Study of the Performance of Wireless Sensor Networks Operating

with Smart Antennas

Skiani, E.D, Mitilineos, S.A,and Thomopoulos, S.C.A

September 2, 2010

Abstract

Wireless Sensor Networks (WSNs) have attracted a great deal of research interest duringthe last few years, with potential applications making them ideal for the development of theenvisioned world of ubiquitous and pervasive computing. Energy and computational efficiencyconstraints are the main key issues when dealing with this type of networks. The main researcheffort has been channeled towards routing and distributed processing in order to achieve bet-ter QoS provisions, lower interference and power consumption rate while data dissemination iscarried out. The embedment of smart antennas on wireless sensor nodes is proposed herein asan alternative and novel approach at the physical layer with a potential of relieving traditionalchallenges faced by current WSN architectures. Studying the behavior of WSNs consisting of dif-ferent types of antennas (omnidirectional or adaptive directional) yielded unexpectedly favorableresults that improve the operation of networking systems of this type.

1 Introduction

Wireless Sensor Networks (WSNs) are a class of distributed computing and communication sys-tems that are an integral part of the physical space they inhabit[1]. Albeit low profile, limitedcomputational power and sparse energy resources, their most interesting feature is the reasoningof and reaction to the world that surrounds them. Recent advances in this field have enabled thedevelopment of WSNs whose functionality relies on the collaborative effort of a large number oftiny, low-cost, low-power, multi-functional sensor nodes able to communicate untethered in shortdistances[2]. Moreover, engineering or pre-determining the nodes positions is not necessary. This al-lows random deployment in hostile environments or disaster relief operations, a unique feature whichaccounts for rendering these network types an integral part of modern life. Smart environmentsrepresent the next evolutionary development step in building, utilities, industrial, home, shipboard,and transportation systems automation[3]. This bridge to the physical world has enabled a growingbouquet of potential services, ranging from health to military and security, such as target tracking,environmental control, habitat monitoring, source detection and localization, vehicular and trafficmonitoring, health monitoring, building and industrial monitoring, etc.[4].

On the other hand, WSNs display undesirable features such as power limitations, frequentlychanged topology, broadcast communication, susceptibility to failure and low memory, while theirarchitecture calls for protocols and algorithms with self-organizing capabilities[2]. Furthermore, inmost cases a WSN will be composed of a large number of densely deployed sensor nodes, whichmeans that neighboring nodes might be very close to each other. Their communication is expectedto produce high interference and power consumption levels. One of the most important constraintsof sensor nodes stems from their limited, generally irreplaceable, power sources. Therefore, while

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traditional networks aim at achieving high Quality of Service (QoS) provisions, WSN protocolsmust focus primarily on power conservation. Trade-off mechanisms seem necessary to increasereliability at the cost of lower throughput or higher latency. An essential design issue is relatedto the investigation of system parameters, such as network size and node density, with regards tosystem metrics including spatial coverage, throughput, latency, network lifetime, energy efficiencyand reliability and how these affect the trade-offs previously mentioned.

These issues have been engaged in the literature, with most approaches focusing on routingoptimization and protocol design. Many researchers have been developing schemes that fulfill therequirements described above, proposing protocols and algorithms for WSNs. Quality of Service(QoS) can be measured in terms of energy efficiency or the optimum number of sensors sendinginformation at any given time[5]. In the latter case, QoS control mechanisms built on the Gur GameParadigm have been put forward to adjust QoS resolution, thus extending network lifetime andmanaging energy depletion. Later, J. Frolik[6] extended the Gur game approach and, additionally,illustrated a second method providing QoS feedback through packet acknowledgments. Apart fromthe introduction of new MAC layer protocols (QUality-of-service specific Information REtrieval(QUIRE)[7], Z-MAC[8], i-GAME[9]) and network layer protocols e.g. MMSPEED (Multi-Path andMulti-SPEED Routing Protocol)[10], cross-layer design[11] is a novel approach that is lately comingunder close scrutiny.

Minimizing node interference is undoubtedly one of the main challenges in WSNs. High in-terference increases the packet collision probability which, in turn, affects efficiency and energyconsumption. Early approaches focus on reducing the node degree[12], [13]. Topology controlmechanisms, inspired by graph theory, have been developed to conserve energy in WSNs withoutbeing able to explicitly guarantee low interference, such as the model by Burkhart et al.[14] us-ing Minimum Spanning Tree (MST), the Highway model proposed by Rickenbach et al.[15] andthe “Minimizing Interference in Sensor Network (MI-S)” algorithm introduced by A.K. Sharma etal.[16]. In addition, Jang[17] drew inspiration from graph theory to propose geometric algorithmsreducing interference based on the conversion of network problems to geometry problems. Last, J.Tang et al.[18] studied multi-channel assignments to achieve interference-aware topology control inwireless mesh networks.

On the other hand, smart antennas have been extensively used in the literature of more conven-tional communications systems, and their usage is considered to expand more, due to their provenbeneficial impact in wireless communications performance[19]. Smart antennas have been suggestedin order to satisfy the demand for spontaneously high data rates to certain users while maintain-ing a high level of QoS for conventional users[20]. They have been also used in order to mitigateinterference and delay spread, increase system capacity and spectral efficiency, combat multipathfading, address the near-far effect and increase cell coverage etc.[21] [22]. Furthermore, they havebeen suggested for radiation pattern diversity, space division multiple access, direction of arrivalestimation and localization etc.[23] [24] [25]

Herein, it is proposed that WSN nodes are equipped with smart antennas, and that certainslight changes are implemented in the node selection and routing processes, in order to address theinherent drawbacks of such networks with an efficient tool, only this time in the physical layer. Ouraim is to analyze a novel technique to overcome the limitations arising from sensor nodes. We alsoattempt an investigation into pertaining design constraints promoting the use of certain tools toattain our main objectives. The emergence of adaptive systems, such as smart antennas, can boostthe performance of WSNs aiming at satisfying the growing demand for robust infrastructures.

