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Enabling context-aware smart home with semantic technology
... In this work a context reasoning was focused on location (bedroom, bathroom, kitchen, …) to derive user's situation in smart phone scenarios. Other work has extended the CANON ontology by integrating a temporal ontology and rules-based context aware smart home [32]. Five rules are presented in [8] for multimedia conferencing process according to the user notification services (email, SMS, voice) and conferencing time efficiency. ...
... A learning object is a sort of digital element that permits content reuse, independence and flexibility in order to give a high quality of control to users [32]. ...
This paper focuses on monitoring and analyzing user activities on collaborative filtering -based recommender system in order to guess suitable and unsuitable items' context information using rating matrix which makes more efficient adaptation task. An ontology-based user profile and rules-based context modeling for reasoning about context information is proposed in this research work, in addition to an investigation to apply Semantic Web technologies in user modeling and context reasoning. This proposal is applied in education field in which we have designed an authoring tool for learning objects within ubiquitous environment. This system aims to improve the learning object production task (creation, review, edition…) on behalf of technologies offered by collaborative filtering systems as well as user behaviors monitoring to improve the recommendation process.
... Thus, it considers modelling approaches as either object-role-based, spatial, ontology-based or hybrid, while key requirements are heterogeneity, mobility, relationships, timeliness, imperfection, reasoning, usability and efficiency [8]. Indeed, an investigation of existing models in literature, reveals that the most dominant approaches are either ontology-based [9,10,11] or graphical [12,13]. Regarding content and domain, most context-modelling approaches so far revolve around the topic of pervasive computing, ambient intelligence and context-aware systems, such as smart homes [14], smart meetings [15], and less often museums [16] and eLearning domains [11]. ...
... Regarding content and domain, most context-modelling approaches so far revolve around the topic of pervasive computing, ambient intelligence and context-aware systems, such as smart homes [14], smart meetings [15], and less often museums [16] and eLearning domains [11]. Overall, the most common concepts in context modelling tend to be Person and Device [9,16,17]. Examining approaches per domain, the ones in pervasive computing typically consider environmental parameters (e.g. weather, temperature, light and sound), location, user preferences, applications and services [13]. ...
The fields of Digital Humanities and Digital Preservation are not yet enjoying the full potential of the Semantic Web and relevant technologies, largely due to the highly contextualized nature of their source materials. This paper addresses the issue of representing context and use-context (i.e. context of use) of digital content, by proposing an ontology-based representation approach , which is based on the LRM, an upper-level ontology for describing dependencies between digital resources.
... There exist numerous representations to define context ontologies, to specify context types and their descriptors and relationships, including OWL; W3C' semantic web activities; and Resource Description Framework (RDF); these logic-based languages probably gave boost to ontologies . For examples of ontology-based context models see Gu et al. (2005), Chen et al. (2004b), and Korpip et al. (2005). Furthermore, ontology researchers have recently started to explore the possibility of integrating different models (e.g., representation sublanguage) and different types of reasoning mechanisms in order to obtain more flexible, robust, and comprehensive systems. ...
... They allow context to be recognized through direct semantic reasoning that make extensive use of semantic content-descriptions and domain knowledge. Numerous studies (e.g., Strimpakou et al. 2006;Gu et al. 2005;Khedr and Karmouch 2005;Chen et al. 2004a, b, c;Korpip et al. 2005;Korpipaa et al. 2003;Wang et al. 2004;Strang et al. 2003) have demonstrated the benefits of using ontology-based context models. Evaluation of a research work on context modeling (Strang and Linnhoff-Popien 2004) shows that the usage of ontologies exhibits prominent benefits in AmI environments. ...
The aim of this chapter is to give insights into the origin and context of the AmI vision; to shed light on the customary assumptions behind the dominant vision of AmI, underlying many of its envisioned scenarios, and provide an account on its current status; to outline and describe a generic typology for AmI; to provide an overview on technological factors behind AmI and the many, diverse research topics and areas associated with AmI; to introduce and describe human-directed sciences as well as artificial intelligence and their relationships and contributions to AmI; and to discuss key paradigmatic, non-paradigmatic, pre-paradigmatic, and post-paradigmatic dimensions of AmI. Moreover, this chapter intends to provide essential underpinning conceptual tools for exploring the subject of AmI further in the remaining chapters.
... There exist numerous representations to define context ontologies, to specify context types and their descriptors and relationships, including OWL; W3C' semantic web activities; and Resource Description Framework (RDF); these logic-based languages probably gave boost to ontologies . For examples of ontology-based context models see Gu et al. (2005), Chen et al. (2004b), and Korpip et al. (2005). Furthermore, ontology researchers have recently started to explore the possibility of integrating different models (e.g., representation sublanguage) and different types of reasoning mechanisms in order to obtain more flexible, robust, and comprehensive systems. ...
... They allow context to be recognized through direct semantic reasoning that make extensive use of semantic content-descriptions and domain knowledge. Numerous studies (e.g., Strimpakou et al. 2006;Gu et al. 2005;Khedr and Karmouch 2005;Chen et al. 2004a, b, c;Korpip et al. 2005;Korpipaa et al. 2003;Wang et al. 2004;Strang et al. 2003) have demonstrated the benefits of using ontology-based context models. Evaluation of a research work on context modeling (Strang and Linnhoff-Popien 2004) shows that the usage of ontologies exhibits prominent benefits in AmI environments. ...
This chapter intends to look into the concept of context in relation to both human interaction and HCI‚ espousing a transdisciplinary approach‚ and to delve into the technological and social dimensions of context awareness, focusing on key aspects which are theoretically disputable and questionable in the realm of AmI and pointing out key challenges‚ open issues‚ and limitations.
... There exist numerous representations to define context ontologies, to specify context types and their descriptors and relationships, including OWL; W3C' semantic web activities; and Resource Description Framework (RDF); these logic-based languages probably gave boost to ontologies . For examples of ontology-based context models see Gu et al. (2005), Chen et al. (2004b), and Korpip et al. (2005). Furthermore, ontology researchers have recently started to explore the possibility of integrating different models (e.g., representation sublanguage) and different types of reasoning mechanisms in order to obtain more flexible, robust, and comprehensive systems. ...
