How to profit from payments channels

TL;DR

This paper explores how payment nodes in blockchain networks can optimize their channel fees and connections to maximize profits, using a formal NP-hard problem and a greedy approximation algorithm validated by real-world data.

Contribution

It formalizes the profit maximization problem for nodes in payment channel networks and proposes a greedy algorithm to approximate optimal fee and channel strategies.

Findings

The optimization problem is NP-hard.

The greedy algorithm achieves at least twice the profit of other strategies.

Simulation with real data confirms the effectiveness of the proposed approach.

Abstract

Payment channel networks like Bitcoin's Lightning network are an auspicious approach for realizing high transaction throughput and almost-instant confirmations in blockchain networks. However, the ability to successfully make payments in such networks relies on the willingness of participants to lock collateral in the network. In Lightning, the key financial incentive is to lock collateral are small fees for routing payments for other participants. While users can choose these fees, currently, they mainly stick to the default fees. By providing insights on beneficial choices for fees, we aim to incentivize users to lock more collateral and improve the effectiveness of the network. In this paper, we consider a node $\mathbf{A}$ that given the network topology and the channel details selects where to establish channels and how much fee to charge such that its financial gain is maximized. We formalize the optimization problem and show that it is NP-hard. We design a greedy algorithm to approximate the optimal solution. In each step, our greedy algorithm selects a node which maximizes the total reward concerning the number of shortest paths passing through $\mathbf{A}$ and channel fees. Our simulation study leverages real-world data set to quantify the impact of our gain optimization and indicates that our strategy is at least a factor two better than other strategies.