A Novel Timechain-Level Approach to the Modeling of the Bitcoin Lightning Network

Abstract

The Lightning Network (LN) emerged in recent years as the most promising solution for scaling the Bitcoin network on a second layer. While the protocol specifications reached a relative maturity level, research efforts on higher-level challenges such as topology discovery, routing, and adversarial scenarios are still in their infancy. On the one hand, traditional network modelling approaches ignore the peculiarity of LN, that is, being based on a time-flow dictated by Bitcoin layer events. On the other, deploying thousands of actual nodes would require massive computational resources and, especially from an academic perspective, non-trivial interventions on complex production-stage code to test more experimental scenarios. With this work, we introduce the concept of Timechain-Ievel model, along with an open-source implementation, to abstract the complexity of the Lightning Network while still providing a vision of base layer events and protocol internals. After describing how each element of the model is mapped into the LN architectural stack, we show a case study to demonstrate its usage in investigating large-scale scenarios for research, development and educational purposes. Finally, we present a detailed comparison to properly contextualize our contribution to the current state of the art of LN modelling, highlighting the advancements introduced by a Timechain-level and future directions of research it opens.