Cooperative Space-Time Block Codes for Wireless Video Sensor Networks

Wireless Pers Commun (2012) 64:123–137DOI 10.1007/s11277-012-0521-x

Cooperative Space-Time Block Codes for Wireless VideoSensor Networks

Marcelo Portela Sousa · Ajey Kumar ·Rafael F. Lopes · Waslon T. A. Lopes ·Marcelo Sampaio de Alencar

Published online: 28 January 2012© Springer Science+Business Media, LLC. 2012

Abstract Wireless video sensor networks (WVSNs) are gaining popularity due to numer-ous potential applications such as video surveillance, environmental and habitat monitoring,and so on. Considering large-scale WVSNs, in which the sink node is not within the rangeof all other nodes, a multi-hop operation is desired. This paper proposes a novel cooperativesystem that uses space time-block codes to enhance the performance of multi-hop wirelessvideo sensor networks. The proposed scheme is designed to deal with large-scale WVSNs,by adapting the cluster size and extending the cooperation between the cluster-heads. Theperformance comparison between the proposed system and a non-cooperative scheme ispresented based on network lifetime for varying propagation scenarios, number of cameranodes, and on the quality of transmitted videos in a WVSN. Simulation results show animprovement in performance for the proposed system.

Keywords Wireless video sensor networks · Cooperative STBC · Full diversity ·SCA with LEACH · Multi-hop communication

M. P. Sousa (B) · A. Kumar · R. F. Lopes · W. T. A. Lopes · M. S. de AlencarDepartment of Electrical Engineering, Institute for Advanced Studies in Communications (Iecom),Federal University of Campina Grande (UFCG), Av. Aprígio Veloso, 882, Bodocongó, Campina Grande,PB 58109-970, Brazile-mail: [email protected]

A. Kumare-mail: [email protected]

R. F. Lopese-mail: [email protected]

W. T. A. Lopese-mail: [email protected]

M. S. de Alencare-mail: [email protected]

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124 M. P. Sousa et al.

1 Introduction

Recent technological advances in microelectronics and nano-systems technologies allow theuse of small low-cost cameras in wireless sensor networks used to capture and transmit vid-eos. A wireless video sensor network (WVSN) interconnects low-cost and low-power sensornodes with limited data processing capabilities, which are equipped with miniature camerasand wireless transceivers. Typically, video sensors are small in size and communicate overshort distances. Each video sensor node captures video scenes and transmits them to a centralbase station or data sink [1,2].

WVSNs can provide better quality for monitored video capturing and processing, thanthe applications in which isolated sensing devices are employed [3]. A distributed system ofmultiple cameras and sensors enables perception of the environment from multiple disparateviewpoints, and can overcome occlusion effects. Furthermore, heterogeneous media streamswith different granularity can be acquired from the same point of view to provide a multi-resolution description of the scene and multiple levels of abstraction. Such feature could beused to recognize people based on their facial characteristics. Other benefit is the redundancyintroduced by multiple, possibly heterogeneous, overlapped sensors can provide enhancedunderstanding and monitoring of the environment. Overlapped cameras can provide differentviews of the same area or target, while the joint operation of cameras and audio or infraredsensors can help disambiguate cluttered situations [4].

An interesting application scenario for WVSNs is the area of monitoring systems. Forinstance, video and audio sensors can be used to enhance and complement existing sur-veillance systems against crime and terrorist attacks. Large-scale networks of video sensorscan extend the ability of law enforcement agencies to monitor areas, public events, privateproperties, borders and rails [4–6].

According to its coverage and density characteristics, a WVSN can be classified as asmall-scale or a large-scale network. The former is usually composed by a small number ofnodes spread over a small area. In this scenario, the nodes are near to each other [7] and thesink node is considered to be within the transmission range of any other node. On the otherhand, a large-scale network is composed of densely deployed sensor nodes [8]. In a large-scale network only a few nodes can reach the sink node, requiring the adoption of a multi-hopcommunication approach and routing protocols to allow the network communication. In thiscontext, the routing protocol should be aware of the energy constraints of the nodes.

