Group-aware Stream Filtering for Bandwidth-efficient Data Dissemination
[li:jfilter]Ming Li and David Kotz. Group-aware Stream Filtering for Bandwidth-efficient Data Dissemination. International Journal of Parallel, Emergent and Distributed Systems (IJPEDS), volume 23, number 6, pages 429–446. Taylor & Francis, December 2008. doi:10.1080/17445760801930955. ©Copyright Taylor & Francis. Invited paper.
In this paper we are concerned with disseminating high-volume data streams to many simultaneous applications over a low-bandwidth wireless mesh network. For bandwidth efficiency, we propose a group-aware stream filtering approach, used in conjunction with multicasting, that exploits two overlooked, yet important, properties of these applications: 1) many applications can tolerate some degree of “slack” in their data quality requirements, and 2) there may exist multiple subsets of the source data satisfying the quality needs of an application. We can thus choose the “best alternative” subset for each application to maximize the data overlap within the group to best benefit from multicasting. An evaluation of our prototype implementation shows that group-aware data filtering can save bandwidth with low CPU overhead. We also analyze the key factors that affect its performance, based on testing with heterogeneous filtering requirements.
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Keywords: [context-aware] [sensors]
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