The vision of pervasive computing is one of a personalized space populated with vast amounts of data that can be exploited by humans. Such personalized network spaces (PNetS) and the requisite support for general-purpose expressive spa-tiotemporal 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. We present 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. We evaluate Gander through a user study that examines the perceived usability and utility of our mobile application, and we benchmark the performance of Gander in large PNetS through network simulation.

The vision of the Internet of Things will enable networked environments populated with vast amounts of data that can be exploited by humans. The volume of digitally available data in such emerging computing spaces presents an imminent need for search mechanisms that enable humans and applications to find relevant information within their digitally accessible physical surroundings. This paper presents Gander, a search engine for these pervasive computing spaces enabled by the Internet of Things and characterized by large volumes of highly transient data. Gander is founded on a novel conceptual model of search that resolves queries about a user’s here and now by leveraging proximally-available resources in the here and now. We formally describe the model underlying Gander, describe the networking protocols that enable Gander’s search, and provide a realization of Gander via an extensible framework. Employing this Gander framework, we describe a concrete middleware implementation for wirelessly networked environments. We evaluate this implementation of Gander through a user study that examines the perceived utility of myGander, a real-world mobile application enabled by the Gander middleware, and we benchmark the performance of Gander in large pervasive computing spaces through network simulation.

The 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 generalpurpose 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. We present the Gander search engine, founded on a novel conceptual model of search in PNetS that connects formal notions of sampling asearchspaceto expressive, spatiotemporal-aware protocols that perform distributed query processing in situ.

Abstract: Queries are frequently used by applications in dynamically formed mo-bile networks to discover and acquire information and services available in the sur-rounding environment. A number of inquiry strategies exist, each of which em-bodies an approach to disseminating a query and collecting results. The choice of inquiry strategy has different tradeoffs under different operating conditions. There-fore, it is beneficial to allow a query-based application to dynamically adapt its in-quiry strategy to the changing environmental conditions. To promote development by non-expert domain programmers, we can automate the decision-making process associated with adapting the inquiry strategy. In this paper, we propose an architec-ture to support automated adaptative query processing for dynamic mobile environ-ments. The decision-support module of our architecture relies on an instance-based learning approach to support context-aware adaptation of the inquiry strategy.