Enhancing IoT Scalability and Interoperability Through Ontology Alignment and FedProx
The rapid expansion of IoT devices has introduced major challenges in ensuring data interoperability, enabling real-time processing, and achieving scalability, especially in decentralized edge computing environments. In this paper, an advanced framework of FedProx with ontology-driven data standardi...
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MDPI AG
2025-03-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/17/4/140 |
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| author | Chaimae Kanzouai Soukaina Bouarourou Abderrahim Zannou Abdelhak Boulaalam El Habib Nfaoui |
| author_facet | Chaimae Kanzouai Soukaina Bouarourou Abderrahim Zannou Abdelhak Boulaalam El Habib Nfaoui |
| author_sort | Chaimae Kanzouai |
| collection | DOAJ |
| description | The rapid expansion of IoT devices has introduced major challenges in ensuring data interoperability, enabling real-time processing, and achieving scalability, especially in decentralized edge computing environments. In this paper, an advanced framework of FedProx with ontology-driven data standardization is proposed, which can meet such challenges comprehensively. On the one hand, it can guarantee semantic consistency across different kinds of IoT devices using unified ontology, so that data from multiple sources could be seamlessly integrated; on the other hand, it solves the non-IID issues of data and limited resources in edge servers by FedProx. Experimental findings indicate that FedProx outperforms FedAvg, with a remarkable accuracy level of 89.4%, having higher convergence rates, and attaining a 30% saving on communication overhead through gradient compression. In addition, the ontology alignment procedure yielded a 95% success rate, thereby ensuring uniform data preprocessing across domains, including traffic monitoring and parking management. The model demonstrates outstanding scalability and flexibility to new devices, while maintaining high performance during ontology evolution. These findings highlight its great potential for deployment in smart cities, environmental monitoring, and other IoT-based ecosystems, thereby enabling the creation of more efficient and integrated solutions in these areas. |
| format | Article |
| id | doaj-art-617eabaffcc54b45bc3a4dac83c5ef1a |
| institution | OA Journals |
| issn | 1999-5903 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj-art-617eabaffcc54b45bc3a4dac83c5ef1a2025-08-20T02:28:14ZengMDPI AGFuture Internet1999-59032025-03-0117414010.3390/fi17040140Enhancing IoT Scalability and Interoperability Through Ontology Alignment and FedProxChaimae Kanzouai0Soukaina Bouarourou1Abderrahim Zannou2Abdelhak Boulaalam3El Habib Nfaoui4LSATE Laboratory, National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez 30000, MoroccoFaculty of Sciences, University Mohamed V, Rabat 10090, MoroccoERCI2A, Faculty of Science and Technology Al Hoceima, Abdelmalek Essaadi University, Tetouan 93000, MoroccoLSATE Laboratory, National School of Applied Sciences, Sidi Mohamed Ben Abdellah University, Fez 30000, MoroccoL3IA Laboratory, Faculty of Sciences Dhar El Mahraz, Sidi Mohamed Ben Abdellah University, Fez 30000, MoroccoThe rapid expansion of IoT devices has introduced major challenges in ensuring data interoperability, enabling real-time processing, and achieving scalability, especially in decentralized edge computing environments. In this paper, an advanced framework of FedProx with ontology-driven data standardization is proposed, which can meet such challenges comprehensively. On the one hand, it can guarantee semantic consistency across different kinds of IoT devices using unified ontology, so that data from multiple sources could be seamlessly integrated; on the other hand, it solves the non-IID issues of data and limited resources in edge servers by FedProx. Experimental findings indicate that FedProx outperforms FedAvg, with a remarkable accuracy level of 89.4%, having higher convergence rates, and attaining a 30% saving on communication overhead through gradient compression. In addition, the ontology alignment procedure yielded a 95% success rate, thereby ensuring uniform data preprocessing across domains, including traffic monitoring and parking management. The model demonstrates outstanding scalability and flexibility to new devices, while maintaining high performance during ontology evolution. These findings highlight its great potential for deployment in smart cities, environmental monitoring, and other IoT-based ecosystems, thereby enabling the creation of more efficient and integrated solutions in these areas.https://www.mdpi.com/1999-5903/17/4/140IoT-edge computingontology-driven standardizationFederated proximal learning (FedProx)data interoperabilityscalability in decentralized systemsnon-IID data processing |
| spellingShingle | Chaimae Kanzouai Soukaina Bouarourou Abderrahim Zannou Abdelhak Boulaalam El Habib Nfaoui Enhancing IoT Scalability and Interoperability Through Ontology Alignment and FedProx Future Internet IoT-edge computing ontology-driven standardization Federated proximal learning (FedProx) data interoperability scalability in decentralized systems non-IID data processing |
| title | Enhancing IoT Scalability and Interoperability Through Ontology Alignment and FedProx |
| title_full | Enhancing IoT Scalability and Interoperability Through Ontology Alignment and FedProx |
| title_fullStr | Enhancing IoT Scalability and Interoperability Through Ontology Alignment and FedProx |
| title_full_unstemmed | Enhancing IoT Scalability and Interoperability Through Ontology Alignment and FedProx |
| title_short | Enhancing IoT Scalability and Interoperability Through Ontology Alignment and FedProx |
| title_sort | enhancing iot scalability and interoperability through ontology alignment and fedprox |
| topic | IoT-edge computing ontology-driven standardization Federated proximal learning (FedProx) data interoperability scalability in decentralized systems non-IID data processing |
| url | https://www.mdpi.com/1999-5903/17/4/140 |
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