Optimal cluster-based energy efficient routing scheme for QoS aware IoT-enabled wireless body area network

Wireless Body Area Networks (WBANs) are an integral component of contemporary IoT-driven healthcare and can enable wearable sensors to continuously monitor a patient's health. Despite their usefulness, routing data in WBANs is challenging because of factors such as tight energy restrictions, frequent node movement, congestion and unreliable trust between nodes. These problems often lead to lower network performance and reduced system life. Clustering techniques may be useful in enhancing energy consumption and scalability, however there are numerous pre-existing approaches, yet they still face issues such as premature convergence, unbalanced workloads and poor selection of cluster heads (CHs). To overcome those limitations, this work proposes a new QoS-aware, energy-efficient clustering-based routing scheme (QEEC-Routing) which combines three new algorithms. The Modified Raccoon Optimization (MRO) algorithm forms well-balanced clusters to distribute the energy usage more evenly. A Two-level Quaternion-Valued Recurrent Neural Network (TQV-RNN) is used to calculate adaptive trust levels to obtain more accurate CH selection. The Improved Hypercube Natural Aggregation (IHNA) algorithm then finds the most reliable routing paths, even with nodes in motion or congestion in the network. Tests conducted in NS3 simulator indicate that QEEC-Routing reduces energy consumption by 51.5%, improves packet delivery by 6.5% and increases the overall network lifetime by 14.9% as compared to current approaches. Altogether, the proposed design proposes a more reliable, energy-aware, and trust-conscious communication strategy that can be used for real-time IoT healthcare applications.