Edge-Cloud Framework for Vehicle-Road Cooperative Traffic Signal Control in Augmented Internet of Things

• Published in: IEEE internet of things journal 2024-10, p.1-1
• Main Authors: Zhang, Lingling, Zhou, Zhenxiong, Yi, Bo, Wang, Jing, Chen, Chien-Ming, Shi, Chunyang
• Format: Article
• Language: English
• Subjects: Augmented IoT; Cloud computing; Computer architecture; Cooperative systems; Decision making; deep reinforcement learning; extended Kalman filter; Internet of Things; Process control; Real-time systems; Resource management; Servers; traffic signal control; vehicle-road cooperation; Wireless communication
• ISSN: 2327-4662; 2327-4662
• DOI: 10.1109/JIOT.2024.3487538

The rapid development of the Internet of Things (IoT) and wireless communication technologies has enabled the realization of vehicle-road cooperative systems. However, the vast amount of data generated by IoT devices in these systems poses challenges for traditional data processing methods. Augmented intelligence, such as deep reinforcement learning (DRL), has emerged as a powerful solution for processing large-scale real-time data and making accurate decisions. This paper proposes an edge-cloud framework for vehicle-road cooperative traffic signal control in the context of Augmented IoT (AIoT). The framework integrates an edge-cloud collaborative resource allocation algorithm based on DRL and a traffic signal timing method that combines DRL with an extended Kalman filter. Simulation results demonstrate the effectiveness of the proposed framework in improving traffic efficiency and reducing vehicle waiting times. The average queue length was reduced by 35.7%, and the average waiting time increased by 29.1%. The proposed edge-cloud framework for vehicle-road cooperative traffic signal control in AIoT provides a promising solution for enhancing traffic management in smart cities.