Resource Optimization for Delay-Tolerant Data in Blockchain-Enabled IoT With Edge Computing: A Deep Reinforcement Learning Approach

Recently, the development of the Internet of Things (IoT) provides plenty of opportunities and challenges in various fields. As an essential part of IoT, machine-to-machine (M2M) communications open a novel way that the machine-type communication devices (MTCDs) are connected and communicated without any human intervention. Meanwhile, delay-tolerant data play an important role in M2M communications-based IoT, and it puts more emphasis on powerful data caching, computing, and processing, as well as the security and stability of data transmission. To meet these requirements in M2M communications networks, in this article, we introduce some promising technologies, such as edge computing and blockchain, and propose a joint optimization framework about caching, computation, and security for delay-tolerant data in M2M communications networks based on dueling deep Q-network (DQN). According to the dynamic decision process by DQN, the optimal selection and decision of caching servers, computing servers, and blockchain systems can be made to achieve maximum system rewards, which includes higher efficiency of data processing, lower network costs, and better security of data interaction. Extensive simulation results with different system parameters show that our proposed framework can effectively improve the system performance for blockchain-enabled M2M communications compared to the existing schemes.