Minimizing Age-of-Information for Fog Computing-supported Vehicular Networks with Deep Q-learning

• URL: http://arxiv.org/abs/2004.04640v1
• Date: Sat, 4 Apr 2020 05:19:25 GMT
• Title: Minimizing Age-of-Information for Fog Computing-supported Vehicular Networks with Deep Q-learning
• Authors: Maohong Chen, Yong Xiao, Qiang Li and Kwang-cheng Chen
• Abstract summary: Age of Information (AoI) is a 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.
• Score: 15.493225546165627
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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