面向节点能量消耗的无线传感器网络故障管理方法研究
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摘要
由于无线传感器网络（Wireless Sensor Networks，WSNs）节点具有能量供应有限、硬件资源有限、节点数目众多、网络自组织和动态拓扑等特点，导致网络故障时有发生，即使设计成多冗余的容错系统，依然无法完全避免故障的发生。尤其是节点能量耗尽导致的能量故障非常普遍。因此，本文从节点能量耗尽导致节点失效进而引起网络故障的角度出发，研究故障预防、故障检测和故障修复等问题，基于减少能量消耗和均衡能量消耗的原则，设计相应的解决方法。本文的研究内容主要包括以下三个方面：
（1）针对冗余通路故障预防方法中工作状态冗余节点过多、能量大量浪费的问题，提出一种基于冗余通路控制的WSNs故障预防方法。该方法利用汇节点收集网络内所有节点能量状态，计算节点健康度等相关参数，使用A-Star算法选择最优工作通路，控制其余冗余通路分批轮流休眠，从而达到减少网络工作过程能量消耗，预防某些节点能量提前耗尽导致网络能量故障发生的目的。仿真实验和实际节点实验的结果表明，在保证网络适当冗余通路的前提下，与其它相关方法比较，该方法可以显著均衡网络能量消耗，有效延长网络寿命。
（2）针对WSNs故障检测的邻居协作方法中，由于邻居数据与被检测节点实际数据存在差异导致的故障检测准确率问题，本文提出一种基于历史数据与邻居协作的节点自检测方法，并据此提出基于簇内数据聚类的故障检测方法来降低虚警率和普通节点的能量消耗。该方法将节点自检测方法和邻居协作方法相结合，首先由普通节点融合节点稳定的历史数据和实时邻居数据，采用分布式故障检测思想，完成自身故障检测工作，然后由能量不受限的汇节点利用聚类算法将按照空间位置分簇的WSNs节点感知数据进行聚类，并通过阈值比较进行故障检测。实验结果表明，与类似的WSNs故障检测方法相比，该方法具有良好的故障检测准确率和较低的故障检测虚警率，且普通节点能量消耗较小。
（3）针对WSNs中节点故障导致网络出现“覆盖空洞”的问题，本文提出一种基于最小距离冗余节点替代的故障修复方法。该方法由能量不受限的汇节点获取全网所有工作节点和冗余节点的位置信息，通过故障节点和邻居节点的位置信息确定“覆盖空洞”面积，计算最佳修复位置，寻找与此位置距离最小的冗余节点作为替代节点加入网络工作，从而达到修复网络故障的目的。在此基础上，提出网络覆盖率的Monte Carlo积分计算方法，对网络的“覆盖空洞”边界进行数学描述，利用概率抽样的方法估算不规则区域的面积，实现对网络覆盖质量性能的评价。然后，根据实验数据，构造网络寿命与节点余活比经验公式、网络覆盖率与网络运行时间、节点余活比经验公式，为网络设计时根据监测区域面积、网络寿命期望和网络覆盖率等参数合理部署节点数目，提供理论参考。实验结果表明，通过预先随机部署的冗余节点，该故障修复方法可以获得较高的故障修复率和较好的网络覆盖，可有效地延长网络寿命。且在保证充分抽样的情况下，网络覆盖率的计算误差控制在5%以内，程序实现简单且计算结果可信。
Wireless Sensor Networks(WSNs) nodes have the features of constrained energysupply, limited hardware resource, large numbers, self-organization and dynamictopology, which make networks prone to have malfunctions. It will not be able to avoidthe faults completely though there is redundant fault-tolerance. The energy fault causedby node energy depletion is widespread typically. Thus, in terms of network faultscaused by failure nodes, this thesis studies fault prevention, fault detection and faultrecovery in WSNs, and proposes corresponding methods based on reducing energyconsumption and balancing energy consumption. Here, the research content mainlyincludes the following five aspects:
(1) For the reason that the redundant routing method for fault prevention in WSNshas the shortcomings of too much traffic and more wasted energy, a Fault PreventionTechnique based on Controlling Redundant Routes into Sleeping (FPCR) is proposed.Sink node which collects each node’s residual energy and number of neighbors, andcalculates energy consumption degree parameter and health degree parameter, which areused to choose the opptimal routing by A-Star algorithm. And then, it controls theredundant routes into sleeping mode in turn. In this way, the purpose of preventingcertain node depletion in advance and prolonging the networks’ lifetime should bereached. The simulation results of NS2and actual node experimental results illustratethat the presented health degree based technique can balance the network’s energy andprolong the network’s lifetime significantly.
