2020/04/04 by Maohong Chen, Yong Xiao, Chen, Maohong +5
Computer Science · Engineering · Medicine · #Age of Information Optimization #Artificial Intelligence (cs.AI) #Congenital Heart Disease Studies #Distributed #FOS: Computer and information sciences #FOS: Electrical engineering #IoT Networks and Protocols #Parallel #Signal Processing (eess.SP) #and Cluster Computing (cs.DC) #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2004.04640
openalex publication_date 2020/04/04 · openalex created_date 2020/04/17 · openalex updated_date 2026/07/28
Connected vehicular network is one of the key enablers for next generation cloud/fog-supported autonomous driving vehicles. Most connected vehicular applications require frequent status updates and Age of Information (AoI) is a more relevant metric to evaluate the performance of wireless links between vehicles and cloud/fog servers. This paper introduces a novel proactive and data-driven approach to optimize the driving route with a main objective of guaranteeing the confidence of AoI. In particular, we report a study on three month measurements of a multi-vehicle campus shuttle system connected to cloud/fog servers via a commercial LTE network. We establish empirical models for AoI in connected vehicles and investigate the impact of major factors on the performance of AoI. We also propose a Deep Q-Learning Netwrok (DQN)-based algorithm to decide the optimal driving route for each connected vehicle with maximized confidence level. Numerical results show that the proposed approach can lead to a significant improvement on the AoI confidence for various types of services supported.