Compared to conventional data sensor networks, WVSNs show unique properties. Thedata generation rate in a video sensor is high, and the required network bandwidth and powerconsumption for wireless transmission are large. In general, sensor networks should havea lifetime long enough to fulfill the application requirements. Several methods have beenproposed to increase the lifetime of sensor networks. Cluster based protocols are one of thesuccessful methods for energy saving in small-scale networks and can be adapted to satisfywith the requirements of large-scale networks [9–12]. In a multi-hop operation, the clus-ter-heads can communicate with each other and forward the sensed information to the sinknode.

In WVSN channels the fading caused by multipath can significantly degrade the perfor-mance of the communication system. In this context, the automatic repeat request (ARQ)protocol at the link layer is an effective means to mitigate the channel fading, in whichcyclic redundancy check (CRC) is used for error check and retransmissions are requested ifthe packet is received erroneously. In practice, the maximum number of retransmissions islimited to minimize the delay and buffer size. This ARQ strategy is called truncated ARQprotocol [13,14].

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Cooperative Space-Time Block Codes 125

On the other hand, diversity techniques can also improve the performance of systems usingtruncated ARQ, since replicas of the transmitted signals are provided to the sink node [15–17].Time diversity, frequency diversity, spatial diversity and modulation diversity are examplesof typical diversity techniques. However, the application of diversity techniques with multipleantennas could be impractical in a wireless video sensor network, due to the sensor nodessize and the network energy constraints. In order to overcome this limitation, a new form oftime-spatial diversity, whereby diversity gains are achieved via the cooperation of the nodes,has been proposed [18–21].

In [22], Sousa et al. proposed an integrated system, SCA (selective cooperative diversitywith ARQ) with LEACH (low-energy adaptive clustering hierarchy), that combines the SCAcross-layer design with the LEACH protocol [9], for wireless sensor networks. The systemuses STBC (space-time block coding), a paradigm for communication over Rayleigh fadingchannels using multiple transmitters [23,24]. Data is encoded and split into N streams whichare simultaneously transmitted using N transmitters. The received signal at the receiver sinknode is a linear superposition of the N transmitted signals perturbed by noise and fading.Maximum likelihood decoding is achieved in a simple way through decoupling of the signalstransmitted from different antennas rather than joint detection. STBC provides full diversityand very low complexity encoding and decoding [25]. However, the performance of LEACHcould be improved for large-scale WSNs. Additionally, the transmission of videos in large-scale networks requires protocols that take into account the residual energy of each node.

In this paper, the operation of SCA with LEACH is modified. An efficient clusteringprotocol is proposed, which uses the residual energy of each node as the election criteriaof the cluster-heads. On the other hand, the SCA operation remains with cooperative space-time block codes, but with modifications to deal with large-scale networks and a multi-hopscheme. The lifetime performance and the quality of transmitted videos are evaluated andcompared with a non-cooperative transmission scheme.

The remaining of this paper is organized as follows: Section 2 presents the related work.Section 3 explains the operation of the proposed clustering protocol. Section 4 describes thesystem configuration and the channel model. The operation of cooperative space-time blocksis described in Section 5. Simulation parameters and results are discussed in Section 6 andthe conclusions are presented in Section 7.

2 Related Work

Several systems with sensor networks for surveillance applications have been proposed. Asurveillance WSN framework based on line sensor architecture, capable of capturing a con-tinuous stream of temporal one dimensional image (line image), is presented in [5], in whichthe trade-off between node processing and communication bandwidth efficiency is investi-gated. In [6], the authors describe the design and implementation of a running system forenergy-efficient surveillance. The system allows a group of cooperating sensor devices todetect and track the positions of moving vehicles in an energy efficient and stealthy manner.A wireless sensor network system for surveillance of critical areas is demonstrated in [26], inwhich it runs protocols to detect trespassers, node failures and network partitioning alongsidea duty cycle management to ensure network longevity. The study of the resource utilizationbehaviour of a wireless video sensor and the analysis of its performance under these resourceconstraints are presented in [27]. The authors consider the scenario of wireless video surveil-lance, and analyse the performance limit of the wireless video sensor. In [28], a multi-agentframework for video sensor-based coordination in surveillance applications is presented.

