基于改进双系统协同进化算法的无线传感器网络节点定位

为进一步提高无线传感器网络(WSN)中节点的定位精度,提出了一种双系统协同进化(BCO)算法。改进算法利用粒子群优化(PSO)算法快速收敛的特性和混合蛙跳算法(SFLA)较高的寻优精度的特性,在较少的迭代次数内快速收敛且实现深度搜索达到较高的精度。仿真实验结果表明:在应用双系统协同进化算法对测试目标函数进行求解时,能非常接近最优解;同时将该算法应用到基于接收信号强度值(RSSI)测距的节点定位中,预测位置与实际位置的绝对误差在0.05 m范围内;相比基于RSSI的分步粒子群算法(IPSO-RSSI),其定位精度至少提高了10倍。     

无线传感器网络局部协同定位算法

针对无线传感器网络中距离定位算法精度和覆盖率低的问题,提出了局部协同定位算法(LCLA)。该算法通过对节点路径损耗指数的局部计算,将通信中受到环境或者障碍物影响的锚节点判定为无效锚节点;同时引入协同定位思想,将满足误差要求的已定位节点升级为锚节点,并参与其他未知节点的定位,以提高定位的覆盖率。节点定位时,若收到多个锚节点信号,优先选取初始的有效锚节点对其进行定位;当有效锚节点个数不足以定位时,再选取升级后的锚节点,以减少累积误差,提高定位精度。仿真结果表明,局部协同定位算法在定位覆盖率和精度方面优于改进的接收信号强度指示(RSSI)定位算法、多维尺度分析(MDS-MAP)算法和协作定位算法。     

A novel heuristic approach for distance- and connectivity-based multihop node localization in wireless sensor networks

The availability of accurate location information of constituent nodes becomes essential in many applications of wireless sensor networks. In this context, we focus on anchor-based networks where the position of some few nodes are assumed to be fixed and known a priori, whereas the location of all other nodes is to be estimated based on noisy pairwise distance measurements. This localization task embodies a non-convex optimization problem which gets even more involved by the fact that the network may not be uniquely localizable, especially when its connectivity is not sufficiently high. To efficiently tackle this problem, we present a novel soft computing approach based on a hybridization of the Harmony Search (HS) algorithm with a local search procedure that iteratively alleviates the aforementioned non-uniqueness of sparse network deployments. Furthermore, the areas in which sensor nodes can be located are limited by means of connectivity-based geometrical constraints. Extensive simulation results show that the proposed approach outperforms previously published soft computing localization techniques in most of the simulated topologies. In particular, to assess the effectiveness of the technique, we compare its performance, in terms of Normalized Localization Error (NLE), to that of Simulated Annealing (SA)-based and Particle Swarm Optimization (PSO)-based techniques, as well as a naive implementation of a Genetic Algorithm (GA) incorporating the same local search procedure here proposed. Non-parametric hypothesis tests are also used so as to shed light on the statistical significance of the obtained results.     

基于MPSO-DV-Hop的无线传感器节点定位

节点定位技术是无线传感器网络的关键技术,为减小DV-Hop算法的节点定位误差,提出一种多子群粒子群（MPSO）算法优化DV-Hop的节点定位算法（MPSO-DV-Hop）。通过设置门限值修正节点间的跳数,提高了跳段距离估算精度,DV-Hop的第3阶段引入MPSO算法,对节点定位误差进行校正,通过引入多子群加快算法收敛速度,提高DV-Hop算法的节点定位精度,在MATLAB2008平台上对算法仿真分析。结果表明,MPSO-DV-Hop算法在不增加成本情况下,提高了传感器的节点定位精度,具有较高的应用价值。     

