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1-D Coordinate Based on Local Information for MAC and Routing Issues in WSNs

  • Alexandre Mouradian
  • Isabelle Augé-Blum
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7363)

Abstract

New Wireless Sensor Networks (WSNs) applications are emerging with new requirements such as reliability and respect of time constraints. The underlying mechanisms such as MAC and routing must handle such requirements. To meet timing constraint, it is necessary to bound the hop-count between a node and the sink and the time it takes to do a hop. Thus, the end-to-end delay can be bounded and the communications are real-time. Due to the efficiency and scalability of greedy routing in WSNs and the financial cost of GPS chips, Virtual Coordinate Systems (VCSs) for WSNs have been proposed. A category of VCSs is based on the hop-count from the sink, this scheme leads to many nodes having the same coordinate. The main advantage of this system is that the hops number of a packet from a source to the sink is known. Nevertheless, it does not allow to differentiate the nodes with the same hop-count. For reliability purpose we propose to select forwarder nodes depending on how they are connected in the direction of the sink. In order to be able to do so we need a metric that gives information on hop-count, that allows to strongly differentiate nodes and gives information on the connectivity of each node. As this metric is linked to physical organization of the network it can be viewed as a virtual coordinate. In this paper we propose a novel hop-count-based VCS which aims at classifying the nodes having the same hop-count depending on their connectivity and at differentiating nodes in a 2-hop neighborhood. Those properties make the coordinates, which also can be viewed as a local identifier, a very powerful metric which can be used in WSNs mechanisms. We evaluate the performances of our solution theoretically and by simulation.

Keywords

Wireless Sensor Network Anchor Node Contention Period Projection Distance Link Quality Estimation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Alexandre Mouradian
    • 1
  • Isabelle Augé-Blum
    • 1
  1. 1.CITIUniversité de Lyon, INRIA, INSA LyonFrance

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