Q-Learning Improved Lightweight Consensus Algorithm for Blockchain-Structured Internet of Things

Security and trust have become the key issues in the Internet of Things (IoT) environment. Characterized by the centralized control and high-energy consumption, the traditional trust management schemes are not suitable for the IoT systems, in which most of the interactions are short-duration, random and maybe one-time, and the terminal devices always have resource constraints. Therefore, this article proposes a distributed and two-layered trust management framework based on blockchain architecture for IoT. The hierarchical architecture of the cloud, the edge, the IoT subgroups, and devices solves the resource limitation problem and improves the privacy protection of the IoT applications. And a novel lightweight Q-learning improved DPoS consensus algorithm named QV-DPoS is proposed to solve the problems of large energy consumption and high complexity of consensus mechanism of blockchain. Ethereum is used to build a blockchain-based IoT prototype system, and some experiments were designed to verify whether the proposed platform can successfully conduct trust management and achieve identity and behavior authentication between the IoT entities. Moreover, the simulation experiments based on NetLogo is also designed to test the performance of the trust and consensus mechanisms. The results of the experiments show that the proposed mechanisms can effectively enhance the credibility of the interactions in the IoT environments, improve the transaction success rate, and reduce energy consumption at least 10% compared with the traditional algorithms.