Distributed event region fault-tolerance based on weighted distance for wireless sensor networks

Event region detection is the important application for wireless sensor networks（WSNs）, where the existing faulty sensors would lead to drastic deterioration of network quality of service.Considering single-moment nodes fault-tolerance, a novel distributed fault-tolerant detection algorithm named distributed fault-tolerance based on weighted distance（DFWD） is proposed, which exploits the spatial correlation among sensor nodes and their redundant information.In sensor networks, neighborhood sensor nodes will be endowed with different relative weights respectively according to the distances between them and the central node.Having syncretized the weighted information of dual-neighborhood nodes appropriately, it is reasonable to decide the ultimate status of the central sensor node.Simultaneously, readings of faulty sensors would be corrected during this process.Simulation results demonstrate that the DFWD has a higher fault detection accuracy compared with other algorithms, and when the sensor fault probability is 10%, the DFWD can still correct more than 91% faulty sensor nodes, which significantly improves the performance of the whole sensor network.

A double weighted LS-SVM model for data estimation in wireless sensor networks

In wireless sensor networks, data missing is a common problem due to sensor faults, time synchronization, malicious attacks, and communication malfunctions, which may degrade the network＇ s performance or lead to inefficient decisions. Therefore, it is necessary to effectively estimate the missing data. A double weighted least squares support vector machines （DWLS-SVM） model for the missing data estimation in wireless sensor networks is proposed in this paper. The algo- rithm first applies the weighted LS-SVM （WLS-SVM） to estimate the missing data on temporal do- main and spatial domain respectively, and then uses the weighted average of these two candidates as the final estimated value. DWLS-SVM considers the possibility of outliers in the dataset and utilizes spatio-temporal dependencies among sensor nodes fully, which makes the estimate more robust and precise. Experimental results on real world dataset demonstrate that the proposed algorithm is outli- er robust and can estimate the missing values accurately.

Weighted Multi-sensor Data Level Fusion Method of Vibration Signal Based on Correlation Function

As the differences of sensor＇s precision and some random factors are difficult to control,the actual measurement signals are far from the target signals that affect the reliability and precision of rotating machinery fault diagnosis.The traditional signal processing methods,such as classical inference and weighted averaging algorithm usually lack dynamic adaptability that is easy for trends to cause the faults to be misjudged or left out.To enhance the measuring veracity and precision of vibration signal in rotary machine multi-sensor vibration signal fault diagnosis,a novel data level fusion approach is presented on the basis of correlation function analysis to fast determine the weighted value of multi-sensor vibration signals.The approach doesn＇t require knowing the prior information about sensors,and the weighted value of sensors can be confirmed depending on the correlation measure of real-time data tested in the data level fusion process.It gives greater weighted value to the greater correlation measure of sensor signals,and vice versa.The approach can effectively suppress large errors and even can still fuse data in the case of sensor failures because it takes full advantage of sensor＇s own-information to determine the weighted value.Moreover,it has good performance of anti-jamming due to the correlation measures between noise and effective signals are usually small.Through the simulation of typical signal collected from multi-sensors,the comparative analysis of dynamic adaptability and fault tolerance between the proposed approach and traditional weighted averaging approach is taken.Finally,the rotor dynamics and integrated fault simulator is taken as an example to verify the feasibility and advantages of the proposed approach,it is shown that the multi-sensor data level fusion based on correlation function weighted approach is better than the traditional weighted average approach with respect to fusion precision and dynamic adaptability.Meantime,the approach is adaptable and easy to use,can be applied to other areas of vibration measurement.

Prediction-Based Distance Weighted Algorithm for Target Tracking in Binary Sensor Network

Binary sensor network(BSN) are becoming more attractive due to the low cost deployment,small size,low energy consumption and simple operation.There are two different ways for target tracking in BSN,the weighted algorithms and particle filtering algorithm.The weighted algorithms have good realtime property,however have poor estimation property and some of them does not suit for target’s variable velocity model.The particle filtering algorithm can estimate target's position more accurately with poor realtime property and is not suitable for target’s constant velocity model.In this paper distance weight is adopted to estimate the target’s position,which is different from the existing distance weight in other papers.On the analysis of principle of distance weight (DW),prediction-based distance weighted(PDW) algorithm for target tracking in BSN is proposed.Simulation results proved PDW fits for target's constant and variable velocity models with accurate estimation and good realtime property.

