In grids, delegation is a key facility that can be used to authenticate and authorize requests on behalf of disconnected users. In current grid systems,delegation is either performed dynamically, in an unrestricted manner, or by a secure but static method. Unfortunately, the former compromises security and the latter cannot satisfy the requirements of dynamic grid application execution. Therefore, development of a delegation framework that enables a restricted and flexible delegation mechanism becomes increasingly urgent as grids are adopted by new communities and grow in size. The main barriers in development of such a mechanism are the requirements for dynamic execution of grid applications, which make it difficult to anticipate required access rights for completing tasks in advance. Another significant architectural requirement in grids is federated security and trust. A considerable barrier to achieving this is cross-organizational authentication and identification. Organizations participating in Virtual Organizations (VOs) may use different security infrastructures that implement different protocols for authentication and identification; thus, there exists a need to provide an architectural mechanism for lightweight, rapid and interoperable translation of security credentials from an original format to a format understandable by recipients. This thesis contributes the development of a delegation framework that utilizes a mechanism for determining and acquiring only required rights and credentials for completing a task, when they are needed. This is what we call an on-demand delegation framework that realizes a bottom-up delegation model and provides a just-in-time acquisition of rights for restricted and dynamic delegation. In this thesis, we further contribute the development of a credential mapping mechanism using off-the-shelf standards and technologies. This mechanism provides support for an on-the-fly exchange of different types of security credentials used by the security mechanisms of existing grids. / QC 20100622

In this thesis, an emergency management system taking advantage of mobile computing and its awareness on context is provided. The framework primarily aims to create an infrastructure for acquiring implicit and explicit data about an emergency situation by using capabilities of smart mobile devices and converting them into value-added information to be used in phases of emergency management. In addition to conceptual description of the framework, a real prototype implementation is developed and successful application of the framework is demonstrated. Sample cases are analyzed in conjunction with the prototype and an experiment for reporting an emergency situation is carried out by a group of participants in order to demonstrate the applicability and feasibility of the framework. Data collected during the experiment are examined in order to determine the advantages of the proposed system in comparison with traditional emergency reporting efforts.

The dream of weaving technology into our everyday fabric of life is recently being made possible by advances in ubiquitous computing and sensor technologies. Countless sensors of various sizes have made their way into everyday commercial applications. Many projects aim to explore new ways to utilize these new technologies to aid and interact with the general population. Context-aware systems use available context information to assist users automatically, without explicit user input. By inferring user intent and configuring the system proactively for each user, context-aware systems are an integral part of achieving user-friendly ubiquitous computing environments. <br /><br /> A common issue with building a distributed context-aware system is the need to develop a supporting infrastructure providing features such as storage, distributed messaging, and security, before the real work on processing context information can be done. This thesis proposes a coordination framework that provides an effective common foundation for context-aware systems. The separation between the context-processing logic component and the underlying supporting foundation allows researchers to focus their energy at the context-processing part of the system, instead of spending their time re-inventing the supporting infrastructure. <br /><br /> As part of an ongoing project, Impress, the framework uses the open standard, Jabber, as its communication protocol. The Publish-Subscribe (pubsub) extension to Jabber provides interesting features that match those needed by a context-aware system. The main contribution of this thesis is the design and implementation of a coordination framework, called the Impress Context Store, that provides an effective common foundation for context-aware systems. The separation between the context-processing logic and the underlying supporting foundation allows researchers to focus their energy at the context-processing part of the system, instead of spending their time re-inventing the supporting infrastructure.

