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基于博弈論能耗均衡的無(wú)線傳感網(wǎng)絡(luò)路由算法
2017年電子技術(shù)應(yīng)用第7期
朱亞?wèn)|1,高翠芳2
1.江蘇聯(lián)合職業(yè)技術(shù)學(xué)院 信息中心,江蘇 南京211135;2.江南大學(xué) 理學(xué)院,江蘇 無(wú)錫214112
摘要: 為了平衡能量消耗,延長(zhǎng)網(wǎng)絡(luò)壽命,提出基于博弈論能耗均衡的無(wú)線傳感網(wǎng)絡(luò)路由算法——EGT-EBGR。EGT-EBGR路由的目的是使節(jié)點(diǎn)能耗均衡,進(jìn)而延長(zhǎng)網(wǎng)絡(luò)壽命。首先,將發(fā)送節(jié)點(diǎn)的傳輸范圍劃分幾個(gè)轉(zhuǎn)發(fā)子區(qū)域,然后再結(jié)合進(jìn)化博弈論EGT(Evolutionary Game Theory),從平衡負(fù)載角度,從轉(zhuǎn)發(fā)子區(qū)域內(nèi)選擇一個(gè)轉(zhuǎn)發(fā)子區(qū)域,再利用貪婪算法從此轉(zhuǎn)發(fā)子區(qū)域內(nèi)選擇一個(gè)節(jié)點(diǎn)作為下一跳的轉(zhuǎn)發(fā)節(jié)點(diǎn)。通過(guò)進(jìn)化博弈論和貪婪算法GA(Greedy Algorithm)平衡負(fù)載,縮短傳輸距離,有效地降低地能量消耗速度,進(jìn)而延長(zhǎng)網(wǎng)絡(luò)壽命。仿真數(shù)據(jù)表明,提出的EGT-EBGR協(xié)議能夠有效地平衡能量消耗,擴(kuò)延了網(wǎng)絡(luò)壽命。
中圖分類號(hào): TN925
文獻(xiàn)標(biāo)識(shí)碼: A
DOI:10.16157/j.issn.0258-7998.2017.07.029
中文引用格式: 朱亞?wèn)|,高翠芳. 基于博弈論能耗均衡的無(wú)線傳感網(wǎng)絡(luò)路由算法[J].電子技術(shù)應(yīng)用,2017,43(7):114-116,126.
英文引用格式: Zhu Yadong,Gao Cuifang. Energy-balanced routing algorithm based on Game-Theory for WSNs[J].Application of Electronic Technique,2017,43(7):114-116,126.
Energy-balanced routing algorithm based on Game-Theory for WSNs
Zhu Yadong1,Gao Cuifang2
1.Information Center,Jiangsu Union Technical Institute,Nanjing 211135,China; 2.School of Science,Jiangnan University,Wuxi 214112,China
Abstract: To extend the network lifetime by balancing energy consumption, evolutionary game theory-based energy balance geographical routing(EGT-EBGR) protocol is proposed in this paper. The objective of the proposed protocol is to make sensor nodes deplete their energy at approximately the same time. The transmission range of a sender is divided into serval forwarding sub-regions, evolutionary game theory(EGT) is used to balance the traffic load to available sub-regions. Greedy algorithm(GA) is used to select the best node to balance the load in the selected sub-region. This EGT and GA is shown to be an effective solution for load balancing and extending network lifetime. Simulation results show that EGT-EBGR protocol offers significant improvement over existing protocols in extending network lifetime.
Key words : WSNs;routing;energy balance;evolutionary game theory;Greedy

0 引言

    提高節(jié)點(diǎn)能量利用率、擴(kuò)延網(wǎng)絡(luò)壽命成為無(wú)線傳感網(wǎng)絡(luò)(Wireless Sensor Networks,WSNs)的研究熱點(diǎn)[1]。通過(guò)協(xié)調(diào)節(jié)點(diǎn)間通信來(lái)平衡網(wǎng)絡(luò)能量消耗,是提高網(wǎng)絡(luò)壽命最為有效的技術(shù)之一[2-3]。在這些技術(shù)中,路由決策起著重要作用,因?yàn)槁窂降倪x擇直接影響到節(jié)點(diǎn)能量消耗[4-5]。

