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EDCCS: effective deterministic clustering scheme based compressive sensing to enhance IoT based WSNs

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Abstract

The problem of Data acquisition in large distributed Wireless Sensor Networks (WSNs) scale is a hindrance in the growth of the Internet of Things (IoT). Recently, the combination of compressive sensing (CS) and routing techniques has attracted great interest from researchers. An open question of this approach is how to effectively integrate these technologies for specific tasks. The objective of this paper is two parts. First, we propose an effective deterministic clustering scheme based CS technique (EDCCS) for data collection in IoT based homogeneous and heterogeneous WSN to deal with the data acquisition problem, reduce the consumption of energy and increase the lifetime of network. Second, we propose random matching pursuit (RMP) as an effective CS reconstruction algorithm to improve the recovery process by reducing the error average at the base station (BS). The simulation results show that our proposed novel EDCCS scheme reduces at least 60% of the average power consumption and increases the network lifetime at least 1.3 times of the other schemes in homogeneous network while, it increases the network lifetime and residual energy by 1.9 times and 1.3 times respectively, compared to the other schemes in heterogeneous network. Also, our proposed RMP algorithm reduces the error average of reconstruction at least 35% compared to other reconstruction algorithms.

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Data availibility

The datasets generated during the Simulation and Analysis of our proposed algorithm are randomly generated and also we used Intel Berkeley Research Lab Data Set. Available at: http://db.csail.mit.edu/labdata/labdata.html where, RMP algorithm is used to reconstruct the data that collected by Intel Berkeley Research Lab Data.

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Authors and Affiliations

  1. Tashkent State University of Economics, Tashkent, Uzbekistan

    Ahmed Aziz

  2. Yeoju Technical Institute in Tashkent, Tashkent, Uzbekistan

    Ahmed Aziz

  3. Faculty of Computers and Artificial Intelligence, Benha University, Benha, Egypt

    Walid Osamy

  4. Department of Applied Natural Science, Applied College in Unaizah, Qassim University, Unaizah, Qassim, Kingdom of Saudi Arabia

    Walid Osamy

  5. Computer Science Department, University of Sharjah, Sharjah, 27272, UAE

    Ahmed M. Khedr

  6. Research Institute of Sciences and Engineering, University of Sharjah, Sharjah, 27272, UAE

    Oruba Alfawaz

  7. Faculty of Science, Zagazig University, Zagazig, Egypt

    Ahmed M. Khedr

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  1. Ahmed Aziz
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Aziz, A., Osamy, W., Alfawaz, O. et al. EDCCS: effective deterministic clustering scheme based compressive sensing to enhance IoT based WSNs. Wireless Netw 28, 2375–2391 (2022). https://doi.org/10.1007/s11276-022-02973-3

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