Research Article A Game Theory Based Strategy for...

Research ArticleA Game Theory Based Strategy for Reducing EnergyConsumption in Cognitive WSN

Elena Romero, Javier Blesa, Alvaro Araujo, and Octavio Nieto-Taladriz

ETSI Telecomunicacion, Universidad Politecnica de Madrid, 28040 Madrid, Spain

Correspondence should be addressed to Elena Romero; [email protected]

Received 6 September 2013; Revised 28 November 2013; Accepted 19 December 2013; Published 28 January 2014

Academic Editor: Al-Sakib Khan Pathan

Copyright © 2014 Elena Romero et al. This is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Wireless sensor networks (WSNs) are one of the most important users of wireless communication technologies in the coming yearsand some challenges in this areamust be addressed for their complete development. Energy consumption and spectrum availabilityare two of the most severe constraints of WSNs due to their intrinsic nature. The introduction of cognitive capabilities into thesenetworks has arisen to face the issue of spectrum scarcity but could be used to face energy challenges too due to their new rangeof communication possibilities. In this paper a new strategy based on game theory for cognitive WSNs is discussed. The presentedstrategy improves energy consumption by taking advantage of the new change-communication-channel capability. Based on gametheory, the strategy decides when to change the transmission channel depending on the behavior of the rest of the network nodes.The strategy presented is lightweight but still has higher energy saving rates as compared to noncognitive networks and even toother strategies based on scheduled spectrum sensing. Simulations are presented for several scenarios that demonstrate energysaving rates of around 65% as compared to WSNs without cognitive techniques.

1. Introduction

Global data traffic in telecommunications grows annually ata rate of 70%. The increasing number of wireless devicesthat are accessing mobile networks worldwide is one ofthe primary contributors to traffic growth. The number ofmobile-connected devices will exceed the world’s populationin 2013 according to the CISCO report [1]. One of the maincauses of this spectacular growth of mobile traffic is theincrease in mobile-connected laptops and tablets and theemergence of smartphones whose use has increased by 82%in 2012. Handsets will exceed 50% of mobile data traffic in2013. All of these devices (smartphones, tablets, and laptops)are usually connected viaWi-Fi or Bluetooth, which work ona 2.4GHz unlicensed band.

Reexamining the CISCO report, machine to machine(M2M) communications are shown as one of the mostimportant trends with a 90% annual growth between 2012and 2017. Typical M2M applications include security andsurveillance, health, or monitoring. These applications areusually supported by wireless sensor networks (WSNs) pro-viding a wireless and flexible structure for the transmission ofthe data acquired by sensors to the rest of the network.

One of the problemswithWSNs is certainly spectral coex-istence. Regarding spectrum scarcity, most WSN solutionsoperate on unlicensed frequency bands. In general they usethe industrial, scientific, and medical (ISM) bands like theworldwide available 2.4GHz band.This band is also used by alarge number of popular wireless applications, as mentionedbefore, or wireless networks based on IEEE 802.15.4. As aresult, coexistence issues on unlicensed bands have been thesubject of extensive research showing that IEEE 802.11 net-works can significantly degrade the performance of 802.15.4networks when operating on overlapping frequency bands[2]. To address the efficient spectrum utilization problem,cognitive radio (CR) [3] has emerged as the key technology,which enables opportunistic access to the spectrum.

One of the most important challenges with WSNs isenergy consumption. Due to the number of nodes, theirwireless nature, and, sometimes, their deployment in difficultaccess areas, nodes should not require any maintenance. Interms of consumption this means that the sensors must beenergetically autonomous, the networks should not requirehuman intervention, and therefore the batteries cannot bechanged or recharged. In these kinds of scenarios, node

Hindawi Publishing CorporationInternational Journal of Distributed Sensor NetworksVolume 2014, Article ID 965495, 9 pageshttp://dx.doi.org/10.1155/2014/965495

2 International Journal of Distributed Sensor Networks

lifetime should last for years, making energy consumption adramatic requirement to establish. If energy consumption hasnot been taken into account, nodes will eventually shut down.

The introduction of CR capabilities in WSNs providesa new paradigm for power consumption reduction offer-ing new opportunities to improve it, but this also impliessome challenges [4]. Specifically, sensing state, collabora-tion among devices—which requires communication, andchanges in transmission parameters all increase the totalenergy consumption.