基于改进鲸鱼优化算法的外骨骼机器人步态检测

针对传统的外骨骼机器人步态检测算法中的信息单一化、准确率低、易陷入局部最优等问题，提出基于改进鲸鱼算法优化的支持向量机（IWOA-SVM）的外骨骼机器人步态检测算法，即在鲸鱼优化算法（WOA）中引入遗传算法（GA）的选择、交叉、变异操作，进而去优化支持向量机（SVM）的惩罚因子与核参数，再使用参数优化后的SVM建立分类模型，从而扩大算法的搜索范围，减小算法陷入局部最优的概率。首先，使用混合传感技术采集步态数据，即通过足底压力传感器和膝关节、髋关节角度传感器采集外骨骼机器人的运动数据，并作为步态检测系统的输入；然后，使用门限法对步态相位进行划分并标记标签；最后，将足底压力信号与髋关节、膝关节角度信号融合作为输入，使用IWOA-SVM算法完成对步态的检测。对6个标准测试函数进行仿真实验，并与GA、粒子群优化（PSO）算法、WOA进行比较，数值实验表明，改进鲸鱼优化算法（IWOA）的鲁棒性、寻优精度、收敛速度均优于其他优化算法。通过分析不同穿戴者的步态检测结果发现，准确率可达98.8%，验证了所提算法在新一代外骨骼机器人中的可行性和实用性，并与基于遗传优化算法的支持向量机（GA-SVM）、基于粒子群优化算法的支持向量机（PSO-SVM）、基于鲸鱼优化算法的支持向量机（WOA-SVM）算法进行比较，结果表明，该算法识别准确率分别提高了5.33%、2.70%、1.44%，能够对外骨骼机器人的步态进行有效检测，进而实现外骨骼机器人的精确控制及稳定行走。     

Research of localization approach for the new comer in wireless sensor networks

When some sensor nodes of wireless sensor networks (WSN) can not work forever because of long-term work or failure caused by attack, a few new comers need to be put into the network. For the application of the new comer in WSN, an accurate and effective localization algorithm based on received signal strength indicator (RSSI) is proposed. Through a few necessary nodes’ participation and the collaboration between the new comer and its one-hop and two-hop neighbor nodes, the accurate localization of the new comer is achieved. Simulation results show that the localization accuracy is about 17% of sensor node’s radio frequency (RF) transmission range, when the measurement error is 10% and the standard deviation for Gauss error of original sensor nodes’ coordinate is about 20% of sensor node’s RF transmission range. Simulation results also verify nice stability and adaptability of the new comer’s location algorithm.     

Efficient and accurate sensor network localization

Wireless sensor networks (WSN) have great potential in ubiquitous computing. However, the severe resource constraints of WSN rule out the use of many existing networking protocols and require careful design of systems that prioritizes energy conservation over performance optimization. A key infrastructural problem in WSN is localization—the problem of determining the geographical locations of nodes. WSN typically have some nodes called seeds that know their locations using global positioning systems or other means. Non-seed nodes compute their locations by exchanging messages with nodes within their radio range. Several algorithms have been proposed for localization in different scenarios. Algorithms have been designed for networks in which each node has ranging capabilities, i.e., can estimate distances to its neighbours. Other algorithms have been proposed for networks in which no node has such capabilities. Some algorithms only work when nodes are static. Some other algorithms are designed specifically for networks in which all nodes are mobile. We propose a very general, fully distributed localization algorithm called range-based Monte Carlo boxed (RMCB) for WSN. RMCB allows nodes to be static or mobile and that can work with nodes that can perform ranging as well as with nodes that lack ranging capabilities. RMCB uses a small fraction of seeds. It makes use of the received signal strength measurements that are available from the sensor hardware. We use RMCB to investigate the question: “When does range-based localization work better than range-free localization?” We demonstrate using empirical signal strength data from sensor hardware (Texas Instruments EZ430-RF2500) and simulations that RMCB outperforms a very good range-free algorithm called weighted Monte Carlo localization (WMCL) in terms of localization error in a number of scenarios and has a similar computational complexity to WMCL. We also implement WMCL and RMCB on sensor hardware and demonstrate that it outperforms WMCL. The performance of RMCB depends critically on the quality of range estimation. We describe the limitations of our range estimation approach and provide guidelines on when range-based localization is preferable.     

面向多跳WSNs的基于LSSVR的节点定位算法

多跳无线传感网络WSNs(Wireless Sensor Networks)中的多类应用均需要准确的位置信息.为此,提出面向多跳WSNs的基于最小二乘支持向量回归机定位算法 LSSVR-LA(Least-Squares Support Vector Regression location algorithm).LSSVR-LA算法先引用转发区域概念,并通过转发区域建立测距模型,然后再利用Secant 算法估计传感节点与锚节点间距离,最后将这些距离作为LSSVR输入,建立了基于LSSVR定位算法模型.最终,估计未知节点的位置.实验数据表明,提出的LSSVR-LA算法的定位精度得到有效地提高.     