    由于地理路由協(xié)議(Geographical Routing Protocols,GRPs)無(wú)需建立路由表,也無(wú)需進(jìn)行路由發(fā)現(xiàn)和路由維護(hù),使得GRPs非常適用于無(wú)線傳感網(wǎng)絡(luò)。典型的地理路由協(xié)議有GPSR(Greedy Perimeter Stateless Routing)[6]、GOAFR[7]、GRR[8]、GAR[9]、BVGF[10]、GEAR(Geographical and Energy Aware Routing)[11]、OVCR[12]、VAA[13]。地理路由協(xié)議GRPs的不足之處在于它沒(méi)有從全局考慮網(wǎng)絡(luò)信息,對(duì)于無(wú)線傳感網(wǎng)絡(luò)而言,能量利用率是非常重要的性能指標(biāo)[14]。

    為此,本文針對(duì)地理路由協(xié)議GRPs的特性及其不足,利用進(jìn)化博弈理論(Evolutionary Game Theory,EGT),平衡了網(wǎng)絡(luò)能量消耗。通過(guò)EGT建立平衡能量消耗的方案,進(jìn)而擴(kuò)延網(wǎng)絡(luò)壽命。此外,EGT能夠在全局信息未知的環(huán)境下進(jìn)行正確的決策。

1 EGT-EBGR算法

    EGT-EBGR算法目的是平衡網(wǎng)絡(luò)能量消耗,使得節(jié)點(diǎn)的能量消耗速度相近。依據(jù)節(jié)點(diǎn)密度,源節(jié)點(diǎn)S將其傳輸范圍劃分為K個(gè)子區(qū)域。首先利用基于EGT的區(qū)域選擇算法(EGT-based Regions Selection,EGT-RS)選擇下一個(gè)轉(zhuǎn)發(fā)子區(qū)域,然后再利用貪婪地理算法選擇轉(zhuǎn)發(fā)節(jié)點(diǎn)。

    如圖1所示,源節(jié)點(diǎn)S將它向目的節(jié)點(diǎn)D的傳輸方向的鄰居節(jié)點(diǎn)劃分了4個(gè)區(qū)域,分別為R1、R2、R3、R4。然后,利用EGT-RS算法,為當(dāng)前數(shù)據(jù)包選擇了一個(gè)轉(zhuǎn)發(fā)區(qū)域。假定選擇了R2作為當(dāng)前數(shù)據(jù)包的轉(zhuǎn)發(fā)區(qū)域,最后,再在R2區(qū)域,利用貪婪轉(zhuǎn)發(fā)算法選擇離目的節(jié)點(diǎn)D最近的節(jié)點(diǎn)作為轉(zhuǎn)發(fā)節(jié)點(diǎn)。

tx5-t1.gif

1.1 基于EGT的區(qū)域選擇算法EGT-RS

tx5-1.1-x1.gif

    復(fù)制動(dòng)態(tài)在每個(gè)博弈理論間隔進(jìn)化一個(gè)新的數(shù)據(jù)包分布矢量[16],不斷進(jìn)化,直到得到最優(yōu)的分布矢量X*。實(shí)際上,計(jì)算分布矢量X*的關(guān)鍵在于設(shè)計(jì)適度函數(shù)FF(Fitness Function),適度函數(shù)Fk(X)的定義如下:

    tx5-gs1.gif

其中Etr、Etx分別節(jié)點(diǎn)接收、發(fā)送一個(gè)數(shù)據(jù)包所需的能量。

1.2 復(fù)制動(dòng)態(tài)

    從子區(qū)域l到子區(qū)域k的切換概率Pk,l(X),其與兩個(gè)子區(qū)域l、k的適度函數(shù)相關(guān),如式(2)所示。

tx5-gs2-3.gif

    從子區(qū)域k到其他所有子區(qū)域的轉(zhuǎn)換概率之和應(yīng)等于1:

    tx5-gs4.gif

    因此,復(fù)制動(dòng)態(tài)的差異值反映了子區(qū)域k的流入和流出的數(shù)據(jù)包凈差:

     tx5-gs5-6.gif

    因此,對(duì)于僅有兩個(gè)子區(qū)域的場(chǎng)景,利用式(2),可計(jì)算過(guò)渡概率矩陣P:

 tx5-gs7-8.gif

    當(dāng)所有子區(qū)域的流入和流出數(shù)據(jù)包相等時(shí),系統(tǒng)就到達(dá)穩(wěn)定狀態(tài)。

1.3 進(jìn)化均衡

tx5-gs9-13.gif

2 性能分析

    利用OMNeT++4.22網(wǎng)絡(luò)仿真器建立仿真平臺(tái),仿真參數(shù)如表1所示。傳感節(jié)點(diǎn)隨機(jī)分布于二維的100×100 m2區(qū)域。

