东篱科研大数据发现系统（DRDS）

位置：成果数据库 > 期刊 > 期刊详情页

带有罚函数的无线传感器网络粒子群定位算法

ISSN号：1000-1239
期刊名称：计算机研究与发展
时间：2012
页码：1228-1234
分类：TP393[自动化与计算机技术—计算机应用技术;自动化与计算机技术—计算机科学与技术]
作者机构：[1]哈尔滨工程大学计算机科学与技术学院,哈尔滨150001, [2]哈尔滨工程大学自动化学院,哈尔滨150001
相关基金：国家自然科学基金项目（41176082,61073182,40827003,60703090,61073183）;中央高校基本科研业务费专项基金项目（HEUCFl007,HEucFl00607）;哈尔滨工程大学青年骨干教师支持项目（HEUQG0804）;哈尔滨工程大学科技园基础研究支持基金项目
相关项目：基于双移动锚节点的3维水下传感器网络安静定位算法及其误差修正技术的研究

关键词：无线传感器网络, 节点, 定位, 粒子群优化, 罚函数, WSN, node, localization, particle swarm optimization （PSO）, penalty function

中文摘要：

无线传感器网络是一种没有基础设施的无线自组织网络，它在军事、环境检测和智能家居等诸多领域具有广泛的应用．在无线传感器网络的绝大多数应用中，只有当节点和被感知的物体的位置是可知的，节点获得的信息才有意义．因此，节点定位技术是无线传感器网络的关键技术之一．近年来，粒子群优化算法（particleswarmoptimization，PSO）等智能算法被用于无线传感器网络节点定位技术的研究．在粒子群优化算法定位技术研究的基础上，提出的带有罚函数的无线传感器网络粒子群定位算法（particleswarmoptimizationwithpenaltyfunction，PSOPF）利用罚函数来加快算法的收敛速度和提高定位算法的定位精度．实验结果表明，和原有的PS0定位算法相比较，PSOPF算法具有更高的定位精度和更快的收敛速度．

英文摘要：

WSN （wireless sensor network） is formed by a large number of cheap sensors, which communicate through an ad hoc wireless network to collect information of sensed objects of a certain area. Hence, it can be used widely in military affairs, environment detection and intelligent home. In most applications of WSN, the acquired information is useful only when the locations of sensors and objects are known. Therefore, localization is one of the most important technologies of WSN. Now, some intelligent algorithms, for example PSO （particle swarm optimization）, are studied for node localization in WSN. However, the existing PSO algorithm has lower localization accuracy and convergence speed. Hence, in order to improve the convergence speed and the localization accuracy further, a new localization algorithm based on PSO with penalty function （PSOPF） is proposed in this paper. In PSOPF, an error correction factor is defined to reflect the average error of distance measure between a node and some sample anchors firstly. And then, a penalty function based on error correction factor for PSO is defined to improve its convergence speed and localization accuracy. The simulation results show that, compared with PSO location algorithms, PSOPF has higher positioning accuracy and higher convergence speed.

同期刊论文项目