... They allow context to be recognized through direct semantic reasoning that make extensive use of semantic content-descriptions and domain knowledge. Numerous studies (e.g., Strimpakou et al. 2006;Gu et al. 2005;Khedr and Karmouch 2005;Chen et al. 2004a, b, c;Korpip et al. 2005;Korpipaa et al. 2003;Wang et al. 2004;Strang et al. 2003) have demonstrated the benefits of using ontology-based context models. Evaluation of a research work on context modeling (Strang and Linnhoff-Popien 2004) shows that the usage of ontologies exhibits prominent benefits in AmI environments. ...
As a conclusion to Part I and Part II, this chapter provides main concluding remarks, discusses key relevant research and practical implications, and presents some critical reflections.
... There exist numerous representations to define context ontologies, to specify context types and their descriptors and relationships, including OWL; W3C' semantic web activities; and Resource Description Framework (RDF); these logic-based languages probably gave boost to ontologies . For examples of ontology-based context models see Gu et al. (2005), Chen et al. (2004b), and Korpip et al. (2005). Furthermore, ontology researchers have recently started to explore the possibility of integrating different models (e.g., representation sublanguage) and different types of reasoning mechanisms in order to obtain more flexible, robust, and comprehensive systems. ...
... They allow context to be recognized through direct semantic reasoning that make extensive use of semantic content-descriptions and domain knowledge. Numerous studies (e.g., Strimpakou et al. 2006;Gu et al. 2005;Khedr and Karmouch 2005;Chen et al. 2004a, b, c;Korpip et al. 2005;Korpipaa et al. 2003;Wang et al. 2004;Strang et al. 2003) have demonstrated the benefits of using ontology-based context models. Evaluation of a research work on context modeling (Strang and Linnhoff-Popien 2004) shows that the usage of ontologies exhibits prominent benefits in AmI environments. ...
The intent of chapter is to review the state-of-the-art sensor devices, recognition approaches, data processing techniques, and pattern recognition methods underlying context recognition in AmI environments. An overview of the recent advances and future development trends in the area of sensor technology is provided, focusing on novel multi-sensor data fusion techniques and related signal processing methods. In addition, the evolving trend of miniaturization is highlighted, with a focus on MEMS technology and its role in the advancement of sensing and computing devices. The observed future development trends include: the miniaturization of sensing devices, the widespread use of multi sensor fusion techniques and systems, and the increasing applicability of autonomous sensors. As to data processing and pattern recognition methods, emphasis is laid on machine learning probabilistic techniques, particularly in relation to emotional and cognitive context awareness and affective systems.
... There exist numerous representations to define context ontologies, to specify context types and their descriptors and relationships, including OWL; W3C' semantic web activities; and Resource Description Framework (RDF); these logic-based languages probably gave boost to ontologies . For examples of ontology-based context models see Gu et al. (2005), Chen et al. (2004b), and Korpip et al. (2005). Furthermore, ontology researchers have recently started to explore the possibility of integrating different models (e.g., representation sublanguage) and different types of reasoning mechanisms in order to obtain more flexible, robust, and comprehensive systems. ...
... They allow context to be recognized through direct semantic reasoning that make extensive use of semantic content-descriptions and domain knowledge. Numerous studies (e.g., Strimpakou et al. 2006;Gu et al. 2005;Khedr and Karmouch 2005;Chen et al. 2004a, b, c;Korpip et al. 2005;Korpipaa et al. 2003;Wang et al. 2004;Strang et al. 2003) have demonstrated the benefits of using ontology-based context models. Evaluation of a research work on context modeling (Strang and Linnhoff-Popien 2004) shows that the usage of ontologies exhibits prominent benefits in AmI environments. ...
The aim of this chapter is to review and show the state-of-the-art in the area of ontological and hybrid context modeling, representation, and reasoning in AmI. In addition to focusing on works on context information representation and reasoning that fall into the ontological category, other relevant representation and reasoning techniques from the literature on context-aware computing are included for comparative purposes. Context is primarily considered from the view point of adaptation in HCI, and ontology is discussed in the applied context of software engineering.
... There exist numerous representations to define context ontologies, to specify context types and their descriptors and relationships, including OWL; W3C' semantic web activities; and Resource Description Framework (RDF); these logic-based languages probably gave boost to ontologies . For examples of ontology-based context models see Gu et al. (2005), Chen et al. (2004b), and Korpip et al. (2005). Furthermore, ontology researchers have recently started to explore the possibility of integrating different models (e.g., representation sublanguage) and different types of reasoning mechanisms in order to obtain more flexible, robust, and comprehensive systems. ...
... They allow context to be recognized through direct semantic reasoning that make extensive use of semantic content-descriptions and domain knowledge. Numerous studies (e.g., Strimpakou et al. 2006;Gu et al. 2005;Khedr and Karmouch 2005;Chen et al. 2004a, b, c;Korpip et al. 2005;Korpipaa et al. 2003;Wang et al. 2004;Strang et al. 2003) have demonstrated the benefits of using ontology-based context models. Evaluation of a research work on context modeling (Strang and Linnhoff-Popien 2004) shows that the usage of ontologies exhibits prominent benefits in AmI environments. ...
The intent of this chapter is to examine and discuss the different aspects and forms of the affective behavior of AmI systems, as well as to highlight the role of affective computing as a research area of AI in AmI in advancing the field of AmI with respect to emotionally human-inspired applications. Examples of HCI application scenarios revealing important emerging trends in this research area include: affective context-aware, emotion-aware, context-aware affective, and emotionally intelligent systems.
... There exist numerous representations to define context ontologies, to specify context types and their descriptors and relationships, including OWL; W3C' semantic web activities; and Resource Description Framework (RDF); these logic-based languages probably gave boost to ontologies . For examples of ontology-based context models see Gu et al. (2005), Chen et al. (2004b), and Korpip et al. (2005). Furthermore, ontology researchers have recently started to explore the possibility of integrating different models (e.g., representation sublanguage) and different types of reasoning mechanisms in order to obtain more flexible, robust, and comprehensive systems. ...
