EASE : Enabling Affordable Smarter Environment

From the pervasive computing envisioned just a few years ago, in which small computers are spread in the environment, the vision of the Internet of Things evolves towards a world where objects have very different characteristics: few of them are able to perform complex tasks, most of them have to be frugal in terms of hardware (low cost) and/or energy (years battery operation), and have to deal with changing network conditions (mobility, volatility).

For some pervasive services, local processing and interactions could be more efficient than cloud-based approaches. More generally, the intelligence is about to be spread in the environment, a lot of small pieces of hardware are now capable to run specialized IA algorithms making decisions depending on local information. The approach to control and to feed all these IA instances from the cloud does not scale even if it is easier to design and to implement. It also makes it difficult to use local interaction to autonomously trigger actions on the physical space.

In our approach, objects and infrastructure integrated into user’s environment could provide a more suitable support to pervasive applications: description of the actual system’s state can be richer, more accurate, and, meanwhile, easier to handle; the applications’ structure can be distributed by being built directly into the environment, facilitating scalability and resilience by the processing autonomy; finally, moving processing closer to the edge of the network avoids major problems of data sovereignty and privacy encountered in infrastructures very dependent on the cloud.

The aim of the team is to ease development, deployment, evolution and maintenance of pervasive applications in complex environments with a strong focus on smart cities and smart agriculture. We are especially interested in cooperative autonomy applied to autonomous vehicles. We develop a comprehensive set of new interaction models, tools to augment and qualify information, and design principles for relevant system architectures.

Fact following the team:  TACOMA