{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T03:48:00Z","timestamp":1781754480409,"version":"3.54.5"},"reference-count":50,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T00:00:00Z","timestamp":1686700800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["BDCC"],"abstract":"<jats:p>Internet of Things (IoT) systems include many smart devices that continuously generate massive spatio-temporal data, which can be difficult to process. These continuous data streams need to be stored smartly so that query searches are efficient. In this work, we propose an efficient method, in the fog-cloud computing architecture, to index continuous and heterogeneous data streams in metric space. This method divides the fog layer into three levels: clustering, clusters processing and indexing. The Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm is used to group the data from each stream into homogeneous clusters at the clustering fog level. Each cluster in the first data stream is stored in the clusters processing fog level and indexed directly in the indexing fog level in a Binary tree with Hyperplane (BH tree). The indexing of clusters in the subsequent data stream is determined by the coefficient of variation (CV) value of the union of the new cluster with the existing clusters in the cluster processing fog layer. An analysis and comparison of our experimental results with other results in the literature demonstrated the effectiveness of the CV method in reducing energy consumption during BH tree construction, as well as reducing the search time and energy consumption during a k Nearest Neighbor (kNN) parallel query search.<\/jats:p>","DOI":"10.3390\/bdcc7020119","type":"journal-article","created":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T03:02:24Z","timestamp":1686711744000},"page":"119","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Efficient Method for Continuous IoT Data Stream Indexing in the Fog-Cloud Computing Level"],"prefix":"10.3390","volume":"7","author":[{"given":"Karima","family":"Khettabi","sequence":"first","affiliation":[{"name":"LabSTIC Laboratory, Department of Computer Science, 8 Mai 1945 University, Guelma 24000, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8105-9810","authenticated-orcid":false,"given":"Zineddine","family":"Kouahla","sequence":"additional","affiliation":[{"name":"LabSTIC Laboratory, Department of Computer Science, 8 Mai 1945 University, Guelma 24000, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1609-6006","authenticated-orcid":false,"given":"Brahim","family":"Farou","sequence":"additional","affiliation":[{"name":"LabSTIC Laboratory, Department of Computer Science, 8 Mai 1945 University, Guelma 24000, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hamid","family":"Seridi","sequence":"additional","affiliation":[{"name":"LabSTIC Laboratory, Department of Computer Science, 8 Mai 1945 University, Guelma 24000, Algeria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0632-3172","authenticated-orcid":false,"given":"Mohamed","family":"Ferrag","sequence":"additional","affiliation":[{"name":"Technology Innovation Institute, Masdar City P.O. Box 9639, Abu Dhabi, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bangui, H., Ge, M., and Buhnova, B. (2018, January 19\u201321). Exploring Big Data Clustering Algorithms for Internet of Things Applications. Proceedings of the IoTBDS, Funchal, Portugal.","DOI":"10.5220\/0006773402690276"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3154525","article-title":"Large-scale indexing, discovery, and ranking for the Internet of Things (IoT)","volume":"51","author":"Fathy","year":"2018","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Demchenko, Y., Grosso, P., De Laat, C., and Membrey, P. (2013, January 20\u201324). Addressing big data issues in scientific data infrastructure. 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