Optimization of tips selection on the IOTA tangle for securing blockchain-based IoT transactions

Abstract

Data integrity and security protection are needed in the Internet of Things. IOTA technology with a Directed Acyclic Graph (DAG) structure is a solution to realize secure and scalable data transactions. Recent research IOTA is still faced with the issue of being vulnerable to splitting attacks and left-behind transactions. The splitting attack causes the network to confirm conflict transactions. Then, left-behind transactions cause the network to generate transactions that will not be confirmed. The selection tip weighted random walk (WRW) algorithm uses the Markov Chain Monte Carlo (MCMC) to overcome these two issues by applying the appropriate bias parameter (α). However, when the α is too large, it will produce a lot of left-behind transactions. Determining the optimal value of α is still an important research topic today. An E-IOTA study that gives several α values statically with random selection but can still produce more left-behind transactions than pure WRW. This paper proposes an optimization of the tip selection algorithm (DA-IOTA) to determine the optimal alpha (α) using an approach to dynamically determine each WRW step. The experimental results show that DA-IOTA produces fewer left-behind transactions than MCMC (WRW) and E-IOTA which use α parameters statically.