Accelerating Deep Learning for Self-Calibration in Large-Scale Uncontrolled Wireless Sensor Networks for Environmental Monitoring

Self-calibration poses one of the primary challenges in deploying wireless sensor networks (WSNs), particularly in uncontrolled environments. While existing deep learning methods have demonstrated their effectiveness in centralized scenarios, their efficiency diminishes when applied in distributed and uncontrolled environments. To tackle this issue, this work evaluates the application of extreme learning machines (ELM) and graph deep learning techniques for self-calibration. The experimental findings reveal that the graph learning approach outperforms state-of-the-art deep learning methods, including LSTM, MLP, and CNN, in terms of accuracy. Additionally, ELM showcases its effectiveness as a suitable machine learning approach under resource-constrained scenarios, with accelerated learning.