Enhancing Data Security in the Internet of Medical Things (IoMT) with a Robust Transmission Model

The Internet of Things (IoT) has made real-time applications more flexible and deployable. IoT has security and energy efficiency issues despite its benefits. This study addresses these issues with a secure data transmission architecture using fuzzy-based secure clustering and hybridised metaheuristic algorithms. The suggested framework contains three phases. The first phase presents an energy-efficient and secure IoT data transfer infrastructure. Secure route selection is done using fuzzy-based secure clustering and the Whale Optimization-based Harris Hawks Optimisation (W-HHO) method. Parallel cypher block chaining and advanced encryption strengthen data security. Secure cluster-based routing is needed for IOMT in the second phase. To group nodes, we use an adaptive Gaussian mutation-based Sine Cosine Optimisation (SCO) method, and we use mutual authentication to make sure everything is secure. Phase last of the research yields the most optimal and safe fuzzy inference route selection approach. With its foundation in Levy Flight Distributed Optimization (LFD-HGSO), the Henry Gas Solubility Optimization Algorithm clusters data and encrypts it concurrently and cypher-block-chains it. The safest routes are selected by fuzzy inference. In comparison to conventional methods, the proposed alternatives achieve better results in several performance metrics, including delivery rate for packet, energy efficiency, throughput and network lifetime. These results demonstrate the framework's secure and energy-efficient data transport for IoT and IoMT applications.