Due to their small form factor and modest energy budget, individual sensors are not expected to be GPS-enabled. Moreover, in most applications, exact geographic location is not necessary, and all that the individual sensors need is a coarse-grain location awareness. The task of acquiring such a coarse-grain location awareness is referred to as training. In this paper, a scalable energy-efficient training protocol is proposed for massively-deployed sensor networks, where sensors are initially anonymous and unaware of their location. The training protocol is lightweight and simple to implement; it is based on an intuitive coordinate system imposed onto the deployment area which partitions the anonymous sensors into clusters where data can be gathered from the environment and synthesized under local control.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • A. A. Bertossi
    • 1
  • S. Olariu
    • 2
  • M. C. Pinotti
    • 3
  1. 1.Department of Computer ScienceUniversity of BolognaBolognaItaly
  2. 2.Department of Computer ScienceOld Dominion UniversityNorfolkUSA
  3. 3.Department of Computer Science and MathematicsUniversity of PerugiaPerugiaItaly

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