陈树, 王明佳. 一种新的多级能量异构WSNs簇优化算法[J]. 微电子学与计算机, 2016, 33(9): 42-46.
引用本文: 陈树, 王明佳. 一种新的多级能量异构WSNs簇优化算法[J]. 微电子学与计算机, 2016, 33(9): 42-46.
CHEN Shu, WANG Ming-jia. A New Clustering Optimization Algorithm for Multi-level Energy Heterogeneous Wireless Sensor Networks[J]. Microelectronics & Computer, 2016, 33(9): 42-46.
Citation: CHEN Shu, WANG Ming-jia. A New Clustering Optimization Algorithm for Multi-level Energy Heterogeneous Wireless Sensor Networks[J]. Microelectronics & Computer, 2016, 33(9): 42-46.

一种新的多级能量异构WSNs簇优化算法

A New Clustering Optimization Algorithm for Multi-level Energy Heterogeneous Wireless Sensor Networks

  • 摘要: 传统多级能量异构无线传感网络(Wireless Sensor Networks, WSNs)的簇优化问题一般只是对簇头选举过程进行优化, 没有对簇的生成过程进行优化.对应簇的生成和运行两个阶段, 分别进行簇生成和簇头选择两级优化.第一级优化采用模糊c均值(Fuzzy c-Means, FCM)算法对传感网络节点进行簇划分, 然后综合簇内节点的剩余能量、簇内节点之间的距离和节点到基站的距离三个方面, 构建簇头选择数学模型, 进行二级优化.仿真结果表明, 运用该算法的无线传感网络能均衡网络节点能耗, 延长了网络的生命周期, 比DEEC、TDEEC协议更加适用于多级能量异构WSNs.

     

    Abstract: The cluster optimization for traditional multi-level energy wireless sensor networks is generally only for cluster head election process optimization without the cluster generation process optimization. Corresponding to two stages including the formation and operation of the clusters, we optimize the formation and election of the clusters with two levels. The first level optimization is organizing the nodes in the network into clusters using Fuzzy c-Means algorithm, then we make the second optimization with the construction of mathematical model for the election of cluster heads considering the residual energy of the nodes, the distance between the nodes in the clusters and the distance between the nodes and the base station. Simulation result shows that the energy consumption of the nodes in the network is balanced and the lifetime of the network is prolonged with the algorithm. The protocol with the algorithm is more suitable than DEEC and TDEEC protocols for multi-level energy heterogeneous wireless sensor networks.

     

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