Özet
Unmanned air vehicles are becoming widespread, driven by improved wireless technologies. However, the WiFi technology used for communication has a highly crowded and unevenly distributed channel occupancy in its spectrum. To overcome this, WiFi resources need to be utilized efficiently. Therefore, this paper proposes the Opportunistic Reinforcement Learning-based WiFi Access scheme, which exploits intermittent channel occupancy to solve the NP-hard channel assignment problem. As a result, the proposed model has improved the accurate channel selection on the UAVs by 9%, performing 91% accuracy, compared to the trivial channel scoring-based selection algorithms.
| Orijinal dil | İngilizce |
|---|---|
| Ana bilgisayar yayını başlığı | 2023 International Conference on Smart Applications, Communications and Networking, SmartNets 2023 |
| Yayınlayan | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Elektronik) | 9798350302523 |
| DOI'lar | |
| Yayın durumu | Yayınlandı - 2023 |
| Harici olarak yayınlandı | Evet |
| Etkinlik | 2023 International Conference on Smart Applications, Communications and Networking, SmartNets 2023 - Istanbul, Türkiye Süre: 25 Tem 2023 → 27 Tem 2023 |
Yayın serisi
| Adı | 2023 International Conference on Smart Applications, Communications and Networking, SmartNets 2023 |
|---|
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| ???event.eventtypes.event.conference??? | 2023 International Conference on Smart Applications, Communications and Networking, SmartNets 2023 |
|---|---|
| Ülke/Bölge | Türkiye |
| Şehir | Istanbul |
| Periyot | 25/07/23 → 27/07/23 |
Bibliyografik not
Publisher Copyright:© 2023 IEEE.
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Opportunistic RL-based WiFi Access for Aerial Sensor Nodes in Smart City Applications' araştırma başlıklarına git. Birlikte benzersiz bir parmak izi oluştururlar.Alıntı Yap
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