{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T16:12:26Z","timestamp":1784131946057,"version":"3.55.0"},"reference-count":59,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,9,3]],"date-time":"2022-09-03T00:00:00Z","timestamp":1662163200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020YFB1807700"],"award-info":[{"award-number":["2020YFB1807700"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["62071356"],"award-info":[{"award-number":["62071356"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["JB210113"],"award-info":[{"award-number":["JB210113"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC)","doi-asserted-by":"publisher","award":["2020YFB1807700"],"award-info":[{"award-number":["2020YFB1807700"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC)","doi-asserted-by":"publisher","award":["62071356"],"award-info":[{"award-number":["62071356"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China (NSFC)","doi-asserted-by":"publisher","award":["JB210113"],"award-info":[{"award-number":["JB210113"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["2020YFB1807700"],"award-info":[{"award-number":["2020YFB1807700"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["62071356"],"award-info":[{"award-number":["62071356"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["JB210113"],"award-info":[{"award-number":["JB210113"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Unmanned aerial vehicles (UAVs) are widely used in Internet-of-Things (IoT) networks, especially in remote areas where communication infrastructure is unavailable, due to flexibility and low cost. However, the joint optimization of locations of UAVs and relay path selection can be very challenging, especially when the numbers of IoT devices and UAVs are very large. In this paper, we formulate the joint optimization of UAV locations and relay paths in UAV-relayed IoT networks as a graph problem, and propose a graph neural network (GNN)-based approach to solve it in an efficient and scalable way. In the training procedure, we design a reinforcement learning-based relay GNN (RGNN) to select the best relay path for each user. The theoretical analysis shows that the time complexity of RGNN is two orders lower than the conventional optimization method. Then, we jointly exploit location GNN (LGNN) and RGNN trained to optimize the locations of all UAVs. Both GNNs can be trained without relying on the training data, which is usually unavailable in the context of wireless networks. In inference procedure, LGNN is first used to optimize the location of UAVs, and then RGNN is used to select the best relay path based on the output of LGNN. Simulation results show that the proposed approach can achieve comparable performance to brute-force search with much lower time complexity when the network is relatively small. Remarkably, the proposed approach is highly scalable to large-scale networks and adaptable to dynamics in the environment, which can hardly be achieved using conventional methods.<\/jats:p>","DOI":"10.3390\/rs14174377","type":"journal-article","created":{"date-parts":[[2022,9,8]],"date-time":"2022-09-08T04:18:32Z","timestamp":1662610712000},"page":"4377","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":75,"title":["Joint Flying Relay Location and Routing Optimization for 6G UAV\u2013IoT Networks: A Graph Neural Network-Based Approach"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1439-4875","authenticated-orcid":false,"given":"Xiucheng","family":"Wang","sequence":"first","affiliation":[{"name":"School of Telecommunications Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lianhao","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Cheng","sequence":"additional","affiliation":[{"name":"School of Telecommunications Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ruijin","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Telecommunications Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tom","family":"Luan","sequence":"additional","affiliation":[{"name":"School of Cyber Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Quan","sequence":"additional","affiliation":[{"name":"School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4051-815X","authenticated-orcid":false,"given":"Khalid","family":"Aldubaikhy","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Qassim University, Buraydah 52571, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1038\/s41928-019-0355-6","article-title":"What should 6G be?","volume":"3","author":"Dang","year":"2020","journal-title":"Nat. Electron."