Optimizing Tip Selection in IOTA Based Intelligent Distributed Ledgers using an Adaptive Approach

The rapid expansion of Internet of Things (IoT) applications has revealed limitations in traditional blockchain systems, particularly in scalability, energy efficiency, and computational overhead. IOTA, a Distributed Ledger Technology (DLT) based on Directed Acyclic Graph (DAG) structure known as the Tangle, offers a lightweight, scalable solution tailored for IoT environments. Key factor in IOTA’s performance and security is its tip selection mechanism, which manages transaction confirmation. This research introduces a novel approach combining Action Candidate-based Clipped Double Q-learning (AC-CDQ) with Biased Thompson Sampling (BTS) to improve tip selection. The proposed model addresses overestimation bias in Q-learning while enhancing robustness against adversarial attacks. Extensive simulations using a ledger simulator and the OpenAI Gym environment shows that model significantly reduces the average number of unapproved transactions. It consistently outperforms baseline methods, including Uniform Random Tip Selection (URTS), Markov Chain Monte Carlo (MCMC), and standard Q-learning, by confirming more transactions with improved reliability. Although the model incurs slightly higher computational costs, it delivers more accurate Q-value estimates and better learning stability. This work advances tip selection algorithms and supports the development of secure, efficient, and scalable distributed ledger systems. It demonstrates the potential of hybrid reinforcement learning techniques in future IoT-oriented ledger technologies.