Meta Reinforcement Learning for UAV-Assisted Energy Harvesting IoT Devices in Disaster-Affected Areas
<p dir="ltr">Over the past decade, Unmanned Aerial Vehicles (UAVs) have attracted significant attention due to their potential applications in emergency-response applications, including wireless power transfer (WPT) and data collection from Internet of Things (IoT) devices in disaste...
محفوظ في:
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| مؤلفون آخرون: | , , |
| منشور في: |
2024
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إضافة وسم
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| _version_ | 1864513510691569664 |
|---|---|
| author | Marwan Dhuheir (19170898) |
| author2 | Aiman Erbad (14150589) Ala Al-Fuqaha (4434340) Abegaz Mohammed Seid (19170901) |
| author2_role | author author author |
| author_facet | Marwan Dhuheir (19170898) Aiman Erbad (14150589) Ala Al-Fuqaha (4434340) Abegaz Mohammed Seid (19170901) |
| author_role | author |
| dc.creator.none.fl_str_mv | Marwan Dhuheir (19170898) Aiman Erbad (14150589) Ala Al-Fuqaha (4434340) Abegaz Mohammed Seid (19170901) |
| dc.date.none.fl_str_mv | 2024-03-18T12:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/ojcoms.2024.3377706 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Meta_Reinforcement_Learning_for_UAV-Assisted_Energy_Harvesting_IoT_Devices_in_Disaster-Affected_Areas/26324875 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Aerospace engineering Electrical engineering Information and computing sciences Artificial intelligence Distributed computing and systems software Machine learning Energy harvesting UAVs positions energy consumption meta-reinforcement learning UAVs strategic locations Internet of Things Autonomous aerial vehicles Optimization Data collection Energy consumption Trajectory Disasters |
| dc.title.none.fl_str_mv | Meta Reinforcement Learning for UAV-Assisted Energy Harvesting IoT Devices in Disaster-Affected Areas |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Over the past decade, Unmanned Aerial Vehicles (UAVs) have attracted significant attention due to their potential applications in emergency-response applications, including wireless power transfer (WPT) and data collection from Internet of Things (IoT) devices in disaster-affected areas. UAVs are more attractive than traditional techniques due to their maneuverability, flexibility, and low deployment costs. However, using UAVs for such critical tasks comes with challenges, including limited resources, energy constraints, and the need to complete missions within strict time frames. IoT devices in disaster areas have limited resources (e.g., computation, energy), so they depend on the UAVs’ resources to accomplish vital missions. To address these resource problems in a disaster scenario, we propose a meta-reinforcement learning (RL)-based energy harvesting (EH) framework. Our system model considers a swarm of UAVs that navigate an area, providing wireless power and collecting data from IoT devices on the ground. The primary objective is to enhance the quality of service for strategic locations while allowing UAVs to dynamically join and leave the swarm (e.g., for recharging). In this context, we formulate the problem as a non-linear programming (NLP) optimization problem aimed at maximizing the total EH IoT devices and determining the optimal trajectory paths for UAVs while adhering to the constraints related to the maximum time duration, the UAVs’ maximum energy consumption, and the minimum data rate to achieve a reliable transmission. Due to the complexity of the problem, the combinatorial nature of the formulated problem, and the difficulty of obtaining the optimal solution using conventional optimization problems, we propose a lightweight meta-RL solution capable of solving the problem by learning the system dynamics. We conducted extensive simulations and compared our approach with two state-of-the-art models using traditional RL algorithms represented by a deep Q-network algorithm, a Particle Swarm Optimization (PSO) algorithm, and one greedy solution. Our simulation results show that the proposed Meta-RL algorithm can outperform the IoT EH of the DQN, PSO algorithm, and the greedy solution by 25%, 32%, and 45%, respectively. The results of our simulations also demonstrate that our proposed approach outperforms the competitive solutions in terms of efficiently covering strategic locations with a high satisfaction rate and high accuracy.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Open Journal of the Communications Society<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" rel="nofollow" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/ojcoms.2024.3377706" target="_blank">https://dx.doi.org/10.1109/ojcoms.2024.3377706</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_36ca9cee87cbc0d7d8e5905bed925e9f |
