| 引用本文: | 陈延军,潘泉,梁彦,李小偎.基于信息量的分布式协同自组织算法[J].控制理论与应用,2011,28(10):1391~1398.[点击复制] |
| CHEN Yan-jun,PAN Quan,LIANG Yan,LI Xiao-wei.Decentralized collaborative self-organization algorithm based on information content[J].Control Theory and Technology,2011,28(10):1391~1398.[点击复制] |
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| 基于信息量的分布式协同自组织算法 |
| Decentralized collaborative self-organization algorithm based on information content |
| 摘要点击 2013 全文点击 2273 投稿时间：2010-08-05 修订日期：2010-11-04 |
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| DOI编号 10.7641/j.issn.1000-8152.2011.10.CCTA100901 |
| 2011,28(10):1391-1398 |
| 中文关键词 无线传感器网络 协同自组织 信息滤波 目标跟踪 |
| 英文关键词 wireless sensor networks collaborative self-organization information filter target tracking |
| 基金项目 国家自然科学基金重点资助项目(60634030); 国家自然科学基金资助项目(60702066); 航天科技创新基金资助项目(CASC0214). |
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| 中文摘要 |
| 无线传感器网络中节点协同自组织主要涵盖传感器管理和状态估计, 也就是如何选择传感器节点、设置传感器参数并估计被监测系统的状态, 因此协同自组织为决策与估计的联合优化. 本文提出了一种自适应动态协同自组织算法, 以量测所提供的信息量和节点自身的剩余能量做为节点选择的综合指标, 根据设定的感知精度, 自适应地选择参与感知任务的节点集合, 在信息滤波的融合框架下完成状态的分布式估计. 相比信息驱动传感器查询(information-driven sensor querying, IDSQ), 算法具有精度可调、强鲁棒, 同时尽可能地延长了网络的生命周期. 以目标跟踪为应用背景, 其仿真结果表明: 以跟踪精度、失跟率和网络生命周期作为评价指标, 该算法优于IDSQ. |
| 英文摘要 |
| Collaborative self-organization of sensor nodes in wireless sensor networks involves sensor management and state estimation, which include the selection of sensor nodes, the configuration of sensors, and the estimation of the states of the inspected system. Thus, this collaborative self-organization performs the joint optimization of decision and estimation. We propose an adaptive dynamic collaborative self-organization algorithm, in which the sensors are selected based on the composite index of the measured information and the residual energy of the sensor node. Given the desired accuracy by the end user, the optimal set of sensors involved in the sensing task is chosen adaptively and instantly. Then, the measured information from selected sensors is fused under the frame of information filter. Compared with the method of information-driven sensor querying(IDSQ), this technique is more advantageous in the adjustable accuracy, the robustness and the network lifetime. When this algorithm is applied to the target tracking, the simulation results validate the superiority of this algorithm to IDSQ in tracking accuracy, the percentage of missing tracking and the lifetime of the network. |
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