Edge Computing QoE Maximization in EV Parking Scenario

Facing the emergence of 6G and the rapid increase in electric vehicles (EVs), smart parking lots providing real-time services like EV charging have become essential. Edge computing, due to its proximity to end devices, offers low latency and high bandwidth, but its limited resources necessitate efficient allocation. We present a parking lots scenario with edge computing system offering four key services: Supply Equipment Communication Controller, charging space detection, monitoring, and video streaming, along with their QoE models and cor-responding estimation models. We predict system requests for the next time slot and employ the Maximum-chosen algorithm and Collaborative Optimal Decision Search method to optimize service deployment and assignment, maximizing QoE values and resource efficiency. Simulation results validate that we can obtain request status that is more similar to real requests by prediction and the Collaborative Optimal Decision Search method can generate optimal service assignment strategy within different methods.