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.


School of Sciences and Engineering


Robotics, Control & Smart Systems Program

Degree Name

MS in Robotics, Control and Smart Systems

Graduation Date

Summer 6-15-2021

Submission Date


First Advisor

Yasser Gadallah

Committee Member 1

Karim Seddik

Committee Member 2

Amr El-Sherif

Committee Member 3

Ahmed El Gendy


78 p.

Document Type

Master's Thesis

Institutional Review Board (IRB) Approval

Not necessary for this item

Available for download on Thursday, May 25, 2023