Latency Minimization for Mobile Edge Computing Enhanced Proximity Detection in Road Networks

In road networks, mobile users (including vehicles and pedestrians) need to know the proximity relationship with other users in real time, referred to as the problem of proximity detection which is very significant for autonomous driving. However, due to limited computing and storage resources of mobile users and real-time changes of road network status, it becomes a difficult task to calculate and update the proximity relationship between users in real time. Therefore, in this paper, we first propose a computation offloading scheme and a dynamic road network state update model for proximity detection in dynamic road networks based on Mobile Edge Computing (MEC), and formulate the latency optimization problem for proximity detection in the dynamic road network as a nonlinear optimization problem. Then we use the Sequential Least Squares Programming (SLSQP) algorithm to solve the latency optimization problem. In addition, to reduce the running time, we also use the deep reinforcement learning approach, i.e., the Deep Deterministic Policy Gradient (DDPG) algorithm, to address the latency optimization problem. Simulation results show that, compared with the SLSQP algorithm, the DDPG algorithm can effectively and efficiently reduce the computational time of the optimal latency each time by continuously adjusting the task allocation strategy, and the computational time of the DDPG algorithm is two orders of magnitude lower than the SLSQP algorithm.