一种无线传感器网络节点故障的检测方法

To reduce excessive computing and communication loads of traditional fault detection methods, a neighbor-data analysis based node fault detection method is proposed. First, historical data is analyzed to confirm the confidence level of sensor nodes. Then a nodes reading data is compared with neighbor nodes which are of good confidence level. Decision can be made whether this node is a failure or not. Simulation shows this method has good effect on fault detection accuracy and transient fault tolerance, and never transfers communication and computing overloading to sensor nodes.     

Low-latency neighbor discovery algorithm based on multi-beacon message in mobile low-duty-cycle sensor network

Neighbor discovery enables nodes in the networks to discover each other through simple information interaction,which was suitable for the new mobile low duty cycle sensor network (MLDC-WSN).However,because the nodes in MLDC-WSN can move randomly and sleep,the network topology was changed frequently,which results in that some nodes need a lot of energy and time to find their neighbors.How to realize fast neighbor discovery for all nodes in the network was a difficult problem in current research.To solve this problem,a new low-latency neighbor discovery algorithm based on multi-beacon messages was proposed.In this algorithm,the nodes were discovered by sending a short beacon message through their neighbor nodes,and by adjusting the time and frequency of beacon message sent,a lower neighbor discovery delay was obtained.Eventually,through quantitative analysis and simulation experiments,it is found that compared with existing algorithms,this algorithm can find all neighbor nodes in MLDC-WSN with less energy consumption,lower latency and greater probability.     

rDFD: reactive distributed fault detection in wireless sensor networks

Generally, fault detection approaches pursue high detection accuracy, but neglect energy consumption due to the high volume of messages exchanged. Therefore, in this work we propose a reactive distributed scheme for detecting faulty nodes. The scheme is able to detect transient and permanent faulty nodes accurately by exchanging fewer messages. In existing fault detection schemes, nodes exchange too many messages after every specific interval to detect suspicious node. However, in the proposed scheme comparatively much less messages are exchanged within a limited geographical area around the suspicious node only and that too when the node suspects its own readings. In the proposed scheme, each node exploits the temporal correlation in its own readings to detect any suspicious behavior. In order to confirm its status, the suspicious node communicates with its immediate neighbors who may be locally good or possible faulty with a certain level of confidence. Thus, the scheme utilizes the strength of both spatial and temporal correlation to find faulty nodes. Also, a confidence level is assigned to each correlated neighbor of suspicious node in order to enhance the detection accuracy. The ns-2 based simulation results show that our scheme performs better by reducing communication overhead and by detecting faulty nodes with high accuracy as compared to existing approaches.     

一种分簇无线传感网络的节点自诊断算法

众多网络为了节省能量、减少链路都是在分簇拓扑结构中实现信息传输。当分簇网络出现故障时,首先要确保簇头节点的诊断精度。文中提出一种分簇网络的WSN节点自诊断算法,算法分为两步诊断来确定节点的最终状态:第一步主要根据相邻节点的信息相似来初步确定节点状态;第二步为了防止节点被诊断错误,对节点采取进一步检验诊断。仿真结果显示,所给出的两步诊断算法有较高的诊断准确率和诊断精度。     

基于网格结构无线传感网络故障诊断算法

无线传感网中节点的数据信息具有空间相关性,可以通过邻居节点数据的比较来完成网络的故障诊断。网络中有时会出现瞬时故障,影响网络的诊断精确度。文中提出了一种基于网格结构的无线传感网络故障诊断算法,算法通过相邻节点历史数据信息之间的比较来确定节点最终状态,有效地避免了瞬时故障对节点诊断的影响。仿真结果表明,文中算法可保证较高地诊断精确度并能节省一定的能量。     

Composite Fault Diagnosis in Wireless Sensor Networks Using Neural Networks

Wireless sensor networks (WSNs) are spatially distributed devices to support various applications. The undesirable behavior of the sensor node affects the computational efficiency and quality of service. Fault detection, identification, and isolation in WSNs will increase assurance of quality, reliability, and safety. In this paper, a novel neural network based fault diagnosis algorithm is proposed for WSNs to handle the composite fault environment. Composite fault includes hard, soft, intermittent, and transient faults. The proposed fault diagnosis protocol is based on gradient descent and evolutionary approach. It detects, diagnose, and isolate the faulty nodes in the network. The proposed protocol works in four phases such as clustering phase, communication phase, fault detection and classification phase, and isolation phase. Simulation results show that the proposed protocol performs better than the existing protocols in terms of detection accuracy, false alarm rate, false positive rate, and detection latency.

