A Fuzzy Logic-Driven Semantic and Binary Tree-Based Indexing Framework for Scalable IoT Data Storage and Retrieval
Abstract
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.
Full Text:
PDFReferences
‘IoT devices installed base worldwide 2015-2025’, Statista. Accessed: Apr. 25, 2025. [Online]. Available: https://www.statista.com/statistics/471264/iot-number-of-connected-devices-worldwide/
S. Benkhaled, M. Hemam, M. Djezzar, and M. Maimour, ‘An Ontology – based Contextual Approach for Cross-domain Applications in Internet of Things’, Informatica, vol. 46, no. 5, Mar. 2022, doi: 10.31449/inf.v46i5.3627.
K. N. Prashanth Kumar, V. Ravi Kumar, and K. Raghuveer, ‘A Survey on Semantic Web Technologies for the Internet of Things’, in 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC), Mysore: IEEE, Sep. 2017, pp. 316–322. doi: 10.1109/CTCEEC.2017.8454974.
J. D. McDonald and M. Levine-Clark, Eds., ‘Resource Description Framework (RDF)’, in Encyclopedia of Library and Information Science, Fourth Edition, 0 ed., CRC Press, 2017, pp. 3961–3969. doi: 10.1081/E-ELIS4-120043688.
K. Gunaratna, S. Lalithsena, and A. Sheth, ‘Alignment and dataset identification of linked data in Semantic Web’, WIREs Data Min. Knowl. Discov., vol. 4, no. 2, pp. 139–151, Mar. 2014, doi: 10.1002/widm.1121.
M. H. Al-Zubaidie and R. H. Razzaq, ‘Maintaining Security of Patient Data by Employing Private Blockchain and Fog Computing Technologies based on Internet of Medical Things’, Informatica, vol. 48, no. 12, Sep. 2024, doi: 10.31449/inf.v48i12.6047.
Y. Bu, ‘Fuzzy Decision Support System for Financial Planning and Management’, Informatica, vol. 48, no. 21, Nov. 2024, doi: 10.31449/inf.v48i21.6718.
C. Hou, N. Xu, and S. Liu, ‘Design of Online Monitoring Method for Distribution IoT Devices Based on DBSCAN Optimization Algorithm’, Informatica, vol. 49, no. 5, Jan. 2025, doi: 10.31449/inf.v49i5.6399.
X. Huo, ‘Blockchain-Based Distributed Network Security Architecture with Smart Contract Vulnerability Detection Using Improved Tree CNN’, Informatica, vol. 49, no. 17, Mar. 2025, doi: 10.31449/inf.v49i17.8050.
F. Firouzi, B. Farahani, and A. Marinšek, ‘The convergence and interplay of edge, fog, and cloud in the AI-driven Internet of Things (IoT)’, Inf. Syst., vol. 107, p. 101840, Jul. 2022, doi: 10.1016/j.is.2021.101840.
H. A. Tran, D. Tran, L. G. Nguyen, Q. T. Ha, V. Tong, and A. Mellouk, ‘SHIOT: A novel SDN-based framework for the heterogeneous Internet of Things’, Informatica, vol. 42, no. 3, Sep. 2018, doi: 10.31449/inf.v42i3.2245.
A. Rhayem, M. B. A. Mhiri, and F. Gargouri, ‘Semantic Web Technologies for the Internet of Things: Systematic Literature Review’, Internet Things, vol. 11, p. 100206, Sep. 2020, doi: 10.1016/j.iot.2020.100206.
A. Gyrard, C. Bonnet, K. Boudaoud, and M. Serrano, ‘LOV4IoT: A Second Life for Ontology-Based Domain Knowledge to Build Semantic Web of Things Applications’, in 2016 IEEE 4th International Conference on Future Internet of Things and Cloud (FiCloud), Vienna, Austria: IEEE, Aug. 2016, pp. 254–261. doi: 10.1109/FiCloud.2016.44.
