A DV-Hop localization algorithm corrected based on multi-strategy sparrow algorithm in sea-surface wireless sensor networks

Sea surface sensor node localization accuracy is often hindered by seawater flow, while sea storms affect the transmission of radio signals. To improve the localization accuracy of the Distance Vector-Hop (DV-Hop) algorithm in Sea surface wireless sensor networks, we propose a DV-Hop localization algorithm enhanced through a multi-strategy sparrow search algorithm. The sea surface communication model is established, with drones as sink nodes, and the number of hops between nodes in the Sea Surface network is subdivided using non-uniform communication radii. Then, the average hop distance of the node is corrected by combining the weighted minimum mean square error and the cosine theorem. Finally, the calculated localization error is used as the fitness function. The localization of unknown nodes is initialized using the elite reversal strategy, and the Harris Hawk optimization method combined with the differential evolution algorithm is used to update the localization of the sparrow population discoverer to improve the population diversity. In the simulation experiments, the effectiveness of our algorithm is verified in anisotropic topologies. After that, we compared DV-Hop, Sparrow Search Algorithm for Optimizing DV-Hop (SSA-DV-Hop), Whale Optimization Algorithm for Optimizing DV-Hop (WOA-DV-Hop), and Harris Hawk Optimization Algorithm for Optimizing DV-Hop (HHO-DV-Hop) with our algorithm to verify the accuracy of the algorithm. The results show that, across various communication radii, the average localization error exhibited a reduction of 66.91% in comparison to DV-Hop. In addition, in different scenarios with different numbers of beacon nodes, the average localization error decreased by 66.78% compared to DV-Hop. Therefore, the proposed algorithm can effectively improve localization accuracy.