An Energy-Delay Optimized Model for Efficient WBAN Communication in Iot-Enabled Autism Monitoring

Abstract

Wireless Body Area Networks (WBANs) have revolutionized healthcare by enabling continuous monitoring of physiological parameters, making them crucial for managing conditions like autism, where real-time data collection and analysis are vital. However, WBANs face challenges such as energy inefficiency, high delay, and communication overhead, particularly in dynamic IoT environments with mobility and emergency scenarios. This study addresses these challenges by proposing an Energy-Delay Optimized Data Communication Model (EDODCM) for WBANs. The primary objective is to enhance energy efficiency, increase network lifetime, reduce end-to-end delay, and minimize communication overhead while ensuring reliable data transmission from wearable sensors to gateways. The EDODCM employs an unequal clustering approach, an optimized duty-cycling mechanism, multi-hop routing for normal and emergency scenarios, and a TDMA-based Optimized Medium Access Control (TDMA-OMAC) for efficient data aggregation and transmission. Simulation results demonstrate that EDODCM improves average energy efficiency by 24.52%, extends average network lifetime by 20.1%, reduces average end-to-end delay by 9.65%, and decreases average communication overhead by 22.18% compared to existing approaches. The novelty of this work lies in its adaptive routing strategies for dynamic scenarios and its focus on integrating WBANs within IoT for real-time autism monitoring. These findings highlight EDODCM’s potential for scalable and efficient WBAN communication, paving the way for improved healthcare solutions.