Repository landing page

We are not able to resolve this OAI Identifier to the repository landing page. If you are the repository manager for this record, please head to the Dashboard and adjust the settings.

Estimating distances via connectivity in wireless sensor networks

Abstract

Distance estimation is vital for localization and many other applications in wireless sensor networks. In this paper, we develop a method that employs a maximum-likelihood estimator to estimate distances between a pair of neighboring nodes in a static wireless sensor network using their local connectivity information, namely the numbers of their common and non-common one-hop neighbors. We present the distance estimation method under a generic channel model, including the unit disk (communication) model and the more realistic log-normal (shadowing) model as special cases. Under the log-normal model, we investigate the impact of the log-normal model uncertainty; we numerically evaluate the bias and standard deviation associated with our method, which show that for long distances our method outperforms the method based on received signal strength; and we provide a Cramér-Rao lower bound analysis for the problem of estimating distances via connectivity and derive helpful guidelines for implementing our method. Finally, on implementing the proposed method on the basis of measurement data from a realistic environment and applying it in connectivity-based sensor localization, the advantages of the proposed method are confirmed. Copyright © 2012 John Wiley & Sons, Ltd

Similar works

Full text

thumbnail-image

OPUS - University of Technology Sydney

redirect
Last time updated on 18/10/2019

This paper was published in OPUS - University of Technology Sydney.

Having an issue?

Is data on this page outdated, violates copyrights or anything else? Report the problem now and we will take corresponding actions after reviewing your request.