Distributed reasoning in ambient environnement

L’informatique pervasive et l’intelligence ambiante visent à créer un environnement intelligent avec des dispositifs électroniques et informatiques mis en réseau tels que les capteurs, qui s’intègrent parfaitement dans la vie quotidienne et offrent aux utilisateurs un accès transparent aux services partout et à tout moment.Pour garantir ce fonctionnement, un système doit avoir une connaissance globale sur son environnement, et en particulier sur les personnes et les dispositifs, leurs intérêts et leurs capacités, ainsi que les tâches et les activités associées. Toutes ces informations relèvent de la notion de contexte. Cela passe par la collecte des données contextuelles de l’utilisateur pour déterminer sa situation/son activité courante ; on parle alors d’identification de situations/d’activités. Pour cela, le système doit être sensible aux variations de son environnement et de son contexte, afin de détecter les situations/les activités et de s’adapter ensuite dynamiquement. Reconnaître une situation/une activité nécessite alors la mise en place de tout un processus : perception des données contextuelles, analyse de ces données collectéeset raisonnement sur celles-ci pour l’identification de situations/d’activités.Nous nous intéressons plus particulièrement aux aspects liés à la modélisation distribuée de l’environnement ambiant et à ceux liés au raisonnement distribué en présence de données imparfaites pour l’identification de situations/d’activités. Ainsi, la première contribution de la thèse concerne la partie perception. Nous avons proposé un nouveau modèle de perception permettant la collecte des données brutes issues des capteurs déployés dans l’environnement et la génération des évènements. Ensuite, la deuxième contribution se focalise sur l’observation et l’analyse de ces évènements en les segmentant et extrayant les attributs lesplus significatifs et pertinents. Enfin, les deux dernières contributions présentent deux propositions concernant le raisonnement distribué pour l’identification de situations/d’activités; l’une représente la principale contribution et l’autre représente sa version améliorée palliant certaines limites. D'un point de vue technique, toutes ces propositions ont été développées, validées et évaluées avec plusieurs outils.Pervasive Computing and Ambient Intelligence aim to create a smart environment withnetworked electronic and computer devices such as sensors seamlessly integrating into everyday life and providing users with transparent access to services anywhere and anytime.To ensure this, a system needs to have a global knowledge of its environment, and inparticular about people and devices, their interests and their capabilities, and associated tasks and activities. All these information are related to the concept of context. This involves gathering the user contextual data to determine his/her current situation/activity; we also talk about situation/activity identification. Thus, the system must be sensitive to environment and context changes, in order to detect situations/activities and then to adapt dynamically.Recognizing a situation/an activity requires the definition of a whole process : perception of contextual data, analysis of these collected data and reasoning on them for the identification of situations/activities.We are particularly interested in aspects related to the distributed modeling of the ambient environment and to those related to distributed reasoning in the presence of imperfect data for the identification of situations/activities. Thus, the first contribution of the thesis concerns the perception part. We have proposed a new perception model that allows the gathering of raw data from sensors deployed in the environment and the generation of events.Next, the second contribution focuses on the observation and analysis of these events by segmenting them and extracting the most significant and relevant features. Finally, the last two contributions present two proposals concerning the distributed reasoning for the identification of situations/activities ; one represents the main contribution and the other represents its improved version overcoming certain limitations. From a technical point of view, all these proposals have been developed, validated and evaluated with several tools

Raisonnement distribué dans un environnement ambiant

Pervasive Computing and Ambient Intelligence aim to create a smart environment withnetworked electronic and computer devices such as sensors seamlessly integrating into everyday life and providing users with transparent access to services anywhere and anytime.To ensure this, a system needs to have a global knowledge of its environment, and inparticular about people and devices, their interests and their capabilities, and associated tasks and activities. All these information are related to the concept of context. This involves gathering the user contextual data to determine his/her current situation/activity; we also talk about situation/activity identification. Thus, the system must be sensitive to environment and context changes, in order to detect situations/activities and then to adapt dynamically.Recognizing a situation/an activity requires the definition of a whole process : perception of contextual data, analysis of these collected data and reasoning on them for the identification of situations/activities.We are particularly interested in aspects related to the distributed modeling of the ambient environment and to those related to distributed reasoning in the presence of imperfect data for the identification of situations/activities. Thus, the first contribution of the thesis concerns the perception part. We have proposed a new perception model that allows the gathering of raw data from sensors deployed in the environment and the generation of events.Next, the second contribution focuses on the observation and analysis of these events by segmenting them and extracting the most significant and relevant features. Finally, the last two contributions present two proposals concerning the distributed reasoning for the identification of situations/activities ; one represents the main contribution and the other represents its improved version overcoming certain limitations. From a technical point of view, all these proposals have been developed, validated and evaluated with several tools.L’informatique pervasive et l’intelligence ambiante visent à créer un environnement intelligent avec des dispositifs électroniques et informatiques mis en réseau tels que les capteurs, qui s’intègrent parfaitement dans la vie quotidienne et offrent aux utilisateurs un accès transparent aux services partout et à tout moment.Pour garantir ce fonctionnement, un système doit avoir une connaissance globale sur son environnement, et en particulier sur les personnes et les dispositifs, leurs intérêts et leurs capacités, ainsi que les tâches et les activités associées. Toutes ces informations relèvent de la notion de contexte. Cela passe par la collecte des données contextuelles de l’utilisateur pour déterminer sa situation/son activité courante ; on parle alors d’identification de situations/d’activités. Pour cela, le système doit être sensible aux variations de son environnement et de son contexte, afin de détecter les situations/les activités et de s’adapter ensuite dynamiquement. Reconnaître une situation/une activité nécessite alors la mise en place de tout un processus : perception des données contextuelles, analyse de ces données collectéeset raisonnement sur celles-ci pour l’identification de situations/d’activités.Nous nous intéressons plus particulièrement aux aspects liés à la modélisation distribuée de l’environnement ambiant et à ceux liés au raisonnement distribué en présence de données imparfaites pour l’identification de situations/d’activités. Ainsi, la première contribution de la thèse concerne la partie perception. Nous avons proposé un nouveau modèle de perception permettant la collecte des données brutes issues des capteurs déployés dans l’environnement et la génération des évènements. Ensuite, la deuxième contribution se focalise sur l’observation et l’analyse de ces évènements en les segmentant et extrayant les attributs lesplus significatifs et pertinents. Enfin, les deux dernières contributions présentent deux propositions concernant le raisonnement distribué pour l’identification de situations/d’activités; l’une représente la principale contribution et l’autre représente sa version améliorée palliant certaines limites. D'un point de vue technique, toutes ces propositions ont été développées, validées et évaluées avec plusieurs outils

