Smart Dust Networks for Predictive Maintenance in Large Industrial Complexes

The growing demand for efficient industrial operations necessitates advanced monitoring and maintenance strategies. Smart dust networks, consisting of ultra-miniaturized wireless microelectromechanical systems (MEMS) sensors, offer a transformative solution for predictive maintenance in large industrial complexes. These networks can continuously monitor temperature, pressure, vibration, and structural stress in real time, transmitting data to centralized systems for predictive analytics. Unlike conventional sensors, smart dust nodes are scalable, energy-efficient, and capable of autonomous operation within challenging industrial environments.This paper explores the architecture of smart dust networks, their integration with predictive maintenance frameworks, and their potential to revolutionize large-scale industrial monitoring. The study reviews enabling technologies including low-power communication protocols, energy harvesting methods, and edge computing for data processing. Practical applications are discussed in the context of oil refineries, power plants, manufacturing hubs, and chemical processing industries. By identifying limitations related to data security, signal interference, and node lifetime, the paper provides insights into the pathways required for widespread industrial adoption of smart dust.