泊松分布下无线传感器网络多目标覆盖控制  

Multi-objective coverage control in wireless sensor network based on Poisson distribution

作　　者：徐奕昕[1] 白焰[1] 赵天阳[2] 王仁书[1] 

机构地区：[1]华北电力大学控制与计算机工程学院,北京102206 [2]华北电力大学新能源电力系统国家重点实验室,北京102206

出　　处：《计算机应用》2013年第7期1820-1824,1832,共6页journal of Computer Applications

基　　金：北京市教育委员会共建项目

摘　　要：针对无线传感器网络中k重覆盖率、能耗、可靠性难以协调的问题,在节点呈泊松分布的假设下,提出了多目标优化的覆盖控制。针对多目标差分进化算法在种群初始化、参数控制和种群维护中的不足,分别设计了种群正交初始化、参数自适应控制和动态种群维护策略,提出了改进的多目标差分进化(I-DEMO)算法对模型进行求解。仿真结果表明,该控制策略能够在达到81.2%的3重覆盖率的同时有效降低能耗并保障可靠性,I-DEMO可以支配传统算法76%的Pareto前沿。该算法同样适用于求解其他多目标问题。A multi-objective optimization coverage control was proposed for solving the intractable problem of k-coverage rate, energy consumption and reliability in wireless sensor networks on the assumption that nodes are in Poisson distribution. In order to overcome the shortcomings of population initialization,parameter control and population maintenance in multi-objective differential evolution algorithm,the author designed tactics of swarm orthogonal initialization, parameter self-adaptive control and dynamic swarm maintenance strategy separately, and an improved multi-objective differential evolutional algorithm （I-DEMO） was proposed to solve this model. The results show that the control strategy can effectively achieve the three-coverage rate of 81.2%, reduce the energy consumption effectively, and ensure the reliability. This algorithm can dominate 76% Pareto fronts of the traditional algorithm and be applied to the solution of other multi-objective problems.

关 键 词：无线传感器网络 泊松分布 k重覆盖率 能耗 可靠性 多目标差分进化算法 

分 类 号：TP393.071[自动化与计算机技术—计算机应用技术]