Node Localization Based on Improved PSO and Mobile Nodes for Environmental Monitoring WSNs

In this paper, a novel iterative localization algorithm based on improved particle swarm optimization (PSO) is proposed for monitoring environment like lakes, rivers or other water bodies. The first step of this algorithm is to get the position of some unknown nodes by using improved PSO algorithm. The second step is to locate other nodes by using these unknown nodes in first step as new anchor nodes. The localization problem of island node in sparse distributed grid is solved by introducing adaptive mobile node in this paper. The simulation results show that the algorithm has the advantages of small location error and little influence by environmental factors.     

一种改进的蒙特卡罗定位算法研究

针对移动无线传感器网络中节点随机运动的情况,蒙特卡罗定位(MCL)算法有较好的定位精度,但由于MCL方法严格过滤而进行的频繁重采样带来大量计算,加重了节点能量消耗,针对上述情况提出了基于接收信号强度(received signal strength,RSS)的蒙特卡罗定位算法,该算法利用锚节点之间的距离及其测得的移动节点的RSS值来校正移动节点与每个锚节点之间的权值,缩小了传统MCL算法的采样范围。仿真表明,该方法降低了蒙特卡罗方法的采样次数以及通信开销,同时提高了节点定位精度。     

Reference node placement and selection algorithm based on trilateration for indoor sensor networks

The key problem of location service in indoor sensor networks is to quickly and precisely acquire the position information of mobile nodes. Due to resource limitation of the sensor nodes, some of the traditional positioning algorithms, such as two‐phase positioning (TPP) algorithm, are too complicated to be implemented and they cannot provide the real‐time localization of the mobile node. We analyze the localization error, which is produced when one tries to estimate the mobile node using trilateration method in the localization process. We draw the conclusion that the localization error is the least when three reference nodes form an equilateral triangle. Therefore, we improve the TPP algorithm and propose reference node selection algorithm based on trilateration (RNST), which can provide real‐time localization service for the mobile nodes. Our proposed algorithm is verified by the simulation experiment. Based on the analysis of the acquired data and comparison with that of the TPP algorithm, we conclude that our algorithm can meet real‐time localization requirement of the mobile nodes in an indoor environment, and make the localization error less than that of the traditional algorithm; therefore our proposed algorithm can effectively solve the real‐time localization problem of the mobile nodes in indoor sensor networks. Copyright © 2008 John Wiley & Sons, Ltd.     

基于连通度和加权校正的移动节点定位算法

移动节点定位问题是无线传感器网络中的研究重点。针对移动节点定位误差大的问题,提出一种基于连通度和加权校正的移动节点定位算法。在未知节点移动过程中,根据节点间连通度大小选取参与定位的信标节点,利用加权校正方法修正RSSI测距信息,然后用最小二乘法对未知节点进行位置估计。仿真分析表明,节点通信半径和信标密度在一定范围内,该算法表现出良好的定位性能,定位精度明显提升。     

无人机辅助的基于前馈神经网络的节点定位算法

针对无线传感网络(WSNs)的节点定位问题,提出无人机辅助的基于前馈神经网络的节点定位(UAV-NN)算法。UAV-NN算法利用无人机(UAV)作为锚节点,并由UAV周期地发射beacon信号,利用极端学习机(LEM)训练单隐藏前向反馈的神经网络(SLFN),未知节点接收来自UAV发射的beacon信号,并记录其接收信号强度指示(RSSI),已训练的SLFN再依据RSSI值估计节点位置。仿真结果表明,相比于传统的基于RSSI定位算法,提出的UAV-NN算法无需部署地面锚节点;相比其他传统的机器学习算法,UAV-NN算法通过引用ELM,减少了定位误差。     

移动无线传感器网络定位算法研究

为了解决移动无线传感器网络中节点的自身定位问题,提出了一种基于运动预测的定位算法。该算法利用节点运动的连续性和接收信号强度测距方式,保存其最近两组历史位置信息,并结合节点当前的状态来估计自身位置。它不需要额外的硬件支持与较高的信标节点密度,满足复杂传输环境的应用要求。仿真结果表明,该算法具有较好的鲁棒性和较高的定位精度,节点随机运动时的定位误差约为15%,而直线运动时只有12%。     

基于边框定界的WSN分布式全搜索定位算法

提出一种基于边框定界的WSN分布式全搜索定位算法。该算法通过节点测距得到邻居节点的坐标和距离信息,然后通过边框定界方法确定节点存在的位置区域,最后将位置区域网格化,并用全搜索方法在该区域搜索最佳估计点,最佳估计点的坐标即为节点的定位坐标。该算法应用到网络时需运行多轮,通过逐步求精得到节点的定位坐标。仿真实验表明该算法达到当前其他复杂定位算法的性能。     

MOBILITY AIDED LOCALIZATION OF WIRELESS SENSOR NETWORKS WITH INACCURATE RANGE MEASUREMENTS

This paper studies the relationship between mobility, navigation and localization in the context of wireless sensor networks with mobile beacons. It is observed that mobility can aid in network node localization and that once localized, the network nodes can localize and track a mobile object and guide its navigation. A distributed kernel-based algorithm is proposed that enables the nodes to establish confident position estimates in the presence of ranging inaccuracies. The proposed approach features robustness with respect to range measurement inaccuracies, low complexity and distributed implementation, using only local information. Simulation validates our approach viable.     