tx5-b1.gif

    提出的EGT-EBGR協(xié)議與3種隨機(jī)選擇方案進(jìn)行比較。這3種隨機(jī)選擇方案分別為:(1)隨機(jī)+隨機(jī)(Random+Random):表示隨機(jī)選擇轉(zhuǎn)發(fā)區(qū)域,并且也隨機(jī)地選擇轉(zhuǎn)發(fā)節(jié)點(diǎn);(2)(EGT-RS+Random):利用EGT-RS算法選擇轉(zhuǎn)發(fā)區(qū)域,然后再?gòu)霓D(zhuǎn)發(fā)區(qū)域內(nèi)隨機(jī)地選擇轉(zhuǎn)發(fā)節(jié)點(diǎn);(3)隨機(jī)+GA(Random+GA):隨機(jī)地選擇轉(zhuǎn)發(fā)區(qū)域,然后再利用貪婪算法從區(qū)域內(nèi)選擇轉(zhuǎn)發(fā)節(jié)點(diǎn)。

2.1 網(wǎng)絡(luò)壽命

    本次實(shí)驗(yàn)中,數(shù)據(jù)包產(chǎn)生率為2 packets/s,節(jié)點(diǎn)數(shù)從120~520變化,仿真結(jié)果如圖2所示。

tx5-t2.gif

    從圖2可知,網(wǎng)絡(luò)壽命隨節(jié)點(diǎn)數(shù)的增加呈上升趨勢(shì)。正如預(yù)期的,Random+Random方案的壽命最短,依次為Random+GA、EGT-RS+Random,而提出的EGT-EBGR協(xié)議最高。原因在于EGT-RS+Random方案利用EGT-RS算法選擇轉(zhuǎn)發(fā)區(qū)域,平衡網(wǎng)絡(luò)能量消耗速度。此外,從圖1可知,提出的EGT-EBGR協(xié)議的網(wǎng)絡(luò)壽命比Random+Random、EGT-RS+Random分別提高了近38%、9%。

2.2 平均每個(gè)數(shù)據(jù)包的能量消耗

    本次實(shí)驗(yàn)分析向目的節(jié)點(diǎn)傳輸一個(gè)數(shù)據(jù)包所消耗的平均能量,實(shí)驗(yàn)數(shù)據(jù)如圖3所示。從圖3可知,提出的EGT-EBGR的能量消耗比Random+Random下降了約64%。原因在于:EGT-EBGR協(xié)議中的每個(gè)節(jié)點(diǎn)利用納什均衡做出最優(yōu)的轉(zhuǎn)發(fā)決策,從能量均衡角度選擇轉(zhuǎn)發(fā)區(qū)域,而隨機(jī)選擇增加了能量消耗。

tx5-t3.gif

3 結(jié)論

    針對(duì)無(wú)線網(wǎng)絡(luò)路由問(wèn)題,本文提出了基于博弈論能耗均衡的無(wú)線傳感網(wǎng)絡(luò)路由算法EGT-EBGR。EGT-EBGR算法通過(guò)平衡網(wǎng)絡(luò)能量消耗,提高網(wǎng)絡(luò)壽命。EGT-EBGR首先將數(shù)據(jù)包攜帶節(jié)點(diǎn)的傳輸范圍劃分幾個(gè)轉(zhuǎn)發(fā)子區(qū)域,然后再利用進(jìn)化博弈算法,從中選擇一個(gè)子區(qū)域作為轉(zhuǎn)發(fā)區(qū)域,再?gòu)倪x擇的子區(qū)域內(nèi),利用貪婪算法選出下一跳轉(zhuǎn)發(fā)節(jié)點(diǎn)。仿真結(jié)果表明,提出的EGT-EBGR協(xié)議的網(wǎng)絡(luò)壽命比隨機(jī)選擇下一跳轉(zhuǎn)發(fā)節(jié)點(diǎn)(Random+Random)高了近38%,能量消耗下降了64%。

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作者信息:

朱亞?wèn)|1,高翠芳2

(1.江蘇聯(lián)合職業(yè)技術(shù)學(xué)院 信息中心,江蘇 南京211135;2.江南大學(xué) 理學(xué)院,江蘇 無(wú)錫214112)

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