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The sensor localization is one of many challenges in wireless sensor networks (WSNs). The perfect usage of WSNs largely depends on an efficient nodes' localization system. This paper proposed an improved localization algorithm in WSNs called DV-hop (FOA) that is based on the classical algorithm DV-Hop (Distance Vector-Hop) and the Fruit Fly metaheuristic to minimize the error on the nodes' position estimation. Fruit fly optimization (FOA) is preferment for the minimization problems due to its quick convergence and less of parameters. In this work, we initialize the flies in the initial search location defined by the DV-hop method and they are given the random value of direction and distance. Then, we find out the flies with the highest smell value using fitness and keep its positions, and we consider it the location of the target node. Simulation results in matlab show that the proposed algorithm DV-hop (FOA) has the better localization error than the original DV-Hop algorithm.

The main purpose of localization in wireless sensor networks is to determine the location of the randomly collected sensors. Recently, bio-inspired localization techniques have become popular thanks to its precise and fast solution. In this paper, a recently developed meta-heuristic algorithm based on the social behavior of chickens called chicken swarm optimization (CSO) is used to solve the node localization problem. The studies that we have carried out have demonstrated the clear effect of our proposed approach by changing the characteristics of the network and the number of chickens used, as well as its ability to improve a classical approach based on CSO; and the comparison results with PSO showing the superiority of proposed approach.