A parallel grey wolf optimization with two objective functions applied in DV-Hop localization algorithm

Wireless sensor networks (WSNs) have the ability to sense and process information. Only when the position of the sensor nodes is available, the information transmitted to the user is meaningful. In this paper, to improve the localization accuracy of DV-Hop, a two-objective DV-Hop localization algorithm based on parallel grey wolf optimization is proposed called PGWO-DV- Hop. Unlike the traditional DV-Hop based on intelligent optimization algorithm, after DV-Hop, two objective functions are established by using the estimated coordinates of neighboring nodes, the estimated distance and the theoretical distance between unknown node and neighboring nodes. To optimize the functions, the parallel grey wolf optimization (PGWO) is proposed. Simulation results show that compared with original DV-Hop and the other two typical improved algorithms, our proposed strategy significantly improves the localization accuracy.