Abstract:

In order to improve the positioning accuracy and convergence speed of localization algorithm for uranium tailings reservoir in WSN, the optimized sparrow search algorithm (SSA) was used to improve the localization algorithm based on signal strength indication (RSSI). Firstly, the chaotic mapping and elite opposition based learning were introduced to initialize the sparrow population, enrich the diversity of the population, and improve the global optimization ability of the algorithm. Secondly, the levy flight strategy was used to improve the searcher's position update method to avoid falling into local optimum. Then, the optimized SSA was used to replace the least square method to locate unknown nodes, and the localization algorithm was applied to the monitoring and positioning of radionuclide pollution in uranium tailings reservoir. Finally, under the conditions of different number of anchor nodes, communication radius and noise standard deviation, the sparrow search optimization localization algorithm (SSOLA) was compared with the weighted centroid localization algorithm (WCLA), received signal strength indicator difference localization algorithm (RSSID), sparrow search localization algorithm (SSA), particle swarm localization algorithm (PSO) and the salp swarm localization algorithm (SAP). The results show that compared with the other five algorithms, the positioning error of SSOLA has decreased by 41.9%, 45.2%, 26.8%, 39.9% and 36.9% on average, with higher positioning accuracy and faster convergence speed.

Key words: sparrow search optimization (SSO), uranium tailings reservoir, wireless sensor network (WSN), localization algorithm, chaotic mapping, elite opposition-based learning, levy flight strategy