动态无线传感器反应网络事件驱动定位算法

动态无线传感器反应网络（WSANs）是传统无线传感器网络（WSNs）的衍生物,是由大量资源受限的传感器节点和少量资源不受限的反应节点组成的动态网络,采用无线通信的方式进行通信,具有很强的实用性和应用前景。在充分分析动态WSNs特点的基础上,提出了一种基于接收信号强度指示（RSSI）测距的分布式事件驱动定位算法,并利用OPNET网络仿真软件对所设计的算法进行了仿真验证,结果表明：所设计算法具有较高的定位精度和能效性。     

基于EKF算法的无线传感器网络定位技术研究

无线传感器网络中,基于RSSI测距技术的定位系统误差较大。对扩展卡尔曼滤波(EKF)算法在抑制测距误差和提高定位精度方面进行了深入的研究。同时根据EKF算法在WSN节点定位中的两种应用方式,以收敛概率和相对误差为指标,在各种拓扑条件下对提高节点定位精度进行了分类探讨。最后结合仿真阐述了EKF算法的适用范围,并分析了影响算法性能的因素。     

基于球面坐标的目标定位位置的封闭解算法

针对无线传感网络WSNs(Wireless Sensor Networks)的三维目标定位问题,提出基于接收信号强度RSS(Received Signal Strength)和到达角度AoA(Angle of Arrival)混合目标定位RATL(RSS and AoA-based Target Localization algorithm)算法.RATL算法融合了RSS和AoA观察值.通过将笛卡尔坐标转换球面坐标,并充分利用AoA测量值的优势,RATL算法建立测量值与未知目标位置间的关系,进而获取简单、封闭解.相比于现在算法,RATL算法并不要求更多松驰.实验数据表明,RATL算法比同类算法的复杂度低、定位精度高.     

一种无线传感器网络中的定位精度改进方法

无线定位是无线传感器网络的一个重要应用,但目前仍然存在着定位精度不高、定位性能差的问题。通过针对TDOA(Time Difference of Arrival)/AOA(Angle of Arrival)混合式方法定位的超宽带(Ultra-Wideband)无线传感器定位系统,提出利用迭代去噪和卡尔曼滤波器的混合式处理方法。在数据处理层面上改善系统的定位性能,从而提高定位可靠性。现场试验的数据表明该方法确实可以改善系统的定位效果。     

DV-Hop localization algorithm based on bacterial foraging optimization for wireless multimedia sensor networks

DV-Hop localization algorithm is a classic range free localization algorithm in wireless sensor networks (WSNs). Although easy to be employed in low cost and resource limited WSNs, DV-Hop localization algorithm suffers from low localization accuracy as the other range free localization approaches. To improve the localization accuracy, in this paper we introduce Bacterial Foraging Optimization (BFO), an efficient optimization method that has been widely applied in a variety of scientific and engineering applications. We conduct extensive simulations under different network setting, the simulation results demonstrate that the proposed algorithm achieves significantly higher positioning accuracy than the basic DV-Hop algorithm.

     

DARE: evaluating Data Accuracy using node REputation

Typical wireless sensor networks (WSNs) applications are characterized by a certain number of different requirements such as: data accuracy, localization, reputation, security, and confidentiality. Moreover, being often battery powered, WSNs face the challenge of ensuring privacy and security despite power consumption limitations. When the application scenario allows their use, data aggregation techniques can significantly reduce the amount of data exchanged over the wireless link at the price of an increased computational complexity and the potential exposition to data integrity risks in the presence of malicious nodes. In this paper, we propose DARE, an hybrid architecture combining WSNs with the wireless mesh networking paradigm in order to provide secure data aggregation and node reputation in WSNs. Finally, the use of a secure verifiable multilateration technique allows the network to retain the trustworthiness of aggregated data even in the presence of malicious node. Extensive performance evaluations carried out using simulations as well as a real-world prototype implementation, show that DARE can effectively reduce the amount of data exchanged over the wireless medium delivering up to 50% battery lifetime improvement to the wireless sensors.     

A survey on sensor localization

Localization is one of the fundamental problems in wireless sensor networks (WSNs), since locations of the sensor nodes are critical to both network operations and most application level tasks. Although the GPS based localization schemes can be used to determine node locations within a few meters, the cost of GPS devices and non-availability of GPS signals in confined environments prevent their use in large scale sensor networks. There exists an extensive body of research that aims at obtaining locations as well as spatial relations of nodes in WSNs without requiring specialized hardware and/or employing only a limited number of anchors that are aware of their own locations. In this paper, we present a comprehensive survey on sensor localization in WSNs covering motivations, problem formulations, solution approaches and performance summary. Future research issues will also be discussed.     

