Transaction Fees Minimization in Blockchain-Based Home Delivery System

This study investigates the impact of Zlib compression on gas consumption within blockchain systems, focusing particularly on Ethereum transactions. By employing the Ethereum simulator Ganache, we simulate 100 realistic home delivery system datasets to evaluate the performance of compressed versus u...

Celý popis

Uložené v:
Podrobná bibliografia
Vydané v:IEEE access Ročník 12; s. 109197 - 109209
Hlavní autori: Jandaeng, Chanankorn, Chukleang, Tirajet, Kongsen, Jonksuk, Koad, Peeravit, Thu, May
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:
Algorithms
Blockchain
Blockchains
Compression algorithms
Cost analysis
Costs
Cryptocurrency
Data analysis
Data compression
home delivery
logistics
Nonlinear dynamics
Open source software
Optimization
Regression models
Security
Smart contracts
Statistical analysis
Text compression algorithm
transaction fees
ISSN:2169-3536, 2169-3536
On-line prístup:Získať plný text
Tagy: Pridať tag
Žiadne tagy, Buďte prvý, kto otaguje tento záznam!
Popis
Shrnutí:This study investigates the impact of Zlib compression on gas consumption within blockchain systems, focusing particularly on Ethereum transactions. By employing the Ethereum simulator Ganache, we simulate 100 realistic home delivery system datasets to evaluate the performance of compressed versus uncompressed data. The methodology encompasses rigorous statistical analysis to ensure robust results. Our findings reveal that using the Zlib algorithm to compress textual data exceeding 141 bytes before submitting transactions on the Ethereum network reduces the gasUsed while maintaining the system time unchanged. This demonstrates the effectiveness of data compression in optimizing transaction costs without affecting operational efficiency. Additionally, our research extends to analyzing real gasPrice trends on the Ethereum network. We propose a non-linear regression model that accurately predicts hourly gasPrice variations based on the day of the week and the specific time. This provides a valuable tool for users to plan transactions strategically. These insights enhance the understanding of blockchain dynamics and offer practical solutions for improving economic and system efficiency in blockchain operations.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3439683