@inbook{, author = {Bnouhanna, Nisrine and C. Sofia, Rute}, title = {IoT Things to Service Matchmaking at the Edge}, booktitle = {Shaping the Future of IoT with Edge Intelligence - How Edge Computing Enables the Next Generation of IoT Applications}, publisher = {Book chapter, Shaping the Future of IoT with Edge Intelligence - How Edge Computing Enables the Next Generation of IoT Applications, River Publishers, USA}, journal = {Book chapter, Shaping the Future of IoT with Edge Intelligence - How Edge Computing Enables the Next Generation of IoT Applications}, pages = {460}, year = {2023}, month = sep, abstract = {This chapter debates on the use of Machine Learning (ML) to support edge-based semantic matchmaking to handle a large-scale integration of IoT data sources with IoT platforms. The chapter starts by addressing the interoperability challenges currently faced by integrators, the role of ontologies in this context. It continues with a perspective on semantic matchmaking approaches, and ML solutions that can best support a cognitive matchmaking. The chapter then covers a use case and pilots that are being developed with a new open-source middleware, TSMatch, in the context of the Horizon 2020 EFPF project, for the purpose of environmental monitoring in smart manufacturing.}, howpublished = {River Publishers, USA}, keywords = {ML, IIoT, Semantic technologies, Matchmaking}, url = {https://www.researchgate.net/publication/373736217_IoT_Things_to_Service_Matchmaking_at_the_Edge#fullTextFileContent}, } @article{, author = {Bnouhanna, Nisrine and Karabulut, Erkan and C. Sofia, Rute and Seder, E. and Insolvibile, G.}, title = {An Evaluation of a Semantic Thing To Service Matching Approach in Industrial IoT Environments}, publisher = {IEEE}, journal = {inProc. 2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 2022, pp. 433-438, doi: 10.1109/PerComWorkshops53856.2022.9767519. Pisa, Italy.}, year = {2022}, month = jan, abstract = {Industrial Internet of Things Platforms enable the use of available data to improve production and business processes. However, the data exchange and provisioning between the data sources and platform services remain a challenge as such platforms are usually vendor specific, proprietary, and associated with specific IoT hardware. Therefore, we propose a detailed description of an open-source software-based solution,Thing to Service Matching (TSMatch) which performs fine-grained semantic matching between available IoT data and services. Moreover,the paper presents the actual implementation of the proposed solution in 2 different Aerospace production environments, and a performance evaluation in a testbed environment.}, howpublished = {2022 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 2022, pp. 433-438, doi: 10.1109/PerComWorkshops53856.2022.9767519. Pisa, Italy.}, doi = {10.1109/PerComWorkshops53856.2022.9767519}, keywords = {semantic matchmaking, IoT, IIoT}, url = {https://www.researchgate.net/publication/357658064_An_Evaluation_of_a_Semantic_Thing_To_Service_Matching_Approach_in_Industrial_IoT_Environments}, } @inproceedings{, author = {Karabulut, Erkan and Bnouhanna, Nisrine and C. Sofia, Rute}, title = {ML-based Data Classification and Data Aggregation on the Edge}, booktitle = {CoNEXT-SW '21: Proceedings of the CoNEXT Student Wokshop}, publisher = {ACM}, journal = {CoNEXT-SW '21: Proceedings of the CoNEXT Student Wokshop}, pages = {21-22}, year = {2021}, month = dec, location = {Munich, Germany}, abstract = {This study focuses on sensor classification using machine learning algorithms, to improve data aggregation on the Edge. This aspect is particularly important in large-scale Internet of Things environments, where data aggregation derived from sensors from different vendors often requires human intervention. The proposed research is focused on relying on machine learning to classify sensors based on their semantic descriptions.}, howpublished = {CoNEXT-SW '21: Proceedings of the CoNEXT Student WorkshopDecember 2021 Pages 21–22 https://doi.org/10.1145/3488658.3493786}, doi = {https://doi.org/10.1145/3488658.3493786}, url = {https://dl.acm.org/doi/abs/10.1145/3488658.3493786}, } @inproceedings{, author = {Bnouhanna, Nisrine and C. Sofia, Rute and Pretschner, Alexander}, title = {IoT Thing To Service Semantic Matching}, booktitle = {inProc IEEE Percom 2021 (Best PhD paper award)}, publisher = {2021 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops)}, year = {2021}, month = mar, abstract = {We propose an automated IoT data source (Thing) to service semantic matching mechanism that selects an optimal set of available IoT data sources in order to satisfy service requirements and needs. The main motivation is to provide a better use of data captured via IoT data sources. To achieve this goal, we propose a three-fold approach: (i) modelling service requirements based on existing service descriptions; (ii) automating the selection of an optimal set of Things that best meet the service requirements; (iii) selecting data processing services that provide additional service requirements.}, howpublished = {IEEE Percom 2021, Best PhD paper award}, isbn = {978-1-6654-4724-9}, doi = {10.1109/PerComWorkshops51409.2021.9431128}, keywords = {IIoT, semantic technologies, matchmaking}, url = {https://www.researchgate.net/publication/349110311_IoT_Thing_To_Service_Semantic_Matching}, } @inproceedings{bnouhanna2019, author = {Bnouhanna, Nisrine and Neugschwandtner, Georg}, title = {Cross-Factory Information Exchange for Cloud-Based Monitoring of Collaborative Manufacturing Networks}, booktitle = {Proceedings of the {IEEE} International Conference on Emerging Technologies And Factory Automation ({ETFA})}, year = {2019}, month = sep, address = {Zaragoza, Spain}, abstract = {Cloud-based platforms and ecosystems are widely considered as a means of supporting collaborative manufacturing networks (CMN) among companies and, in particular, manufacturing SMEs. Due to the spatial separation of manufacturing activities among the CMN partners, there is a need for monitoring and coordinating the collaborative manufacturing processes. However, little research investigates inter-enterprise information exchange systems required for such monitoring and coordination. This paper proposes an approach for cross-factory information exchange considering connectivity and interoperability between various factory data sources and cloud-based collaborative production monitoring services. The paper also introduces a cross-factory information exchange mechanism, meta-models, and architecture to facilitate the ad-hoc, target specific data exchange and addresses SMEs’ adoption barriers, namely trust, security, data control, and integration efforts. A proof of concept of the proposed approach is presented.}, }