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A visualization framework to enable a more effective visual analysis of large-scale videosis proposed in [29]. The area coverage problem of randomly deployed nodes in a WVSN isaddressed in [30]. The proposed approach aims to increase the network lifetime by the useof a distributed algorithm dedicated to save energy and to reduce ambiguities in WVSN. Theapproach proposed by Huang et al. [31] employs a novel real-time change mining algorithmbased on an extracted profile model of moving objects learnt from frog’s eyes. The authorshave applied their work for the surveillance of residential districts.

An important aspect to assure a distributed and self organized operation is the combat of thechannel fading, also inherent in wireless channels. Cooperative communications have beendemonstrated to be effective in combating the multiple fading effects in wireless networks,and improving the network performance in terms of adaptivity, reliability, data throughputand network lifetime [19]. The energy efficiency of MIMO (multiple input multiple output)transmissions in wireless sensor networks is analysed in [32]. Energy consumed in transmis-sion, processing circuitry and cooperation show that the optimal energy efficiency requires theexploitation of both the diversity and the multiplexing gains. Heinzelman et al. [9] developedLEACH, a clustering-based protocol that minimizes energy dissipation in sensor networks.In [33], the efficiency of STBC in cooperative transmission is studied within LEACH proto-col. Simulations show that with proper design, cooperative transmission can enhance energyefficiency and prolong sensor network lifetime. Chen et al. [34] investigated the use of cooper-ative communications for reliable data dissemination in wireless sensor networks. The authorsproposed a distance-based forwarding node selection scheme that yields smaller end-to-enddelays. Liang et al. investigate the use of cooperative communications for QoS (quality ofservice) provisioning in resource-constrained WSNs, and propose MRL-CC, a Multi-agentreinforcement learning based cooperative communication protocol. Lin and Letaief [20] pro-posed the SCA scheme, in which adaptive cooperative diversity gain can be achieved anderror propagation is therefore avoided.

3 The Proposed Clustering Protocol

The proposed clustering protocol is designed to deal with large-scale networks and is basedon a multi-hop routing scheme. As in LEACH, the nodes organize themselves into localclusters, with one node per cluster acting as the cluster-head. The conception of round isused [35]. A round is the time used for the reporting of data from all the clusters to sink node.Each round contains two states: the cluster set-up state and the steady state.

In the cluster set-up state, the clusters are created in a self-adaptive mode. In the firstround, the cluster-heads are randomly elected. The remaining nodes compare the locationsof the cluster-heads and decide to join to the closest one. When each node reports its senseddata to the respective cluster-head, an information about its residual energy is attached to themessage, and it is shared with the other nodes due to the broadcast nature of the wirelesschannel. Then, each node decides whether to become a cluster-head in the next round. Thisdecision is based on the comparison between its residual energy and the energy reported fromthe other nodes. If its energy value is greater than the others, it elects itself as a cluster-head.Otherwise, in the next round the node joins in the cluster indicated by the nearest cluster-head.Current cluster-heads must be saved for the next round and do not participate in the currentelection. A design-dependent timeout may be configured to check if the elected cluster-headis available at the beginning of each round.

Once the clusters are created and a TDMA (time division multiple access) schedulingfor transmissions of each cluster is fixed, the steady state begins. The aggregated data is

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transmitted in a multi-hop scheme, from a source cluster-head to the next hop, which iseither another cluster-head, or the sink node. A source cluster-head is the start point in therouting process, towards the sink node. Between the source cluster-head and the sink node,intermediate cluster-heads forward the information originated at the scheduled cluster. If thesensed data of all the clusters is reported to the final destination, i.e., the sink node, one roundis said to be completed.

The major differences between the clustering protocol proposed in this paper and LEACHare the cluster-head election criteria and the routing process. Those differences were designedto attain the requirements of large-scale networks. In opposite of LEACH, that uses in allthe rounds a random election criteria, the proposed scheme utilizes a random election onlyin the first round, and turns the remaining elections more efficient by the use of a residualenergy criteria. Furthermore, the multi-hop routing process is realized between the electedcluster-heads, which guarantees that the nodes with more residual energy participate in theforwarding process.