The remainder of this paper is organized as follows. In section 2 the simulated WSN model isestablished, with the network setup, its operating modes and the basic transmission mechanism.Section 3 demonstrates the improved network performance evaluated in terms of QoS, efficiency

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and node activity, while section 4 considers energy consumption associated with the studied modes.Section 5 offers a performance comparison among smart-antenna modes with increased number ofbeams (5.1), or among WSNs with mixed smart and omnidirectional nodes (5.2). Finally, section 6concludes the paper.

2 WSN Model And Formulation

This section provides an overview of the proposed approach and the general framework upon whichsimulation is based, along with the principal assumptions associated with our modeling. Further-more, the basic mechanisms that define the network operation are introduced.

2.1 Network Setup

Smart antennas can substitute omnidirectional antennas at WSN nodes, as they can transmit withinthe total coverage area of an omnidirectional one, yet with a directional gain which depends on thebeam activated at each time-slot. This means that the relative angular position of the source -destination nodes pair will determine the beam which will be activated for each node, serving foreither transmissions or receptions. This way, a well-defined area with locally specified bounds isconsidered busy for each ongoing transmission and every node within this range is rendered incapableof transmitting data. In other words, the utilization of beams can actually reduce the interferenceand limit the collision areas to narrower sectors instead of full discs of the same radii, as in the caseof omnidirectional antennas. However, the gain will differ with angle, since it will generally dependon the actual angle between source - destination nodes.

Different network topologies are examined in this work, each one characterized by a differentnode density, node deployment mode and, essentially, the way nodes are linked. The latter isreflected by the adjacency matrix, which is essentially a record of the various node pairs within thenetwork that are within communication range with each other. The adjacency matrix is generatedas follows: For each pair of nodes, e.g n1 and n2, we compute the receiving power of the secondone (n2) with the first one considered to be the transmitting node. The receiving power is hereincalculated by the Friis Equation:

Prec = Pt ·G1 ·G2

L·(

λ

4 · π · d

)2

(2.1)

where:Prec: receiving powerPt: transmit powerG1: transmission gain of node n1

G2: receiving gain of node n2

L: propagation lossesλ: wavelength which equals 3×108

fd: distance between n1 and n2

At the receiver side, a power threshold determines whether it can successfully accept the trans-mitted signal, i.e. whether Prec > Prthres. In this case a directional link pointing from n1 to n2 isadded in the adjacency matrix, denoted by an 1 at the element [n1 , n2]. Otherwise, there is nodirect connection from n1 to n2, and this element of the adjacency matrix has a zero value.

Upon network simulation setup, we need to specify the value of the maximum gain of each beamwith respect to the omnidirectional gain so that the networks generated are comparable in terms

3

1

2

3

4

30

210

60

240

90

270

120

300

150

330

180 0

G1

G2

G3

G4

(a) 4-beam Smart Antennas

1

2

3

4

30

210

60

240

90

270

120

300

150

330

180 0

(b) Omnidirectional Antennas

Fig. 1: Radiation Patterns: (a) and (b)

of link density1. We thus assume that the gain of omnidirectional antennas equals 3 dB, which isa popular gain for commercial ISM antennas, while the maximum value of each of the beams isapproximately doubled, which is a reasonable assumption for an array of 4 commercial elements[26][27] [28] . This means that Gi = 2 ∗ 100.3 = 4 or, equivalently, 6 dB. The radiation pattern of eachnode is shown in Fig. 1.

2.2 Network Operating Modes

In this section, we compare the different modes in which networks operate. First, we deploy a num-ber of nodes, e.g. N = 25, within a deployment area of size 50 x 50 m2, and build the adjacencymatrices for each operating mode (nodes are uniformly distributed in the deployment region). Weassume that the area is free of impediments, with propagation losses of 6 dB. Frequency is set at2.4 GHz, transmission rate is 1 Mbps while the receiver’s sensitivity approximates −100 dBm. Inaddition, we need to adjust the number of time-slots which will enable us to evaluate the perfor-mance of each network type on the same merit.

Omnidirectional Antennas Operating Mode: Nodes are devices equipped with omnidirec-tional antennas. In this mode, the gain of transmitter and receiver are equal to each other, i.e.G1 = G2; thus, every link becomes reciprocal.

Smart Antennas Operating Mode: Nodes are equipped with smart antennas. In this mode,G1 = G2. In order to compute Eq. (2.1), we need to find the angle between n1 and n2. Next, forthis angle, we find the beam that is going to be activated for each node, when n1 is the transmitterand n2 the receiver, so as to compute the transmitting and the receiving gain accordingly. Last, weexamine whether the receiving power is above or below the threshold (receiver’s sensitivity) and weeither add a link in the adjacency matrix or not.

The main difference between these two modes lies in the configuration of the collision areas, i.e.the sectors occupied by active transmissions. A model to calculate these areas will be provided later

1Link density also determines the Average Path Length(APL)[29], a parameter that partly defines a sensor network.It is the average number of hops between every node pair. The larger the APL, the greater the number of simulationsteps needed so that the results be highly regarded.

4

n1 n2

R

(a) Omnidirectional Mode

R

n2n1

(b) 4-beam Smart Antennas

R

n1

n2

(c) 4-beam Smart Antennas

Fig. 2: Interference between active nodes

herein (sub-section 2.6).Fig. 2 shows the way nodes interfere with each other and form coverage areas while attempting

transmission. In Fig. 2(a), the common coverage area is defined as the intersection of the discs,while their union defines the blockage area, in which every other node is unable to transmit. Fig.2(b) shows the total coverage area during an ongoing transmission between nodes n1 and n2 whenthey use smart antennas and, consequently, only one of the four beams is activated. It becomes clearthat total interference is reduced significantly, a smaller number of nodes are blocked and more freespace becomes available for the nodes remaining inactive in the network. Fig. 2(c) shows a differentexample, where node n1 does not transmit towards n2, but instead it exchanges information withanother node within the sector covered by its activated beam (steered towards the left). Althoughthese two nodes are able to communicate with each other, they do not block each other whencommunicating with other nodes; this would not be the case in omnidirectional antennas mode.In Fig. 2(a), n2 remains always within the range of n1 and is thus excluded from transmitting oncondition that n1 is currently sending data to a third node.

