LSTM-MM: Efficient LSTM-Based Mobility Management for Power Inspection Vehicles in Smart Grids

The smart grid achieves efficient power management and resource allocation through various advanced technologies, among which the Internet of Vehicles communication technology is particularly important. This technology enables power inspection vehicles to seamlessly connect to the smart grid system, allowing for real-time acquisition and transmission of critical data, thereby further enhancing the efficiency and safety of power inspections. However, the high-speed movement of power inspection vehicles and frequent base station handovers pose challenges to their mobility management. To address the cost issues arising from frequent base station handovers in dense networks, this paper proposes an efficient mobility management solution based on trajectory prediction, suitable for Software-Defined Vehicular Networks. This scheme leverages Software-Defined Networking (SDN) technology to centrally manage vehicle communication and network traffic, integrating trajectory prediction algorithms to forecast the future movements of power inspection vehicles, optimizing route planning, and improving operational efficiency and network performance. Through detailed theoretical analysis and simulation experiments, the results indicate that this scheme significantly outperforms existing solutions in terms of signaling costs, handover delays, and packet delivery costs, substantially enhancing the efficiency of mobility management for power inspection vehicles.