论文标题
观点年龄：一种用于评估车辆网络物理系统中异质信息融合的新指标
Age of View: A New Metric for Evaluating Heterogeneous Information Fusion in Vehicular Cyber-Physical Systems
论文作者
论文摘要
异构信息融合是实现车辆网络物理系统（VCPSS）的最关键问题之一。这项工作首次尝试通过设计一种称为“视图时代（AOV）”的新指标来定量测量VCP中异质信息融合的质量。具体而言，我们根据多级M/g/1优先级队列和基于香农理论的传输模型得出一个感应模型。在此基础上，我们通过对VCP中异质信息融合的及时性，完整性和一致性进行建模，并提出旨在最大程度减少系统平均AOV的问题来正式定义AOV。此外，我们提出了一种新的解决方案，称为多代理差异 - 基于差异 - 基于奖励的深度加强学习，并通过贪婪的带宽分配（MDR-GBA）来解决问题。特别是，每辆车都是独立的代理，并确定传感频率和上载异质信息的优先级。同时，路边单位（RSU）根据贪婪方案决定了每辆车的车辆到基础设施（V2I）带宽分配。最后，我们构建了仿真模型，并将提出解决方案的性能与最新算法进行比较。实验结果最终证明了新指标的重要性和所提出的解决方案的优势。
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.
