Ambient Intelligence / Intelligent system: definition, concepts and an deployment example
Pervasive Computing has been envisioned by Mark D. Weiser as the third era of modern computing, in which the user is confronted with a multitude of wireless computers communicating discreetly with each other. Pervasive computing lies in the ability of digital systems to be integrated into the physical environment to the point where they spontaneously merge into it at multiple scales. By integrating Weiser’s prediction from the end of the 90s, we see the emergence of Ambient Intelligence (AmI) concept, a term that appeared at the beginning of the 2000s under the impetus of Juan Carlos Augusto Wrede, with a new environment typology based on four pillars : 1- Ubiquitous, integrated, ubiquitous computer systems, 2 – Wired or wireless networks, deployed, robust, resilient networks that provide permanent connectivity, 3 – Sensors, 4 – Human-machine interfaces. Ambient intelligence may be associated with the term ubiquitous computing or pervasive or diffuse computing and also intelligent system, a new scientific area about ten years ago. We also observe the recent popularization of Artificial Intelligence (AI) algorithms (automatic learning, deep learning…) with a growing media coverage around the application topics that result from it. This amplifies the ongoing digital transition which can be seen as a disruptive transformation of today’s world. These tools use raw data to build exploitable knowledge by replacing explicit programming by human developers with machine learning from a large data set. The advent of artificial intelligence tools is part of a complementarity and maturity in other technological fields such as the Internet of Things (IoT) or the storage, indexing and accessibility of huge databases. This convergence of these worlds – AI – high-speed wireless internet communication and Big Data – is leading to the creation of a new definition of Ambient Intelligence, intimately intertwined with the objects around us, sensitive and capable of creating, from the users’ point of view, a world that is responsive and attentive to their presence and needs. Such a concept, aggregating as many promises around services rendered to society and to humans, can find an echo within areas where humans are present, such as cities and in particular smart cities. The Smart City places the citizen at the heart of its development strategy thanks, in particular, to robust IT infrastructures (communication, storage, security, responsiveness, sensor networks, etc.) and the creation of digital, participatory and inclusive services. We introduce a real deployment of AmI for a small town or smart village. The Intelligent System is based on four elements: sustainable development, IT/digital infrastructures, education/e-citizenship and agricultural activities. The system stores, analyzes, predict and restitutes. It is built around the collection of data on the village environment through LoRa Wireless Sensors Network (WSN). The collected data are used for direct visualization of the data but also for prediction with the help of a Machine Learning model. The predicted data will also be used to work on observed system optimization. The whole must allow to realize a tool for observation and decision support, but also for other services.
Smart City, Smart Village, ambient intelligence, Big data, WSN, ML Algorithm, Environment
Thierry Antoine-Santoni is an associate Professor, research leadership at the University of Corsica in France, in In Mixed unit of Research CNRS Sciences for Environment 6134, where he has been since 2010. His research interests span both Wireless Sensors Network and complex systems modeling. Much of his work has been on improving the understanding, design, and performance simulation of WSN and complex system mainly through the application of modeling/simulation, communication, real deployments. He has also investigated the implications of Machine algorithms integration in Ambient Intelligence and Intelligent systems. He has participated in numerous research projects and since 2017 he is the leader of the Smart Village scientific program.