The versatility and dimension of smart phone applications is   increasing at magnificent rate and getting more and more advanced in a level that could solve complicated real time tasks. One of the important factors for such advancement has been the powerful sensors embedded on a Smartphone devices and sensory networks. Moreover, Context and Context-awareness would have remained a myth without the advent of sensors. The objective of this thesis has been to contribute to the research work carried out under the MediaSense project. Accordingly, the ultimate purpose of the thesis has been to evaluate and study the feasibility of the adaptive context view proposed in MediaSense Platform. In precise words, the thesis has done three core tasks. Firstly, the theoretical presentation of related works and the significance of the research question have been discussed through various social applications. Secondly, a proof-of-concept application has been developed to simulate what has been proposed in the research work. Finally, Android application has been designed and implemented in order to evaluate and study the techniques presented in a practical scenario. Moreover, in the android application known as SundsvallBIGBuddies, we have used the extensions designed for the existing MediaSense platform. The impact of using Android app relaying on a continuous stream of context data has been presented using graphs and tables.  In order to study the impact we used smart phone and tablets from Samsung.

Because electronic commerce has been flourishing in recent year, the amount and the variety of information on the web have also been rapidly increasing. However, many problems occur as the result of information overload. This thesis is to study the issue of information overload in the field of multimedia that covers not only medium of diffuse knowledge but also entertainment of everyday life. The main goal of this work is to use personalized recommendation technologies to help users select multimedia he is interested in. The thesis investigates two types of personalized recommendation: tag-based recommendation and context-aware recommendation. Regarding the former kind of recommendation, Folksonomy is the popular Web2.0 application that allows users tagging items to indicate the corresponding characteristics. These tags, provided by the users, directly or indirectly reflect his personal interests. Therefore the recommendation performance is enhanced when the tags are used with computational methods. On the other hand, the latter kind focuses on the contents and the relevant situations, because what multimedia is considered suitable for users can be different under different situations. The advantages of the personalized recommendation technology can improve performance of recommendation and take the context into account at the same time. Meanwhile this study also implements a working system for personalized multimedia recommendation.

Doctor en Ciencias, Mención Computación / En escenarios ubicuos, conocer la posición de un dispositivo es imperativo para proveer al usuario de servicios personalizados basados en location awareness, un aspecto de diseño clave en la mayoría de las aplicaciones ubicuas que dependiente de las capacidades de los dispositivos para sentir cambios en su ambiente de trabajo. No existe una solución que aborde todos los tipos de posicionamiento, pues distintos tipos de aplicaciones requieren información de posicionamiento variada en términos de exactitud, precisión, complejidad, escalabilidad, y costo. En escenarios ubicuos estándar, suele más de una estrategia de posicionamiento disponible, pero en general los dispositivos móviles no son capaces de determinar cuál es la más adecuada dado el contexto de trabajo del usuario. Además, este contexto está en constante cambio a medida que el usuario se mueve, perdiéndose conexiones a ciertos elementos del ambiente y ganándose otras. Aunque existen soluciones que abordan el posicionamiento en escenarios específicos de manera efectiva, hacerlo tomando en cuenta la mayoría de estos escenarios sigue siendo un problema abierto. La propuesta presentada en esta tesis es un modelo de posicionamiento sensible al contexto (CAMPOS), que permite a dispositivos que realizan actividades débilmente acopladas en escenarios ad-hoc al aire libre, elegir estrategias de posicionamiento adecuadas a su contexto, basado en variables contextuales predefinidas. El modelo elabora un "catálogo" de estrategias disponibles y los puntos de referencia, usando las variables contextuales como entrada para un clasificador RandomForest, el cual determina un orden de idoneidad para las estrategias de posicionamiento, lo que permite acceder a estrategias ajustadas al contexto del usuario. CAMPOS fue diseñado usando una metodología iterativa basada en casos de estudio. Primero, se realizó una revisión de literatura para determinar umbrales y valores promedio iniciales para las métricas y variables del modelo. Luego, se implementaron dos conjuntos de simulaciones; el primero para experimentar con distintos escenarios y configuraciones de dispositivos; y el segundo para evaluar el rendimiento del modelo. La batería de pruebas incluyó 27 plantillas de escenario, ejecutadas 15 veces para un total de 405 experimentos. Las variables observadas incluyen el efecto de variar la cantidad de beacons (dispositivos con capacidad de posicionamiento), la cantidad total de dispositivos, y el rango de comunicación. Todos los experimentos presentados en este trabajo se realizaron utilizando el ns-3, un simulador de redes de eventos discretos orientado a la investigación. El aporte de CAMPOS reside en que no es una nueva propuesta de estrategia de posicionamiento, ni busca mejorar el estado del arte en términos de precisión. En vez de ello, proporciona a los dispositivos de una red los medios para censar su entorno y determinar qué estrategia de posicionamiento es más adecuada para su contexto. Además, dado que CAMPOS es independiente del proceso formal de posicionamiento, si apareciesen nuevas estrategias de posicionamiento en el futuro, éstas podrían añadirse a CAMPOS con relativa facilidad, permitiendo que los dispositivos potencialmente tengan acceso a dichas estrategias a través del modelo. / El trabajo presentado en esta tesis ha sido financiado por el Programa de Becas NIC Chile, y parcialmente por Fondecyt (Chile), Proyecto 1150252

