A Lightweight Smart Contract Framework for Behavior-Based Dynamic Identity Revocation in IoT Systems
Year: 2025
Type: article
Abstract: With the Internet of Things (IoT) venturing into life and death area, distributing identity management has never become an important condition to meet for trust model such like security, auditability and being capable of failure. Traditional blockchain identity systems are mostly limited to registration, verification, without dynamic revocation and recovery features. In this paper, we propose a lightweight smart contract-driven framework to deal with dynamic trust based on real-time events in IoT networks. The research contribution is a modular architecture with which to programmatic revocation using behavior-based triggers, threshold consensus voting, and timelocked expiry as part of on-chain policy logic. In addition, a re-enrollment protocol tied to the certification ensures authenticity and lifetime after revocation. From a methodological point of view, the system is deployed as an Ethereum-based system and consists of 3 main building block components: (1) an Identity Registry, (2) a Revocation Policy Manager, and (3) an Access Monitor coupled with offchain oracles. The system guarantees constant-time (O(1)) lookups for identity checks and policy evaluation, that are protected by unforgeable event logs. Performance analysis shows that our proposed solution incurs low computational overhead (≤ 45 ms on ARM Cortex-M4), small communication cost (2.1 KB per transaction), and little gas consumption (≤ 115,000 units). The model achieves 2× speedup over state-of-the-art models EBIAS, Zhang et al., and Song et al. when preserving auditability and privacy. To summarize, the framework provides a scalable, secure and autonomous trust lifecycle management solution for IoT. The model will be extended in future with post-quantum cryptographic building blocks and machine learning—based anomaly scoring to develop the adaptive trust dynamics in real-world test beds.
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Funder: King Faisal University
OpenAlex ID: W7160919540