Localization in Wireless Sensor Networks Using Visible Light in Non-Line of Sight Conditions

Localization in Wireless Sensor Networks Using Visible Light in Non-Line of Sight Conditions In this paper, a novel method to localize indoor wireless sensor nodes using visible light in non-line of sight (NLOS) condition is proposed. The proposed method is able to identify NLOS condition in a sensor network and subsequently localize the sensor nodes. Since visible light is used for localization, the reflection points are first localized using time difference of arrival in a maximum likelihood framework. The location of the sensor nodes is then estimated using range and reflection angles which are themselves computed using novel geometric methods. Simulations and real field wireless sensor node deployments are then used to evaluate the performance of the proposed method. Experimental results indicate that the proposed method is able to localize sensor nodes with a high degree of accuracy when compared to conventional methods under NLOS conditions. The method also demonstrates reasonable robustness under sensor and ambient noise conditions. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Wireless Personal Communications Springer Journals

Localization in Wireless Sensor Networks Using Visible Light in Non-Line of Sight Conditions

Loading next page...
 
/lp/springer_journal/localization-in-wireless-sensor-networks-using-visible-light-in-non-6W3wKn9adl
Publisher
Springer Journals
Copyright
Copyright © 2017 by Springer Science+Business Media, LLC
Subject
Engineering; Communications Engineering, Networks; Signal,Image and Speech Processing; Computer Communication Networks
ISSN
0929-6212
eISSN
1572-834X
D.O.I.
10.1007/s11277-017-4853-4
Publisher site
See Article on Publisher Site

Abstract

In this paper, a novel method to localize indoor wireless sensor nodes using visible light in non-line of sight (NLOS) condition is proposed. The proposed method is able to identify NLOS condition in a sensor network and subsequently localize the sensor nodes. Since visible light is used for localization, the reflection points are first localized using time difference of arrival in a maximum likelihood framework. The location of the sensor nodes is then estimated using range and reflection angles which are themselves computed using novel geometric methods. Simulations and real field wireless sensor node deployments are then used to evaluate the performance of the proposed method. Experimental results indicate that the proposed method is able to localize sensor nodes with a high degree of accuracy when compared to conventional methods under NLOS conditions. The method also demonstrates reasonable robustness under sensor and ambient noise conditions.

Journal

Wireless Personal CommunicationsSpringer Journals

Published: Aug 14, 2017

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off