Q-Learning Model for Blockchain Security in Internet of Medical Things Networks

Abstract

Secure Internet of Medical Things (IoMT) installations must take a long time to complete, which leaves them vulnerable to low energy performance, low throughput, and low packet delivery rates. This limits performance in real-time deployments, which has an effect on healthcare efficiency. Many optimization approaches have been proposed by researchers to deal with these issues, but most of them increase the complexity of mining and thus reduce its scalability. In order to improve the temporal QoS performance of IoMT deployments, this paper proposes the creation of a novel bio-inspired model. In order to evaluate the temporal mining performance (of miners) in terms of mining throughput, block mining ratio, latency, and energy, the proposed model makes use of the Mayfly Optimization (MO) Method. A fitness function based on these indices is then used to decide whether or not the underlying blockchains should be combined or split up. During a split operation, a blockchain is divided into two equal parts, and during a merge operation, the blockchain is saved for later retrieval. The proposed model improves block mining performance by 3.2%, compared to the average QoS optimization model under similar conditions, increases mining speed by 3.5%, decreases energy consumption by 4.9%, increases throughput by 2.8%, and improves performance by 3.2%.