M. Compton et al., ‘The SSN ontology of the W3C semantic sensor network incubator group’, J. Web Semant., vol. 17, pp. 25–32, Dec. 2012, doi: 10.1016/j.websem.2012.05.003.
M. B. Alaya, S. Medjiah, T. Monteil, and K. Drira, ‘Toward semantic interoperability in oneM2M architecture’, IEEE Commun. Mag., vol. 53, no. 12, pp. 35–41, Dec. 2015, doi: 10.1109/MCOM.2015.7355582.
L. Daniele, F. Den Hartog, and J. Roes, ‘Created in Close Interaction with the Industry: The Smart Appliances REFerence (SAREF) Ontology’, in Formal Ontologies Meet Industry, vol. 225, R. Cuel and R. Young, Eds., in Lecture Notes in Business Information Processing, vol. 225. , Cham: Springer International Publishing, 2015, pp. 100–112. doi: 10.1007/978-3-319-21545-7_9.
‘Ontology (DBO)’, DBpedia Association. Accessed: Nov. 28, 2024. [Online]. Available: https://www.dbpedia.org/resources/ontology/
‘dblp /rdf’. Accessed: Nov. 28, 2024. [Online]. Available: https://dblp.org/rdf/
‘Bio2RDF v2.7a’. Accessed: Apr. 25, 2025. [Online]. Available: https://bio2rdf.org/
S. Duan, A. Kementsietsidis, K. Srinivas, and O. Udrea, ‘Apples and oranges: a comparison of RDF benchmarks and real RDF datasets’, in Proceedings of the 2011 ACM SIGMOD International Conference on Management of data, Athens Greece: ACM, Jun. 2011, pp. 145–156. doi: 10.1145/1989323.1989340.
‘Lehigh University Benchmark (LUBM)’. Accessed: Apr. 25, 2025. [Online]. Available: https://swat.cse.lehigh.edu/projects/lubm/
L. Ma, Z. Su, Y. Pan, L. Zhang, and T. Liu, ‘RStar: an RDF storage and query system for enterprise resource management’, in Proceedings of the thirteenth ACM international conference on Information and knowledge management, Washington D.C. USA: ACM, Nov. 2004, pp. 484–491. doi: 10.1145/1031171.1031264.
D. J. Abadi, A. Marcus, S. R. Madden, and K. Hollenbach, ‘SW-Store: a vertically partitioned DBMS for Semantic Web data management’, VLDB J., vol. 18, no. 2, pp. 385–406, Apr. 2009, doi: 10.1007/s00778-008-0125-y.
‘Jena Property Table Implementation’. Accessed: Apr. 25, 2025. [Online]. Available: http://shiftleft.com/mirrors/www.hpl.hp.com/techreports/2006/HPL-2006-140.html
‘Apache Jena - Home’. Accessed: Apr. 25, 2025. [Online]. Available: https://jena.apache.org/
‘Blazegraph Database’. Accessed: Apr. 25, 2025. [Online]. Available: https://blazegraph.com/
S. Sakr and A. Y. Zomaya, Eds., ‘Graph Databases’, in Encyclopedia of Big Data Technologies, Cham: Springer International Publishing, 2019, pp. 835–835. doi: 10.1007/978-3-319-77525-8_100147.
S. Benedict, ‘IoT-Enabled Remote Monitoring Techniques for Healthcare Applications -- An Overview’, Informatica, vol. 46, no. 2, Jun. 2022, doi: 10.31449/inf.v46i2.3912.
A. Cimmino et al., ‘VICINITY: IoT Semantic Interoperability Based on the Web of Things’, in 2019 15th International Conference on Distributed Computing in Sensor Systems (DCOSS), Santorini Island, Greece: IEEE, May 2019, pp. 241–247. doi: 10.1109/DCOSS.2019.00061.
A. Broring et al., ‘The BIG IoT API - Semantically Enabling IoT Interoperability’, IEEE Pervasive Comput., vol. 17, no. 4, Art. no. 4, Oct. 2018, doi: 10.1109/MPRV.2018.2873566.
