Age of View: A New Metric for Evaluating Heterogeneous Information Fusion in Vehicular Cyber-Physical Systems

TL;DR

This paper introduces the Age of View (AoV), a novel metric for evaluating the quality of heterogeneous information fusion in vehicular cyber-physical systems, and proposes a reinforcement learning-based solution to optimize it.

Contribution

It is the first to quantitatively measure information fusion quality in VCPS using AoV and develops a multi-agent deep reinforcement learning approach for optimization.

Findings

AoV effectively measures fusion quality in VCPS.

The proposed MDR-GBA algorithm outperforms existing methods.

Simulation results validate the significance of AoV and the effectiveness of the solution.

Abstract

Heterogeneous information fusion is one of the most critical issues for realizing vehicular cyber-physical systems (VCPSs). This work makes the first attempt at quantitatively measuring the quality of heterogeneous information fusion in VCPS by designing a new metric called Age of View (AoV). Specifically, we derive a sensing model based on a multi-class M/G/1 priority queue and a transmission model based on Shannon theory. On this basis, we formally define AoV by modeling the timeliness, completeness, and consistency of the heterogeneous information fusion in VCPS and formulate the problem aiming to minimize the system's average AoV. Further, we propose a new solution called Multi-agent Difference-Reward-based deep reinforcement learning with a Greedy Bandwidth Allocation (MDR-GBA) to solve the problem. In particular, each vehicle acts as an independent agent and decides the sensing frequencies and uploading priorities of heterogeneous information. Meanwhile, the roadside unit (RSU) decides the Vehicle-to-Infrastructure (V2I) bandwidth allocation for each vehicle based on a greedy scheme. Finally, we build the simulation model and compare the performance of the proposed solution with state-of-the-art algorithms. The experimental results conclusively demonstrate the significance of the new metric and the superiority of the proposed solution.