Joint Communications, Sensing, and MEC for AoI-Aware V2I Networks

As a large variety of applications emerge in vehicle-to-infrastructure (V2I) networks, the explosion of data places greater demands on communications and computing. Sensing technology offers accurate data by collecting real-time environmental information. Meanwhile, mobile edge computing (MEC) can significantly reduce communication latency and enhance computational efficiency. Therefore, integrating the two novel technologies into V2I applications to improve overall performance has become a research hotspot. In this paper, we focus on the optimization problem for determining caching, offloading, and matching strategies to minimize system cost under the joint communications, sensing, and MEC framework of V2I networks. First, we analyze the positive impact of sensing on signaling overhead and age of information (AoI), and derive a linear relationship between delay and AoI. Next, we formulate the optimization function of system cost, which is defined as the weighted sum of AoI and energy consumption and is proved to be NP-hard. To address this problem, we leverage an improved quantum particle swarm optimization (QPSO) algorithm to acquire a suboptimal solution of caching and offloading strategies. This significantly reduces computational complexity compared to the optimal solution obtained via the branch-and-bound (B&B) method. According to the vehicles’ AoI, we design a matching algorithm for the roadside unit (RSU) with vehicles. Building on these, we propose a QPSO-based algorithm under the joint communications, sensing, and MEC framework (QJCSM). Simulation results demonstrate that the QJCSM algorithm outperforms other baseline algorithms in terms of AoI and energy consumption and achieves near-optimal performance with low complexity.