无线传感器网络三维节点定位优化算法研究

发布时间：2018-04-21 00:25

本文选题：无线传感器网络 + 三维节点定位　；参考：《厦门大学》2014年硕士论文

【摘要】：无线传感器网络是一个多学科交叉的、新兴、前沿的热点研究领域,它将会对人类的生产和生活产生深远的影响。与普通通信网络相比,无线传感器网络在军事、环境、医疗、家庭和工业等领域的应用十分广阔。而节点定位技术作为无线传感器网络的关键支撑技术之一,是必须要解决的难题。本文主要探讨了无线传感器网络基于测距技术的三维节点定位优化方法。由于定位问题在本质上是一个优化问题,所以引入智能算法——模拟退火算法和遗传算法进行优化,并通过仿真实验将它们与经典的极大似然估计算法进行比较和分析。将定位精度和计算时间作为算法优良的评价标准,分别比较三种算法在不同测距误差、不同信标节点密度、及不同节点数目条件下对应的平均定位误差和计算时间。仿真实验表明,在不同测距误差下,两种智能算法的定位精度都比ML算法的高,且测距误差越大智能算法的优势越明显；在不同信标节点密度下,两种智能算法的定位精度也都比ML算法的高,而信标节点密度对它们影响却不大,因而采用这两种算法只需在网络中部署少量的信标节点就可以获得较高的定位精度,从而降低了成本；三种算法都表现出了较好的自适应性；GA-L算法的定位精度最高,但计算时间最长,而SA-L算法既能获得较好的定位精度,也不需花太多的计算时间。总之,在无线传感器网络三维节点定位中,应用智能算法可以有效避免过大的定位误差,获得更高的定位精度和更好定位性能,其中GA-L算法的定位精度最高,而在需要同时考虑算法定位精度和计算时间的情况下,SA-L算法更合适。
[Abstract]:Wireless sensor network (WSN) is a multi-disciplinary, emerging, cutting-edge research field, which will have a profound impact on human production and life. Compared with ordinary communication networks, wireless sensor networks are widely used in military, environmental, medical, home and industrial fields. As one of the key supporting technologies in wireless sensor networks, node location is a difficult problem to be solved. This paper mainly discusses the three-dimensional node location optimization method based on ranging technology in wireless sensor networks. Because the localization problem is essentially an optimization problem, an intelligent algorithm, simulated annealing algorithm and genetic algorithm, is introduced to optimize it, and the simulation experiments are carried out to compare them with the classical maximum likelihood estimation algorithm. The positioning accuracy and computing time are taken as the evaluation criteria of the algorithm, and the mean location error and calculation time of the three algorithms under different ranging errors, different beacon node densities, and different number of nodes are compared respectively. The simulation results show that the location accuracy of the two intelligent algorithms is higher than that of ML algorithm under different ranging errors, and the larger the ranging error is, the more obvious the advantages of the intelligent algorithm are, and under the different beacon node densities, the location accuracy of the two intelligent algorithms is higher than that of the ML algorithm. The localization accuracy of the two intelligent algorithms is also higher than that of ML algorithm, but the density of beacon nodes has little effect on them. Therefore, using these two algorithms, only a small number of beacon nodes can be deployed in the network to obtain higher localization accuracy. All of the three algorithms have the highest localization accuracy but the longest computation time, while the SA-L algorithm can not only obtain better positioning accuracy, but also need not spend too much time. In short, in the wireless sensor network 3D node location, the application of intelligent algorithm can effectively avoid excessive positioning errors, obtain higher positioning accuracy and better positioning performance, among which GA-L algorithm has the highest positioning accuracy. The SA-L algorithm is more suitable when the accuracy and computing time of the algorithm are taken into account at the same time.
【学位授予单位】：厦门大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TN929.5;TP212.9

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