Browsing by Subject "Pervasive computing"
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Item Design of platforms for computing context with spatio-temporal locality(2011-05) Ziotopoulos, Agisilaos Georgios; De Veciana, Gustavo; Garg, Vijay; Mok, Al; Julien, Christine; Touba, Nur; Breternitz, MauricioThis dissertation is in the area of pervasive computing. It focuses on designing platforms for storing, querying, and computing contextual information. More specifically, we are interested in platforms for storing and querying spatio-temporal events where queries exhibit locality. Recent advances in sensor technologies have made possible gathering a variety of information on the status of users, the environment machines, etc. Combining this information with computation we are able to extract context, i.e., a filtered high-level description of the situation. In many cases, the information gathered exhibits locality both in space and time, i.e., an event is likely to be consumed in a location close to the location where the event was produced, at a time whic h is close to the time the event was produced. This dissertation builds on this observation to create better platforms for computing context. We claim three key contributions. We have studied the problem of designing and optimizing spatial organizations for exchanging context. Our thesis has original theoretical work on how to create a platform based on cells of a Voronoi diagram for optimizing the energy and bandwidth required for mobiles to exchange contextual information t hat is tied to specific locations in the platform. Additionally, we applied our results to the problem of optimizing a system for surveilling the locations of entities within a given region. We have designed a platform for storing and querying spatio-temporal events exhibiting locality. Our platform is based on a P2P infrastructure of peers organized based on the Voronoi diagram associated with their locations to store events based on their own associated locations. We have developed theoretical results based on spatial point processes for the delay experienced by a typical query in this system. Additionally, we used simulations to study heuristics to improve the performance of our platform. Finally, we came up with protocols for the replicated storage of events in order to increase the fault-tolerance of our platform. Finally, in this thesis we propose a design for a platform, based on RFID tags, to support context-aware computing for indoor spaces. Our platform exploits the structure found in most indoor spaces to encode contextual information in suitably designed RFID tags. The elements of our platform collaborate based on a set of messages we developed to offer context-aware services to the users of the platform. We validated our research with an example hardware design of the RFID tag and a software emulation of the tag's functionality.Item Easing software development for pervasive computing environments(2009-12) Stovall, Andrew Erich; Julien, ChristineIn 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.Item The Gander search engine for personalized networked spaces(2012-12) Michel, Jonas Reinhardt; Julien, Christine; Garg, VijayThe vision of pervasive computing is one of a personalized space populated with vast amounts of data that can be exploited by humans. Such Personalized Networked Spaces (PNetS) and the requisite support for general-purpose expressive spatiotemporal search of the “here” and “now” have eluded realization, due primarily to the complexities of indexing, storing, and retrieving relevant information within a vast collection of highly ephemeral data. This thesis presents the Gander search engine, founded on a novel conceptual model of search in PNetS and targeted for environments characterized by large volumes of highly transient data. We overview this model and provide a realization of it via the architecture and implementation of the Gander search engine. Gander connects formal notions of sampling a search space to expressive, spatiotemporal-aware protocols that perform distributed query processing in situ. This thesis evaluates Gander through a user study that examines the perceived usability and utility of our mobile application, and benchmarks the performance of Gander in large PNetS through network simulation.Item Grapevine : efficient situational awareness in pervasive computing environments(2012-12) Grim, Evan Tyler; Julien, Christine; Garg, VijayMany pervasive computing applications demand expressive situational awareness, which entails an entity in the pervasive computing environment learning detailed information about its immediate and surrounding context. Much work over the past decade focused on how to acquire and represent context information. However, this work is largely egocentric, focusing on individual entities in the pervasive computing environment sensing their own context. Distributed acquisition of surrounding context information is much more challenging, largely because of the expense of communication among these resource-constrained devices. This thesis presents Grapevine, a framework for efficiently sharing context information in a localized region of a pervasive computing network, using that information to dynamically form groups defined by their shared situations, and assessing the aggregate context of that group. Grapevine’s implementation details are presented and its performance benchmarked in both simulation and live pervasive computing network deployments.Item MobiShare : mobile computing with no strings attached(2013-12) Castillo, Jason Moses; Julien, ChristineIn today’s world, technology is growing at a fast rate compared at other times. Sales have increased in the smart phone market, which has created new opportunities in pervasive computing. In pervasive computing, nodes enter and leave a network at any time. Within the network, nodes can transfer data to other nodes. The information is not retained in any static location such as a server. The mobile infrastructure requires a way to handle all the information in a dynamic way. The use of a centralized server in a mobile environment creates deterioration in the performance of obtaining information. The main goal of this paper is to provide data persistence using a “substrate” that is inherently not persistent. The data will be stored within the network for availability to all users. Saving data within a network would provide a means to obtain any type of information without relying on the source of where the data came from in the network. Users would also be able to continue