Wireless Sensor Networks Calibration using Attention-based Gated Recurrent Units for Air Pollution Monitoring

Calibration in wireless sensor networks (WSNs) poses a significant challenge, particularly in uncontrolled environmental deployments for environmental monitoring, such as air pollution. Traditional calibration methods rely on centralized reference stations, which are costly to maintain, offer limited coverage, and calculate measurements as averages. However, with the rise of the Internet of Things (IoT), sensors present a cost-effective alternative for calibration compared to fixed reference stations. Nevertheless, in uncontrolled environments, sensors require self-recalibration to ensure accurate measurements for the reliable operation of WSNs without human intervention. Existing calibration approaches, such as LSTM, are computationally expensive, have higher memory requirements, and exhibit training instability, making them unsuitable for resource-constrained WSNs. This paper proposes a self-calibration approach for WSNs using the Gated Recurrent Unit (GRU) coupled with the attention mechanism (Attention-GRU). The Attention-GRU selectively focuses on relevant features while capturing long-term dependencies, akin to Recurrent Neural Networks (RNNs), thereby mitigating overfitting. Experimental results demonstrate that the Attention-GRU model outperforms other models with an R-squared value of 0.97 and accelerated learning. These accurate sensor recalibration predictions promote sustainability by supporting IoT-enabled air pollution monitoring efforts.