... They allow context to be recognized through direct semantic reasoning that make extensive use of semantic content-descriptions and domain knowledge. Numerous studies (e.g., Strimpakou et al. 2006;Gu et al. 2005;Khedr and Karmouch 2005;Chen et al. 2004a, b, c;Korpip et al. 2005;Korpipaa et al. 2003;Wang et al. 2004;Strang et al. 2003) have demonstrated the benefits of using ontology-based context models. Evaluation of a research work on context modeling (Strang and Linnhoff-Popien 2004) shows that the usage of ontologies exhibits prominent benefits in AmI environments. ...
This chapter seeks to address computational intelligence in terms of conversational and dialog systems and computational processes and methods to support complex communicative tasks. In so doing, it explores human verbal and nonverbal communication behavior and sheds light on the recent attempts undertaken to investigate different aspects of human communication with the aim to replicate and implement them into ECAs. In HCI, ECAs represent multimodal user interfaces where modalities are the natural modalities of human conversation, namely speech, facial expressions and gestures, hand gestures, and body postures.
... The application of data mining and machine learning techniques to this data can be used to discover frequent activity patterns and predict events in order to automate interactions with the environment and respond in a context-aware manner. Context awareness can also be used to facilitate adaptation to changing requirements [98]. ...
... Other approaches include the use of a peer to peer architecture with multiple OSGi platforms to distribute the working load over the system with service-oriented components augmented by mobile agent technology for system interaction [102], and a generic five-layer context stack with each layer having a different function [98]. ...
The paper discusses the potential of assistive service robots to support disabled and elderly people. It shows that they have considerable untapped potential in this area, but also that inappropriate implementations could increase isolation, reduce independence and lead to users feeling as though they are under surveillance. The main body of the paper presents an overview of existing applications and discusses their benefits and potential problems. This is organized by an extension of the common classification into socially and physically assistive robots by the two categories of sensory assistive and mixed assistance robots. Another more detailed classification is also presented. This discussion is introduced by an overview of many of the technological components of smart mobile robots. It is followed by a discussion of user acceptance. The problems of existing models based on either solely positive or solely negative factors are noted and a model containing both types of factors is proposed. The need for continuing research is noted and various proposals are made.
... The details of the context processing are hidden from other components and encapsulated in the object level (Strang & Linnhoff-Popien, 2004). Mshali et al. (2018) reported that the research by Zhang et al. (2005) proposed a context-aware system for smart homes using an object-oriented context model. Context data is structured around a set of entities in this model, and each entity describes a physical or contextual object such as a person or an activity. ...
The impact of digital technology on healthcare delivery services is increasing as new technologies evolve and current technologies expand. These technologies have the potential to provide a platform to reason about the health condition of a patient using relevant contextual information. Context-aware reasoning is particularly important in cardiac health monitoring because of the increasing number of deaths resulting from cardiac diseases. As a result, several efforts have been made to develop intelligent systems for cardiac condition monitoring. Nevertheless, most of the existing systems for cardiac health monitoring are generally based on physiological information, mainly the heart rate or electrocardiogram(ECG) signals, while the few research that does integrate contextual information has not considered the privacy of the patients in the development process. This research proposes a privacy-preserving context-aware framework for cardiac health monitoring using contextual information from the patient's behavior data to facilitate physicians' decision-making. The framework considers the patient's privacy in the architectural design by allowing the user to take control of the data generated from the sensors as information is stored in the user's device and not transferred to any server. Furthermore, the user's privacy is also considered at the algorithm training and model generation stage by adopting a federated machine learning approach. Federated learning allows different clients in different locations to train a global model without sending their dataset to a central server. In addition, the framework addresses the issue of context acquisition by engaging healthcare professionals in the development process. A prototype tagged "mCardiac" is presented as a proof of concept. The design, implementation, and evaluation of mCardiac was made possible by constant interaction with healthcare professionals. mCardiac was also evaluated with cardiac patients who were asked to use the system to validate the effectiveness of the approach.
... Context Modelling refers to defining and storing context data in a machine-processable form [13]. Literature has revealed various context modelling approaches including Key-Value Models [14,15], Markup Scheme Models [16,17], Graphical Model [18,19], Logic-Based Modelling [20,21], Object-Oriented Models [22][23][24] and Ontology-Based Models [25][26][27]. Among others, the ontology-based context-modelling approach has been advocated as the most promising approach due to its modular structuring mechanism, reusability, independence of its own identity [28][29][30][31][32]. In this paper, we choose the ontology-driven context modelling approach for the proposed intelligent assistive formalism. ...
... Various OWL ontologies have been proposed for representing shared descriptions of context. Among the most prominent proposals are the SOUPA [17] ontology for modeling context in pervasive environments, and the CONON [9] ontology for smart home environments. OWL-DL ontological models of context have been adopted in several architectures for contextawareness. ...
the big needs of disabled and normal users who want to receive the needed information using computational system at anytime and everywhere is now a necessity. The need for services architectures that is aware of the context that to build content adaptation applications that maximize the user satisfaction. The development of context-aware applications should be supported by adequate context information modeling and reasoning techniques. In this paper, we present how to design a conceptually layered framework that supports context aware application to explain the different elements common to most context-aware architectures, but also how a context can be modeled and shared. In this paper we focus on existing work in this research issue in order to determine the different elements common to most context-aware architectures and determine different approaches of modeling context in ubiquitous computing.
... Various OWL ontologies have been proposed for representing shared descriptions of context. Among the most prominent proposals are the SOUPA [17] ontology for modeling context in pervasive environments, and the CONON [9] ontology for smart home environments. OWL-DL ontological models of context have been adopted in several architectures for contextawareness. ...
the big needs of disabled and normal users who want to receive the needed information using computational system at anytime and everywhere is now a necessity. The need for services architectures that is aware of the context that to build content adaptation applications that maximize the user satisfaction. The development of context-aware applications should be supported by adequate context information modeling and reasoning techniques. In this paper, we present how to design a conceptually layered framework that supports context aware application to explain the different elements common to most context-aware architectures, but also how a context can be modeled and shared. In this paper we focus on existing work in this research issue in order to determine the different elements common to most context-aware architectures and determine different approaches of modeling context in ubiquitous computing.
