Deep reinforcement learning for AoI minimization in UAV-aided data collection for WSN and IoT applications: a survey
Deep reinforcement learning (DRL) has emerged as a promising technique for optimizing the deployment of unmanned aerial vehicles (UAVs) for data collection in wireless sensor networks (WSNs) and Internet of Things (IoT) applications. With DRL, UAV trajectory can be optimized, optimal data collecti...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/112003/ http://psasir.upm.edu.my/id/eprint/112003/1/published-10589619-Deep_Reinforcement_Learning_for_AoI_Minimization_in_UAV-Aided_Data_Collection_for_WSN_and_IoT_Applications_A_Survey.pdf |
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| author | Ahmed Amodu, Oluwatosin Jarray, Chedia Raja Mahmood, Raja Azlina Althumali, Huda Ali Bukar, Umar Nordin, Rosdiadee Abdullah, Nor Fadzilah Cong Luong, Nguyen |
| author_facet | Ahmed Amodu, Oluwatosin Jarray, Chedia Raja Mahmood, Raja Azlina Althumali, Huda Ali Bukar, Umar Nordin, Rosdiadee Abdullah, Nor Fadzilah Cong Luong, Nguyen |
| author_sort | Ahmed Amodu, Oluwatosin |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Deep reinforcement learning (DRL) has emerged as a promising technique for optimizing the
deployment of unmanned aerial vehicles (UAVs) for data collection in wireless sensor networks (WSNs) and
Internet of Things (IoT) applications. With DRL, UAV trajectory can be optimized, optimal data collection
points can be determined, sensor node transmissions can be scheduled efficiently, and irregular traffic
patterns can be learned effectively. In view of the significance of DRL for UAV-assisted IoT research in
general and, more specifically, its use for time-critical applications, this paper presents a review of the
existing literature on UAV-aided data collection for WSN and IoT applications related to the application
of DRL to minimize the Age of Information (AoI), a recent metric used to measure the degree of freshness
of transmitted information collected in data-gathering applications. This review aims to provide insights into
the state-of-the-art techniques, challenges, and opportunities in this domain through an extensive analysis of
a sizable range of related research papers in this domain. It discusses application areas of UAV-assisted IoT,
such as environmental monitoring, infrastructure inspection, and disaster response. Then, the paper focuses
on the proposed works, their optimization objectives, architectures, simulation libraries and complexities of
the various DRL-based approaches used. Thereafter discussion, challenges, and some opportunities for future
work are provided. The findings of this review serve as a valuable resource for researchers and practitioners,
guiding further advancements and innovations in the field of DRL for UAV-aided data collection in WSN
and IoT applications. |
| first_indexed | 2025-11-15T14:10:57Z |
| format | Article |
| id | upm-112003 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:10:57Z |
| publishDate | 2024 |
| publisher | Institute of Electrical and Electronics Engineers |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1120032024-09-17T01:55:47Z http://psasir.upm.edu.my/id/eprint/112003/ Deep reinforcement learning for AoI minimization in UAV-aided data collection for WSN and IoT applications: a survey Ahmed Amodu, Oluwatosin Jarray, Chedia Raja Mahmood, Raja Azlina Althumali, Huda Ali Bukar, Umar Nordin, Rosdiadee Abdullah, Nor Fadzilah Cong Luong, Nguyen Deep reinforcement learning (DRL) has emerged as a promising technique for optimizing the deployment of unmanned aerial vehicles (UAVs) for data collection in wireless sensor networks (WSNs) and Internet of Things (IoT) applications. With DRL, UAV trajectory can be optimized, optimal data collection points can be determined, sensor node transmissions can be scheduled efficiently, and irregular traffic patterns can be learned effectively. In view of the significance of DRL for UAV-assisted IoT research in general and, more specifically, its use for time-critical applications, this paper presents a review of the existing literature on UAV-aided data collection for WSN and IoT applications related to the application of DRL to minimize the Age of Information (AoI), a recent metric used to measure the degree of freshness of transmitted information collected in data-gathering applications. This review aims to provide insights into the state-of-the-art techniques, challenges, and opportunities in this domain through an extensive analysis of a sizable range of related research papers in this domain. It discusses application areas of UAV-assisted IoT, such as environmental monitoring, infrastructure inspection, and disaster response. Then, the paper focuses on the proposed works, their optimization objectives, architectures, simulation libraries and complexities of the various DRL-based approaches used. Thereafter discussion, challenges, and some opportunities for future work are provided. The findings of this review serve as a valuable resource for researchers and practitioners, guiding further advancements and innovations in the field of DRL for UAV-aided data collection in WSN and IoT applications. Institute of Electrical and Electronics Engineers 2024 Article PeerReviewed text en cc_by_nc_nd_4 http://psasir.upm.edu.my/id/eprint/112003/1/published-10589619-Deep_Reinforcement_Learning_for_AoI_Minimization_in_UAV-Aided_Data_Collection_for_WSN_and_IoT_Applications_A_Survey.pdf Ahmed Amodu, Oluwatosin and Jarray, Chedia and Raja Mahmood, Raja Azlina and Althumali, Huda and Ali Bukar, Umar and Nordin, Rosdiadee and Abdullah, Nor Fadzilah and Cong Luong, Nguyen (2024) Deep reinforcement learning for AoI minimization in UAV-aided data collection for WSN and IoT applications: a survey. IEEE Access, 12. pp. 108000-108040. ISSN 2169-3536 https://ieeexplore.ieee.org/document/10589619 10.1109/ACCESS.2024.3425497 |
| spellingShingle | Ahmed Amodu, Oluwatosin Jarray, Chedia Raja Mahmood, Raja Azlina Althumali, Huda Ali Bukar, Umar Nordin, Rosdiadee Abdullah, Nor Fadzilah Cong Luong, Nguyen Deep reinforcement learning for AoI minimization in UAV-aided data collection for WSN and IoT applications: a survey |
| title | Deep reinforcement learning for AoI minimization in UAV-aided data collection for WSN and IoT applications: a survey |
| title_full | Deep reinforcement learning for AoI minimization in UAV-aided data collection for WSN and IoT applications: a survey |
| title_fullStr | Deep reinforcement learning for AoI minimization in UAV-aided data collection for WSN and IoT applications: a survey |
| title_full_unstemmed | Deep reinforcement learning for AoI minimization in UAV-aided data collection for WSN and IoT applications: a survey |
| title_short | Deep reinforcement learning for AoI minimization in UAV-aided data collection for WSN and IoT applications: a survey |
| title_sort | deep reinforcement learning for aoi minimization in uav-aided data collection for wsn and iot applications: a survey |
| url | http://psasir.upm.edu.my/id/eprint/112003/ http://psasir.upm.edu.my/id/eprint/112003/ http://psasir.upm.edu.my/id/eprint/112003/ http://psasir.upm.edu.my/id/eprint/112003/1/published-10589619-Deep_Reinforcement_Learning_for_AoI_Minimization_in_UAV-Aided_Data_Collection_for_WSN_and_IoT_Applications_A_Survey.pdf |