Improved DV-HOP localization algorithm based on grey wolf optimization

DV-HOP is a widely used localization algorithm, commonly applied in areas such as node localization in Wireless Sensor Networks (WSNs), deployment of Internet of Things (IoT) devices and navigation for mobile robots. However, the DV-HOP algorithm faces challenges in practical applications, including cumulative hop count errors between nodes, inaccuracies in estimated hop distances and computational bias introduced by the least squares method when dealing with nonlinear problems. To address these issues, this paper proposes an improved DV-HOP localization algorithm based on Grey Wolf Optimization (GWO). By incorporating dual communication radii, weighted hop distance correction, and an Improved Grey Wolf Optimization (IGWO) algorithm, the proposed approach enhances the localization accuracy of nodes in WSN. First, the dual communication radii strategy is utilized to refine the hop count between nodes, improving the accuracy of hop estimations. Second, a hop adjustment factor is introduced to further correct the minimum hop count between anchor nodes, resulting in more precise average hop distances. Weighted optimization of estimated hop distances from unknown nodes to anchor nodes is achieved using the minimum mean square error criterion. Finally, the IGWO algorithm replaces the least squares method for solving the coordinates of unknown nodes. Simulation results demonstrate that the proposed improved DV-HOP algorithm consistently achieves lower localization errors under various experimental conditions. Compared with other methods, it provides higher localization accuracy, verifying its effectiveness and advantages.