2.3 Simulation Algorithm

Apart from the way nodes get connected in the network, we need to clarify how the procedure takesplace. Consequently, at each time-slot:

1. For every single node, packet generation follows the Poisson distribution with parameter λ.Packets with the same identity number (id) comprise the same message, which has a uniquedestination, source, transmission time and success field (updated only if the packet reaches its

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final destination within the simulation time). It is worth noting that a message might containmore than one packets as it represents the total amount of information generated at the sourceaiming at being delivered to the desired destination.

2. Each node that has data to send senses the wireless medium and attempts transmission in thecase where it is not blocked by another transmission. No priority scheme is set and transmittingnodes are selected randomly, as in a realistic case where nodes are not centrally controlled whentrying to sense the medium and initiate transmission.

3. The queues of each node are First-In-First-Out and thus the first packet is captured, and, afterdetermining its destination, the shortest path is computed according to Dijkstras algorithm[31].In the case where the next node in the path is busy the packet stays in the current node’s queueuntil it can be retransmitted; else, it is transmitted to the next node. By counting the averagenumber of hops (average path length) and the total time from source to destination, we caneasily calculate the average delay time at each intermediate node.

4. The pair of nodes that is currently exchanging data does not allow other nodes within the samerange to start transmitting. These nodes are disabled and cannot transmit at the same time-slot.

2.4 Definitions & Assumptions

A list of definitions and assumptions follows herein, in order to put the numerical results into theright context:

i. the number of the execution steps stands for the number of time-slots; we speed up convergenceby prohibiting packet generation from a pre-determined simulation step and onwards. However,the number of time-slots which is considered sufficient so as to reach a desirable steady stateand thus avoid overflows in node queues needs to be determined. After simulating the samenetwork for different parameter values, ranging from 100 to 5000 timeslots, we concluded thatthe fluctuations can be considered negligible since the results vary only slightly as the numberof time-slots increases, i.e. they practically stay unaffected. Hence, the number of time-slotsdoes not necessarily have to be too large in order for the results to be indicative of the networkperformance. Therefore, we set a moderate number of time-slots equal to 1000, since our aimis to check the efficiency of the network when it operates under normal circumstances as usual.Thus, we are able to compare the different operating modes and the improvements the networksare subject to due to the installment of smart antennas on the nodes.

ii. the nodes whose queue is not empty i.e. for which there is available information to be sent,attempt transmissions at most once during each time-slot. We assume that the MAC protocolused is CSMA/CA, which means that transmitters avoid transmission whenever they detectongoing traffic and keep the packets in their queue until the next time-slot. The collision areasare determined in the way previously described (the areas specified by the sectors of the pairof beams). When their queues are full, they drop the packets; this case is translated to packetloss (failure).

2.5 Basic Network Metrics And Parameters

In order to evaluate network performance we consider the following performance metrics:

• Quality Of Service (QoS): the number of packets whose transmission has commenced tothe total number of packets that have been generated (total network load).

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• Efficiency: the number of messages successfully delivered from source to destination to thetotal network load generated throughout the simulation.

• Percentage of Active Nodes, A(%): the average number of nodes allowed to transmitwithin the same time-slot without being blocked due to interference caused by ongoing networktraffic. A small percentage of active nodes correspond to more collisions, which reducesnetwork efficiency.

We consider the above metrics with respect to a set of parameters, which are the following:

◃ Node Density (nodes per sq.m)

◃ Poisson parameter λ

◃ Transmit Power Pt

As far as node density is concerned, it is - by definition - the number of nodes deployed withinthe network region to the total deployment area. Parameter λ determines the rate at which packetsare generated during each time-slot. We should take care of the maximum value this parameter cantake, given that if we let the number of packets increase arbitrarily and at a high rate, the resultswill not be representative of the network performance. Last, by transmit power, we refer to thetransmit power of each node, which goes for the whole network since we assume that the networkis homogeneous, i.e. each node displays identical features.

2.6 Collision Areas, Probability of Transmission & Energy Consumption

We now present a mathematical analysis of the collision areas and the transmission probabilitybased on the mechanisms described in section 2. In Table 1, An denotes the coverage area of noden. For instance, let as assume that we place one node every approximately 10 meters; the meandistance between each pair is then 10 meters. We could alternatively compute the maximum radius,R, using Friis Equation and setting Prec = Prthres. Assuming that R is known, we can calculatethe collision areas analytically. Defining the collision areas of node n1, n2 by An1 and An1 , as wellas the source - destination pairs common area by An1,n2, it can be easily deducted that on theomnidirectional mode it holds that An1 = An2 = πR2, i.e. An1 and An2 correspond to the areacovered by a full disk. Furthermore, their common coverage area, An1,n2 , has been calculated by

Wei et al. [32] and is given by 4 ·∫ R

d2

√R2 − x2 dx (see Table 1).

Regarding the Smart Antennas mode, the coverage areas are defined as the sectors covered byeach nodes transmission range and, therefore, are equal to one quarter of the area covered by fulldiscs in case there are four beams, i.e. 1

4πR2. As for the source - destination pairs common area,

it varies since it is a function of the actual angle between them. Here, we can make the assump-tion that the antennas are optimally oriented, hence the maximum value of their common area,An1,n2 , approximates the one in the previous case, i.e. 4 ·

∫ Rd2

√R2 − x2 dx, since this area is the

intersection between the radiation patterns of the pair of nodes, as they were presented in Fig. 1(a).

We estimate the probability of transmission for a single node. This incurs as the probability ofthe node being selected earlier than its neighbors, which means that it is not in a disabled statedue to ongoing transmissions.

