With the advent of portable devices (e.g., smartphones) and the advances in wireless networking technologies (e.g., WLAN, GPRS, UMTS), the vision of ubiquitous computing is becoming a reality. It aims to facilitate user tasks through the seamless utilization of heterogeneous computing and communication capabilities (represented as services) available in the environment. Service discovery, which is necessary for achieving the above goal, must be aware of the service's non-functional properties due to the challenges posed by ubiquitous computing, such as device portability and mobility. This thesis proposes an overall solution that supports QoS-aware service discovery in ubiquitous computing environments. Our contribution lies in substantiating QoS awareness in the following three aspects. Firstly, during the process of discovering services, the expiring wireless links resulting from device mobility are identified and avoided since they cause service failures and thus hamper service reliability. Secondly, as mu tiple services can be discovered, a comprehensive utility function is proposed to evaluate services in terms of their various non-functional properties, meanwhile taking into account the service user's preferences among them, for the purpose of selecting the best one. Thirdly, to avoid untrustworthy services, a distributed reputation mechanism is proposed to facilitate the evaluation of the service host's trustworthiness. The above three proposed solutions are extensively evaluated respectively, based on analysis and simulation. They are further incorporated into a middleware that supports QoS aware Web service discovery in ubiquitous computing environments. A prototype implementing the middleware is deployed and evaluated. The results show that the overhead introduced by QoS awareness seems reasonable.

Location is a primary cue in many context-aware computing systems, and is often represented as a global coordinate, room number, or Euclidean distance various landmarks. A user?s concept of location, however, is often defined in terms of regions in which common activities occur. We show how to partition a space into such regions based on patterns of observed user location and motion. These regions, which we call activity zones, represent regions of similar user activity, and can be used to trigger application actions, retrieve information based on previous context, and present information to users. We suggest that context-aware applications can benefit from a location representation learned from observing users. We describe an implementation of our system and present two example applications whose behavior is controlled by users? entry, exit, and presence in the zones.

Thesis (M. Phil.)--University of Hong Kong, 2005. / Title proper from title frame. Also available in printed format.

Recent technological advancements are enabling the vision of pervasive or ubiquitous computing to become a reality. Service discovery is vital in such a computing paradigm, where a great number of devices and software components collaborate unobtrusively and provide numerous services. Current service-discovery protocols do not make use of contextual information in discovering services, and as a result, fail to provide the most appropriate and relevant services for users. In addition, current protocols rely on keyword-based search techniques and do not consider the semantic description of services. Thus, they suffer from poor precision and recall. To address the need for a discovery architecture that supports the envisioned scenarios of pervasive computing, we propose a context-aware service-discovery protocol that exploits meaningful contextual information, either static or dynamic, to provide users with the most suitable and relevant services. The architecture relies on a shared, ontology-based, semantic representation of services and context to enhance precision and recall, and to enable knowledge sharing, capability-based search, autonomous reasoning, and semantic matchmaking. Furthermore, the architecture facilitates a dynamic service-selection mechanism to filter and rank matching services, based on their dynamic contextual attributes, which further enhances the discovery process and saves users time and effort. Our empirical results indicate the effectiveness and feasibility of the proposed architecture.

Pervasive computing is an important trend today. It concerns devices and services in a smart space that interact with users in a simple, natural, and harmonious way. Many problems in this domain have been studied from different perspectives in various projects. However, one important characteristic of pervasive computing, which is how to make it non-intrusive so that users can focus on their tasks, has received little formal attention. Nowadays, many computing entities including smart devices, and software components, are involved in our daily lives, and users need to deal with them as well as with other people. Besides, people are easy to reach with multiple devices. We believe there should be a systematic way to help users avoid intrusive ones. We propose a model for posing and answering two questions: will an interaction intrude on its receiver if delivered, and given that the interaction is deliverable, how can it be delivered effectively and not too overtly? With this model, the intrusion problem is analyzed and the essential factors are identified. A quantitative approach is used, so that factors have quantitative values for comparison and computation. We also apply context to refine them in order to achieve better results. We then illustrate how to materialize the model and build a system whose design is inspired by the Jabber framework that includes a collection of standards, technologies, and projects for instant messaging. The discussion is at a general level that does not depend on Jabber. However, by choosing Jabber in implementation, we reuse existing software and technologies, and benefit from Jabber/XMPP standardization, its low entry barrier for application developers, and its rich community support. The main contributions of our work are two-fold. First, we propose a model for intrusiveness in pervasive computing. Second, we address the problem at the system level by designing and realizing it. We also make use of standardized instant-messaging technologies, more precisely Jabber, in the system instantiation to reuse existing software, making the system more flexible and extensible.

