Learning-based resilience guarantee for multi-UAV collaborative QoS management
Tài liệu tham khảo
Mahdavinejad, 2018, Machine learning for internet of things data analysis: a survey, Digit. Commun. Netw., 4, 161, 10.1016/j.dcan.2017.10.002
Nguyen, 2019, Machine learning and deep learning frameworks and libraries for large-scale data mining: a survey, Artif. Intell. Rev., 52, 77, 10.1007/s10462-018-09679-z
Yan, 2017, Coclustering of multidimensional big data—A useful tool for genomic, financial, and other data analysis, IEEE Syst. Man Cybern. Mag., 23, 10.1109/MSMC.2017.2664218
L. Bai, L. Cui, Y. Wang, Y. Jiao, E. Hancock, A quantum-inspired entropic kernel for multiple financial time series analysis (2020) 4453–4460.
Huang, 2014, A kernel entropy manifold learning approach for financial data analysis, Decis. Support Syst., 64, 31, 10.1016/j.dss.2014.04.004
Cui, 2018, A preliminary survey of analyzing dynamic time-varying financial networks using graph kernels, Struct., Syntactic, Stat. Pattern Recognit., 237, 10.1007/978-3-319-97785-0_23
Cui, 2021, Internet financing credit risk evaluation using multiple structural interacting elastic net feature selection, Pattern Recognit., 114, 107835, 10.1016/j.patcog.2021.107835
Stockinger, 2019, Scalable architecture for big data financial analytics: user-defined functions vs. SQL, J. Big Data, 6, 46, 10.1186/s40537-019-0209-0
Jabbour, 2019, Unlocking the circular economy through new business models based on large-scale data: an integrative framework and research agenda, Technol. Forecast. Soc. Change, 144, 546, 10.1016/j.techfore.2017.09.010
Fikri, 2019, An adaptive and real-time based architecture for financial data integration, J. Big Data, 6, 97, 10.1186/s40537-019-0260-x
Shakhatreh, 2019, Unmanned aerial vehicles (UAVs): a survey on civil applications and key research challenges, IEEE Access, 7, 48572, 10.1109/ACCESS.2019.2909530
Sargolzaei, 2020, Control of cooperative unmanned aerial vehicles: review of applications, challenges, and algorithms, 229
Santos, 2019, Scene wireframes sketching for unmanned aerial vehicles, Pattern Recognit., 86, 354, 10.1016/j.patcog.2018.09.017
Ren, 2020, A three-step classification framework to handle complex data distribution for radar UAV detection, Pattern Recognit., 111, 107709, 10.1016/j.patcog.2020.107709
Ren, 2017, Regularized 2-D complex-log spectral analysis and subspace reliability analysis of micro-doppler signature for UAV detection, Pattern Recognit., 69, 225, 10.1016/j.patcog.2017.04.024
Zhao, 2019, UAV-assisted emergency networks in disasters, IEEE Wirel. Commun., 26, 45, 10.1109/MWC.2018.1800160
Liu, 2018, Energy-efficient UAV control for effective and fair communication coverage: a deep reinforcement learning approach, IEEE J. Sel. Areas Commun., 36, 2059, 10.1109/JSAC.2018.2864373
Mnih, 2015, Human-level control through deep reinforcement learning, Nature, 518, 529, 10.1038/nature14236
Silver, 2017, Mastering the game of go without human knowledge, Nature, 550, 354, 10.1038/nature24270
Sihang, 2020, Precise detection of Chinese characters in historical documents with deep reinforcement learning, Pattern Recognit., 107, 107503, 10.1016/j.patcog.2020.107503
Teng, 2020, Three-step action search networks with deep Q-learning for real-time object tracking, Pattern Recognit., 101, 107188, 10.1016/j.patcog.2019.107188
J. Schulman, F. Wolski, P. Dhariwal, A. Radford, O. Klimov, Proximal policy optimization algorithms, arXiv preprint arXiv:1707.06347(2017).
