Distributed Graph-Based Optimization of Multicast Data Dissemination for Internet of Vehicles

Abstract

The Internet of Vehicles (IoV) is a promising paradigm for autonomous driving, where the sensing data from the onboard sensors can be disseminated and processed cooperatively via vehicle-to-vehicle links. Autonomous vehicles can share their local views for cooperative, reliable, and robust driving decisions. However, the limited wireless resources may become the bottleneck with the increasing number of vehicles. The technical challenges also arise from the decentralized control, the spatial couplings of decisions, and the complexity of combinatorial optimization. This paper proposes a novel fully distributed graph-based approach to jointly optimize multicast link establishment with data dissemination and processing decisions by only exchanging partial information among neighboring vehicles. The mixed-integer programming problem aims to maximize system energy efficiency while achieving maximum data throughput. We prove that maximizing data processing throughput is submodular optimization to find the local optimum efficiently. The optimization of data dissemination and processing is reformulated to a minimum-cost maximum-flow problem in a three-layer graph, and efficiently solved by exploiting the graphical interdependence. Both simulation-generated and trace-based datasets are evaluated to validate the effectiveness of the proposed approach in terms of data throughput and energy efficiency.