基于无线传感器网络的智能交通测速系统

介绍了一种基于无线传感器网络的交通检测系统,通过在路边布置两个距离已知的信标节点接收的RSSI值大小来判断移动节点的到达信标节点的时刻,因为距离越近RSSI值越大,所以当RSSI值达到最大时就是移动节点到达信标节点的时刻,最后根据已知的距离和分别到达两个信标的时刻计算出速度。文章对该测速系统进行了实物实验,并用MATLAB对实验数据进行处理,分析结果表明这个系统具有一定的实用性。     

基于无线传感器网络的移动机器人定位导航系统

设计了一种基于无线传感器网络(WSN)的移动机器人轨迹跟踪定位系统,通过测算移动机器人和信标节点之间的无线电接收信号强度(RSSI)进而估计出它们之间的距离,采用自适应的扩展卡尔曼滤波算法对RSSI数据处理得到机器人的当前位置、速度、加速度等状态信息.为了提高系统的实时性和计算效率,动态选用网络中一部份信标节点进行轨迹跟踪定位计算,降低了移动机器人CPU的负担.采用CC2430芯片作为节点的通信和数据处理单元,现场试验结果表明该方法具有轨迹跟踪定位精度高、实现简单、成本低廉等特点.     

无线传感器网络中基于RSSI的质心定位算法研究

无线传感器网络中,由于传统质心算法普遍存在信标节点分布不均与中心化问题,导致定位误差相对较大。针对这些问题,提出了基于RSSI的改进算法。在APIT的基础上,改进算法依靠未知节点接收到不同信标节点的RSSI数值,判断其周围是否存在最佳三角形,若存在则利用最佳三角形进行定位;若不存在则选出一个距其较近的三角形,利用移动信标节点的办法来缩小此三角形的范围进行定位。Matlab平台仿真结果表明,与传统质心算法相比,改进算法减少了定位误差,节点定位精度有所提高。     

无线传感器网络混合定位技术研究

在大规模复杂无线传感器网络中往往采用多种节点定位技术,在此结合现有无线传感器定位技术的现状,提出了一种混合定位技术以实现不同定位方法之间的互补。一方面利用RSSI定位弥补TDOA定位覆盖范围小的缺点;另一方面将测距信息引入到非测距定位DV—Hop算法中,用RSSI测距模型来提高DV-Hop算法中定位节点与信标节点间有效距离的精度。实验结果表明,该混合定位技术实现了TDOA,RSSI以及DV-HOP等定位技术的融合,有效地提高了复杂大规模无线传感器网络的节点定位精度。     

WSN中基于RSSI的加权质心定位算法的改进

自身节点定位是无线传感器网络的关键技术之一。本文对距离无关定位算法中的质心定位算法进行了分析,在基于RSSI的质心定位算法的基础上提出了一种新的校正RSSI测距值的加权定位算法。测距阶段将信标节点之间的距离和信号强度信息同时考虑在内进行RSSI值校正,权值选择阶段采用了修正传统权重的计算方法,权值取距离倒数之和。通过仿真证明,本文提出的算法相对于传统的加权质心定位算法有明显改进,获得较好的定位精度。     

基于RSSI的精确室内定位算法

无线传感器网络的关键问题是实现节点的精确定位。为了解决基于RSSI的无线传感器网络三角形质心定位算法在有些情况不适用的问题,本文提出一种新型的基于RSSI的精确室内定位算法,此算法提出了虚拟信标节点的概念并用此来修正未知节点位置。实验表明,该算法具有较高的定位精度,能满足大多数的应用场合,具有一定的实用价值。     

基于RSSI测距的无线传感器网络定位算法

提出了一种采用最小二乘法对环境参数进行拟合的方法,获得损耗模型,同时对所测得的RSSI数据进行高斯处理并优选信标节点,最后对目标节点采用改进后的三边测量定位算法进行节点定位.实验结果表明,本算法定位受环境因素影响减小,比传统RSSI定位算法精度更高,可应用于无线传感器网络中.     