When designing WSN optimization strategies, the factthat WSN nodes are very limited in terms of memory, com-putational power, or energy consumption is not insignificant.Thus, light strategies that require low computing capacitymust be found. In this way, different previousworks, shown inSection 2, have demonstrated the feasibility and effectivenessof implementing game-theory-based strategies to optimizelimited resources on WSNs. Since the field of energy conser-vation in WSNs has been widely explored, we assumed thatnew strategies should emerge from the new opportunitiespresented by cognitive networks.

In this paper a new game-theory-based strategy to opti-mize energy consumption inWSNs is presented.This strategytakes advantage of a new opportunity offered by cognitivewireless sensor networks (CWSNs): the ability to change thetransmission and reception channel.

The organization of the paper is as follows. Section 2presents the review of the state of the art. Assumptions aboutthe network are exposed in Section 3. The game-theory-based strategy is presented in Section 4. Section 5 describesthe baseline scenario and tools used in the simulations andthe results are presented and discussed. Finally, Section 6presents the conclusions from this work.

2. Related Work

CWSNs are a young technology and there is not a widerange of contributions in this area. Most works found in theliterature on CWSNs introduce the general idea and promoteresearch in this field. Zahmati et al. present in [4] an overviewof CWSNs, discussing the emerging topics and the potentialchallenges in the area. Moving on to energy efficiency, thereare several approaches to reduce power consumption forCNs but not specifically for CWSNs. Most of the researchwork focuses on achieving power-efficient spectrum use.In [5] a transmission power management is proposed tominimize interference with primary users and to guaranteean acceptable quality of service (QoS) level for cognitivetransmission. In [6] the power constraint is integrated intothe objective function which is a combination of the mainsystem parameters of the cognitive network.

If we move to the specific area of consumption reductionin CWSNs, there is still much work to do. Focusing onlow-power networks that exploit CR features, [7] notes theimportance of CR features to improve power consumption,as in [8] where it is noted that CR could be able toadapt to varying channel conditions, which would increasetransmission efficiency and hence help reduce power used

for transmission and reception.These papers address the newopportunities offered but lack in specific solutions. In [9]two main problems related to energy consumption are listed:network lifetime maximization and energy efficient routing.

Specific solutions are given in [10] where authors proposea routing scheme optimizing the size of transmitted data andthe transmission distance. Also, Stabellini and Zander centertheir work on reducing power consumption in the sensingstep [11]. They use an energy constrained system comprisingof two sensor nodes that avoid interference by exploitingspectrum holes in the time domain to prove its algorithm.

Given that the contributions in the field of reducingenergy consumption in CWSNs are still scarce, it is possibleto use the advances in WSNs to inspire new strategies forCWSNs. This way, it is possible to find a wide range ofprevious works in the area of algorithms based on gametheory seeking energy optimizations in sensor networks. Asstated in [12], more than 330 research articles related to gametheory andWSNswere published from 2003 to 2011.Modeledgames range from routing, task scheduling, or MAC energyefficient implementations.

Moreover, the use of game theory based algorithms issuited well to the characteristics of CNs. There are severalstudies based on game theory that model CN resources, froman overview presented in 2010 by [13] to models of channelselection [14], power allocation [15], or the mixture of both[16].

However, the introduction of intrinsic characteristics ofWSNSs makes it essential to model energy consumptiongames. In addition, games designed for CWSNs should belighter in terms of processing and energy consumption. Inthis area, a game-theory-based energy-efficient approach topower allocation in CWSNs is presented in [17]. Even ifthe approach takes energy efficiency into account, the gamemodels power allocation instead of energy consumption.

Even though the research in this area looks to be veryinteresting, the use of CR to improve energy consumption inWSNs is not a mature research area. Some ideas are given butreal proposals outside of the efficient sensing area or routingprotocols are missing.

3. Assumptions and CWSNS Scenario

CWSNs are based on typical WSNs, improved with sev-eral features provided by cognitive networks. Thus, typicalCWSNs are similar in components, distribution, and behav-ior to WSNs.

In this model, a CWSNS consists of a set𝑁 = {1, 2, . . . , 𝑛}of 𝑛 cognitive wireless sensor nodes which could implementdifferent final applications. Each node can communicate withothers depending on their position and the transmissionrange. A typical CWSN consists of a number of nodes whichcan vary from tens to thousands of devices. These nodes arebattery powered. CWSNS communicate over IEEE 802.15.4specification with rates of up to 250Kbps. However, typicalCWSN rates are lower. Transmission power is limited dueto energy consumption constraints. Nodes could performin transmission mode, reception mode, or standby mode.