Lu, 2020, A cost-efficient elastic UAV relay network construction method with guaranteed QoS, Ad Hoc Netw., 107, 102219, 10.1016/j.adhoc.2020.102219
Lyu, 2019, Online UAV scheduling towards throughput QoSguarantee for dynamic IoVs, 1
Lin, 2016, A tube-and-droplet-based approach for representing and analyzing motion trajectories, IEEE Trans. Pattern Anal. Mach. Intell., 39, 1489, 10.1109/TPAMI.2016.2608884
Cheng, 2018, UAV trajectory optimization for data offloading at the edge of multiple cells, IEEE Trans. Veh. Technol., 67, 6732, 10.1109/TVT.2018.2811942
Samir, 2019, Joint optimization of UAV trajectory and radio resource allocation for drive-thru vehicular networks, 1
Bejaoui, 2020, A QoS-oriented trajectory optimization in swarming unmanned-aerial-vehicles communications, IEEE Wirel. Commun. Lett., 9, 791, 10.1109/LWC.2020.2970052
Perabathini, 2019, Efficient 3D placement of UAVs with QoS assurance in ad hoc wireless networks, 1
Li, 2019, Rechargeable multi-UAV aided seamless coverage for QoS-guaranteed iot networks, IEEE Internet Things J., 6, 10902, 10.1109/JIOT.2019.2943147
Roth, 2019, Base-stations up in the air: multi-UAV trajectory control for min-rate maximization in uplink C-RAN, 1
Hu, 2020, Reinforcement learning for a cellular internet of UAVs: protocol design, trajectory control, and resource management, IEEE Wirel. Commun., 27, 116, 10.1109/MWC.001.1900262
Koushik, 2019, Deep Q-learning-based node positioning for throughput-optimal communications in dynamic UAV swarm network, IEEE Trans. Cogn. Commun. Netw., 5, 554, 10.1109/TCCN.2019.2907520
Bayerlein, 2018, Trajectory optimization for autonomous flying base station via reinforcement learning, 1
Ghanavi, 2018, Efficient 3D aerial base station placement considering users mobility by reinforcement learning, 1
Wu, 2019, Trajectory design for overlay UAV-to-device communications by deep reinforcement learning, 1
Hu, 2018, Reinforcement learning for decentralized trajectory design in cellular UAV networks with sense-and-send protocol, IEEE Internet Things J., 6, 6177, 10.1109/JIOT.2018.2876513
Cui, 2019, The application of multi-agent reinforcement learning in UAV networks, 1
Wu, 2020, Cellular UAV-to-device communications: trajectory design and mode selection by multi-agent deep reinforcement learning, IEEE Trans. Commun., 68, 4175, 10.1109/TCOMM.2020.2986289
Salehi, 2020, A QoS-aware, energy-efficient trajectory optimization for UAV base stations using Q-learning, 329
Qiu, 2020, A multi-objective pigeon-inspired optimization approach to UAV distributed flocking among obstacles, Inf. Sci., 509, 515, 10.1016/j.ins.2018.06.061
S. Ivanov, A. D’yakonov, Modern deep reinforcement learning algorithms, arXiv preprint arXiv:1906.10025(2019).
Luong, 2019, Applications of deep reinforcement learning in communications and networking: a survey, IEEE Commun. Surv. Tutor., 21, 3133, 10.1109/COMST.2019.2916583
Kingma, 2015, Adam: a method for stochastic optimization
Paszke, 2019, Pytorch: An imperative style, high-performance deep learning library, 8026
Choi, 2009, Consensus-based decentralized auctions for robust task allocation, IEEE Trans. Robot., 25, 912, 10.1109/TRO.2009.2022423
Chopra, 2017, A distributed version of the hungarian method for multirobot assignment, IEEE Trans. Robot., 33, 932, 10.1109/TRO.2017.2693377