Schnorr approval-based secure and privacy-preserving data aggregation with dynamic clustering and fault tolerance for wireless body area networks

Abstract Wireless Body Area Networks play a pivotal role in enabling real-time health monitoring within the Internet of Medical Things, yet their resource-constrained sensors, sensitive health data, and dynamic operational conditions introduce significant challenges, such as limited battery life, network disruptions due to patient mobility, and vulnerabilities to sensor failures or cyberattacks that jeopardize data integrity and privacy. This paper presents a novel Schnorr Approval-Based Secure and Privacy-Preserving Data Aggregation system tailored for Wireless Body Area Networks, integrating a lightweight Modified MuSig2 Schnorr signature scheme for robust, rogue-key-resistant data authentication, a Dynamic Clustering Algorithm to seamlessly adapt to mobility-induced topology changes, a Recoverable Masking Technique leveraging SipHash-2–4 to ensure data confidentiality without sacrificing accuracy, and a Fault-Tolerant Data Approval mechanism employing Reed-Solomon coding to mitigate failures and malicious attacks. In contrast to computationally intensive methods like homomorphic encryption, this proposed system is meticulously designed for low-power sensors, achieving a balance of security, efficiency, and scalability. Comprehensive simulations across diverse scenarios demonstrate that our system delivers minimal latency, low energy consumption, high aggregation accuracy, and exceptional resilience against data injection attacks and node failures, positioning it as an innovative and reliable solution for next generation of Wireless Body Area Networks for health monitoring applications.