The smarter homes of tomorrow promise to increase comfort, aid elderly and disabled people, and help inhabitants save energy. Unfortunately, smart homes today are far from this vision – people who already live in such a home struggle with complicated user interfaces, inflexible home configurations, and difficult installation procedures. Under these circumstances, smart homes are not ready for mass adoption. This dissertation addresses these issues by proposing two smart assistants for smart homes. The first assistant is a recommender system that suggests useful services (i.e actions that the home can perform for the user). The recommended services are fitted to the user’s current situation, habits, and preferences. With these recommendations it is possible to build much simpler user interfaces that highlight the most interesting choices currently available. Configuration becomes much more flexible: since the recommender system automatically learns user habits, user routines no longer have to be manually described. Evaluations with two smart home datasets show that the correct service is included in the top five recommendations in 90% of all cases. The second assistant addresses the difficult installation procedures. The unique feature of this assistant is that it removes the need for manually describing device functionalities (such descriptions are needed by the recommender system). Instead, users can simply plug in a new device, begin using it, and let the installation assistant identify what the device is doing. The installation assistant has minimal requirements for manufacturers of smart home devices and was successfully integrated with an existing smart home. Evaluations performed with this smart home show that the assistant can quickly and reliably learn device functionalities. / <p>QC 20130924</p>

Recent technological advancements are enabling the vision of pervasive or ubiquitous computing to become a reality. Service discovery is vital in such a computing paradigm, where a great number of devices and software components collaborate unobtrusively and provide numerous services. Current service-discovery protocols do not make use of contextual information in discovering services, and as a result, fail to provide the most appropriate and relevant services for users. In addition, current protocols rely on keyword-based search techniques and do not consider the semantic description of services. Thus, they suffer from poor precision and recall. To address the need for a discovery architecture that supports the envisioned scenarios of pervasive computing, we propose a context-aware service-discovery protocol that exploits meaningful contextual information, either static or dynamic, to provide users with the most suitable and relevant services. The architecture relies on a shared, ontology-based, semantic representation of services and context to enhance precision and recall, and to enable knowledge sharing, capability-based search, autonomous reasoning, and semantic matchmaking. Furthermore, the architecture facilitates a dynamic service-selection mechanism to filter and rank matching services, based on their dynamic contextual attributes, which further enhances the discovery process and saves users time and effort. Our empirical results indicate the effectiveness and feasibility of the proposed architecture.

Pervasive computing is an important trend today. It concerns devices and services in a smart space that interact with users in a simple, natural, and harmonious way. Many problems in this domain have been studied from different perspectives in various projects. However, one important characteristic of pervasive computing, which is how to make it non-intrusive so that users can focus on their tasks, has received little formal attention. Nowadays, many computing entities including smart devices, and software components, are involved in our daily lives, and users need to deal with them as well as with other people. Besides, people are easy to reach with multiple devices. We believe there should be a systematic way to help users avoid intrusive ones. We propose a model for posing and answering two questions: will an interaction intrude on its receiver if delivered, and given that the interaction is deliverable, how can it be delivered effectively and not too overtly? With this model, the intrusion problem is analyzed and the essential factors are identified. A quantitative approach is used, so that factors have quantitative values for comparison and computation. We also apply context to refine them in order to achieve better results. We then illustrate how to materialize the model and build a system whose design is inspired by the Jabber framework that includes a collection of standards, technologies, and projects for instant messaging. The discussion is at a general level that does not depend on Jabber. However, by choosing Jabber in implementation, we reuse existing software and technologies, and benefit from Jabber/XMPP standardization, its low entry barrier for application developers, and its rich community support. The main contributions of our work are two-fold. First, we propose a model for intrusiveness in pervasive computing. Second, we address the problem at the system level by designing and realizing it. We also make use of standardized instant-messaging technologies, more precisely Jabber, in the system instantiation to reuse existing software, making the system more flexible and extensible.

Peoples daily lives and experiences often contain memories and information that they may want to recall again at a later time. Human memory, however, has its limitations and many times it alone may not be sufficient. People sometimes have difficulty recalling salient information and can forget important details over time. To complement what they can remember naturally, people must expend much time and manual effort to record desired content from their lives for future retrieval. Unfortunately, manual methods for capturing information are far from ideal. Over the years, ubiquitous computing researchers have constructed devices and applications to support the automated capture of live experiences and access to those records. At Georgia Tech, we have also investigated the benefits of automated capture and access in over half a dozen projects since 1995. As we encountered challenges in developing these systems, we began to understand how the difficulty of building capture and access systems can prevent exploration of the hard issues intertwined with understanding how capture impacts our everyday lives. These challenges illustrate the need for support structures in building this class of ubiquitous computing systems. This dissertation presents a set of abstractions for a conceptual framework and a focused design process that encourages designers to decompose the design of capture and access applications into a set of concerns that will be easier to develop and to manage. In addition, an implementation of the framework called the INCA Toolkit is discussed, along with a number of capture and access applications that have been built with it. These applications illustrate how the toolkit is used in practice and supports explorations of the capture and access design space.

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