... There is an opportunity to leverage context-awareness typically used to provide context-relevant information and/or services to users [20] in various service domains such as healthcare [23], smart-home [83], safety [9], and location-based recommendations [69]. A previous study highlighted that "it is desirable for mobile devices to automatically configure themselves based on the context of the environment and user preferences." ...
Smartphones are often distraction for everyday life activities. In this work, we envision designing a context-aware system that helps users better manage smartphone distractions. This system design requires us to have an in-depth understanding of users' contexts of smartphone distractions and their coping strategies. However, there is a lack of knowledge regarding the contexts in which users perceive that smartphones are distracting in their everyday lives. Furthermore, prior studies did not systematically examine users' preferred coping strategies for handling interruptions caused by smartphones, possibly supported by context-aware systems that proactively manage smartphone distraction. To bridge this gap, we collect in-situ user contexts and their corresponding levels of perceived smartphone distraction as well as analyze the daily contexts in which users perceive smartphones as distracting. Moreover, we also explore how users want to manage phone distraction by asking them to write simple if-then rules. Our results on user contexts and coping strategies provide important implications for designing and implementing context-aware distraction management systems.
... The study in [32][33][34][35] focused on a model based on reasoning in an ontology-based system. The model SOUPA discussed in the study [33][34][35] used context ontology in the pervasive computing environment while CONON [36][37][38] make use of extensible context ontology. It lacks the generality and the classification of context for reasoning purpose. ...
The study presents a Formal Contextual Security Privacy model for healthcare application with aim to provide services in the secure manner. The contextual information helps in improving the dynamic aspects of security and privacy as compared to existing security mechanisms which works only in static environments. Semantic web-based technique and reasoning mechanism are used for representation. Various scenarios relating to health care services as well as challenges faced by the system are presented by this study. The performance overhead of the model is evaluated by implementing simulator in Java. The system performance is studied by testing the scalability of the system and its throughput by measuring the memory usage and average inference time by varying the number of policies, size of ontology and number of instances, etc. Comparative analysis of average inference time is done for different types of reasoning engines. The response time of our proposed system is compared with the traditional model without security and privacy parameters shows significant improvements.
... The study in [32][33][34][35] focused on a model based on reasoning in an ontology-based system. The model SOUPA discussed in the study [33][34][35] used context ontology in the pervasive computing environment while CONON [36][37][38] make use of extensible context ontology. It lacks the generality and the classification of context for reasoning purpose. ...
The study presents a Formal Contextual Security Privacy model for healthcare application with aim to provide services in the secure manner. The contextual information helps in improving the dynamic aspects of security and privacy as compared to existing security mechanisms which works only in static environments. Semantic web–based technique and reasoning mechanism are used for representation. Various scenarios relating to health care services as well as challenges faced by the system are presented by this study. The performance overhead of the model is evaluated by implementing simulator in Java. The system performance is studied by testing the scalability of the system and its throughput by measuring the memory usage and average inference time by varying the number of policies, size of ontology and number of instances, etc. Comparative analysis of average inference time is done for different types of reasoning engines. The response time of our proposed system is compared with the traditional model without security and privacy parameters shows significant improvements.
... Many studies have concentrated on connecting heterogeneous devices and subsystems together, and providing a unified interface on top [23,24,25,26]. Recently, the use of semantic technologies in smart environments has been suggested by many authors [27,28,29], as it provides a way to represent data on a higher semantically meaningful level, and share common understanding of the concepts using ontologies. In addition, different application domains can define their own ontologies. ...
Buildings' energy consumption has dramatically increased over the last decade, accounting for more than 35% of global energy use. In this article, RESPOND's approach to improve building operation is shown for reducing energy demand peaks. Namely, RESPOND develops an AI system to dispatch optimal DR events maximizing the renewable energy generation and leveraging energy storage units. Furthermore, with views to ensuring dwellers' engagement with the project, the proposed DR events are aimed to generate the least disturbance in their everyday activities.
... Une entité est définie comme «une personne, un lieu ou des objets considérés comme pertinents pour l'interaction entre un utilisateur et une application, y compris l'utilisateur et les applications ellesmêmes»[220]. Zhang et al.[221] proposent un modèle de contexte général orienté objet à utiliser dans un système de maison intelligente sensible au contexte. Dans leur modèle contextuel, l'information est structurée autour d'un ensemble d'entités. ...
Les progrès de la technologie des capteurs et leur disponibilité ont permis de mesurerdiverses propriétés et activités des habitants dans une maison intelligente.Cependant, l’obtention de connaissances significatives à partir d’une grande quantitéd’informations collectées à partir d’un réseau de capteurs n’est pas une tâchesimple. En raison de la complexité du comportement des habitants, l’extraction d’informationssignificatives et la prédiction précise des valeurs représentant les activitésfutures d’un occupant sont des défis de recherche [5].L’objectif principal de notre travail de thèse est d’assurer une analyse efficace desdonnées recueillies à partir des capteurs d’occupation dans une maison intelligente.Cette recherche tente de trouver une solution efficace pour surveiller les personnesâgées vivant d’une façon autonome dans leur propre maison. Par conséquent, cetravail se base sur la reconnaissance et l’évaluation des activités quotidiennes d’unepersonne âgée dans le but d’observer, de prédire et de suivre l’évolution de son étatde dépendance, de santé et de détecter par la même occasion, la présence d’une perteou d’une perturbation de l’autonomie en temps réel.Afin d’atteindre l’objectif principal de cette recherche, les objectifs suivants sontidentifiés :— Étudier différentes méthodes pour présenter et extraire l’énorme ensembledes données hétérogènes (bas niveau) détectées par les capteurs pour lesadapter dans un format approprié, lisible (haut niveau) pour reconnaitre etprédire le comportement de la personne.— Suivre l’état de santé de l’habitant via son comportement quotidien et selonsa routine.— Étudier les moyens appropriés d’exploration et de prédiction des comportementsdans la maison intelligente pour extraire le modèle comportementalde la personne.— Proposer un modèle de reconnaissance et de prédiction des activités quotidiennesadaptables à la personne, performant de point de vue de la précisionet de la rapidité.— Comparer les performances des différentes techniques de prédiction (les modèlesproposés) pour évaluer la technique la plus appropriée pour les donnéescollectées à partir d’un habitat intelligent.— Examiner les différentes techniques de détection pour évaluer les informationsimportantes concernant les valeurs aberrantes et tout comportementanormal.— Évaluer l’état de la santé de la personne à partir de son comportementquotidien, son profil et ses habitudes.
