PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
September 12, 2025Journal of Current Research in Blockchain.0 citations

Analysis of Gas Fee Patterns in Blockchain Transactions - A Case Study on Ethereum Smart Contracts

View Full Paper
APAdi Suryaputra Paramitha

Key Points

  • The study found an average gas price of 120.5 Gwei, showing high variability in transaction costs.
  • Mint operations in smart contracts used the highest gas, averaging 1,500,000 units, compared to approve and transfer functions.
  • A positive correlation (r = 0.65) exists between gas price and transaction value, indicating higher fees for valuable transactions.
  • Temporal patterns show peak gas prices during high network activity, highlighting opportunities for cost optimization.

Abstract

Gas fees play a crucial role in Ethereum blockchain transactions, directly affecting the cost and efficiency of decentralized applications. This study analyzes gas fee patterns across transaction types, temporal trends, and anomalous behaviors using a dataset of 1,000 Ethereum transactions. The results reveal that the average gas price was 120.5 Gwei, with a standard deviation of 45.2 Gwei, highlighting significant variability. Smart contract functions exhibited varying gas usage, with mint operations consuming the highest average gas (1,500,000 units) compared to approve (1,200,000 units) and transfer (800,000 units). A positive correlation (r = 0.65) was observed between gas price and value transferred, suggesting that higher-value transactions often incur elevated gas fees. Temporal analysis showed predictable patterns, with peak gas prices occurring between 13:00 - 17:00 UTC during high network activity and lower prices between 02:00 - 06:00 UTC. Additionally, anomaly detection identified 15 outlier transactions, including one with an unusually high gas price of 500 Gwei, reflecting network congestion or prioritization strategies. These findings provide actionable insights for optimizing transaction costs and improving smart contract efficiency. Future research could explore layer-2 scaling solutions, alternative fee mechanisms, and machine learning approaches for gas price prediction. This study contributes to a deeper understanding of Ethereum’s gas fee dynamics, offering valuable guidance for developers, users, and researchers in the blockchain ecosystem.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Adi Suryaputra Paramitha (2025) studied this question.

synapsesocial.com/papers/68d44a3031b076d99fa532d3https://doi.org/10.47738/jcrb.v2i3.41
Ask AI
Helpful
Bookmark
Share
View Full Paper