From Traditional Adaptive Data Caching to Adaptive Context Caching: A Survey

Context data is in demand more than ever with the rapid increase in the development of many context-aware Internet of Things applications. Research in context and context-awareness is being conducted to broaden its applicability in light of many practical and technical challenges. One of the challenges is improving performance when responding to large number of context queries. Context Management Platforms that infer and deliver context to applications measure this problem using Quality of Service (QoS) parameters. Although caching is a proven way to improve QoS, transiency of context and features such as variability, heterogeneity of context queries pose an additional real-time cost management problem. This paper presents a critical survey of state-of-the-art in adaptive data caching with the objective of developing a body of knowledge in cost- and performance-efficient adaptive caching strategies. We comprehensively survey a large number of research publications and evaluate, compare, and contrast different techniques, policies, approaches, and schemes in adaptive caching. Our critical analysis is motivated by the focus on adaptively caching context as a core research problem. A formal definition for adaptive context caching is then proposed, followed by identified features and requirements of a well-designed, objective optimal adaptive context caching strategy.Comment: This paper is currently under review with ACM Computing Surveys Journal at this time of publishing in arxiv.or

Reconnaissance de contexte stable pour l'habitat intelligent

L'habitat intelligent est l'objet de nombreux travaux de recherche. Il permet d'assister des personnes âgées ou handicapées, d'améliorer le confort, la sécurité ou encore d'économiser de l'énergie. Aujourd'hui, l'informatique ubiquitaire se développe et s'intègre dans l'habitat intelligent notamment en apportant la sensibilité au contexte. Malheureusement, comprendre ce qui se passe dans une maison n'est pas toujours facile. Dans cette thèse, nous explicitons comment le contexte peut permettre de déployer des services adaptés aux activités et aux besoins des habitants. La compréhension du contexte passe par l'installation de capteurs mais aussi par l'abstraction des données brutes en données intelligibles facilement exploitables par des humains et des services. Nous mettons en avant une architecture multi-couches de fusion de données permettant d'obtenir des données contextuelles de niveaux d'abstraction différents. La mise en place des couches basses y est présentée en détail avec l'application de la théorie des fonctions de croyance pour l'abstraction de données brutes issues de capteurs. Enfin, sont présentés le déploiement d'un prototype nous ayant permis de valider notre approche, ainsi que les services déployés.Smart home is a major subject of interest. It helps to assist elderly or disabled people, improve comfort, safety, and also save energy. Today, ubiquitous computing is developed and integrated into the smart home providing context-awareness. Unfortunately, understanding what happens in a home is not always easy. In this thesis, we explain how context can be used to deploy services tailored to the activities and needs of residents. Understanding context requires the installation of sensors but also the abstraction of raw data into easily understandable data usable by humans and services. We present a multi-layer architecture of data fusion used to obtain contextual information of different levels of abstraction. The implementation of the lower layers is presented in detail with the application of the theory of belief functions for the abstraction of raw sensor data. Finally, are presented the deployment of a prototype that allowed us to validate our approach and the deployed services.RENNES1-Bibl. électronique (352382106) / SudocSudocFranceF