Visibility Graph Analysis of IOTA and IoTeX Price Series: An Intentional Risk-Based Strategy to Use 5G for IoT

Alberto Partida,Regino Criado,Miguel Romance

doi:10.3390/electronics10182282

Abstract

The transformation of time series into complex networks through visibility graphs is an innovative way to study time-based events. In this work, we use visibility graphs to transform IOTA and IoTeX price volatility time series into complex networks. Our aim is twofold: first, to better understand the markets of the two most capitalised Internet of Things (IoT) platforms at the time of writing. IOTA runs on a public directed acyclic graph (DAG) and IoTeX on a blockchain. Second, to suggest how 5G can improve information security in these two key IoT platforms. The analysis of the networks created by the natural and horizontal visibility graphs shows, first, that both IOTA and IoTeX are still at their infancy in their development, with IoTex seemingly developing faster. Second, both IoT tokens form communities in a hierarchical structure, and third, 5G can accelerate their development. We use intentional risk management as a lever to understand the impact of 5G on IOTA and IoTeX. Our results lead us to provide a set of design recommendations that contribute to improving information security in future 5G-based IoT implementations.

Highlights

Our results lead us to provide a set of design recommendations that contribute to improving information security in future 5G-based Internet of Things (IoT) implementations
The first future work path mentioned in [26] is the transformation of the time series created by IOTA and IoTeX transactions, i.e., the value transacted at each point of time, into complex networks using the visibility graph technique proposed by Lacasa et al [3]
In this study, we analyse the visibility graphs created from IOTA and IoTeX tokens’ daily price volatility information since day one of trading

Summary

Introduction

Network science is a scientific discipline at the crossroad of mathematics, statistics and physics [2]. It characterises systems composed of a collection of nodes and edges between them. It is possible to transform a time series into a complex network thanks to an easy-to-implement algorithm, i.e., a visibility graph [3]. This conversion facilitates the study of the system whose events create a time series with the powerful analysis toolkit that complex network science provides

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Methods

Conclusion