4 Network Configuration

Consider a network that uses orthogonal relay transmission to achieve diversity gain, in whicheach node has one or several partners. The node and its partners are responsible for trans-mitting not only their own information, but also information from their partners. Therefore,a virtual antenna array is obtained with the use of the relays’ antennas without a complexsignal design [20]. Error propagation can be avoided, since only the relay candidates whocorrectly detect the packet are selected to be relay nodes. Furthermore, node cooperationis adopted only when the receiver fails to detect the packet correctly. Thus, the cooperativediversity is employed in a selective and adaptive manner. This is the contribution of the SCAin the proposed scheme.

The WVSN is composed by K camera nodes and each node is equipped with one antenna.Among the K nodes, Q nodes (Q ≤ K ) are the cluster-heads of the corresponding roundselected by the proposed clustering algorithm. In the multi-hop operation, the set of λ trans-mission hops from the source cluster-head to the sink node is represented by H, in whichλ = 1, . . . , Λ. For each value of λ the closest neighbour cluster-head towards the sink nodeis selected as the next hop. If λ = Λ, the next hop is the sink node. In the end of each hop, thereceiver node represents the source cluster-head for the next hop. Then, the set of ω sourcecluster-heads is represented by S, in which ω = λ − 1. If ω = 0, the source cluster-head isthe first one, that is, the one which on fact is the source of sensed data. Indeed, during thereporting of cluster fused data, the source cluster-head can either forward that data to oneof the Q − 1 idle cluster-head (λ �= Λ) or transmit it directly to the sink node (λ = Λ),according to a distance comparison towards the sink node.

Q − m idle cluster-heads are assumed available as possible relays for the source cluster-head during a packet retransmission (if needed), in which

m ={

1, if ω = Λ − 1,

2, otherwise.(1)

Let a source cluster-head, Sω, which transmits a data packet with a C-bit CRC attachedand the respective next hop, Hλ, which detects the CRC. Then, an acknowledgement that iseither positive (ACK) or negative (NACK) is sent back to Sω, from Hλ. At the same time, allthe Q − m relay candidates check the CRC and the ones who get positive results are selectedto be relay cluster-heads. If the packet is correctly detected by Hλ (with ACK feedback),

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Sω continues to transmit a new data packet and the previous process is repeated. Otherwise,retransmission starts. Both Sω and the relay cluster-heads jointly retransmit the packet by uti-lizing a suitable orthogonal cooperative STBC. The retransmission continues until the packetis successfully delivered, or the number of retransmissions exceeds Nmax

r , which is a presetparameter indicating the maximum number of retransmissions allowed per packet [22].

4.1 The Channel Model

The adopted channel model was introduced in [20]. The communication channel between thesource cluster-head and the sink node is a flat Rayleigh fading channel. The communicationis facilitated by v relay cluster-heads, which are selected from Q − m relay candidates. Inaddition, perfect channel knowledge is assumed to be available at the receiver side, by usingtraining sequences.

At time slots t0 + 1, . . . , t0 + ς , the ωth source cluster-head, Sω, ω = λ − 1, sends apacket xSω

t0+1, . . . , xSωt0+ς with transmission power Pt per symbol, in which xSω

t0+i is a QPSKmodulated symbol and ς = L/b is the number of symbols per packet with a total length ofL bits and b = 2 bits per symbol. Then, the signal received by Hλ, λ = 1, . . . , Λ, at timeslot t0 + i, i = 1, . . . , ς , is given by

yHλ

t0+i = h0xSω

t0+i + z0, (2)

in which the channel gain h0 is a complex Gaussian random variable with zero-mean and var-iance σ 2

0 (corresponding to the effect of large-scale path loss and shadowing), z0 representsthe additive white Gaussian noise with zero-mean and variance N0.

At the j th relay candidate, j = 1, . . . , Q − m, the received signal is given by

yr jt0+1 = hs j xSω

t0+i + zs j , (3)

in which the noise zs j and the fading coefficient hs j are complex Gaussian random variableswith zero-mean and variance N0 and σ 2

s j, respectively, j = 1, . . . , Q − m. In the following,

the packet xSω

t0+1, . . . , xSωt0+ς is directly transmitted.

