Edge-to-Cloud Long Short-Term Memory Model for Ambient Carbon Monoxide Level Prediction

Carbon monoxide (CO) is a harmful gas from incomplete fuel combustion, often found in motor vehicle emissions. Prolonged exposure can cause serious health issues or death. While existing Internet-of-Things (IoT) systems monitor CO levels, most lack predictive capability. One prior study used an Artificial Neural Network with limited accuracy (79%). To address this, a new IoT-based CO prediction model is proposed using a Long Short-Term Memory (LSTM) algorithm. The model predicts future CO concentrations based on seasonal patterns, empowering users to anticipate and proactively respond to potential exposure. By leveraging Edge-to-Cloud architecture, this approach enables low-power edge devices to send data to the cloud for accurate forecasting without local model deployment. Based on the evaluation, the model achieved 98.42% accuracy, outperforming previous approaches by 19.42%. It also showed superior performance against other algorithms, with the lowest MAE (0.026305), MSE (0.016004), RMSE (0.126506), and the highest R² (0.997647). Evaluation with AIC and BIC confirmed its reliability, scoring zero after MinMax scaling. The model demonstrates a substantial advancement in predictive CO monitoring, giving users actionable insights to protect health and safety.