Optimizing security and energy efficiency in IoT-Based health monitoring systems for wireless body area networks

Wireless Body Area Networks (WBANs) play a critical role in real-time healthcare monitoring by enabling continuous data collection from body-worn sensors. However, energy inefficiency and data security vulnerabilities limit their large-scale deployment and long-term reliability. The primary aim of this research is to optimize both energy consumption and data integrity within WBAN-based health monitoring systems. To address these issues, a novel multi-layered framework is proposed. First, an energy-efficient mechanism is introduced using a threshold-based transmission controller. Each sensor node monitors its power levels and enters sleep mode when a defined energy threshold (0.4 J) is reached. A flag is transmitted to the sink node to indicate potential data unavailability, preventing misinterpretation of sensor silence. Transmission power is dynamically scaled to further minimize energy usage without compromising data delivery. Second, a lightweight security mechanism is incorporated using echo packet validation. A dedicated security block monitors the reverse packet flow for anomalies. Any deviation from the expected flow pattern triggers a breach alert, effectively identifying and mitigating spoofing or reverse-channel attacks. This ensures data integrity and confidentiality across the WBAN. Additionally, a trust aggregation engine is implemented to quantify the trustworthiness of nodes using both global and local ratings. The healthcare provider assigns global ratings, while local ratings are crowdsourced from remote users or monitoring agents. The aggregation logic at the Cluster Head (CH) ensures that only data with validated origin and computation consistency are accepted, significantly increasing trust in remote diagnostics. Simulations were conducted using MATLAB R2022b with 50 nodes across a 20 × 20 m² area. The proposed system achieved a 35% reduction in energy consumption, lowering it from 20 Joules (baseline) to 13 Joules, and improved data accuracy to 98%, compared to 78% and 80% in existing methods. The system also demonstrated a reduction in false positive alerts below 3.8%, a packet delivery ratio of 97.5%, and latency under 92 ms, indicating its real-time applicability and reliability. These results confirm that the proposed framework not only prolongs network lifetime but also secures data communication while maintaining high diagnostic fidelity, thereby offering a robust and scalable solution for next-generation WBAN healthcare applications.