水声传感器网络节点自适应加权定位算法

对水声传感器网络节点定位进行研究,针对水声传感器网络节点间测距精度不高的问题,提出一种水声传感器网络节点自适应加权定位算法。考虑到水声传感器网络节点间的测距误差随着节点间距离的增大而增大,算法改进了锚节点选择机制,并且对不同锚节点在定位测度中的权重进行加权,改进定位测度,提高了测距信息的利用效率。仿真实验表明该算法提高了节点定位精度。     

Research of localization approach for the new comer in wireless sensor networks

When some sensor nodes of wireless sensor networks (WSN) can not work forever because of long-term work or failure caused by attack, a few new comers need to be put into the network. For the application of the new comer in WSN, an accurate and effective localization algorithm based on received signal strength indicator (RSSI) is proposed. Through a few necessary nodes’ participation and the collaboration between the new comer and its one-hop and two-hop neighbor nodes, the accurate localization of the new comer is achieved. Simulation results show that the localization accuracy is about 17% of sensor node’s radio frequency (RF) transmission range, when the measurement error is 10% and the standard deviation for Gauss error of original sensor nodes’ coordinate is about 20% of sensor node’s RF transmission range. Simulation results also verify nice stability and adaptability of the new comer’s location algorithm.     

Metaheuristics Based Node Localization Approach for Real-Time Clustered Wireless Networks

In recent times, real time wireless networks have found their applicability in several practical applications such as smart city, healthcare, surveillance, environmental monitoring, etc. At the same time, proper localization of nodes in real time wireless networks helps to improve the overall functioning of networks. This study presents an Improved Metaheuristics based Energy Efficient Clustering with Node Localization (IM-EECNL) approach for real-time wireless networks. The proposed IM-EECNL technique involves two major processes namely node localization and clustering. Firstly, Chaotic Water Strider Algorithm based Node Localization (CWSANL) technique to determine the unknown position of the nodes. Secondly, an Oppositional Archimedes Optimization Algorithm based Clustering (OAOAC) technique is applied to accomplish energy efficiency in the network. Besides, the OAOAC technique derives a fitness function comprising residual energy, distance to cluster heads (CHs), distance to base station (BS), and load. The performance validation of the IM-EECNL technique is carried out under several aspects such as localization and energy efficiency. A wide ranging comparative outcomes analysis highlighted the improved performance of the IM-EECNL approach on the recent approaches with the maximum packet delivery ratio (PDR) of 0.985.     

基于改进蛙跳算法的WSN移动节点部署研究

针对传统无线传感移动节点部署方法存在节点分布不均匀、覆盖不完全等问题,提出一种基于改进混合蛙跳算法(SFLA)的移动节点部署方法。根据节点位置信息建立部署模型,利用改进SFLA算法求解该模型,将得到的解作为节点最终位置。仿真实验结果表明,相对于微粒群、虚拟力、基本混合蛙跳算法,改进SFLA算法可提高网络覆盖率和降低移动节点能耗。     

基于多粒度流形学习的无线传感器网络定位方法*

针对FastMDS-MAP定位算法存在对不规则无线传感器网络定位误差大,选取的框架节点不能很好的体现网络的拓扑结构实现不同粒层定位的问题,通过选择不同的筛选半径获得不同粒度的框架节点,结合绝对坐标变换加权策略提出了基于多粒度流形学习的无线传感器网络定位方法(MG-MDS)。仿真实验结果表明,不规则网络中MG-MDS算法定位精度比FastMDS-MAP算法有明显的提高;且定位误差随着网络节点粒度的变细而变小。     

