Adoption of the LoRa transmission protocol for a low power indoor air quality monitoring system

Abstract

Indoor air quality (IAQ) is a critical, often-overlooked public health concern, driving the need for robust Internet of Things (IoT) monitoring systems to optimise building ventilation and energy efficiency. This research addresses two major gaps: the high power consumption of existing wireless sensor nodes and the lack of cost-effective, scalable big data systems for large-scale IAQ monitoring. The core contribution is an ultra-low-power, low-cost wireless sensor node integrating state-of-the-art (SOA) sensors for carbon dioxide, volatile organic compounds, particulate matter, temperature, humidity, and pressure. Utilising dynamic power management, a sleep mode current draw of 270 nA and an average active current of 38 mA is achieved. This translates to an overall energy consumption of approximately 327 μAh per hour, and a projected battery life of 40 months on a 10,500 mAh battery. The achieved power efficiency is significantly better than both comparable academic and commercial SOA devices, even while offering a broader range of sensing capabilities. Complementary to this, the work introduces a cost-effective, LoRa-based big data system for large-scale IAQ monitoring. This system features a novel data forwarding server that calculates Air Quality Index (AQI) and Thermal Comfort Index (TCI) values, storing the enriched data in a document-oriented database. The research also validated a theoretical simulation model for indoor LoRa propagation. Advanced data visualisation was also developed, including a coordinate-based AQI heatmap, enabling smarter building management system (BMS) control. This research establishes a new benchmark for ultra-low-power, modular IAQ technology, coupled with a proven, scalable big data solution, accelerating the adoption of high-density IoT for healthier, smarter buildings.