基于改进蝙蝠算法的无线分簇路由算法
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摘要
针对无线传感器网络节点能量有限的问题,给出了一种基于改进蝙蝠算法的分簇路由算法。利用K-means聚类算法通过迭代过程初始化蝙蝠种群,使种群更均匀地分布在监测区域中;为了使蝙蝠个体跳出局部最优解,避免早熟收敛,采用控制概率判断算法进行高斯变异。该算法结合传感器节点本身剩余能量和到基站距离建立适应度函数,通过改进蝙蝠算法实现适应度函数的最优求解,从而获得合适的分簇,簇头节点间采用多跳动态路由。仿真结果表明,使用本文提出的无线分簇路由算法使得所有节点死亡时间明显推迟,能有效提高传感器节点能量的利用率,降低网络能耗,延长整个网络的生存周期。
In accordance with the problem that the wireless sensor network node's energy is limited. A clustering routing algorithm based on improved bat algorithm is presented. K-means clustering algorithm was put forward to use the iterative procedure to initialize the bat population to make the population distribution more homogeneous in the monitor space and the control probability judgment algorithm was used to make gauss variation to make individual bat out of local optima and avoid premature convergence. A fitness function was set up combined the residual energy of the sensor nodes and the distance between the sensor nodes and the base station. The optimal solution of the fitness function was realized by the improved bat algorithm and the suitable clustering was gained. The multi hop dynamic routing was used among the cluster nodes. The simulation result shows that the wireless clustering routing algorithm put forward in this paper make the death time of all nodes obviously delayed, effectively improve the energy utilization of sensor nodes, reduce the network energy consumption and prolong the network life cycle.
In accordance with the problem that the wireless sensor network node's energy is limited. A clustering routing algorithm based on improved bat algorithm is presented. K-means clustering algorithm was put forward to use the iterative procedure to initialize the bat population to make the population distribution more homogeneous in the monitor space and the control probability judgment algorithm was used to make gauss variation to make individual bat out of local optima and avoid premature convergence. A fitness function was set up combined the residual energy of the sensor nodes and the distance between the sensor nodes and the base station. The optimal solution of the fitness function was realized by the improved bat algorithm and the suitable clustering was gained. The multi hop dynamic routing was used among the cluster nodes. The simulation result shows that the wireless clustering routing algorithm put forward in this paper make the death time of all nodes obviously delayed, effectively improve the energy utilization of sensor nodes, reduce the network energy consumption and prolong the network life cycle.
引文
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[3]刘健苗,许新忠,黄书广,等.改进的分布式无线传感器网络多维标度定位算法[J].传感技术学报,2014,27(5):692-697.
[4]Kulkarni R V,Forster A,Venayagamoorthy G K.Computa tional intelligence in wireless sensor network:A survey[J]Communications Surveys&Tutorials,IEEE,2011,13(1)68-96.
[5]Heinzelman W,Chandrakasan A,Balakrishnan H.An app lication-specific protocol architecture for wireless microse nsor networks[J].IEEE Transactions on Wireless Communic ations,2002,1(4):660-670.
[6]宋文.无线传感器网络技术与应用[M].北京:电子工业出版社,2007.
[7]孙利民,李建中,陈渝.无线传感器网络[M].北京:清华大学出版社,2005.
[8]任丰原,黄海宁,林闯.无线传感器网络[J].软件学报,2003,14(7):1282-1291.
[9]陈志军,李明.基于免疫退火的WSN簇首节点选择方法研究[J].重庆师范大学学报(自然科学版),2014,02(31):72-76.
[10]何延杰,李腊元,邢明彦.WSN中一种能量均衡的分簇路由协议的设计[J].传感技术学报,2009,22(10):1510-1514.
[11]刘园莉,李腊元,卢迪.节能的无线传感器网络分簇路由协议的研究[J].传感技术学报,2010,23(12):1792-1797.
[12]韩万强,刘云.WSN中基于分簇的改进路由协议[J].计算机工程,2012,38(5):105-107.
[13]郭剑,孙力娟,王汝传.基于最佳簇数的无线传感器网络粒子群分簇协议[J].南京邮电大学学报:自然科学版,2010,30(2):36-40.
[14]Ye Mao,Li Chengfa,Chen Guihai,et al.EECS:an energy efficient clustering scheme in wireless sensor networks[J].Journal of Frontiers of Computer Science&Technology,2007,3(2-3):535-540
[15]Li Chengfa,Ye Mao,Chen Guihai,et al.An energy-efficient unequal clustering mechanism for wireless sensor networks:MASS 2005,2005[C].Washington:IEEE Press,2005.
[16]Tashtarian F,Haghighat A T,Honary M T,et al.A new energy-efficient clustering algorithm for wireles sensor networks:Soft COM 2007,2007[C].Split:IEEE Press,2007.
[17]王宏岩,李英顺,王德彪,等.低能耗和低时延的无线传感器网络数据融合算法[J].电子设计工程,2013,21(7):47-50.
[18]Yang X S,Gandomi A H.Bat algorithm:a novel approach for global engineering optimization[J].Neural Computing&Applications,2013,22(6):1239-1225.