2d denotes the distance between the node pairs [30]

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Table 1: Collision Areas Calculation

Collision Area = An1 +An2 −An1,n2

Mode An1 An2 An1,n2

Omnidirectional Antennas πR2 πR2 4 ·∫ R

d2

√R2 − x2 dx 2

4-beam Smart Antennas πR2

4πR2

4 varies

The transmit probability Pt of a single node i is yielded by Eq. (2.2):

Pt(i) = pn(i) · (1−n

N)nodeDegree(i,µ) (2.2)

Proof. Let p be the probability that a node i is the nth one to be selected within a specific timeslot,which is a random event, and thus pn(i) =

1N . The probability q that a node j has not yet been

selected within the same timeslot is one minus the union of the following possibilities: it wasselected 1st (let us define it as P1), 2

nd (P2), ..., or nth(Pn). These events are mutually exclusivesince, obviously, they do not occur simultaneously. This yields:

qn(j) = 1− (P1

∪P2

∪...∪

Pn)

= 1− (p1(j) + p2(j) + ...+ pn(j))

= 1− (1

N+

1

N+ ...+

1

N)

= 1− n

N

The probability that a node does not interfere with its neighbors, inducing that neither of theneighbors has been selected so far, is the intersection of the events that the neighbors have not yet

8

been selected. Those events are independent since the occurrence of one does not interfere with theothers i.e. their intersection comes as the product of the single probabilities.∩

all nodes j; lij ∈ E

qn(j) =∏

all nodes j; lij ∈ E

(1− n

N) = (1− n

N)nodeDegree(i,µ) (2.3)

Thus, the transmission probability for node i is defined as the combined probability that the nodeis selected nth (pn(i)) and is not blocked by neighboring nodes (Eq. (2.3))

Pt(i) = pn(i) · (1−n

N)nodeDegree(i,µ)

where nodeDegree(i, µ) is the degree of node i when the network operates on mode µ, with values0 and 1, holding for omnidirectional and smart antennas respectively. �

As for nodeDegree(i, µ), i.e. the number of nodes j|lij ∈ |E| where |E| is the set of the edgesof the graph, it is explicitly computed using spatial analysis, i.e. techniques based on analyticapproaches to study topological and geometric properties, since we have assumed uniformly dis-tributed nodes within the total coverage area. A uniform node distribution implies that since thenumber of nodes within the total region of L2 sq. m. is N , the number of nodes within an area ofA sq. m is expected to be A

L2 x N. Thus, the average node degree, i.e. a node’s neighbors, is easilycomputed when its coverage area is known. Considering this analysis in association with Table 1showing the area covered by a single node on the two different operating modes, it follows that:

nodeDegree(i, µ) = nodeDegree(µ) =

{πR2

L2 ·N = πR2ρ µ = 0πR2

4L2 ·N = 14πR

2ρ µ = 1(2.4)

The nominator is the coverage area of node i as described in Table 1 and has a fixed value regardlessof node i since we have assumed that nodes have equal transmit power and, therefore, the sametransmission range, R; this explains why we substitute nodeDegree(i, µ) with nodeDegree(µ). Wealso set N/L2 as node density, defined by ρ.

From Eq. (2.2), it is yielded that the higher the node degree, the smaller the possibility oftransmission. It also becomes evident that node degree depends exclusively on the µ parameter.This makes clear that the average node degree for the network is equal to each individual node’sdegree. Besides, the dependence of a node’s degree on the µ parameter indicates the superiority ofsmart antennas over omnidirectional ones. When smart antennas are used, node degree is modifiedwith respect to the activated beam; on the omnidirectional mode, node degree remains fixed andexhibits a fourfold increase compared to the Smart Antennas Mode, which is verified by Eq. (2.4).Each transmitting node induces the deactivation of its neighbors or, in other words, for every singlenode i, nodeDegree(µ) nodes are blocked. Defining the percentage of active nodes as A(%), it holdsthat A

100 ·N + A100 ·N · nodeDegree(µ) = N , i.e. the number of nodes equals the number of active

nodes and the number of the nodes blocked owing to active ones. Substituting the values for eachoperating mode, the Active Nodes Percentage is computed by Eq. (2.5).

A(%) =

{100

1+πR2ρµ = 0

1001+ 1

4πR2ρ

µ = 1(2.5)

Following the previous analysis, energy consumption can be estimated both for individual nodesand for the entire network, taking into account the percentage of active nodes, A(%), and theirtransmit power. More specifically, the percentage of active nodes multiplied by the average power

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corresponds to the energy consumed by active nodes. Assuming that the rest of the nodes remainidle, the corresponding energy consumption is given by the number of the idle nodes multiplied bythe idle state consumption rate. Representing the consumption rate by a(%) and the rate at whichenergy is depleted at the idle state by γ(%), it follows that at time step t it will hold that:

E(t) = A · E(t− 1) · a

100+ (1−A) · E(t− 1) · γ

100(2.6)

where E(t) and E(t − 1) correspond to the total available energy at time t and t − 1 respectively,while A denotes the number of Active Nodes.

2.7 Network Topology

Network topology and the way operating modes are differentiated may be better explained througha graphical example (Fig. 3). A network model and its state after adding the links is presented,first having nodes transmitting omnidirectionally (Fig. 3(a)) and then using smart antennas (Fig.3(b)). Their difference lies in the number of links added, making the nodes equipped with smartantennas able to communicate in greater distances, since the gain is higher towards every direction.This explains why the graph becomes denser, with regard to its set of edges, when it operates onthe Smart Antennas Mode.

0 10 20 30 40 50

50

(a) Omnidirectional Mode

0 10 20 30 40 5050

50

(b) Smart Antennas

Fig. 3: Network Topology

All numerical results presented in the following Sections have been calculated by averaging thecorresponding network metrics for a large number (more than 100) of random topologies like thoseshown in Fig. 3. This aids towards proving the validity of our conclusions and contributes to thesuccessful evaluation of the performance of both network operating modes.

3 Metrics Evaluation With Respect to Network Parameters

In this section, we present certain numerical results of the network simulation. Our analysis isperformed vs. node density and parameter λ, while taking into account the following system metrics:Quality of Service(QoS), Efficiency and Percentage of Active Nodes.

3.1 Node Density

Node density plays a vital role in network efficiency as it constitutes a determining factor for boththe collision areas and the shortest paths used in information dissemination. As node density

10

0.005 0.01 0.015 0.02 0.0250

0.2

0.4

0.6

0.8

1

Node Density

QoS

Omnidirectional Antennas4−beam Smart Antennas

(a) Quality Of Service

0.005 0.01 0.015 0.02 0.0250.05

0.1

0.15

0.2

0.25

0.3

Node Density

Effi

cien

cy

Omnidirectional Antennas4−beam Smart Antennas

(b) Efficiency

0.005 0.01 0.015 0.02 0.02510

20

30

40

50

Node Density

Act

ive

Nod

es

Omnidirectional Antennas4−beam Smart Antennas

(c) Active Nodes

Fig. 4: Network performance with respect to Node Density.

increases, more nodes lie in the same sectors and are disabled due to ongoing transmissions. As willbe demonstrated by numerical results, sparse networks do not demonstrate significant improvementafter installing smart antennas while the opposite phenomenon is observed for denser networks.The collisions detected are fewer but as the network increases in size, the link density increases at ahigh rate, thus impeding successful transmissions without collisions. The problem gets worse whennodes use omnidirectional antennas.

