Blockchain-Enabled Deep Reinforcement Learning Approach for Performance Optimization on the Internet of Things

Abstract

Internet of Things (IoT) networks are rapidly expanding, which requires adequate and reliable infrastructure and a large amount of data. The IoT device security and technical confidentiality may benefit from using Blockchain, a decentralised and trustworthy ledger. Increasing transaction throughput and coping with big data transfer situations is critical when dealing with significant volumes of IoT data on the Blockchain. Consequently, this research investigates the Deep Reinforcement Learning (DRL) crucial functioning of the blockchain-enabled IoT structure, wherever transactions are instantaneously expanded and public divisibility is confirmed. It is important to note that DRL and Blockchain are two separate advancements devoted to the reliability and usefulness of system operation. These are both transactional systems. Technology integration into information exchange and research solutions is becoming increasingly critical. As a result of Blockchain, information may be exchanged securely and decentralised. When used in tandem with DRL, it can significantly improve communication efficiency. By combining DRL and Blockchain throughout the IoT, the author first presents a decentralised and efficient communication structure that allows for scalable and trustworthy information allocation and better performance than earlier options. The DRL approach assesses whether to offload and which service to dump to improve performance up to 87.5%. Furthermore, this method focuses on constructing an effective offloading mechanism for Blockchain-based communication systems to boost performance.