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

Fayed, Salema and Youssef, Sherin and El-Helw, Amr and Patwary, Mohammad and Moniri, Mansour (2017) Analytical framework for Adaptive Compressive Sensing for Target Detection within Wireless Visual Sensor Networks. Multimedia Tools and Applications. ISSN 1380-7501

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Abstract

Wireless visual sensor networks (WVSNs) are composed of a large
number of visual sensor nodes covering a specific 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

Item Type: Article
Additional Information: The final publication is available at Springer via https://doi.org/10.1007/s11042-017-5227-3
Uncontrolled Keywords: Compressive sensing, Duty cycles, Target detection, Wireless visual sensor networks
Subjects: G400 Computer Science
Divisions: Faculty of Computing, Engineering and the Built Environment
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology
Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology > Cloud Computing
UoA Collections > UoA11: Computer Science and Informatics
Depositing User: $ Ian McDonald
Date Deposited: 08 Aug 2017 08:49
Last Modified: 14 Nov 2017 11:46
URI: http://www.open-access.bcu.ac.uk/id/eprint/4960

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