Internet of Things Communication protocols optimization using Blockchain Technology integrated with Reinforcement Learning

Under the IoT vision, conventional items become sophisticated and self-contained. This ideal has become an actuality due to its technological breakthroughs, although there are still challenges to face. Especially in the field of security, like data accuracy. All academia and commerce are curious about the combined study of block chain and computational modelling (ML) because it may offer significant advantages for achieving decentralized, safe, intelligent, and advanced information management operations and administration. Considering the expected IoT will have to establish relationships in this massive incoming data stream as it evolves in the coming years Data sharing will change as a result of the crucial software known as the blockchain. A scientific innovation that can transform several industries, such as the Internet of Things, is the capacity to make connections within dispersed systems without the need for power. As a result, the controller operates well and may be modified to fit into different, dynamic contexts. Additionally, since greater-level systems are taught with deep reinforcement learning, the machine can expect to study even after it is put into use, which makes it perfect for practical uses. Based on their personal experience with the employment of an agent, learning through Reinforcement: S (State), A (Action), and R (Reaction) are the parameters (Reward Under the IoT vision, ordinary items become clever and self-contained. This paper investigates this relationship, analyses issues with blockchain and IoT systems, and assesses the most pertinent studies in order to establish how blockchain with reinforcement learning may enhance IoT. Depending on the finding, the study then suggests using supervised learning techniques to address some of the major problems faced by blockchain-enabled IIoT systems, such as block time reduction and operations throughput development. There will be a thorough case study that demonstrates how a Q-learning strategy can be used to minimize latency problems for a miner and hence lower the likelihood of forking events.