User-centred design of flexible hypermedia for a mobile guide: Reflections on the hyperaudio experience

A user-centred design approach involves end-users from the very beginning. Considering users at the early stages compels designers to think in terms of utility and usability and helps develop the system on what is actually needed. This paper discusses the case of HyperAudio, a context-sensitive adaptive and mobile guide to museums developed in the late 90s. User requirements were collected via a survey to understand visitors’ profiles and visit styles in Natural Science museums. The knowledge acquired supported the specification of system requirements, helping defining user model, data structure and adaptive behaviour of the system. User requirements guided the design decisions on what could be implemented by using simple adaptable triggers and what instead needed more sophisticated adaptive techniques, a fundamental choice when all the computation must be done on a PDA. Graphical and interactive environments for developing and testing complex adaptive systems are discussed as a further step towards an iterative design that considers the user interaction a central point. The paper discusses how such an environment allows designers and developers to experiment with different system’s behaviours and to widely test it under realistic conditions by simulation of the actual context evolving over time. The understanding gained in HyperAudio is then considered in the perspective of the developments that followed that first experience: our findings seem still valid despite the passed time

Leveraging and Fusing Civil and Military Sensors to support Disaster Relief Operations in Smart Environments

Natural disasters occur unpredictably and can range in severity from something locally manageable to large scale events that require external intervention. In particular, when large scale disasters occur, they can cause widespread damage and overwhelm the ability of local governments and authorities to respond. In such situations, Civil-Military Cooperation (CIMIC) is essential for a rapid and robust Humanitarian Assistance and Disaster Relief (HADR) operation. These type of operations bring to bear the Command and Control (C2) and Logistics capabilities of the military to rapidly deploy assets to help with the disaster relief activities. Smart Cities and Smart Environments, embedded with IoT, introduce multiple sensing modalities that typically provide wide coverage over the deployed area. Given that the military does not own or control these assets, they are sometimes referred to as gray assets, which are not as trustworthy as blue assets, owned by the military. However, leveraging these gray assets can significantly improve the ability for the military to quickly obtain Situational Awareness (SA) about the disaster and optimize the planning of rescue operations and allocation of resources to achieve the best possible effects. Fusing the information from the civilian IoT sensors with the custom military sensors could help validate and improve trust in the information from the gray assets. The focus of this paper is to further examine this challenge of achieving Civil-Military cooperation for HADR operations by leveraging and fusing information from gray and blue assets

Design Time Methodology for the Formal Modeling and Verification of Smart Environments

Smart Environments (SmE) are intelligent and complex due to smart connectivity and interaction of heterogeneous devices achieved by complicated and sophisticated computing algorithms. Based on their domotic and industrial applications, SmE system may be critical in terms of correctness, reliability, safety, security and other such vital factors. To achieve error-free and requirement-compliant implementation of these systems, it is advisable to enforce a design process that may guarantee these factors by adopting formal models and formal verification techniques at design time. The e-Lite research group at Politecnico di Torino is developing solutions for SmE based on integration of commercially available home automation technologies with an intelligent ecosystem based on a central OSGi-based gateway, and distributed collaboration of intelligent applications, with the help of semantic web technologies and applications. The main goal of my research is to study new methodologies which are used for the modeling and verification of SmE. This goal includes the development of a formal methodology which ensures the reliable implementation of the requirements on SmE, by modeling and verifying each component (users, devices, control algorithms and environment/context) and the interaction among them, especially at various stages in design time, so that all the complexities and ambiguities can be reduced

Users' trust in information resources in the Web environment: a status report

This study has three aims; to provide an overview of the ways in which trust is either assessed or asserted in relation to the use and provision of resources in the Web environment for research and learning; to assess what solutions might be worth further investigation and whether establishing ways to assert trust in academic information resources could assist the development of information literacy; to help increase understanding of how perceptions of trust influence the behaviour of information users

