Section: New Results

Context modeling for Smart Spaces

Participants : Yoann Maurel, Frédéric Weis [contact] .

To provide services for Smart Building, automation based on pre-set scenarios is ineffective: human behavior is hardly predictable and application should be able to adapt their behavior at runtime depending on the context. We focused on recognizing user's activities to adapt applications behaviors. Our aim is to compute small pieces of context we called context attributes. Those context attributes are diverse, for example a presence in a room, the number of people in a room etc. Building efficient and accurate context information using inexpensive and non-invasive sensors was and is still a great challenge. We proved, through the use of dedicated algorithms and a layered architecture that it is achievable when the targeted space (controlled environment) is known - due to the specific and non automated calibration process we used. Among all the available theories, we used the Belief Function Theory (BFT) [8] [9] as it allows to express uncertainty and imprecision.

Context is computed by a chain if three a tasks:

• The transition between a raw sensor value and a belief function is made through the use of a belief model which maps a sensor value to a belief function. A belief function represents the degree of belief associated to each possible value of the context attribute.

• Then a set of belief functions (corresponding to a set of sensors) can be combined (fused).

• Finally the system can decide what is the "best" value for the context attribute.

Currently the BFT theories requires a huge calibration process. In 2016, we obtained new results on the semi-automated building of belief functions, that have to be provided by each sensor, using our BFT Java implementation (see section 5.1).