If Hλ fails to detect the packet correctly retransmission starts at time slot tL + 1. Let Nr

be the number of retransmissions, 1 ≤ Nr ≤ Nmaxr . The received signal at Hλ at time slot

tL + i is given by

yHλ

tL+i = h0xSω

tL+i + z0 +v∑

j=1

hr j xr jtL+i + zr j , (4)

for i = 1, . . . , Nrς/R, in which R is the rate of the STBC code. The symbols xSω

tL+i and xr jtL+i

are the STBC symbols transmitted by the source cluster-head and the j th relay cluster-headwith the transmission power Pt/(v + 1), respectively. The additive noise zr j and the fadingcoefficient hr j are complex Gaussian random variables with zero-mean and variance N0 andσ 2

r j, respectively, j = 1, . . . , v.

5 Cooperative Space-Time Block Codes

Consider a system composed by a source cluster-head, Sω, a relay cluster-head, r j , and areceiver (next-hop), Hλ, λ = 1, . . . , Λ and ω = λ−1, both with just one antenna employingspace-time block codes and using the QPSK modulation. First, Sω picks the symbol (xSω

tL+1)

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from the constellation and r j picks the symbol (xr jtL+1). Then, they transmit these respec-

tive symbols at time slot one and, at time slot two, they transmit −(xr jtL+1)

∗ and (xSω

tL+1)∗,

respectively. Therefore, the transmitted codeword is

C =(

(xSω

tL+1) (xr jtL+1)

−(xr jtL +1)

∗ (xSω

tL+1)∗

). (5)

The path gains from Sω and r j to Hλ are (hSω ) and (hr j ), respectively. Then, the decoder

receives signals (yHλ

tL+1) and (yHλ

tL+2) at times one and two, respectively, such that{

(yHλ

tL+1) = (hSω )(xSω

tL+1) + (hr j )(xr jtL +1) + zSω

(yHλ

tL+2) = −(hSω )(xr jtL+1)

∗ + (hr j )(xSω

tL +1)∗ + zr j .

(6)

For a coherent detection scheme the sink node knows the channel path gains (hSω ) and(hr j ), the maximum-likelihood detection criterion minimizes the decision metric

|(yHλ

tL+1) − (hSω )(xSω

tL+1) − (hr j )(xr jtL +1)|2

+ |(yHλ

tL+2) + (hSω )(xr jtL+1)

∗ − (hr j )(xSω

tL+1)∗|2, (7)

over all possible combinations of (xSω

tL+1) and (xr jtL+1). Such decoding scheme requires a

full search over all possible pairs ((xSω

tL+1), (xr jtL+1)) and, in general, its complexity grows

exponentially with the number of transmitter cluster-heads. Expanding the cost function (7),one can ignore the common term |(yHλ

tL+1)|2 +|(yHλ

tL+2)|2. Then, the metric (7) can be splittedinto two parts, one of which [25]

|(xSω

tL+1)|2(|hSω |2 + |hr j |2) −[(yHλ

tL+1)(hSω )∗(xSω

tL+1)∗ + (yHλ

tL+1)∗(hSω )(xSω

tL+1)

+(yHλ

tL+2)(hr j )∗(xSω

tL+1) + (yHλ

tL+2)∗(hr j )(xSω

tL +1)∗] , (8)

is a function of (xSω

tL+1) only, and the other one

|(xr jtL+1)|2(|hSω |2 + |hr j |2) −

[(yHλ

tL+1)(hr j )∗(x

r jtL+1)

∗ + (yHλ

tL+1)∗(hr j )(x

r jtL+1)

− (yHλ

tL+2)(hSω )∗(xr jtL+1) − (yHλ

tL+2)∗(hSω )(x

r jtL+1)