移动无线传感器网络采样区域自调整的MCL定位算法

定位技术是无线传感器网络中关键的支撑技术之一。现有的无线传感器网络定位算法大多是针对静态场景的,不能直接应用于移动无线传感器网络。针对移动无线传感器网络的特点,在深入分析现有蒙特卡洛算法的基础上,提出一种改进机制,即采样区域自调整的蒙特卡洛节点定位(SA_MCL)算法。该算法通过对节点历史位置信息插值模拟获得节点的运动速度和方向,目的是为了自动调整采样区域,从而提高定位精度。仿真结果表明,采用SA_MCL算法,节点的定位精度有较大提高。     

Node localization algorithm for wireless sensor networks using compressive sensing theory

In recent years, localization has been recognized as an important supporting technology for wireless sensor networks (WSNs). Along with the increase in WSN indoor applications, indoor localization has become a hot research topic and many localization algorithms have been studied. Among these algorithms, the localization method based on compressive sensing theory emerges as a popular approach to indoor localization. In this approach, the nodes are sparse when compared to the number of grids utilized to represent the locations of the nodes, so the locations are considered as sparse signal and can be reconstructed using the compressive sensing techniques. The localization problem is formulated as the sparse reconstruction of sparsifying matrix which is comprised of measurement of received signal at grids. In order to improve the localization accuracy and meet the real-time requirement of localization applications in large indoor area, an indoor localization algorithm based on dynamic measurement compressive sensing for wireless sensor networks is proposed. Using the bounding-box method, we firstly identify a potential area that possesses the independent features. Instead of using the entire node deployment region as the measurement area, our method can decrease the number of meshing and also the dimension of measurement matrix. Meanwhile, we assume that only the anchor nodes which have communication relationship with the unknown nodes can be used as the measuring nodes; the measurement matrix of unknown nodes which need to be localized can be dynamically constructed according to the potential area and the received anchor node information, and the maximum number of measurement is decided by the number of grids of potential area. The proposed algorithm can mitigate the measurement redundancy and improve the real-time feature. Simulation results indicate that the proposed algorithm can reduce the time complexity and also maintain good localization accuracy and localization efficiency.     

无线传感器网络距离自调整的MDS定位算法

定位技术是无线传感器网络中的关键支撑技术之一。针对移动无线传感器网络的特点,在深入分析现有多维定标节点定位算法的基础上,提出一种改进机制,即距离自调整的多维定标节点定位算法(SA_MDS)。该算法运用3种方法估算节点的两跳距离,然后自动调整节点间的估算距离,从而提高定位精度。仿真结果表明,采用SA_MDS算法,节点的定位精度有较大提高。     

基于半定规划的恶劣环境下定位修正算法

无线传感网络WSNs(Wireless Sensor Networks)的定位精度严重受到环境的影响,尤其是高噪声电平和非视距连接的恶劣环境,定位精度急剧下降.为此,提出基于半定规划的恶劣环境下定位修正算法,记为ESDP O算法.该修正算法以半定规划ESDP(Edge-Semi-Definite Programming)算法为基础,旨在提高定位精度,并降低算法复杂性,进而减少定位时间.ESDP O算法通过引用抖动矩阵,对ESDP算法进行修改,提高了算法在恶劣环境的健壮性.同时,ESDP O算法通过寻找低秩解,减少高噪声和非视距偏差.仿真结果表明,在高噪声和非视距NLOS(Non Line of Sight)的恶劣环境下,ESDP O算法的定位精度优于基于同类算法,并且降低了定位的复杂度.     