... Ontology modeling is adopted as a means to build a shareable vocabulary among the different actors operating in the SH, such as sensors, actuators, inhabitants. In [24] an ontology for context modeling is used to facilitate explicit context representation and to allow services to share contextual information, as well as inferring new pieces of information through reasoning processes. Abdulrazak et al. [25] presented a novel ontology to describe contexts in smart spaces; although the ontology does not provide concepts for comfort description, it encompasses several representations of the actors that operate within a smart environment, with a focus on beings-a concept including both physical and non-physical entities. ...
This work introduces ComfOnt, a semantic framework developed within the context of ambient assisted living, context awareness, and ambient intelligence Italian research projects. ComfOnt leverages knowledge regarding Smart Home inhabitants and their particular needs, the devices deployed inside the domestic environment (appliances, sensors, and actuators), the amount of their energy consumption, and indoor comfort metrics to provide dwellers with customized services. Developed reusing widely adopted ontologies, ComfOnt aims at providing inhabitants with the possibility of having personalized indoor comfort in their living environments and at helping them in scheduling their daily activities requiring appliances; in fact, the proposed semantic framework enables the representation of appliances’ energy consumption and the energy profile of the Smart Home, thus assisting the dwellers in avoiding power cuts and fostering energy savings. ComfOnt serves as a knowledge base for a prototypical application (DECAM) dedicated to Smart Home inhabitants; the architecture and the functionalities of DECAM are here presented.
... Other works that take into account the existence of an intelligent home and user dened rules to cover dierent aspects are Bernardos et al. [2013], Leong et al. [2009], Papadopoulos et al. [2009], Ricquebourg et al. [2006], Gouin-Vallerand and Giroux [2007], Lyle et al. [2012], Zhang et al. [2005], Catania et al. [2012]. Most of these works should be revised, due to the advancements in IT and electronic technologies. ...
... Other works that take into account the existence of an intelligent home and user dened rules to cover dierent aspects are Bernardos et al. [2013], Leong et al. [2009], Papadopoulos et al. [2009], Ricquebourg et al. [2006], Gouin-Vallerand and Giroux [2007], Lyle et al. [2012], Zhang et al. [2005], Catania et al. [2012]. Most of these works should be revised, due to the advancements in IT and electronic technologies. ...
... Authors in [26] suggest a layered model for generic context-aware system that most current context-aware systems can mapped onto. This layered model is proposed in line to the OSI model where distinct functionality is implemented through Context Acquisition Layer is the lowest layer where context is acquired using various context sensors. ...
With escalation in adoption of the technology for smart homes and smart building, it becomes absolutely necessary to devise an energy efficient ecosystem. This requirement for energy efficient system is based on the statistics released by The Statistics Portal. This report results into tightening the environmental regulations and increased concern about climate change among the public. As a result, energy efficient solution has been recognized as a high priority international goal in order to improve sustainability of the planet. In order to achieve the goal, governing bodies across the world are taking conscious and sincere efforts. For example, The U.S. Environmental Protection Agency’s Building Technologies Office (BTO) has set a target of 20% energy use reduction in commercial buildings. Here, authors attempt to understand the basic architecture of IoT ecosystems and its adaptation for providing an energy efficient architecture. Smart homes and buildings have been considered to simulate IoT ecosystems throughout the chapter.
... Home automation technology has been studied for well over a decade. For example, Zhang et al. [102] have investigated how to add context awareness to smart home technology. Similarly, security and privacy research has recognized the smart home as a relevant domain [5,19,54,91]. ...
A key issue for smart home systems is supporting non-expert users in their management. Whereas feedback design on use cases (such as energy feedback) have gained attention, current approaches to providing awareness on the system state typically provide a rather technical view. Long-term investigations of the practices and resources needed for maintaining Do-It-Yourself smart home systems, are particularly scarce. We report on a design case study in which we equipped 12 households with DIY smart home systems for two years and studied participants' strategies for maintaining system awareness, from learning about its workings to monitoring its behavior. We find that people's needs regarding system accountability changed over time. Their privacy needs were also affected over the same period. We found that participants initially looked for in-depth awareness information from the dedicated web-based dashboard. In the later phases of appropriation, however, their interaction and information needs shifted towards management by exception on mobile or ambient displays -- only focusing on the system when things were 'going wrong'. In terms of system accountability, we find that a system's self-declaration should focus on being socially meaningful rather than technically complete, for instance by relating itself to people's activities and the home routines.
... However, the availability of ubiquitous computing devices has enabled the use of distributed architectures with incompatibility between different products avoided by the use of open standards. Options include the Open Services Gateway Initiative (OSGi); a peer-to-peer architecture with multiple platforms [10]; and a generic five-layer context stack with each layer having a different function [11]. The OSGi is generally based on the client-server model, and is unfortunately at risk of single point of failure in the home gateway [10]. ...
... In machine learning based architectures the engine is usually one or more machine learning algorithms that are trained on data that they can aggregate and produce an output accordingly, while continuously learning from data and human feedback. Our proposed system architecture, presented in the Figure 2, builds on existing proposed architectures from both semantic webcentered [44] and machine learning-centered architectures [17] and puts forward the Hybrid-Knowledge Engine (HKE) at the center of the system. Dedicated interfaces, the Data API and the Knowledge API, allows adapted processing methods for the knowledge type and make the appropriate knowledge available for the proper circumstances. ...
The Internet of Things allowed us to seamlessly integrate communication and computational capabilities into
everyday things that resulted in a technologically enhanced environment. However, we still need to work on integrating high level understanding and intelligence in this connected system. The IoT is a mean that enables the possibility of integrating intelligent behavior and services into surrounding environments. One of the most representative examples of artificial environments are buildings. Residential buildings (e.g. homes, apartment blocks) or dedicated public buildings (educational, medical, commercial, governmental) serve different purposes and needs, and therefore they have different characteristics and constraints. However, every building uses some form of resource (e.g. energy, water) in order to assure the required level of comfort, safety and conditions for carrying out the desired activities. In this paper we take a look at some questions regarding the construction and the exploitation of knowledge related to different types of buildings in order to optimize the use of different resources while still assuring the occupants’ comfort. We enumerate some of the elements that characterize a building as smart and finally, we present a model for a building management system based on hybrid knowledge.
