引用本文:代亮,沈中,常义林,张颖,闫中江.用可分负载理论分析无线传感器网络任务调度[J].控制理论与应用,2011,28(9):1119~1129.[点击复制]
DAI Liang,SHEN Zhong,CHANG Yi-lin,ZHANG Ying,YAN Zhong-jiang.Analysis of task scheduling in wireless sensor networks based on divisible load theory[J].Control Theory and Technology,2011,28(9):1119~1129.[点击复制]
用可分负载理论分析无线传感器网络任务调度
Analysis of task scheduling in wireless sensor networks based on divisible load theory
摘要点击 2601  全文点击 1355  投稿时间：2009-11-11  修订日期：2010-12-07
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DOI编号  10.7641/j.issn.1000-8152.2011.9.CCTA091427
  2011,28(9):1119-1129
中文关键词  无线传感器网络  分群结构  任务调度  可分负载理论
英文关键词  wireless sensor networks  clustering structure  task scheduling  divisible load theory
基金项目  国家自然科学基金资助项目(60972047); 高等学校学科创新引智计划资助项目(B08038).
作者单位E-mail
代亮* 长安大学 电子与控制工程学院 ldai1981@gmail.com 
沈中 西安电子科技大学 综合业务网理论与关键技术国家重点实验室  
常义林 西安电子科技大学 综合业务网理论与关键技术国家重点实验室  
张颖 西安电子科技大学 综合业务网理论与关键技术国家重点实验室  
闫中江 西安电子科技大学 综合业务网理论与关键技术国家重点实验室  
中文摘要
      针对无线传感器网络的任务如何在最短时间内完成且充分利用网络资源的问题, 提出了一种基于可分负载理论的无线传感器网络任务调度算法(WDTA). 该算法根据网络中各个节点的处理能力和节点间的通信能力, 将总任务从SINK节点下发至网络中. 通过去除节点间的通信干扰来提高资源利用率和减少总任务完成时间. 算法在两种分群结构的异构网络环境下进行了分析, 得到了在各个节点上最合理的任务分配方案, 以及最短的任务完成所需时间. 理论分析了基于可分负载理论的无线传感器网络任务调度的极限情况. 实验结果表明WDTA算法能够通过合理分配任务, 而减少任务完成时间及节点能耗. 该方案可以作为设计大规模无线传感器网络的原则.
英文摘要
      To accomplish the tasks within a possibly shortest time, and fully use network resources, a task scheduling algorithm(WDTA) based on divisible load theory is proposed under clustering structure in wireless sensor networks. In WDTA, the tasks are distributed to wireless sensor network based on the processing and communication capacity of each sensor by SINK. By removing communication interference between each sensor, this algorithm achieves the reduction of the task-accomplishment time and improves the network resource utilization. Two kinds of heterogeneous clustering network settings are then analyzed in WDTA. The most reasonable scheme for task allocation in all nodes and the shortest time required to accomplish the tasks are also obtained. The limit case of task scheduling in wireless sensor networks is theoretically analyzed. Finally, simulation results show that the algorithm can reasonably distribute tasks to each sensor, and then effectively reduces the time-consumption and energy-consumption in task accomplishment. This scheme can be used as the design principle for large-scale wireless sensor networks.