Position paper on realizing smart products: challenges for Semantic Web technologies

In the rapidly developing space of novel technologies that combine sensing and semantic technologies, research on smart products has the potential of establishing a research field in itself. In this paper, we synthesize existing work in this area in order to define and characterize smart products. We then reflect on a set of challenges that semantic technologies are likely to face in this domain. Finally, in order to initiate discussion in the workshop, we sketch an initial comparison of smart products and semantic sensor networks from the perspective of knowledge technologies

Smart campuses : extensive review of the last decade of research and current challenges

Novel intelligent systems to assist energy transition and improve sustainability can be deployed at different scales, ranging from a house to an entire region. University campuses are an interesting intermediate size (big enough to matter and small enough to be tractable) for research, development, test and training on the integration of smartness at all levels, which has led to the emergence of the concept of “smart campus” over the last few years. This review article proposes an extensive analysis of the scientific literature on smart campuses from the last decade (2010-2020). The 182 selected publications are distributed into seven categories of smartness: smart building, smart environment, smart mobility, smart living, smart people, smart governance and smart data. The main open questions and challenges regarding smart campuses are presented at the end of the review and deal with sustainability and energy transition, acceptability and ethics, learning models, open data policies and interoperability. The present work was carried out within the framework of the Energy Network of the Regional Leaders Summit (RLS-Energy) as part of its multilateral research efforts on smart region

SeMoM: a semantic middleware for IoT healthcare applications

De nos jours, l'internet des objets (IoT) connaît un intérêt considérable tant de la part du milieu universitaire que de l'industrie. Il a contribué à améliorer la qualité de vie, la croissance des entreprises et l'efficacité dans de multiples domaines. Cependant, l'hétérogénéité des objets qui peuvent être connectés dans de tels environnements, rend difficile leur interopérabilité. En outre, les observations produites par ces objets sont générées avec différents vocabulaires et formats de données. Cette hétérogénéité de technologies dans le monde IoT rend nécessaire l'adoption de solutions génériques à l'échelle mondiale. De plus, elle rend difficile le partage et la réutilisation des données dans d'autres buts que ceux pour lesquels elles ont été initialement mises en place. Dans cette thèse, nous abordons ces défis dans le contexte des applications de santé. Pour cela, nous proposons de transformer les données brutes issues de capteurs en connaissances et en informations en s'appuyant sur les ontologies. Ces connaissances vont être partagées entre les différents composants du système IoT. En ce qui concerne les défis d'hétérogénéité et d'interopérabilité, notre contribution principale est une architecture IoT utilisant des ontologies pour permettre le déploiement d'applications IoT sémantiques. Cette approche permet de partager les observations des capteurs, la contextualisation des données et la réutilisation des connaissances et des informations traitées. Les contributions spécifiques comprennent : * Conception d'une ontologie " Cognitive Semantic Sensor Network ontology (CoSSN) " : Cette ontologie vise à surmonter les défis d'interopérabilité sémantiques introduits par la variété des capteurs potentiellement utilisés. CoSSN permet aussi de modéliser la représentation des connaissances des experts. * Conception et mise en œuvre de SeMoM: SeMoM est une architecture flexible pour l'IoT intégrant l'ontologie CoSSN. Elle s'appuie sur un middleware orienté message (MoM) pour offrir une solution à couplage faible entre les composants du système. Ceux-ci peuvent échanger des données d'observation sémantiques de manière flexible à l'aide du paradigme producteur/consommateur. Du point de vue applicatif, nous sommes intéressés aux applications de santé. Dans ce domaine, les approches spécifiques et les prototypes individuels sont des solutions prédominantes ce qui rend difficile la collaboration entre différentes applications, en particulier dans un cas de patients multi-pathologies. En ce qui concerne ces défis, nous nous sommes intéressés à deux études de cas: 1) la détection du risque de développement des escarres chez les personnes âgées et 2) la détection des activités de la vie quotidienne (ADL) de personnes pour le suivi et l'assistance à domicile : * Nous avons développé des extensions de CoSSN pour décrire chaque concept en lien avec les deux cas d'utilisation. Nous avons également développé des applications spécifiques grâce à SeMoM qui mettent en œuvre des règles de connaissances expertes permettant d'évaluer et de détecter les escarres et les activités. * Nous avons mis en œuvre et évaluer le framework SeMoM en se basant sur deux expérimentations. La première basée sur le déploiement d'un système ciblant la détection des activités ADL dans un laboratoire d'expérimentation pour la santé (le Connected Health Lab). La seconde est basée sur le simulateur d'activités ADLSim développé par l'Université d'Oslo. Ce simulateur a été utilisé pour effectuer des tests de performances de notre solution en générant une quantité massive de données sur les activités d'une personne à domicile.Nowadays, the adoption of the Internet of Things (IoT) has received a considerable interest from both academia and industry. It provides enhancements in quality of life, business growth and efficiency in multiple domains. However, the heterogeneity of the "Things" that can be connected in such environments makes interoperability among them a challenging problem. Moreover, the observations produced by these "Things" are made available with heterogeneous vocabularies and data formats. This heterogeneity prevents generic solutions from being adopted on a global scale and makes difficult to share and reuse data for other purposes than those for which they were originally set up. In this thesis, we address these challenges in the context of healthcare applications considering how we transform raw data to cognitive knowledge and ontology-based information shared between IoT system components. With respect to heterogeneity and integration challenges, our main contribution is an ontology-based IoT architecture allowing the deployment of semantic IoT applications. This approach allows sharing of sensors observations, contextualization of data and reusability of knowledge and processed information. Specific contributions include: * Design of the Cognitive Semantic Sensor Network ontology (CoSSN) ontology: CoSSN aims at overcoming the semantic interoperability challenges introduced by the variety of sensors potentially used. It also aims at describing expert knowledge related to a specific domain. * Design and implementation of SeMoM: SeMoM is a flexible IoT architecture built on top of CoSSN ontology. It relies on a message oriented middleware (MoM) following the publish/subscribe paradigm for a loosely coupled communication between system components that can exchange semantic observation data in a flexible way. From the applicative perspective, we focus on healthcare applications. Indeed, specific approaches and individual prototypes are preeminent solutions in healthcare which straighten the need of an interoperable solution especially for patients with multiple affections. With respect to these challenges, we elaborated two case studies 1) bedsore risk detection and 2) Activities of Daily Living (ADL) detection as follows: * We developed extensions of CoSSN to describe each domain concepts and we developed specific applications through SeMoM implementing expert knowledge rules and assessments of bedsore and human activities. * We implemented and evaluated the SeMoM framework in order to provide a proof of concept of our approach. Two experimentations have been realized for that target. The first is based on a deployment of a system targeting the detection of ADL activities in a real smart platform. The other one is based on ADLSim, a simulator of activities for ambient assisted living that can generate a massive amount of data related to the activities of a monitored person

