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                          <header>
                              <identifier>oai:research.chalmers.se:506105</identifier>
                              <datestamp>2019-02-13T18:17:21Z</datestamp>
                              <setSpec>openaire</setSpec>
                          </header>
                          <metadata>
                              <oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
                                  <dc:title>Geometric Monitoring in Action: a Systems Perspective for the Internet of Things</dc:title>
                                  <dc:identifier>https://research.chalmers.se/en/publication/506105</dc:identifier>
                                  <dc:date>2018</dc:date>
                                              <dc:creator>Stylianopoulos, Charalampos</dc:creator>
                                              <dc:creator>Almgren, Magnus</dc:creator>
                                              <dc:creator>Landsiedel, Olaf</dc:creator>
                                              <dc:creator>Papatriantafilou, Marina</dc:creator>
                                                  <dc:subject>Computer Engineering</dc:subject>
                                                  <dc:subject>Communication Systems</dc:subject>
                                                  <dc:subject>Computer Systems</dc:subject>
                                              <dc:subject>lifetime improvement</dc:subject>
                                              <dc:subject>geometric monitoring</dc:subject>
                                              <dc:subject>IoT</dc:subject>
                                          <dc:description>Applications for IoT often continuously monitor sensor values and react if the network-wide aggregate exceeds a threshold. Previous work on  Geometric monitoring (GM) has promised a several-fold reduction in communication but been limited to analytic or high-level simulation results. In this paper, we build and evaluate a full system design for GM on resource-constrained devices. In particular, we provide an algorithmic implementation for commodity IoT hardware and a detailed study regarding duty cycle reduction and energy savings. Our results, both from full-system simulations and a publicly available testbed, show that GM indeed provides several-fold energy savings in communication. We see up to 3x and 11x reduction in duty-cycle when monitoring the variance and average temperature of a real-world data set, but the results fall short compared to the reduction in communication (4.3x and 44x, respectively). Hence, we investigate the energy overhead imposed by the network stack and the communication pattern of the algorithm and summarize our findings. These insights may enable the design of protocols that will unlock more of the potential of GM and similar algorithms for IoT deployments.</dc:description>
                                                                  <dc:relation>info:eu-repo/grantAgreement/EC/H2020/773717//Integrated cyber-physical solutions for intelligent distribution grid with high penetration of renewables (UNITED-GRID)/</dc:relation>
                                          <dc:type>info:eu-repo/semantics/article</dc:type>
                                                      <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:language>eng</dc:language>
                                  <dc:audience>Researchers</dc:audience>
                                  <dc:format>application/pdf</dc:format>
                              </oai_dc:dc>
                          </metadata>
                      </record>