无线传感网中DV-Hop定位算法研究

发布时间：2018-06-24 07:45

本文选题：无线传感器网络 + DV-Hop算法　；参考：《吉林大学》2015年硕士论文

【摘要】：无线传感器网络(Wireless Sensor Network,WSN)是一种由众多能够检测和感知客观世界并能够通过电磁波、超声波、红外线等无线通信方式发送和接收信息的节点以自组织的方式形成的多跳分布式网络。它具有广阔的应用前景未来将会被广泛应用于地震监测、动物生活习性监测、深海探测、精细农业、智能家居等各个领域，必将对人类的生产和生活产生极为深远的影响。在其众多应用中节点自身位置的获取是必不可少的，一方面是因为没有采集位置的信息是没有任何价值和意义的，另一方面是因为节点的管理与分簇、监测目标的跟踪、网络拓扑的获取与控制以及路由的生成与优化等系统功能的实现也需要节点位置信息的支持，所以说节点定位技术是WSN中十分重要和非常关键的技术。经过多年的研究和发展，虽然目前已经提出了很多用于无线传感器网络的节点定位技术，但是其定位精度和实现成本仍然是鱼和熊掌的关系，高精度节点定位的实现都依赖于成本和能耗较高的测量模块的支持，因此十分有必要研究低成本、高精度的节点定位技术。本文在深入研究了DV-Hop算法原理的基础上，详细分析了算法误差的外在和内在两方面因素，，并通过查阅大量的文献研究了学者们对本算法进行的一些优化改进并总结了研究现状。为了进一步提高算法的定位性能，本文以减小算法的内在误差为出发点，提出了基于跳数优化和迭代加权的改进算法──TDDV-Hop定位算法和基于RSSI和优化鱼群算法的改进算法──RPFDV-Hop定位算法。 TDDV-Hop定位算法进行的改进工作如下：首先基于相邻两个节点的邻居集合的异同性与两者距离的关系对相邻节点的跳数进行了优化；其次未知节点以周围几个距离较近的信标节点的坐标和跳数信息计算自身的加权跳距；然后未知节点基于三角形的边长与内角的关系以及原始算法中的方法计算自身与信标节点之间的加权距离，进而提高了两者之间距离估计的准确性；最后对方程组最后一个方程的选择进行了优化处理，并利用迭代加权最小二乘估计法计算未知节点的坐标，从而提高了未知节点坐标估计的精度。Matlab仿真结果表明，TDDV-Hop定位算法的稳定性和定位精度都明显高于原始算法以及文献[33]和文献[35]的改进算法。 RPFDV-Hop定位算法进行的改进工作如下：首先对节点接收到的RSSI值利用MeanShift算法进行滤波处理以获取RSSI的真实值并计算节点间的路径损耗值，信标节点计算自身平均每跳距离和平均每跳路径损耗值并进行广播，网络中的节点接收周围信标节点的广播信息并基于最小二乘估计法估计自身的路径损耗指数等参数，之后利用对数-常态分布无线信道模型计算自身与相邻节点之间的距离，并将此值作为两者之间优化后的跳数；其次未知节点基于加权的方法估计与周围不同信标节点之间的跳距；最后采用基于粒子群算法进行优化改进的人工鱼群算法计算未知节点的坐标。Matlab仿真结果表明，RPFDV-Hop定位算法性能由于RSSI测距技术和改进人工鱼群智能算法的应用得到了明显提升，好于原始算法以及文献[27]和文献[37]的改进算法。
[Abstract]:Wireless Sensor Network ( WSN ) is a multi - hop distributed network formed by many nodes capable of detecting and sensing objective world and capable of transmitting and receiving information in wireless communication mode such as electromagnetic wave , ultrasonic wave , infrared ray , etc .

After many years of research and development , although many node positioning technologies for wireless sensor networks have been put forward , the positioning accuracy and realization cost are still the relations of fish and bear palm , and the realization of high - precision node positioning depends on the support of the measurement module with high cost and energy consumption , so it is necessary to study the node positioning technology with low cost and high precision .

On the basis of studying the principle of DV - Hop algorithm , this paper analyzes the external and internal factors of the algorithm error , and studies the current situation of the research . In order to further improve the positioning performance of the algorithm , this paper presents an improved algorithm based on hop count optimization and iterative weighting , which is based on the internal error of the algorithm .

The improvement of the TDDV - Hop localization algorithm is as follows : Firstly , the hop count of adjacent nodes is optimized based on the relationship between the difference of the neighbor set of two adjacent nodes and the distance between the two nodes ;
secondly , the unknown node calculates its own weighted hop distance based on the coordinate and the hop count information of the beacon node which is closer to the surrounding several distances ;
then the unknown node calculates the weighted distance between itself and the beacon node based on the relationship between the edge length and the inner angle of the triangle and the method in the original algorithm , thereby improving the accuracy of the distance estimation between the two nodes ;
Finally , the selection of the last equation of the equations is optimized , and the coordinates of the unknown nodes are calculated by the iterative weighted least squares estimation method . The simulation results show that the stability and the positioning accuracy of the TDDV - Hop positioning algorithm are obviously higher than those of the original algorithm and the improved algorithm of the literature review 33 and the literature review 35 .

The improved operation of RPFDV - Hop positioning algorithm is as follows : firstly , the RSSI value received by the node is filtered by the MeanShift algorithm to obtain the real value of the RSSI and calculate the path loss value between the nodes , the beacon node calculates the average per - hop distance and the average per - hop path loss value and carries out broadcasting ;
secondly , the unknown node estimates the hop distance from the surrounding different beacon nodes based on the weighted method ;
Simulation results show that the performance of RPFDV - Hop localization algorithm is improved obviously due to the RSSI ranging technology and the application of improved artificial fish swarm intelligence algorithm .
【学位授予单位】：吉林大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TN929.5;TP212.9

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