采用RSSI提高无线传感网络定位精度的算法

为了提高无线传感网络中节点的定位精度,同时又希望降低节点的定位开销,提出接收信号强度(RSSI)定位算法。以RSSI和三边定位原理为基础,详细阐述了该算法的定位思想,以伪代码的形式描述未知节点定位的实现过程。从存储、计算和通信开销3个方面与ALA方案做了定性分析,针对不同的冗余系数、不同的定位轮数和不同的信标数量进行了仿真,与ALA方案做了定量分析。分析结果表明,该算法达到了提高无线传感网络定位精度的目的。     

基于 RSSI 的无线传感器网络节点定位算法研究

节点位置信息是无线传感器网络应用的基础。基于RSSI（Receive Signal Strength Indicator）的测距技术因其低成本和低复杂度的优点而被广泛用于无线传感器网络的定位技术中。介绍了RSSI信号传输模型,在介绍无线传感器网络定位基本原理的基础上,分析了影响定位精度的因素。综述了近几年提出的无线传感器网络中基于RSSI的节点定位算法及其改进算法,现有基于RSSI定位算法的改进算法主要从测距精度改进、定位精度改进或误差修正改进等方面进行。最后,指出了基于RSSI的无线传感器网络节点定位算法的不足,并进行展望。     

无线传感器网络中一种信标节点可迁移的协作定位方法

 提出一种协作定位方法.利用邻居信标交互,获取定位环境信息,并在此基础上实现基于无效信标过滤的信号强度定位.同时,在多种定位方法协作判定下,将精度较高的已定位节点升级为信标节点.仿真结果表明,该方法同加权RSSI及APIT定位方法相比,具有较高的定位精度和定位成功率.     

基于无线传感器网络的移动机器人导航方法

为移动机器人在无定位信息的无线传感器网络(WSN)中选择路程短、代价低的导航路径,提出了一种基于无线传感器网络的移动机器人导航方法,包括全网络导航路径规划和局部节点趋近算法。该方法通过结合各节点传感器数据,构造代价函数,在网络中建立伪梯度势场,为移动机器人规划最优路径;移动机器人通过探测接收信号强度指示(RSSI),逐一趋近该路径上的传感器节点到达目标节点。仿真结果表明,该方法能够根据移动机器人的导航要求,引导移动机器人迅速沿最优路径到达目标节点。     

Mobile Robot Navigation Using Wireless Sensor Networks Without Localization Procedure

In this paper, algorithms for navigating a mobile robot through wireless sensor networks are presented. The mobile robot can navigate without the need for a map, compass, or GPS module while interacting with neighboring sensor nodes. Two navigation algorithms are proposed in this paper: the first uses the distance between the mobile robot and each sensor node and the second uses the metric calculated from one-hop neighbors’ hop-counts. Periodically measuring the distance or metric, the mobile robot can move toward a point where these values become smaller and finally come to reach the destination. These algorithms do not attempt to localize the mobile robot for navigation, therefore our approach permits cost-effective robot navigation while overcoming the limitations of traditional navigation algorithms. Through a number of experiments and simulations, the performance of the two proposed algorithms is evaluated.     

一种新的无线传感网络三维定位方法

定位是无线传感器网络的基础问题之一,文章提出利用均值法对接收信号强度指示(RSSI)数据进行处理,筛选出RSSI值较优的锚节点,以解决RSSI易受干扰的问题,减小RSSI的测距误差。在此基础上,提出动态修正三维三边测量方法。该方法利用筛选出的RSSI值较优的3个锚节点进行测距,在一个移动锚节点辅助下进行三维三边定位,提高定位精确度。仿真结果表明,与传统三边测量定位算法相比,此方法可明显减少定位误差。     

基于RSSI和惯性导航的融合室内定位算法

针对目前对高精度室内定位算法的需求,提出一种基于接收信号强度识别（RSSI）和惯性导航的融合室内定位算法。基于无线传感网中ZigBee节点的RSSI值,采用位置指纹识别算法,对网络中的未知节点进行定位。结合惯性传感单元（IMU）提供的惯性数据,对RSSI定位结果进行融合修正。利用Kalman滤波器,采用状态方程描述待定位节点位置坐标的动态变化规律,从而实现一种以无线传感网络定位为主、IMU为辅的融合定位方法。仿真结果表明,提出的融合定位算法既能改善单独使用RSSI定位受环境干扰较大的问题,又能避免单独使用惯性导航带来的累积误差,极大地提高了定位精度。     

Adaptive directed evolved NSGA2 based node placement optimization for wireless sensor networks

Wireless sensor network (WSN) is a wireless network composed of a large number of static or mobile sensors in a self-organizing and multi-hop manner. In WSN research, node placement is one of the basic problems. In view of the coverage, energy consumption and the distance of node movement, an improved multi-objective optimization algorithm based on NSGA2 is proposed in this paper. The proposed algorithm is used to optimize the node placement of WSN. The proposed algorithm can optimize both the node coverage and lifetime of WSN while also considering the moving distance of nodes, so as to optimize the node placement of WSN. The experiments show that the improved NSGA2 has improvements in both searching performance and convergence speed when solving the node placement problem.