     

WSN中一种改进的目标跟踪方法

目标跟踪作为无线传感器网络(WSN)的一项基本应用,已得到广泛研究。提出了一种改进的目标跟踪方法,该方法主要分为邻域检测、目标跟踪和目标修正3个阶段。节点通过获取对于目标的感知信息收益值来实现邻域检测。每个节点通过计算自己的节点权值来决定是否参与目标的跟踪。基于目标的运动趋势,通过发送数据报告来自适应地对目标进行修正。仿真实验表明,该算法减少了参与目标跟踪的节点数,节省了能量,与PM算法相比,该算法提高了目标跟踪的准确率。     

Research and Application of a Fault Self-Diagnosis Method for Roots Flowmeter Based on WSN Node

In order to diagnose whether the photoelectric encoder of Roots flow meter is working well in assuring the wireless sensor networks (WSN) work properly, this paper discusses the features of Roots flow meter and proposes an on-line fault self-diagnosis method. First, a flexible fault sensing circuit is designed as a state detection module on WSN node. Second, a fault self-diagnosis method is proposed. The fault diagnosis method is based on the vibration frequency of Roots flow meter. The failure is diagnosed by whether the relationship between the measurement of instantaneous flow and the vibration frequency is correct. Experiment results demonstrate the fault self-diagnosis method is suit to the application in WSN nodes and the WSN nodes have been successfully applied to the oil tank trucks.

     

WSNs中基于数据匹配的分布式故障节点检测算法

针对无线传感网络的节点故障问题,提出一种新的分布式故障节点检测算法(DFDA).DFDA算法利用节点度信息估计节点对网络的重要性,并尽可能将节点度高的节点保存到网络中.通过比较节点间感测的数据,检测故障节点.为了增强检测的准确性,采用双重测定策略.仿真结果表明,相比于同类算法,DFDA算法提高了检测故障节点的精确度,并...     

信标节点漂移情况下的无线传感器网络节点定位机制

针对信标节点漂移情况下的节点定位问题,提出了一种分布式的信标节点漂移检测方法,采用节点自评分和协商机制,自动寻找可能发生了漂移的信标节点,同时针对大量信标节点发生漂移后的定位覆盖率下降问题,构建普通节点的定位可信度模型,并在定位盲区内使用一些较为可靠的普通节点作为临时信标节点进行定位。仿真实验表明,该算法在误检测、定位误差方面性能优于传统算法,具有较低的通信开销、较高的实用性和灵活性。     

移动无线传感网的生存时间优化算法研究

当sink节点位置固定不变时,分布在sink 节点周围的传感节点很容易成为枢纽节点,因转发较多的数据而过早失效。为解决上述问题,提出移动无线传感网的生存时间优化算法（LOAMWSN）。LOAMWSN算法考虑sink节点的移动,采用减聚类算法确定sink节点移动的锚点,采用最近邻插值法寻找能遍历所有锚点的最短路径近似解,采用分布式非同步Bellman-Ford算法构建sink节点k跳通信范围内的最短路径树。最终,传感节点沿着最短路径树将数据发送给sink节点。仿真结果表明：在节点均匀分布和非均匀分布的无线传感网中,LOAMWSN算法都可以延长网络生存时间、平衡节点能耗,将平均节点能耗保持在较低水平。在一定的条件下,比Ratio_w、TPGF算法更优。     

一种带有可控动态参数的优化覆盖算法

能耗与覆盖问题是无线传感器网络研究领域的基本问题,也是一个重点问题。针对传感器节点所呈现的同构性特点,提出了一种带有可控动态参数的优化覆盖算法（OCCDP）。该算法首先给出了3节点联合覆盖时,最大无缝覆盖率的求解过程;其次,给出了在监测区域内存在传感器节点覆盖时,覆盖质量期望值求解方法以及与邻居节点进行覆盖比对时覆盖率的判定方法;当存在冗余覆盖时,给出了任意传感器节点处于冗余节点覆盖时的覆盖率的计算过程;最后,通过仿真实验与其他算法在覆盖质量和网络生存周期等方面进行对比,其性能指标平均提升了11.02%和13.27%,从而验证了提出算法的有效性和可行性。     

移动低占空比传感网中基于分段拟合压缩的数据容灾存储算法

数据存储是无线传感器网络中数据管理的基础操作.在移动低占空比传感网中,由于节点的移动性,每个节点需要频繁更新邻居节点集合,使得节点能量消耗过大;同时,节点大部分时间处于睡眠状态,仅在少部分时间内苏醒工作,造成数据备份的通信延迟过大.提出一种快速的低能耗数据保存机制.首先,源节点基于连续时间序列对感知数据进行分段线性拟合压缩;接着,节点根据预估故障概率和存储空间大小,计算出合理的压缩数据备份数量.在此基础上,设计一种动态自适应传输协议.实验仿真表明,与已有存储算法比较,该机制具有更低的传输能耗和通信延迟.     

无线传感器网络节点复制攻击和女巫攻击防御机制研究

在无线传感器网络(WSNs)中,节点复制攻击和女巫攻击可扰乱数据融合和阈值选举等网络操作.发起这两种攻击需先通过邻居发现认证过程.考虑到在WSNs中发起邻居认证是不频繁的,提出了一种基于单向密钥链的ID认证防御机制(OKCIDA),降低攻击者在任何时间段发起这两种攻击的可能性.然后基于椭圆曲线离散对数问题,构造对称参数,并组合OKCIDA和利用节点邻居关系,提出了一种无需位置的邻居认证协议(LFNA),以阻止复制节点和女巫节点成功加入网络.最后给出了安全性证明和分析,并在安全和开销方面将LFNA与已有典型防御方案进行了比较,结果表明该方案具有一定的优势.     

