Raspberry Pi–Driven Digital Twin of Optical Sensor Networks for Predictive Maintenance in Smart Campuses

In this paper, I have introduced the Raspberry Pi-based digital twin architecture of optical sensor networks in smart-campus settings to support predictive maintenance. It uses low-cost optical sensors (LED/photodiode arrays) in conjunction with Raspberry Pi edge nodes and transmits preprocessed telemetry to a digital twin to predict the state of the components and provide maintenance warnings. We detail the system design, state estimation mathematical models, an implementation in commodity hardware and lightweight protocols, and experimental outcomes through proper representation of the initial warning indications of a degradation and an anomaly.

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