Scalability is essential for next generation blockchain technology to integrate with large mobile networks like Internet of Things (IoT). The IOTA distributed ledger protocol has combined transaction generation and verification to address this, but at the expense of increased reliance on connectivity to resolve conflicts with a novel ledger data structure. Intelligent Ledger Construction (ILC) was proposed as an auditable lightweight reinforcement-learning scheme to address this constraint with proposal of local conflict resolution with machine-learning classification. This effort presents an improved reliability reward model to enhance training for ILC and further reduce adversarial gaming and resource usage. Testing this revision in an idealistic setting showed vast improvement in transaction throughput efficiency and delay while also reducing unapproved transactions by 60% in a realistic IoT-like network. Future work will improve auditability of the underlying intelligence for stronger security.
C. Rawlins and S. Jagannathan, "Improved Intelligent Ledger Construction For Realistic IoT Blockchain Networks," 2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023 - Proceedings, Institute of Electrical and Electronics Engineers, Jan 2023.
The definitive version is available at https://doi.org/10.1109/AIBThings58340.2023.10292467
Electrical and Computer Engineering
Keywords and Phrases
Blockchain and Machine Learning/Artificial Intelligence; Blockchain for Internet of Things; IOTA
Article - Conference proceedings
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01 Jan 2023