Context-aware adaptation in DySCAS

DySCAS is a dynamically self-configuring middleware for automotive control systems. The addition of autonomic, context-aware dynamic configuration to automotive control systems brings a potential for a wide range of benefits in terms of robustness, flexibility, upgrading etc. However, the automotive systems represent a particularly challenging domain for the deployment of autonomics concepts, having a combination of real-time performance constraints, severe resource limitations, safety-critical aspects and cost pressures. For these reasons current systems are statically configured. This paper describes the dynamic run-time configuration aspects of DySCAS and focuses on the extent to which context-aware adaptation has been achieved in DySCAS, and the ways in which the various design and implementation challenges are met

Towards Activity Context using Software Sensors

Service-Oriented Computing delivers the promise of configuring and reconfiguring software systems to address user's needs in a dynamic way. Context-aware computing promises to capture the user's needs and hence the requirements they have on systems. The marriage of both can deliver ad-hoc software solutions relevant to the user in the most current fashion. However, here it is a key to gather information on the users' activity (that is what they are doing). Traditionally any context sensing was conducted with hardware sensors. However, software can also play the same role and in some situations will be more useful to sense the activity of the user. Furthermore they can make use of the fact that Service-oriented systems exchange information through standard protocols. In this paper we discuss our proposed approach to sense the activity of the user making use of software

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

A user-centric approach for developing and deploying service front-ends in the future internet of services

Service-Oriented Architectures (SOAs) based on web services have attracted a great deal of interest and Internet Technology (IT) investment over the last few years, principally in the context of business-to-business integration within corporate intranets. However, they are now evolving and breaking through enterprise boundaries in a revolutionary attempt to make the approach pervasive. This is leading to what we call a user-centric SOA. A user-centric SOA is an SOA conceived as an internet of services made up of compositional resources empowering end users to collaboratively remix and ubiquitously exploit these resources. In this paper we explore the architectural basis, technologies, frameworks and tools considered necessary to tackle this novel vision of SOA. We also present the rationale behind Ez Web/FAST, an ongoing EU-funded project whose first outcomes could serve as a preliminary proof of concept

Paving the way to collaborative context-aware mobile applications: a case study on preventing worsening of allergy symptoms

En los últimos años, la evolución de los teléfonos inteligentes y sus aplicaciones de software ha crecido exponencialmente; junto con el avance del Internet de las Cosas y las ciudades inteligentes, ha generado una gran demanda de servicios y aplicaciones en estos dominios. Aunque la amplia gama de aplicaciones móviles es incuestionable, los ciudadanos ya exigen que las aplicaciones se adapten a sus necesidades y situaciones específicas en tiempo real, es decir, que sean conscientes del contexto. Sin embargo, las aplicaciones móviles conscientes del contexto a menudo son muy limitadas y pierden la oportunidad de beneficiarse de la retroalimentación proporcionada por la colaboración ciudadana. Para llenar este vacío, este documento propone una arquitectura de software y una aplicación móvil colaborativas y conscientes del contexto. En particular, los hemos implementado en el ámbito de la e-salud, más específicamente en el área de las alergias estacionales, que causan que las personas alérgicas experimenten síntomas molestos que podrían evitarse si tuvieran acceso a información sobre el polen en tiempo real. Además, también se beneficiarán de la colaboración ciudadana a través del conocimiento de los síntomas que otras personas alérgicas con la misma alergia y en la misma ubicación están experimentando. Para ello, los usuarios podrán proporcionar sus síntomas en cualquier momento a través de su aplicación móvil y la arquitectura propuesta procesará constantemente esa información en tiempo real, enviando notificaciones a los usuarios tan pronto como se vea que los síntomas reportados superan un cierto umbral. Se han probado el rendimiento de la arquitectura, el consumo de recursos de la aplicación y una encuesta de satisfacción sobre la usabilidad y utilidad de la aplicación; todos los resultados han sido completamente satisfactorios.In recent years, the evolution of smartphones and their software applications has grown exponentially; together with the advance of the Internet of Things and smart cities, it has raised huge demand for services and applications in these domains. Although the wide range of mobile applications is unquestionable, citizens already demand that applications adapt to their specific needs and situations in real time, that is, that they are context-aware. However, context-aware mobile applications are often very limited and miss out on the opportunity of benefiting from feedback provided by citizen collaboration. In order to fill this gap, this paper proposes a context-aware and collaborative software architecture and mobile application. In particular, we have implemented them in the scope of e-health, more specifically in the area of seasonal allergies, which cause allergic people to experience annoying symptoms that could be avoided by having access to pollen information in real time. Furthermore, they will also benefit from citizen collaboration through the knowledge of the symptoms other allergic people with the same allergy and in the same location are experiencing. To this end, users will be able to provide their symptoms at any time through their mobile application and the proposed architecture will constantly process that information in real time, sending notifications to users as soon as reported symptoms are seen to exceed a certain threshold. The architecture’s perfor mance, the application’s resource consumption and a satisfaction survey of the app’s usability and usefulness have been tested; all results have been fully satisfactoryThis work was supported by the Spanish Ministry of Science and Innovation and the European Union FEDER Funds [grant numbers RTI2018-093608-B-C33, RED2018-102654-T

