One of the main objectives of the ELEDIA@USIL is to deepen the knowledge of smart self-adaptive wireless systems and to investigate their application to real-world problems.
A fundamental feature of smart monitoring systems is the ability to adapt at runtime according to the environmental changes, the resource variability, and the objectives redefinition.
In this context, the Wireless Sensor Networks (WSN) and Internet of Things (IoT) technologies enable the system to interact with the application scenario and to evaluate the effects of the actuation.
The reconfiguration capability is provided by smart methodologies able to learn the complex relations among the measured parameters and to suggest new actions. Such methods are often based on machine learning techniques like Neural Network (NN), Support Vector Machine (SVM), Gaussian Process (GP).
Starting from the know-how in wireless monitoring technologies and smart methods, the ELEDIA@USIL is involved in the design, implementation, and performance assessment of hardware and software solutions applied to real-world scenarios such as Precision Agriculture and Landslide Monitoring.