Context-aware mobile applications design: implications and challeges for a new indusy

Context-aware computing is slowly becoming the new mobile paradigm in which applications can discover and use information “out and about”. Typical sources of knowledge about context are the device’s location, data about the environment at large, the mobile device’s prior activity log and even the user’s biometrics. The mobile industry agrees that this paradigm improves the appeal and value of applications by personalising and adapting them to the context in which they run. However, capturing contextual information and processing it to enhance or create a new application is a daunting task: it involves scattered systems and infrastructures and an increasingly wide array of heterogeneous data, architectures and technological tools. In this paper, we explore and analyse existing mobile context-aware applications and the proposed frameworks that enable them. The paper aims to clarify the echnological choices behind context-aware mobile applications and the challenges that still remain ahead for this area to fulfil the promises it offers

Integration of passive RFID location tracking for real-time visualization in building information models (BIM)

Navigation through large and unfamiliar facilities with labyrinths of corridors and rooms is difficult and often results in a person being lost. Additionally, locating a specific utility within a facility is often a tough task. The hypothesis tested in this research is that integrating real-time automated sensing technology and a Building Information Model will provide real time visualization that can assist in localization and navigation of a facility. The scope of this research is facility maintenance management during the Operation and Maintenance (O&M) phase of a facility. The thesis demonstrates how the integration of passive Radio Frequency Identification (RFID) tracking technology and Building Information Modeling (BIM) can assist in facilities maintenance management. The objectives of this research included 1) developing a framework that utilizes the integration of commercially-available RFID and a BIM model; 2) evaluating the framework for real-time resource location tracking within an indoor environment; and 3) developing an algorithm for real-time localization and visualization in a BIM model. A prototype application has been developed that simultaneously connects the RFID readers, a database, and a BIM model. The goal of this system is to have a real-time localization accuracy of 3 meters at 95% confidence. Testing was conducted in laboratory conditions, and the results show that the system error was within the 3 meters goal.M.S

Conception et mise en place sur le web d'un système interactif d'aide à la décision utilisant des bases de connaissances

La facilité grandissante de la communication entre les intervenants d’une équipe de projet n’a pas résolu le problème de la capitalisation des connaissances malgré l’apparition de l’Internet. La construction d’une logique d’exécution adaptée aux projets lors de la phase de conception est souvent une étape répétitive. Nous chercherons à fournir une méthodologie de conception d’un outil d’aide à la décision basé sur des connaissances en ordonnancement dont le but est de générer des échéanciers. Ces connaissances seront capitalisées à une grande échelle grâce au Web. Notre premier objectif sera de stocker et gérer des connaissances portant sur la logique d’ordonnancement. Les planificateurs pourront contribuer à l’amélioration continue du système de décision en maintenant les connaissances sur une base publique. Nous mettrons ensuite en place un protocole d’évaluation et de consolidation des connaissances par des experts désignés au sein du système. Enfin nous présenterons l’outil d’aide à la décision basé sur les connaissances, pour la génération d’une logique d’ordonnancement. Nous développerons donc une méthodologie de conception d’un SIAD dans un environnement Web 2.0. Nous étudierons la conception de la base de connaissances, du système d’inférence, de l’outil d’ingénierie des connaissances et des comportements de l’interface. L’application de cette méthodologie aboutira sur la conception d’un prototype. Une validation sera effectuée à travers la construction d’une base de connaissances de 4000 activités permettant de s’assurer de la fonctionnalité du prototype. L’environnement Web permettra ensuite de le valider à grande échelle à travers des tests d’application effectués en simultané à travers le monde

Information behaviour of construction project actors

Construction is one of the largest industry sectors in terms of size and output in the United Kingdom (UK). The sector contributes about 10% directly to the UK’s Gross Domestic Product (GDP) and drives historical GDP growth. However, Construction projects and Organisations continue to underperform at significant levels which is underpinned by actors' Information Behaviours (IBs). [Continues.