International Journal of Distributed Sensor Networks 3

Typical current consumptions are 20mA in transmissionor reception mode and below 1mA in standby mode [18].Moreover, the mode usually described as sensing refers to along-lasting reception mode.

As mentioned before, CWSN nodes communicate on anISM band in coexistence with Wi-Fi or Bluetooth devices.Due to their bandwidth and their transmission power, eachWi-Fi channel can mask up to four 802.15.4 channels whenboth technologies coexist on the 2.4GHz band.

Even if one of the main characteristics of CR is theexistence of primary users (PUs) and secondary users (SUs),in this scenario no distinction shall be made between them.According to their formal definition, PUs are the “owner”of the spectrum band with right to communicate withoutrestrictions, while SUs can use the spectrum if they do notjam PUs. Because of the CWSN use of unlicensed bands, thedefinition in this case refers to the information importance orrelevance. Moreover, the strategy could apply to PUs and SUsimproving their energy consumption in both cases.

4. Game Theory Strategy

As mentioned in Section 1, constrained resources are anintrinsic challenge related to WSNs. The additional com-plexity added to the nodes to enable cognitive capabilitiesmakes nodes have higher energy consumption. Moreover,processing capability of WSN nodes is limited; thus, thestrategies implemented should have low complexity.

There are many new different opportunities for reducingenergy consumption in CWSNs. The proposal presented isto divide the opportunities for energy consumption opti-mization into three groups, namely, those that are obtainedthrough spectrum sensing, those related to the capability tochange transmission parameters, and those that depend onthe ability to share network knowledge. The first two groupsare directly derived from the cognitive capabilities added tothe WSN nodes. However, the third one, related to the coop-eration between devices, is one of the basic characteristics ofWSNs, now enriched with cognitive information.

The proposed strategy addressed in this paper focuseson the ability to change transmission parameters based onsensed information. In addition, this strategy takes advantageof the cooperation in the network to share the information.In this work, a channel shift strategy to prevent unnecessaryretransmissions has been selected. The use of less noisychannels avoids extra retransmissions and makes the globalconsumption reduction of the network possible.

As shown in Section 2, game theory is widely acceptedfor resource optimization in cooperative WSNs, and, now,with cognitive capabilities, it could fit even more. Althoughother approaches to optimize energy consumption such asgenetic algorithms have been explored, their implementationinWSN nodes is expensive in terms of cost in computationalresources and energy consumption [19, 20].

By its intrinsic nature, a sensor-network resource prob-lem can be easilymodeled like a game. In addition, games canbe simplified enough without losing functionality to make

Table 1: Payoff matrix for player 𝑛.

Payoff for node 𝑛 𝑚 changes channel 𝑚 does not changechannel𝑛 changes channel −𝐶

𝑐ℎ−𝐶𝑐ℎ− 𝐶𝑛

𝑛 does not changechannel −𝐶

𝑛−𝐶𝑜

them supported by a WSNS node, even if its processingcapability is limited.

A game is defined by several characteristics. The resourcebeing modeled, the players, their strategies, and the actionsthey can take. Thereon, costs associated with each action willbe defined, and, by combining this with the odds (suspectedor known) of such actions occurring, the payoff matrix andfunction will be obtained.

In the approach described in this paper, the game ismodeled as a finite resource game due to the battery-powerednodes, which provide them with a finite energy. The resourceis the energy available in each node. Players are CWSN nodes𝑁 = {1, 2, . . . , 𝑛} and the strategies are those relating to theselection of the communication channel 𝑆 = {𝑠

1, 𝑠2, . . . , 𝑠

𝑖}.

The feasible actions that each one of the players can carry outare to change or not change the transmission channel. Thisaction can arise from themselves or after a move—request—from another player. Energy consumption is modeled as theresource forwhich players compete.Thus, the payoffs and costsare those energy expenses associated with the actions taken.𝑃𝑛(𝑠) where 𝑛 ∈ 𝑁 is the 𝑛th payoff function.This game can be described as a hybrid game because

although it is noncooperative in game theory terminology,communication between nodes can lead to the commongood. It is a non-zero-sum game, in which there is nocorrelation between one player’s payoffs and another player’slosses. In fact, there may be values that maximize the payoffsof every player. The game is sequential because actions areperformed sequentially. This game is asymmetrical sincepayoffs are different depending on the players. In this casethis dependence refers to the position of the players and thetraffic between them. It is also an evolutionary game, sinceplayers can learn, adapt, and evolve their actions based on theinformation shared and the odds perceived from the rest ofthe nodes.

