Augmented Reality Based Switching with Resource Utilization Analyzing Using Data Science

Abstract

Conventional electrical switchboards demand physical contact for operation and generate no operational intelligence, leaving accessibility gaps for elderly and mobility-impaired users while depriving homeowners and facility managers of energy-use insights. This paper presents AR Autonomous Switching with Resource Utilization Analyzing, an integrated system that retrofits existing switchboards with smart control capabilities at a fraction of commercial replacement cost. The architecture fuses three tiers: an ESP8266 microcontroller–relay module that attaches externally to existing switches without rewiring; a Firebase Realtime Database cloud layer that maintains sub-200 ms state synchronisation across all devices; and a Python-based augmented reality application that exploits OpenCV ARUCO marker detection to overlay interactive virtual switches onto a live camera view of the physical panel. A fourth analytics tier records every switching event and device-health metric, aggregating raw logs into interactive Plotly–Dash dashboards that surface hourly usage heatmaps, response-time trends, and statistically derived anomaly alerts. Evaluation across 5,000 operations on ten prototype modules yielded 98.7% command success, 180 ms average end-to-end latency, and 99.2% ARUCO detection accuracy under standard indoor lighting. A 50-participant user study produced an overall satisfaction score of 4.8 / 5.0 and a 96% recommendation rate, with average system familiarisation taking 3.2 minutes. Total hardware cost is $4–6 per switch—60–75% below comparable commercial offerings—and battery-powered modules achieve 45-day autonomy through ESP8266 deep-sleep scheduling. The system demonstrates that integrated IoT, augmented reality, and data science can deliver spatially intuitive control, accessibility, and operational transparency simultaneously on commodity hardware.