Gander: Mobile, pervasive search of the here and now in the here and now

The vision of the Internet of Things (IoT) 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 IoT 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.