networks,” in Proceedings of the InternationalMultiConference of Engineers and Computer Scientists, 2009.

[22] W. Heinzelman, A. Chandrakasan, and H. Balakrishnan,“An application-specific protocol architecture for wirelessmicrosensor networks,” IEEE Trans. on Wireless Communi-cations, vol. 1, pp. 660–670, 2002.

[23] S. Shi, L. X., and X. Gu, “An energy-efficiency optimizedleach-c for wireless sensor networks,” in International ICSTConference on Communication and Networking in China(CHINACOM), 2012.

[24] N. M. A. Latiff, C. C. Tsimenidis, and B. S. Sharif, “Energy-aware clustering for wireless sensor networks using particleswarm optimization,” in Proc. of the IEEE InternationalSymposium on Personal, Indoor and Mobile Radio Commu-nications, 2007.

[25] B. Singh and D. Lobiyal, “A novel energy-aware cluster headselection based on particle swarm optimization for wirelesssensor networks,” Journal of Human-centric Computing andInformation Sciences, vol. 2, pp. 1–18, 2012.

[26] J. Kennedy and R. Eberhart, “Particle swarm optimization,”in Proceedings of IEEE International Conference on NeuralNetworks, 1995.

[27] D. W. van der Merwe and A. P. Engelbrecht, “Data clusteringusing particle swarm optimization,” in Proc. of the Congresson Evolutionary Computation, 2003, pp. 215–220.

[28] A. Goldsmith, Wireless Communications. Cambridge Uni-versity Press, 2005.

[29] D. T. Ho, J. Park, S. Shimamoto, and J. Kitaori, “Performanceevaluation of multi hop relay network for oceanic air trafficcontrol communication,” IEICE TRANSACTIONS on Commu-nications, vol. E94-B, no. 1, pp. 86–96, 2011.

[30] K. Savla, E. Frazzoli, and F. Bullo, “Traveling salespersonproblems for the dubins vehicle,” IEEE Transaction on Auto-matic Control, vol. 53, pp. 1378–1391, 2008.

68