DCR: a new distributed model for human activity recognition in smart homes

International audienceHuman Activity Recognition (HAR) is an important research issue for pervasive computing that aims to identify human activities in smart homes. In the literature, most reasoning approaches for HAR are based on centralized approach where a central system is responsible for processing and reasoning about sensor data in order to recognize activities. Since sensor data are distributed, heterogeneous, and dynamic (i.e., whose characteristics are varying over time) in the smart home, reasoning process on these data for HAR needs to be distributed over a group of heterogeneous, autonomous and interacting entities in order to be more efficient. This paper proposes a main contribution, the DCR approach, a fully Distributed Collaborative Reasoning multi-agent approach where agents, with diverse classifiers, observe sensor data, make local predictions, communicate and collaborate to identify current activities. Then, an improved version of the DCR approach is proposed, the DCR-OL approach, a distributed Online Learning approach where learning agents learns from their collaborations to improve their own performance in activity recognition. Finally, we test our approaches by performing an evaluation study on Aruba dataset, that indicates an enhancement in terms of accuracy, F-measure and G-mean metrics compared to the centralized approach and also compared to a distributed approach existing in the literature

Distributed collaborative reasoning for HAR in smart homes

International audienceDistributed Human Activity Recognition (D-HAR) is an active research issue for pervasive computing that aims to identify human activities in smart homes. This paper proposes a fully distributed multi-agent reasoning approach where agents, with diverse classifiers, observe sensor data, make local predictions and collaborate to identify current activities. Experimental tests on Aruba dataset indicate an enhancement in terms of accuracy and F-measure metrics compared either to a centralized approach or a distributed approach from the literatur

A fuzzy semantic CEP model for situation identification in smart homes

International audienceUncertainty is an essential issue for smart home applications. Events generated from sensors can be outdated, inaccurate, imprecise or in contradiction with other ones. These unreliable data can lead to dysfunction in smart home applications. To tackle these challenges, we propose a new model named FSCEP (Fuzzy Semantic Complex Event Processing) that integrates fuzzy logic paradigm, semantic features through an ontology and traditional CEP. We confronted FSCEP with other works tackling uncertainty for CEP and experimented it through simulation with early but promising resul

FSCEP: a new model for context perception in smart homes

International audienceWith the emergence of the Internet of Things and smart devices, smart homes are becoming more and more popular. The main goal of this study is to implement an event driven system in a smart home and to extract meaningful information from the raw data collected by the deployed sensors using Complex Event Processing (CEP). These high-level events can then be used by multiple smart home applications in particular situation identification. However, in real life scenarios, low-level events are generally uncertain. In fact, an event may be outdated, inaccurate, imprecise or in contradiction with another one. This can lead to misinterpretation from CEP and the associated applications. To overcome these weaknesses, in this paper, we propose a Fuzzy Semantic Complex Event Processing (FSCEP) model which can represent and reason with events by including domain knowledge and integrating fuzzy logic. It handles multiple dimensions of uncertainty, namely freshness, accuracy, precision and contradiction. FSCEP has been implemented and compared with a well known CEP. The results show how some ambiguities are solve

Identification of compound heterozygous patients with primary hyperoxaluria type 1: clinical evaluations and in silico investigations

Abstract Background Primary hyperoxaluria type 1 (PH1) is an autosomal recessive inherited disorder of glyoxylate metabolism in which excessive oxalates are formed by the liver and excreted by the kidneys. Calcium oxalate crystallizes in the urine, leading to urolithiasis, nephrocalcinosis, and consequent renal failure if treatment is not initiated promptly. Mutations in the AGXT gene which encodes the hepatic peroxisomal enzyme alanine:glyoxylate aminotransferase are responsible of PH1. In the present work, we aimed to analyze AGXT gene and in silico investigations performed in four patients with PH1 among two non consanguineous families. Methods Exhaustive gene sequencing was performed after PCR amplification of coding exons and introns boundaries. Bioinformatic tools were used to predict the impact of AGXT variants on gene expression as well as on the protein structure and function. Results Direct sequencing of all exons of AGXT gene revealed the emergence of multiple mutations in compound heterozygous state in the two studied families. Two patients were compound heterozygous for the c.731 T > C, c.32C > T, c.1020A > G and c.33_34insC and presented clinically with recurrent urinary tract infection, multiple urolithiasis and nephrocalcinosis under the age of 1 year and a persistent hyperoxaluria at the age of diagnosis. The two other patients presenting a less severe phenotypes were heterozygous for c.731 T > C and homozygous for the c.32C > T and c.1020A > G or compound heterozygous for c.26C > A and c.65A > G variants. Conclusion In Summary, we provided relevance regarding the compound heterozygous mutations in non consanguineous PH1 families with variable severity