Modeling Ledger Dynamics in IOTA Blockchain

Abstract

IOTA blockchain is a new type of distributed ledger systems that is lightweight without mining and feeless-of-using. Rather than using a chain structure as in traditional blockchains, IOTA organizes ledger records with a directed acyclic graph (DAG), called Tangle. When message entries are committed into the ledger, the ledger tangle grows in a special way where multiple messages could be attached by different processing nodes in parallel. Such a unique evolution process motivates us to study the ledger tangle dynamics, which is unexplored so far. In this paper, we present the first generative modeling for IOTA tangle based on stochastic analysis. A key finding is that IOTA tangle renders a double Pareto Lognormal (dPLN) distribution, rather not typical network models (e.g., Power-Law and Exponential distributions). Quantitative comparisons show that the fitting quality of our model outperforms existing popular models on official real world datasets published by IOTA Foundation. Estimated model parameters are provided, which is immediately instrumental for a more realistic IOTA network generator design. The proposed generative model also provides a deeper understanding of the internal mechanics of IOTA network.