Cooperative Nodes Localization for Three-Dimensional Underwater Wireless Sensor Network Based on Weighted Centroid Localization Algorithm

The underwater wireless sensor network(UWSN) has the features of mobility by drifting,less beacon nodes,longer time for localization and more energy consumption than the terrestrial sensor networks,which makes it more difficult to locate the nodes in marine environment.Aiming at the characteristics of UWSN,a kind of cooperative range-free localization method based on weighted centroid localization(WCL) algorithm for three-dimensional UWSN is proposed.The algorithm assigns the cooperative weights for the beacon nodes according to the received acoustic signal strength,and uses the located unknown nodes as the new beacon nodes to locate the other unknown nodes,so a fast localization can be achieved for the whole sensor networks.Simulation results indicate this method has higher localization accuracy than the centroid localization algorithm,and it needs less beacon nodes and achieves higher rate of effective localization.

Two-stage prediction and update particle filtering algorithm based on particle weight optimization in multi-sensor observation

The reasonable measuring of particle weight and effective sampling of particle state are considered as two important aspects to obtain better estimation precision in particle filter.Aiming at the comprehensive treatment of above problems,a novel two-stage prediction and update particle filtering algorithm based on particle weight optimization in multi-sensor observation is proposed.Firstly,combined with the construction of multi-senor observation likelihood function and the weight fusion principle,a new particle weight optimization strategy in multi-sensor observation is presented,and the reliability and stability of particle weight are improved by decreasing weight variance.In addition,according to the prediction and update mechanism of particle filter and unscented Kalman filter,a new realization of particle filter with two-stage prediction and update is given.The filter gain containing the latest observation information is used to directly optimize state estimation in the framework,which avoids a large calculation amount and the lack of universality in proposal distribution optimization way.The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.

AN ADAPTIVE-WEIGHTED TWO-DIMENSIONAL DATA AGGREGATION ALGORITHM FOR CLUSTERED WIRELESS SENSOR NETWORKS

In this paper,an Adaptive-Weighted Time-Dimensional and Space-Dimensional(AWTDSD) data aggregation algorithm for a clustered sensor network is proposed for prolonging the lifetime of the network as well as improving the accuracy of the data gathered in the network.AWTDSD contains three phases:(1) the time-dimensional aggregation phase for eliminating the data redundancy;(2) the adaptive-weighted aggregation phase for further aggregating the data as well as improving the accuracy of the aggregated data; and(3) the space-dimensional aggregation phase for reducing the size and the amount of the data transmission to the base station.AWTDSD utilizes the correlations between the sensed data for reducing the data transmission and increasing the data accuracy as well.Experimental result shows that AWTDSD can not only save almost a half of the total energy consumption but also greatly increase the accuracy of the data monitored by the sensors in the clustered network.

基于高斯滤波与均值聚类的异质多源传感器数据加权融合

异质多源传感器之间工作频率存在差异,导致数据之间的一致性较差,加权融合后的观测误差较大,因此提出基于高斯滤波与均值聚类的异质多源传感器数据加权融合方法。采用高斯滤波对异质多源传感器数据空间单元格进行划分,建立基于单元格的最佳连通域,保留传感器内部数据,完成传感器数据的高斯滤波平滑处理。引入均值聚类对异质多源传感器数据进行一致性处理。通过免疫粒子群搜索最优权重和参数,利用最优权重和参数完成异质多源传感器数据加权融合。仿真结果表明,所提方法能够降低融合后传感器数据的观测误差与均方误差,观测误差与均方误差最小值均为0.002。因此,说明所提方法提高了融合后异质多源传感器数据的可利用性。

基于WTGWO的无线传感器网络三维部署优化方法

为优化无线传感器网络的部署问题,提出一种新的无线传感器网络三维部署优化方法.在增强灰狼优化算法的基础上,通过在外层位置更新策略中引入自适应权重方法,平衡了增强灰狼优化算法开发与勘探之间的搜索.在马鞍形曲面山坡上进行仿真实验,实验结果表明,在50个节点下,该方法在保证连通的情况下最高覆盖率可达97.58%,平均覆盖率可达96.74%,与其他算法相比提高了1.64%~3.87%,可以有效提升无线传感器网络的覆盖率,增强无线传感器网络的服务质量.

