Abstract

This study investigates the application of Artificial Neural Network (ANN), Long Short-Term Memory network (LSTM) as a representative of Recurrent Neural Network (RNN), and one-dimensional Convolutional Neural Network (1D-CNN) for time series prediction, demonstrated through a use case of low-cost gas sensor readings from transient signals. Despite the widespread use of these architectures in IoT forecasting applications, there is a lack of systematic comparative studies that evaluate their performance under identical experimental conditions, particularly in energy-constrained sensing scenarios. The primary objective is to evaluate the trade-offs between model accuracy, computational cost, and memory requirements under energy-efficient data acquisition scenarios. A comprehensive experimental analysis was conducted using 186 recorded transient samples, where all models were trained and evaluated under consistent preprocessing, identical data splits, and uniform hyperparameter settings. Performance was assessed using RMSE, MAE, R2, training time, and model size as key evaluation metrics under varying input sequence lengths. The results show that the LSTM model achieved the highest accuracy, with an RMSE of 3.69%, R2 of 0.85 and scaled MAE of 0.04, effectively capturing long-term temporal dependencies. The 1D-CNN exhibited a balanced compromise between accuracy and training efficiency, while the ANN provided the shortest training time but lower overall performance. Reducing the number of input readings from 186 to as few as 10–20 resulted in only a 2–4% increase in RMSE, with model size reductions of up to 50%, making such configurations particularly suitable for edge or embedded IoT devices. The findings demonstrate that artificial neural networks can maintain high prediction accuracy even under reduced data conditions, contributing to the development of low-power, resource-efficient sensing systems for intelligent and distributed IoT environments.

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