Initially, we assume that the network covers a square region whose area equals 50 x 50 m2. Inthis area, we deploy a fixed number of nodes and, thus, we start from placing 1 node every 15meters, which corresponds to node density of 0.005 nodes per square meter. We then graduallyincrease the number of nodes deployed until there exists approximately one node every 6m (in thiscase density is 0.025). We show that the QoS is improved as the network becomes denser. Thisis mainly due to the lack of connectedness that appears in more sparse networks. However, thisparameter tends to converge to a constant value as node density rises over 0.015 nodes per squaremeter. The improvement of the QoS with smart antennas over omnidirectional is approximately20%, at almost every density value which is a considerable difference since more transmissions getactivated within the same time period.

Furthermore, we discuss how efficiency is influenced by node density. It is noteworthy that,

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when omnidirectional antennas are used, network’s efficiency drops both significantly and rapidlyas node density increases. Initially, as long as the network is sparse, the efficiency achieved is high.However, sparse networks - inducing low link density and, consecutively, low interference - are notwithin our areas of interest given that conventional WSNs need to be connected. By this, it ismeant that the phenomenon of the existence of isolated nodes has to be eliminated; this is securedfor density values above 0.015. On the other hand, networks using smart antennas diverge fromthis behavior, and show a tendency to keep efficiency rates at the same levels. In other words,they guarantee that most packets will be delivered to the destination successfully, mainly due todecreased interference levels.

Finally, there is a performance improvement regarding Active Nodes, as illustrated in Fig. 4(c),since the percentage of active nodes is always higher compared to the omnidirectional mode. Thisdifference ranges from 10 % and rises up to 20 % of the total number of nodes N ; this percentagecorresponds to 5-10 more active nodes when N = 50, 10-20 when N = 100 etc.

3.2 Parameter λ

Furthermore, the performance of each operating mode with respect to the Poisson parameter λ isexamined; the λ parameter essentially reflects the network’s traffic. A busy network, for example,where information flows continuously exhibits a large value of λ and tends to display an undesirablebehavior when queues overflow. On the other hand, networks in which information flows steadilyand at lower rates tend to provide a better quality of service and be far more efficient compared tothe previous case. Thus, we need to study the network performance under different network trafficconditions.

Fig. 5 demonstrates the way parameter λ affects the network performance for both operatingmodes. As this parameter increases, performance deteriorates for both operating modes, with SmartAntennas displaying a sharper decrease mainly due to the high values achieved when the informationis generated at lower rates. Nevertheless, smart-antenna WSN performance is improved with respectto omnidirectional WSNs in all cases. More specifically, the Quality Of Service and the efficiencytake the value ‘1’ as long as the network traffic remains low while they drop significantly as packetsare generated at higher rates.

Furthermore, as long as the percentage of active nodes is concerned, smart-antenna mode alwaysdelivers higher performance, which steadily increases with increasing values of the parameter λ. Thisis expected, since on the one hand, the interference levels increase on the omnidirectional mode sincealmost every node has packets to send, and most of them block the nodes they are connected with.Besides that, the high packet generation rate does not affect both modes at the same degree; thesmart-antenna mode is less affected in that more nodes are able to transmit due to the smallercoverage areas formed and the smaller number of nodes blocked. Also, the percentage of activenodes increases with parameter λ as expected; a node can be active only when it transmits data,which presupposes that packets have been generated within its queue. When λ is low, most nodequeues are empty and the ongoing activity is small. This attitude inverses as λ increases. We donot consider greater values (e.g. λ > 1) since in that case the queues do most likely overflow, thusnot allowing for fair evaluation of the operating modes.

4 Energy Consumption

This section is dedicated to one of the most important factors of WSNs. Energy consumptionplays a key role to the network operation and should be taken into consideration upon designingand manufacturing wireless nodes and sensors. On condition that every node has the same energy

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0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

λ

Qo

S

Omnidirectional Antennas4−beam Smart Antennas

(a) Quality Of Service

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

λ

Eff

icie

ncy

Omnidirectional Antennas4−beam Smart Antennas

(b) Efficiency

0 0.2 0.4 0.6 0.8 10

5

10

15

20

λ

Act

ive

No

de

s

Omnidirectional Antennas4−beam Smart Antennas

(c) Active Nodes

Fig. 5: Network performance with respect to λ. Node Density = 1 node per 100 m2

capabilities, i.e. energy reservoirs, transmit power, energy depletion time, energy consumption rateetc., we can easily deduce that a decrease in the transmit power can affect all the rest energydeterminants. This decrease is herein achieved by increasing directionality via the use of smartantennas. Since the distances between each pair of nodes are known in advance, we can have thenodes adjust their transmit power accordingly. Thus, instead of increasing the number of linksof the networks produced by keeping the transmit power fixed, we modify our simulation plan byreducing the transmit power of nodes equipped with smart antennas. This approach is consideredto be more “fair when comparing smart antenna nodes with omnidirectional ones. Later in thissection we examine the possibility of adjusting the transmit power of each node with regard to theglobal threshold value. Despite this being a costly solution, it can improve network efficiency byreducing interference and energy consumption simultaneously.

