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An effective blockchain evaluation system based on entropy-CRITIC weight method and MCDM techniques

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Abstract

Public blockchain enables decentralized trust models in distributed systems. The programmable features such as smart contracts and decentralized applications are attracting application developers, systems integrators, and users to adopt public blockchain systems for a large plethora of applications across all industries. However, the abundance and variety of features in immature blockchain ecosystems make it hard to select an appropriate and useful public blockchain platform. This paper employs a multi-criteria decision-making (MCDM) approach to rank and outline the suitable public blockchain platforms. To this end, we present ECWM, a new weight assignment technique, which is a combination of Entropy and CRITIC method. We applied ECWM on a diverse dataset curated with 16 features (i.e., indicators representing different criteria for blockchain adoption) from 30 public blockchain systems. We apply three MCDM techniques, namely WSM, TOPSIS, and VIKOR, to generate ranks. These techniques produce divergent rankings; therefore, we use Spearman’s rank correlation coefficient to resolve disagreements and outline the best possible ranking with the given dataset. We also rank blockchains according to three categories: popularity, sustainability, and profitability. The ranks are meticulously evaluated by domain experts and are deemed quite effective to guide future researchers and system developers.

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Authors and Affiliations

  1. National University of Computer and Emerging Sciences, Punjab, Pakistan

    Shakeel Zafar, Zareen Alamgir & M. H. Rehman

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  1. Shakeel Zafar
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  2. Zareen Alamgir
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  3. M. H. Rehman
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Correspondence to Zareen Alamgir.

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This article is part of the Topical Collection: Special Issue on Blockchain for Peer-to-Peer Computing Guest Editors: Keping Yu, Chunming Rong, Yang Cao, and Wenjuan Li

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Zafar, S., Alamgir, Z. & Rehman, M.H. An effective blockchain evaluation system based on entropy-CRITIC weight method and MCDM techniques. Peer-to-Peer Netw. Appl. 14, 3110–3123 (2021). https://doi.org/10.1007/s12083-021-01173-8

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  • DOI: https://doi.org/10.1007/s12083-021-01173-8

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