... It employs encapsulation, reusability, and inheritance to represent context data. The work in [101] used a general object-oriented context model to propose a context-aware system in smart environments. In this model, the context data is structured around a set of entities, each describes a physical or conceptual object such as a person or an activity. ...
Aging population ratios are rising significantly. Health monitoring systems (HMS) in smart environments have evolved rapidly to become a viable alternative to traditional healthcare solutions. The aim of HMS is to not only reduce costs but to also provide timely e-health services to individuals wishing to maintain their independence. In this way, elderly people can avoid, for as long as possible, any interaction with healthcare institutions (e.g. nursing homes and hospitals), which in turn reduces pressure on the health system. To fully realise this vision of seamless e-health services supporting people in need of them, a number of challenges that need further investigation still exist. To this end, we provide an overview of the current state of the art for smart health monitoring systems. We review HMS in smart environments from a general perspective and with a particular focus on systems for the elderly and dependent people. We look at the challenges for these systems from the perspective of developing the technology itself, system requirements, system design and modelling. We present a consolidated picture of the most important functions and services offered by HMS for monitoring and detecting human behaviour including its concepts, approaches, and processing techniques. Moreover, we provide an extensive, in-depth analysis and evaluation of the existing research findings in the area of e-health systems. Finally, we present challenges and open issues facing the smart HMS field and we make recommendations on how to improve future systems.
... A notable example of domain ontology is FOAF 1 , which describes a taxonomy of concepts and relations that are common in the social networks domain. SOUPA [8] and CONON [41] are the most prominent ontology proposals for representing context according to the requirements of pervasive computing and ambient intelligence. For instance, the SOUPA ontology for ubiquitous and pervasive systems includes concepts about agents, beliefs, desires, time, space and privacy. ...
The paradigm of Internet of Robotic Things (IoRT) extends the scope of the Internet of Things by endowing any object with the three main typical functions of any robotic system: Perception, actuation, and control. This paper presents a semantic framework for context-aware IoRT systems to support the development of applications for monitoring and managing IoRT systems. A knowledge representation framework, called SmartRules, is proposed for context modeling. SmartRules is a production rules language that enables reactive reasoning based on the closed world and unique name assumptions. It allows producing actions based on contextual information represented in a dedicated ontology language, called-Concept. An operational platform, centered on the notion of manageable object (MO), is also proposed to abstract the access to any physical or virtual device, which can communicate through the Internet. In addition, an integrated methodology and tools are proposed for guiding the development and deployment of context-aware Semantic IoRT systems, and, in particular, for defining context semantics and creating context management rules. To show the effectiveness of the proposed framework in ambient assisted living (AAL) applications, an IoRT system dedicated to the monitoring and assistance of an elderly person during his/her daily living activities is described and evaluated.
... Context stack [40] is another layered architecture. It has five layers: acquisition, representation, aggregation, interpretation and utilization. ...
Context awareness is relevant to embedded processing and machine to machine communication. It is, therefore, a critical element of research in smart environment. In this chapter our focus is on the various issues related to defining a context aware infrastructure around which smart environments can be designed and implemented. As far as the design aspects are concerned, the framework of context consists of representation, extraction, adaptability and formal modeling of contextual data. From prospectives of implementation, many technology related issues such as network, nodes, softwares, and the middleware architectures context management should be addressed comprehensively in order to establish a robust context aware infrastructure. This chapter touches all relevant issues in some details.
... Object-oriented models provide formal models for supporting reasoning and providing inherent characteristics of object oriented paradigms (such as encapsulation, inheritance, etc.). This model implements class hierarchies and relationships between objects as attributes or subobjects (Zhang, 2005). ...
The smart home concept offers a world of possibilities in the development of context-aware homes able to take intelligent decisions and respond autonomously according to the environmental situation at any moment. Artificial intelligence provides ad hoc concepts and techniques that support this view from a computational perspective. However, research on smart homes has traditionally prioritized network and hardware-based solutions. Thus, the ambient assisted living (AAL) paradigm has focused more on active ageing approaches aimed at building safer environments that seek to promote elderly people or people with disabilities benefiting from more independent living in their own homes. AAL provides important opportunities to deliver information and communication technology-based services that improve quality of life and personal autonomy, such as proactive monitoring of users and environments; smart control of physiological measures; detection of abnormal situations; and customization according to each user's needs and preferences. This chapter includes the main building blocks and their interrelationship that is required to create a sustainable and replicable people-centered smart home.
... However, they require ontology engines which have high requirements on resources producing negative performance impact on local context processing where resource-constrained devices are employed. Among the prominent proposal on ontology based modeling techniques are SOUPA [33] for pervasive environment and CONON [34] for smart home environment. The figure below illustrates the ontology model of the above case study. ...
... Rule-based reasoning engines are often coupled with ontological models, to perform context-awareness. Ontological models and reasoning are popular since early days of AmI [1], for sharing and reusing knowledge of an environment. Most ontological models rely on the Semantic Web technologies, such as Resource Description Framework (RDF) and OWL [2]. ...
... On the other hand, lowest layers describe specific domains. In (Zhang et al., 2005) a five layered model named Context Stack is presented and used to describe context-aware systems. The lowest layer acquires raw data from sensors, whereas the upper levels are aimed at creating a basis of the context model that allows interoperability and integration of heterogeneous sensors. ...
One of the most critical issues in Ambient Assisted Living (AAL) is the design of systems that can evolve to meet the requirements of individuals as their needs and health conditions change. Although much work has been done on home and building automation systems for AAL, often referred to as assistive domotics, there is in fact still a substantial lack of solutions capable to support system designers in the early stage of development of such assistive systems. To this aim, the work contributes to the research on design of assistive domotic systems by presenting an ontology-driven methodology aimed to guide the development process. The novel contributions of the paper include the goal-oriented approach of the methodology, which involves the elicitation and analysis of AAL requirements and their formal representation in an ontology, where high-level goals are described in terms of subgoals and tasks, that are then linked to corresponding measures and devices. Moreover, logic-based reasoning enables more advanced functionalities useful at design time. We present a validation of the methodology showing typical use cases both related to the development from scratch of a domotic system with assistive capabilities starting from a set of high-level user requirements and the redesign of existing implementations according to changed requirements.