Given that the transmit power for networks operating on the omnidirectional mode is fixed, westudy the behavior of networks with smart antennas and examine lower values of transmitting powerfor different network topologies. Hence, we can draw conclusions about the point where throughputsare equalized and the amount of energy saved after the evaluation period. Let us elaborate on Fig.6, where parameter a corresponds to a fraction of the initial transmit power, whose value is universalin the network (given that the network is considered a homogeneous one, every node having thesame transmit power capability). Transmit power on the omnidirectional mode is 10mW and the

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Omni a = 100% a = 75% a = 50% a = 25%0

0.2

0.4

0.6

0.8

1Q

oS

(a) Quality Of Service

Omni a = 100% a = 75% a = 50% a = 25%0

0.1

0.2

0.3

0.4

Eff

icie

ncy

(b) Efficiency

Omni a = 100% a = 75% a = 50% a = 25%0

10

20

30

40

50

Acti

ve N

od

es (

%)

(c) Active Nodes %

Fig. 6: Energy Consumption: Network performance with respect to Transmit Power

cases evaluated include networks using smart antennas with reduced power (ranging from 0.01Wwhere a = 100% to 0.0025W corresponding to a = 25%) reflected by the a parameter. As expected,the most efficient network corresponds to a = 100%. However, the point where the QoS with smartantenna nodes remains at the same levels compared to omnidirectional nodes, corresponds to amuch lower transmitting power, which equals the 75% of its initial (i.e. with omnidirectional nodes)value. This is somewhat expected, but the energy conservation is spectacular. Due to the highergains of each beam of the smart antennas, high transmit power leads to greater number of linksand therefore higher levels of interference. By reducing transmit power we achieve the following:

• The QoS is approximately equal to the level achieved when nodes transmit with the maximumpower. Thus, approximately the same number of packets are being serviced in the same timeperiod.

• The percentage of active nodes within the same time-slot is greater by almost 2% comparedto the case of a = 100% Pt and is almost doubled with reference to the omnidirectional mode.

• On the antipode, this network is not as efficient as the first one. Heavy traffic causes mostqueues to keep packets for longer time periods and this probably accounts for the lowerefficiency rates.

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0 10 20 30 40 500

0.5

1

1.5

2x 10

−3

Node Distribution

Tran

smit

Pow

er (W

)

(a) Node Distribution: Transmit Power

0 0.01 0.02 0.03 0.04−32.5

−32

−31.5

−31

−30.5

−30

Node Density

Tra

nsm

it P

ower

(dB

)

Omnidirectional Antennas4−beam Smart Antennas

(b) Average Transmit Power to Node Density

Fig. 7: Transmit Power

The procedure followed in this section differs from previous approaches. In this endeavor, ourobjective is to keep the number of links unaffected (|E| set of the graph). Although we build theadjacency matrix in exactly the same way, we modify the transmit power for each individual node todetermine the minimum power required for the transmission between each adjacent node pair to besuccessful. This power value is considerably lower compared to the one used with omnidirectionalantennas. For instance, a packet is received under −70dB while the threshold set by the receiverequals −100dB; the node transmitting could save valuable energy (approximately 10 − 15dBs inthis case) by reducing its transmit power. Modifying the transmit power is allowed only if alltransmissions for this node can be carried out successfully after this modification, which is ensuredby setting transmit power equal to the minimum power required for every existing link of thenode(the complexity of this estimation is O(NodeDegree), as we need to consider every one-hopneighbor (NodeDegree) of the specific node and find the maximum required power required toestablish the link).

Then, we compare the necessary transmit power when antennas transmit omnidirectionally vs.the power required when nodes are equipped with smart antennas enabling them to transmit towardsdifferent directions contingent on the target for various node density values. We assume that thenetwork is a sparse sensor network deploying N = 50 nodes placed at a distance of approximately10 meters from their neighbors. More specifically, we intend to estimate the average transmit powerthat ensures connectedness for the network we examine; in this way, we can easily determine asuitable threshold value for the receiver’s sensitivity. Therefore, assuming that R = 10m, and aftersubstituting the values for Losses, gains, λ, etc., Eq. (2.1) yields:

Prec(dB) = 10 log

(Pt · 100.3 · 100.3

100.3·(

0.125

4 · π · 10

)2)

≃ −57 + Pt(dB) (4.1)

The transmit power is 1mW (−30dB) and the threshold is set accordingly i.e. −57 − 30 =−87dB. Fig. 7(a) is a characteristic example of a single network where each node has differenttransmit power, determined as explained above. This distribution was displayed by almost everynetwork of identical node density. Every node requires greater transmit power in the traditionaloperating mode which surpasses 40% of the power needed by smart antennas. This difference isclose to 2 dB, i.e. the gain difference between the modes.

Finally, two more diagrams are introduced; the first one (Fig. 7(a)) demonstrates the necessary

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0.01 0.012 0.014 0.016 0.018 0.02 0.0220

0.2

0.4

0.6

0.8

1

Node Density

Qo

S

Omnidirectional Antennas4−beam Smart Antennas6−beam Smart Antennas

(a) Quality Of Service

0.01 0.012 0.014 0.016 0.018 0.02 0.022

0.1

0.2

0.3

Node Density

Effic

ien

cy

Omnidirectional Antennas4−beam Smart Antennas6−beam Smart Antennas

(b) Efficiency

0.01 0.012 0.014 0.016 0.018 0.02 0.02210

20

30

40

50

Node Density

Active

No

de

s

Omnidirectional Antennas4−beam Smart Antennas6−beam Smart Antennas

(c) Active Nodes %

Fig. 8: Smart Antennas: Network performance with respect to Node Density

transmit power so that the signals are received with the minimum power required, while the secondone (Fig. 7(b)) illustrates the comparison of the mean transmit power with regard to node density.Omnidirectional antennas exhibit an average value close to the transmit power (−30dB) whilesmart antennas require less power to establish the same links in the network, with the percentageimprovement ranging from 15-30%.

5 Further Considerations

This section deals with further improvements and extensions of the previously discussed networkmodel with a view of assessing the contribution of two alternative approaches. Increasing thenumber of beams of the smart antennas constitutes the first approach, yet a costly one. The secondapproach (Hybrid Model), which lies in the idea of installing smart antennas exclusively on a smallnumber of the nodes, aims to bridge the gap between cost efficiency and performance enhancement.

