Distance estimation by mining characteristics in anisotropic sensor networks

  • Authors:
  • Kai Li;Yun Wang

  • Affiliations:
  • School of Computer Science & Engineering, Southeast University, Nanjing, China 210096;School of Computer Science & Engineering, Southeast University, Nanjing, China 210096

  • Venue:
  • Cluster Computing
  • Year:
  • 2010

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Abstract

Localization is useful for many position-dependent applications in wireless sensor networks, where distance estimation from sensor nodes to beacon nodes plays a fundamental role. Most current ranging methods rely on an assumption that deployed WSNs are isotropic. Hence, adjustments on measured distances are the same in all directions. Unfortunately, this assumption does not hold in practice. Present methods introduce such great ranging errors that they are not feasible for real applications. In order to obtain better distance estimation in anisotropic WSNs, we propose a new metric, Dominating Degree, to describe the local deployment characteristics of sensor nodes, and to identify turning nodes along paths. We further propose a method to scale deployment irregularities of WSNs as global characteristics. Finally, appropriate adjustments to distance measurements are performed by synthesizing both local and global characteristics. Simulation results show that the proposed method outperforms PDM and DV-distance especially when beacon nodes are not deployed uniformly.