Contribution to improve mobility uses through context-awareness

Dey, in his paper “Towards a Better Understanding of Context and Context-Awareness”, argues that context-awareness is important in applications in which the user’s context changes rapidly, such as in mobile environments for ubiquitous computing. In his paper, Dey defines context as “any information that can be used to characterize the situation of an entity”. In mobile environments, the entity is the mobile device itself. The device is both pervasive and person-­centric; it can continuously capture information about its users and their context through its sensors. The use of context has gained importance in ubiquitous computing since the 1990s, and the technique has recently been used in mobile devices to improve their uses and applications. For mobile context-awareness to become a reality, further research is required, particularly in the field of context prediction, which can expand the possibilities of context-awareness applications by expanding the applications’ situation awareness. In this PhD dissertation, we focus on the use of data obtained through mobile device sensors and user behavior to derive and predict context to improve mobility for both the users’ experience and for the applications’ functionality. We contribute to context-­aware mobile computing by showing how mobile devices can automatically learn from the user’s context and can adapt to improve the mobile experience. We begin our work with a state-­‐of-­‐the-­‐art analysis of “context-awareness” proposals for mobile systems and applications and of the current tools used to infer context from the existing environmental variables. In this dissertation, we analyze the existing gaps in mobile environments and propose solutions to resolve these issues. We first define “context-­awareness” and propose an architecture to predict context from a mobility perspective. Numerous definitions of context, context-­awareness and architectures exist, but few focus exclusively on mobility. Moreover, all of the definitions are oriented towards context inference rather than towards a prediction of future context. We develop a model that captures, processes and unifies variables from heterogeneous sources for use by a machine-­learning algorithm that infers and predicts the context. We also test and benchmark several machine-­learning algorithms in our architecture so that we can recommend those algorithms that we consider most appropriate for inferring context in mobility environments. We propose the combination of on-­‐line prediction algorithms and classifier algorithms to enhance context derivation with future context prediction. We evaluate our proposal utilizing real data from the Reality Mining project, which captures data from the daily mobile usage of c.100 Nokia smart phones during an academic year. We conclude with an example of how to apply our proposed architecture and model, and we demonstrate its enrichment of the search experience with a mobile device by including a “context-awareness” module in mobile search engines. We use Bing as the search engine for all of our search examples. ----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------Describe Dey, en su artículo “Towards a Better Understanding of Context and Context-Awareness” cómo la percepción del contexto (context-awareness) cobra importancia en las aplicaciones en las que el contexto del usuario cambia con rapidez, como es el caso en los entornos móviles de la computación ubicua. Dey, en su artículo, define contexto como “cualquier información que pueda usarse para caracterizar la situación de una entidad”. En entornos móviles, dicha entidad es el dispositivo móvil en sí mismo. Este aparato, al ser ubicuo y centrado en las personas, puede captar continuamente información tanto de los usuarios como de su contexto a través de sus sensores. El uso del contexto ha cobrado importancia en entornos de computación ubicua desde la década de los 90, y esta técnica se ha empleado en dispositivos móviles para mejorar su utilización y aplicación. Para que el área de percepción de contexto se convierta en una realidad, se necesita más investigación, sobre todo en el área de predicción de contexto que amplíe las posibilidades de las aplicaciones que usan información de su contexto. En esta tesis doctoral, nos centramos en el uso de los datos obtenidos de los sensores del móvil y en el comportamiento del usuario, para deducir el contexto presente predecir el contexto futuro, mejorando así la usabilidad del móvil y las funcionalidades de sus aplicaciones. Contribuimos a la computación de percepción del contexto móvil demostrando cómo los dispositivos móviles pueden aprender automáticamente sobre el contexto en el que está el usuario y adaptarse al mismo para mejorar la experiencia de movilidad. Comenzamos nuestro trabajo realizando un estudio del estado del arte de propuestas de percepción de contexto para sistemas y aplicaciones móviles, así como de las herramientas para intuir el contexto a partir de variables existentes del entorno. Analizamos las carencias que tienen en su aplicación al área de la movilidad y hacemos propuestas de cómo resolverlas a lo largo de la tesis. Primero sentamos las bases de la tesis definiendo el concepto de percepción de contexto (“context-awarenes”) y realizamos una propuesta de arquitectura de derivación del contexto actual y predicción del contexto futuro desde un punto de vista de un entorno móvil. Existen muchas definiciones de contexto, percepción de contexto y arquitecturas, pero hay pocas orientadas exclusivamente a movilidad. Además todas se centran en la derivación del contexto actual en vez de hacerlo en la predicción del contexto futuro. Desarrollamos un modelo que nos permite captar, procesar y unificar variables de fuentes heterogéneas para que puedan ser utilizadas por el algoritmo de aprendizaje automático para intuir y predecir contexto. También probamos y referenciamos varios algoritmos de aprendizaje automático para poder recomendar los algoritmos que consideramos más apropiados para intuir contexto en entornos de movilidad. Hacemos una propuesta de mejora en la que combinamos los algoritmos de predicción en línea con los algoritmos de clasificación para poder así predecir el contexto futuro además del contexto actual intuido por el clasificador. Evaluamos nuestra propuesta con datos reales de uso del móvil disponibles en el proyecto “Reality Mining”, en el cual se captan datos de uso diario de móviles de aproximadamente 100 Smartphones Nokia usados por estudiantes universitarios durante un año académico. Finalmente concluimos dando un ejemplo de cómo aplicar nuestra arquitectura y el modelo propuesto demostrando como enriquece la experiencia de búsqueda en un dispositivo móvil el hecho de incluir un módulo de percepción de contexto en los buscadores móviles. Usamos el buscador Bing para todos los ejemplos de búsquedas

