EPIDEMIOLOGICAL MODELING APPROACH FOR PERFORMANCE ASSESSMENT OF WIRELESS SENSOR NETWORKS

Abstract

Wireless Sensor Networks (WSNs) have emerged as a fundamental technology for environmental monitoring, healthcare, industrial automation, and smart infrastructure. However, their performance is often constrained by factors such as energy depletion, node failure, congestion, and security vulnerabilities. Traditional analytical models sometimes fail to capture the dynamic and stochastic behavior of such networks. This paper explores an alternative framework by adopting epidemiological modeling—originally developed to study the spread of infectious diseases—to assess the performance of WSNs. By drawing parallels between disease transmission and information dissemination, node failure propagation, and energy depletion, this study develops a conceptual and mathematical understanding of network dynamics. The paper discusses key epidemiological models such as the Susceptible-Infected-Recovered (SIR) and Susceptible-Infected-Susceptible (SIS) models, adapting them to WSN contexts. The findings demonstrate that epidemiological models provide deeper insights into network resilience, fault tolerance, and scalability, offering a robust tool for performance evaluation.