Increased life expectancy has led to a 15% growth in the population of seniors (aged 65 and above) in Canada, in the last 5 years and this trend is expected to grow. However, the provision of personalized care is bottlenecked, due a severe shortage of formal caregivers in the healthcare industry. Technological solutions are proposed to supplement or replace human care, but have not been widely accepted due to their inability of dynamically adapting to user needs and context of respective situations. Affective data (i.e., emotions and moods of individuals), can be utilized to induce context-awareness and artificial emotional intelligence in such technological solutions, and thereby provide personalized support. Moreover, the capacity of brain to process affective phenomenon can serve as an indicator of onsetting neuro-degenerative diseases. This research thoroughly investigated what affect is, and how it can be used in computing in real-life scenarios. Particularly, evidence was obtained on which biological signals collected using a wearable sensor device were capable of capturing the arousal dimension of affective states (emotions and moods) of individuals. Furthermore, a qualitative study was conducted with older adults using semi-structured interviews, to determine the feasibility and acceptability of different self-report measures of mood, which are crucial to capture the valence dimension of affect. As the hypothesis that older adults would prefer a pictorial measure to self-report their mood failed, we proposed an adjective-based mood reporting instrument prototype, and laid down implications for future research.

In modern software development, the gap between software requirements and implementation is not always conciliated. Typically, for Web services-based context-aware systems, reconciling this gap is even harder. The aim of this research is to explore how software reengineering can facilitate the reconciliation between requirements and implementation for the said systems. The underlying research in this thesis comprises the following three components. Firstly, the requirements recovery framework underpins the requirements elicitation approach on the proposed reengineering framework. This approach consists of three stages: 1) Hypothesis generation, where a list of hypothesis source code information is generated; 2) Segmentation, where the hypothesis list is grouped into segments; 3) Concept binding, where the segments turn into a list of concept bindings linking regions of source code. Secondly, the derived viewpoints-based context-aware service requirements model is proposed to fully discover constraints, and the requirements evolution model is developed to maintain and specify the requirements evolution process for supporting context-aware services evolution. Finally, inspired by context-oriented programming concepts and approaches, ContXFS is implemented as a COP-inspired conceptual library in F#, which enables developers to facilitate dynamic context adaption. This library along with context-aware requirements analyses mitigate the development of the said systems to a great extent, which in turn, achieves reconciliation between requirements and implementation.

Mobile applications can benefit from increased awareness of the device's context. Unfortunately, existing solutions for inferring context require special purpose sensors or beacons on the mobile devices or in the physical environment. This requirement significantly limits the deployment of these solutions. In this thesis, I argue that mobile devices can infer a substantial amount of their context by leveraging their existing wireless interfaces to monitor ambient radio sources, such as GSM cell towers or WiFi access points. I focus on two important problems in context-aware computing: localization of mobile devices and detecting proximity between mobile devices for authentication purposes. Specifically, I present an accurate localization system based on fingerprinting of GSM signals. I show that the key to more accurate GSM localization is the use of wide signal strength fingerprints that include readings from a large number of base stations. Next, I present a method that addresses the key drawback of fingerprint-based localization systems - the need to collect extensive measurements to train the system in every target environment. Finally, I show how radio environment sensing can be used to secure the communication of devices that come within close proximity. Removing the need for additional hardware on the mobile devices and in the physical environment renders the approach that I present amenable for widespread deployment.

