Design and Evaluation of an Adaptive Duty-Cycled Iot Sensor Network for Energy-Efficient Urban Air Pollution Monitoring: Case Study of Effurun, Nigeria

Abstract

Urban air pollution remains a growing public health challenge in rapidly expanding cities within developing regions, where continuous monitoring is often constrained by limited energy resources and infrastructure. This study presents the design and evaluation of an adaptive duty-cycled Internet of Things (IoT) sensor network for energy-efficient, real-time air quality monitoring, using Effurun, Nigeria, as a case study. The system integrates a PMS5003 particulate matter sensor, MQ-135 gas sensor, and an ESP32 microcontroller with LoRa (SX1276) wireless communication modules to enable distributed data collection across five urban monitoring nodes. An adaptive duty-cycling algorithm dynamically adjusts sensing and transmission intervals based on pollution variability—increasing sampling to 10-second intervals during spike events (PM2.5 > 55 µg/m³) and extending intervals to 120 seconds during stable conditions—reducing unnecessary energy consumption while preserving responsiveness during pollution events. A 14-day field deployment demonstrated a 42% reduction in average power draw compared to fixed 30-second sampling, extending estimated node battery life from 4.2 days to 7.3 days. Data accuracy was validated through co-location with a reference TSI DustTrak II monitor (R² = 0.91, RMSE = 3.4 µg/m³ for PM2.5). The system demonstrated stable performance under real-world conditions and effective detection of short-term pollution events, highlighting its suitability for resource-constrained environments