AI-Enabled Reliable QoS in Multi-RAT Wireless IoT Networks: Prospects, Challenges, and Future Directions
Wireless IoT networks have seen an unprecedented rise in number of devices, heterogeneity and emerging use cases which led to diverse throughput, reliability and latency (Quality of Service) requirements. Fulfilling these diverse requirements in a rapidly changing and dynamic wireless environment is...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Open Journal of the Communications Society |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9924256/ |
| _version_ | 1811196909526712320 |
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| author | Kamran Zia Alessandro Chiumento Paul J. M. Havinga |
| author_facet | Kamran Zia Alessandro Chiumento Paul J. M. Havinga |
| author_sort | Kamran Zia |
| collection | DOAJ |
| description | Wireless IoT networks have seen an unprecedented rise in number of devices, heterogeneity and emerging use cases which led to diverse throughput, reliability and latency (Quality of Service) requirements. Fulfilling these diverse requirements in a rapidly changing and dynamic wireless environment is a complex and challenging task. On top of including new technologies and wireless standards, one solution is to deploy cross-layer Design (CLD) and multiple Radio Access Technologies (Multi-RAT) to develop scalable QoS-aware IoT networks. However, the complexity of such solutions is high as it involves complex inter-layer interactions and dependencies and inter-application dependencies in multi-RAT networks. Moreover, the wireless environment is very dynamic, so having an optimal constellation of parameters is a challenging task. In this paper, we address the possibilities of using Artificial Intelligence (AI) and Machine Learning (ML) to address these high dimensional and dynamic problems. Based on our findings, we have proposed a distributed network management framework employing AI & ML for studying inter-layer dependencies and developing cross-layer design, traffic classification and traffic prediction at the edge devices for reliable QoS in multi-RAT IoT networks. A thorough discussion on future directions and emerging challenges related to our proposed framework has also been provided for further research in this field. |
| first_indexed | 2024-04-12T01:06:32Z |
| format | Article |
| id | doaj.art-772982a511b348d0929976906c5fee6e |
| institution | Directory Open Access Journal |
| issn | 2644-125X |
| language | English |
| last_indexed | 2024-04-12T01:06:32Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj.art-772982a511b348d0929976906c5fee6e2022-12-22T03:54:13ZengIEEEIEEE Open Journal of the Communications Society2644-125X2022-01-0131906192910.1109/OJCOMS.2022.32157319924256AI-Enabled Reliable QoS in Multi-RAT Wireless IoT Networks: Prospects, Challenges, and Future DirectionsKamran Zia0https://orcid.org/0000-0003-1630-0801Alessandro Chiumento1https://orcid.org/0000-0003-1248-5327Paul J. M. Havinga2Pervasive Systems Research Unit, University of Twente, Enschede, The NetherlandsPervasive Systems Research Unit, University of Twente, Enschede, The NetherlandsPervasive Systems Research Unit, University of Twente, Enschede, The NetherlandsWireless IoT networks have seen an unprecedented rise in number of devices, heterogeneity and emerging use cases which led to diverse throughput, reliability and latency (Quality of Service) requirements. Fulfilling these diverse requirements in a rapidly changing and dynamic wireless environment is a complex and challenging task. On top of including new technologies and wireless standards, one solution is to deploy cross-layer Design (CLD) and multiple Radio Access Technologies (Multi-RAT) to develop scalable QoS-aware IoT networks. However, the complexity of such solutions is high as it involves complex inter-layer interactions and dependencies and inter-application dependencies in multi-RAT networks. Moreover, the wireless environment is very dynamic, so having an optimal constellation of parameters is a challenging task. In this paper, we address the possibilities of using Artificial Intelligence (AI) and Machine Learning (ML) to address these high dimensional and dynamic problems. Based on our findings, we have proposed a distributed network management framework employing AI & ML for studying inter-layer dependencies and developing cross-layer design, traffic classification and traffic prediction at the edge devices for reliable QoS in multi-RAT IoT networks. A thorough discussion on future directions and emerging challenges related to our proposed framework has also been provided for further research in this field.https://ieeexplore.ieee.org/document/9924256/QoS in IoT networksAI & ML for cross-layer designcross-layer optimizationreliable QoSmulti-RAT networksedge intelligence |
| spellingShingle | Kamran Zia Alessandro Chiumento Paul J. M. Havinga AI-Enabled Reliable QoS in Multi-RAT Wireless IoT Networks: Prospects, Challenges, and Future Directions IEEE Open Journal of the Communications Society QoS in IoT networks AI & ML for cross-layer design cross-layer optimization reliable QoS multi-RAT networks edge intelligence |
| title | AI-Enabled Reliable QoS in Multi-RAT Wireless IoT Networks: Prospects, Challenges, and Future Directions |
| title_full | AI-Enabled Reliable QoS in Multi-RAT Wireless IoT Networks: Prospects, Challenges, and Future Directions |
| title_fullStr | AI-Enabled Reliable QoS in Multi-RAT Wireless IoT Networks: Prospects, Challenges, and Future Directions |
| title_full_unstemmed | AI-Enabled Reliable QoS in Multi-RAT Wireless IoT Networks: Prospects, Challenges, and Future Directions |
| title_short | AI-Enabled Reliable QoS in Multi-RAT Wireless IoT Networks: Prospects, Challenges, and Future Directions |
| title_sort | ai enabled reliable qos in multi rat wireless iot networks prospects challenges and future directions |
| topic | QoS in IoT networks AI & ML for cross-layer design cross-layer optimization reliable QoS multi-RAT networks edge intelligence |
| url | https://ieeexplore.ieee.org/document/9924256/ |
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