Resource Efficient Federated LoRaWAN Architecture for Far-Edge IoT Applications
This study introduces an innovative, resource-efficient architecture that incorporates advanced technologies to create a lightweight, flexible, and scalable framework for remote and limited data collecting and processing in next-generation Internet of Things (IoT) applications. It specifically propo...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11037774/ |
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| author | Anna Triantafyllou Ilias Siniosoglou Vasileios Argyriou Sotirios K. Goudos Georgios Th. Papadopoulos Konstantinos Panitsidis Panagiotis Sarigiannidis |
| author_facet | Anna Triantafyllou Ilias Siniosoglou Vasileios Argyriou Sotirios K. Goudos Georgios Th. Papadopoulos Konstantinos Panitsidis Panagiotis Sarigiannidis |
| author_sort | Anna Triantafyllou |
| collection | DOAJ |
| description | This study introduces an innovative, resource-efficient architecture that incorporates advanced technologies to create a lightweight, flexible, and scalable framework for remote and limited data collecting and processing in next-generation Internet of Things (IoT) applications. It specifically proposes an integrated Federated LoRaWAN (LoRA-FL) system that combines hierarchical, privacy-preserving Federated Learning (FL), Knowledge Distillation (KD), and a customised Medium Access Control (MAC) protocol, identified as FCA-LoRa, to address the significant limitations of LoRaWAN networks. These encompass strict duty cycle rules, restricted bandwidth, and energy limitations, elements that conventionally hinder the implementation of intelligent IoT devices in rural or isolated settings. The proposed architecture features a hierarchical FL approach enabling multi-tier Artificial Intelligence (AI) model aggregation across edge nodes, gateways, and a central server. The effectiveness of the proposed system is confirmed by two practical applications, smart agriculture and smart livestock farming, which exemplify standard situations for far-edge intelligence. The results indicate that the distilled model consistently attains over 90% packet delivery success, illustrating the architecture’s capacity to provide scalable and energy-efficient intelligence at the edge. This research addresses a significant gap in previous studies that frequently examine FL, communication optimisation, and model compression independently. This study offers a comprehensive, implementable approach that tackles model scalability, and network-layer issues within a cohesive architecture, enhancing the practical implementation of AI-driven IoT deployments over LoRaWAN. |
| format | Article |
| id | doaj-art-ea0dffaa21f249ddaa8cd89cdc8e803b |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-ea0dffaa21f249ddaa8cd89cdc8e803b2025-08-20T03:31:52ZengIEEEIEEE Access2169-35362025-01-011310876610878510.1109/ACCESS.2025.358037511037774Resource Efficient Federated LoRaWAN Architecture for Far-Edge IoT ApplicationsAnna Triantafyllou0https://orcid.org/0000-0003-4019-6610Ilias Siniosoglou1https://orcid.org/0000-0001-9844-8185Vasileios Argyriou2https://orcid.org/0000-0003-4679-8049Sotirios K. Goudos3https://orcid.org/0000-0001-5981-5683Georgios Th. Papadopoulos4https://orcid.org/0000-0003-1686-421XKonstantinos Panitsidis5https://orcid.org/0000-0001-8299-1511Panagiotis Sarigiannidis6https://orcid.org/0000-0001-6042-0355Department of Electrical and Computer Engineering, University of Western Macedonia, Kozani, GreeceDepartment of Electrical and Computer Engineering, University of Western Macedonia, Kozani, GreeceDepartment of Networks and Digital Media, Kingston University London, London, U.K.Physics Department, Aristotle University of Thessaloniki, Thessaloniki, GreeceDepartment of Informatics and Telematics, Harokopio University of Athens, Athens, GreeceDepartment of Management Science and Technology, University of Western Macedonia, Kozani, GreeceDepartment of Electrical and Computer Engineering, University of Western Macedonia, Kozani, GreeceThis study introduces an innovative, resource-efficient architecture that incorporates advanced technologies to create a lightweight, flexible, and scalable framework for remote and limited data collecting and processing in next-generation Internet of Things (IoT) applications. It specifically proposes an integrated Federated LoRaWAN (LoRA-FL) system that combines hierarchical, privacy-preserving Federated Learning (FL), Knowledge Distillation (KD), and a customised Medium Access Control (MAC) protocol, identified as FCA-LoRa, to address the significant limitations of LoRaWAN networks. These encompass strict duty cycle rules, restricted bandwidth, and energy limitations, elements that conventionally hinder the implementation of intelligent IoT devices in rural or isolated settings. The proposed architecture features a hierarchical FL approach enabling multi-tier Artificial Intelligence (AI) model aggregation across edge nodes, gateways, and a central server. The effectiveness of the proposed system is confirmed by two practical applications, smart agriculture and smart livestock farming, which exemplify standard situations for far-edge intelligence. The results indicate that the distilled model consistently attains over 90% packet delivery success, illustrating the architecture’s capacity to provide scalable and energy-efficient intelligence at the edge. This research addresses a significant gap in previous studies that frequently examine FL, communication optimisation, and model compression independently. This study offers a comprehensive, implementable approach that tackles model scalability, and network-layer issues within a cohesive architecture, enhancing the practical implementation of AI-driven IoT deployments over LoRaWAN.https://ieeexplore.ieee.org/document/11037774/LoRaWANfederated learningknowledge distillationInternet of Thingsschedulingarchitecture |
| spellingShingle | Anna Triantafyllou Ilias Siniosoglou Vasileios Argyriou Sotirios K. Goudos Georgios Th. Papadopoulos Konstantinos Panitsidis Panagiotis Sarigiannidis Resource Efficient Federated LoRaWAN Architecture for Far-Edge IoT Applications IEEE Access LoRaWAN federated learning knowledge distillation Internet of Things scheduling architecture |
| title | Resource Efficient Federated LoRaWAN Architecture for Far-Edge IoT Applications |
| title_full | Resource Efficient Federated LoRaWAN Architecture for Far-Edge IoT Applications |
| title_fullStr | Resource Efficient Federated LoRaWAN Architecture for Far-Edge IoT Applications |
| title_full_unstemmed | Resource Efficient Federated LoRaWAN Architecture for Far-Edge IoT Applications |
| title_short | Resource Efficient Federated LoRaWAN Architecture for Far-Edge IoT Applications |
| title_sort | resource efficient federated lorawan architecture for far edge iot applications |
| topic | LoRaWAN federated learning knowledge distillation Internet of Things scheduling architecture |
| url | https://ieeexplore.ieee.org/document/11037774/ |
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