A small world approach for scalable and resilient position estimation algorithms for wireless sensor networks

  • Authors:

  • Daniel L. Guidoni;Azzedine Boukerche;Leandro A. Villas;Fernanda S.H. de Souza;Horacio A.B.F. Oliveira;Antonio A.F. Loureiro

  • Affiliations:

  • Federal University of São João del-Rei, São João del-Rei, Brazil & University of Ottawa, Ottawa, Canada;University of Ottawa, Ottawa, ON, Canada;University of Ottawa, Ottawa, Canada & Federal University of Minas Gerais, Belo Horizonte, Brazil;Federal University of Minas Gerais, Belo Horizonte, Brazil;Federal University of Amazonas, Manaus, Brazil;Federal University of Minas Gerais, Belo Horizonte, Brazil

  • Venue:
  • Proceedings of the 10th ACM international symposium on Mobility management and wireless access
  • Year:
  • 2012

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Abstract

In Wireless Sensor Networks (WSNs) there are network functions and applications that need to know the localization of a node such as the association of the sensed data with a given position. In general, it may be impossible or unfeasible to have either a planned deployment or a localization hardware. Thus, an important problem in wireless sensor networks is to find out the localization of sensor nodes. In this work, we propose a heterogeneous wireless sensor network topology based on small world concepts to improve the Recursive Position Estimation algorithm. By using the small world topology we have two goals: (i) the error in position estimation for large wireless sensor networks is reduced, and (ii) the resilience in the position estimation in the presence of node failures is increased. We evaluate the Recursive Position Estimation algorithm in the proposed model. Simulation results show that a small set of powerful nodes with a higher communication range can reduce significantly the error and increase the resilience in the position estimation.