The dream of weaving technology into our everyday fabric of life is recently being made possible by advances in ubiquitous computing and sensor technologies. Countless sensors of various sizes have made their way into everyday commercial applications. Many projects aim to explore new ways to utilize these new technologies to aid and interact with the general population. Context-aware systems use available context information to assist users automatically, without explicit user input. By inferring user intent and configuring the system proactively for each user, context-aware systems are an integral part of achieving user-friendly ubiquitous computing environments. <br /><br /> A common issue with building a distributed context-aware system is the need to develop a supporting infrastructure providing features such as storage, distributed messaging, and security, before the real work on processing context information can be done. This thesis proposes a coordination framework that provides an effective common foundation for context-aware systems. The separation between the context-processing logic component and the underlying supporting foundation allows researchers to focus their energy at the context-processing part of the system, instead of spending their time re-inventing the supporting infrastructure. <br /><br /> As part of an ongoing project, Impress, the framework uses the open standard, Jabber, as its communication protocol. The Publish-Subscribe (pubsub) extension to Jabber provides interesting features that match those needed by a context-aware system. The main contribution of this thesis is the design and implementation of a coordination framework, called the Impress Context Store, that provides an effective common foundation for context-aware systems. The separation between the context-processing logic and the underlying supporting foundation allows researchers to focus their energy at the context-processing part of the system, instead of spending their time re-inventing the supporting infrastructure.

In the last decade, with the extensive use of mobile electronic and wireless communication devices, there is a growing need for context aware applications and many pervasive computing applications have become integral parts of our daily lives. Context aware recommender systems are one of the popular ones in this area. Such systems surround the users and integrate with the environment / hence, they are aware of the users&#039 / context and use that information to deliver personalized recommendations about everyday tasks. In this manner, predicting user&rsquo / s next activity preferences with high accuracy improves the personalized service quality of context aware recommender systems and naturally provides user satisfaction. Predicting activities of people is useful and the studies on this issue in ubiquitous environment are considerably insufficient. Thus, this thesis proposes an activity prediction model to forecast a user&rsquo / s next activity preference using past preferences of the user in certain contexts and current contexts of user in ubiquitous environment. The proposed model presents a new approach for activity prediction by taking advantage of ontology. A prototype application is implemented to demonstrate the applicability of this proposed model and the obtained outputs of a sample case on this application revealed that the proposed model can reasonably predict the next activities of the users.

In this thesis work, a certificate based authentication model supported by mobile devices is provided for ubiquitous computing environments. The model primarily aims to create an infrastructure for controlling and regulating access requests through mobile devices to local resources and services. The model also allows users from different domains to use local resources and services within the scope of agreements between domains. In addition to conceptual description of the model, a real prototype implementation is developed and successful application of the model is demonstrated. Within the prototype implementation, a mobile application is developed for access requests and sensors are used as representative local resources. Sample cases applied on the prototype demonstrate applicability and feasibility of the model.

Delivering accurate and opportune information on people's activities and behaviors has become one of the most important tasks within pervasive computing. Its wide spectrum of potential applications in medical, entertainment, and tactical scenarios, motivates further research and development of new strategies to improve accuracy, pervasiveness, and eciency. This dissertation addresses the recognition of human activities (HAR) with wearable sensors in three main regards: In the rst place, physiological signals have been incorporated as a new source of information to improve the recognition accuracy achieved by conventional approaches, which rely on accelerometer signals solely. A new HAR system, Centinela, was born from such concept, employing structural feature extraction along with classier ensembles, and achieving over 95% of recognition accuracy. In the second place, real time activity recognition was enabled by Vigilante, a mobile HAR framework under the AndroidTM platform. Providing immediate feedback on the user's activities is especially benecial in healthcare and military applications, which may require alert triggering or support of decision making. The evaluation demonstrates that Vigilante is energy ecient while maintaining high accuracy (i.e., up to 96.8%) and low response time. The system features MECLA, a mobile library for the evaluation of classification algorithms, which is also suitable for further machine learning applications. Finally, the activity recognition accuracy is improved by two new strategies for decision fusion and selection in multiple classier systems: the failure product and the precision-recall dierence. The experimental analysis conrms that the presented methods are benecial, not only for recognizing human activities, but also for many other classication problems.

