SimNet: UAV-Integrated Sensor Nodes Localization for Communication Intelligence in 6G Networks

Abstract

Achieving communication intelligence with a low computational cost is necessary for wireless sensor networks for the drone transportation system. This work proposed a novel localization system with low computational and time efficiency that uses an unmanned aerial vehicle (UAV) as anchor node and lightweight neural networks to evaluate the UAV position information. The scheme takes advantage of the capabilities of artificial intelligence models. Simulation results indicate that the proposed scheme displayed a good performance with the least localization error of 1.75, least training time of 0.0032s, and testing time of 0.00039s without requiring GPS when compared to other algorithms and previous schemes.