For the calculation of the payoff matrix of this game,the resulting payoffs coming from the combination of theactions taken by the players (to change or not to change thetransmission channel) are taken into account.

The payoff matrix for player 𝑛 that communicates withplayer 𝑚 is shown in Table 1.

And the payoff matrix for node 𝑛 could be expressed as

𝑃𝑛= (−𝐶𝑐ℎ−𝐶𝑐ℎ− 𝐶𝑛

−𝐶𝑛−𝐶𝑜

) , (1)

where 𝐶𝑐ℎ

is defined as the energy cost associated with achange of the communication channel. It is calculated as theaddition of the extra energy cost associated with the sensingmode (𝐶sensing) and the cost of the transmission (𝐶

𝑡𝑥) and

reception (𝐶𝑟𝑥) caused by the agreement messages needed to


negotiate the channel change (𝑛 msg). Thus, the energy costof the action of change in this case is

𝐶𝑐ℎ= 𝐶sensing + (𝐶𝑡𝑥 + 𝐶𝑡𝑥) ⋅ 𝑛 msg. (2)

𝐶𝑜is the energy cost of transmission in noisy channels. It

is calculated as the cost of a packet transmission taking intoaccount that it requires a number of retransmissions named𝑛 𝑟𝑡𝑥.This 𝑛 𝑟𝑡𝑥depends on the observed and stored numberof retransmissions needed by previous packets and is calcu-lated as the average of the needed message retransmissionsfor the previous 𝑘 (parameterizable) messages:

𝐶𝑜= 𝐶𝑡𝑥⋅ 𝑛 𝑟𝑡𝑥. (3)

𝐶𝑛is the energy cost associated to communications in a chan-

nel not shared with the receiver. Even though this situation isnot very common, it could happen if several CWSNs performthe strategy without agreement.𝐶

𝑛is calculated as the cost of

transmission when the number of retransmission has run outand consequently the maximum allowed has been reached(max 𝑟𝑡𝑥):

𝐶𝑛= 𝐶𝑡𝑥⋅max 𝑟𝑡𝑥. (4)

Naming 𝑥 the odds of node 𝑛 and 𝑦 the supposedprobability of node 𝑚 to take the action to change thecommunication channel and calculating the total payoff ofnode 𝑛 result in

𝑃𝑛= − 𝐶

𝑐ℎ⋅ 𝑥 ⋅ 𝑦 − (𝐶

𝑐ℎ+ 𝐶𝑛) ⋅ 𝑥 ⋅ (1 − 𝑦)

− 𝐶𝑛⋅ (1 − 𝑥) ⋅ 𝑦 − 𝐶

𝑜⋅ (1 − 𝑥) ⋅ (1 − 𝑦) ,

𝑃𝑛= (2 ⋅ 𝐶

𝑛− 𝐶𝑜) ⋅ 𝑥 ⋅ 𝑦 + (𝐶

𝑜− 𝐶𝑐ℎ− 𝐶𝑛) ⋅ 𝑥

+ (𝐶𝑜− 𝐶𝑛) ⋅ 𝑦 − 𝐶

𝑜.

(5)

To determine the optimal value of 𝑥, each node 𝑛 storesthe observed number of accepted and sent requests from itsneighbor nodes. From this stored data it is possible to extractthe supposed probability of change 𝑦. Evaluating𝐶

𝑐ℎ,𝐶𝑛, and

𝐶𝑜at the time of the channel change request, the optimal

value of 𝑥 that maximizes the payoff can be obtained.Applying the maximization criterion of this simple algo-

rithm in every device, it is possible to optimize the consump-tion of each node without impacting the breakdown of othernodes in the network. Thus, by maximizing the lifetime ofeach network node, the lifetime of the network as a wholeis prolonged. Although this statement is not always true, theenergy saved in each node has a positive effect on the overalloperation of the network.

For the implementation of this strategy, it could bepossible to always run the maximization of the payoff inthe background, but in terms of energy conservation andcomputing capabilities it is more efficient to optimize onlywhen the transmission channel is noisy enough. In thisway, the strategy considers that the optimization will betriggered, taking into account other parameters such as theRSSI received in the communication channel, which is relatedto noise presence.