汽车踏板力计便携式校准装置的研制

踏板力计的准确度直接关系着车辆制动系统的可靠性,影响车辆行驶的安全性。本文设计了一种便携式的汽车踏板力计校准装置,结合砝码或测力传感器,对计轴向和倾斜向进行校准。实验证明:该装置能提高力计的精度,不受场地制约,结构稳定可靠,安装方便;适用于车企和生产厂家对设备的自校;采用Solidworks软件进行结构设计及有限元分析,不仅性能达到设计要求,而且对汽车踏板力计的校准结果符合JJF 1169-2007校准规范。

基于鲁棒马氏距离统计量的多源融合抗差估计方法

为了有效抵御复杂多变城市环境下的全球卫星导航系统(GNSS)信号干扰、增强多源融合定位可靠性,提出一种基于鲁棒马氏距离的多源融合抗差估计方法。该方法在分析观测值故障传播特点以及典型方差膨胀抗差估计模型基础上,基于相邻新息序列构造鲁棒马氏距离检验统计量。历史新息的引入能够提高系统观测冗余,同时不同观测量间的新息交互增强了异常检验统计量的鲁棒性。根据鲁棒马氏距离的统计特性,给出抗差关键门限取值规则并分别结合两种典型加权策略自适应调节观测值噪声矩阵。利用典型城市峡谷环境下惯性导航系统(INS)/GNSS/激光雷达(LiDAR)/VINS多源融合车载数据进行相关实验,与现有方法相较,所提方法能够将三维均方根定位误差最低限制在3.37 m。通过对比不同组显著性水平下的定位结果,进一步说明所提方法在城市峡谷环境下定位的优越性。

基于Hall和GMR的多传感器融合方法及实现

目前霍尔传感器(Hall)和巨磁阻(GMR)传感器均广泛地应用于电力系统电流测量。为同时发挥二者的优势、降低各自的局限性,在分析Hall和GMR的温度特性、噪声特性和被测电流范围的基础上,提出了一种基于Hall和GMR的多传感器融合方案。在定义GMR和Hall的灵敏度差值ΔS基础上,将被测电流i和灵敏度差值ΔS构成的融合域划分为四个域,在域Ⅱ采用多传感加权观测融合Kalman滤波算法,将Hall和GMR的观测量和观测噪声融合后与状态方程联立进行Kalman滤波;在域Ⅰ采用数据加权融合最优权值分配的方法,给Hall的测量数据赋予较大权值,GMR的测量数据赋予较小的权值;在域Ⅲ,权值分配情况相反,各域之间可实现数据融合的平滑过渡。基于多传感器融合方法,设计了一种组合式闭环电流传感器,包括磁芯、电路部分设计及仿真。仿真和样机实验结果表明,在域Ⅱ时多传感器融合值与真实值的均方根误差低至0.004;在域Ⅰ、Ⅲ时电流测量的相对误差E_(i)均在0.255%以下。与单一传感器相比,多传感器融合的方法使组合式传感器测量电流范围增大,适用于温度变化范围较大的场景,电流测量精度及可信度更高。

基于RSSI的弦相交加权质心定位算法

无线传感器网络中节点定位是其重要技术,其中基于测距的RSSI定位技术具有成本低、易实现等优点。为解决传统三边质心算法在确定相交区域顶点时存在一定缺陷导致误差较大的问题,提出弦相交-加权质心定位算法。该算法采用分组、分步式定位方式。首先通过弦相交法进行初步定位,将有效节点三三分组,组内各圆公共弦所在直线交点作为本组的定位结果;再采用加权质心算法将初步定位中各组结果进行二次精确定位,得到最终信号源预测位置。在平均边长近似20 m的不规则五边形区域进行仿真验证,信号源随机分布于15 m×15 m的正方形内,结果显示相较于加权三边质心算法,所换算法平均误差减少了2.028 m,定位精度提升了61.14%,具有良好效果。

基于分类平均跳距的无线传感器网络节点CADV-Hop定位方法

针对原型DV-Hop定位算法中平均跳距的计算产生较大定位误差的问题,提出了一种分类平均跳距的CADV-Hop算法。首先,揭示了无线传感网络中不同跳数路径的单跳距离分布不一致的现象,并分析了这种差异出现的原因与规律。其次,按照跳数对信标间路径分类再计算各类路径平均跳距的分类平均跳距。最后,在分类平均跳距所提供未知节点与最近信标节点之间更精确距离估计的基础上,结合加权最小二乘法最终实现节点坐标解算。仿真实验表明,CADV-Hop算法在不增加算法复杂度以及额外硬件的情况下有效地降低了定位误差,随着信标节点数量增加,CADV-Hop算法比原型DV-Hop算法和两种改进的DV-Hop算法具有更高的定位精度。