∗] , (9)

depends on (xr jtL+1). Therefore, instead of minimizing the cost function (7) over all pos-

sible values of ((xSω

tL+1), (xr jtL+1)), one can simultaneously minimize the cost functions (8)

and (9) over all possible values of (xSω

tL+1) and (xr jtL+1), respectively. As a result the decod-

ing complexity of the code increases linearly, instead of exponentially, with the number oftransmitter cluster-heads. In addition, since the constellation symbols have equal energy, asa QPSK scheme, the terms |(xSω

tL+1)|2(|hSω |2 + |hr j |2) and |(xr jtL+1)|2(|hSω |2 + |hr j |2) can

be ignored. In fact, Hλ should minimize

|(xSω

tL+1) − (yHλ

tL+1)(hSω )∗ − (yHλ

tL+2)∗(hr j )|2 (10)

to decode (xSω

tL+1) and minimize

|(xr jtL+1) − (yHλ

tL+1)(hr j )∗ + (yHλ

tL+2)∗(hSω )|2 (11)

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to decode (xr jtL+1). Therefore, the decoding consists of first by calculating

{x̃Sω

tL+1 = (yHλ

tL+1)(hSω )∗ + (yHλ

tL+2)∗(hr j )

x̃r jtL+1 = (yHλ

tL+1)(hr j )∗ − (yHλ

tL+2)∗(hSω ).

(12)

Then, to decode (xSω

tL+1), Hλ finds the closest symbol to x̃Sω

tL+1 in the constellation. Sim-

ilarly, the decoding of (xr jtL+1) consists of finding the closest symbol to x̃

r jtL+1 in the

constellation [25].The cooperative space-time block coding achieves the full diversity with a very simple

maximum-likelihood decoding algorithm. The key feature of this scheme is orthogonalitybetween the sequences generated by two transmitters.

6 Simulation Parameters and Results

All simulations were developed using Matlab 7. The performance evaluation of the videotransmissions over the WVSN considers that digital cameras are embedded in the nodes,which are deployed on the monitored environment. Although the simulations do not dealwith any fusion or video compression method, it is assumed that the camera nodes reporttheir sensed information (aggregation) and the source cluster-head generates a resultant video,which is forwarded to the sink node. Several fusion methods, which are aimed to compressthe distributed information, to generate a resultant video or select which reported informationis relevant to be forwarded could be used, but this is beyond the scope of this paper. However,the energy needed for both aggregation and forwarding processes of each video transmissionis considered. Actually, there is a trade-off between the energy spent to generate a resultantvideo and the energy spent in the forwarding process.

The simulation considers the forwarding of two different videos, which are generated atthe source cluster-head. The former is the Coastguard video, with 300 frames, and the QCIFformat (176×144 pixels). The video frame number 88 is presented in Fig. 1. The Coastguardvideo presents relative movements between the boats and the surveillance of a wide area.The second video is Foreman, also with 300 frames, and the QCIF format. The video framenumber 131 is presented in Fig. 2. The Foreman video reproduces a good scenario for peoplerecognizing based on their facial characteristics.

Each node has an initial energy of 500 J. The dissipation radio model used in the sim-ulations was proposed in [9]. The radio dissipates Eelec = 50 nJ/bit to run the transmitter

Fig. 1 Original coastguard(Frame no. 88)

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Fig. 2 Original foreman(Frame no. 131)

or receiver circuitry and εamp = 100 pJ/bit/m2 for the transmitting amplifier to achieve anacceptable Eb

N0(the loss of energy in the transmission is proportional to the square of the

distance). Thus, to transmit a k-bit message at a distance d using the radio model, the radiospends [9]

ET x (k, d) = Eelec · k + εamp · k · d2 (13)

and to receive this message, the radio spends:

ERx (k) = Eelec · k. (14)

The performance evaluation is done by means of a comparison between the proposedcooperative STBC system and a non-cooperative system. Both systems use a truncatedARQ scheme. A CRC with C = 16 bits is assumed with a cyclic generator polynomialof GCRC16(D) = D16 + D12 + D5 + 1. QPSK is the modulation scheme adopted and themaximum number of retransmissions in the simulations is Nmax

r = 4. The deployment ofnodes is randomly configured on an area of 62,500 m2 (250 m × 250 m), in which the sinknode is located at coordinates x = 125 and y = 300 m. The formation of five clusters isconsidered per round, with each cluster composed by 100 nodes (except in the experimentwhose results are shown in Fig. 4, in which the number of nodes varies from 100 to 700 withan increment of 100). The considered termination criteria is the inactivation of the last sensornode.

