John Heidemann / Papers / Building Efficient Wireless Sensor Networks with Low-Level Naming

Building Efficient Wireless Sensor Networks with Low-Level Naming
John Heidemann, Fabio Silva, Chalermek Intanagonwiwat, Ramesh Govindan, Deborah Estrin and Deepak Ganesan
USC/Information Sciences Institute

Citation

John Heidemann, Fabio Silva, Chalermek Intanagonwiwat, Ramesh Govindan, Deborah Estrin and Deepak Ganesan. Building Efficient Wireless Sensor Networks with Low-Level Naming. Proceedings of the Symposium on Operating Systems Principles (Chateau Lake Louise, Banff, Alberta, Canada, Oct. 2001), 146–159. [DOI] [PDF] [alt PDF]

Abstract

In most distributed systems, naming of nodes for low-level communication leverages topological location (such as node addresses) and is independent of any application. In this paper, we investigate an emerging class of distributed systems where low-level communication does not rely on network topological location. Rather, low-level communication is based on attributes that are \emphexternal to the network topology and \emphrelevant to the application. When combined with dense deployment of nodes, this kind of named data enables \emphin-network processing for data aggregation, collaborative signal processing, and similar problems. These approaches are essential for emerging applications such as sensor networks where resources such as bandwidth and energy are limited. This paper is the first description of the software architecture that supports named data and in-network processing in an operational, multi-application sensor-network. We show that approaches such as in-network aggregation and nested queries can significantly affect network traffic. In one experiment aggregation reduces traffic by up to 42% and nested queries reduce loss rates by 30%. Although aggregation has been previously studied in simulation, this paper demonstrates nested queries as another form of in-network processing, presents the first evaluation of these approaches over an operational testbed.

Bibtex Citation

@inproceedings{Heidemann01c,
  author = {Heidemann, John and Silva, Fabio and Intanagonwiwat, Chalermek and Govindan, Ramesh and Estrin, Deborah and Ganesan, Deepak},
  title = {Building Efficient Wireless Sensor Networks with Low-Level Naming},
  booktitle = {Proceedings of the  Symposium on Operating Systems Principles},
  year = {2001},
  sortdate = {2001-10-01},
  project = {ilense, scadds, scowr},
  jsubject = {sensornet_data_dissemination},
  publisher = {ACM},
  address = {Chateau Lake Louise, Banff, Alberta, Canada},
  month = oct,
  pages = {146--159},
  location = {johnh: folder: xxx},
  brag = {60th most cited paper in CS for 2001 according to CiteSeer (as of Feb. 2005)},
  keywords = {sensor networks, apis, in-network processing,
                           possible 551 or 555 paper, event distribution
                           systems},
  doi = {http://dx.doi.org/10.1145/502059.502049},
  url = {http://www.isi.edu/%7ejohnh/PAPERS/Heidemann01c.html},
  pdfurl = {http://www.isi.edu/%7ejohnh/PAPERS/Heidemann01c.pdf},
  psurl = {http://www.isi.edu/%7ejohnh/PAPERS/Heidemann01c.ps.gz},
  otherurl = {http://www-cse.ucsd.edu/sosp01/papers/heidemann.pdf},
  myorganization = {USC/Information Sciences Institute},
  copyrightholder = {ACM},
  copyrightterms = {
  	Permission to make digital or
  	hard copies of part or all 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 new copies bear this notice and the full
  	citation on the first page. Copyrights for components of this
  	work owned by others than ACM must be honored. Abstracting with
  	credit  is permitted.
   
  	To copy otherwise, to republish, to post on servers or to
  	redistribute to lists, requires prior specific permission
  	and/or a fee. Request Permissions from
  	Publications Dept, ACM Inc.,
  	Fax +1 (212) 869--0481, or
  	permissions@acm.org.
  }
}

Copyright

Permission to make digital or hard copies of part or all 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 new copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request Permissions from Publications Dept, ACM Inc., Fax +1 (212) 869–0481, or permissions@acm.org.
Copyright © by John Heidemann