Performance Improvement of Wireless Multimedia Sensor Networks Using MIMO and Compressive Sensing

  • Arjav Bavarva
  • Preetida Vinayakray Jani
  • Komal Ghetiya
Research paper
  • 32 Downloads

Abstract

Wireless Multimedia Sensor Networks (WMSN) are designed to transmit audio and video streams, still images, and scalar data. Multimedia transmission over wireless sensor networks has many applications, such as video surveillance, object tracking, telemedicine, theft control systems, and traffic monitoring. Researchers face many challenges, such as higher data rates, lower energy consumption, reliability, signal detection and estimation, uncertainty in network topology, quality of service (QoS), and security-and privacy-related issues to accomplish various applications of WMSN. This paper presents multiple input multiple output (MIMO) along with compressive sensing (CS) properties to improve system performance in terms of energy consumption and QoS in deep fade environments. The CS theory model has been proposed to reduce energy consumption by taking fewer measurements of the original signal or information and reconstructing it with acceptable image quality at the receiver side. The transmission and processing energy can be reduced by transmitting fewer measurements from the sensor side itself. The MIMO model and CS algorithm have been simulated, and results show that CS performs well on images.

Keywords

MIMO wireless multimedia sensor networks compressive sensing energy-efficient WMSN 

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References

  1. [1]
    A. Bavarva, P. Jani. An introduction to wireless multimedia sensor networks [M]. Electronics for you, April 2015: 46–50.Google Scholar
  2. [2]
    I. F. Akyildiz, T. Melodia, K. R. Chowdury. Wireless multimedia sensor networks: a survey [J]. IEEE Wireless Communications, 2007, 14(6): 32–39.CrossRefGoogle Scholar
  3. [3]
    A. Bavarva, P. V. Jani. Improve the channel performance of wireless multimedia sensor network using MIMO properties [C]//International Conference on Advances in Computing, Chennai, 2015.Google Scholar
  4. [4]
    F. Salahdine, N. Kaabouch, H. El. Ghazi. A survey on compressive sensing techniques for cognitive radio networks [J]. Physical Communication, 20, 2016(9): 61–73.CrossRefGoogle Scholar
  5. [5]
    N. Eslahi, A. Aghagolzadeh, S. Andargoli. Image/video compressive sensing recovery using joint adaptive sparsity measure [J]. Neurocomputing, 2016, 200(3): 88–109.CrossRefGoogle Scholar
  6. [6]
    R. Hemalatha, S. Radha, S. Sudharsan. Energy-efficient image transmission in wireless multimedia sensor networks using block-based Compressive Sensing [J]. Computers and Electrical engineering, 2015, 44(2): 67–79.CrossRefGoogle Scholar
  7. [7]
    Y. Cho, J. Kim, W. Yang, et al. MIMO-OFDM wireless communications with MATLAB [M]. IEEE press, Noida, 2010.CrossRefGoogle Scholar
  8. [8]
    S. Chu, X. Wang, Y. Yang. Adaptive scheduling in MIMO-based heterogeneous Ad Hoc networks [J]. IEEE Transactions on mobile computing, 2014, 13(5): 964–978.CrossRefGoogle Scholar
  9. [9]
    D. Gong, M. Zhao, Y. Yang. A multi-channel cooperative MIMO MAC protocol for clustered wireless sensor networks [J]. Journal of Parallel and Distributed Computing, 2014, 74: 3098–3114.CrossRefGoogle Scholar
  10. [10]
    D. L. Donoho. Compressed sensing [J]. IEEE Transactions on Information Theory, 2006, 52(4): 1289–1306.MathSciNetCrossRefMATHGoogle Scholar
  11. [11]
    E. J. Candes, M. B. Wakin. An introduction to compressive sampling [J]. IEEE Signal Processing Magazine, 2008, 25(2): 21–30.CrossRefGoogle Scholar
  12. [12]
    B. Bah, J. Tanner. Improved bounds on restricted isometry constants for Gaussian Matrices [J]. Siam Journal on Matrix Analysis & Applications, 2010, 31(5): 2882–2898.MathSciNetCrossRefMATHGoogle Scholar
  13. [13]
    V. Tiwari, P. P. Bansod, A. Kumar. Designing sparse sensing matrix for compressive sensing to reconstruct high resolution medical images [EB/BL]. https://www.cogentoa.com/article/10.1080/23311916. 2015.1017244.pdf, Cogent Eng., 2015.Google Scholar
  14. [14]
    M. Dhasmana, S. Budhiraja. A survey of compressive sensing based on Greedy Pursuit reconstruction algorithms [J]. International Journals of Image Graphics and Signal Processing, 2015, 10(1): 1–10.Google Scholar
  15. [15]
    X. L. Liu, W. Hu, C. Luo, et al. Compressive image broadcasting in MIMO systems with receiver antenna heterogeneity [J]. Signal processing: image communication, 2014, 29(1): 361–374.Google Scholar

Copyright information

© Posts & Telecom Press and Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Arjav Bavarva
    • 1
  • Preetida Vinayakray Jani
    • 2
  • Komal Ghetiya
    • 1
  1. 1.Department of Electronics and Communication, School of EngineeringRK UniversityRajkotIndia
  2. 2.Department of Electronics and TelecommunicationSardar Patel Institute of TechnologyMumbaiIndia

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