Optimization strategy performs as follows.

(1) Every node in the CWSN receives messages by theassigned channel and saves RSSI samples from eachmessage received.

(2) If the RSSI value saved in node 𝑛 is above a certainthreshold (in a certain number of samples), node 𝑁activates the optimization algorithm that evaluatesthe payoff function to decide if changing the channelis interesting at that moment or not.

(3) If the result of this evaluation is a change, node𝑁 senses the spectrum and chooses the least noisychannel.

(4) Node 𝑁 communicates its decision to the rest of thenetwork nodes and the new chosen channel accordingto its sensing values.

(5) The rest of nodes evaluate this change and decidewhether to change the channel depending on itspayoff function. This decision is communicated backto other nodes in the network.

(6) The value of the stored accepted channel changerequests is updated in order to calculate 𝑦 in futuresituations.

Although in this work the sensing state only involves onenode, this approach could be adapted to any type of sensingdepending on the network features. To demonstrate the valid-ity of this algorithm, only the triggered node is responsible forsensing. However, new collaborative techniques or channelnegotiation in clusters could be included according to thelocation of the nodes.

5. Experimental Results

In this section results of different simulations are presented.First, the simulation tool used to perform them is presented.The baseline scenario and the different scenario configura-tions are shown. Finally, the simulations performed and theresults obtained are presented and discussed.

5.1. Simulation Tools. In this work the architecture of cogni-tivity brokerage framework [21] is used. For simulation resultsthe framework used is composed of two fundamental ele-ments: a CWSN simulator and low power cognitive radio realdevices. This framework [22] has been tested and referencedin previousworks. Both the simulator (based onCastalia) andreal nodes implement the cognitivity brokerage architecturementioned.

The structure of Castalia simulator has been enhancedto provide cognitive features. The simulator can carry outsensing tasks in order to acquire and share spectrum infor-mation.This information may include received signal power,noise power, or time between packets. The information isprocessed, stored, and shared according to the implementedstrategy. A virtual control channel (VCC) also exists to sharesensed information, with no extra overhead over regularcommunications.


The simulator is also responsible for the scenario defini-tion, the simulation of the spectrum state, and the commu-nication between nodes from the physical to the applicationlayer.

Real nodes are used just to confirm, as empirical testing,results for small-scale networks. So that all the results pre-sented in this paper are extracted from the simulator.

5.2. Cognitive Baseline Scenario. The baseline scenario whichcarries out the simulation tests is deployed in a 100m × 10marea, such as an example of a WSN scenario. It contains twocoexisting networks, a CWSN and a Wi-Fi network. In thebaseline scenario 100 Wi-Fi nodes are assumed, but a simu-lation with a varying number of Wi-Fi nodes is performed(from 50 to 200 devices). CWSN results show the energyconsumption of a cognitive device which communicates onlywith the network coordinator (as a WSN star topology).

CWSNs are modeled with a Texas Instrument CC2420transceiver. Values of energy consumption are extracted fromdatasheet (for transmission, reception and idle modes, andenergy costs of transitions between modes) and verifiedthrough experimental measurement. Sensing stage is mod-eled as a reception mode lasting for 200ms.

CWSN nodes transmit commonWSN packets of 50 bytesat −5 dBm while Wi-Fi network transmits the usual Wi-Fipackets of 2000 bytes at −3 dBm. Both networks use ISMband at 2.4GHz. A maximum number of 20 retransmissionsare set for CWSN and Wi-Fi nodes in the baseline scenario.However, it is interesting to check the behavior by varying themaximum number of retransmissions allowed. Therefore, asimulation for this is included.

For the baseline scenario, a RSSI threshold of −150 dBmtaking into account 5 samples is assumed. Nevertheless, asimulation with a different number of samples and a variablethreshold is also considered.

In order to facilitate simulations of different configu-rations, a reduction in simulation 10 times lower in everymagnitude is assumed. For this assumption, a long-termsimulation is performed, showing similar results to thosepresented in the paper. Thus, simulation time is 300 s, andnetwork rates of 1 packet per second on CWSNs and 50packets per second on Wi-Fi are chosen. The spectrumsensing period for CWSNs is 2 s.

In order to simulate new Wi-Fi configurations or theappearance of newWi-Fi networks or nodes in the area, thesesimulated nodes change their communication channel every30 s.

In all the results shown, figures show the energy con-sumption in accumulated Joules over time. For a real ref-erence, typical batteries for CWSNs have a total energy of18,000 J.

