Due to the mobility and flexibility, using unmanned aerial vehicles (UAVs) for data collection from wireless sensors in the Internet of Things will be a promising solution. In this paper, we propose a fairness-aware trajectory design for UAV-aided data collection network. Particularly, we establish a Markov decision process (MDP) to represent the interaction between UAV and sensors, and a fairness-aware reward function is defined to evaluate the performance of UAV. Through the reinforcement learning algorithm deep deterministic policy gradient (DDPG), the optimal flight trajectory of UAV is obtained. Simulation results demonstrate that the proposed fairness-aware trajectory design method can significantly improve data collection ratio (DCR) and system fairness index compared with the common baseline method.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A Fairness-Aware UAV Trajectory Design with Reinforcement Learning


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Sun, Songlin (Herausgeber:in) / Hong, Tao (Herausgeber:in) / Yu, Peng (Herausgeber:in) / Zou, Jiaqi (Herausgeber:in) / Xu, Bowei (Autor:in) / Peng, Liang (Autor:in) / Wang, Xiaoxiang (Autor:in) / Jiang, Lixin (Autor:in) / Zhang, Yunxia (Autor:in) / Zhang, Peng (Autor:in)

    Kongress:

    International Conference On Signal And Information Processing, Networking And Computers ; 2021 December 27, 2021 - December 29, 2021



    Erscheinungsdatum :

    13.10.2022


    Format / Umfang :

    6 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Reinforcement Learning-Based Solar Sail Trajectory Design

    Yuan, Hao / Zhong, Zikai / Wang, Jie et al. | IEEE | 2024





    Machine Learning for QoS-Aware Fairness of a D2D Network

    Liu, Xian / Huang, Changcheng | IEEE | 2020