A copy of this work was available on the public web and has been preserved in the Wayback Machine. The capture dates from 2017; you can also visit <a rel="external noopener" href="http://publications.mircoschoenfeld.de/2013-HierarchicalHashing.pdf">the original URL</a>. The file type is <code>application/pdf</code>.
<i title="Springer International Publishing">
<a target="_blank" rel="noopener" href="https://fatcat.wiki/container/jajl7qtqc5cy7oavratsldrv2y" style="color: black;">Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering</a>
Interest dissemination in constrained environments such as wireless sensor networks utilizes Bloom filters commonly. A Bloom filter is a probabilistic data structure of fixed length, which can be used to encode the set of sensor nodes to be awake. In this way an application can disseminate interest in specific sensor nodes by broadcasting the Bloom filter throughout the complete wireless sensor network. The probabilistic nature of a Bloom filter induces false positives, that is some sensor<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-14225-8_8">doi:10.1007/978-3-319-14225-8_8</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/mfjtn6o35zhtfipf2ey4xdtdmm">fatcat:mfjtn6o35zhtfipf2ey4xdtdmm</a> </span>
more »... will be awake without the application having interest in their sensor values. As the interest is often depending on location such as in adaptive sampling applications, we present a novel method to encode both interest and possible location of information into one probabilistic data structure simultaneously. While our algorithm is able to encode any kind of treestructured information into a fixed length bit array we exemplify its use through a wireless sensor network. In comparison to traditional Bloom encoding techniques we are able to reduce the overall number of false positives and furthermore reduce the average distance of false positives from the next true positive of the same interest. In our example this helps to reduce the overall energy consumption of the sensor network by only requesting sensor nodes that are likely to store the requested information.
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20170809020202/http://publications.mircoschoenfeld.de/2013-HierarchicalHashing.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/a2/71/a2719206d268d18f635d75b16bb10cb9103d932d.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-319-14225-8_8"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> springer.com </button> </a>