John Heidemann / Papers / RMST: Reliable Data Transport in Sensor Networks

RMST: Reliable Data Transport in Sensor Networks
Fred Stann and John Heidemann
USC/Information Sciences Institute

Citation

Fred Stann and John Heidemann. RMST: Reliable Data Transport in Sensor Networks. Proceedings of the First International Workshop on Sensor Net Protocols and Applications (Anchorage, Alaska, USA, Apr. 2003), 102–112. [PDF] [alt PDF]

Abstract

Reliable data transport in wireless sensor networks is a multifaceted problem influenced by the physical, MAC, network, and transport layers. Because sensor networks are subject to strict resource constraints and are deployed by single organizations, they encourage revisiting traditional layering and are less bound by standardized placement of services such as reliability. This paper presents analysis and experiments resulting in specific recommendations for implementing reliable data transport in sensor nets. To explore reliability at the transport layer, we present RMST (Reliable Multi- Segment Transport), a new transport layer for Directed Diffusion. RMST provides guaranteed delivery and fragmentation/reassembly for applications that require them. RMST is a selective NACK-based protocol that can be configured for in-network caching and repair.

Bibtex Citation

@inproceedings{Stann03a,
  author = {Stann, Fred and Heidemann, John},
  title = {RMST: Reliable Data Transport in Sensor Networks},
  booktitle = {Proceedings of the First International Workshop on Sensor Net Protocols and Applications },
  year = {2003},
  sortdate = {2003-04-01},
  project = {ilense, scadds},
  jsubject = {sensornet_data_dissemination},
  publisher = {IEEE},
  address = {Anchorage, Alaska, USA},
  month = apr,
  pages = {102--112},
  location = {johnh: pafile},
  brag = {99th most cited paper in CS for 2003 according to CiteSeer (as of Feb. 2005)},
  keywords = {sensor networks, reliable transport layer,
                           RMST, PDSQ},
  url = {http://www.isi.edu/%7ejohnh/PAPERS/Stann03a.html},
  pdfurl = {http://www.isi.edu/%7ejohnh/PAPERS/Stann03a.pdf},
  myorganization = {USC/Information Sciences Institute},
  copyrightholder = {IEEE},
  copyrightterms = {
  	Personal use of this material is permitted.  However,
  	permission to reprint/republish this material for advertising
  	or promotional purposes or for creating new collective works
          for resale or redistribution to servers or lists,
  	or to reuse any copyrighted component of this work in other works
  	must be obtained from the IEEE.
  }
}

Copyright

Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Copyright © by John Heidemann