An Energy-Optimized Artificial Intelligence of Things (AIoT)-Based Biosensor Networking for Predicting COVID-19 Outbreaks in Healthcare Systems

Abstract

By integrating energy-efficient AIoT-based biosensor networks, healthcare systems can now predict COVID-19 outbreaks with unprecedented accuracy and speed, revolutionizing early detection and intervention strategies. Therefore, this paper explores the rapid growth of electronic technology in today’s environment, driven by the proliferation of advanced devices capable of monitoring and controlling various healthcare systems. However, these devices’ limited resources necessitate optimizing their utilization. To tackle this concern, we propose an enhanced Artificial Intelligence of Things (AIoT) system that utilizes the networking capabilities of IoT biosensors to forecast potential COVID-19 outbreaks. The system aims to efficiently collect data from deployed sensor nodes, enabling accurate predictions of possible disease outbreaks. By collecting and pre-processing diverse parameters from IoT nodes, such as body temperature (measured non-invasively using the open-source thermal camera TermoDeep), population density, age (captured via smartwatches), and blood glucose (collected via the CGM system), we enable the AI system to make accurate predictions. The model’s efficacy was evaluated through performance metrics like the confusion matrix, F1 score, precision, and recall, demonstrating the optimal potential of the IoT-based wireless sensor network for predicting COVID-19 outbreaks in healthcare systems.