Content area
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.
Details
Interoperability;
Datasets;
Classification;
Internet of Things;
Data exchange;
Multilayers;
Ontology;
Fuzzy logic;
Real time;
Semantic web;
Data retrieval;
Heterogeneity;
Efficiency;
Data management;
Health care;
Hypotheses;
Decision making;
Indexing;
Connectivity;
Performance degradation;
Data storage;
Resource Description Framework-RDF;
Semantics;
Information retrieval
1 Department of Computer Science, LabSTIC Laboratory, University of Guelma 08 May 1945, Guelma, Algeria
2 Abdelhafid Boussouf University Center, Mila, Algeria