(2) Because of the physical space distance, the sensing data have distinguisheddifferences between neighbors in neighbor coordination method for fault detection inWSNs, which will influence the fault detection accuracy. To solve this problem, A nodeSelf Detection Technique by History data and Neighbors (NDHN), and meanwhile, aFault Detection Technique based on Clustering (FDTC) is proposed to reduce the falsealarm probability of NDHN. It weights the nodes’ historical data and the neighbors’sensing data in NDHN, and run the judgment task in each node. Then, by usingmathematical clustering algorithm, the sink node clusters each node by its sensing data.Employing carefully-chosen threshold, fault detection task is implemented on the sinknode. The simulation results show that the presented method can obtain high faultdetection accuracy and low false alarm probability, while greatly reduce nodes’ energyconsumption.
(3)“Coverage hole” caused by failure nodes is a typical fault in WSNs, which isvital to evaluate the quality of networks coverage. To recover the “coverage hole”, aRecovery Algorithm based on Minimum Distance Redundant Nodes (MDRN) is proposed. The recovery algorithm is implemented on the sink node with unconstrainedenergy consumption. The locations of all active nodes and redundant nodes, known bysink node, are used to calculate the “coverage hole”. Then, sink node calculates theoptimal location to recover the “coverage hole”, and searches for the minimum distanceredundant node from which as the alternative node. On this basis, a Method to Estimatethe Network Coverage based on Monte Carlo Integration (ECMC) is proposed toevaluate the quality of coverage of MDRN, by using probability sampling method toestimate the area of hole’s irregular region. According to the experimental data, theexperience formula of network lifetime and the ratio of redundant nodes to active nodesis constructed, and in the same way, the experience formula of network coverage andthe ratio of redundant nodes to active nodes. Those provide theoretical reference inchoosing reasonable number of deployment nodes at the beginning of the networkdesign, in accordance with the monitoring area, expected monitoring lifetime andnetwork coverage. Simulation results demonstrate that, by choosing appropriate numberof redundant nodes, this algorithm will be feasible and efficient for dealing with the“coverage hole”, simultaneously get great recoveryaccuracy and network coverage, andalso achieve the purpose of prolonging the lifetime of WSNs. In the case of guaranteesufficient sampling, the calculation error of network coverage can be controlled within5%, where the program is simple and the calculation result is believable.
（1）针对冗余通路故障预防方法中工作状态冗余节点过多、能量大量浪费的问题，提出一种基于冗余通路控制的WSNs故障预防方法。该方法利用汇节点收集网络内所有节点能量状态，计算节点健康度等相关参数，使用A-Star算法选择最优工作通路，控制其余冗余通路分批轮流休眠，从而达到减少网络工作过程能量消耗，预防某些节点能量提前耗尽导致网络能量故障发生的目的。仿真实验和实际节点实验的结果表明，在保证网络适当冗余通路的前提下，与其它相关方法比较，该方法可以显著均衡网络能量消耗，有效延长网络寿命。
（2）针对WSNs故障检测的邻居协作方法中，由于邻居数据与被检测节点实际数据存在差异导致的故障检测准确率问题，本文提出一种基于历史数据与邻居协作的节点自检测方法，并据此提出基于簇内数据聚类的故障检测方法来降低虚警率和普通节点的能量消耗。该方法将节点自检测方法和邻居协作方法相结合，首先由普通节点融合节点稳定的历史数据和实时邻居数据，采用分布式故障检测思想，完成自身故障检测工作，然后由能量不受限的汇节点利用聚类算法将按照空间位置分簇的WSNs节点感知数据进行聚类，并通过阈值比较进行故障检测。实验结果表明，与类似的WSNs故障检测方法相比，该方法具有良好的故障检测准确率和较低的故障检测虚警率，且普通节点能量消耗较小。
（3）针对WSNs中节点故障导致网络出现“覆盖空洞”的问题，本文提出一种基于最小距离冗余节点替代的故障修复方法。该方法由能量不受限的汇节点获取全网所有工作节点和冗余节点的位置信息，通过故障节点和邻居节点的位置信息确定“覆盖空洞”面积，计算最佳修复位置，寻找与此位置距离最小的冗余节点作为替代节点加入网络工作，从而达到修复网络故障的目的。在此基础上，提出网络覆盖率的Monte Carlo积分计算方法，对网络的“覆盖空洞”边界进行数学描述，利用概率抽样的方法估算不规则区域的面积，实现对网络覆盖质量性能的评价。然后，根据实验数据，构造网络寿命与节点余活比经验公式、网络覆盖率与网络运行时间、节点余活比经验公式，为网络设计时根据监测区域面积、网络寿命期望和网络覆盖率等参数合理部署节点数目，提供理论参考。实验结果表明，通过预先随机部署的冗余节点，该故障修复方法可以获得较高的故障修复率和较好的网络覆盖，可有效地延长网络寿命。且在保证充分抽样的情况下，网络覆盖率的计算误差控制在5%以内，程序实现简单且计算结果可信。
