​A Three-Dimensional DV-Hop Algorithm Based on the Improved Grey Wolf Algorithm for Wireless Sensor Networks

Abstract

Location technology has become one of the core technologies of the Internet of Things, especially for wireless sensor networks. The DV-Hop algorithm is used in various situations as one of the representative algorithms. Aiming at the large positioning error of the DV-Hop algorithm, this paper proposes a three-dimensional DV-Hop algorithm based on the improved Grey Wolf algorithm (TDIGW). First of all, the algorithm refines the number of hops through a multi-communication radius strategy, which reduces errors caused by the number of node hops. Then, mean square error is introduced to optimize the average hop distance of the beacon nodes. Finally, the grey wolf algorithm with dynamic weight factor optimization is used instead of least squares to calculate the coordinate positions of the unknown nodes. The simulation results show that compared with the traditional 3D-DVHop algorithm and other algorithms. the average localization error of this algorithm is reduced by 20.13%, 12.62% and 8.98% respectively. The algorithm proposed in this paper reduces the localization error effectively.