To chain or not to chain: A reinforcement learning approach for blockchain-enabled IoT monitoring applications

Abstract

Traceability and autonomous business logic execution are highly desirable features in IoT monitoring applications. Traceability enables verifying signals history for security or analytical purposes. On the other hand, the autonomous execution of pre-defined rules establishes trust between parties involved in such applications and improves the efficiency of their workflow. Smart Contracts (SCs) firmly guarantee these two requirements due to the blockchain's immutable distributed ledger and secure cryptographic consensus rules. Thus, SCs emerged as an appealing technology for monitoring applications. However, the cost of using public blockchains to harvest these guarantees can be prohibitive, especially with the considerable fluctuation of coin prices and different use case requirements. In this paper, we introduce a general SC-based IoT monitoring framework that can leverage the security features of public blockchains while minimizing the corresponding monetary cost. The framework contains a reinforcement learning agent that adapts to users needs and acts in real-time to smartly set the data submission rate of IoT sensors. Results based on the Ethereum protocol show significant potential cost saving depending on users preferences.