Improved DV-Hop based on parallel and compact whale optimization algorithm for localization in wireless sensor networks

被引:0
作者
Ruo-Bin Wang
Wei-Feng Wang
Lin Xu
Jeng-Shyang Pan
Shu-Chuan Chu
机构
[1] North China University of Technology,School of Information Science and Technology
[2] North China University of Technology,Beijing Urban Governance Research Center
[3] University of South Australia,STEM
[4] Shandong University of Science and Technology,College of Computer Science and Engineering
[5] Flinders University,College of Science and Engineering
来源
Wireless Networks | 2022年 / 28卷
关键词
Whale optimization algorithm; Compact technique; Parallel technique; DV-Hop; Wireless sensor networks;
D O I
暂无
中图分类号
学科分类号
摘要
Improving localization performance is one of the critical issues in Wireless Sensor Networks (WSN). As a range-free localization algorithm, Distance Vector-Hop(DV-Hop) is well-known for its simplicity but is hindered by its low accuracy and poor stability. Therefore, it is necessary to improve DV-Hop to achieve a competitive performance. However, the comprehensive performance of WSN is limited by computing and storage capabilities of sensor nodes. In this paper, we propose an algorithm with parallel and compact techniques based on Whale Optimization Algorithm (PCWOA) to improve DV-Hop performance. The compact technique saves memory consumption by reducing the original population. The parallel techniques enhance the ability to jump out of local optimization and improve the solution accuracy. The proposed algorithm is tested on CEC2013 benchmark functions and compared with some popular algorithms and compact algorithms. Experimental results show that the improved algorithm achieves competitive results over compared algorithms. Finally, simulation research is conducted to verify the localization performance of our proposed algorithm.
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页码:3411 / 3428
页数:17
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