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Compressive sensing-based energy consumption model for data gathering techniques in wireless sensor networks

Author

Listed:
  • Mohammad Reza Ghaderi

    (Islamic Azad University)

  • Vahid Tabataba Vakili

    (Islamic Azad University
    Iran University of Science and Technology)

  • Mansour Sheikhan

    (Islamic Azad University)

Abstract

Nowadays, wireless sensor networks (WSNs) have found many applications in a variety of topics. The main objective in WSNs is to measure environmental phenomena and send reading data to the sink in multi-hop paths. The most important challenge in WSNs is to minimize energy consumption in the sensor nodes and increase the network lifetime. One of the most effective techniques for reducing energy consumption in WSNs is the compressive sensing (CS) which has recently been considered by the researchers. CS reduces the network energy consumption by reducing the number and size of transmitted data packets over the network. On the other hand, in order to overcome the challenge of energy consumption in the network, it is necessary to identify and analyze the energy consumption resources of the network. Although many models have been proposed for energy consumption analysis in the WSN, but these models were not based on the CS technique. Therefore, we have proposed a complete model in this work for energy consumption analysis in various CS-based data gathering techniques in WSNs. This model can be very effective in energy consumption optimization when designing a CS-based data gathering technique for WSN.

Suggested Citation

  • Mohammad Reza Ghaderi & Vahid Tabataba Vakili & Mansour Sheikhan, 2021. "Compressive sensing-based energy consumption model for data gathering techniques in wireless sensor networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 77(1), pages 83-108, May.
    • Handle: RePEc:spr:telsys:v:77:y:2021:i:1:d:10.1007_s11235-020-00748-9
    DOI: 10.1007/s11235-020-00748-9
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    References listed on IDEAS

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    1. Carolina Del-Valle-Soto & Carlos Mex-Perera & Juan Arturo Nolazco-Flores & Ramiro Velázquez & Alberto Rossa-Sierra, 2020. "Wireless Sensor Network Energy Model and Its Use in the Optimization of Routing Protocols," Energies, MDPI, vol. 13(3), pages 1-33, February.
    2. Wenyu Cai & Meiyan Zhang, 2018. "Spatiotemporal correlation–based adaptive sampling algorithm for clustered wireless sensor networks," International Journal of Distributed Sensor Networks, , vol. 14(8), pages 15501477187, August.
    3. Saeed Mehrjoo & Farshad Khunjush, 2018. "Accurate compressive data gathering in wireless sensor networks using weighted spatio-temporal compressive sensing," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 68(1), pages 79-88, May.
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    Cited by:

    1. N. Nisha Sulthana & M. Duraipandian, 2024. "EELCR: energy efficient lifetime aware cluster based routing technique for wireless sensor networks using optimal clustering and compression," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 85(1), pages 103-124, January.
    2. Ahmed Salim & Ahmed Ismail & Walid Osamy & Ahmed M. Khedr, 2021. "Compressive sensing based secure data aggregation scheme for IoT based WSN applications," PLOS ONE, Public Library of Science, vol. 16(12), pages 1-27, December.

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