Moving anchor node localization algorithm based on network connectivity

In order to better solve the contradiction between precision of localization and the number of anchor nodes in wireless sensor network,a mobile anchor node localization technology based on connectivity was proposed.First,the coverage characteristic of the network nodes was analyzed,and a critical value was found between the mobile step and the anchor node communication radius,mobile anchor nodes＇ coverage characteristic would change when near this critical value.Second,a mobile anchor node followed a planning path to form a positioning area seamless coverage was used.Finally,when there was no need for high-precision technology,node position would been estimated according with the connectivity of the network and the receiving information of the node.The simulation results show that the proposed algorithm can realize coarse-grained localization,and paths perform complete localization.     

基于EKF的无线传感器网络移动信标定位算法

针对无线传感器网络节点定位精度较低的问题,提出一种基于扩展卡尔曼滤波的移动信标节点定位算法。该算法采用等距三重优化覆盖思想确定虚拟信标分布,利用蚁群算法获取最优遍历路径,同时引入扩展卡尔曼滤波算法以提高节点定位精度。通过对节点通信半径、虚拟信标数目、路径长度、迭代次数等参数分别进行仿真验证,结果表明本文算法定位精度明显优于普通质心定位算法,同时该算法在提高网络覆盖度、降低网络成本等方面也有较大优势。     

Range‐free intersecting chord‐based geometric localization scheme for wireless sensor networks

Recent advancement in wireless sensor network has contributed greatly to the emerging of low‐cost, low‐powered sensor nodes. Even though deployment of large‐scale wireless sensor network became easier, as the power consumption rate of individual sensor nodes is restricted to prolong the battery lifetime of sensor nodes, hence the heavy computation capability is also restricted. Localization of an individual sensor node in a large‐scale geographic area is an integral part of collecting information captured by the sensor network. The Global Positioning System (GPS) is one of the most popular methods of localization of mobile terminals; however, the use of this technology in wireless sensor node greatly depletes battery life. Therefore, a novel idea is coined to use few GPS‐enabled sensor nodes, also known as anchor nodes, in the wireless sensor network in a well‐distributed manner. Distances between anchor nodes are measured, and various localization techniques utilize this information. A novel localization scheme Intersecting Chord‐Based Geometric Localization Scheme (ICBGLS) is proposed here, which loosely follows geometric constraint‐based algorithm. Simulation of the proposed scheme is carried out for various communication ranges, beacon broadcasting interval, and anchor node traversal techniques using Omnet++ framework along with INET framework. The performance of the proposed algorithm (ICBGLS), Ssu scheme, Xiao scheme, and Geometric Constraint‐Based (GCB) scheme is evaluated, and the result shows the fact that the proposed algorithm outperforms the existing localization algorithms in terms of average localization error. The proposed algorithm is executed in a real‐time indoor environment using Arduino Uno R3 and shows a significant reduction in average localization time than GCB scheme and similar to that of the SSU scheme and Xiao scheme.     

基于定向天线的无线传感网络环境自适应定位算法

在无线传感网络的各类应用中,传感器节点位置信息有着重要价值,在现有基于锚节点的各类定位算法设计中,锚节点大多采用全向天线技术,这虽然增加了算法设计的便利性,但是因其固有的物理缺陷,定位效果不理想。在充分分析现有各类天线技术的基础上,提出了一种基于定向天线测距的环境自适应分布式定位算法,并利用实验室现有设备搭建实验环境,对所设计的算法进行实验验证,结果表明其具有较高的定位精度和应用价值。     

基于ToA和误差抑制的大规模网络节点定位

针对混合视距/非视距环境中的移动节点定位,提出一种基于到达时间测量值和误差抑制的定位方案。首先,配备有超宽带无线电的节点随机移动,以收集到达时间测量数据,并执行最短路径距离选择算法得到包括一跳节点距离在内的非视距误差减小后的多跳节点距离;采用多维标度确定节点的初始位置;采用迭代三边测量法和误差积累管理相结合来获得定位节点位置。仿真实验结果表明,提出的定位方案的定位精确度优于其他几种常用方案。     

一种基于网络密度分簇的移动信标辅助定位方法

现有移动信标辅助定位算法未充分利用网络节点分布信息,存在移动路径过长及信标利用率较低等问题。该文把网络节点分簇、增量定位与移动信标辅助相结合,提出了一种基于网络密度分簇的移动信标辅助定位算法(MBL(ndc))。该算法选择核心密度较大的节点作簇头,采用基于密度可达性的分簇机制把整个网络划分为多个簇内密度相等的簇,并联合使用基于遗传算法的簇头全局路径规划和基于正六边形的簇内局部路径规划方法,得到信标的优化移动路径。当簇头及附近节点完成定位后,升级为信标,采用增量定位方式参与网络其它节点的定位。仿真结果表明,该算法定位精度与基于HILBERT路径的移动信标辅助定位算法相当,而路径长度不到后者的50％。     

