CBEAOR: An Energy Aware Optimal Clustering and Routing Protocol for Sustainable IoT Healthcare Networks

The Internet of Things (IoT) is the network of numerous smart sensors and physical devices connected via the internet. It can be used in many areas like agriculture, military, environmental and pollution control, smart cities, and health care (HC). Because IoT devices are resource-constrained in nature, energy consumption (EC), battery lifetime, and frequent and reliable communication are challenging issues to handle when transmitting medical data from the patient to the hospital via the wireless channel. Clustering and routing techniques are considered better solutions to offer energy-efficient communication for IoT applications with extended network lifetimes (NL). Most existing techniques treated clustering and routing as separate solutions in the IoT network. So there is a need for a framework to offer both clustering and routing optimally and efficiently. This paper proposes a novel clustering-based optimal routing (CBEAOR) methodology for IoT-HC systems to attain the network's maximum level of energy utilization and lifetime. Initially, a modified K-means (MKM) clustering algorithm is proposed for the creation of clusters. Further, the optimal cluster heads (CHs) are chosen according to the enhanced arithmetic optimization algorithm (EAOA). After CHs selection, the highly disruptive polynomial mutation an adaptive inertia weighted grasshopper optimization algorithm (HAGOA) is applied for the optimum selection of routes to destinations. The simulations were carried out to evaluate the effectiveness of the proposed method. The performance is evaluated for different number of nodes; when number of nodes is 250, energy consumption, throughput and packet delivery rate (PDR) are 0.274 mj, 0.956% and 97.87%. The results proved that the proposed CBEAOR achieves superior performance than the existing routing models.