Intelligent Nanosensor Network for Real-Time Environmental and Biomedical Monitoring using Machine Learning and IoT

The real-time environment and biomedical monitoring continue to drive innovation and pose stringent challenge towards accuracy of data collected, detection of anomalies and prediction of sets of data. The legacy sensor networks are ineffective, inaccurate, and rigid in the completion of more intricate tasks such as monitoring of pollution, detection of pathogens in the air, as well as industrial safety. The challenge is how to do this without bringing too much noise interference while maintaining the high volume of sensor data collected. While new developments in nanosensors based on quantum dots in graphene may provide higher sensitivity, the efficacy of these sensors will be limited by the means of data processing. In this paper, we present novel signal integration methodologies that merge nanosensors with sophisticated technology approaches, machine learning, and reinforcement learning for optimization. The system utilized LSTM deep learning for predictive analytics, applied clustering for anomaly detection, and utilized Fourier Transform for reducing noise. Moreover, the application of reinforcement learning facilitates optimal sensor positioning as well as optimal knowledge with regards to what data should be processed. This approach is differ from other from other competing ones because it is adaptive and self-learning. There are positive experimental results when self-learning system is applied. These results are more accurate in the determination of the presence or absence of harmful substances, less chances of false claims, and more accurate changes in the environment. Additionally, without loss of flexibility the amount of energy used is lowered greatly. Next steps will be focused on miniature sensors, Data secured by blockchain and bi crossing domains for medicine industry and environmental health.