无线传感网络任务分配的能效性控制策略  

Energy-efficiency task allocation control strategy in wireless sensor networks

作　　者：王雪[1] 王晟[1] 马俊杰[1] 

机构地区：[1]清华大学精密仪器与机械学系精密测试技术及仪器国家重点实验室,北京100084

出　　处：《控制理论与应用》2007年第3期490-493,共4页Control Theory & Applications

基　　金：国家重点基础研究发展计划973资助项目(2006CB303000);国家自然科学基金资助项目(60673176;60373014;50175056).

摘　　要：无线传感网络包含大量密集分布传感节点,各节点测量产生大量数据给传输、存储、管理和分析带来困难,无线传感网络能源不可更换性限制了网络寿命.本文提出基于熵理论和欧式距离的网络能耗评价指标,采用对等(peer-to-peer,简称P2P)计算方法,利用基于蚁群智能的能效性优化任务分配控制策略,针对中心节点工作状态、传输能耗和网络寿命实现动态实时任务控制分配,完成多中心节点并行计算,提高网络工作效率,节约能耗.实验表明基于蚁群智能的能效性任务分配控制策略能实时有效地缩短无线传感网络计算时间,减少网络能耗,提高网络寿命.Wireless sensor networks （WSNs） consist of many densely deployed wireless sensor nodes. A lot of realtime data stream produced by sensor nodes is difficult to transport, store, manage and analyze. Strict constraints, such as limited on-board battery power and limited network communication bandwidth, restrict the lifetime of WSNs. A peer-topeer （P2P） multi-sink hierarchical structure is presented in this paper. Several practically feasible measures of execution time and energy consumption are introduced, which are based on entropy theory and Euclidian distance. Ant colony optimization （ACO） is then imported for realizing dynamic real-time allocation according to energy consumption, lifetime and the states of each individual node. Because of the advantages in parallelism and robustness, ACO has high-speed regional convergence and efficient global searching ability, especially for complex problem. The experimental results also verify that energy-efficiency task allocation control strategy with ACO can decrease the execution time and energy consumption, improve efficiency, reduce the requirement of bandwidth and prolong the lifetime of WSNs.

关 键 词：无线传感网络 蚁群智能 任务分配 网络能效性 

分 类 号：TP212.9[自动化与计算机技术—检测技术与自动化装置] TN929.5[自动化与计算机技术—控制科学与工程]