ASSESSING WIRELESS SENSOR NETWORK PERFORMANCE USING EPIDEMIOLOGICAL MODELING: A QUANTITATIVE STUDY

Abstract

Wireless Sensor Networks (WSNs) are widely used in applications such as environmental monitoring, healthcare, industrial automation, and smart cities. Despite their importance, WSNs face persistent challenges related to energy efficiency, data reliability, fault propagation, and network longevity. Traditional performance evaluation methods often rely on protocol-level simulations or mathematical optimization techniques that may fail to capture the dynamic and collective behavior of sensor nodes under varying conditions. In recent years, epidemiological modeling—originally developed to study the spread of infectious diseases—has emerged as a promising interdisciplinary approach for analyzing complex networked systems.

This study explores the application of epidemiological models to assess the performance of Wireless Sensor Networks. By drawing analogies between disease transmission and data or fault propagation among sensor nodes, the research adopts compartmental models such as Susceptible–Infected–Recovered (SIR) and Susceptible–Exposed–Infected–Recovered (SEIR) to quantitatively analyze network behavior. The proposed framework maps sensor states to epidemiological compartments and evaluates key performance metrics, including packet delivery ratio, energy consumption, network stability, and resilience against node failures.

Through analytical modeling and numerical simulations, the study demonstrates that epidemiological models can effectively capture temporal dynamics, congestion effects, and recovery mechanisms in WSNs. The results indicate that infection and recovery rates strongly influence network throughput and lifetime, providing valuable insights for protocol design and network management. The paper concludes that epidemiological modeling offers a flexible and scalable tool for WSN performance evaluation and can complement conventional analytical and simulation-based methods.