... В статье [11] описывается основанный на контексте подход к построению умного дома будущего. Авторы считают, что сервисы в мобильном окружении должны быть основаны на контексте, для возможности самоадаптирования в быстроменяющемся окружении. ...
The paper describes ontological approach to context-oriented knowledge management in smart environment. A conceptual model of knowledge management system in smart environment has been developed. The knowledge management system for intelligent conference management system in smart environment has been developed. This system has been successfully tested in the FRUCT association conferences.
... Several interesting architectures and middleware systems for smart spaces have been developed in the last years. Several of them were layered architectures most of which have used agent technologies like the ones proposed by [3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. The Smart-M3 was based on a blackboard architecture model [19]. ...
Technology is moving beyond the personal computer towards a trend of embedded microprocessors in everyday objects and home appliances. The recent advances in sensor networks and devices with computing ability have provided us the necessary technology to make smart spaces. In such spaces, user activity and behavior are taken into account in order to provide adequate and accurate adapted services to the current context. Services are provided proactively (without explicit user intervention) and in an unobtrusive manner. The main objective of smart spaces is to provide services to the user for improved comfort, energy savings, security, and tremendous benefits for elderly persons living alone or persons with disabilities. Despite the interesting number of proposed architecture for building smart spaces, there still exists a lack of a generic software architecture for the development of such spaces. The major weakness of proposed architecture is that they have not dealt in depth with context-awareness, which is a key feature especially in context-aware services adaptation in smart spaces. In this paper, we propose a multi-agent architecture for building a smart living room with a focus on context-awareness aspects. The proposed architecture is generic enough to be easily used in any smart space.
... Context awareness is crucial for smart home systems to succeed [63]. Other works provide insights of applications in the so-called smart cities, where variety of networked sensor-based systems and devices are deployed on the scale of cities [35]. ...
Mobile applications often adapt their behavior according to user context, however, they are often limited to consider few sources of contextual information, such as user position or language. This article reviews existing work in context-aware systems (CAS), e.g., how to model context, and discusses further development of CAS and its potential applications by looking at available information, methods and technologies. Social Media seems to be an interesting source of personal information when appropriately exploited. In addition, there are many types of general information, ranging from weather and public transport to information of books and museums. These information sources can be combined in previously unexplored ways, enabling the development of smarter mobile services in different domains. Users are, however, reluctant to provide their personal information to applications; therefore, there is a crave for new regulations and systems that allow applications to use such contextual data without compromising the user privacy.
... Various OWL ontologies have been proposed for representing shared descriptions of context. Among the most prominent proposals are the SOUPA [17] ontology for modeling context in pervasive environments, and the CONON [9] ontology for smart home environments. OWL-DL ontological models of context have been adopted in several architectures for contextawareness. ...
... The CONON ontology [Wang et al., 2004] [Gu et al., 2004] has been designed and integrated to the Service-Oriented Context-Aware Middleware (SOCAM) [Zhang et al., 2005] [ Wang et al., 2002] architecture. Neither prototype nor ontologies are available online. ...
Selon les prévisions de Cisco , il y aura plus de 50 milliards d'appareils connectés à Internet d'ici 2020. Les appareils et les données produites sont principalement exploitées pour construire des applications « Internet des Objets (IdO) ». D'un point de vue des données, ces applications ne sont pas interopérables les unes avec les autres. Pour aider les utilisateurs ou même les machines à construire des applications 'Internet des Objets' inter-domaines innovantes, les principaux défis sont l'exploitation, la réutilisation, l'interprétation et la combinaison de ces données produites par les capteurs. Pour surmonter les problèmes d'interopérabilité, nous avons conçu le système Machine-to-Machine Measurement (M3) consistant à: (1) enrichir les données de capteurs avec les technologies du web sémantique pour décrire explicitement leur sens selon le contexte, (2) interpréter les données des capteurs pour en déduire des connaissances supplémentaires en réutilisant autant que possible la connaissance du domaine définie par des experts, et (3) une base de connaissances de sécurité pour assurer la sécurité dès la conception lors de la construction des applications IdO. Concernant la partie raisonnement, inspiré par le « Web de données », nous proposons une idée novatrice appelée le « Web des règles » afin de partager et réutiliser facilement les règles pour interpréter et raisonner sur les données de capteurs. Le système M3 a été suggéré à des normalisations et groupes de travail tels que l'ETSI M2M, oneM2M, W3C SSN et W3C Web of Things. Une preuve de concept de M3 a été implémentée et est disponible sur le web (http://www.sensormeasurement.appspot.com/) mais aussi embarqué
... It has been designed to support the building of context-aware services. SOCAM proposes and uses CONON [15] ontology model based on a two layer design that supports separation of concepts considering generality and specificity. SOCAM is a distributed middleware that has a layered architecture that aims to provide an efficient infrastructure. ...
Context management is one of the keys to creating context-aware systems for ubiquitous computing. Contextual informations comes from a large variety of sources and mostly in raw format without any interpretation which could be meaningless. Nowadays context management systems, as context information providers, build contextual semantic models with collected raw informations as their main task. There are different technologies for context modeling, such as Object-Role Modeling and Ontology modeling. Each technology has its own advantages and limitations. However, until now, there is no general standardized context model. Furthermore, a context management system for ubiquitous environment should be able to dynamically change the executing environment the same way other context-aware ubiquitous applications do. Therefore, we have designed Kali2Much Context Middleware, which offers a context management service based on an adaptation-driven architecture. It handles distributed context collection, context notifications and context source searching. Kali2Much is aimed to provide a service allowing its consumers to describe semantically what they need as context informations.
Energy harvesting is the demand of present day wireless communication
for improving the energy efficiency of a network and a way to green
communication as well. Wireless sensor networks (WSNs) suffer from energy
depletion of nodes and energy harvesting is a promising solution to enhance
the life-time of sensor nodes in the area having lesser human intervention.
