Secure data communication framework based on fuzzy based secure clustering and hybridized metaheuristic algorithms

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 the Internet of Medical Things (IoMT) in the second phase. An adaptive Gaussian mutation-based Sine Cosine Optimisation (SCO) approach clusters nodes, and mutual authentication assures security. Final phase of research delivers optimal and secure fuzzy inference route selection policy. The Levy Flight Distributed optimization-based Henry Gas Solubility Optimisation Algorithm (LFD-HGSO) clusters data, while concurrent encryption and cypher block chaining secure it. Fuzzy inference selects safest paths. Performance tests show that the proposed solutions improve packet delivery rate, throughput, energy efficiency, and network lifetime over conventional methods. These results demonstrate the framework's safe and energy-efficient data transfer for IoT and IoMT applications. Experimental findings show that the suggested strategy outperforms various QoS metrics like packet delivery ratio, throughput, network longevity, and energy usage. Time efficiency is also improved by the PAES-CBC key generation algorithm.