Extreme Learning Machines for Calibration and Prediction in Wireless Sensor Networks: Advancing Environmental Monitoring Efficiency

Calibrating wireless sensor network deployments, especially in uncontrolled environments, poses a significant challenge. Existing deep learning approaches for calibration perform well when resource requirements are not constrained However, conventional deep learning models are not well suited for resource constraint environments due to their computational complexity and requirement of resources. To tackle this issue, this paper introduces an extreme learning machine (ELM)-based calibration solution. ELM leverages a single-layer neural network with random weight initialization, enabling faster training and inference. Experimental results demonstrate that ELM results in accelerated learning while maintaining competitive accuracy compared to deep learning approaches like neural networks (NNs).