Privacy-Preserving Data Aggregation Techniques for Wireless Body Area Networks: A Comprehensive Survey and Comparative Analysis

Wireless Body Area Networks (WBANs) are crucial for advancing healthcare monitoring, enabling continuous data collection directly from patients. Efficient data management strategies are essential to maximize the utility of the sensitive health data these networks gather. Existing WBAN systems often face significant challenges in balancing data privacy with network performance, potentially compromising patient safety and system effectiveness. Traditional data aggregation methods in WBANs generally emphasize enhancing transmission speeds and reducing energy usage, but they frequently neglect critical aspects of privacy protection and data integrity. This study evaluates advanced data aggregation techniques need to prioritize privacy preservation while enhancing system efficiency, specifically addressing the limitations in latency and energy consumption inherent to existing methods. The efficacy of the projected solutions is assessed using various datasets, including the comprehensive MIMIC-III and PhysioNet for real-world healthcare scenarios, and custom simulated WBAN data to test under controlled conditions. Comparative evaluation of existing techniques show that significantly improve network performance, achieving packet delivery ratios up to 99%, reducing latency to as low as 80 ms, and minimizing energy consumption to 0.33 J, all while maintaining high levels of data integrity and privacy. The advanced data aggregation strategies analysed in this study, which surveys and comparatively evaluates techniques effectively address the critical challenges in WBANs, demonstrating substantial enhancements in both privacy and operational efficiency. This balanced approach paves the way for broader adoption and more reliable deployment of WBAN technologies in sensitive health monitoring applications.