| identifier_str_mv | 10.1109/ojcoms.2024.3377706 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26324875 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Meta Reinforcement Learning for UAV-Assisted Energy Harvesting IoT Devices in Disaster-Affected AreasMarwan Dhuheir (19170898)Aiman Erbad (14150589)Ala Al-Fuqaha (4434340)Abegaz Mohammed Seid (19170901)EngineeringAerospace engineeringElectrical engineeringInformation and computing sciencesArtificial intelligenceDistributed computing and systems softwareMachine learningEnergy harvestingUAVs positionsenergy consumptionmeta-reinforcement learningUAVsstrategic locationsInternet of ThingsAutonomous aerial vehiclesOptimizationData collectionEnergy consumptionTrajectoryDisasters<p dir="ltr">Over the past decade, Unmanned Aerial Vehicles (UAVs) have attracted significant attention due to their potential applications in emergency-response applications, including wireless power transfer (WPT) and data collection from Internet of Things (IoT) devices in disaster-affected areas. UAVs are more attractive than traditional techniques due to their maneuverability, flexibility, and low deployment costs. However, using UAVs for such critical tasks comes with challenges, including limited resources, energy constraints, and the need to complete missions within strict time frames. IoT devices in disaster areas have limited resources (e.g., computation, energy), so they depend on the UAVs’ resources to accomplish vital missions. To address these resource problems in a disaster scenario, we propose a meta-reinforcement learning (RL)-based energy harvesting (EH) framework. Our system model considers a swarm of UAVs that navigate an area, providing wireless power and collecting data from IoT devices on the ground. The primary objective is to enhance the quality of service for strategic locations while allowing UAVs to dynamically join and leave the swarm (e.g., for recharging). In this context, we formulate the problem as a non-linear programming (NLP) optimization problem aimed at maximizing the total EH IoT devices and determining the optimal trajectory paths for UAVs while adhering to the constraints related to the maximum time duration, the UAVs’ maximum energy consumption, and the minimum data rate to achieve a reliable transmission. Due to the complexity of the problem, the combinatorial nature of the formulated problem, and the difficulty of obtaining the optimal solution using conventional optimization problems, we propose a lightweight meta-RL solution capable of solving the problem by learning the system dynamics. We conducted extensive simulations and compared our approach with two state-of-the-art models using traditional RL algorithms represented by a deep Q-network algorithm, a Particle Swarm Optimization (PSO) algorithm, and one greedy solution. Our simulation results show that the proposed Meta-RL algorithm can outperform the IoT EH of the DQN, PSO algorithm, and the greedy solution by 25%, 32%, and 45%, respectively. The results of our simulations also demonstrate that our proposed approach outperforms the competitive solutions in terms of efficiently covering strategic locations with a high satisfaction rate and high accuracy.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Open Journal of the Communications Society<br>License: <a href="http://creativecommons.org/licenses/by/4.0/" rel="nofollow" target="_blank">http://creativecommons.org/licenses/by/4.0/</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/ojcoms.2024.3377706" target="_blank">https://dx.doi.org/10.1109/ojcoms.2024.3377706</a></p>2024-03-18T12:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/ojcoms.2024.3377706https://figshare.com/articles/journal_contribution/Meta_Reinforcement_Learning_for_UAV-Assisted_Energy_Harvesting_IoT_Devices_in_Disaster-Affected_Areas/26324875CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/263248752024-03-18T12:00:00Z |
| spellingShingle | Meta Reinforcement Learning for UAV-Assisted Energy Harvesting IoT Devices in Disaster-Affected Areas Marwan Dhuheir (19170898) Engineering Aerospace engineering Electrical engineering Information and computing sciences Artificial intelligence Distributed computing and systems software Machine learning Energy harvesting UAVs positions energy consumption meta-reinforcement learning UAVs strategic locations Internet of Things Autonomous aerial vehicles Optimization Data collection Energy consumption Trajectory Disasters |
| status_str | publishedVersion |
| title | Meta Reinforcement Learning for UAV-Assisted Energy Harvesting IoT Devices in Disaster-Affected Areas |
| title_full | Meta Reinforcement Learning for UAV-Assisted Energy Harvesting IoT Devices in Disaster-Affected Areas |
| title_fullStr | Meta Reinforcement Learning for UAV-Assisted Energy Harvesting IoT Devices in Disaster-Affected Areas |
| title_full_unstemmed | Meta Reinforcement Learning for UAV-Assisted Energy Harvesting IoT Devices in Disaster-Affected Areas |
| title_short | Meta Reinforcement Learning for UAV-Assisted Energy Harvesting IoT Devices in Disaster-Affected Areas |
| title_sort | Meta Reinforcement Learning for UAV-Assisted Energy Harvesting IoT Devices in Disaster-Affected Areas |
| topic | Engineering Aerospace engineering Electrical engineering Information and computing sciences Artificial intelligence Distributed computing and systems software Machine learning Energy harvesting UAVs positions energy consumption meta-reinforcement learning UAVs strategic locations Internet of Things Autonomous aerial vehicles Optimization Data collection Energy consumption Trajectory Disasters |