bullet Sensors & Transducers Journal

    (ISSN 1726- 5479)


2008 e-Impact Factor

25 Top Downloaded Articles

Best Selling Articles 2012

Journal Subscription 2013

Editorial Calendar 2013

Submit an Article

Editorial Board

Current Issue

S&T 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. 158, Issue 11, November 2013, pp. 332-337




A Sparse Optimization Method for Distributed Hydrology Information Monitoring System
1 Huang LIJUN, 1 Zhang SHUFANG, 2 Yao HAN, 1 Hu XUEQING, 3 Zhang JIAHUA

1 College of Mechanical and Electronic Engineering, Suzhou University, 234000, China

2 Suzhou Construction Investment Co., Ltd, 234000, China

3 School of Mathematics and Information Technology, Nanjing Xiaozhuang University, 211171, China

1 Tel.: 15705577979, fax: 05573048275

1 E-mail: ljhuang@mail.ustc.edu.cn


Received: 3 September 2013   /Accepted: 25 October 2013   /Published: 30 November 2013

Digital Sensors and Sensor Sysstems


Abstract: The sparse optimization method is an effective technological approach to solve information detection and spectrum sensing in hydrology information monitoring sensor network and it is at the frontier domain of water data collection and transmission field in home and abroad. By combining sparse optimization with distributed hydrology information monitoring and based on the theoretical framework of sparse optimization of distributed water level monitoring, the paper puts forward the distributed sparse optimization monitoring method of water environment information, which expands the new application of sparse optimization in distributed network on one hand. On the other hand, it points out the reconstruction method of joint sparse signals based on the block coordinate descent method. Simulation experiment shows that the proposed method can converge quickly to the approximate optimal solution and has a good robustness for calculation error caused by inaccurate average and other uncertain factors in the network.


Keywords: Hydrology information, Sparse representation, Distributed data collection, Optimization algorithm, Wireless sensor networks.


Acrobat reader logo Click <here> or title of paper to download the full pages article in pdf format



Download <here> the Library Journal Recommendation Form



Read more about Wireless Sensor Networks






1999 - 2018 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