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Energy Efficient 3-D UAV Control for Persistent Communication Service and Fairness: A Deep Reinforcement Learning Approach

Hang Qi, Zhiqun Hu, Hao Huang, Xiangming Wen, Zhaoming Lu
2020 IEEE Access  
INDEX TERMS UAV communication, energy efficiency, fairness, energy replenishment, deep reinforcement learning, DDPG. I.  ...  Inspired by the success of deep reinforcement learning, we propose a UAV Control policy based on Deep Deterministic Policy Gradient (UC-DDPG) to address the combination problem of 3-D mobility of multiple  ...  Then, the problem definition of energy efficient 3-D UAV control for persistent communication service and fairness is presented. A.  ... 
doi:10.1109/access.2020.2981403 fatcat:zm5hcazrmzfi5dtjgvujel3hte

A reawakening of Machine Learning Application in Unmanned Aerial Vehicle: Future Research Motivation

Wasswa Shafik, S. Mojtaba Matinkhah, Fawad Shokoor, Lule Sharif
2022 EAI Endorsed Transactions on Internet of Things  
Supervised, unsupervised, semi-supervised, and Reinforcement Learning (RL) are the main types of ML.  ...  This study mainly focuses on RL and Deep learning, since necessitates mainly sequential and consecutive decision-making context.  ...  The authors in nutshell would like to distinguish the support and comments shared with us from the computer engineering department members to attain this paper's quality.  ... 
doi:10.4108/eetiot.v8i29.987 fatcat:hkyfcvmj5bdt7gmiigvzlvgpvi

On-board Deep Q-Network for UAV-assisted Online Power Transfer and Data Collection

Kai Li, Wei Ni, Eduardo Tovar, Abbas Jamalipour
2019 IEEE Transactions on Vehicular Technology  
A key challenge is online MPT and data collection in the presence of on-board control of a UAV (e.g., patrolling velocity) for preventing battery drainage and data queue overflow of the sensing devices  ...  A key challenge is online MPT and data collection in the presence of on-board control of a UAV (e.g., patrolling velocity) for preventing battery drainage and data queue overflow of the sensing devices  ...  Acknowledgements This work was partially supported by National Funds through FCT/MCTES (Portuguese Foundation for Science and Technology), within the CISTER Research Unit (CEC/04234); also by the Operational  ... 
doi:10.1109/tvt.2019.2945037 fatcat:t2g73wxqirdh5nde32nwovepge

2020 Index IEEE Transactions on Vehicular Technology Vol. 69

2020 IEEE Transactions on Vehicular Technology  
Yu, X., A Joint Design of Platoon Communication and Control Based on LTE-V2V; 15893-15907 Hong, C.S., see Nguyen, M.N.H., TVT May 2020 5618-5633 Hong, C.S., see Chen, D., TVT May 2020 5634-5646 Hong  ...  , C.S., see Le, T.H.T., TVT Dec. 2020 15162-15176 Hong, D., Lee, S., Cho, Y.H., Baek, D., Kim, J., and Chang, N Guo, H., Liu, J., and Zhang, Y., Toward Swarm Coordination: Topol-ogy-Aware Inter-UAV  ...  ., +, TVT Aug. 2020 8577-8585 UAV Virtualization for Enabling Heterogeneous and Persistent UAV-as-a- Service.  ... 
doi:10.1109/tvt.2021.3055470 fatcat:536l4pgnufhixneoa3a3dibdma

Space-Air-Ground Integrated Mobile Crowdsensing for Partially Observable Data Collection by Multi-Scale Convolutional Graph Reinforcement Learning

