Group-aware stream filtering for bandwidth-efficient data dissemination

Abstract

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 maximise 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.

Keywords:

• data filtering
• data dissemination
• overlay multicasting
• bandwidth reduction

Acknowledgements

The authors want to thank Guanling Chen, Kazuhiro Minami, members of the ARTEMIS project at the Institute for Security Technology Studies, and other members of Center for Mobile Computing at Dartmouth College, for their valuable suggestions and feedback. This research program is a part of the Institute for Security Technology Studies, supported under Award number 2000-DT-CX-K001 from the U.S. Department of Homeland Security, Science and Technology Directorate, and by Grant number 2005-DD-BX-1091 awarded by the Bureau of Justice Assistance. Points of view in this document are those of the authors and do not necessarily represent the official position of the US Department of Homeland Security or the US Department of Justice.

Notes

^1. Here, we represent each tuple as a single integer; in reality, each tuple may have several fields, but for simplicity we represent each by the value of its ‘temperature’ field since it is that field that is used for filtering.

^2. http://cens.ucla.edu

^3. http://www.emulab.net is a cluster for distributed-systems research.