Learning from sensor network data

  • Authors:
  • Matthias Keller;Jan Beutel;Andreas Meier;Roman Lim;Lothar Thiele

  • Affiliations:
  • ETH Zurich, Zurich, Switzerland;ETH Zurich, Zurich, Switzerland;ETH Zurich, Zurich, Switzerland;ETH Zurich, Zurich, Switzerland;ETH Zurich, Zurich, Switzerland

  • Venue:
  • Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
  • Year:
  • 2009

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Abstract

Within the PermaSense project, two wireless sensor networks have been deployed for a long-term operation in the Swiss Alps. For enabling state-of-the-art permafrost research based on the collected data, highest possible data quality and yield have to be ensured. But, the operation of wireless sensors networks remains a hard research problem. Firstly, deployed wireless sensors networks are subject to continuous changes. Second, there are scenarios that can only be tested in the field as the capabilities of testbeds are too limited. Basically, it is not possible to test for many months before deploying in the field. In this poster, we present an analysis of our data that has been collected over nine months. In addition to describing our system design and methods, we also share our experiences from discovered severe incidences.