TRULLO - local trust bootstrapping for ubiquitous devices

Handheld devices have become sufficiently powerful that it is easy to create, disseminate, and access digital content (e.g., photos, videos) using them. The volume of such content is growing rapidly and, from the perspective of each user, selecting relevant content is key. To this end, each user may run a trust model - a software agent that keeps track of who disseminates content that its user finds relevant. This agent does so by assigning an initial trust value to each producer for a specific category (context); then, whenever it receives new content, the agent rates the content and accordingly updates its trust value for the producer in the content category. However, a problem with such an approach is that, as the number of content categories increases, so does the number of trust values to be initially set. This paper focuses on how to effectively set initial trust values. The most sophisticated of the current solutions employ predefined context ontologies, using which initial trust in a given context is set based on that already held in similar contexts. However, universally accepted (and time invariant) ontologies are rarely found in practice. For this reason, we propose a mechanism called TRULLO (TRUst bootstrapping by Latently Lifting cOntext) that assigns initial trust values based only on local information (on the ratings of its user’s past experiences) and that, as such, does not rely on third-party recommendations. We evaluate the effectiveness of TRULLO by simulating its use in an informal antique market setting. We also evaluate the computational cost of a J2ME implementation of TRULLO on a mobile phone

Context-awareness to increase inclusion of people with DS in society

Assistive technologies have the potential to enhance the quality of life of citizens. Most especially of interest are those cases where a person is affected by some physical or cognitive impairment. Whilst most work in this area have been focused on assisting people indoors to support their independence, the POSEIDON project is focused on empowering citizens with Down’s Syndrome to support their independence outdoors. This paper explains the POSEIDON module which we are in the process of developing to make the system context-aware,reactive and adaptive

Towards a Semantic-Aware Collaborative Working Environment

Collaborative Working Environments (CWEs) enable an efficient collaboration between professionals, specially those settled in different locations of a company or stakeholders from different companies. This can be of great help for small and medium enterprises (SMEs), as an effective way to share information. However, it can be difficult for SMEs to have access to a fully integrated CWE providing different tools (e.g., videoconferencing, instant messaging, etc.). Currently, they may define a CWE as a combination of heterogeneous and non-integrated tools which are not able to share information between them. An integrated CWE would provide SMEs with the necessary means to collaborate, making information exchange easier.&nbsp

Using Probabilistic Temporal Logic PCTL and Model Checking for Context Prediction

Context prediction is a promoting research topic with a lot of challenges and opportunities. Indeed, with the constant evolution of context-aware systems, context prediction remains a complex task due to the lack of formal approach. In this paper, we propose a new approach to enhance context prediction using a probabilistic temporal logic and model checking. The probabilistic temporal logic PCTL is used to provide an efficient expressivity and a reasoning based on temporal logic in order to fit with the dynamic and non-deterministic nature of the system's environment. Whereas, the probabilistic model checking is used for automatically verifying that a probabilistic system satisfies a property with a given likelihood. Our new approach allows a formal expressivity of a multidimensional context prediction. Tested on real data our model was able to achieve 78% of the future activities prediction accuracy