Details
| Original language | English |
|---|---|
| Article number | 112620 |
| Number of pages | 11 |
| Journal | Neural Computing and Applications |
| Early online date | 11 Dec 2024 |
| Publication status | E-pub ahead of print - 11 Dec 2024 |
Abstract
The lightning network (LN) is a technological solution designed to solve the bitcoin blockchain transaction speed problem by introducing off-chain transactions. Since LN is a sparse and highly distributed network with three predominant routing protocols, its native pathfinding algorithms can potentially find multi-hop payment paths similar from the payment sender’s perspective, but the algorithms themselves have different performance, computational cost, energy consumption, and ultimately different CO2 emissions per step in the pathfinding phase. Bitcoin itself generates approximately 61.4 million tons of CO2 eq. per year. Since the LN is built on top of bitcoin, every small change in its energy consumption can have a significant impact on overall pollution. In this paper, we show that the reinforcement learning (RL) approach can reduce these costs and achieve better performance in terms of energy consumption at each pathfinding step. We introduce one-shot path prediction and propose a RL solution for a network agent that learns its neighborhood and uses local knowledge to cleverly solve the pathfinding problem and outperform native pathfinding algorithms.
Keywords
- Bitcoin infrastructure, Green computing, Lightning network, Pathfinding, Reinforcement learning
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Artificial Intelligence
Sustainable Development Goals
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In: Neural Computing and Applications, 11.12.2024.
Research output: Contribution to journal › Article › Research › peer review
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TY - JOUR
T1 - Increasing energy efficiency of bitcoin infrastructure with reinforcement learning and one-shot path planning for the lightning network
AU - Valko, Danila
AU - Kudenko, Daniel
N1 - Publisher Copyright: © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.
PY - 2024/12/11
Y1 - 2024/12/11
N2 - The lightning network (LN) is a technological solution designed to solve the bitcoin blockchain transaction speed problem by introducing off-chain transactions. Since LN is a sparse and highly distributed network with three predominant routing protocols, its native pathfinding algorithms can potentially find multi-hop payment paths similar from the payment sender’s perspective, but the algorithms themselves have different performance, computational cost, energy consumption, and ultimately different CO2 emissions per step in the pathfinding phase. Bitcoin itself generates approximately 61.4 million tons of CO2 eq. per year. Since the LN is built on top of bitcoin, every small change in its energy consumption can have a significant impact on overall pollution. In this paper, we show that the reinforcement learning (RL) approach can reduce these costs and achieve better performance in terms of energy consumption at each pathfinding step. We introduce one-shot path prediction and propose a RL solution for a network agent that learns its neighborhood and uses local knowledge to cleverly solve the pathfinding problem and outperform native pathfinding algorithms.
AB - The lightning network (LN) is a technological solution designed to solve the bitcoin blockchain transaction speed problem by introducing off-chain transactions. Since LN is a sparse and highly distributed network with three predominant routing protocols, its native pathfinding algorithms can potentially find multi-hop payment paths similar from the payment sender’s perspective, but the algorithms themselves have different performance, computational cost, energy consumption, and ultimately different CO2 emissions per step in the pathfinding phase. Bitcoin itself generates approximately 61.4 million tons of CO2 eq. per year. Since the LN is built on top of bitcoin, every small change in its energy consumption can have a significant impact on overall pollution. In this paper, we show that the reinforcement learning (RL) approach can reduce these costs and achieve better performance in terms of energy consumption at each pathfinding step. We introduce one-shot path prediction and propose a RL solution for a network agent that learns its neighborhood and uses local knowledge to cleverly solve the pathfinding problem and outperform native pathfinding algorithms.
KW - Bitcoin infrastructure
KW - Green computing
KW - Lightning network
KW - Pathfinding
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=85212085649&partnerID=8YFLogxK
U2 - 10.1007/s00521-024-10588-2
DO - 10.1007/s00521-024-10588-2
M3 - Article
AN - SCOPUS:85212085649
JO - Neural Computing and Applications
JF - Neural Computing and Applications
SN - 0941-0643
M1 - 112620
ER -