FULFIL: production control system for managing workflow, quality and flexibility in construction

Construction is an important sector of every economy. Evidence of below par performance in construction projects has been recognized by government and industry bodies. Traditional control systems with project-based approaches have not overcome endemic problems in the industry such as cost and schedule overruns and quality issues. The innovative control system proposed in this research takes a production-based approach (as opposed to a project-based approach). The FULFIL system, aims to stabilise the work f low, minimise interruptions caused by q u a l ity problems and maximise the f lexib il ity in process design. The FULFIL system of production control is based on four pillars: queuing theory, transformation/flow/value theory, factory physics, and theory of constraints. In order to propose the principles of the FULFIL system, analytical and simulation models of construction production are developed. This thesis is driven by seven research objectives: 1) To analyse the impact of workflow variability on construction production. The research results confirm that performance in construction is adversely affected by workflow variability caused by factors such as rework and capacity imbalance. 2) To establish a tailored modelling approach that precisely quantifies variability in the workflow amongst specialty contractors. This thesis proposes a new modelling approach using a relative indicator of variability, which takes both the standard deviation of time between completions and average processing times into consideration. 3) To explore approaches to stabilising the workflow in construction. Two principles for stabilising the workflow are proposed and tested. Limiting the number of jobs under construction and integrating work processes are confirmed to prevent frequent work starvations and overloads in the production network. 4) To explore opportunities for variability reduction in construction. Tangible performance measures in due-date-driven and rate-driven production are compared. FULFIL analysis shows that when new construction is authorised, not scheduled, the production is more efficient, controllable and robust against control errors. 5) To explore opportunities for variability buffering in construction production. The user-friendly framework for defining optimum-sized capacity buffers in the FULFIL system is developed and tested. 6) To explore opportunities for improving the flexibility in construction processes. Two sources of inflexibility in process designs are analysed and addressed. Depending on the level of capacity imbalance and processing time variability, different cross-training strategies are proposed and tested. When processing times are variable, capacity should be shifted in an indirect path to the bottlenecks. 7) To explore opportunities for reducing interruptions caused by quality problems and rework. Three variables of rework are analysed and strategies to address them are proposed. Rework duration and intervals, and the timeframe of call-backs are shown to have significant impacts on the performance of construction and can be effectively offset by using FULFIL protocols. This thesis contributes to the body of knowledge by developing a deeper insight into the dynamics of workflow, quality and flexibility management, and the resulting impacts on construction plan reliability. Furthermore it can assist industry practitioners in finding the most cost-effective way to operate and control production networks. Easy-to-use models developed and tested in this thesis can improve the traditional project-based controls in construction

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

Implementation of smart devices in the construction industry

A thesis submitted in partial fulfilment of the requirements of the University of Wolverhampton for the degree of Doctor of Philosophy.The construction industry has a fragmented nature, which accounts for the highest degree of decentralisation of information and the highest mobile content access. The exchange of information made possible by smart devices. This creates an opportunity to enhance productivity and communication among stakeholders of the construction industry. Firstly, this thesis explored the concept of smart devices. Secondly, the drivers, challenges and Critical Success Factors for implementing smart devices were investigated. This study adopted a qualitative approach using semi-structured interviews. A total of Thirty-nine interviewees which includes professionals from the construction sector of the Dominican Republic (DR) and the United Kingdom (UK) were interviewed. Thematic analysis was used to analyse the collected data. The drivers for the adoption of smart devices were grouped into internal and external drivers. The challenges found in the interviews were grouped into three categories, namely, economic, cultural and technological. The Critical Success Factors (CSFs) for implementing smart devices in the construction industry are leadership, training and development, organisational culture, technology awareness, cost, company size and usability. These findings were used to develop a strategic framework which has two sub-frameworks. This study concluded that a specific culture must be adopted on behalf of the government and construction companies to successfully adopt smart devices. Furthermore, this investigation found various similarities and differences regarding the drivers, challenges and CSFs for implementing smart devices in the UK and the DR. This study recommends integrating smart devices in data collection techniques in academia. Also, for construction companies to embrace technological innovation it is recommended to be willing to start new ventures, to be open to the participation of all members of the company, and be creative and client-oriented.Ministerio de Educacion Superior, Ciencia y Tecnología (MESCyT) - Ministry of Higher education, Science and technology of the Dominican Republic