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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. Laye...
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| Format: | Thesis |
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AUC Knowledge Fountain
2021
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| _version_ | 1867613419321950208 |
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| access_status_str | Open Access |
| author | Kadry, Heba |
| author_browse | Kadry, Heba |
| author_facet | Kadry, Heba |
| author_sort | Kadry, Heba |
| collection | Thesis |
| description | 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. |
| format | Thesis |
| id | oai:fount.aucegypt.edu:etds-2671 |
| institution | American University in Cairo (Egypt) |
| last_indexed | 2026-06-10T12:35:50.652Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from AUC Knowledge Fountain — bepress |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | AUC Knowledge Fountain |
| publisherStr | AUC Knowledge Fountain |
| record_format | dspace |
| source_str | AUC Knowledge Fountain — bepress |
| spelling | oai:fount.aucegypt.edu:etds-2671 Off-chain Transaction Routing in Payment Channel Networks: A Machine Learning Approach Kadry, Heba 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. 2021-06-15T07:00:00Z thesis application/pdf https://fount.aucegypt.edu/etds/1647 https://fount.aucegypt.edu/context/etds/article/2671/viewcontent/heba_ahmed_kadry_elriedy_thesis.pdf Theses and Dissertations AUC Knowledge Fountain blockchain machine learning payment channel network routing scalability bitcoin lightning network cryptocurrency off-chain transactions Digital Communications and Networking |
| spellingShingle | blockchain machine learning payment channel network routing scalability bitcoin lightning network cryptocurrency off-chain transactions Digital Communications and Networking Kadry, Heba Off-chain Transaction Routing in Payment Channel Networks: A Machine Learning Approach |
| title | Off-chain Transaction Routing in Payment Channel Networks: A Machine Learning Approach |
| title_full | Off-chain Transaction Routing in Payment Channel Networks: A Machine Learning Approach |
| title_fullStr | Off-chain Transaction Routing in Payment Channel Networks: A Machine Learning Approach |
| title_full_unstemmed | Off-chain Transaction Routing in Payment Channel Networks: A Machine Learning Approach |
| title_short | Off-chain Transaction Routing in Payment Channel Networks: A Machine Learning Approach |
| title_sort | off chain transaction routing in payment channel networks a machine learning approach |
| topic | blockchain machine learning payment channel network routing scalability bitcoin lightning network cryptocurrency off-chain transactions Digital Communications and Networking |
| url | https://fount.aucegypt.edu/etds/1647 https://fount.aucegypt.edu/context/etds/article/2671/viewcontent/heba_ahmed_kadry_elriedy_thesis.pdf |
| work_keys_str_mv | AT kadryheba offchaintransactionroutinginpaymentchannelnetworksamachinelearningapproach |