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.
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 pa
per 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)doi: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
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