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

作     者：Wang, Ruo-Bin Wang, Wei-Feng Xu, Lin Pan, Jeng-Shyang Chu, Shu-Chuan 

作者机构：North China Univ Technol Sch Informat Sci & Technol Beijing 100043 Peoples R China North China Univ Technol Beijing Urban Governance Res Ctr Beijing 100043 Peoples R China Univ South Australia STEM Adelaide SA 5095 Australia Shandong Univ Sci & Technol Coll Comp Sci & Engn Qingdao 266590 Peoples R China Flinders Univ S Australia Coll Sci & Engn 1284 South Rd Tonsley SA 5042 Australia 

出 版 物：《WIRELESS NETWORKS》 (无线网络)

年 卷 期：2022年第28卷第8期

页      面：3411-3428页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 0809[工学-电子科学与技术（可授工学、理学学位）] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Whale optimization algorithm Compact technique Parallel technique DV-Hop Wireless sensor networks 

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.