Easing software development for pervasive computing environments

textIn recent years pervasive computing has enjoyed an amazing growth in both research and commercial fields. Not only have the number of available techniques and tools expanded, but the number of actual deployments has been underwhelming. With this growth however, we are also experiencing a divergence of software interfaces, languages, and techniques. This leads to an understandably confusing landscape which needlessly burdens the development of applications. It is our sincere hope that through the use of specialized interfaces, languages, and tools, we can make pervasive computing environments more approachable and efficient to software developers and thereby increase the utility and value of pervasive computing applications. In this dissertation, we present a new method for creating and managing the long-term conversations between peers in pervasive computing environments. The Application Sessions Model formally describes these conversations and specifies techniques for managing them over their lifetimes. In addition to these descriptions, this dissertation presents a prototype implementation of the model and results from its use for realistic scenarios. To address the Application Sessions Model's unique needs for resource discovery in pervasive computing environments, we also present the Evolving Tuples Model. This model is also formally defined in this dissertation and practical examples are used to clarify its features. A prototype for both sensor hardware and software simulation of this model is described along with results characterizing the behavior of the model. The models, prototypes, and evaluations of both models presented here form the basis of a new and interesting line of research into support structures for pervasive computing application development.Electrical and Computer Engineerin

Self-organising agent communities for autonomic resource management

The autonomic computing paradigm addresses the operational challenges presented by increasingly complex software systems by proposing that they be composed of many autonomous components, each responsible for the run-time reconfiguration of its own dedicated hardware and software components. Consequently, regulation of the whole software system becomes an emergent property of local adaptation and learning carried out by these autonomous system elements. Designing appropriate local adaptation policies for the components of such systems remains a major challenge. This is particularly true where the system’s scale and dynamism compromise the efficiency of a central executive and/or prevent components from pooling information to achieve a shared, accurate evidence base for their negotiations and decisions.In this paper, we investigate how a self-regulatory system response may arise spontaneously from local interactions between autonomic system elements tasked with adaptively consuming/providing computational resources or services when the demand for such resources is continually changing. We demonstrate that system performance is not maximised when all system components are able to freely share information with one another. Rather, maximum efficiency is achieved when individual components have only limited knowledge of their peers. Under these conditions, the system self-organises into appropriate community structures. By maintaining information flow at the level of communities, the system is able to remain stable enough to efficiently satisfy service demand in resource-limited environments, and thus minimise any unnecessary reconfiguration whilst remaining sufficiently adaptive to be able to reconfigure when service demand changes

Theory and Practice of Data Citation

Citations are the cornerstone of knowledge propagation and the primary means of assessing the quality of research, as well as directing investments in science. Science is increasingly becoming "data-intensive", where large volumes of data are collected and analyzed to discover complex patterns through simulations and experiments, and most scientific reference works have been replaced by online curated datasets. Yet, given a dataset, there is no quantitative, consistent and established way of knowing how it has been used over time, who contributed to its curation, what results have been yielded or what value it has. The development of a theory and practice of data citation is fundamental for considering data as first-class research objects with the same relevance and centrality of traditional scientific products. Many works in recent years have discussed data citation from different viewpoints: illustrating why data citation is needed, defining the principles and outlining recommendations for data citation systems, and providing computational methods for addressing specific issues of data citation. The current panorama is many-faceted and an overall view that brings together diverse aspects of this topic is still missing. Therefore, this paper aims to describe the lay of the land for data citation, both from the theoretical (the why and what) and the practical (the how) angle.Comment: 24 pages, 2 tables, pre-print accepted in Journal of the Association for Information Science and Technology (JASIST), 201

Precisely Analyzing Loss in Interface Adapter Chains

Interface adaptation allows code written for one interface to be used with a software component with another interface. When multiple adapters are chained together to make certain adaptations possible, we need a way to analyze how well the adaptation is done in case there are more than one chains that can be used. We introduce an approach to precisely analyzing the loss in an interface adapter chain using a simple form of abstract interpretation.Comment: 12 pages, 1 figure. Submitted to IASTED SE 201