Optimized cluster based routing protocol for IoT enabled healthcare data networks

The integration of Internet of Things (IoT) technologies into healthcare has transformed real-time monitoring and data communication. However, IoT-enabled Cognitive Radio Networks (CRNs) face persistent challenges such as high energy consumption, routing delays, and security risks. This paper proposes a novel Grey Wolf Optimization-based Multi-Adaptive Routing Protocol (GWO-MARP) that combines cluster-based routing with adaptive meta-heuristic intelligence. Unlike traditional cluster-based routing approaches that rely on static or energy-weighted selection, GWO-MARP dynamically determines cluster heads through a multi-objective fitness function that balances residual energy, lifetime, link quality, and security cost. The adaptive hunting behaviour of the GWO algorithm guides optimal path formation and ensures secure and energy-aware transmission, making the protocol distinct from existing methods. The proposed method is implemented and evaluated using MATLAB, and its performance is benchmarked against existing protocols including DA-EDC, MT-DQL, and SDL. Experimental results demonstrate significant improvements in throughput (44.30 kbps), end-to-end delay (1.175 ms), packet delivery ratio (99.6%), and energy consumption (0.103 J) for a network of 200 nodes. These outcomes validate the proposed protocol's capability to support robust, scalable, and energy-efficient data transmission in IoT-enabled healthcare networks.