Vehicular Ad Hoc Networks Topology Optimization for Autonomous Intersection Management System: A Periodic Intervention-Based Approach

It is important to achieve the real-time and reliable vehicular ad hoc networks (VANETs) for the safety and efficiency of the autonomous intersection management (AIM) system. However, there are challenges in establishing the low-delay and robustness VANETs topology in a horizon due to the high mobility, intersection shelters and malicious attacks. This paper investigates the long-horizon VANETs networking problem with the objective of producing the robust topology in the process of routing paths planning. To achieve this goal, a centralized periodic intervention networking (CPIN) approach is proposed, which consists of three main modules: long-horizon information mapping, full-path routing modeling and proactive optimization mechanism. The first module aims to provide a spatio-temporal delay tensor, considering the mobility and the quality of service (QoS). The second module converts routing path planning into a combinatorial optimization problem to achieve long-horizon full-path routing. The third module introduces a proactive optimization mechanism based on node degree centrality to evolve the VANETs networking process toward high robustness. To find the optimal solution efficiently, we propose an improved Monte Carlo Tree Search (MCTS) algorithm based on the operational characteristics of the AIM system. A series of simulation experiments are carried out to verify the effectiveness of CPIN. The results show that the CPIN outperforms the benchmark method in terms of time delay, wireless hops, and network robustness. Specifically, the CPIN reduces the average time delay by at most 44.4%-48.3% while ensuring relative high robustness.