基于加权马氏距离判别的计算机实验室火灾风险预警方法

为识别计算机实验室内潜在的火情,并及时发出分级火灾预警信息,该文提出一种基于加权马氏距离判别的计算机实验室火灾风险预警方法,通过部署多种类型传感器采集并分析易引起火灾风险的多项指标数据,判断实验室内火灾风险等级,并针对不同火灾风险等级提出相应的预警处理措施。通过模拟实验证明,基于加权马氏距离判别的计算机实验室火灾风险预警方法具有较高的预警精度。

WSN中基于Stateless Weight的移动代理路由设计

为了使移动代理的路由不再过分依赖于网络结构,提出了一种应用在无线传感器网络(WSN)中的移动代理的路由设计(SWR-MA)。在SWR-MA中,引入了一个与节点位置有关的参数Weight,通过比较Weight值,移动代理可以自主的确定自己的路径。SWR-MA可以应用在拓扑改变的网络中,比如带有移动Sink节点的网络中。最后,对该路由算法进行了仿真评价。

两级融合的多传感器数据融合算法研究

针对智慧工厂监测环境中多源数据融合精度问题,提出了一种两级融合的多传感器数据融合方法,旨在提高多源数据融合的准确性和可靠性。该方法分为一级数据融合和二级决策融合,首先采用卡尔曼滤波结合自适应加权平均对同类型传感器进行数据降噪融合处理,其次利用人工兔优化算法(ARO)优化ELM神经网络进行决策融合。实验结果表明,基于ARO优化ELM神经网络的多传感器数据融合算法在融合精度方面优于其他先进算法。经验证,所提出的两级融合多传感器数据融合方法具有更好的融合性能,有效提升感知系统的可靠性和鲁棒性,实现更加准确和可靠的监测和预测。

基于加权最小二乘法的光纤光栅应变传感器参数敏感性自动优化算法

常规光纤光栅应变传感器参数优化方法主要依托于麻雀搜索算法,但此方法忽略了应变传递的影响,导致传感器灵敏度较低,无法保证测量精度。为此,提出基于加权最小二乘法的光纤光栅应变传感器参数敏感性自动优化算法。依据光纤光栅应变传感器的组成结构与工作原理,充分考虑应变传递系数的变化规律,计算应变传递损耗,并采用光传输矩阵计算光纤中心波长的电场振幅,由此得到传感器敏感场分布,结合模式耦合理论,以敏感场分布均匀性和最大电容与最小电容比值最小为目标函数建立参数优化模型,并采用加权最小二乘法对采样点加权获取优化响应值,进而得到传感器最优结构参数。对比实验结果表明,利用所提方法对光纤光栅应变传感器参数进行优化后,传感器的灵敏度较高,保证了传感器的测量精度。

Cluster Based Secure Dynamic Keying Technique for Heterogeneous Mobile Wireless Sensor Networks

In Heterogeneous Wireless Sensor Networks, the mobility of the sensor nodes becomes essential in various applications. During node mobility, there are possibilities for the malicious node to become the cluster head or cluster member. This causes the cluster or the whole network to be controlled by the malicious nodes. To offer high level of security, the mobile sensor nodes need to be authenticated. Further, clustering of nodes improves scalability, energy efficient routing and data delivery. In this paper, we propose a cluster based secure dynamic keying technique to authenticate the nodes during mobility. The nodes with high configuration are chosen as cluster heads based on the weight value which is estimated using parameters such as the node degree, average distance, node's average speed, and virtual battery power. The keys are dynamically generated and used for providing security. Even the keys are compromised by the attackers, they are not able to use the previous keys to cheat or disuse the authenticated nodes. In addition, a bidirectional malicious node detection technique is employed which eliminates the malicious node from the network. By simulation, it is proved that the proposed technique provides efficient security with reduced energy consumption during node mobility.

Energy efficient clustering algorithm based on neighbors for wireless sensor networks

In this paper, an energy efficient clustering algorithm based on neighbors （EECABN） for wireless sensor networks is proposed. In the algorithm, an optimized weight of nodes is introduced to determine the priority of clustering procedure. As improvement, the weight is a measurement of energy and degree as usual, and even associates with distance from neighbors, distance to the sink node, and other factors. To prevent the low energy nodes being exhausted with energy, the strong nodes should have more opportunities to act as cluster heads during the clustering procedure. The simulation results show that the algorithm can effectively prolong whole the network lifetime. Especially at the early stage that some nodes in the network begin to die, the process can be postponed by using the algorithm.