Energy-Effective Optimal Routing–Driven Hybrid Optimizations–Enabled IoT-Based Wearable Wireless Body Area Network

Abstract

Wireless body area networks (WBAN) are considerably playing a remarkable role in healthcare monitoring systems with the capacity to provide real-time information about patients. The challenging factor about WBAN is providing a better energy efficiency system with higher reliability. For better monitoring of health, wearable IoT smart devices are utilized. To achieve this, the work focused on energy-effective clustering and optimal routing with an improved optimization algorithm. The proposed work combines the Levy Flight and Frilled Lizard Optimization (LF²ZO) algorithm. The Levy flight strategy is used to enhance the exploration stages of FLO for the optimal energy-efficient routing system. This proposed method mitigates energy expenditure with high transmission reliability. The cluster formation in the area is effectuated with the Dynamic Self-executing Active Cross-propagated (DSAC) method with various factors such as Murkowski distance, Euclidian distance, and the desired number of clusters. Based on the connectivity and residual energy, the cluster head (CH) is selected using the Momentum Sparrow Search (MSS) algorithm. With the precise selection of CH, the transmission number and energy are reduced within interclustering and intraclustering. Simulation outcomes validate the robustness of the proposed work with the remarkable improvement of the network's lifespan with a higher packet delivery ratio. The energy expenditure is also lower and provides a promising solution for the optimal routing system. This work provides better sustainability of WBAN for the healthcare monitoring system.