The University of Southampton
University of Southampton Institutional Repository

Tackling the flip ambiguity in wireless sensor network localization and beyond

Tackling the flip ambiguity in wireless sensor network localization and beyond
Tackling the flip ambiguity in wireless sensor network localization and beyond
There have been significant advances in range-based numerical methods for sensor network localizations over the past decade. However, there remain a few challenges to be resolved to satisfaction. Those issues include, for example, the flip ambiguity, high level of noises in distance measurements, and irregular topology of the concerning network. Each or a combination of them often severely degrades the otherwise good performance of existing methods. Integrating the connectivity constraints is an effective way to deal with those issues. However, there are too many of such constraints, especially in a large and sparse network. This presents a challenging computational problem to existing methods. In this paper, we propose a convex optimization model based on the Euclidean Distance Matrix (EDM). In our model, the connectivity constraints can be simply represented as lower and upper bounds on the elements of EDM, resulting in a standard 3-block quadratic conic programming, which can be efficiently solved by a recently proposed 3-block alternating direction method of multipliers. Numerical experiments show that the EDM model effectively eliminates the flip ambiguity and retains robustness in terms of being resistance to irregular wireless sensor network topology and high noise levels.
85-97
Bai, Shuanghua
91e9bb33-da7d-4316-beb7-1e774032c2ba
Qi, Hou-Duo
e9789eb9-c2bc-4b63-9acb-c7e753cc9a85
Bai, Shuanghua
91e9bb33-da7d-4316-beb7-1e774032c2ba
Qi, Hou-Duo
e9789eb9-c2bc-4b63-9acb-c7e753cc9a85

Bai, Shuanghua and Qi, Hou-Duo (2016) Tackling the flip ambiguity in wireless sensor network localization and beyond. Digital Signal Processing, 55, 85-97. (doi:10.1016/j.dsp.2016.05.006).

Record type: Article

Abstract

There have been significant advances in range-based numerical methods for sensor network localizations over the past decade. However, there remain a few challenges to be resolved to satisfaction. Those issues include, for example, the flip ambiguity, high level of noises in distance measurements, and irregular topology of the concerning network. Each or a combination of them often severely degrades the otherwise good performance of existing methods. Integrating the connectivity constraints is an effective way to deal with those issues. However, there are too many of such constraints, especially in a large and sparse network. This presents a challenging computational problem to existing methods. In this paper, we propose a convex optimization model based on the Euclidean Distance Matrix (EDM). In our model, the connectivity constraints can be simply represented as lower and upper bounds on the elements of EDM, resulting in a standard 3-block quadratic conic programming, which can be efficiently solved by a recently proposed 3-block alternating direction method of multipliers. Numerical experiments show that the EDM model effectively eliminates the flip ambiguity and retains robustness in terms of being resistance to irregular wireless sensor network topology and high noise levels.

Text
EDMSNL_Revised_Journal_Style.pdf - Accepted Manuscript
Download (782kB)

More information

Accepted/In Press date: 23 May 2016
e-pub ahead of print date: 2 August 2016
Published date: August 2016
Organisations: Operational Research

Identifiers

Local EPrints ID: 394185
URI: https://eprints.soton.ac.uk/id/eprint/394185
PURE UUID: fdc2605b-5e60-431d-a401-a8b4c82207ac
ORCID for Hou-Duo Qi: ORCID iD orcid.org/0000-0003-3481-4814

Catalogue record

Date deposited: 12 May 2016 12:51
Last modified: 06 Oct 2018 00:35

Export record

Altmetrics

Contributors

Author: Shuanghua Bai
Author: Hou-Duo Qi ORCID iD

University divisions

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of https://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×