{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T10:00:41Z","timestamp":1771668041945,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,7,3]],"date-time":"2020-07-03T00:00:00Z","timestamp":1593734400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"PHC-Tassili","award":["18MDU114"],"award-info":[{"award-number":["18MDU114"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Large Scale Wireless Sensor Networks (LS-WSNs) are Wireless Sensor Networks (WSNs) composed of an impressive number of sensors, with inherent detection and processing capabilities, to be deployed over large areas of interest. The deployment of a very large number of diverse or similar sensors is certainly a common practice that aims to overcome frequent sensor failures and avoid any human intervention to replace them or recharge their batteries, to ensure the reliability of the network. However, in practice, the complexity of LS-WSNs pose significant challenges to ensuring quality communications in terms of symmetry of radio links and maximizing network life. In recent years, most of the proposed LS-WSN deployment techniques aim either to maximize network connectivity, increase coverage of the area of interest or, of course, extend network life. Few studies have considered the choice of a good LS-WSN deployment strategy as a solution for both connectivity and energy consumption efficiency. In this paper, we designed a LS-WSN as a tool for collecting big data generated by smart cities. The intrinsic characteristics of big data require the use of heterogeneous sensors. Furthermore, in order to build a heterogeneous LS-WSN, our scientific contributions include a model of quantifying the kinds of sensors in the network and the multi-level architecture for LS-WSN deployment, which relies on clustering for the big data collection. The results simulations show that our proposed LS-WSN architecture is better than some well known WSN protocols in the literature including Low Energy Adaptive Clustering Hierarchy (LEACH), E-LEACH, SEP, DEEC, EECDA, DSCHE and BEENISH.<\/jats:p>","DOI":"10.3390\/sym12071113","type":"journal-article","created":{"date-parts":[[2020,7,6]],"date-time":"2020-07-06T11:07:42Z","timestamp":1594033662000},"page":"1113","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["A Framework of Modeling Large-Scale Wireless Sensor Networks for Big Data Collection"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2711-3710","authenticated-orcid":false,"given":"Asside Christian","family":"Djedouboum","sequence":"first","affiliation":[{"name":"LI-PaRAD Laboratory, Universit\u00e9 Paris Saclay, Versailles Saint-Quentin-en-Yvelines University, 45 Avenue des \u00c9tats-Unis, 78000 Versailles, France"},{"name":"LaRI Laboratory, University of Maroua, P.O. Box 814 Maroua, Cameroon"},{"name":"Faculty of Exact and Applied Sciences, University of Moundou, P.O. Box 206 Moundou, Chad"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5660-0660","authenticated-orcid":false,"given":"Ado Adamou Abba","family":"Ari","sequence":"additional","affiliation":[{"name":"LI-PaRAD Laboratory, Universit\u00e9 Paris Saclay, Versailles Saint-Quentin-en-Yvelines University, 45 Avenue des \u00c9tats-Unis, 78000 Versailles, France"},{"name":"LaRI Laboratory, University of Maroua, P.O. Box 814 Maroua, Cameroon"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6082-6574","authenticated-orcid":false,"given":"Abdelhak Mourad","family":"Gueroui","sequence":"additional","affiliation":[{"name":"LI-PaRAD Laboratory, Universit\u00e9 Paris Saclay, Versailles Saint-Quentin-en-Yvelines University, 45 Avenue des \u00c9tats-Unis, 78000 Versailles, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alidou","family":"Mohamadou","sequence":"additional","affiliation":[{"name":"LaRI Laboratory, University of Maroua, P.O. Box 814 Maroua, Cameroon"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9560-1196","authenticated-orcid":false,"given":"Ousmane","family":"Thiare","sequence":"additional","affiliation":[{"name":"LANI Laboratory, Gaston Berger University of Saint-Louis, P.O. Box 234 Saint-Louis, Senegal"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7007-7607","authenticated-orcid":false,"given":"Zibouda","family":"Aliouat","sequence":"additional","affiliation":[{"name":"LRSD Laboratory, University Ferhat Abbes Setif 1, El Bez, Setif 19000, Algeria"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,7,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Djedouboum, A., Ari, A.A.A., Gueroui, A., Mohamadou, A., and Aliouat, Z. (2018). Big Data Collection in Large-Scale Wireless Sensor Networks. Sensors, 18.","DOI":"10.3390\/s18124474"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"106957","DOI":"10.1016\/j.comnet.2019.106957","article-title":"Resource allocation scheme for 5G C-RAN: A Swarm Intelligence based approach","volume":"165","author":"Ari","year":"2019","journal-title":"Comput. 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