Improving Transaction Success Rate via Smart Gateway Selection in Cryptocurrency Payment Channel Networks

The last decade has experienced a vast interest in Blockchain-based cryptocurrencies with a specific focus on the applications of this technology. However, slow confirmation times of transactions and unforeseeable high fees hamper their wide adoption for micro-payments. The idea of establishing payment channel networks is one of the many proposed solutions to address this scalability issue where nodes, by utilizing smart contracting, establish payment channels between each other and perform off-chain transactions. However, due to the way these channels are created, both sides have a certain one-way capacity for making transactions. Consequently, if one sides exceeds this one-way capacity, the channel becomes useless in that particular direction, which causes failures of payments and eventually creates an imbalance in the overall network. To keep the payment channel network sustainable, in this paper, we aim to increase the overall success rate of payments by effectively exploiting the fact that end-users are usually connected to the network at multiple points (i.e., gateways) any of which can be used to initiate the payment. We propose an efficient method for selection of the gateway for a user by considering the gateway's inbound and outbound payment traffic ratio. We then augment this proposed method with split payment capability to further increase success rate especially for large transactions. The evaluation of the proposed method shows that compared to greedy and maxflow-based approaches, we can achieve much higher success rates, which are further improved with split payments.Comment: arXiv admin note: text overlap with arXiv:2003.0029

Optimizing Closed Payment Networks on the Lightning Network: Dual Central Node Approach

The Lightning Network, known for its millisecond settlement speeds and low transaction fees, offers a compelling alternative to traditional payment processors, which often have higher fees and longer processing times. This is particularly significant for the unbanked population, which lacks access to standard financial services. Our research targets businesses looking to shift their client to client payment processes, such as B2B invoicing, remittances, and cross-border transactions, to the Lightning Network. We compare the efficiency of interconnected mesh nodes (complete graph topology) with central routing nodes (star graph topology), with a specific focus on the dual central node approach. This approach introduces features like circular rebalancing, redundancy, and a closed network system. Through a basic SimPy model, we assess the network's throughput in a 100 node scenario. While this approach centralizes a technology initially designed for decentralization, it fosters broader enterprise adoption of Bitcoin-based payment networks and encourages participation in the decentralized financial ecosystem. Our study also considers the regulatory implications of using central routing nodes, possibly classified as payment processors under Money Transmission Laws (MTL). These findings aim to contribute to the discourse on the Lightning Network's application in business, highlighting its potential to drive shifts in financial technology towards more decentralized systems.Comment: 21 pages, 8 figures, 1 tabl

Off-chain Transaction Routing in Payment Channel Networks: A Machine Learning Approach

Blockchain is a foundational technology that has the potential to create new prospects for our economic and social systems. However, the scalability problem limits the capability to deliver a target throughput and latency, compared to the traditional financial systems, with increasing workload. Layer-two is a collective term for solutions designed to help solve the scalability by handling transactions off the main chain, also known as layer one. These solutions have the capability to achieve high throughput, fast settlement, and cost efficiency without sacrificing network security. For example, bidirectional payment channels are utilized to allow the execution of fast transactions between two parties, thus forming the so-called payment channel networks (PCNs). Consequently, an efficient routing protocol is needed to find the payment path from the sender to the receiver, with the lowest transaction fees. This routing protocol needs to consider, among other factors, the unexpected online/offline behavior of the constituent payment nodes as well as payment channel imbalance. This study proposes a novel machine learning-based routing technique for fully distributed and efficient off-chain transactions to be used within the PCNs. For this purpose, the effect of the offline nodes and channel imbalance on the payment channels network are modeled. The simulation results demonstrate a good tradeoff among success ratio, transaction fees, routing efficiency, transaction overhead, and transaction maintenance overhead as compared to other techniques that have been previously proposed for the same purpose

A Private Bitcoin Payment Network with Reduced Transaction Fees and Confirmation Times

Since its introduction, Bitcoin cryptocurrency has revolutionized the way payment systems can be designed in a purely distributed manner through its novel Blockchain data structure. While Bitcoin has opened new opportunities, it has been long criticized for its slow transaction confirmation times and high transaction fees. To address this issue, one of the recently emerging solutions is to build a payment channel network (PCN) on top of Bitcoin where the transactions can be made without writing to blockchain. Specifically, a PCN is a network where the users connect either directly or indirectly to send payments to each other in a trustless way. Being backed by the blockchain technology, PCNs satisfy a robust and flexible medium where the exchange of assets become frictionless and thus enable faster transactions with negligible fees. For example, Lightning Network, a second layer network built on top of the Bitcoin network, is being actively developed and it makes Bitcoin possible to be used for micro-payments. However, PCNs including LN bring new challenges on centralization, robustness and privacy as they accept more users. Such problems threaten the very idea of decentralization that comes with blockchain. Therefore, in this dissertation we target the problem of PCN topology formation that will come with ideal features and continue to grow without violating such characteristics. Specifically, we focused on the design of methods for obtaining peer-to-peer (P2P) decentralized PCN topologies. Inspiring from the multi-commodity flow problem, we first developed an optimal solution to establish the perfect PCN topology by utilizing mixed-integer programming. We solve this problem for the required capacities within the network for uninterrupted operation. Second, as mixed integer programming is proved to be NP-compete in complexity, we developed a heuristic optimization approach to take the solution into the polynomial-time domain. Third, to further enable scalability, we developed a new sub-optimal heuristic algorithm using the Dijkstra\u27s shortest path algorithm. Finally, we turned our attention to privacy preservation problem for transactions and augmented each of the proposed approaches with privacy guarantees. Evaluation results indicate that our proposed approaches can enable desirable PCN topology features while respecting the privacy requirements