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A Lossless Distributed Data Compression and Aggregation Methods for Low Resources Wireless Sensors Platforms

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Abstract

Wireless Multimedia Sensor Networks (WMSN) are undoubtedly one of the technologies that will transform the world around all of us. Still, they have been as useful and beneficial as resource-constrained distributed event-based system for several scenarios. Yet, in WMSN, optimisation of limited resources (energy, computing memory, bandwidth, storage and so on) during data collection, processing and communication process is a major challenge to guarantee the high performance of the system. Unfortunately, data redundancy involves a large consumption of sensor resources during processing and transferring information to an analysis centre. As a matter of fact, most of energy consumption (as much as 80%) for standard WSN applications lies in the radio module where receiving and sending packets is necessary to communicate between stations. To tackle this issue, this paper proposes an approach to achieve optimal sensor resources by data compression and aggregation regarding integrity of raw data. Then, the main objective is to reduce this redundancy by discarding a certain number of packets of information and keeping only the most meaningful and informative ones for the reconstruction. Data aggregation discarded a certain sensing data packet, which lead to low data-rate communication and low likelihood of packet collisions on the wireless medium. Data compression reduces a redundancy in keeping aggregated data, which leads to storage saving and sending only a small data stream in the bandwidth of communication. The performances of the proposed approach are qualified using experimental simulation on OMNeT +  + /Castalia. The performance metrics were evaluated in terms of data Aggregation Rate (AR), Compression Ratio (CR), Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR) and Energy Consumption (EC).The obtained results have significantly increased the life span of the sensors and thus the lifetime of the network. Furthermore, the integrity (quality) of the raw data is guaranteed.

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Data Availability

The datasets used and/or analysed during the current research are available from the corresponding author on reasonable request.

Code Availability

The custom code is available from the corresponding author on reasonable request.

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Acknowledgements

We gratefully acknowledge that this work has been partially supported by the Directorate of Central Africa and Great Lakes Region (DCAGLR) of “AgenceUniversitaire de la Francophonie” (AUF) for the financial support of ourresearch in the "Laboratoire de GénieInformatique et d’Automatique de l'Artois—UR 3926", University of Artois, France.

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Authors and Affiliations

  1. Department of Mathematics and Computer Science, Faculty of Science, University of Dschang, Dschang, Cameroon

    Elie Fute Tagne & Hugues Marie Kamdjou

  2. Department of Computer Engineering, Faculty of Engineering and Technology, University of Buea, Buea, Cameroon

    Elie Fute Tagne

  3. LGI2A (Laboratoire de Génie Informatique et d’Automatique de l’Artois), University of Artois, 3926, F-62400, Bethune, UR, France

    Adnen El Amraoui

  4. Department of Industrial Systems and Environmental Engineering, Fotso Victor University Institute of Technology, Faculty of Science, University of Dschang, Bandjoun, Cameroon

    Armand Nzeukou

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  1. Elie Fute Tagne
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All authors participated during the design, implementation and writing of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Elie Fute Tagne.

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Tagne, E.F., Kamdjou, H.M., Amraoui, A.E. et al. A Lossless Distributed Data Compression and Aggregation Methods for Low Resources Wireless Sensors Platforms. Wireless Pers Commun 128, 621–643 (2023). https://doi.org/10.1007/s11277-022-09970-x

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