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.
翻译:信息杂交是实现视觉网络物理学系统(VCPS)的最关键问题之一。 这项工作首次试图通过设计名为 " 观察时代 " (AoV)的新指标,从数量上衡量VCPS中各种信息融合的质量。 具体地说,我们根据一个多级M/G/1优先排队和基于香农理论的传输模式,获得一个基于多级M/G/1优先排队和传输模式的感测模型。 在此基础上,我们正式定义AoV,对VCPS中各种信息融合的及时性、完整性和一致性进行建模,并制定问题,旨在最大限度地减少系统平均AOV。 此外,我们提出一个新的解决方案,称为多剂差异奖励性深加固学习,与Greedy Bandwidth分配(MDR-GBA)相结合,以解决该问题。 特别是,我们作为独立代理,决定不同信息的感测频率和上传优先事项。 同时,路边单位(RSU)根据一个贪婪方案,决定每部车辆到基础设施(V2I)的带宽分配问题。 此外,我们提出一个新的解决方案,我们将拟议模型和指标性模型与拟议模型的模型的精确性结果进行比较。