无线传感器网络远程监测节点的功能扩展电路

在节点现有功能基础上进行外围电路功能扩展是传感器网络应用开发中的常见问题之一。本文以JN5139系列节点为基础,用测量范围较大的高温采集电路,替换了原有的常温传感器;配备了蓄电池电压检测电路,使得监测中心能及时了解节点能量供应情况;通过纽扣电池为节点时钟芯片提供电源,保证节点时钟计时准确性;采用GPRS开关控制电路,降低了汇聚节点能量损耗;设计的看门狗控制电路,提高了节点工作的可靠性和稳定性。     

Fault diagnosis and its prediction in wireless sensor networks using regressional learning to achieve fault tolerance

Due to the wide range of critical applications and resource constraints, sensor node gives unexpected responses, which leads to various kind of faults in sensor node and failure in wireless sensor networks. Many research studies focus only on fault diagnosis, and comparatively limited studies have been conducted on fault diagnosis along with fault tolerance in sensor networks. This paper reports a complete study on both 2 aspects and presents a fault tolerance approach using regressional learning with fault diagnosis in wireless sensor networks. The proposed method diagnose the different types of faulty nodes such as hard permanent, soft permanent, intermittent, and transient faults with better detection accuracy. The proposed method follows a fault tolerance phase where faulty sensor node values would be predicted by using the data sensed by the fault free neighbors. The experimental evaluation of the fault tolerance module shows promising results with R² of more than 0.99. For the periodic fault such as intermittent fault, the proposed method also predict the possible occurrence time and its duration of the faulty node, so that fault tolerance can be achieved at that particular time period for better performance of the network.     

An Energy Efficient Barrier Coverage Algorithm for Wireless Sensor Networks

Intrusion detection is one of the most important applications of wireless sensor networks. When mobile objects are entering into the boundary of a sensor field or are moving cross the sensor field, they should be detected by the scattered sensor nodes before they pierce through the field of sensor (barrier coverage). In this paper, we propose an energy efficient scheduling method based on learning automata, in which each node is equipped with a learning automaton, which helps the node to select best node to guarantee barrier coverage, at any given time. To apply our method, we used coverage graph of deployed networks and learning automata of each node operates based on nodes that located in adjacency of current node. Our algorithm tries to select minimum number of required nodes to monitor barriers in deployed network. To investigate the efficiency of the proposed barrier coverage algorithm several computer simulation experiments are conducted. Numerical results show the superiority of the proposed method over the existing methods in term of the network lifetime and our proposed algorithm can operate very close to optimal method.     

Balance energy-efficient and real-time with reliable communication protocol for wireless sensor network

In many wireless sensor network applications, it should be considered that how to trade off the inherent conflict between energy efficient communication and desired quality of service such as real-time and reliability of transportation. In this paper, a novel routing protocols named balance energy-efficient and real-time with reliable communication (BERR) for wireless sensor networks (WSNs) are proposed, which considers the joint performances of real-time, energy efficiency and reliability. In BERR, a node, which is preparing to transmit data packets to sink node, estimates the energy cost, hop count value to sink node and reliability using local information gained from neighbor nodes. BERR considers not only each sender’ energy level but also that of its neighbor nodes, so that the better energy conditions a node has, the more probability it will be to be chosen as the next relay node. To enhance real-time delivery, it will choose the node with smaller hop count value to sink node as the possible relay candidate. To improve reliability, it adopts retransmission mechanism. Simulation results show that BERR has better performances in term of energy consumption, network lifetime, reliability and small transmitting delay.     

基于云计算和动态贝叶斯博弈的WSN恶意程序传播优化抑制方法

为有效地应用入侵检测系统检测WSN（wireless sensor network,无线传感网络）恶意程序从而抑制WSN恶意程序传播,在考虑WSN节点资源有限和云计算平台资源几乎无限的现状基础上,借助云计算平台提出WSN入侵检测网络结构。依据传感节点和WSN入侵检测代理之间博弈过程的分析,使用动态贝叶斯博弈建立了考虑WSN入侵检测代理监控数据发送能耗和传感节点隐私保护需求的WSN恶意程序传播抑制博弈模型。依据建立的博弈类型,并基于精炼贝叶斯均衡提出抑制WSN恶意程序传播的优化策略,并给出具体的算法。实验分析了影响WSN入侵检测代理选择优化策略的因素,为具体应用提供了实验依据。     

Choice of Detection Parameters on Fault Detection in Wireless Sensor Networks: A Multiobjective Optimization Approach

In this paper, the intermittent fault detection in wireless sensor networks is formulated as an optimization problem and a recently introduced multiobjective swarm optimization (2LB-MOPSO) algorithm is used to find an optimum trade-off between detection accuracy and detection latency. Faulty sensor nodes are identified based on comparisons of sensed data between one-hop neighboring nodes. Time redundancy is used to detect intermittent faults since an intermittent fault does not occur consistently. Simulation and analytical results show that sensor nodes with permanent faults are identified with high accuracy and by properly choosing the inter-test interval most of the intermittent faults are isolated with negligible performance degradation.