Edge-Cloud IoT Framework for Energy Efficiency and Indoor Air Quality Monitoring in Smart Buildings

Small-office smart buildings require reliable indoor air quality and energy monitoring, but many IoT prototypes emphasize sensors and dashboards while leaving the end-to-end telemetry pipeline insufficiently evaluated. This study presents an edge-cloud IoT framework for monitoring indoor air quality and energy-related variables in small-office environments. The framework integrates low-cost sensing nodes, a Raspberry Pi edge gateway, MQTT and/or HTTP telemetry transport, cloud-based time-series storage, data cleaning and aggregation, dashboard visualization, and threshold-based alerts. It monitors carbon dioxide, temperature, relative humidity, illuminance, motion or occupancy indication, and energy-related readings when available. The main outcome of the study is a reproducible telemetry architecture in which each pipeline stage, from data perception to dashboard delivery, is explicitly defined and linked to measurable engineering indicators. The framework establishes baseline, network-disturbance, and reconnect-forwarding evaluation scenarios and specifies five metrics: end-to-end latency, message success rate, data completeness, successful storage-write rate, and dashboard update delay. This structure makes missing, delayed, repeated, and out-of-range records visible instead of treating them as hidden system errors. The contribution is therefore a practical method for assessing telemetry readiness and data quality before adding forecasting, anomaly detection, or actuator-based control. The proposed framework supports the development of more dependable smart-building monitoring systems by clarifying how sensor data are validated, transmitted, stored, processed, visualized, and evaluated under normal and disturbed network conditions.