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Section: New Results
Pervasive support for Smart Homes
Participants : Michele Dominici, Bastien Pietropaoli, Sylvain Roche, Frédéric Weis [contact] .
A smart home is a residence equipped with information-and-communication-technology (ICT) devices conceived to collaborate in order to anticipate and respond to the needs of the occupants, working to promote their comfort, convenience, security and entertainment while preserving their natural interaction with the environment.
The idea of using the Ubiquitous Computing paradigm in the smart home domain is not new. However, the state-of-the-art solutions only partially adhere to its principles. Often the adopted approach consists in a heavy deployment of sensor nodes, which continuously send a lot of data to a central elaboration unit, in charge of the difficult task of extrapolating meaningful information using complex techniques. This is a logical approach. Aces proposed instead the adoption of a physical approach, in which the information is spread in the environment, carried by the entities themselves, and the elaboration is directly executed by these entities "inside" the physical space. This allows performing meaningful exchanges of data that will thereafter need a less complicate processing compared to the current solutions. The result is a smart home that can, in an easier and better way, integrate the context in its functioning and thus seamlessly deliver more useful and effective user services. Our contribution aims at implementing the physical approach in a domestic environment, showing a solution for improving both comfort and energy savings.
Most existing smart home solutions were designed with a technology-driven approach. That is, the designers explored which services, functionalities, actions and controls could be performed exploiting available technologies. This led to solutions for human activity recognition relying on wearable sensors, microphones or video cameras. Those technologies may be difficult to deploy and get accepted in real-world households, because of convenience and privacy concerns. Many people have concerns on carrying equipments or feeling observed or recorded while living their private life. This could seriously impact the acceptability of the smart home system or reduce its diffusion in real households. To avoid such kind of issues, we designed our system with an acceptability-driven approach. That is, we selected technologies that respond to the constraints of a real-world deployment of the future smart home system, namely, convenience and privacy concerns. We decided to take a very conservative approach, choosing technologies that are as unobtrusive as possible, in order to explore the frontiers of what can be done in a smart home with a very limited instrumentation. Following the same considerations, the adopted technologies and techniques had to guarantee a fast and easy configuration, ultimately allowing a plug-and-play deployment.
Design and implementation of a system architecture
In 2012, we have designed and experimented a system architecture of a smart home prototype currently under development. It is the demonstrator of an interdisciplinary project that brings together industrials and researchers, from the fields of ubiquitous computing and cognitive ergonomics. The aim is to develop a smart home system that is able to prevent energy waste and preserve inhabitants' comfort. The key requirement is to provide functionalities that are seamlessly adapted to ongoing situations and activities of inhabitants, avoiding bothering them with inappropriate interventions. The architecture of such a system has been designed so as to respect the principles and constraints illustrated in the introduction of this section. Namely, we have chosen the necessary equipments among those that should guarantee privacy preservation and high acceptability. When designing the algorithms for context and situation recognition and the human-computer interaction aspects of the system, we have kept in mind the model of human activity described in the previous section. Finally, we have designed the architecture of the system so as to realize successive abstraction of contextual information and to allow uncertainty, imprecision and ignorance to flow between the layers [2] .
Layered architecture
The system architecture relies on the principles of the ubiquitous computing paradigm. It also draws its inspiration from the work of Coutaz, who suggest a four-layer model to build context-aware applications. The first layer, "sensing", is in charge of sensing the environment. It is realized by augmented appliances and physical sensors. The augmented household appliances provide information about their state, while the sensors measure physical phenomena (sound level, motion, vibration, etc.). The second layer, called "perception", realizes the abstraction from the raw data. These are processed to obtain more abstract information about the context (e.g. the detection of presence in a room can be obtained combining motion, sound and vibration measures). "Situation and context identification", the third layer, identifies the occurring situations and the activities of inhabitants. For instance, the fact that a given moment a person is ironing can be modeled combining the information that a person is present in a room with the fact that the iron is on and that it is being moved. The top layer, called "exploitation", provides contextual information to applications. More specifically, the contextual information is used to adapt the behavior of the augmented appliances in a semi-automatic way and to allow lowly interruptive takeover by inhabitants.
