MULTI-FACTOR HYBRID APPROACH FOR BLOCKCHAIN-ENABLED IOT EDGE COMPUTING

Abstract

The integration of Internet of Things (IoT) devices with edge computing supports applications in industry, healthcare, and smart cities. Edge computing moves data processing closer to the data source, reducing reliance on central cloud infrastructure. However, managing heterogeneous and geographically distributed systems introduces challenges in coordination, software distribution, and trust establishment. Centralized approaches create single points of failure and limit scalability. Blockchain technology addresses trust and coordination issues. Nonetheless, existing implementations lack on-chain coordination with real-time resource metrics and do not support load-aware task assignments.

This work introduces a hybrid blockchain-IoT edge computing platform. The platform integrates multi-factor node selection based on node reputation and resource availability. The developed approach includes timeout management, queue capacity control, thread-safe concurrent transactions, and event-driven task orchestration with staleness detection. Off-chain computation is separated from on-chain coordination. Node selection uses a scoring function that combines success rate with real-time resource metrics. Coordination logic is implemented through Ethereum-based smart contracts, ensuring transparency and immutability. Validation was conducted in a simulated Docker network environment.

Testing shows that nodes with higher scores are selected in all observed cases. However, when multiple tasks are submitted within the same inter-block period, node scores do not update as expected. Selecting nodes based on the following block rather than the current one improves selection reliability. The platform addresses gaps identified in existing literature and can be used in industrial edge computing scenarios.