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

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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.

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Authors and Affiliations

  1. Department of Electrical Engineering, South Tehran Branch, Islamic Azad University, Tehran, Iran

    Mohammad Reza Ghaderi, Vahid Tabataba Vakili & Mansour Sheikhan

  2. Department of Electrical Engineering, Iran University of Science and Technology, Tehran, Iran

    Vahid Tabataba Vakili

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  1. Mohammad Reza Ghaderi
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  2. Vahid Tabataba Vakili
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Correspondence to Vahid Tabataba Vakili.

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Ghaderi, M.R., Tabataba Vakili, V. & Sheikhan, M. Compressive sensing-based energy consumption model for data gathering techniques in wireless sensor networks. Telecommun Syst 77, 83–108 (2021). https://doi.org/10.1007/s11235-020-00748-9

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  • DOI: https://doi.org/10.1007/s11235-020-00748-9

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