Analytical framework for Adaptive Compressive Sensing for Target Detection within Wireless Visual Sensor Networks

Article


Fayed, Salema, Youssef, Sherin, El-Helw, Amr, Patwary, Mohammad and Moniri, M. 2017. Analytical framework for Adaptive Compressive Sensing for Target Detection within Wireless Visual Sensor Networks. Multimedia Tools and Applications. 77 (13), pp. 16533-16559. https://doi.org/10.1007/s11042-017-5227-3
AuthorsFayed, Salema, Youssef, Sherin, El-Helw, Amr, Patwary, Mohammad and Moniri, M.
Abstract

Wireless visual sensor networks (WVSNs) are composed of a large number of visual sensor nodes covering a specifc geographical region. This paper addresses the target detection problem within WVSNs where visual sensor nodes are left unattended for long-term deployment. As battery energy is a critical issue it is always challenging to maximize the network's lifetime. In order to reduce energy consumption, nodes undergo cycles of active-sleep periods that save their battery energy by switching sensor nodes ON and OFF, according to predefined duty cycles. Moreover, adaptive compressive sensing is expected to dynamically reduce the size of transmitted data through the wireless channel, saving communication bandwidth and consequently saving energy. This paper derives for the first time an analytical framework for selecting node's duty cycles and dynamically choosing the appropriate compression rates for the captured images and videos based on their sparsity nature. This reduces energy waste by reaching the maximum compression rate for each dataset without compromising the probability of detection. Experiments were conducted on different standard datasets resembling different scenes; indoor and outdoor, for single and multiple targets detection. Moreover, datasets were chosen with different sparsity levels to investigate the effect of sparsity on the compression rates. Results showed that by selecting duty cycles and dynamically choosing the appropriate compression rates, the desired performance

JournalMultimedia Tools and Applications
Journal citation77 (13), pp. 16533-16559
ISSN1380-7501
Year2017
PublisherSpringer Verlag
Accepted author manuscript
License
Digital Object Identifier (DOI)https://doi.org/10.1007/s11042-017-5227-3
Web address (URL)https://doi.org/10.1007/s11042-017-5227-3
Publication dates
Online31 Oct 2017
Publication process dates
Deposited04 Oct 2017
Accepted14 Sep 2017
Accepted14 Sep 2017
Copyright informationThis is a post-peer-review, pre-copyedit version of an article published in Multimedia Tools and Applications. The final authenticated version is available online at: https://doi.org/10.1007/s11042-017-5227-3
Permalink -

https://repository.uel.ac.uk/item/84q12

Download files

Accepted author manuscript
Moniri_Analytical framework for Adaptive Compressive Sensing for Target Detection within Wireless Visual Sensor Networks.pdf
License: Springer Nature terms of use for archived author accepted manuscripts (AAMs) of subscription articles, books and chapters

  • 41
    total views
  • 104
    total downloads
  • 1
    views this month
  • 1
    downloads this month

Export as

Related outputs

A Framework for Augmented Reality Based Shared Experiences
Ali, A., Glackin, C., Cannings, N., Wall, J., Sharif, S. and Moniri, M. 2019. A Framework for Augmented Reality Based Shared Experiences. Immersive Learning Research Network - iLRN. London, UK 23 - 27 Jun 2019 Technischen Universit├Ąt Graz. https://doi.org/10.3217/978-3-85125-657-4-24
Comparative Analysis on the Competitiveness of Conventional and Compressive Sensing-based Query Processing
Fayed, Salema, Youssef, Sherin, El-Helw, Amr, Akbari, Akbar Sheikh, Patwary, Mohammad and Moniri, M. 2014. Comparative Analysis on the Competitiveness of Conventional and Compressive Sensing-based Query Processing. in: Advances in Information Science and Applications, Volume 1: Proceedings of the 18th International Conference on Computers (part of CSCC '14) Institute for Natural Sciences and Engineering (INASE).
Evaluation of Performance Enhancement for Crash Constellation Prediction via Car-to-Car Communication
Kuehbeck, Thomas, Hakobyan, Gor, Sikora, Axel, Chibelushi, Claude C. and Moniri, M. 2014. Evaluation of Performance Enhancement for Crash Constellation Prediction via Car-to-Car Communication. in: Communication Technologies for Vehicles Springer.
Compressive Sensing-based Target Tracking for Wireless Visual Sensor Networks
Fayed, Salema, Youssef, Sherin, El-Helw, Amr, Patwary, Mohammad and Moniri, M. 2014. Compressive Sensing-based Target Tracking for Wireless Visual Sensor Networks. in: Advances in Information Science and Applications, Volume I: Proceedings of the 18th International Conference on Computers (part of CSCC '14) Institute for Natural Sciences and Engineering (INASE).
Towards a fully automated monitoring system for Manhole Cover: Smart cities and IOT applications
Aly, Hesham H., Soliman, Abdel Hamid and Moniri, M. 2015. Towards a fully automated monitoring system for Manhole Cover: Smart cities and IOT applications. in: 2015 IEEE First International Smart Cities Conference (ISC2) Institute of Electrical and Electronics Engineers (IEEE). pp. 24-30
Prediction architecture based on block matching statistics for mixed spatial-resolution multi-view video coding
Said, Hany, Moniri, M. and Chibelushi, Claude C. 2017. Prediction architecture based on block matching statistics for mixed spatial-resolution multi-view video coding. EURASIP Journal on Image and Video Processing. 2017 (1). https://doi.org/10.1186/s13640-017-0164-7
A Hybrid Adaptive Compressive Sensing Model for Visual Tracking in Wireless Visual Sensor Networks
Fayed, Salema, Youssef, Sherin, El-Helw, Amr, Patwary, Mohammad and Moniri, M. 2015. A Hybrid Adaptive Compressive Sensing Model for Visual Tracking in Wireless Visual Sensor Networks. International Journal of Circuits, Systems, and Signal Processing. 9, pp. 134-144.
Adaptive compressive sensing for target tracking within wireless visual sensor networks-based surveillance applications
Fayed, Salema, M.Youssef, Sherin, El-Helw, Amr, Patwary, Mohammad and Moniri, M. 2015. Adaptive compressive sensing for target tracking within wireless visual sensor networks-based surveillance applications. Multimedia Tools and Applications. 75 (11), pp. 6347-6371. https://doi.org/10.1007/s11042-015-2575-8
Image segmentation using adaptive video analytics, Image processingUS 9047677 B2
Sedky, Mohamed Hamed Ismail, Chibelushi, Claude Chilufya and Moniri, M. 2015. Image segmentation using adaptive video analytics, Image processingUS 9047677 B2. US 13/140,378
Spectral-360: A Physics-Based Technique for Change Detection
Sedky, Mohamed, Moniri, M. and Chibelushi, Claude C. 2014. Spectral-360: A Physics-Based Technique for Change Detection. 2014 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW 2014). Columbus, OH, USA 23 - 28 Jun 2014 Institute of Electrical and Electronics Engineers (IEEE). pp. 405-408 https://doi.org/10.1109/CVPRW.2014.65