Joint Sensing, Communication, and Computation Resource Allocation for Cooperative Perception in Fog-Based Vehicular Networks

TL;DR

This paper proposes a resource allocation scheme in fog-based vehicular networks to optimize cooperative perception, balancing message sharing, bandwidth, and computation resources to improve perception satisfaction under latency constraints.

Contribution

It introduces a joint resource allocation framework that maximizes perception satisfaction by considering spatial-temporal value and latency, with solutions for non-convex optimization problems.

Findings

Proposed scheme significantly improves perception satisfaction over baselines.

Joint allocation of sensing, communication, and computation resources enhances network performance.

Simulation results validate the effectiveness of the proposed approach.

Abstract

To enlarge the perception range and reliability of individual autonomous vehicles, cooperative perception has been received much attention. However, considering the high volume of shared messages, limited bandwidth and computation resources in vehicular networks become bottlenecks. In this paper, we investigate how to balance the volume of shared messages and constrained resources in fog-based vehicular networks. To this end, we first characterize sum satisfaction of cooperative perception taking account of its spatial-temporal value and latency performance. Next, the sensing block message, communication resource block, and computation resource are jointly allocated to maximize the sum satisfaction of cooperative perception, while satisfying the maximum latency and sojourn time constraints of vehicles. Owing to its non-convexity, we decouple the original problem into two separate sub-problems and devise corresponding solutions. Simulation results demonstrate that our proposed scheme can effectively boost the sum satisfaction of cooperative perception compared with existing baselines.