AI-Driven Autocalibration of a Heterogeneous Sensor Network

This project develops algorithms for automatic calibration of heterogeneous IoT sensor networks using AI and self-learning approaches. It focuses on environmental and industrial sensor systems with varying sensitivities and offsets.

Enable self-calibration and drift compensation in distributed sensor systems. Use machine learning to harmonise readings across heterogeneous sensors. Improve long-term accuracy of environmental and industrial IoT deployments.