Yixiang Ren, Zhenhui Ye, Guanghua Song, Xiaohong Jiang
2022 Entropy  
To address this multi-agent problem, we propose a novel deep reinforcement learning (DRL) based method called Multi-Scale Soft Deep Recurrent Graph Network (ms-SDRGN).  ...  We also use a graph attention mechanism to model inter-UAV communications and aggregate extra neighboring information, and utilize a gated recurrent unit for long-term performance.  ...  Acknowledgments: We gratefully acknowledge the reviewers for their comments and suggestions. Conflicts of Interest: The authors declare no conflict of interest. Entropy 2022, 24, 638  ... 
doi:10.3390/e24050638 pmid:35626523 pmcid:PMC9140918 fatcat:ol64sxrv65cshaw3ucrtc23l64

2021 Index IEEE Internet of Things Journal Vol. 8

2021 IEEE Internet of Things Journal  
Departments and other items may also be covered if they have been judged to have archival value. The Author Index contains the primary entry for each item, listed under the first author's name.  ...  The primary entry includes the coauthors' names, the title of the paper or other item, and its location, specified by the publication abbreviation, year, month, and inclusive pagination.  ...  ., +, JIoT June 15, 2021 9995-10009 Mean Field Deep Reinforcement Learning for Fair and Efficient UAV Control.  ... 
doi:10.1109/jiot.2022.3141840 fatcat:42a2qzt4jnbwxihxp6rzosha3y

Proactive Handover Decision for UAVs with Deep Reinforcement Learning

Younghoon Jang, Syed M. Raza, Moonseong Kim, Hyunseung Choo
2022 Sensors  
This paper presents a novel handover decision scheme deploying Deep Reinforcement Learning (DRL) to prevent unnecessary handovers while maintaining stable connectivity.  ...  The proposed DRL framework takes the UAV state as an input for a proximal policy optimization algorithm and develops a Received Signal Strength Indicator (RSSI) based on a reward function for the online  ...  Illustrative comparison between Reinforcement Learning (RL) and Deep Reinforcement Learning (DRL). (a) RL with Q-table for policy update. (b) DRL with neural network for policy update.  ... 
doi:10.3390/s22031200 pmid:35161945 pmcid:PMC8838000 fatcat:egj3e4lk35h4dlp3mdb4ij7pf4

Machine Learning Methods for Management UAV Flocks - a Survey

Rina Azoulay, Yoram Haddad, Shulamit Reches
2021 IEEE Access  
UAV technology can be used in a wide range of domains, including communication, agriculture, security, and transportation.  ...  The development of unmanned aerial vehicles (UAVs) has been gaining momentum in recent years owing to technological advances and a significant reduction in their cost.  ...  [75] developed a deep learning system for energy-efficient power control in wireless networks.  ... 
doi:10.1109/access.2021.3117451 fatcat:f6xli6srencw3ezqg5fyzwmuie

Networking of Internet of UAVs: Challenges and Intelligent Approaches [article]

Peng Yang, Xianbin Cao, Tony Q. S. Quek, Dapeng Oliver Wu
2021 arXiv   pre-print
I-UAV networks and presents the corresponding networking challenges and intelligent approaches.  ...  Internet of unmanned aerial vehicle (I-UAV) networks promise to accomplish sensing and transmission tasks quickly, robustly, and cost-efficiently via effective cooperation among UAVs.  ...  Figure 3 illustrates the tendency of the obtained energy efficiency and Jain's fairness index with a constant UAV transmit power P D = 24 dBm and the number of users N = 100.  ... 
arXiv:2111.07078v1 fatcat:yqnuhkoxxrcn3czqeurbht7cqy

Applications of Multi-Agent Reinforcement Learning in Future Internet: A Comprehensive Survey [article]

Tianxu Li, Kun Zhu, Nguyen Cong Luong, Dusit Niyato, Qihui Wu, Yang Zhang, Bing Chen
2022 arXiv   pre-print
The issues consist of network access, transmit power control, computation offloading, content caching, packet routing, trajectory design for UAV-aided networks, and network security issues.  ...  Standard learning algorithms such as single-agent Reinforcement Learning (RL) or Deep Reinforcement Learning (DRL) have been recently used to enable each network entity as an agent to learn an optimal  ...  ., mobile users, vehicles, UAVs, and IoT devices, and establish lowlatency, ultra-reliable, and energy-efficient networks, Reinforcement Learning (RL) [1] and Deep Reinforcement Learning (DRL) [2] ,  ... 
arXiv:2110.13484v2 fatcat:u2o5uxms65gmnp3q7xbh35l5oi