Design and experimentation of the "perception" layer
In the second layer called "perception", raw sensor data are processed to obtain more abstract information about context called Context Attributes. These are small pieces of context easily understandable by humans and that can be provided to the upper layer. Examples of Context Attributes are the presence, the number of people in a room or the posture of someone. Some raw data are immediately exploitable, like temperature or light level. Others require data fusion in order to obtain more abstract contextual information, such as inhabitants' presence or movement. A certain number of sensors is necessary to obtain sufficient certainty when fusing data, as redundancy can significantly increase the reliability of the sources. Furthermore, heterogeneous sensors allow collecting different physical measurements that can enrich the data fusion process.
Data fusion is a large problem. Many theories offer tools to handle it. In our approach, the main aim of the perception layer is to abstract imperfect raw data to make it computable by higher level reasoning algorithms. Data may be imperfect for different reasons:
Inconsistency, due to overload of data or conflicting sources.
Incompleteness, due to loss of data which may easily happen with wireless communication.
Ambiguity (or fuzziness), due to models or to natural language imprecision.
Redundancy, due to multiple sources measuring the same parameter.
In order to manage many of those imperfections and respect the theoretical constraints, we decided to use as a first layer of abstraction the belief functions theory (BFT). The BFT can be seen as a generalization of the Bayesian theory of subjective probability. It can be used to model probabilities if only atomic focal sets are used in mass functions. Thus, it is totally possible to mix probabilities with real belief functions.
In our approach, we considered that sensors should duce belief for a certain amount of time after the measures because of the continuity of studied context. For instance, a motion sensor in a room could be able to induce a belief on the presence of someone for a longer time than the exact moment at which the measure has been obtained. It is a matter of physical system with inertia. In this example, it is easy to take into account that physical persons cannot move too fast and thus will certainly be there for some seconds before they can exit the room. Thus, this little example brings two questions: how to build evidence from raw data and how to take into account evidence over time? We proposed a simple method already existing to build belief functions from raw data and propose an improvement to take into account timed evidence [5] .
Design and experimentation of the "situation and context identification" layer
"Situation and context identification", the third layer, identifies the occurring situations and the activities of inhabitants. For instance, the fact that a given moment a person is ironing can be modeled combining the information that a person is present in a room with the fact that the iron is on and that it is being moved. Having obtained the Context Attributes through abstraction from the raw sensor data, the system has to reason about context, in order to infer higher-level context information, needed to make decisions concerning the functionalities to offer to inhabitants. We needed a unified theory for modeling contextual information, also offering a generic framework for applying different reasoning techniques to infer higher-level context.
We adopted a situation-centric modeling and reasoning approach called Context Spaces, based on a unified context modeling and reasoning theory. Using this theory, interesting situations can be modeled as combinations of basic contextual information provided both by a sensor-data-fusion technique and by augmented appliances. Adapted functionalities can be provided when the interesting situations are triggered. The recognition of ongoing situations is made possible by reasoning about available context information. The Context Spaces theory allows managing and propagating uncertainty and ignorance, reasoning on ambiguous contexts and assessing the degree of uncertainty of the resulting inference. It also provides tools to reason on complex logical expressions that combine elementary situations. The use and the extension of the Context Spaces is the core of a PhD thesis that has been finished at the end of 2012 by Michele Dominici (to be defended in March 2013).
Uncertainty and ignorance management
Given the gap between contextual capture capabilities of our architecture and actual complexity of real-world human activities and context, an important issue arises: the management of uncertainty and ignorance. If contextual information has to be abstracted in successive steps, sources are not always reliable. In particular, uncertainty is intrinsic to the physical sensors that are used in the capture. Thus, the uncertainty of lower abstraction layers will negatively impact the inference and decisions of the upper layers. Furthermore, due to the contextual gap illustrated above, any computing model that tries to represent the complexity of real activity will be affected by a certain degree of uncertainty. This reflects on the recognition of the activity itself and can lead to wrong conclusions, which in turn negatively impact the provision of adapted functionalities to inhabitants. As a consequence, we considered that information about uncertainty and ignorance has to be propagated, cumulated and considered at every layer of our pervasive architecture. Whenever the level of uncertainty becomes excessively high, the system tried to evaluate the tradeoff between the potential benefit of providing the right functionality and the risk associated with an unsuitable functionality, which would be provided in case the situation has not been correctly recognized.