Deep learning techniques for Intelligent Cyber Physical Systems: towards a new generation of smart and autonomous things

We are living an era constantly surrounded by objects blended with the environment. During the last decade, the advent of the Internet of Things totally revolutionized the way we interact with these objects, and acted as a catalyzer for the creation of a wide variety of Cyber Physical Systems exposing services to support the human being under different aspects of his life. Today, the maximum expression of Cyber Physical Systems are smart environments whose task is to simplify (or automate) certain types of operations to help their ``guests'' during the daily activities. Cloud and Edge computing paradigms play a fundamental role for the realization of these systems; the first providing storage and high performance computing capabilities, the second allowing a pervasive monitoring and an early processing of the data gathered from sensors. In such a context, Artificial Intelligence is another very important player which gained a lot of interest during these years, thanks also to the advancement in ICT and the huge amount of available data. Leveraging the above mentioned technologies, it allows the implementation of a new type of intelligent systems (e.g., Intelligent Cyber Physical Systems) able to make ``reasonings'' and perform autonomous actions according to the context. This thesis work presents a deep study of all these technologies and proposes intelligent systems and algorithms solutions applied to several fields (i.e., smart home, smart city, smart industry, smart health, and smart agriculture) focusing also on the challenges emerged during the design and implementation processes. Experimental results demonstrate the feasibility of the proposed approaches and show the benefits derived from using them.