Green Intelligence Networking for Connected and Autonomous Vehicles in Smart Cities
Yuzheng Ren, Renchao Xie, F. Richard Yu, Tao Huang, Yunjie Liu
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.