Node location using cuckoo search algorithm with grouping and drift strategy for WSN

Abstract

Wireless sensor networks (WSNs) have been used extensively in many fields due to their convenience in data collection, processing, and transmission, and the stability and reliability of information processing and transmission is dependent on the accuracy of the locations of sensor nodes in the WSNs. Therefore, in order to improve the accuracy of WSN node localization, an improved cuckoo search algorithm (ICS-GD) based on population grouping and drifting strategy is proposed in this manuscript for solving the WSN node localization error problem. The first step is to design a Mahalanobis distance-based method of expressing population diversity, and population clustering is also implemented on this basis in concert with individual evolution; an information interaction based drift strategy is then proposed to improve the search capability of the algorithm at the local development stage depending on the clustering; finally, the effectiveness of the ICS-GD algorithm is verified through benchmark test functions and WSN node localization error experiments and superiority of the ICS-GD algorithm through benchmark test functions and WSN node localization error experiments. Experimental results show that the ICS-GD algorithm has more clear advantages over state-of-the-art algorithms such as IAGA, BBPSO, and HGWO, and the algorithm obtains more satisfactory performance in all of the 26 benchmark test functions, and the localization error is as small as 22.21% in the solution of the WSN node localization problem.