Many 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. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/19705 |
| Date | 04 March 2013 |
| Creators | Grim, Evan Tyler |
| Source Sets | University of Texas |
| Language | en_US |
| Detected Language | English |
| Format | application/pdf |
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