Enhancing Scalability and Network Efficiency in IOTA Tangle Networks: A POMDP-Based Tip Selection Algorithm

Abstract

The fairness problem in the IOTA (Internet of Things Application) Tangle network has significant implications for transaction efficiency, scalability, and security, particularly concerning orphan transactions and lazy tips. Traditional tip selection algorithms (TSAs) struggle to ensure fair tip selection, leading to inefficient transaction confirmations and network congestion. This research proposes a novel partially observable Markov decision process (POMDP)-based TSA, which dynamically prioritizes tips with lower confirmation likelihood, reducing orphan transactions and enhancing network throughput. By leveraging probabilistic decision making and the Monte Carlo tree search, the proposed TSA efficiently selects tips based on long-term impact rather than immediate transaction weight. The algorithm is rigorously evaluated against seven existing TSAs, including Random Walk, Unweighted TSA, Weighted TSA, Hybrid TSA-1, Hybrid TSA-2, E-IOTA, and G-IOTA, under various network conditions. The experimental results demonstrate that the POMDP-based TSA achieves a confirmation rate of 89-94%, reduces the orphan tip rate to 1-5%, and completely eliminates lazy tips (0%). Additionally, the proposed method ensures stable scalability and high security resilience, making it a robust and efficient solution for decentralized ledger networks. These findings highlight the potential of reinforcement learning-driven TSAs to enhance fairness, efficiency, and robustness in DAG-based blockchain systems. This work paves the way for future research into adaptive and scalable consensus mechanisms for the IOTA Tangle.