WSN node location based on beetle antennae search to improve the gray wolf algorithm

• Published in: Wireless networks Vol. 28; no. 2; pp. 539 - 549
• Main Authors: Yu, Xiu-wu, Huang, Lu-ping, Liu, Yong, Zhang, Ke, Li, Pei, Li, Ying
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.02.2022; Springer Nature B.V
• Subjects: Algorithms; Animal populations; Antennae; Communications Engineering; Computer Communication Networks; Electrical Engineering; Engineering; IT in Business; Local optimization; Localization; Networks; Nodes; Original Paper; Predation; Searching; Wireless networks; Wireless sensor networks; Wolves; Wireless sensor network; Gray wolf algorithm; Positioning accuracy; Node positioning; Good point set; Beetle antennae search; Intelligent optimization algorithm
• ISSN: 1022-0038; 1572-8196
• DOI: 10.1007/s11276-021-02875-w

With the rapid development of the Internet, more and more people pay attention to wireless sensor networks. Localization technology plays a vital role in wireless sensor networks. To reduce the localization error and improve the localization stability, a gray wolf localization algorithm based on beetle antennae search (BASGWO) is proposed, transforming the node localization problem into function constrained optimization. Firstly, the excellent point set method is used to initialize the gray wolf population, improving the richness. Secondly, the beetle antennae search mechanism with good global search ability is introduced into the gray wolf algorithm to avoid the gray wolf algorithm falling into local optimization in the late iteration. The gray wolf is the beetle antennae in search of excellence. The location of the gray wolf was updated according to the fitness value of the gray wolf and beetle antennae. The optimal global solution can be obtained, and then the unknown node coordinates can be obtained. The improved gray wolf algorithm improves the localization accuracy by 24% through simulation comparison and reduces the localization error fluctuation by 23%. Compared with the classical localization algorithm of WSN, the solution ability and localization accuracy of the BASGWO algorithm are improved.