In this work, different energy harvesting techniques have been presented and
electromagnetic-based energy harvester model is deployed with WSN to
evaluate its performance. Low energy adaptive clustering hierarchy protocol
has been used as a routing protocol for sensor nodes. The performance of the
proposed model is evaluated with variation in hardware characteristics of
energy harvester and analyzed for sensor characteristics such as the number
of dead nodes, alive nodes as well. The energy harvester model and WSN
have been implemented on the MATLAB platform.
The recent advent of Internet of Things (IoT), has given rise to a plethora of smart verticals- smart homes being one of them. Smart Home is a classic example of IoT, wherein smart appliances connected via home gateways constitute a local home network to assist people in activities of daily life. Smart Home involves IoT-based automation (such as smart lighting, heating, surveillance etc.), remote monitoring and control of smart appliances. Besides automation, human-in-the-loop is a unique characteristic of Smart home to offer personalized services. Understanding the human behavior requires context processing. Thus, enablement of Smart home involves two prominent technologies IoT and context-aware computing. Further, local devices lying in the smart home have the implicit location and situational information, hence fog computing can offer real-time smart home services. In this paper, the authors propose ICON (IoT-based CONtext-aware) framework for context-aware IoT applications such as smart home, further ICON leverages fog-based IoT middleware to perform context-aware processing.
The recent advent of Internet of Things (IoT), has given rise to a plethora of smart verticals- smart homes being one of them. Smart Home is a classic example of IoT, wherein smart appliances connected via home gateways constitute a local home network to assist people in activities of daily life. Smart Home involves IoT-based automation (such as smart lighting, heating, surveillance etc.), remote monitoring and control of smart appliances. Besides automation, human-in-the-loop is a unique characteristic of Smart home to offer personalized services. Understanding the human behavior requires context processing. Thus, enablement of Smart home involves two prominent technologies IoT and context-aware computing. Further, local devices lying in the smart home have the implicit location and situational information, hence fog computing can offer real-time smart home services. In this paper, the authors propose ICON (IoT-based CONtext-aware) framework for context-aware IoT applications such as smart home, further ICON leverages fog-based IoT middleware to perform context-aware processing.
In a plethora of smart phones and related mobile applications, users crave innovative and personalized services that adapt to their situation. To achieve that, smart phones need to understand user context and needs for latter providing them with adequate services. This chapter discusses how context can be understood, represented and exploited in smart phones, using techniques from the fields of Semantic Computing, Machine Learning and Graph Theory.
Context-Awareness system provides an appropriate service to user by recognizing situation from surrounding environment. There are many successful studies on this framework, but still has some limitations. In this paper, we are describing a context-awareness middleware that can enhance the limitation of the previous approaches. We first defined a new concept of context-awareness environment as a social intelligence. This concept implies that intelligent objects can make relationships, can aware of situation from surrounding environment, and can collaborate to accomplish a given task. The significance of the study is as follows. First, the system is capable of multi context-awareness since it is designed with a structure that supports multiple lines of reasoning. Second, the system is capable of context planning by adapting AI planning mechanism. Third, the system is capable of making the intelligent objects as a group for collaboration, and provides adaptive service to user. We have developed a prototype of the system and tested with a virtual scenario.
Smart homes are equipped with multiple sensors and actuators to observe the residents and environmental phenomena, to interpret the situation out of that, and finally, to react accordingly. While the data processing for a single smart home is facile, the data processing for multiple smart homes in one smart building is more complex because there are different people (e.g., like several residents, administrators, or a property management) with different interests concerning the processed data. On that point, this chapter shows which kind of typical roles can be found in a smart building and what requirements and challenges they demand for managing and processing the data. Secondly, Data Stream Management Systems (DSMS) are introduced as an approach for processing and managing data in a smart building by presenting an appropriate architecture. Finally, the chapter discusses further concepts from DSMS and illustrates how they additionally meet and solve the requirements and the challenges.
The dynamics of the world today demands a change in traditional education paradigms to enable the creation of more efficient learning environments, where students will learn more effectively and will play a more active role in their education. They should interact with the knowledge at anytime-anywhere. In order to tackle this problem we should take advantage of mobile communication devices (e.g., smartphones and tablets) which are widely used by students and which have excellent processing storage and connectivity capabilities. In this research work, a context-aware system stimulating active learning by students was developed. This system places the student in an intelligent learning environment and is capable of delivering appropriate context-related learning contents, based on location, time, date, interaction of the student, profile of the student, and so on. Within its architecture, the system includes reasoning capability that using context-based ontology is able to deliver efficient learning resources. The experimental results obtained from various learning experiences in nursery, medicine, and systems engineering support the validity of our approach.
Pervasive healthcare applications aim at improving habitability by assisting individuals in living autonomously. To achieve this goal, data on an individual's behavior and his or her environment (often collected with wireless sensors) is interpreted by machine learning algorithms; their decision finally leads to the initiation of appropriate actions, e.g., turning on the light. Developers of pervasive healthcare applications therefore face complexity stemming, amongst others, from different types of environmental and vital parameters, heterogeneous sensor platforms, unreliable network connections, as well as from different programming languages. Moreover, developing such applications often includes extensive prototyping work to collect large amounts of training data to optimize the machine learning algorithms. In this chapter the authors present a model-driven prototyping approach for the development of pervasive healthcare applications to leverage the complexity incurred in developing prototypes and applications. They support the approach with a development environment that simplifies application development with graphical editors, code generators, and pre-defined components.
Today, users need to interact with the UIs of the computer systems at any time and in any place. In fact, users have to deal with diverse devices supporting diverse interfaces and used in diverse environments. Thus, research must to be devoted to adapting the content, presentation and also the navigation scheme of the user interface not only for people without disabilities but also to impaired users according to updated context of use. In fact, context is captured from the surrounding environment in which the user is interacting with the application, gathered from a variety of sources and changed dynamically over time. For that reason, we propose to include context awareness system as a solution for adaptation of user interface tailored to user with special needs. In this paper, we try to introduce our contribution in how to build a user interface which is aware and capable to adapt depending of context. Our contribution is demonstrated through the behaviour of task model.
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