Optimization of Weighted Random Walk Tip Selection Algorithm on the IOTA Tangle

IOTA represents a distinctive form of distributed ledger technology (DLT) that utilizes a Directed Acyclic Graph (DAG) topology known as the Tangle. The Tangle offers numerous benefits, including high scalability, feelessness, and almost immediate transaction confirmation. Each transaction in the Tangle must approve two existing terminal transactions (tips) using dedicated algorithms called tip selection algorithms, which include Uniform Random Tip Selection (URTS), Weighted Random Walk (WRW), and Unweighted Random Walk (URW). In this paper, we focus on analyzing the WRW algorithm. This paper introduces Age Aware Weighted Random Walk (AA-WRW), an enhancement to the Weighted Random Walk (WRW) tip selection algorithm used in the IOTA blockchain. We propose an optimization aimed at reducing the occurrence of permanent tips while maintaining a balance between performance and security. By prioritizing older tips and selecting those with high cumulative weight, our approach enhances transaction validation in the Tangle. Experimental results demonstrate the effectiveness of our optimization, achieving a reduction in permanent tips by over 30% compared to the original WRW approach. Notably, these results also demonstrate that our optimization does not lead to a significant increase in execution time.