To provide users with personalized adaptive services only using the accessible computing resources in a cloud environment, context aware applications need to assimilate both the accessed and derived context, i.e. a combination of more than one sensed data and information in the environment. Context data dependency, dependency that arises between the context data producer and consumer, may get introduced in a system due to numerous reasons. But as the number of context dependencies for a service increases, the more complex the system becomes to manage. The thesis addresses issues of how to identify context dependencies, represent such context dependencies and then reduce them in a system. In the first part of the thesis, we present two efficient approaches to determine context dependency relations among various services in ubiquitous computing environment to help better analyse the pervasive services. One approach is based on graph theory, and we have used the topological sort to determine the context dependencies. The second approach is based on solving constraint networks which determines whether an entity is affected when the state of a certain other entity has its state changed, i.e. determining the dynamic nature of context dependency. In the second part of the thesis, we present a mode for representation of context dependencies within a system. Our model that represents context dependencies is based on set theory and first-order predicate logic. The context dependency representation model also represents alternative sources for context acquisition that can be utilized in a case in which the preferred context producers are not available to service the desired context to the relevant context consumer any more. Further, we try to reduce the context dependencies by presenting the idea of profile context, which is based on the proposal of an open framework for context acquisition, management and distribution. This heuristic approach is based on the idea of utilizing mobile nodes in an ad hoc overlay network with more resources than the context producer itself to store various contextual information under the banner of profile context, and further, provide profile context instead of each context individually based on the queries the nodes receive from the context consumers. Bringing together the context information and context updates from various sources, support for context aware decisions can be implemented efficiently in a mobile environment by addressing the issues of context dependency using profile context

Service composition integrates existing services to fulfill specific tasks using a set of standards and tools. Existing service composition techniques and tools are mainly designed for SOA professionals. The business processes used in the service composition systems are primarily designed by experienced business analysts who have extensive process knowledge. Process knowledge is the information about a process, including the tasks in a process, the control flow and data flow among tasks. It is challenging for end-users without sufficient service composition skills and process knowledge to find desired services then compose services to perform their daily activities, such as planning a trip. Context-aware techniques provide a promising way to help end-users find services using the context of end-users. However, existing context-aware techniques have limited support for dynamic adapting to new context types (e.g., location, time and activity) and context values (e.g., “New York City”). To shelter end-users from the complexity of service composition, we present our techniques that assist non-IT professional end-users in service composition by dynamically composing and recommending services to meet their requirements. To acquire the desired process knowledge for service composition, we propose an approach to automatically extract process knowledge from existing commercial applications on the Web. By analyzing the context of end-users, our techniques can dynamically adapt to new context types or values and provide personalized service recommendation for end-users. Instead of requiring end-users to specify detailed steps for service composition, the end-users only need to describe their goals using a few keywords. Our approach expands the meaning of an end-user's goal using process knowledge then derives a group of tasks to help the end-user fulfill the goal. The effectiveness of our proposed techniques is demonstrated through a set of case studies. / Thesis (Ph.D, Computing) -- Queen's University, 2011-09-30 11:43:39.151

Hardware evolves faster than software. While a hardware system might need replacement every one to five years, the average lifespan of a software system is a decade, with some instances living up to several decades. Inevitably, code outlives the platform it was developed for and may become legacy: development of the software stops, but maintenance has to continue to keep up with the evolving ecosystem. No new features are added, but the software is still used to fulfil its original purpose. Even in the cases where it is still functional (which discourages its replacement), legacy code is inefficient, costly to maintain, and a risk to security. This thesis proposes methods to leverage the expertise put in the development of legacy code and to extend its useful lifespan, rather than to throw it away. A novel methodology is proposed, for automatically exploiting platform specific optimisations when retargeting a program to another platform. The key idea is to leverage the optimisation information embedded in vector processing intrinsic functions. The performance of the resulting code is shown to be close to the performance of manually retargeted programs, however with the human labour removed. Building on top of that, the question of discovering optimisation information when there are no hints in the form of intrinsics or annotations is investigated. This thesis postulates that such information can potentially be extracted from profiling the data flow during executions of the program. A context-aware data dependence profiling system is described, detailing previously overlooked aspects in related research. The system is shown to be essential in surpassing the information that can be inferred statically, in particular about loop iterators. Loop iterators are the controlling part of a loop. This thesis describes and evaluates a system for extracting the loop iterators in a program. It is found to significantly outperform previously known techniques and further increases the amount of information about the structure of a program that is available to a compiler. Combining this system with data dependence profiling improves its results even more. Loop iterator recognition enables other code modernising techniques, like source code rejuvenation and commutativity analysis. The former increases the use of idiomatic code and as a result increases the maintainability of the program. The latter can potentially drive parallelisation and thus dramatically improve runtime performance.