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A Hybrid Mobile Node Localization Algorithm Based on Adaptive MCB-PSO Approach in Wireless Sensor Networks
Wireless Communications and Mobile Computing ( IF 2.146 ) Pub Date : 2020-06-13 , DOI: 10.1155/2020/3845407
Hua Wu 1, 2 , Ju Liu 1 , Zheng Dong 1 , Yang Liu 2, 3
Affiliation  

In this paper, a hybrid adaptive MCB-PSO node localization algorithm is proposed for three-dimensional mobile wireless sensor networks (MWSNs), which considers the random mobility of both anchor and unknown nodes. An improved particle swarm optimization (PSO) approach is presented with Monte Carlo localization boxed (MCB) to locate mobile nodes. It solves the particle degeneracy problem that appeared in traditional MCB. In the proposed algorithm, a random waypoint model is incorporated to describe random movements of anchor and unknown nodes based on different time units. An adaptive anchor selection operator is designed to improve the performance of standard PSO for each particle based on time units and generations, to maintain the searching ability in the last few time units and particle generations. The objective function of standard PSO is then reformed to make it obtain a better rate of convergence and more accurate cost value for the global optimum position. Furthermore, the moving scope of each particle is constrained in a specified space to improve the searching efficiency as well as to save calculation time. Experiments are made in MATLAB software, and it is compared with DV-Hop, Centroid, MCL, and MCB. Three evaluation indexes are introduced, namely, normalized average localization error, average localization time, and localization rate. The simulation results show that the proposed algorithm works well in every situation with the highest localization accuracy, least time consumptions, and highest localization rates.

中文翻译:

无线传感器网络中基于自适应MCB-PSO方法的混合移动节点定位算法

针对三维移动无线传感器网络(MWSN),提出了一种混合自适应MCB-PSO节点定位算法,该算法考虑了锚点和未知节点的随机移动性。提出了一种改进的粒子群优化(PSO)方法和蒙特卡洛定位盒装(MCB)来定位移动节点。它解决了传统MCB中出现的粒子退化问题。在所提出的算法中,并入了一个随机航路点模型来描述基于不同时间单位的锚点和未知节点的随机运动。自适应锚点选择运算符旨在根据时间单位和世代为每个粒子改进标准PSO的性能,以保持最近几个时间单位和粒子世代的搜索能力。然后,对标准PSO的目标函数进行改革,以使其对于全球最优排名获得更好的收敛速度和更准确的成本值。此外,每个粒子的移动范围被限制在指定的空间中,以提高搜索效率并节省计算时间。在MATLAB软件中进行了实验,并将其与DV-Hop,质心,MCL和MCB进行了比较。介绍了三个评估指标,即归一化平均定位误差,平均定位时间和定位率。仿真结果表明,该算法在定位精度最高,时间消耗最少,定位率最高的各种情况下都能很好地工作。此外,每个粒子的移动范围被限制在指定的空间中,以提高搜索效率并节省计算时间。在MATLAB软件中进行了实验,并将其与DV-Hop,质心,MCL和MCB进行了比较。介绍了三个评估指标,即归一化平均定位误差,平均定位时间和定位率。仿真结果表明,该算法在定位精度最高,时间消耗最少,定位率最高的各种情况下都能很好地工作。此外,每个粒子的移动范围被限制在指定的空间中,以提高搜索效率并节省计算时间。在MATLAB软件中进行了实验,并将其与DV-Hop,质心,MCL和MCB进行了比较。介绍了三个评估指标,即归一化平均定位误差,平均定位时间和定位率。仿真结果表明,该算法在定位精度最高,时间消耗最少,定位率最高的情况下都能很好地工作。介绍了三个评估指标,即归一化平均定位误差,平均定位时间和定位率。仿真结果表明,该算法在定位精度最高,时间消耗最少,定位率最高的各种情况下都能很好地工作。介绍了三个评估指标,即归一化平均定位误差,平均定位时间和定位率。仿真结果表明,该算法在定位精度最高,时间消耗最少,定位率最高的情况下都能很好地工作。
更新日期:2020-06-13
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