9,301 research outputs found
Towards an Economic Analysis of Routing in Payment Channel Networks
Payment channel networks are supposed to overcome technical scalability
limitations of blockchain infrastructure by employing a special overlay network
with fast payment confirmation and only sporadic settlement of netted
transactions on the blockchain. However, they introduce economic routing
constraints that limit decentralized scalability and are currently not well
understood. In this paper, we model the economic incentives for participants in
payment channel networks. We provide the first formal model of payment channel
economics and analyze how the cheapest path can be found. Additionally, our
simulation assesses the long-term evolution of a payment channel network. We
find that even for small routing fees, sometimes it is cheaper to settle the
transaction directly on the blockchain.Comment: 6 pages, 3 figures, SERIAL '17 Worksho
Spectrum Trading: An Abstracted Bibliography
This document contains a bibliographic list of major papers on spectrum
trading and their abstracts. The aim of the list is to offer researchers
entering this field a fast panorama of the current literature. The list is
continually updated on the webpage
\url{http://www.disp.uniroma2.it/users/naldi/Ricspt.html}. Omissions and papers
suggested for inclusion may be pointed out to the authors through e-mail
(\textit{[email protected]})
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
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