A. Gyrard, C. Bonnet, K. Boudaoud, and M. Serrano, ‘LOV4IoT: A Second Life for Ontology-Based Domain Knowledge to Build Semantic Web of Things Applications’, in 2016 IEEE 4th International Conference on Future Internet of Things and Cloud (FiCloud), Vienna, Austria: IEEE, Aug. 2016, pp. 254–261. doi: 10.1109/FiCloud.2016.44.
M. G. Kibria, S. Ali, M. A. Jarwar, and I. Chong, ‘A framework to support data interoperability in web objects based IoT environments’, in 2017 International Conference on Information and Communication Technology Convergence (ICTC), Jeju: IEEE, Oct. 2017, pp. 29–31. doi: 10.1109/ICTC.2017.8190935.
D. Lymperis and C. Goumopoulos, ‘SEDIA: A Platform for Semantically Enriched IoT Data Integration and Development of Smart City Applications’, Future Internet, vol. 15, no. 8, Art. no. 8, Aug. 2023, doi: 10.3390/fi15080276.
A. Pliatsios, D. Lymperis, and C. Goumopoulos, ‘S2NetM: A Semantic Social Network of Things Middleware for Developing Smart and Collaborative IoT-Based Solutions’, Future Internet, vol. 15, no. 6, Art. no. 6, Jun. 2023, doi: 10.3390/fi15060207.
M. Banane, A. Belangour, and L. El Houssine, ‘Storing RDF Data into Big Data NoSQL Databases’, in Lecture Notes in Real-Time Intelligent Systems, vol. 756, J. Mizera-Pietraszko, P. Pichappan, and L. Mohamed, Eds., in Advances in Intelligent Systems and Computing, vol. 756. , Cham: Springer International Publishing, 2019, pp. 69–78. doi: 10.1007/978-3-319-91337-7_7.
C. K. Wu et al., ‘An IoT Tree Health Indexing Method Using Heterogeneous Neural Network’, IEEE Access, vol. 7, pp. 66176–66184, 2019, doi: 10.1109/ACCESS.2019.2918060.
M. D. Le Lagadec, T. Dwyer, and M. Browne, ‘Indicators of patient deterioration in poorly resourced private hospitals: Which vital sign to watch? A retrospective case–control study’, Aust. Crit. Care, vol. 37, no. 3, pp. 461–467, May 2024, doi: 10.1016/j.aucc.2023.05.006.
S. Nasiri, F. Sadoughi, A. Dehnad, M. H. Tadayon, and H. Ahmadi, ‘Layered Architecture for Internet of Things-based Healthcare System: A Systematic Literature Review’, Informatica, vol. 45, no. 4, Dec. 2021, doi: 10.31449/inf.v45i4.3601.
M. Belkebir, T. M. Maarouk, and B. Nini, ‘Realtime Semantic Healthcare System: Visual Risks Identification for Elders and Children’, Informatica, vol. 48, no. 14, Sep. 2024, doi: 10.31449/inf.v48i14.6271.
J. Martinez-Gil and J. M. Chaves-Gonzalez, ‘Interpretable ontology meta-matching in the biomedical domain using Mamdani fuzzy inference’, Expert Syst. Appl., vol. 188, p. 116025, Feb. 2022, doi: 10.1016/j.eswa.2021.116025.
M. Pimentel et al., ‘BIDMC PPG and Respiration Dataset’. physionet.org, 2018. doi: 10.13026/C2208R.
A. L. Goldberger et al., ‘PhysioBank, PhysioToolkit, and PhysioNet: Components of a New Research Resource for Complex Physiologic Signals’, Circulation, vol. 101, no. 23, Jun. 2000, doi: 10.1161/01.CIR.101.23.e215.
‘Respiratory Rate Estimation by peterhcharlton’. Accessed: Apr. 25, 2025. [Online]. Available: https://peterhcharlton.github.io/RRest/datasets.html
DOI: https://doi.org/10.31449/inf.v49i24.8039
This work is licensed under a Creative Commons Attribution 3.0 License.