skip to main content
10.1145/1176866.1176876acmconferencesArticle/Chapter ViewAbstractPublication PagesmiddlewareConference Proceedingsconference-collections
Article

Multi-training sensor networks with bipartite conflict graphs

Published: 28 November 2006 Publication History

Abstract

Due to their potential applications in various situations such as battlefield communications, emergency relief, environmental monitoring, and other special-purpose operations, wireless sensor networks have recently emerged as a new and exciting research area that has attracted a good deal of well-deserved attention in the literature. In this work we take the view that a sensor network consists of a set of tiny sensors, massively deployed over a geographical area. The sensors are capable of performing processing, sensing and communicating with each other by radio links. Alongside, with the tiny sensors, more powerful devices referred as Aggregating and Forwarding Nodes, (AFN, for short) are also deployed. In support of their mission, the AFNs are endowed with a special radio interface for long distance communications, miniaturized GPS, and appropriate networking tools for data collection and aggregation. As a fundamental prerequisite for self-organization, the sensors need to acquire some form of location awareness. Since fine-grain location awareness usually assumes that the sensors are GPS-enabled, in the case of tiny sensors the best we can hope for is to endow them with coarse-grain location awareness. This task is referred to as training and its responsibility lies with the AFNs. However, due to the random deployment, some of the sensors fall under the coverage area of several AFNs, in which case the goal is for these sensors to acquire location information relative to all the covering AFNs. The corresponding task is referred to as multi-training.The main contribution of this work is to show that in case the conflict graphs of the AFN coverage is bipartite, multi-training can be completed very fast by a simple algorithm.

References

[1]
F. Akyldiz, W. Su, Y. Sankarasubramanian, and E. Cayirci, Wireless sensor networks: A Survey, Computer Networks, 38(4), 2002, 393--422.
[2]
N. Bulusu, J. Heidemann, and D. Estrin, GP-less low cost outdoor localization for very small devices, IEEE Personal Communications, 7(5): 28--34, 2000.
[3]
D. Culler, D. Estrin, and M. Srivastava, Overview of sensor networks, IEEE Computer, 37(8), 2004, 41--49.
[4]
D. Culler and W. Hong, Wireless sensor networks, Communications of the ACM, 47(6), 2004, 30--33.
[5]
K. Martinez, J. K. Hart and R. Ong, Environmental sensor networks, IEEE Computer, 37(8), 2004, 50--56.
[6]
R. Ishak, S. Olariu, S. Salleh and Q. Xu, Dual-training for Massively- Deployed Sensor Networks, 13th International Conference of Telecommunications, Portugal, May 2006.
[7]
Karl, H., and Willig, A. (2005). Protocols and Architectures for Wireless Sensor Networks.John Wiley and Sons Ltd, England.
[8]
S. Olariu, Q. Xu, A. Wadaa and I. Stojmonovic, A virtual infrastructure for wireless sensor networks, in I. Stojmenivc, Ed., Handbook of Sensor Networks, Wiley 2005, 107--140.
[9]
S. Olariu, A. Wadaa, L. Wilson and M. Eltoweissy, Wireless sensor networks: leveraging the virtual infrastructure, IEEE Network, 18(4), 204, 51--56.
[10]
S. Olariu, M. Eltoweissy, and M. Younis, ANSWER: Autonomous Wireless Sensor Network, Proc. ACM Q2SWinet, Montreal, Canada, October 2005.
[11]
K. Sohrabi, J. Gao, V. Ailawadhi, and G. Pottie, Protocols for self-organization of a wireless sensor network, IEEE Personal Communications, October 2000, 16--27.
[12]
A. Wadaa, S. Olariu, L. Wilson, M. Eltoweissy, and K. Jones, Training a Wireless Sensor Network, Mobile Networks and Applications 10, 151--168, 2005.

Cited By

View all
  • (2008)Emergent behavior in massively-deployed sensor networksMobile Information Systems10.1155/2008/7316814:4(313-331)Online publication date: 1-Dec-2008

Index Terms

  1. Multi-training sensor networks with bipartite conflict graphs

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      MidSens '06: Proceedings of the international workshop on Middleware for sensor networks
      November 2006
      71 pages
      ISBN:1595934243
      DOI:10.1145/1176866
      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. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 28 November 2006

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. aggregating and forwarding nodes
      2. bipartite
      3. multi-training

      Qualifiers

      • Article

      Conference

      Middleware06
      Sponsor:
      Middleware06: 7th International Middleware Conference
      November 28, 2006
      Melbourne, Australia

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)0
      • Downloads (Last 6 weeks)0
      Reflects downloads up to 13 Jan 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2008)Emergent behavior in massively-deployed sensor networksMobile Information Systems10.1155/2008/7316814:4(313-331)Online publication date: 1-Dec-2008

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media