泊松分布下无线传感器网络多目标覆盖控制
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收稿日期:20130118
;修回日期:20130224。
基金项目:北京市教育委员会共建项目。
作者简介:
徐奕昕(1990-)，女，江西景德镇人，硕士研究生，主要研究方向：无线传感器网络； 白焰(1954-)，男，辽宁沈阳人，教授，博士生导师，主要研究方向：智能系统、工业现场总线、无线传感器网络； 赵天阳(1989-)，男，河南南阳人，硕士研究生，主要研究方向：电动汽车、电力系统分析； 王仁书(1986-)，男，福建福州人，博士研究生，主要研究方向：无线技术在工业控制系统中的应用。
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(IDEMO)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，IDEMO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。
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
中图分类号: TP393.071文献标志码:A
英文标题
Multiobjective coverage control in wireless sensor network based on Poisson distribution
英文作者名
XU Yixin1*, BAI Yan1, ZHAO Tianyang2, WANG Renshu1
英文地址(
1. School of Control and Computer Engineering, North China Electric Power University， Beijing 102206, China;
2. State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China英文摘要)
Abstract:
A multiobjective optimization coverage control was proposed for solving the intractable problem of kcoverage 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 multiobjective differential evolution algorithm，the author designed tactics of swarm orthogonal initialization, parameter selfadaptive control and dynamic swarm maintenance strategy separately, and an improved multiobjective differential evolutional algorithm (IDEMO) was proposed to solve this model. The results show that the control strategy can effectively achieve the threecoverage 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 multiobjective problems.
A multiobjective optimization coverage control was proposed for solving the intractable problem of kcoverage rate, energy consumption and reliability in wireless sensor networks on the assumption that node was passion distribution. Aiming to overcome shortcomings of population initialization，parameter control and population maintenance in multiobjective differential evolution algorithm，the author designed tactics of swarm orthogonal initialization, parameter selfadaptive control and dynamic swarm maintenance strategy separately, and an improved multiobjective differential evolutional algorithm(IDEMO) was presented to solve this model. The result shows that the control strategy can effectively achieve the 3coverage rate of 81.2% and reduce the energy consumption effectively ensuring reliability in the meantime. This algorithm can dominate 76% of the traditional algorithm Pareto front and be applied to the solution of other multiobjective problems.