Real-Time Air Quality Monitoring Using Edge IoT Gateways

Abstract

Reliable, high-resolution air quality information is essential for urban planning, public health alerts, and environmental governance. Yet conventional monitoring networks—based on a few expensive reference-grade analyzers—lack spatial granularity, while large fleets of low-cost sensors suffer from drift, cross-sensitivity, connectivity constraints, and cloud-processing latency. This manuscript proposes and evaluates an edge-centric architecture for real-time air quality monitoring that fuses low-cost sensing with gateway-level analytics. Each edge IoT gateway locally aggregates streams from particulate (PM2.5/PM10) and gas sensors (NO₂, O₃, CO, VOCs), performs multi-variate calibration with temperature/humidity compensation, filters anomalies, computes short-term Air Quality Index (AQI) values, compresses and prioritizes data, and synchronizes with cloud services for model updates and long-term storage.

We detail the hardware/software stack, communication protocols (LoRaWAN/NB-IoT/MQTT), security hardening (secure boot, mTLS), and power-aware scheduling. A field deployment with 60 nodes over 25 km² and a discrete-event simulation demonstrate that edge analytics reduce mean absolute error for PM2.5 from 8.7 to 3.2 µg/m³ (vs. a co-located reference monitor), cut p95 latency from 14.8 s to 2.6 s, and shrink uplink volume by 62% without sacrificing coverage. Statistical analysis shows improved R², lower bias, higher uptime, and reduced packet loss compared to cloud-only baselines. The results validate edge gateways as a cost-effective and scalable substrate for real-time environmental intelligence, with implications for hyperlocal mapping, personal exposure analytics, and municipal alerting systems.