@article{li:jfilter,
    author = {Ming Li and David Kotz},
    title = {Group-aware Stream Filtering for Bandwidth-efficient Data
Dissemination},
    journal = {International Journal of Parallel, Emergent and Distributed
Systems (IJPEDS)},
    year = {2008},
    month = {December},
    volume = {23},
    number = {6},
    pages = {429--446},
    publisher = {Taylor \& Francis},
    copyright = {Taylor \& Francis},
    address = {London, UK},
    doi = {10.1080/17445760801930955},
    url = {http://www.cs.dartmouth.edu/~dfk/papers/internal/li-jfilter.pdf},
    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 \textit{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.}
}