5.3. Results and Discussion. In this section results of differentsimulations are discussed. Even if the simulations do not lastas long as the battery life, energy consumption reduction canbe appreciated for enhancing the network lifetime. Presentedresults always show the energy consumption for a CWSNnode.

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Figure 1: Baseline scenario.

For the first simulation, the baseline scenario is simulatedwith three different CWSN optimization strategies. The firstone, marked in red, is a typical WSN without cognitivecapabilities—noCR, whichmust remain on their initial chan-nel even if this channel becomes very noisy. Green line—simpleCR, shows a first strategy approach to the CWSN,where cognitive nodes are able to sense the spectrum andchange their transmission parameters accordingly. This firstapproach to the CWSN senses the spectrum every 2 s andchanges the transmission and reception channel for thewholenetwork. In this way, the least noisy channel is assuredeach 2 s. In the third CWSN strategy—gtCR, the proposedoptimization strategy based on game theory explained inSection 4 is shown in purple. In order to compare energyconsumption, the chosen sensing period is 2 s as well.

Figure 1 shows that even in the first 300 s gtCR strategyprovides energy consumption savings of around 65% com-pared to the noCR scenario. Furthermore, these savings willincrease over time as both noCR and simpleCR have steeperslopes in energy consumption. In relation to simpleCR, gtCRsaves energy consumption by approximately 30% in the first300 s. As in the previous case, these energy savings areincreasing over time.

Figure 2 shows the energy consumed each second insteadof the accumulated consumption over time for the baselinescenario. This figure shows the detailed energy consumptionof the algorithm each time it is triggered.

Looking at the noCR line, energy consumption is lowerthan simpleCRwhenWi-Fi channel does not overlapwith theCWSN one. However, when the channel does coincide (0–30or 90–120 seconds) energy consumption spikes.

Focusing on simpleCR energy consumption is almostregular throughout the simulation.This is due to the fact thatenergy consumption imposed by the sensing state is muchhigher than the energy consumption in transmission andreception modes in an ideal situation (without coexistenceWi-Fi) so the biggest amount of energy consumed comesfrom the sensing state.


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Figure 2: Instantaneous energy consumption for the baselinescenario.

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Figure 3: Baseline scenario with 50 Wi-Fi nodes.

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Figure 4: Baseline scenario with 200 Wi-Fi nodes.

Even though in some cases gtCR increases node process-ing consumption, for the strategy calculation, this energy costis offset by the energy consumption savings by avoiding noisychannels.

The next simulations show the results of varying thenumber of Wi-Fi nodes on the baseline scenario in order tochange noise in the area.These results can be seen in Figure 3,which uses 50 Wi-Fi nodes instead of 100 in the baselinescenario, and Figure 4, with 200Wi-Fi nodes for a very noisyambience.

As can be seen, both simulations are similar in shape andvalues as baseline scenario, so it can be concluded that theproposed algorithm is not influenced by the amount of noisepresent in the scenario.

The next scenariomodifies the RSSI threshold used by thegtCR strategy as a first decision mechanism to show if thisthreshold influences the behavior of the strategy. In Figure 5,the behavior of the algorithm for different decision thresholdsin dBm is shown.

As shown, algorithm performance is also not greatlyinfluenced by the chosen threshold. This is because of thegame-theory-based strategy design which takes into accountsubsequent corrections such as the number of retransmis-sions used to calculate the best moment to change thechannel.

Small changes that are seen in the center values of thefigure are due to the random character of the channel chosenby the Wi-Fi nodes, which makes CWSN nodes take thedecision of change at 90 s or at 180 s depending on the existingnoise in the channel. But energy consumption at 300 s issimilar for every chosen threshold.

The next scenario changes the number of RSSI samplestaken into account in order to calculate the RSSI value. Theintention of these different values is to probe the strength ofthe strategy employed against anomalous measures of RSSI.For the simulation shown in Figure 6, different numbers ofsamples are taken.

The increased value of energy consumption shown inFigure 6 for 10 samples is due to the time waste produced bythe sampling RSSI with every received packet. As the packetrate for the CWSN is 1 packet per second, it must wait longerto obtain more samples, thereby making the algorithm takelonger to react to changes and trigger the game-theory-basedstrategy. Although increasing the number of samples protectsthe algorithm from possible erroneous samples, it is shownthat the reaction time makes the CWSN node remain on anoisy channel longer; thus, the number of retransmissionsincreases, raising its consumption. The number of samplesmust be chosen depending on the WSN application scenarioand the randomness of the noise.

