An Improvement of DV-Hop Localization Algorithm Based on Improved Adaptive Genetic Algorithm for Wireless Sensor Networks

Wireless sensor networks (WSNs) are networks consisting of many sensors, each of which acquires data and communicates with each other through wireless equipment in time. To make the data obtained by each sensor node meaningful, the precise localization technology of WSNs should be investigated. As an easy-to-implement localization algorithm, DV-Hop has been studied by many researchers. But its localization accuracy needs to be further improved. In this paper, an improved DV-Hop localization algorithm (2DHYP-GA DV-Hop) is proposed, which combines the 2D hyperbolic localization algorithm and an improved adaptive genetic algorithm (IAGA) to estimate the unknown node coordinates, and improves the localization accuracy. In addition, the radio irregularity model is considered in this paper to evaluate the proposed algorithm in anisotropic networks. Simulation results show that the accuracy of the proposed algorithm is 15.9%, 11.1%, and 7.6% than the GA DV-Hop, the PSO DV-Hop, and the IAGA DV-Hop, respectively. The stability of our proposed algorithm is 11.3%, 26.5%, and 16.6% higher than the GA DV-Hop, the PSO DV-Hop, and the IAGA DV-Hop, respectively, and the convergence speed is also the best.