Intelligent Resource Optimization for Blockchain-Enabled IoT in 6G via Collective Reinforcement Learning

Abstract

Artificial intelligence (AI)-enabled Internet of Things (IoT) has attracted great interests. The accuracy of data training model in AI is vital for further development of IoT. In addition, with the increasing number of intelligent IoT devices, the amounts of data available for transmission, learning and training can lead to serious communication burdens and data reliability issues. In order to address these issues, we study novel network architectures in future 6G networks to support the intelligent IoT. Moreover, inspired by the collective learning of humans, we introduce and adopt a novel method named as collective reinforcement learning (CRL) in the intelligent IoT to realize the sharing of learning and training results. To ensure security and privacy, as well as improve computing efficiency, blockchain, mobile edge computing (MEC) and cloud computing are applied to protect data security and enrich computing resources. On this basis, we formulate an optimization problem in the intelligent IoT based on the proposed framework to optimize transmission latency and energy consumption. Simulation results demonstrate that the system performance has improved significantly. At last, some research challenges and open issues are pointed out to the intelligent IoT in future networks.