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0.005 0.01 0.015 0.020

0.2

0.4

0.6

0.8

Node Density

Qo

S

p = 0p = 0.2p = 0.5

(a) Quality Of Service

0.005 0.01 0.015 0.020.05

0.1

0.15

0.2

0.25

Node Density

Eff

icie

nc

y

p = 0p = 0.2p = 0.5

(b) Efficiency

0.005 0.01 0.015 0.0210

15

20

25

30

35

40

45

Node Density

Ac

tiv

e N

od

es

p = 0p = 0.2p = 0.5

(c) Active Nodes %

Fig. 9: Hybrid Model: Heterogenous Network performance with respect to Node Density

5.1 Multi-beam smart antennas

In this section, we discuss the benefits emerging from increasing the number of beams of the smartantennas used in WSNs. It is understood that an increase in the number of beams will accordinglyincrease the directionality of the links, yielding lower interference between the nodes-transmittersand, in turn, smaller number of “blocked” nodes, i.e. nodes within the collision areas of activetransmissions. In other words, when node n1 attempts transmission towards node n2, every nodewithin the area defined by the radiation pattern of each pair of nodes is rendered unable to transmitdata since it senses the medium and detects the ongoing information exchange. The state of thenode is altered only for as long as the current time-slot lasts, as it is now considered incapable ofinitiating transmission. However, it is able to receive data from neighboring nodes.

Computing the total area covered by active transmissions has shown that as the number of beamsis increased the network’s performance is enhanced - though not proportionately. This enhancementis evaluated by the parameters described in section 2. The results are shown in the following figureswhere the study has been carried out for a WSN of size 50 x 50 m2 with varying node density.

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5.2 Hybrid model

In this section, we consider a heterogeneous network, i.e a network consisting of nodes with differ-ent characteristics, some with fewer capabilities and lower cost and others with better features andhigher cost respectively. This means that we build an adjacency matrix with a slightly differentmethod, so as to include both nodes with omnidirectional antennas and nodes with smart anten-nas. To attain this, each node is selected with probability p and is supplied with smart antennas;consequently, its capabilities are modified. Finally, a heterogeneous network which lies between thetwo types of networks studied in section 2 is produced.

0 0.2 0.4 0.6 0.8 1

0.2

0.4

0.6

0 0.2 0.4 0.6 0.8 120

25

30

35

40

p

Act

ive

Nod

es

QoSEfficiency

Fig. 10: Hybrid Model: Heterogenous Network performance with respect to p. Parameter p stands for thepercentage of nodes equipped with smart antennas.

The purpose of the hybrid approach is to trade-off WSN deployment and operating cost vs. per-formance, as there will be few expensive nodes with higher power resources while the majority of therest will be common ones operating on the simplest mode, thus being less power consuming. Later,we present the figures for three network structures, the ‘plain one’ - which points to homogeneity-and two ‘hybrid’ models built from nodes with different features (here probability p denotes thepercentage of the nodes that operate with 4-beam smart antennas). For instance, p = 0.20 meansthat approximately 20 % of the nodes are equipped with smart antennas. The same explanationstands for p = 0.50. For the same network types of size 50 x 50 m2 and equal density, we evaluateQoS, Efficiency and Percentage of Active Nodes as the number of nodes increases.

As shown in Fig. 9, network performance is enhanced; nevertheless, this improvement is notas significant as the previously studied network type. The model lies somewhere between theomnidirectional and the smart antennas mode for the parameter tested, i.e. node density. QoS,Efficiency and Percentage of Active Nodes all increase without, however, reaching the values of thesmart antennas mode studied in Section 3.1. Assuming, for instance, that node density is 0.01; inthis case, the classic approach provides an value of 60% for the QoS, 22% for the efficiency and40% for the Active Nodes Percentage metrics, while, the corresponding values in the hybrid modelof p = 0.50 are 40, 20 and 30%, respectively.

The comparison between different hybrid models reveals a small difference between hybrid net-works, although the improvement is noticeable compared to the plain network. From Fig. 10, itfollows that the improvement in QoS, efficiency and Active Nodes percentage is considerable evenfor a small number of smart antennas used, thus indicating that the proposed approach is valuableeven in a hybrid (and more cost-efficient) approach.

These results, together with a cost analysis of deploying smart antennas over WSNs, could be

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used in order to estimate the optimal trade-off point between cost and performance and determinethe number of smart antennas that should be used in the network.

6 Conclusion

In this paper, it is proposed that WSN performance can be improved in terms of various metricsin the case where smart antennas are used in the network nodes. A simulator has been alsobuilt in order to numerically evaluate the proposed approach. Numerical results are presented,confirming our expectations, indicating that the performance of a WSN is significantly improvedwith respect to QoS, efficiency, active nodes percentage and energy consumption. WSNs equippedwith smart antennas demonstrate improved features even in the case where these antennas areinstalled only on a fraction of nodes (hybrid network model). It was impressive that the performanceof smart-antenna equipped WSNs is doubled with respect to omnidirectional-only, while increasingthe number of beams results in even higher performance. Furthermore, it was found that, in general,the performance of the network is independent of the network size, which guarantees scalability.

The proposed approach reveals the importance of incorporating smart antennas into wirelessnetwork systems, yielding desirable results without modifying the features which characterize anetwork as a WSN (self-organization, limited transmission range, highly clustered nodes). It hasbeen shown that smart antennas can be designed to fit a broader range of applications, catering forhigher efficiency and improved quality almost at no cost. Considering this alternative could opennew avenues in research, offering incentives for innovative ideas as well as further improvements andalterations in existing projects.

References

[1] Raghavendra, C.N., Sivalingham, K.M., and Znati, T., Wireless Sensor Networks, SpringerScience and Business Media Inc., New York, USA, 2004.

[2] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, “Wireless sensor networks: asurvey”, Computer Networks Journal, pp. 393 - 422, 2002.

[3] Lewis, F.L., “Wireless Sensor Networks”, appears in: Smart Environments: Technologies, Pro-tocols and Applications, John Wiley and Sons, New York, USA, 2004.

[4] Callaway, E.H., Wireless Sensor Networks Architectures and Protocols, CRC Press, Boca Raton,FL, USA, 2004.

[5] R. Iyer, L. Kleinrock, “QoS Control for Sensor Networks”, IEEE International Conference onCommunications, vol. 1, pp. 517-521, 2003.

[6] J. Frolik, “QoS Control for Random Access Wireless Sensor Networks”, Proc. of the WirelessCommunications and Networking Conference, vol. 3, pp. 1522 - 1527, 2004.