基于人工蜂群算法的DV-Hop定位改进

针对无线传感器网络无需测距依赖的DV-Hop定位算法节点定位精度不高的问题,将鲁棒性强、收敛速度快且全局寻优性能优异的人工蜂群算法引入到DV-Hop算法的设计中,提出了一种ABDV-Hop(Artificial Bee ColonyDV-Hop)算法。该算法在传统DV-Hop算法的基础上,利用节点间的距离和锚节点的位置信息,在DV-Hop算法的最后阶段,通过建立目标优化函数,实现对未知节点坐标的估计。仿真结果表明,与传统DV-Hop算法相比,在不增加传感器节点的硬件开销的基础上,改进算法能有效降低定位误差。     

运用海伦公式的最优化非视距抑制定位算法

无线网络中非视距误差对于三边定位算法精度的影响较大。因此本文针对存在固定节点与移动节点的无线传感网络系统,提出了一种基于面积残差最小化的最优化定位算法,算法中非视距误差影响下的定位问题被建模为二次规划问题,其核心在于运用海伦公式构建面积残差和优化目标函数。仿真结果在非视距误差较大或网络中固定节点较多时,本文提出的算法可以有效消除非视距误差引起的定位精度损失,同时本文算法还具有对节点数目要求小的优势。     

一种改进的无线自主传感器网络定位算法

节点定位技术是无线自主传感器网络中的关键技术之一。为了提高定位精度,提出一种基于几何斜率的无线传感器网络（WSN）定位算法。网络区域中的节点分为锚节点和未知节点,利用几何学斜率的方法选取合适的锚节点,能够更精确地确定未知节点的位置。在三边测量法上运用最小平方误差方法求解,能够提高算法的精度。在新算法的基础上建立Matlab仿真。仿真结果表明改进的DV-HOP算法,在相同的锚节点数量的情况下,节点定位精度有明显的提高。     

群混合算法应用于异构传感网络节点的优化部署

针对异构传感网络节点初始随机部署时产生覆盖盲区和覆盖冗余的问题,以降低节点成本和提高网络覆盖率为目标,引入ε-目标约束法,提出一种基于粒子群算法和鱼群算法的群混合算法。该群混合算法首先建立个体中心的概念,将鱼群算法的聚群行为和追尾行为的思想引入到粒子群算法中以快速寻取个体的最优位置的解域,再利用粒子群算法对个体的速度和位置进行迭代寻优。仿真结果表明,该群混合算法与标准粒子群算法和标准鱼群算法相比,在网络覆盖率和成本目标之间能达到更好的平衡和优化。     

基于蒙特卡罗算法煤矿井下人员定位研究

对比分析几种常用的无线传感器网络节点定位方法.针对煤矿井下节点移动性可能导致普通的定位算法变得不精确,提出了蒙特卡罗定位(Monte Carlo Localization)算法.该方法利用物体运动的连续性,通过选取合适的模型完成移动节点位置预测与定位.经仿真验证在低密度锚节点环境下,蒙特卡罗方法位置估计误差明显低于其它方法,提高了移动节点定位算法的准确性.     

无线传感器网络中的定位技术研究

传感器网络是综合了传感器、嵌入式计算、网络及无线通信等技术的一种全新信息获取和处理技术。由于许多应用需要精确的定位,因此过去几年无线传感器网络定位技术得到广泛关注。研究了几种典型的定位技术,并根据距离误差、节点密度、anchor节点数量和所需设备等要求对各算法进行了性能分析、比较。最后总结了这些定位技术用于无线传感器网络中存在的问题,并提出了下一步工作的设想,即研究一个公共的三阶段的分布定位算法。     

非合作信标条件下水下无线传感网可靠定位技术

传统假设水下无线传感器网络的传感器节点和信标节点都是合作的,但是在军事应用等特殊场合下,某些节点容易被敌方捕获或入侵,因而水下无线传感网络中有时会存在一些非合作的恶意节点。针对存在若干非合作信标的水下无线传感器网络定位应用,提出了一种非合作信标节点约束下水下无线传器网的可靠节点定位算法。本文算法利用一跳邻居范围内信标节点独自投票机制实现对非合作信标的判决与剔除,从而减少由于存在非合作信标节点对定位误差的影响,同时也分析了不同比例非合作信标下的定位误差界限。仿真结果验证了本文提出的算法相比传统定位算法,在平均定位精度和定位覆盖率等方面都有所提高。