bullet Sensors & Transducers Journal

    (ISSN 1726- 5479)

205.767

2008 e-Impact Factor

25 Top Downloaded Articles

Journal Subscription 2012

Editorial Calendar 2012

Submit an Article

Editorial Board

Current Issue

Sensors & Transducers journal's cover

Sensors & Transducers Journal 2011

Sensors & Transducers Journal 2010

Sensors & Transducers Journal 2009

Sensors & Transducers Journal 2008

Sensors & Transducers Journal 2007

2000-2002 S&T e-Digest Contents

2003 S&T e-Digest Contents

2004 S&T e-Digest Contents

2005 S&T e-Digest Contents

2006 S&T e-Digest Contents

Best Articles 2011

 

 

 

Vol. 14-2, Special Issue, March 2012, pp.111-124

 

Bullet

 

Variable Step Size LMS Algorithm for Data Prediction in Wireless Sensor Networks

 

1 Biljana RISTESKA STOJKOSKA, 2 Dimitar SOLEV, 3 Danco DAVCEV

1 Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University,

Skopje, 1000, Macedonia, Tel.: +38923099157
2 Faculty of Computer Science and Engineering, Ss. Cyril and Methodius University,
Skopje, 1000, Macedonia

3 University for Information Science & Technology St. Paul the Apostle, Ohrid, 6000, Macedonia, Tel.: +38946550001, fax: +38946550004

E-mail: biljana.stojkoska@finki.ukim.mk, dimitar.solev@gmail.com, dancho.davchev@uist.edu.mk

 

Received: 15 November 2011   /Accepted: 20 December 2011   /Published: 12 March 2012

Digital Sensors and Sensor Sysstems

 

Abstract: Wireless communication itself consumes the most amount of energy in a given WSN, so the most logical way to reduce the energy consumption is to reduce the number of radio transmissions. To address this issue, there have been developed data reduction strategies which reduce the amount of sent data by predicting the measured values both at the source and the sink, requiring transmission only if a certain reading differs by a given margin from the predicted values. While these strategies often provide great reduction in power consumption, they need a-priori knowledge of the explored domain in order to correctly model the expected values. Using a widely known mathematical apparatus called the Least Mean Square Algorithm (LMS), it is possible to get great energy savings while eliminating the need of former knowledge or any kind of modeling. In this paper with we use the Least Mean Square Algorithm with variable step size (LMS-VSS) parameter. By applying this algorithm on real-world dataset, we achieved maximum data reduction of over 95 % for star topology and around 97 % when data aggregation was taken into account for cluster-based topology, both for error margin of 0.5 C. Using mean square error as metric for evaluation, we show that our algorithm outperforms classical LMS technique.

 

Keywords: Wireless sensor network, Data prediction, Least mean square algorithm, Time series forecasting

 

Acrobat reader logo Click <here> or title of paper to download the full pages article (1.13 Mb)

 

 

Sensors & Transducers journal subscription

only 450 $ US per year:

 

 

 

Read more about Wireless Sensors and Wireless Sensor Networks

 

 

 

 


1999 - 2012 Copyright , International Frequency Sensor Association (IFSA). All Rights Reserved.


Home - News - Links - Archives - Tools - Voltage-to-Frequency Converters - Standardization - Patents - Marketplace - Projects - Wish List - e-Shop - Sensor Jobs - Membership - Videos - Publishing - Site Map - Subscribe - Search

 Members Area -Sensors Portal -Training Courses - S&T Digest - For advertisers - Bookstore - Forums - Polls - Submit Press Release - Submit White Paper - Testimonies - Twitter - Facebook - LinkedIn