The ever increasing capabilities of mobile devices such as smartphones and their ubiquity in daily life has resulted in a large and interesting body of research into context awareness { the `awareness of a situation' { and how it could make people's lives easier. There are, however, diculties involved in realising and implementing context aware systems in the real world; particularly in a mobile environment. To address these diculties, this dissertation tackles the broad problem of designing and implementing mobile context aware systems in the eld. Spanning the elds of Articial Intelligence (AI) and Human Computer Interaction (HCI), the problem is broken down and scoped into two key areas: context sensing and interactive intelligence. Using a simple design model, the dissertation makes a series of contributions within each area in order to improve the knowledge of mobile context aware systems engineering. At the sensing level, we review mobile sensing capabilities and use a case study to show that the everyday calendar is a noisy `sensor' of context. We also show that its `signal', i.e. useful context, can be extracted using logical data fusion with context supplied by mobile devices. For interactive intelligence, there are two fundamental components: the intelligence, which is concerned with context inference and machine learning; and the interaction, which is concerned with user interaction. For the intelligence component, we use the case of semantic place awareness to address the problems of real time context inference and learning on mobile devices. We show that raw device motion { a common metric used in activity recognition research { is a poor indicator of transition between semantically meaningful places, but real time transition detection performance can be improved with the application of basic machine learning and time series processing techniques. We also develop a context inference and learning algorithm that incorporates user feedback into the inference process { a form of active machine learning. We compare various implementations of the algorithm for the semantic place awareness use case, and observe its performance using a simulation study of user feedback. For the interaction component, we study various approaches for eliciting user feedback in the eld. We deploy the mobile semantic place awareness system in the eld and show how dierent elicitation approaches aect user feedback behaviour. Moreover, we report on the user experience of interacting with the intelligent system and show how performance in the eld compares with the earlier simulation. We also analyse the resource usage of the system and report on the use of a simple SMS place awareness application that uses our system. The dissertation presents original research on key components for designing and implementing mobile context aware systems, and contributes new knowledge to the eld of mobile context awareness.

This thesis investigates whether the use of hand-held technology affects motivation and learning in science. An innovative mixed methods approach was used to provide new insights into an emerging area of research. First, two pilot observational studies were conducted, which aimed to establish how a school currently uses hand held dataloggers, and gain further insight into how learners respond to this technology. This was followed by a primarily quantitative experiment that was concerned with the role of data ownership and the impact of ‘seams’ on the transformation process of the collected data. The results indicated that a hands-on experience increased confidence among students in explaining their own data, as opposed to data collected by someone else. A third study was designed to compare how student motivation and learning were affected when carrying out the same inquiry task either with or without the support of dataloggers. The results revealed no difference in accuracy or motivation for learning. The final, fourth, study was a longitudinal study designed in collaboration with a secondary science teacher, comparing three conditions: the inclusion of cameras to support student reflection, the inclusion of both cameras and the use of dataloggers to support teaching, and a control condition where the lessons were not changed. This study found that inclusion of dataloggers into modules led to increased assessment scores, while the use of cameras indicated that students are adept at taking relevant photos, and did not suffer from an extensive novelty effect. The results highlighted the importance of using a range of methods and tools for teaching students. The thesis concludes with recommendations and future research ideas, including exploring how data is visualised and the role of physical context. Of key importance is that future work is conducted in collaboration with educators in the wild.

<p>This project aims to describe how workflow systems can integrate and make use of context information from context rich environments, to enhance the execution of workflow processes. Context information can for example be used to control transitions between activities, activity enactment and process/activity coordination. A dynamic contextual environment also requires that a workflow system is capable of responding to contextual events. A set of requirements for a context-aware workflow system, based on existing workflow standards, theory behind context-aware computing and activity theory, will be presented and elaborated. Prototypes, which illustrate how these requirements can be implemented in a standard based workflow system, are also provided. Based on the solutions presented in the prototypes, a new interface for a workflow enactment service is presented. This new interface serves as the link between the contextual environment and the workflow system. We also present a solution for handling context related exception states. The definition of basic terms in workflow systems are expanded to better support context-aware behaviour. Ideas and solutions for more complex requirements not met in our prototypes are also discussed, such as situated activity coordination.</p>

<p>This paper intends to describe the work and result of the design project Meet App. Meet App lets users interact around their current locations in a direct manner. The user experience is evaluated to get an understanding of the usefulness and interaction with this type of design. The project is related to the context-awareness research field where findings put the project in a greater whole. The result indicates usefulness and enjoyment interacting with the application, but because of the low number of participants the findings cannot be validated.</p>