For comparing the bit error rate (BER) between the schemes with and without diversity,the Fig. 3 presents a BER curve. The results are very similar to those found in [25]. The bestperformance is for the STBC scheme, with three transmitters and one receiver, followed bythe Alamouti scheme, with two transmitters and one receiver. They are used for the diversitytransmission stage. The poor performance corresponds to the QPSK scheme, which is usedfor the direct transmission stage (without diversity).

Network lifetime is the time span from the deployment to the instant when the network isconsidered nonfunctional [36]. In this paper, the network lifetime measurement is based onthe amount of rounds (time steps) in which the last camera node dies.

The amount of rounds for the death of the last node, as function of the channel SNRvalues and the number of camera nodes deployed on the monitored area, are shown in Fig. 4.Varying the number of nodes is equivalent to take into account different cluster sizes, whichserves as an analysis of scalability. With more nodes, the load for transmitting videos isbetter distributed, which increases the amount of operation rounds. Furthermore, for betterchannel conditions (higher SNR), the lifetime is extended, since a few errors occurs and

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Fig. 3 Bit error rate as a function of the channel SNR

100200300400500600700

510

1520

2530

35

0

50

100

150

200

250

300

Channel SNR (dB)Number of camera nodes

Ro

un

d in

wh

ich

th

e n

od

e d

ies

50

100

150

200

250Cooperative STBCNon−cooperative

Fig. 4 Number of rounds for the last dead node, as a function of the channel SNR and the number of cameranodes, which also impacts the cluster size

less retransmissions are needed. For all the propagation conditions, the cooperative STBCscheme presents better performance, since higher lifetime values are obtained.

To evaluate the quality of the transmitted videos over the network, the peak signal-to-noise ratio (PSNR) metric is used, as a function of the seven values of SNR (see Fig. 4).Examples of received frames for the SNR value of 15 dB are illustrated in Figs. 5, 6, 7and 8. The channel impairments caused by the bit errors reflect on the inferior quality ofthe received frame. If the SNR value is below 15 dB, the videos can not be recovered. Ascan be noticed, the proposed system improves the subjective quality for all propagation con-ditions. The objective evaluation by means of the PSNR is shown in Table 1. For all thescenarios, the cooperative STBC system presents better performance, since higher values ofPSNR indicate the superiority in the relationship between the signal power and the noisepower.

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Fig. 5 Coastguard videoreception in a non-cooperativescheme with channelSNR = 15 dB

Fig. 6 Coastguard videoreception in a cooperative STBCscheme with channelSNR = 15 dB

Fig. 7 Foreman video receptionin a non-cooperative scheme withchannel SNR = 15 dB

Fig. 8 Foreman video receptionin a cooperative STBC schemewith channel SNR = 15 dB

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Table 1 PSNR of coastguard and foreman as a function of the channel signal-to-noise ratio

Channel SNR (dB) PSNR of costguard PSNR of foreman

Cooperative STBC Non cooperative Cooperative STBC Non cooperative

5 17.36 15.91 16.21 14.65

10 23.07 18.64 22.58 18.53

15 31.88 23.56 30.92 23.12

20 40.75 28.31 40.31 28.00

25 52.63 33.52 50.86 32.87

30 59.43 38.49 58.86 37.86

35 69.25 43.18 68.98 42.89

7 Conclusion

This paper proposed a large-scale wireless video sensor network system that uses cooperativespace-time block codes in a multi-hop way to enhance the quality of the transmitted videosand improve the network lifetime. The integrated performance improvement is related tothe fact that STBCs help to mitigate the undesirable effects of fading in wireless channelsand the cooperative employment of the nodes contributes to save energy in the network. Thesystem modifies the SCA with LEACH operation, with an efficient clustering protocol andthe multi-hop forwarding between the cluster-heads. Each node is equipped with a cameradevice. The design is based in a clustered architecture and, for each round, five cluster-headsare elected according to the proposed algorithm. The system assures that cooperative diver-sity can be invoked only if an error occurs in the transmission. Relay cluster-heads are electedbased on the results of the truncated ARQ process. The performance analysis was carried outconsidering a flat Rayleigh fading channel. The performance of the cooperative STBC systemand the non-cooperative system for wireless video sensor networks were compared based ontwo metrics: the network lifetime, considering seven different SNR values of propagationscenarios and the number of camera nodes; and the PSNR of the videos transmitted over thenetwork. The cooperative STBC system achieves better performance when compared to thenon-cooperative system. Future research includes to investigate the effect of the proposedscheme in the network end-to-end delay.

