Next Article in Journal
Optimization of the KNN Supervised Classification Algorithm as a Support Tool for the Implantation of Deep Brain Stimulators in Patients with Parkinson’s Disease
Next Article in Special Issue
Using the Data-Compression Method for Studying Hunting Behavior in Small Mammals
Previous Article in Journal
Measuring Software Modularity Based on Software Networks
Previous Article in Special Issue
Approximations of Shannon Mutual Information for Discrete Variables with Applications to Neural Population Coding
Article Menu

Export Article

Open AccessArticle
Entropy 2019, 21(4), 345; https://doi.org/10.3390/e21040345

CDCS: Cluster-Based Distributed Compressed Sensing to Facilitate QoS Routing in Cognitive Video Sensor Networks

1
College of Computer Science and Technology, Nanjing Tech University, Nanjing 211816, China
2
National Engineering Research Center of Communications and Networking, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
*
Author to whom correspondence should be addressed.
Received: 4 February 2019 / Revised: 14 March 2019 / Accepted: 25 March 2019 / Published: 28 March 2019
  |  
PDF [960 KB, uploaded 28 March 2019]
  |  

Abstract

Compressed sensing based in-network compression methods which minimize data redundancy are critical to cognitive video sensor networks. However, most existing methods require a large number of sensors for each measurement, resulting in significant performance degradation in energy efficiency and quality-of-service satisfaction. In this paper, a cluster-based distributed compressed sensing scheme working together with a quality-of-service aware routing framework is proposed to deliver visual information in cognitive video sensor networks efficiently. First, the correlation among adjacent video sensors determines the member nodes that participate in a cluster. On this basis, a sequential compressed sensing approach is applied to determine whether enough measurements are obtained to limit the reconstruction error between decoded signals and original signals under a specified reconstruction threshold. The goal is to maximize the removal of unnecessary traffic without sacrificing video quality. Lastly, the compressed data is transmitted via a distributed spectrum-aware quality-of-service routing scheme, with an objective of minimizing energy consumption subject to delay and reliability constraints. Simulation results demonstrate that the proposed approach can achieve energy-efficient data delivery and reconstruction accuracy of visual information compared with existing quality-of-service routing schemes. View Full-Text
Keywords: spatial correlation; quality-of-service; distributed compressed sensing; information theory; cognitive video sensor networks spatial correlation; quality-of-service; distributed compressed sensing; information theory; cognitive video sensor networks
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
SciFeed

Share & Cite This Article

MDPI and ACS Style

Shen, H.; Li, L.; Wang, T.; Bai, G. CDCS: Cluster-Based Distributed Compressed Sensing to Facilitate QoS Routing in Cognitive Video Sensor Networks. Entropy 2019, 21, 345.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Entropy EISSN 1099-4300 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top