ArticleScientific reports2026
Optimized cluster based routing protocol for IoT enabled healthcare data networks.
Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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3 authors.
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Abstract
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.
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