A Unique Approach for Performance Analysis of a Blockchain and Cryptocurrency based Carbon Footprint Reduction System

IF 0.2 Q4 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Web Intelligence Pub Date : 2023-08-11 DOI:10.3233/web-220049

Ankit Panch, Dr. Om Prakash Sharma

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引用次数: 0

Abstract

Blockchain technology is commonly used as a replicated and distributed database in different areas. In this paper, a smart home blockchain network connects smart homes through smart devices for reducing carbon footprint and thereby earning bitcoin value in the network. The network is composed of different smart homes interconnected with smart devices. The user makes a transaction request through the network layer and matches the user’s activity with the reward table located at the incentive layer to estimate the bitcoin value. Furthermore, the miner verifies the transaction and sends the bitcoin value to the user, and adds the respective block to the network structure. The optimal parameter used to estimate the bitcoin value is computed using the proposed Improved Invasive Weed Mayfly Optimization (IIWMO) algorithm. The developed method attained higher performance with the metrics, like coins earned, Annual Carbon Reduction (ACR), and fitness as 0.00357BTC, 23.891, and 0.6618 for 200 users. For 200 users the fitness obtained by the proposed method is 14.41%, 16.68%, and 11.68% higher when compared to existing approaches namely, Without optimization, IIWO, and MA, respectively.

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基于区块链和加密货币的碳足迹减少系统性能分析的独特方法

区块链技术通常用作不同领域的复制和分布式数据库。在本文中，智能家居区块链网络通过智能设备连接智能家居，以减少碳足迹，从而在网络中获得比特币价值。该网络由不同的智能家居与智能设备相互连接而成。用户通过网络层发出交易请求，并将用户的活动与位于激励层的奖励表进行匹配，从而估算出比特币的价值。此外，矿工验证交易并将比特币价值发送给用户，并将相应的块添加到网络结构中。使用提出的改进入侵杂草蜉蝣优化(IIWMO)算法计算用于估计比特币价值的最优参数。所开发的方法获得了更高的性能，例如获得的硬币，年度碳减少(ACR)和健身指标为0.00357BTC, 23.891和0.6618(200个用户)。对于200个用户，本文方法获得的适应度分别比现有方法(Without optimization、IIWO和MA)高14.41%、16.68%和11.68%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Web Intelligence COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-

CiteScore

0.90

自引率

0.00%

发文量

期刊介绍： Web Intelligence (WI) is an official journal of the Web Intelligence Consortium (WIC), an international organization dedicated to promoting collaborative scientific research and industrial development in the era of Web intelligence. WI seeks to collaborate with major societies and international conferences in the field. WI is a peer-reviewed journal, which publishes four issues a year, in both online and print form. WI aims to achieve a multi-disciplinary balance between research advances in theories and methods usually associated with Collective Intelligence, Data Science, Human-Centric Computing, Knowledge Management, and Network Science. It is committed to publishing research that both deepen the understanding of computational, logical, cognitive, physical, and social foundations of the future Web, and enable the development and application of technologies based on Web intelligence. The journal features high-quality, original research papers (including state-of-the-art reviews), brief papers, and letters in all theoretical and technology areas that make up the field of WI. The papers should clearly focus on some of the following areas of interest: a. Collective Intelligence[...] b. Data Science[...] c. Human-Centric Computing[...] d. Knowledge Management[...] e. Network Science[...]