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Analytical framework for adaptive compressive sensing for target detection within wireless visual sensor networks

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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 of detection can be achieved with adaptive CS and at the same time saving energy, where the proposed framework results in an 70% on average energy saving as compared to transmitting the captured image without CS.

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Authors and Affiliations

  1. Computer Engineering Department, College of Engineering and Technology, AAST, Alexandria, Egypt

    Salema Fayed & Sherin M. Youssef

  2. Electronics and Communication Department, College of Engineering and Technology, AAST, Alexandria, Egypt

    Amr El-Helw

  3. School of Computing and Digital Technology, Birmingham City University, Birmingham, B47XG, UK

    Mohammad Patwary

  4. School of Architecture, Computing and Engineering, University of East London, London, UK

    Mansour Moniri

Authors
  1. Salema Fayed
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  2. Sherin M. Youssef
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  3. Amr El-Helw
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  4. Mohammad Patwary
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  5. Mansour Moniri
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Correspondence to Salema Fayed.

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Fayed, S., Youssef, S., El-Helw, A. et al. Analytical framework for adaptive compressive sensing for target detection within wireless visual sensor networks. Multimed Tools Appl 77, 16533–16559 (2018). https://doi.org/10.1007/s11042-017-5227-3

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  • DOI: https://doi.org/10.1007/s11042-017-5227-3

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