Acknowledgments The authors would like to thank the Institute for Advanced Studies in Communications(Iecom), the Federal University of Campina Grande (UFCG), the National Council for Scientific and Tech-nological Development (CNPq), Research Support Foundation of Maranhão (FAPEMA), Federal Institute ofParaíba (IFPB) and Federal Institute of Maranhão (IFMA) for supporting this research.

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Author Biographies

Marcelo Portela Sousa was born in Salvador, Bahia, Brazil, onOctober 31, 1983. He received the B.Sc. degree from ÁREA1 Col-lege and the M.Sc. degree from Federal University of Campina Grande(UFCG), Brazil, both in Electrical Engineering. Currently, he is a Ph.D.candidate at UFCG, and a researcher at Institute for Advanced Studiesin Communications (Iecom), Brazil. He is an assistant professor at Fed-eral Institute of Paraíba (IFPB). He has worked as a fellow researcherin several R&Ds. His research interests include Fuzzy Systems, Bio-Inspired Communications, Wireless Sensor Networks and CognitiveNetworks.

Ajey Kumar is currently a post-doctoral fellow at Institute forAdvanced Studies in Communications, UFCG, Brazil. He received hisPh.D. Degree from Indian Institute of Technology Roorkee in 2008.Prior to Ph.D., he did his BE in Computer Science and Engineeringand ME in Software Engineering in 2000 and 2002 respectively. He isassociated as a reviewer for various international conferences and refer-eed journals. His research interest includes wireless/mobile computing,location based services and spatial databases.

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Rafael F. Lopes was born in São Luís, Maranhão, Brazil, on January13, 1982. He received his B.Sc. degree in Computer Science (2003)and M.Sc. degree in Electrical and Computer Engineering (2006) bothfrom the Federal University of Maranhão (UFMA), Brazil. He is nowa D.Sc. candidate in Federal University of Campina Grande (UFCG),Brazil. He is an assistant professor in the Federal Institute of Educa-tion, Science and Technology of Maranhão (IFMA) and a researcherin the Institute for Advanced Studies in Communications (Iecom). Hisresearch interests include wireless communications, multimedia com-munications, cognitive and ad hoc networks and mobile computing.

Waslon T. A. Lopes was born in Petrolina, Pernambuco, Brazil, onDecember 29, 1974. In 2003, He received his D.Sc. degree from Fed-eral University of Campina Grande, Brazil. From 2003 to 2009 heworked at ÁREA1—College of Science and Engineering in Salvador,Bahia, Brazil. He is now with Federal University of Campina Grande,Campina Grande, Paraíba, Brazil. His research interests include robustvector quantization, modulation and wireless communications.

Marcelo Sampaio de Alencar was born in Serrita, Brazil in 1957.He received his Ph.D. from the University of Waterloo in 1994. He isa Registered Professional Engineer and Full Professor at the Depart-ment of Electrical Engineering, Federal University of Campina Grande,Brazil. He is founder and President of the Institute for Advanced Stud-ies in Communications (Iecom). He has been awarded several scholar-ships and grants, including an achievement award from the College ofEngineering of the Federal University of Pernambuco, Brazil, and twogrants from the IEEE Foundation. He published over 200 papers, eightbooks, and wrote chapters for six books. He supervised five Post-doc-toral Fellows, seven Ph.D. theses and 19 Master’s dissertations. He isEditor-in-Chief of the Journal of Communication and Information Sys-tems. He is a columnist of the Jornal do Commercio, Recife, Brazil, andVice-President External Relations of the Brazilian TelecommunicationsSociety (SBrT).

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Cooperative Space-Time Block Codes for Wireless Video Sensor Networks

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