Pervasive computing reference architecture from a software engineering perspective (PervCompRA-SE)

Pervasive computing (PervComp) is one of the most challenging research topics nowadays. Its complexity exceeds the outdated main frame and client-server computation models. Its systems are highly volatile, mobile, and resource-limited ones that stream a lot of data from different sensors. In spite of these challenges, it entails, by default, a lengthy list of desired quality features like context sensitivity, adaptable behavior, concurrency, service omnipresence, and invisibility. Fortunately, the device manufacturers improved the enabling technology, such as sensors, network bandwidth, and batteries to pave the road for pervasive systems with high capabilities. On the other hand, this domain area has gained an enormous amount of attention from researchers ever since it was first introduced in the early 90s of the last century. Yet, they are still classified as visionary systems that are expected to be woven into people’s daily lives. At present, PervComp systems still have no unified architecture, have limited scope of context-sensitivity and adaptability, and many essential quality features are insufficiently addressed in PervComp architectures. The reference architecture (RA) that we called (PervCompRA-SE) in this research, provides solutions for these problems by providing a comprehensive and innovative pair of business and technical architectural reference models. Both models were based on deep analytical activities and were evaluated using different qualitative and quantitative methods. In this thesis we surveyed a wide range of research projects in PervComp in various subdomain areas to specify our methodological approach and identify the quality features in the PervComp domain that are most commonly found in these areas. It presented a novice approach that utilizes theories from sociology, psychology, and process engineering. The thesis analyzed the business and architectural problems in two separate chapters covering the business reference architecture (BRA) and the technical reference architecture (TRA). The solutions for these problems were introduced also in the BRA and TRA chapters. We devised an associated comprehensive ontology with semantic meanings and measurement scales. Both the BRA and TRA were validated throughout the course of research work and evaluated as whole using traceability, benchmark, survey, and simulation methods. The thesis introduces a new reference architecture in the PervComp domain which was developed using a novel requirements engineering method. It also introduces a novel statistical method for tradeoff analysis and conflict resolution between the requirements. The adaptation of the activity theory, human perception theory and process re-engineering methods to develop the BRA and the TRA proved to be very successful. Our approach to reuse the ontological dictionary to monitor the system performance was also innovative. Finally, the thesis evaluation methods represent a role model for researchers on how to use both qualitative and quantitative methods to evaluate a reference architecture. Our results show that the requirements engineering process along with the trade-off analysis were very important to deliver the PervCompRA-SE. We discovered that the invisibility feature, which was one of the envisioned quality features for the PervComp, is demolished and that the qualitative evaluation methods were just as important as the quantitative evaluation methods in order to recognize the overall quality of the RA by machines as well as by human beings