For the next scenario it is interesting to vary the max-imum number of retransmissions allowed by the CWSNnodes, as it is a parameter that depends directly on thedecision to change the channel or not in the game-theory-based strategy design. Figure 7 shows the results.

As can be seen, the greater the maximum number ofretransmissions set, the higher the energy consumptionproduced. In this case, it should reach a compromise betweenconsumption and network reliability depending on the final


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Figure 5: Scenario varying RSSI threshold.

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Figure 6: Scenario varying number of RSSI samples.

application of the WSN or the importance of the datatransmitted by the nodes.

One of the most interesting questions is how this strat-egy evolves depending on the initialization values of theprobability of accepting the changes request 𝑦 stored forthe rest of the network nodes. This evolution could showif the strategy could adapt to real behavior or, instead,relies heavily on initialization values. In this case severalexperiments that include initialization values from 0 to 100%of change requests accepted are performed showing thefollowing results.

Figure 8 shows that values from 0 to 5% demonstratevalues similar to noCR techniques in energy consumption,shown in Figure 1. Moreover, 80% to 100% values are exactlythe same, as are 50% to 60% and 20% to 40%.

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Figure 7: Scenario varying number of rtx.

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Figure 8: Scenario varying init probY.

In any case, it is shown that initialization values between20% and 100% are quite similar in terms of long-term powerconsumption, indicating that the algorithm, regardless of itsinitialization, moves rapidly toward a stationary situationbased on the sensed spectrum around the node and thebehavior of the rest of the devices.

To summarize results, the algorithm shows improvementrates of over 65% compared to WSNs without cognitivetechniques and over 30% compared to sensing strategies forchanging channels based on a decision threshold. For thedependence of the values used in the payoff function, resultsare shown in Table 2.

6. Conclusions

WSNs are shown as one of the most important trendsin wireless communication. Energy consumption became


Table 2: Results summary.

Parameter Dependence FactorsNumber of nodes NoRSSI threshold No

RSSI samples YesApplicationData rate

Sensing period

Number of rtx Yes Networkreliability

Init probY No (above 20%)

an important problem to face in typical WSN applicationbecause of the use of batteries. The introduction of CNfeatures opens up new interesting research challenges rangingfrom CR capabilities to WSN intrinsic features.

In this paper, a new strategy based on game theoryfor reducing energy consumption in CWSNs has been pre-sented. This is a light optimization algorithm that enablesits implementation in CWSNs although the nodes com-puting resources are limited. The strategy is applicable inconjunction with other energy consumption optimizations.Thisway the results can be further improved by incorporatingrouting protocols that have been proven efficient or MACimplementations for low consumption.

The developed algorithm has been tested on a frame-work based on Castalia adapted to incorporate cognitivecapabilities. As seen in the results section, the algorithmshows improvement rates of over 65% compared to WSNswithout cognitive techniques and over 30% compared tosensing strategies for changing channels based on a decisionthreshold.

It can also be seen that the algorithm behaves similarlyevenwith significant variations in the number of noisy nodes.Likewise, RSSI decision threshold and the number of samplestaken into account for their calculation do not influencethe operation of the algorithm. In regard to the numberof samples, the only relationship arises from taking a largenumber of samples, which increases the consumption as thenode keeps in the noisy channel for too long.

Concerning the maximum number of retransmissionsallowed for CWSN nodes, energy consumption increasesalong with them but is caused by the retransmissions itselfand completely unrelated to the algorithm. In this case, acompromise should be reached between consumption andnetwork reliability. The initialization value of the odds ofchange from other nodes does not significantly affect theperformance of the algorithm for values above 20%, sincethis probability evolves based on the noise and not on itsinitialization.

Reducing energy consumption in WSNs is an interestingfield in which there is much work to be done. CWSNsintroduce new features to take advantage of and also newchallenges to face. It would be interesting to see how thisalgorithm behaves on a larger network or a comparison withother game models.

Conflict of Interests

The authors declare that there is no conflict of interestsregarding the publication of this paper.

Acknowledgment

This work was funded by the Spanish Ministry of Economyand Competitiveness, under INNPACTO Program (Refer-ence Grant IPT-2011-1197-370000, PRECOIL; IPT-2011-1241-390000, LINEO; IPT-2011-0782-900000, S2S).

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