Special Session 10

SPECIAL SESSION #10

Enza Panzardi

University of Siena, Italy

• enza.panzardi@unisi.it

Barbara Toniella Corradini

University of Siena, Italy

• barbaratoniella.c@student.unisi.it

Irene Cappelli

University of Siena, Italy

• cappelli@diism.unisi.it

Franco Scarselli

University of Siena, Italy

• franco.scarselli@unisi.it

Valerio Vignoli

University of Siena, Italy

• valerio.vignoli@unisi.it

The advent of Industry 4.0 and the growing need to create ever more capillary monitoring networks have led to an increasing amount of real-time data provided by sensors and, in general, by available data sources covering a wide range of applications. In this context, the interest of the research community in developing new processing strategies and techniques when dealing with such a large volume of sensor data has grown significantly. Data processing using Artificial Intelligence (AI) algorithms and the use of machine learning, neural networks (including deep architectures), and information fusion methods have become common in this field, as they allow automated analysis of complex sensor data, through which, the desired information and, thus, an added value to the data along the entire sensing chain are automatically derived. AI algorithms have been recently adapted to be executed by limited computational power devices, such as microcontrollers, giving rise to the new concept of embedded AI-enabled sensor nodes.

This type of approach can be applied in the context of different IoT architectures and sensing systems, where sensor and actuator nodes communicate and create networks. These types of networks can often be autonomous networks that adapt to different conditions, creating smart IoT networks that would not be possible without exploiting artificial intelligence algorithms.

This Special Session will focus on the applications of AI to sensor data analysis. Potential topics include but are not limited to the following:

• AI to process sensor data
• Information fusion methods to combine multiple sensor data
• Machine learning and decision making to issue responses to sensor data
• Deep learning architectures for sensor applications
• Embedded AI solutions
• Smart IoT networks
• AI-based sensors for efficient energy management
• Databases to enable research on AI-based sensor applications
• AI for environmental monitoring data analysis
• Software engineering for sensor data stream management
• Intelligent devices and instruments for smart building/city design
• Security and privacy in intelligent IoT systems