Stochastic Modeling Approaches for Analyzing Blockchain: A Survey

TL;DR

This survey reviews stochastic modeling techniques applied to blockchain, focusing on network performance, security, and cryptocurrency price prediction, highlighting their strengths, weaknesses, and future research directions.

Contribution

It provides a comprehensive classification and analysis of stochastic models in blockchain, filling a gap in existing literature and guiding future research efforts.

Findings

Stochastic models are categorized into network-oriented and application-oriented.

Cryptocurrency price prediction is the most common application of stochastic models.

The survey discusses strengths, weaknesses, and future challenges of these models.

Abstract

Blockchain technology has been attracting much attention from both academia and industry. It brings many benefits to various applications like Internet of Things. However, there are critical issues to be addressed before its widespread deployment, such as transaction efficiency, bandwidth bottleneck, and security. Techniques are being explored to tackle these issues. Stochastic modeling, as one of these techniques, has been applied to analyze a variety of blockchain characteristics, but there is a lack of a comprehensive survey on it. In this survey, we aim to fill the gap and review the stochastic models proposed to address common issues in blockchain. Firstly, this paper provides the basic knowledge of blockchain technology and stochastic models. Then, according to different objects, the stochastic models for blockchain analysis are divided into network-oriented and application-oriented (mainly refer to cryptocurrency). The network-oriented stochastic models are further classified into two categories, namely, performance and security. About the application-oriented stochastic models, the widest adoption mainly concentrates on the price prediction of cryptocurrency. Moreover, we provide analysis and comparison in detail on every taxonomy and discuss the strengths and weaknesses of the related works to serve guides for further researches. Finally, challenges and future research directions are given to apply stochastic modeling approaches to study blockchain. By analyzing and classifying the existing researches, we hope that our survey can provide suggestions for the researchers who are interested in blockchain and good at using stochastic models as a tool to address problems.