The Speedy laboratory investigates the principles, models, methodologies and tools needed for the design and development of high-performance, distributed and pervasive systems. Such systems are a complex ecosystem of heterogeneous entities (services, smart objects/M2M, people, etc.) that cooperate to provide the related functionalities, and are able to evolve and quickly adapt to changing requirements.
The focus is on combining Cloud Computing and the Internet of Things (IoT), in order to enable the convergence of smart objects, big data and Clouds, and create new frameworks able to offer services for detecting, controlling and processing big data. Cloud platforms are used to store data, use it for the creation of "smart environments", and develop advanced models which can help to identify, monitor, optimize, learn and process our environment. The IOT will evolve towards a cognitive IOT model in which objects have the ability to learn and understand the physical and the social world. Pattern mining algorithms, machine learning methods and artificial intelligence techniques will be designed and executed on the Cloud in a distributed and elastic fashion to automate decision making processes.
The software development methodology is founded on decentralized and self-organizing agent-based solutions and on the swarm intelligence paradigm inspired by biological systems. The Cloud is itself a subject of research, aimed at optimizing the use of computational resources through the efficient use of virtual machines, both classic and "light-weight" (i.e., "containers").
The research interests of the laboratory staff include: Cloud Computing, InterCloud, fog computing, urban computing, social pervasive and mobile computing, Internet/web of everthing and services, cognitive IoT, platforms for the support of cooperating smart objects, swarm intelligence, cyber physical social systems (CPSS), scalable data analytics, multicore and GPU computing, stream mining.
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