[7] Q. Zhao and L. Tong, “QoS Specific Information Retrieval for Densely Deployed Sensor Net-work”, Proc. of 2003 Military Communications International Symposium, Boston, MA, Oct2003.

[8] I. Rhee, A. Warrier, M. Aia and J. MinZ, “Z-MAC: a Hybrid MAC for Wireless Sensor Net-works”, Technical Report, Department of Computer Science, North Carolina State University,April 2005.

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[9] A. Koubaa, M. Alves, E. Tovar, “i-GAME: An Implicit GTS Allocation Mechanism in IEEE802.15.4”, Euromicro Conference on Real-Time Systems, July 2006.

[10] E. Felemban, C. Lee, E. Ekici, “MMSPEED: Multipath Multi-SPEED Protocol for QoS Guar-antee of Reliability and Timeliness in Wireless Sensor Networks”, Mobile Computing IEEETransactions, vol. 5, pp. 738-754, June 2006.

[11] V. Srivastava and M. Motani, “Cross-layer design: a survey and the road ahead”, IEEE Com-munications Magazine, pp. 112 - 119, vol. 43, 2005.

[12] Wattenhofer, R., Li, L., Bahl, P., Wang, “Distributed Topology Control for Power EfficientOperation in Multi-hop Wireless Ad Hoc Networks”, Proc. of the 20th Annual Joint Conf. ofthe IEEE Computer and Communications Societies, INFOCOM, 2001.

[13] P. Santi, “Topology Control in Wireless Ad Hoc and Sensor Networks”, Wiley, Chichester 2005.

[14] M. Burkhart , P. von Rickenbach , R. Wattenhofer , A. Zollinger, “Does Topology Control Re-duce Interference?”, Proc. of 5th ACM International Symposium on Mobile Ad Hoc Networkingand Computing, MOBIHOC 2004, pp. 919, 2004.

[15] P. von Rickenbach, S. Schmid, R. Wattenhofer, and A. Zollinger, “A robust interference modelfor wireless ad hoc networks”, 5th Int. Workshop on Algorithms for Wireless, Mobile, Ad Hocand Sensor Networks (WMAN), Denver, CO, Apr. 2005.

[16] A.K. Sharma, N. Thakral, S.K. Udgata and A.K. Pujari, “Heuristics for Minimizing Interfer-ence in Sensor Networks”, ICDCN, pp. 49-54, 2009.

[17] H. Jang, “Applications of Geometric Algorithms to Reduce Interference in Wireless MeshNetwork”, International journal on applications of graph theory in wireless ad hoc networks andsensor networks (Graph Hoc), Vol.2, No.1, March 2010.

[18] J. Tang , G. Xue , W. Zhang, “Interference-aware topology control and QoS routing in multi-channel wireless mesh networks”, Proc. of the 6th ACM international symposium on Mobile adhoc networking and computing, 2005.

[19] Liberti, J.C., and Rappaport, T.S., “Smart Antennas for Wireless Communications: IS-95 andThird Generation CDMA Application”, Prentice Hall PTR, Upper Saddle River, New Jersey,1999.

[20] Lozano, A., Farrokhi, F.R., and Valenzuela, R.A., “Lifting the limits on high-speed wirelessdata access using antenna arrays”, IEEE Communications Magazine, Vol. 39, No. 9, pp. 156-162,September 2001.

[21] Winters, J.H., “Smart antennas for wireless systems”, IEEE Personal Communications, Vol.1,pp. 23-27, February 1998.

[22] Sklar, B., “Rayleigh fading channels in mobile digital communication systems Part I: Charac-terization”, IEEE Communications Magazine, Vol. , pp. 90-100, July 1997.

[23] Paulraj, A., Nabar, R., and Gore, D., “Introduction to Space-Time Wireless Communications”,Cambridge University Press, Cambridge, UK, 2003.

20

[24] Stuber, G.L., Barry, J.R., McLaughlin, S.W., Li, Y.G., Ingram, M.A., Pratt, T.G., “BroadbandMIMO-OFDM wireless communications”, Proceedings of the IEEE, Vol. 92, No. 2, pp. 271-294,February 2004.

[25] Bellofiore, S., Balanis, C.A., Foutz, J., and Spanias, A.S., “Smart-antenna systems for mobilecommunication networks Part 1: Overview and antenna design”, IEEE Antennas and Propaga-tion Magazine, Vol. 44, No. 3, pp. 145-154, June 2002.

[26] Mitilineos, S.A., and Capsalis, C.N., “A new, low-cost, switched beam and fully adaptiveantenna array for 2.4GHz ISM applications”, IEEE Transactions on Antennas and Propagation,Vol. 55, No. 9, pp. 2502-2508, September 2007.

[27] Mitilineos, S.A., Mougiakos, K.S., and Thomopoulos, S.C.A., “Design and optimization ofESPAR antennas via impedance measurements and a genetic algorithm”, IEEE Antennas andPropagation Magazine, Vol. 51, No. 2, pp. 118-123, April 2009.

[28] Mitilineos, S.A., Thomopoulos, S.C.A., and Capsalis, C.N., “On array failure mitigation withrespect to probability of failure, using constant excitation coefficients and a genetic algorithm”,IEEE Antennas and Wireless Propagation Letters, Vol. 5, pp. 187-190, 2006.

[29] R. Albert and A-L. Barabasi, “Statistical mechanics of complex networks”, Department ofPhysics, University of Notre Dame, Reviews of Modern Physics, vol. 74, Jan. 2002.

[30] W. An, F. Shao and H. Meng, “The coverage-control optimization in sensor network subjectto sensing area”, Journal of Computers And Mathematics with Applications, vol. 57, pp 529 -539, 2009.

[31] Cormen, T. H., Leiserson, C. E., Rivest, R. L., Stein, C. (2001), “Section 24.3: Dijkstra’salgorithm”, Introduction to Algorithms (Second ed.), MIT Press and McGraw-Hill. pp.595601,ISBN0-262-03293-7.

[32] An W., Shao F-M., Meng H., “The coverage-control optimization in sensor network subject tosensing area”, Computers and Mathematics with Applications, vol. 57, pp. 529-539, 2009.

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