A Fuzzy Logic-Driven Semantic and Binary Tree-Based Indexing Framework for Scalable IoT Data Storage and Retrieval

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Veröffentlicht in:Informatica vol. 49, no. 24 (Jul 2025), p. 85-111
1. Verfasser: Halimi, Khaled
Weitere Verfasser: Hadjadj, Abelhalim, Kouahla, Zineddine, Farou, Brahim
Veröffentlicht:
Slovenian Society Informatika / Slovensko drustvo Informatika
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024 7 |a 10.31449/inf.v49i24.8039  |2 doi 
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100 1 |a Halimi, Khaled  |u Department of Computer Science, LabSTIC Laboratory, University of Guelma 08 May 1945, Guelma, Algeria 
245 1 |a A Fuzzy Logic-Driven Semantic and Binary Tree-Based Indexing Framework for Scalable IoT Data Storage and Retrieval 
260 |b Slovenian Society Informatika / Slovensko drustvo Informatika  |c Jul 2025 
513 |a Journal Article 
520 3 |a The rapid growth of Internet of Things (IoT) devices presents significant data management challenges due to heterogeneity, interoperability issues, and massive data volumes, which hinder seamless data exchange and limit the IoT's potential. While the Semantic Internet of Things (SIoT) offers improvements through semantic web technologies, existing approaches often struggle with scalable data storage and efficient retrieval. To address this, the paper proposes a comprehensive, multi-layered architecture for efficient, scalable semantic IoT data handling. The architecture comprises: (1) an Edge Layer that utilizes the SAREF ontology to standardize heterogeneous device data into RDF format; (2) a Fog Layer performing fuzzy logic-based classification for enhanced data organization under uncertainty and binary tree-based indexing for efficient retrieval; and (3) a Cloud Layer for centralized storage. This approach integrates fuzzy logic for improved data categorization, particularly demonstrated through enhanced MEWS classification in healthcare, and a novel binary tree indexing method optimized for RDF file retrieval based on semantic content and fuzzy scores. Three dedicated algorithms govern the classification, indexing, and retrieval phases. Experimental validation using healthcare datasets demonstrates the framework's effectiveness. Specifically, the binary tree indexing reduces average retrieval times by orders of magnitude compared to non-indexed. Furthermore, the complete framework maintains stable and low query execution times (<0.01 s) even with 100,000 RDF files, significantly outperforming traditional RDF triple stores, which exhibit substantial performance degradation at scale. By significantly improving RDF data organization and retrieval efficiency, this work offers a scalable and innovative solution for managing Big IoT data, paving the way for advancements across various sectors. 
653 |a Accuracy 
653 |a Interoperability 
653 |a Datasets 
653 |a Classification 
653 |a Internet of Things 
653 |a Data exchange 
653 |a Multilayers 
653 |a Ontology 
653 |a Fuzzy logic 
653 |a Real time 
653 |a Semantic web 
653 |a Data retrieval 
653 |a Heterogeneity 
653 |a Efficiency 
653 |a Data management 
653 |a Health care 
653 |a Hypotheses 
653 |a Decision making 
653 |a Indexing 
653 |a Connectivity 
653 |a Performance degradation 
653 |a Data storage 
653 |a Resource Description Framework-RDF 
653 |a Semantics 
653 |a Information retrieval 
700 1 |a Hadjadj, Abelhalim  |u Abdelhafid Boussouf University Center, Mila, Algeria 
700 1 |a Kouahla, Zineddine  |u Department of Computer Science, LabSTIC Laboratory, University of Guelma 08 May 1945, Guelma, Algeria 
700 1 |a Farou, Brahim  |u Department of Computer Science, LabSTIC Laboratory, University of Guelma 08 May 1945, Guelma, Algeria 
773 0 |t Informatica  |g vol. 49, no. 24 (Jul 2025), p. 85-111 
786 0 |d ProQuest  |t Advanced Technologies & Aerospace Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3254673917/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch 
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856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3254673917/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch