Smart City Gnosys

Smart city article details

Title	Edge-Cloud Framework For Vehicle-Road Cooperative Traffic Signal Control In Augmented Internet Of Things
ID_Doc	21830
Authors	Zhang L.; Zhou Z.; Yi B.; Wang J.; Chen C.-M.; Shi C.
Year	2025
Published	IEEE Internet of Things Journal, 12, 5
DOI	http://dx.doi.org/10.1109/JIOT.2024.3487538
Abstract	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 article 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. © 2024 IEEE.
Author Keywords	Augmented Internet of Things (AIoT); deep reinforcement learning (DRL); extended Kalman filter; traffic signal control; vehicle-road cooperation

Similar Articles

Id	Similarity	Authors	Title	Published
18053	0.924	Kansal V.; Shnain A.H.; Deepak A.; Rana A.; Manjunatha; Dixit K.K.; Rajkumar K.V.	Deep Reinforcement Learning For Iot-Based Smart Traffic Management Systems	Proceedings of International Conference on Contemporary Computing and Informatics, IC3I 2024 (2024)
15627	0.899	Sachan A.; Chauhan N.S.; Kumar N.	Congestion Minimization Using Fog-Deployed Drl-Agent Feedback Enabled Traffic Light Cooperative Framework	Proceedings - 23rd IEEE/ACM International Symposium on Cluster, Cloud and Internet Computing, CCGrid 2023 (2023)
24038	0.89	Vieira M.A.; Galvão G.; Vieira M.; Véstias M.; Louro P.; Jardim-Goncalves R.	Enhancing Traffic Flow With Visible Light Communication: A Deep Reinforcement Learning Approach	Proceedings of SPIE - The International Society for Optical Engineering, 13374 (2025)
7021	0.887	Qaffas A.A.	Ai-Driven Distributed Iot Communication Architecture For Smart City Traffic Optimization	Journal of Supercomputing, 81, 8 (2025)
39913	0.886	Annunziata D.; Chiaro D.; Qi P.; Piccialli F.	On The Road To Aiot: A Framework For Vehicle Road Cooperation	IEEE Internet of Things Journal, 12, 5 (2025)
23735	0.885	Sattarzadeh A.R.; Pathirana P.N.	Enhancing Adaptive Traffic Control Systems With Deep Reinforcement Learning And Graphical Models	Proceedings - 2024 IEEE International Conference on Future Machine Learning and Data Science, FMLDS 2024 (2024)
6730	0.882	Qu A.; Tang Y.; Ma W.	Adversarial Attacks On Deep Reinforcement Learning-Based Traffic Signal Control Systems With Colluding Vehicles	ACM Transactions on Intelligent Systems and Technology, 14, 6 (2023)
7619	0.881	Kommineni M.; Baseer K.K.	An Architecture And Review Of Intelligence Based Traffic Control System For Smart Cities	EAI Endorsed Transactions on Energy Web, 11 (2024)
25829	0.88	Thamaraiselvi K.; Bohra A.R.; Vishal V.; Sunkara P.S.; Sunku B.; Nityajignesh B.	Exploring Traffic Signal Control: A Comprehensive Survey On Reinforcement Learning Techniques	3rd IEEE International Conference on Industrial Electronics: Developments and Applications, ICIDeA 2025 (2025)
18057	0.88	Mittal M.; Sehgal A.; Varshney N.; Kumar S.P.; Boob N.S.; Reddy R.A.	Deep Reinforcement Learning For Optimizing Route Planning In Urban Traffic	IEEE International Conference on "Computational, Communication and Information Technology", ICCCIT 2025 (2025)