downloading where they left off when they return to the network. Consider an environment where people can share music or books. For example, say that John Doe was searching for a particular song to download and in the network Jane has the song that was requested. John decides to download the song without knowing that it is from Jane. Then John decides to leave the network and the download stops. Whenever John rejoins the network the download of his song will continue where he left off, and his ability to access the information will not depend whether or not Jane is present in the network. John may retrieve the file from any other user who has the exact same file. The requested information that the user queries in a search engine will be stored as a metadata within the network, either by other nodes or a temporary server. This allows data to be obtained without relying on the "main user" or creator of the data to be present in the network. The users would also be able to retrieve the data at multiple times.Item Navigating tradeoffs in context sharing among the Internet of Things(2016-12) Cho, Samuel Sungmin; Julien, Christine; Khurshid, Sarfraz; Perry, Dewayne E; Tiwari, Mohit; Qiu, LiliThis dissertation introduces new perspectives on the sharing context (situational information) among Internet of Things (IoT) devices having different processing power, storage capacity, communication bandwidth, and energy supply. Emerging IoT applications require devices to share information about their context with one another, often over device-to-device wireless links. However, as each IoT device has different capabilities, it may also have different priorities with respect to sharing its context with other nearby devices; low- end IoT devices with limited communication bandwidth and energy supply can prioritize a small context size (and therefore a reduced burden associated with sharing context information), while high-end IoT devices can prioritize communicating context without loss in data quality. Different IoT applications can also impact the priorities; real-time applications can prioritize fast data processing times, whereas big data server applications can prioritize reduced context sizes due to required massive storage. Prioritizing entails tradeoffs. For example, reducing context size through compression requires more energy consumption; in the case of using lossy compression for even smaller output, the data quality can be degraded. In this dissertation, we explore the tradeoffs in sharing context among IoT devices. Specifically, we present our solutions in three stages; theory, implementation, and execution models. In the theory stage, we present our context sharing model using four strategies; we start with strategies that prioritize a single factor, data quality or size, then, we introduce a novel tunable strategy where users can control the tradeoff factors to meet their application’s requirements. We build a mathematical model, and we analyze and experiment with the model to assess the performance relative to tradeoff factors including size, data quality, and energy consumption. An aggregation strategy, which shows an excellent performance in size reduction and energy consumption will be our fourth strategy. In the implementation stage, we introduce a programming model for IoT devices. We stress three principles: easy availability to accessibility of core functions, simple extension to meet application demands, and portability to the other multiple platforms. We demonstrate how these considerations drive the development of the programming model by providing programming tools that realize the model; developers can use these tools to build context sharing activities into their applications. Ultimately, users’ applications will be deployed on a variety of IoT devices. In the third stage of this research, execution models, we categorize IoT devices using three models: tiny devices, mobile devices, and server/cloudlet devices, depending on how the programming tools are employed. We present how context sharing IoT applications can be developed, deployed, and executed within each of these execution models. We expect that IoT developers can benefit in creating new context sharing applications from not only the tools we present but also the ideas behind the tools.Item Supporting device-to-device search and sharing of hyper-localized data(2015-05) Michel, Jonas Reinhardt; Julien, Christine; Garg, Vijay; Lam, Simon; de Veciana, Gustavo; Vishwanath, SriramSupporting emerging mobile applications in densely populated environments requires connecting mobile users and their devices with the surrounding digital landscape. Specifically, the volume of digitally-available data in such computing spaces presents an imminent need for expressive mechanisms that enable humans and applications to share and search for relevant information within their digitally accessible physical surroundings. Device-to-device communications will play a critical role in facilitating transparent access to proximate digital resources. A wide variety of approaches exist that support device-to-device dissemination and query-driven data access. Very few, however, capitalize on the contextual history of the shared data itself to distribute additional data or to guide queries. This dissertation presents Gander, an application substrate and mobile middleware designed to ease the burden associated with creating applications that require support for sharing and searching of hyper-localized data in situ. Gander employs a novel trajectory-driven model of spatiotemporal provenance that enriches shared data with its contextual history -- annotations that capture data's geospatial and causal history across a lifetime of device-to-device propagation. We demonstrate the value of spatiotemporal data provenance as both a tool for improving ad hoc routing performance and for driving complex application behavior. This dissertation discusses the design and implementation of Gander's middleware model, which abstracts away tedious implementation details by enabling developers to write high-level rules that govern when, where, and how data is distributed and to execute expressive queries across proximate digital resources. We evaluate Gander within several simulated large-scale environments and one real-world deployment on the UT Austin campus. The goal of this research is to provide formal constructs realized within a software framework that ease the software engineering challenges encountered during the design and deployment of several applications in emerging mobile environments.