基于无线传感器网络的同时定位与跟踪

研究无线传感器网络在位置信息不确定时,同时定位无线传感器网络节点并跟踪移动目标。利用RSSI测量节点对之间的距离,多维定标技术根据距离矩阵完成传感器网络的初始定位。估计与更新阶段提出了压缩EKF滤波确定传感器节点位置和目标位置。仿真结果显示:算法在较低的网络覆盖率下有较高的定位和跟踪准确度,在初始定位误差为5m时,节点和跟踪误差均小于3m,特别是在长距离的跟踪任务中有很好的精度和实时性。     

无线传感器网络中基于有效三角形判定的改进APIT定位方法

在无线传感器网络中,节点自定位是网络的基本功能,是定位跟踪的前提。基于近似三角形内点测试（APIT）的自定位算法在定位过程中计算量大,耗能较高。针对上述问题,提出了一种利用分离定理和面积法排除无效三角形的方法,该方法在保证一定定位精度的基础上,降低了APIT的次数和传感器的通信量,延长了无线传感器网络的使用寿命。     

An efficient range-free localization algorithm for wireless sensor networks

Node positioning is a fundamental problem in applications of wireless sensor networks (WSNs). In this paper, a new range-free algorithm, called spring swarm localization algorithm (SSLA), is proposed for positioning WSNs. To determine the locations of sensor nodes, the proposed algorithm uses network topology information and a small fraction of sensor nodes which know their locations. Numerical simulations show that high positioning accuracy can be obtained by using the algorithm. Some examples are given to...     

基于前进跳距期望的异构WSNs非测距定位算法

无线传感网络(WSNs,wireless sensor networks)中传感节点的传输范围直接决定节点的通信区域,对定位精度有直接的影响.为此,针对异构WSNs,提出基于前进跳距期望的非测距定位算法.首先,分析传统推导前进跳距期望(EHP,expected hop progress)方法的不足,并证实了EHP值只依赖锚节点的传输范围是不准确的;然后,采用新方法推导了EHP,并结合泰勒级数展开以及加权最小二乘算法估计未知传感节点位置;最后,以降低误差为目的,迭代修正未知传感节点位置的估计值,从而提高定位精度.仿真结果表明,与传统的非测距定位算法相比,提出的算法的定位精度得到有效提升.     

Sensor selection for received signal strength-based source localization in wireless sensor networks

Generally, localization is a nonlinear problem, while linearization is used to simplify this problem. Reasonable approximations could be achieved when signal-to-noise ratio (SNR) is large enough. Energy is a critical resource in wireless sensor networks, and system lifetime needs to be prolonged through the use of energy efficient strategies during system operation. In this paper, a closed-form solution for received signal strength (RSS)-based source localization in wireless sensor network (WSN) is obtained...     

无线传感器网络在电力系统中的应用

概括了目前应用于电力系统中的几种主要的通信技术,包括电力线载波通信、光纤通信和无线通信技术,分析了几种通信方式的优势和不足。介绍了无线传感器网络的应用现状,研究了无线传感器节点构成与工作原理,并探讨了无线传感器网络在变电站自动化系统、电能质量监测、配电网设备监控与故障定位、输电线路实时监测等方面的应用。     

无线传感器网络节点自定位算法研究

节点位置信息是无线传感器网络应用的基础,该文介绍了几种典型的节点自定位算法,并对其进行了分析比较。其中ACT算法是分布式的算法,定位精度最高,但是最大的缺点就是计算发杂性高。该文对传统的ACT算法进行了改进,降低了算法的复杂性,提高了定位的精度。仿真试验表明:改进的ACT算法大幅降低了算法的运算复杂性,对于定位精度也有一定的提高。     

基于Kullback-Leibler分歧的变分滤波WSNs贝叶斯移动定位跟踪西

为了在降低资源能耗和带宽占用情况下,提高无线传感器网络WSNs移动目标定位跟踪的精度,提出了基于Kullback-Leibler分歧的变分滤波的WSNs贝叶斯移动目标定位跟踪算法。首先,利用高斯和Wishart分布在不考虑速度限制和方向移动限制情况下,构建WSNs移动定位的贝叶斯状态演化模型,并基于路径损耗模型构建移动目标定位的观测模型;其次,利用Kullback-Leibler分歧构建变分滤波的误差计算模型,通过周围激活节点实现移动节点目标的位置估计,设计了递归概率计算过程综合预测和更新两个过程,并实现了定位和目标跟踪的同步化;最后,通过仿真验证了所提模型在跟踪精度和资源节约上的优势。     

基于无线传感器网络节点的单兵鞋设计

无线传感器网络(WSNs)定位是传感器定位方法中比较实用、可靠的一种技术.为满足单兵作战环境需要,传感器定位技术只能采用无线、可靠、低功耗的技术.在基于WSNs背景下,将传感器定位系统安装鞋底,并通过定位系统自身感知、处理能力将周围环境定位结果实时显示在单兵作战头盔上.无线传输方式可以将战场环境信息实时返回到指挥中心,方便指挥员根据实时战场环境做出正确判断.