     

WSNs中基于无人机的强健节点定位算法

通过移动无人机(UAV)收集无线传感网络数据的方案已受到广泛关注,将感测的数据与产生此数据的传感节点位置关联起来是十分必要的。为此提出了基于无人机的强健节点定位算法(UAV-NL)。UAV-NL算法将UAV位置作为未知信息。传感节点接收由UAV在随机位置传输的beacon包,并记录接收信号强度指示(RSSI)矢量;通过理论推导2个RSSI矢量的范数距离与这2节点距离的线性关系;最后,通过RSSI值测距,并利用半定规划(SDP)算法估计节点位置。仿真结果表明,提出的UAV-NL算法即使在噪声信道条件下仍具有高的定位精确度。     

基于跳数修正的DV-Hop改进算法

针对传统DV-Hop算法中,跳数信息无法如实反应节点实际距离关系,而导致节点在无线传感网络定位过程中存在较大误差的问题,提出一种对跳数进行水平及垂直修正的改进算法。在相邻节点间,跳数进行水平修正,修正过程引入RSSI技术,对1跳按节点间距离与节点通信半径比值分段,细化跳数。同时在水平修正的基础上,分析节点间可能性分布,对不相邻节点引入修正角度进行垂直修正。仿真结果证明,在相同网络情况下,与传统DV-HOP算法相比,改进算法在增加少量计算量的前提下有效提高了定位精度。     

A chaos particle swarm optimization ranging correction location in complex environment

In complex environment, issues such as reflection, multipath propagation, non-line of sight and antenna gain, etc. would result in significant propagation losses as for the same distance. In order to effectively reduce ranging error and location error caused by received signal strength indication (RSSI) measurement distance, a location algorithm based on chaos particle swarm optimization ranging (CPSOR) is proposed for indoor location and navigation applications. By setting reference beacon nodes within location region, the relationship between distance and RSSI which is measured from target node to each beacon node is automatically corrected, and RSSI ranging error is effectively reduced, thus the objective of improving location accuracy is achieved. Numerical results show that the processing time of CPSOR location algorithm is reduced by 62% and the location accuracy of CPSOR is improved by 72% in contrast that of back propagation (BP) neural network location algorithm. Besides, practicality experiment results show that when the distance between beacon nodes is 50 m, the average location error of CPSOR location algorithm is 1.21 m and the location error of BP location algorithm is 3.36 m, thus the location accuracy is improved by 63%.     

Fish bone structure based data aggregation and routing in wireless sensor network: multi-agent based approach

Wireless sensor networks (WSNs) are constrained by limited node (device) energy, low network bandwidth, high communication overhead and latency. Data aggregation alleviates the constraints of WSN. In this paper, we propose a multi-agent based homogeneous temporal data aggregation and routing scheme based on fish bone structure of WSN nodes by employing a set of static and mobile agents. The primary components of fishbone structure are backbone and ribs connected to both sides of a backbone. A backbone connects a sink node and one of the sensor nodes on the boundary of WSN through intermediate sensor nodes. Our aggregation scheme operates in the following steps. (1) Backbone creation and identifying master centers (or nodes) on it by using a mobile agent based on parameters such as Euclidean distance, residual energy, backbone angle and connectivity. (2) Selection of local centers (or nodes) along the rib of a backbone connecting a master center by using a mobile agent. (3) Local aggregation process at local centers by considering nodes along and besides the rib, and delivering to a connected master center. (4) Master aggregation process along the backbone from boundary sensor node to the sink node by using a mobile agent generated by a boundary sensor node. The mobile agent aggregates data at visited master centers and delivers to the sink node. (5) Maintenance of fish bone structure of WSN nodes. The performance of the scheme is simulated in various WSN scenarios to evaluate the effectiveness of the approach by analyzing the performance parameters such as master center selection time, local center selection time, aggregation time, aggregation ratio, number of local and master centers involved in the aggregation process, number of isolated nodes, network lifetime and aggregation energy. We observed that our scheme outperforms zonal based aggregation scheme.     

Neural-based approach for localization of sensors in indoor environment

Location of wireless sensor nodes is an important piece of information for many applications. There are many algorithms present in literature based on Received Signal Strength (RSSI) to estimate the location. However the radio signal propagation is easily influenced by diffraction, reflection and scattering. Therefore algorithms purely based on RSSI may not accurately predict the position of the node. In the present work, an algorithm for estimating the position of mobile nodes is proposed which is based on a combination of Received Signal Strength (RSSI) and Link Quality Indicator (LQI). Artificial Neural Networks are used to establish the relationship between the location of the mobile node and the experimentally obtained values of RSSI and LQI. Two different algorithms namely, Bayesian Regularization and Gradient Descent are used to develop the neural network model. Proposed algorithms improve the localization accuracy and perform better than other state-of-the-art algorithms.