Using an epidemiological approach to maximize data survival in the internet of things

TL;DR

This paper introduces an epidemiological SIR model to enhance data survivability in IoT networks, accounting for dynamic topology and environmental hazards through theoretical analysis and simulations.

Contribution

It presents a novel compartmental SIR model tailored for IoT data survivability, incorporating network dynamics like node failure and regeneration.

Findings

The model effectively predicts data survival rates under various attack scenarios.

Simulations demonstrate the importance of network topology dynamics on data resilience.

The approach offers a new framework for designing robust IoT data management strategies.

Abstract

The internet of things (IoT) has gained worldwide attention in recent years. It transforms the everyday objects that surround us into proactive actors of the Internet, generating and consuming information. An important issue related to the appearance of such large-scale self-coordinating IoT is the reliability and the collaboration between the objects in the presence of environmental hazards. High failure rates lead to significant loss of data. Therefore, data survivability is a main challenge of the IoT. In this paper, we have developed a compartmental e-Epidemic SIR (Susceptible-Infectious-Recovered) model to save the data in the network and let it survive after attacks. Furthermore, our model takes into account the dynamic topology of the network where natural death (crashing nodes) and birth are defined and analyzed. Theoretical methods and simulations are employed to solve and simulate the system of equations developed and to analyze the model.