Revolutionizing FANETs With Reinforcement Learning: Optimized Data Forwarding and Real-Time Adaptability
Uncrewed Aerial Vehicles (UAVs), commonly known as drones, have significantly advanced wireless communication frameworks by enabling the formation of Flying Ad-Hoc Networks (FANETs). FANETs facilitate autonomous collaboration among UAVs through decentralized and self-organizing communication protoco...
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| Format: | Article |
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IEEE
2025-01-01
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| Series: | IEEE Open Journal of the Communications Society |
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| Online Access: | https://ieeexplore.ieee.org/document/10979975/ |
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| author | Yasir Ibraheem Mohammed Rosilah Hassan Mohammad Kamrul Hasan Shayla Islam Huda Saleh Abbas Muhammad Asghar Khan Muhammad Attique Khan |
| author_facet | Yasir Ibraheem Mohammed Rosilah Hassan Mohammad Kamrul Hasan Shayla Islam Huda Saleh Abbas Muhammad Asghar Khan Muhammad Attique Khan |
| author_sort | Yasir Ibraheem Mohammed |
| collection | DOAJ |
| description | Uncrewed Aerial Vehicles (UAVs), commonly known as drones, have significantly advanced wireless communication frameworks by enabling the formation of Flying Ad-Hoc Networks (FANETs). FANETs facilitate autonomous collaboration among UAVs through decentralized and self-organizing communication protocols, proving especially effective in dynamic applications such as military surveillance, disaster management, and environmental monitoring. Nevertheless, traditional routing algorithms, initially developed for terrestrial networks, often fail to meet the unique challenges of FANETs, notably their high mobility and frequently changing network topologies. A framework was proposed to address these challenges; this paper formulates a multi-objective optimization problem aimed at optimizing UAV trajectories, enhancing energy efficiency, and maximizing communication range to improve overall data forwarding performance. A Reinforcement Learning (RL)-based agent is created that constantly enhances its decision-making capacity by utilizing real-time feedback and dynamically chooses best forwarding tactics. This work also combines developments in large-scale data collecting from Wireless Sensor Networks (WSNs), using mobile sinks supported by FANETs in conjunction with multi-objective optimization approaches to improve data collecting efficiency greatly. Experimental tests show that the suggested RL-based techniques outperform conventional routing protocols by properly lowering delays and raising the Packet Delivery Ratio (PDR). Moreover, simulation findings show the better scalability and adaptability of RL-enabled UAV networks, stressing its possible use in dynamic real-world situations such as disaster relief operations and environmental monitoring tasks. |
| format | Article |
| id | doaj-art-16babd3814ae49e1a24cbb4c402be299 |
| institution | DOAJ |
| issn | 2644-125X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of the Communications Society |
| spelling | doaj-art-16babd3814ae49e1a24cbb4c402be2992025-08-20T03:13:43ZengIEEEIEEE Open Journal of the Communications Society2644-125X2025-01-0164295431010.1109/OJCOMS.2025.356547110979975Revolutionizing FANETs With Reinforcement Learning: Optimized Data Forwarding and Real-Time AdaptabilityYasir Ibraheem Mohammed0Rosilah Hassan1Mohammad Kamrul Hasan2https://orcid.org/0000-0001-5511-0205Shayla Islam3https://orcid.org/0000-0002-0490-7799Huda Saleh Abbas4Muhammad Asghar Khan5https://orcid.org/0000-0002-1351-898XMuhammad Attique Khan6https://orcid.org/0000-0001-5723-3858Center for Cyber Security, Faculty of Information Science and Technology, National University of Malaysia, Bangi, MalaysiaCenter for Cyber Security, Faculty of Information Science and Technology, National University of Malaysia, Bangi, MalaysiaCenter for Cyber Security, Faculty of Information Science and Technology, National University of Malaysia, Bangi, MalaysiaInstitute of Computer Science and Digital Innovation, UCSI University, Kuala Lumpur, MalaysiaDepartment of Computer Science, Computer Science and Engineering College, Taibah University, Madinah, Saudi ArabiaDepartment of EE, College of Engineering, Prince Mohammad Bin Fahd University, Al-Khobar, Saudi ArabiaDepartment of AI, College of Computer Engineering and Science, Prince Mohammad Bin Fahd University, Al-Khobar, Saudi ArabiaUncrewed Aerial Vehicles (UAVs), commonly known as drones, have significantly advanced wireless communication frameworks by enabling the formation of Flying Ad-Hoc Networks (FANETs). FANETs facilitate autonomous collaboration among UAVs through decentralized and self-organizing communication protocols, proving especially effective in dynamic applications such as military surveillance, disaster management, and environmental monitoring. Nevertheless, traditional routing algorithms, initially developed for terrestrial networks, often fail to meet the unique challenges of FANETs, notably their high mobility and frequently changing network topologies. A framework was proposed to address these challenges; this paper formulates a multi-objective optimization problem aimed at optimizing UAV trajectories, enhancing energy efficiency, and maximizing communication range to improve overall data forwarding performance. A Reinforcement Learning (RL)-based agent is created that constantly enhances its decision-making capacity by utilizing real-time feedback and dynamically chooses best forwarding tactics. This work also combines developments in large-scale data collecting from Wireless Sensor Networks (WSNs), using mobile sinks supported by FANETs in conjunction with multi-objective optimization approaches to improve data collecting efficiency greatly. Experimental tests show that the suggested RL-based techniques outperform conventional routing protocols by properly lowering delays and raising the Packet Delivery Ratio (PDR). Moreover, simulation findings show the better scalability and adaptability of RL-enabled UAV networks, stressing its possible use in dynamic real-world situations such as disaster relief operations and environmental monitoring tasks.https://ieeexplore.ieee.org/document/10979975/Data gatheringFANETUAVsIoTreinforcement learningWSNs |
| spellingShingle | Yasir Ibraheem Mohammed Rosilah Hassan Mohammad Kamrul Hasan Shayla Islam Huda Saleh Abbas Muhammad Asghar Khan Muhammad Attique Khan Revolutionizing FANETs With Reinforcement Learning: Optimized Data Forwarding and Real-Time Adaptability IEEE Open Journal of the Communications Society Data gathering FANET UAVs IoT reinforcement learning WSNs |
| title | Revolutionizing FANETs With Reinforcement Learning: Optimized Data Forwarding and Real-Time Adaptability |
| title_full | Revolutionizing FANETs With Reinforcement Learning: Optimized Data Forwarding and Real-Time Adaptability |
| title_fullStr | Revolutionizing FANETs With Reinforcement Learning: Optimized Data Forwarding and Real-Time Adaptability |
| title_full_unstemmed | Revolutionizing FANETs With Reinforcement Learning: Optimized Data Forwarding and Real-Time Adaptability |
| title_short | Revolutionizing FANETs With Reinforcement Learning: Optimized Data Forwarding and Real-Time Adaptability |
| title_sort | revolutionizing fanets with reinforcement learning optimized data forwarding and real time adaptability |
| topic | Data gathering FANET UAVs IoT reinforcement learning WSNs |
| url | https://ieeexplore.ieee.org/document/10979975/ |
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