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Two-level energy-efficient data reduction strategies based on SAX-LZW and hierarchical clustering for minimizing the huge data conveyed on the internet of things networks

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Abstract

The Internet of things (IoT) is an omnipresent system that can be accessed from a long distance, linking a variety of devices (things), including wireless sensor networks (WSNs). Cyber-physical systems monitor things from a distance and control them. Because of its widespread usage in a variety of applications, WSN is among the most essential contributors to the IoT and plays a key part in the daily lives of people. The battery’s energy is a vital source in the sensor node, impacting the lifespan of the WSN. Energy scarcity is a serious concern in WSN, as a large volume of redundant data is gathered and transferred on a regular basis. As a result, efficient energy consumption is the fundamental approach to maximizing network lifetime. This article proposes a two-level data reduction approach for use at two network levels: sensor nodes and gateways (GWs). A novel Compression-Based Data Reduction (CBDR) technology and an effective transmitting data strategy derived from data correlation are being developed at the sensor node level. These strategies are designed to more efficiently compress data readings from IoT devices. CBDR compresses data in two stages: lossy SAX quantization and lossless LZW compression. The suggested approaches function as filtering at the GW level, allowing the GW to discover and subsequently delete groups of data that are duplicated and provided by surrounding nodes. At this level, two strategies are advised: the first is based on the data compression concept, and the second is to identify all couples of member nodes that produce duplicated sets so that redundancy may be eliminated before they are delivered to the sink. The proposed solutions are evaluated using extensive simulation tests made available by the network’s OMNeT++ simulator. The proposed methodologies’ efficiency is tested using four related works: the PFF protocol, the ATP protocol, the AVMDA protocol, and the PIP-DA protocol. The proposed solution uses up to 79%, 80%, 90%, and 6% less for each of the remaining data, transmitted data, energy, and data loss, respectively, depending on the results.

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Data availability

The data that support the findings of this study are openly available in [Intel Lab Data] at [35].

Code availability

The software application or custom code used to solve the proposed methods of this study is available from the corresponding author upon request.

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Acknowledgements

The authors would like to gratefully acknowledge the University of Babylon, Iraq, for the supported.

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Authors and Affiliations

  1. Department of Computer Science, College of Science for Women, University of Babylon, Babylon, Iraq

    Ali Kadhum M. Al-Qurabat & Ali Kadhum Idrees

  2. Department of Dentistry, Al-Mustaqbal University College, Babylon, Iraq

    Suha Abdulhussein Abdulzahra

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  1. Ali Kadhum M. Al-Qurabat
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  3. Ali Kadhum Idrees
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Correspondence to Ali Kadhum M. Al-Qurabat.

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Al-Qurabat, A.K.M., Abdulzahra, S.A. & Idrees, A.K. Two-level energy-efficient data reduction strategies based on SAX-LZW and hierarchical clustering for minimizing the huge data conveyed on the internet of things networks. J Supercomput 78, 17844–17890 (2022). https://doi.org/10.1007/s11227-022-04548-7

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