分布式交互方向拉格朗日乘子声源定位算法

如何实现高效的分布式声源定位是无线传感器网络研究的热点。通过一种基于声源信号能量的分布式声源定位算法,采用交互方向的拉格朗日乘子方法将最大似然声源定位问题拆分到单个传感器节点,通过桥接传感器节点实现传感器节点之间的信息融合。由于采用声源信号衰减模型,交互方向拉格朗日乘子方法中的最优化目标函数成为非凸函数,导致定位算法容易陷入局部最优,为此提出了多重网格搜索方法。仿真结果表明,新算法与现有的分布式声源定位算法相比,具有可并行实现,可应用于任意网络拓扑,不易陷于局部最优等优点。     

无线传感器网络中基于潜在博弈的分布式节点定位

分布式定位是无线传感器网络研究中的热点问题.传统的分布式定位机制存在定位精度低,算法求解复杂等问题.为此,以与邻居节点集距离误差和作为效益函数,提出基于博弈论的分布式定位模型.给出了该博弈模型为潜在博弈的形式化证明,并从理论上证明了纳什均衡的存在性及最终收敛解的有效性.进一步,仅通过与邻居节点进行策略信息交互,提出基于潜在博弈的分布式定位算法.最后设计了博弈策略空间的决策机制与未知节点的升级机制,以避免陷入局部最优并加速收敛至全网最优解.一系列仿真实验验证了所提算法的有效性.     

一种快速低开销的移动节点分布式寻路RPL路由协议

针对低功耗有损网络(Low Power and Lossy Network, LLN)中移动节点作为中继节点时,存在无线链路易断开、数据传输成功率较低和控制开销较高的问题,提出了一种快速低开销的移动节点分布式寻路RPL路由协议(Mobile Node Distributed Pathfinding RPL Routing Protocol, MNA-RPL)。首先,提出一种邻居表自适应更新机制,依据邻居表快速切换父节点;其次,提出一种父子节点双断开机制,能够有效减少数据包丢失;最后,提出一种基于速度动态管理的Trickle计时器,根据自身速度动态调整DIO广播周期。理论分析和仿真结果表明,与主流路由协议相比,该协议在数据传输和控制开销等方面的性能均有一定的提升。     

LIS: Localization based on an intelligent distributed fuzzy system applied to a WSN

The localization of the sensor nodes is a fundamental problem in wireless sensor networks. There are a lot of different kinds of solutions in the literature. Some of them use external devices like GPS, while others use special hardware or implicit parameters in wireless communications.In applications like wildlife localization in a natural environment, where the power available and the weight are big restrictions, the use of hungry energy devices like GPS or hardware that add extra weight like mobile directional antenna is not a good solution.Due to these reasons it would be better to use the localization’s implicit characteristics in communications, such as connectivity, number of hops or RSSI. The measurement related to these parameters are currently integrated in most radio devices. These measurement techniques are based on the beacons’ transmissions between the devices.In the current study, a novel tracking distributed method, called LIS, for localization of the sensor nodes using moving devices in a network of static nodes, which have no additional hardware requirements is proposed.The position is obtained with the combination of two algorithms; one based on a local node using a fuzzy system to obtain a partial solution and the other based on a centralized method which merges all the partial solutions. The centralized algorithm is based on the calculation of the centroid of the partial solutions.Advantages of using fuzzy system versus the classical Centroid Localization (CL) algorithm without fuzzy preprocessing are compared with an ad hoc simulator made for testing localization algorithms.With this simulator, it is demonstrated that the proposed method obtains less localization errors and better accuracy than the centroid algorithm.     

SENMA网络加权质心定位算法

移动代理传感器网络(SENMA)由移动代理节点负责数据处理、接入、转发、传输和路由等工作,更加节能。通过分析指出SENMA网络进行节点定位最适于采用基于距离定位算法,依据网络特点基于接收信号强度指示(RSSI)测距技术较为可行。依据信道RSSI特点,提出了一种加权质心定位算法,并利用Matlab编程进行了仿真验证,证明了其性能优于极大似然估计定位算法,更为适用于SENMA网络。     

基于自适应采样的移动WSN定位算法

节点自定位是无线传感器网络的关键技术之一。当前对无线传感器网络定位的研究主要集中静态节点定位,移动无线传感器网络定位研究相对较少。研究了基于序列蒙特卡罗方法的移动无线传感器网络定位。针对蒙特卡罗定位采用固定样本数,计算量大的缺点,根据蒙特卡罗定位盒（MCB）算法的锚盒子大小动态设置样本数,提出一种自适应采样蒙特卡罗盒定位算法。仿真表明,该算法在保持定位精度的同时有效地减小了采样次数,节约了计算量。