Abstract: To address the issues of low node localization accuracy and large errors in traditional three dimensional DV-Hop algorithms in wireless sensor networks when dealing with complex environments,an improved beluga whale optimization(IBWO) based three dimensional localization algorithm(IBWO-DV-Hop) is proposed.Firstly,by optimizing the minimum hop count of nodes through multiple communication radius and introducing a correction factor,and using a hop distance weighted optimization method to correct the average hop distance,the impact of communication radius uncertainty and hop count error on positioning accuracy is reduced.Secondly,IBWO is introduced instead of the least squares method to estimate the position of unknown nodes.The improvements include using a combination of Sobol sequence and reverse learning strategy in the initialization stage of the Beluga algorithm to improve the initial population and increase population diversity.Then,adaptive t-distribution mutation and adaptive Levy flight strategy are introduced in the exploration and development stages respectively to enhance the algorithm’s optimization ability.Finally,a lens imaging reverse learning strategy is introduced in the whale landing stage to enhance the algorithm’s global optimization ability.Experimental results show that compared with traditional three dimensional DV-Hop algorithms and other similar algorithms,the proposed algorithm has higher positioning accuracy.

Key words: Wireless sensor networks, Three dimensional DV-Hop algorithms, Beluga whale optimization algorithms, Multiple communication radius, Hop distance weighted optimization, Adaptive t-distribution mutation, Lens imaging reverse learning strategy