Cuckoo search algorithm with fuzzy logic and Gauss–Cauchy for minimizing localization error of WSN

Abstract

The location of the sensor node is critical in wireless sensor networks (WSN) as the information acquired by the sensor node may be worthless without knowing its source. However, high accurate positioning of sensor nodes remains a big challenge. To overcome the barrier, an improved cuckoo search algorithm with fuzzy logic and Gauss–Cauchy strategy (ICS-FG) is proposed, that integrates the meta-heuristic algorithm with the traditional method. To regulate the dynamic adjustment of parameters, our study proposes a fuzzy logic based on population diversity. The proposed Gauss–Cauchy strategy significantly improve the algorithm’s search accuracy while enhancing its robustness when evaluated on several selected benchmark functions and locating unknown nodes in WSN. Experimental results obtained from well-known benchmark functions demonstrate the advance of the ICS-FG approach over the parallel compact cuckoo search algorithm (pcCS), improved adaptive genetic algorithm (IAGA), and other remarkable methods. By quantitative assessment, the proposed ICS-FG approach achieves a lower positioning error than pcCS, IAGA, and other state-of-the-art algorithms.