| DOI | Resolve DOI: https://doi.org/10.1109/ICC52391.2025.11161352 |
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| Author | Search for: Fettes, Ethan; Search for: Madoery, Pablo G.; Search for: Yanikomeroglu, Halim; Search for: Kurt, Gunes Karabulut; Search for: Naik, Abhishek1; Search for: Bellinger, Colin1ORCID identifier: https://orcid.org/0000-0002-3567-7834; Search for: Martel, Stéphane; Search for: Ahmed, Khaled; Search for: Siddiqui, Sameera |
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| Affiliation | - National Research Council of Canada. Digital Technologies
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| Funder | Search for: National Research Council Canada |
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| Format | Text, Article |
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| Conference | ICC 2025, IEEE International Conference on Communications, June 8-12, 2025, Montreal, Quebec, Canada |
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| Subject | LEO; free space optical; IoT; satellite networks; delay tolerant networking; reinforcement learning |
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| Abstract | Internet of Things (IoT) devices have become increasingly ubiquitous with applications not only in urban areas but remote areas as well. These devices support industries such as agriculture, forestry, and resource extraction. Due to the device location being in remote areas, satellites are frequently used to collect and deliver IoT device data to customers. As these devices become increasingly advanced and numerous, the amount of data produced has rapidly increased potentially straining the ability for radio frequency (RF) downlink capacity. Free space optical communications with their wide available bandwidths and high data rates are a potential solution, but these communication systems are highly vulnerable to weather-related disruptions. This results in certain communication opportunities being inefficient in terms of the amount of data received versus the power expended. In this paper, we propose a deep reinforcement learning (DRL) method using Deep Q-Networks that takes advantage of weather condition forecasts to improve energy efficiency while delivering the same number of packets as schemes that don't factor weather into routing decisions. We compare this method with simple approaches that utilize simple cloud cover thresholds to improve energy efficiency. In testing the DRL approach provides improved median energy efficiency without a significant reduction in median delivery ratio. Simple cloud cover thresholds were also found to be effective but the thresholds with the highest energy efficiency had reduced median delivery ratio values. |
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| Publication date | 2025-06-08 |
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| Publisher | IEEE |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 8d930904-66d5-4426-8741-825e35081498 |
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| Record created | 2025-10-22 |
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| Record modified | 2025-11-03 |
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