A Review of Epidemic-Style Modelling for Smart City Sensor Fabrics: Intelligent Modeling, Electronics Integration, and Real-World Applications

Abstract

Epidemic-style modeling has emerged as a powerful paradigm for understanding information diffusion, fault propagation, and adaptive communication in large-scale smart city sensor fabrics. These networks, characterized by massive heterogeneity, constrained resources, and dynamic topologies, demand robust, scalable, and intelligent mechanisms for data dissemination and resilience. This paper presents a comprehensive review of epidemic-style models applied to smart city sensor infrastructures, focusing on intelligent modeling techniques, electronics-level integration, and real-world deployment scenarios. The study synthesizes advances from stochastic epidemic theory, network science, embedded systems, and AI-driven optimization to evaluate how epidemic protocols enable efficient data propagation, fault tolerance, and decentralized coordination. Key findings highlight the transition from classical Susceptible-Infected-Recovered (SIR) models to hybrid AI-enhanced epidemic frameworks that incorporate reinforcement learning, graph neural networks, and adaptive thresholding. The review also identifies challenges in energy efficiency, security vulnerabilities, and hardware-software co-design constraints. Contributions include a structured taxonomy of epidemic modeling techniques, a comparative evaluation of recent studies, and identification of future research directions for integrating epidemic intelligence into next-generation smart city systems.