An IoT-enabled environmental monitoring system built in collaboration with the Central University of Technology Free State, designed to track air and water pollution around active mining sites.
Mining operations in South Africa are required to monitor and report environmental pollution levels to the Department of Mineral Resources and Energy (DMRE). However, traditional manual sampling methods are infrequent, costly, and fail to capture real-time pollution events such as dust clouds, chemical spills, or sudden air quality deterioration.
The Central University of Technology Free State (CUT) identified the need for an automated, continuous monitoring system that could provide real-time data to mining companies, regulators, and surrounding communities. The solution needed to be rugged enough to withstand harsh mining environments, affordable enough for widespread deployment, and capable of transmitting data from remote locations with limited cellular infrastructure.
Inovosystems partnered with CUT to design a three-tier IoT system: sensor hardware deployed at strategic monitoring points around mine sites, an Android field data collection app for environmental officers, and a web-based analytics dashboard for real-time visualisation and reporting.
The sensor nodes measure particulate matter (PM1, PM2.5, PM10), sulphur dioxide (SO₂), nitrogen oxides (NOₓ), ground-level ozone, water pH and turbidity, wind speed and direction, and ambient temperature. Data is transmitted via LoRaWAN and cellular backup to a central cloud platform where AI models analyse patterns, detect anomalies, and generate automated compliance reports aligned with DMRE and NEMA (National Environmental Management Act) requirements.
Custom-designed IoT sensor nodes using ESP32 microcontrollers with industrial-grade environmental sensors. Enclosures rated IP67 for dust and water ingress. Solar-powered with battery backup for off-grid deployment.
LoRaWAN for low-power wide-area communication, with automatic failover to GSM cellular (2G/3G/4G) when LoRa gateways are out of range. Data is encrypted end-to-end using AES-128.
Native Android application for field officers to perform spot measurements, tag sensor readings with GPS coordinates, capture photographic evidence, and submit reports while offline (sync on connectivity).
AWS IoT Core for device management, TimescaleDB for time-series sensor data, Grafana for real-time dashboards, and automated PDF report generation via Node.js backend. REST API for third-party integration.
AI and machine learning transform raw sensor data into actionable insights for mine operators and regulators.
Anomaly detection models identify sudden spikes in particulate matter or gas levels, distinguishing between planned blasting events and unexpected pollution incidents. Alerts are sent within 60 seconds of detection.
Machine learning models combine real-time sensor data with weather forecasts (wind direction, temperature inversion) to predict how pollution plumes will disperse, enabling proactive mitigation measures.
Natural language generation (NLG) engines produce draft compliance reports aligned with DMRE, NEMA, and IFC standards. Reports include data visualisations, exceedance summaries, and trend analysis — reducing reporting time by 70%.
AI models analyse sensor node telemetry (battery voltage, signal strength, internal temperature) to predict when hardware maintenance is required, reducing field service costs and preventing data gaps.
The mining pollution monitoring system has been deployed at two active mine sites in the Free State province. The continuous real-time monitoring enabled mine operators to detect and respond to three significant pollution events within the first six months that would have gone unnoticed with manual sampling. Automated compliance reporting reduced the environmental team's administrative workload by 70%, and the community-facing dashboard improved trust and transparency between the mine and surrounding settlements. The project was recognised in a CUT research publication on IoT for environmental sustainability in African mining.
Custom hardware design, firmware development, and industrial IoT deployment for environmental and industrial monitoring.
Learn more →Field data collection apps with offline-first architecture, GPS integration, and real-time sync.
Learn more →Anomaly detection, predictive modelling, and automated reporting powered by machine learning.
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