Joint Computing and Caching in 5G-Envisioned Internet of Vehicles: A Deep Reinforcement Learning-Based Traffic Control System

• Published in: IEEE transactions on intelligent transportation systems Vol. 22; no. 8; pp. 5201 - 5212
• Main Authors: Ning, Zhaolong, Zhang, Kaiyuan, Wang, Xiaojie, Obaidat, Mohammad S., Guo, Lei, Hu, Xiping, Hu, Bin, Guo, Yi, Sadoun, Balqies, Kwok, Ricky Y. K.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.08.2021; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Artificial intelligence; Caching; content caching; Control systems; Deep learning; deep reinforcement learning; Edge computing; Intelligent transportation systems; Internet of connected vehicles; Internet of Vehicles; Machine learning; Optimization; Quality of experience; Resource management; Servers; Task analysis; Traffic control; traffic control system; Traffic information; Transportation networks; User experience; Vehicles; Wireless networks
• DOI: 10.1109/TITS.2020.2970276
• ISSN: 1524-9050

Recent developments of edge computing and content caching in wireless networks enable the Intelligent Transportation System (ITS) to provide high-quality services for vehicles. However, a variety of vehicular applications and time-varying network status make it challenging for ITS to allocate resources efficiently. Artificial intelligence algorithms, owning the cognitive capability for diverse and time-varying features of Internet of Connected Vehicles (IoCVs), enable an intent-based networking for ITS to tackle the above-mentioned challenges. In this paper, we develop an intent-based traffic control system by investigating Deep Reinforcement Learning (DRL) for 5G-envisioned IoCVs, which can dynamically orchestrate edge computing and content caching to improve the profits of Mobile Network Operator (MNO). By jointly analyzing MNO's revenue and users' quality of experience, we define a profit function to calculate the MNO's profits. After that, we formulate a joint optimization problem to maximize MNO's profits, and develop an intelligent traffic control scheme by investigating DRL, which can improve system profits of the MNO and allocate network resources effectively. Experimental results based on real traffic data demonstrate our designed system is efficient and well-performed.