Self-Evolving Integrated Vertical Heterogeneous Networks [article]

Amin Farajzadeh, Mohammad G. Khoshkholgh, Halim Yanikomeroglu, Ozgur Ercetin
2021 arXiv   pre-print
and use cases while dealing with network complexity efficiently.  ...  6G and beyond networks tend towards fully intelligent and adaptive design in order to provide better operational agility in maintaining universal wireless access and supporting a wide range of services  ...  [43] × × × Survey of routing, seamless handover, and energy efficiency aspects of UAV communications.  ... 
arXiv:2106.13950v2 fatcat:z3a6vig6fza2bmnll4fodgczie

Bridging the Urban-Rural Connectivity Gap through Intelligent Space, Air, and Ground Networks [article]

Fares Fourati, Saeed Hamood Alsamhi, Mohamed-Slim Alouini
2022 arXiv   pre-print
Connectivity in rural areas is one of the main challenges of communication networks. To overcome this challenge, a variety of solutions for different situations are required.  ...  AI enables intelligent communications and can integrate space, air, and ground networks for rural connectivity.  ...  The suggested technique optimizes the energy efficiency function while considering the fairness, coverage, connectivity, and energy consumption of the communications.  ... 
arXiv:2202.12683v1 fatcat:jzp3kthojbdldlf5tfofvdjtxm

Intelligent Reflecting Surfaces Assisted UAV Communications for Massive Networks: Current Trends, Challenges, and Research Directions

Syed Agha Hassnain Mohsan, Muhammad Asghar Khan, Mohammed H. Alsharif, Peerapong Uthansakul, Ahmed A. A. Solyman
2022 Sensors  
It is predicted to be a revolutionizing technology with the capability to alter wireless communication to enhance both spectrum and energy efficiencies with low expenditure and low energy consumption.  ...  Finally, we shed some light on open challenges and future research directions for IRS-assisted UAV communication.  ...  Similarly, deep reinforcement learning (DRL) algorithms are proposed as a viable solution instant decisionmaking and embedded optimization in wireless communications.  ... 
doi:10.3390/s22145278 pmid:35890955 pmcid:PMC9322292 fatcat:hesqzgn2u5defolpwzkmf3brcu

Mobile Wireless Rechargeable UAV Networks: Challenges and Solutions [article]

Yuntao Wang, Zhou Su, Ning Zhang, Ruidong Li
2022 arXiv   pre-print
In this article, we present a comprehensive study of vehicle-assisted wireless rechargeable UAV networks (VWUNs) to promote on-demand, secure, and efficient UAV recharging services.  ...  We then propose a secure and privacy-preserving VWUN framework for UAVs and ground vehicles based on differential privacy (DP).  ...  U20A20175, U1808207), the Fundamental Research Funds for the Central Universities, and JSPS KAKENHI Grant Number 19H04105.  ... 
arXiv:2203.13139v1 fatcat:5qq2mhehujfzbku27mvzggazaq

Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges

Hazim Shakhatreh, Ahmad H. Sawalmeh, Ala Al-Fuqaha, Zuochao Dou, Eyad Almaita, Issa Khalil, Noor Shamsiah Othman, Abdallah Khreishah, Mohsen Guizani
2019 IEEE Access  
We also discuss current research trends and provide future insights for potential UAV uses.  ...  Furthermore, we present the key challenges for UAV civil applications, including: charging challenges, collision avoidance and swarming challenges, and networking and security related challenges.  ...  Zhang et al. in [305] use deep reinforcement learning to determine the fastest path to a charging station.  ... 
doi:10.1109/access.2019.2909530 fatcat:xgknpyuqazhpvferjkkdohxmtu
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