Virtual and Augmented Sensing Techniques via Embedded ML Models for IoT Measurement Infrastructures

Dear Colleagues,

In recent years, we have witnessed the exponential growth of virtual and augmented sensing techniques, and of embedded machine learning (ML) as well. Virtual sensing encompasses all those methods using to estimate parameters that cannot be directly measured due to the unavailability of dedicated sensors, or due to a general unfeasibility (e.g., logistical constraints, expensiveness of infrastructures, etc.). On the other hand, augmented sensing includes all those techniques in which the performance of standard sensors, or of standard measuring systems, are enhanced, thus obtaining finer results with reduced associated uncertainties (e.g., augmenting a cheap and coarse sensor, thus avoiding the use of an expensive alternative). Both approaches are usually implemented by resorting to ML, and artificial intelligence (AI) in general, models applied to the gathered data. When such a step is directly performed on the sensor node, the recent paradigm of embedded ML is encountered, which has proven to be valid despite the limit imposed by the hardware platforms using simple, yet effective, ML models; otherwise, it is remotely accomplished on data collection centers, thus enabling to develop more sophisticated ML/AI models. The application scenarios are countless, especially whenever Internet of Things (IoT) measurement infrastructures are exploited: from environmental monitoring to smart cities, distributed and pervasive measurement infrastructures in critical environments, and industrial monitoring, etc.

This Special Issue aims to collect original research and review articles on recent advances, technologies, solutions, applications, and new challenges in the field of virtual and augmented sensing techniques, as well as of embedded ML.

Potential topics include, but are not limited to, the following:

• Virtual sensing;
• Augmented sensing;
• Embedded ML;
• IoT measurement infrastructures;
• Distributed and pervasive measurement systems;
• Wireless sensor networks;
• Environmental monitoring in a broad sense with virtual and augmented sensing.

Dr. Giacomo Peruzzi
Dr. Alessandro Pozzebon
Prof. Dr. Matteo Bertocco
Guest Editors

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