John Heidemann / Papers / An Evaluation of Multi-resolution Storage for Sensor Networks

An Evaluation of Multi-resolution Storage for Sensor Networks
Deepak Ganesan, Ben Greenstein, Denis Perelyubskiy, Deborah Estrin and John Heidemann
USC/Information Sciences Institute

Citation

Deepak Ganesan, Ben Greenstein, Denis Perelyubskiy, Deborah Estrin and John Heidemann. An Evaluation of Multi-resolution Storage for Sensor Networks. Proceedings of the First ACM SenSys Conference (Los Angeles, California, USA, Nov. 2003), 89–102. [PDF] [alt PDF]

Abstract

Wireless sensor networks enable dense sensing of the environment, offering unprecedented opportunities for observing the physical world. Centralized data collection and analysis adversely impact sensor node lifetime. Previous sensor network research has, therefore, focused on in network aggregation and query processing, but has done so for applications where the features of interest are known a priori. When features are not known a priori, as is the case with many scientific applications in dense sensor arrays, efficient support for multi-resolution storage and iterative, drill-down queries is essential. Our system demonstrates the use of in-network wavelet-based summarization and progressive aging of summaries in support of long-term querying in storage and communication-constrained networks. We evaluate the performance of our linux implementation and show that it achieves: (a) low communication overhead for multi-resolution summarization, (b) highly efficient drill-down search over such summaries, and (c) efficient use of network storage capacity through load-balancing and progressive aging of summaries.

Bibtex Citation

@inproceedings{Ganesan03a,
  author = {Ganesan, Deepak and Greenstein, Ben and Perelyubskiy, Denis and Estrin, Deborah and Heidemann, John},
  title = {An Evaluation of Multi-resolution Storage for Sensor Networks},
  booktitle = {Proceedings of the First ACM {SenSys} Conference },
  year = {2003},
  sortdate = {2002-10-01},
  project = {ilense, cens, scadds},
  jsubject = {sensornet_general},
  publisher = {ACM},
  address = {Los Angeles, California, USA},
  month = nov,
  pages = {89--102},
  jlocation = {johnh: pafile},
  copyrightholder = {ACM},
  copyrightterms = {
  Permission to make digital or hard copies of all or part of this work
  for personal or classroom use is granted without fee provided that
  copies are not made or distributed for profit or commercial advantage
  and that copies bear this notice and the full citation on the first
  page. To copy otherwise, to republish, to post on servers or to
  redistribute to lists, requires prior specific permission and/or a
  fee.
  },
  myorganization = {USC/Information Sciences Institute},
  keywords = {multiresolution sensor network data storage},
  url = {https://ant.isi.edu/%7ejohnh/PAPERS/Ganesan03a.html},
  pdfurl = {https://ant.isi.edu/%7ejohnh/PAPERS/Ganesan03a.pdf},
  otherurl = {http://lecs.cs.ucla.edu/%7edeepak/PAPERS/storage.pdf}
}

Copyright

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee.
Copyright © by John Heidemann