Decentralized IoMT Architecture for Privacy-Preserving Remote Patient Monitoring
Li Yin, Xuefeng Du, Yanqi Cheng, Jia Li, Ning Tong, Fengqi Li · IEEE Sensors Journal · 2025
Underscored by the pivotal role of smart IoT devices and AI-driven medical models in remote patient monitoring (RPM), the prevailing reliance on centralized infrastructures poses critical challenges in data privacy, computational resource allocation, and system scalability. To overcome these limitations, we propose APPBIS: a novel Authentication and Privacy-Preserving Blockchain-Based IoT System tailored for decentralized RPM. APPBIS integrates secure hardware certification, smart contractbased orchestration, and an innovative Things-On-Chain (TOC) mechanism, which enables direct interaction between portable medical devices and the blockchain network, minimizing dependency on centralized gateways. At the core of APPBIS is a Float Proof-of-Work (FPoW) consensus scheme, which leverages Bessel functionbased puzzles and floating-point computation to provide verifiable workload proofs across heterogeneous devices. FPoW ensures fair competition among resource-constrained nodes while supporting scalable task delegation. Extensive simulationsspanning up to 1000 concurrent nodesdemonstrate that FPoW outperforms traditional PoW, PoS, and PBFT protocols in terms of throughput, latency, and fairness, while maintaining robust security under adversarial conditions. A complete hardware-software prototype is implemented to validate system feasibility, with energy-efficient performance across devices ranging from embedded sensors to edge servers. The results provide a pathway toward scalable, secure, and trustworthy RPM infrastructures.