Context Aware Middleware Architectures: Survey and Challenges

Abstract: Context aware applications, which can adapt their behaviors to changing environments, are attracting more and more attention. To simplify the complexity of developing applications, context aware middleware, which introduces context awareness into the traditional middleware, is highlighted to provide a homogeneous interface involving generic context management solutions. This paper provides a survey of state-of-the-art context aware middleware architectures proposed during the period from 2009 through 2015. First, a preliminary background, such as the principles of context, context awareness, context modelling, and context reasoning, is provided for a comprehensive understanding of context aware middleware. On this basis, an overview of eleven carefully selected middleware architectures is presented and their main features explained. Then, thorough comparisons and analysis of the presented middleware architectures are performed based on technical parameters including architectural style, context abstraction, context reasoning, scalability, fault tolerance, interoperability, service discovery, storage, security & privacy, context awareness level, and cloud-based big data analytics. The analysis shows that there is actually no context aware middleware architecture that complies with all requirements. Finally, challenges are pointed out as open issues for future work

An Evidence-based Roadmap for IoT Software Systems Engineering

Context: The Internet of Things (IoT) has brought expectations for software inclusion in everyday objects. However, it has challenges and requires multidisciplinary technical knowledge involving different areas that should be combined to enable IoT software systems engineering. Goal: To present an evidence-based roadmap for IoT development to support developers in specifying, designing, and implementing IoT systems. Method: An iterative approach based on experimental studies to acquire evidence to define the IoT Roadmap. Next, the Systems Engineering Body of Knowledge life cycle was used to organize the roadmap and set temporal dimensions for IoT software systems engineering. Results: The studies revealed seven IoT Facets influencing IoT development. The IoT Roadmap comprises 117 items organized into 29 categories representing different concerns for each Facet. In addition, an experimental study was conducted observing a real case of a healthcare IoT project, indicating the roadmap applicability. Conclusions: The IoT Roadmap can be a feasible instrument to assist IoT software systems engineering because it can (a) support researchers and practitioners in understanding and characterizing the IoT and (b) provide a checklist to identify the applicable recommendations for engineering IoT software systems

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions