Smart City Gnosys

Smart city article details

Title	Green Intelligence Networking For Connected And Autonomous Vehicles In Smart Cities
ID_Doc	28394
Authors	Ren Y.; Xie R.; Yu F.R.; Huang T.; Liu Y.
Year	2022
Published	IEEE Transactions on Green Communications and Networking, 6, 3
DOI	http://dx.doi.org/10.1109/TGCN.2022.3148293
Abstract	Advanced communication and artificial intelligence (AI) technologies facilitate the development of green smart cities. Connected and automated vehicles (CAVs) are crucial components, which are aware of the environment by collecting data from sensors and modeling through AI to realize automatic decision-making. Intelligence networking enables each CAV to train appropriate models locally to learn how to drive in different environments, which can make up for the lack of experience of single-vehicle. However, model training and intelligence networking consume a lot of energy, calling on high-energy-efficient solutions. In this paper, we propose a non-fungible token (NFT)-based green intelligence networking scheme (NGIN) for CAVs in smart cities. We use NFT to tokenize and describe intelligence by metadata, enabling applications to network intelligence efficiently, thereby reducing energy consumption. We present the architecture, modules, and efficient mechanisms of distributed intelligence networking. Moreover, we formulate the core problem as a discrete Markov decision process (MDP) and adopt the quantum-inspired reinforcement learning (QRL) algorithm to solve it. Also, the convergence rate and performance are evaluated. Simulation results demonstrate the effectiveness of the proposed scheme. © 2022 IEEE.
Author Keywords	green; intelligence networking; non-fungible token (NFT); Smart cities

Similar Articles

Id	Similarity	Authors	Title	Published
23443	0.866	Pramod M.S.; Balodi A.; Pratik A.; Satya Sankalp G.; Varshita B.; Amrit R.	Energy-Effcient Reinforcement Learning In Wireless Sensor Networks Using 5G For Smart Cities	Applications of 5G and Beyond in Smart Cities (2023)
27553	0.865	Subrahmanyam S.	Future Trends And Research Directions	Neural Networks and Graph Models for Traffic and Energy Systems (2025)
20702	0.863	Kumar P.L.; Jayanthi M.; Singh J.; Gupta M.; Bobba P.B.; Albawi A.; Shubhra	Distributed Reinforcement Learning Framework For Collaborative Energy Management In Connected Hybrid Electric Vehicle Ecosystems	2025 International Conference on Intelligent Control, Computing and Communications, IC3 2025 (2025)
28578	0.861	Ngai E.C.H.; Barnaghi P.; Kanhere S.; Leung V.C.M.; Liu J.	Guest Editorial Special Issue On Green Communications And Networking With Machine Intelligence For Smart Cities	IEEE Transactions on Green Communications and Networking, 6, 3 (2022)
46607	0.859	Salama R.; Alturjman S.; Altrjman C.; Al-Turjman F.	Reward-Based Energy-Aware Routing Protocols In Wireless Sensor Networks	Sustainable Civil Infrastructures (2024)
32921	0.859	Reis M.J.C.S.	Internet Of Things And Artificial Intelligence For Secure And Sustainable Green Mobility: A Multimodal Data Fusion Approach To Enhance Efficiency And Security	Multimodal Technologies and Interaction, 9, 5 (2025)
36061	0.857	Karthikeyan N.; Sivaprakash P.; Periyakaruppan K.; Shobana M.	Machine Learning-Based Quantum Modeling To Classify The Traffic Flow In Smart Cities	Quantum-Safe Cryptography Algorithms and Approaches: Impacts of Quantum Computing on Cybersecur (2023)
2234	0.857	Chalaki B.; Malikopoulos A.A.	A Hysteretic Q-Learning Coordination Framework For Emerging Mobility Systems In Smart Cities	2021 European Control Conference, ECC 2021 (2021)
53959	0.856	Zhong Y.; Qin Z.; Alqhatani A.; Metwally A.S.M.; Dutta A.K.; Rodrigues J.J.P.C.	Sustainable Environmental Design Using Green Iot With Hybrid Deep Learning And Building Algorithm For Smart City	Journal of Grid Computing, 21, 4 (2023)
32449	0.856	Priya B.; Malhotra J.	Intelligent Multi-Connectivity Based Energy-Efficient Framework For Smart City	Journal of Network and Systems Management, 31, 3 (2023)