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<p>One of the most promising industries for the adoption of Internet of Things (IoT) based technology is healthcare, where patients can measure medical parameters at any time and from any place by using wearable or implanted medical sensors. The use of Wireless Body Area Networks (WBANs) in emergency medical situations involving faraway patients has grown in importance. Healthcare data prioritization significantly affects the energy and latency of the WBAN routing process. To overcome the drawbacks of the existing models, a novel approach called Priority-based Energy Efficient Routing Protocols using Multi-objective Optimization in Healthcare using IoT is proposed. Initially, the patient data are collected, which is categorized based on normal and emergency using a hybrid artificial neural network (ANN) in which the ANN and support vector machine (SVM) models are utilized. Following that, a multi-objective-based routing is performed, which involves the calculation of the fitness function. Finally, the best path is selected by multi-objective Avian Harmony Optimization (AHO), which is a hybrid of Egret Swarm Optimization (ESO) and the Lyrebird Optimization Algorithm (LOA). The suggested model attained an Accuracy of 98.57% and Energy consumption of 23.26&#xa0;mJ by implementing it in a MATLAB tool. Thus, from the results, it is evident that the suggested model is superior in comparison to the existing models.</p>

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Pr-EERP: A Priority-Aware Hybrid Optimization and Machine Learning Framework for Energy-Efficient Routing in Healthcare IoT

  • 期刊论文

  • 发表日期：

    2026年7月9日

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  • Monika Pahuja,
  • Dinesh Kumar

摘要

One of the most promising industries for the adoption of Internet of Things (IoT) based technology is healthcare, where patients can measure medical parameters at any time and from any place by using wearable or implanted medical sensors. The use of Wireless Body Area Networks (WBANs) in emergency medical situations involving faraway patients has grown in importance. Healthcare data prioritization significantly affects the energy and latency of the WBAN routing process. To overcome the drawbacks of the existing models, a novel approach called Priority-based Energy Efficient Routing Protocols using Multi-objective Optimization in Healthcare using IoT is proposed. Initially, the patient data are collected, which is categorized based on normal and emergency using a hybrid artificial neural network (ANN) in which the ANN and support vector machine (SVM) models are utilized. Following that, a multi-objective-based routing is performed, which involves the calculation of the fitness function. Finally, the best path is selected by multi-objective Avian Harmony Optimization (AHO), which is a hybrid of Egret Swarm Optimization (ESO) and the Lyrebird Optimization Algorithm (LOA). The suggested model attained an Accuracy of 98.57% and Energy consumption of 23.26 mJ by implementing it in a MATLAB tool. Thus, from the results, it is evident that the suggested model is superior in comparison to the existing models.