Wireless Sensor Networks(WSNs) nodes have the features of constrained energysupply, limited hardware resource, large numbers, self-organization and dynamictopology, which make networks prone to have malfunctions. It will not be able to avoidthe faults completely though there is redundant fault-tolerance. The energy fault causedby node energy depletion is widespread typically. Thus, in terms of network faultscaused by failure nodes, this thesis studies fault prevention, fault detection and faultrecovery in WSNs, and proposes corresponding methods based on reducing energyconsumption and balancing energy consumption. Here, the research content mainlyincludes the following five aspects:
(1) For the reason that the redundant routing method for fault prevention in WSNshas the shortcomings of too much traffic and more wasted energy, a Fault PreventionTechnique based on Controlling Redundant Routes into Sleeping (FPCR) is proposed.Sink node which collects each node’s residual energy and number of neighbors, andcalculates energy consumption degree parameter and health degree parameter, which areused to choose the opptimal routing by A-Star algorithm. And then, it controls theredundant routes into sleeping mode in turn. In this way, the purpose of preventingcertain node depletion in advance and prolonging the networks’ lifetime should bereached. The simulation results of NS2and actual node experimental results illustratethat the presented health degree based technique can balance the network’s energy andprolong the network’s lifetime significantly.
(2) Because of the physical space distance, the sensing data have distinguisheddifferences between neighbors in neighbor coordination method for fault detection inWSNs, which will influence the fault detection accuracy. To solve this problem, A nodeSelf Detection Technique by History data and Neighbors (NDHN), and meanwhile, aFault Detection Technique based on Clustering (FDTC) is proposed to reduce the falsealarm probability of NDHN. It weights the nodes’ historical data and the neighbors’sensing data in NDHN, and run the judgment task in each node. Then, by usingmathematical clustering algorithm, the sink node clusters each node by its sensing data.Employing carefully-chosen threshold, fault detection task is implemented on the sinknode. The simulation results show that the presented method can obtain high faultdetection accuracy and low false alarm probability, while greatly reduce nodes’ energyconsumption.
(3)“Coverage hole” caused by failure nodes is a typical fault in WSNs, which isvital to evaluate the quality of networks coverage. To recover the “coverage hole”, aRecovery Algorithm based on Minimum Distance Redundant Nodes (MDRN) is proposed. The recovery algorithm is implemented on the sink node with unconstrainedenergy consumption. The locations of all active nodes and redundant nodes, known bysink node, are used to calculate the “coverage hole”. Then, sink node calculates theoptimal location to recover the “coverage hole”, and searches for the minimum distanceredundant node from which as the alternative node. On this basis, a Method to Estimatethe Network Coverage based on Monte Carlo Integration (ECMC) is proposed toevaluate the quality of coverage of MDRN, by using probability sampling method toestimate the area of hole’s irregular region. According to the experimental data, theexperience formula of network lifetime and the ratio of redundant nodes to active nodesis constructed, and in the same way, the experience formula of network coverage andthe ratio of redundant nodes to active nodes. Those provide theoretical reference inchoosing reasonable number of deployment nodes at the beginning of the networkdesign, in accordance with the monitoring area, expected monitoring lifetime andnetwork coverage. Simulation results demonstrate that, by choosing appropriate numberof redundant nodes, this algorithm will be feasible and efficient for dealing with the“coverage hole”, simultaneously get great recoveryaccuracy and network coverage, andalso achieve the purpose of prolonging the lifetime of WSNs. In the case of guaranteesufficient sampling, the calculation error of network coverage can be controlled within5%, where the program is simple and the calculation result is believable.
引文
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