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"128125","DOI":"10.1109\/ACCESS.2019.2934998","article-title":"Survey on Collaborative Smart Drones and Internet of Things for Improving Smartness of Smart Cities","volume":"7","author":"Alsamhi","year":"2019","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Alsamhi, S.H., Shvetsov, A.V., Kumar, S., Shvetsova, S.V., Alhartomi, M.A., Hawbani, A., Rajput, N.S., Srivastava, S., Saif, A., and Nyangaresi, V.O. (2022). UAV Computing-Assisted Search and Rescue Mission Framework for Disaster and Harsh Environment Mitigation. Drones, 6.","DOI":"10.3390\/drones6070154"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1117","DOI":"10.1109\/JSAC.2019.2906789","article-title":"Space\/Aerial-Assisted Computing Offloading for IoT Applications: A Learning-Based Approach","volume":"37","author":"Cheng","year":"2019","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Gholami, A., Torkzaban, N., Baras, J.S., and Papagianni, C. (2020, January 19\u201321). Joint mobility-aware UAV placement and routing in multi-hop UAV relaying systems. Proceedings of the International Conference on Ad Hoc Networks, Bari, Italy.","DOI":"10.1007\/978-3-030-67369-7_5"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1109\/TNET.2020.2970744","article-title":"Joint optimization of relay deployment, channel allocation, and relay assignment for UAVs-aided D2D networks","volume":"28","author":"Zhong","year":"2020","journal-title":"IEEE\/ACM Trans. Netw."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Alsamhi, S.H., Ma, O., Ansari, M.S., and Gupta, S.K. (2019). Collaboration of Drone and Internet of Public Safety Things in Smart Cities: An Overview of QoS and Network Performance Optimization. Drones, 3.","DOI":"10.3390\/drones3010013"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1109\/MCOM.2016.7470933","article-title":"Wireless communications with unmanned aerial vehicles: Opportunities and challenges","volume":"54","author":"Zeng","year":"2016","journal-title":"IEEE Commun. Mag."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2739","DOI":"10.1109\/TITS.2021.3090017","article-title":"UAV-Assisted Physical Layer Security in Multi-Beam Satellite-Enabled Vehicle Communications","volume":"23","author":"Yin","year":"2022","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Yin, Z., Jia, M., Wang, W., Cheng, N., Lyu, F., and Shen, X. (2019, January 9\u201313). Max-min secrecy rate for NOMA-based UAV-assisted communications with protected zone. Proceedings of the 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA.","DOI":"10.1109\/GLOBECOM38437.2019.9013815"},{"key":"ref_11","first-page":"309","article-title":"Collaboration of UAV and HetNet for better QoS: A comparative study","volume":"5","author":"Gupta","year":"2020","journal-title":"Int. J. Veh. Inf. Commun. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1031","DOI":"10.1109\/TCOMM.2018.2875081","article-title":"Multiple antenna aided NOMA in UAV networks: A stochastic geometry approach","volume":"67","author":"Hou","year":"2018","journal-title":"IEEE Trans. Commun."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"911","DOI":"10.1109\/TWC.2020.3029143","article-title":"Deep reinforcement learning for delay-oriented IoT task scheduling in SAGIN","volume":"20","author":"Zhou","year":"2020","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Xia, J.Y., Li, S., Huang, J.J., Yang, Z., Jaimoukha, I.M., and G\u00fcnd\u00fcz, D. (2022). Metalearning-Based Alternating Minimization Algorithm for Nonconvex Optimization. IEEE Trans. Neural Netw. Learn. Syst.","DOI":"10.1109\/TNNLS.2022.3165627"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"11891","DOI":"10.1109\/JIOT.2021.3063686","article-title":"Enabling massive IoT toward 6G: A comprehensive survey","volume":"8","author":"Guo","year":"2021","journal-title":"IEEE Internet Things J."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13638-016-0659-4","article-title":"Mobility management for IoT: A survey","volume":"2016","author":"Ghaleb","year":"2016","journal-title":"Eurasip J. Wirel. Commun. Netw."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"312","DOI":"10.1016\/j.icte.2020.04.010","article-title":"The effect of batch size on the generalizability of the convolutional neural networks on a histopathology dataset","volume":"6","author":"Kandel","year":"2020","journal-title":"ICT Express"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Nguyen, D.C., Ding, M., Pathirana, P.N., Seneviratne, A., Li, J., Niyato, D., Dobre, O., and Poor, H.V. (2021). 6G Internet of Things: A comprehensive survey. IEEE Internet Things J., 359\u2013383.","DOI":"10.1109\/JIOT.2021.3103320"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"101","DOI":"10.1109\/JSAC.2020.3036965","article-title":"Graph neural networks for scalable radio resource management: Architecture design and theoretical analysis","volume":"39","author":"Shen","year":"2020","journal-title":"IEEE J. Sel. Areas Commun."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/TNNLS.2020.2978386","article-title":"A comprehensive survey on graph neural networks","volume":"32","author":"Wu","year":"2020","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_21","unstructured":"Xu, K., Hu, W., Leskovec, J., and Jegelka, S. (2019, January 6\u20139). How powerful are graph neural networks?. Proceedings of the 7th International Conference on Learning Representations (ICLR 2019), New Orleans, LA, USA."},{"key":"ref_22","unstructured":"Chen, Z., Li, L., and Bruna, J. (2019, January 6\u20139). Supervised community detection with line graph neural networks. Proceedings of the 7th International Conference on Learning Representations (ICLR 2019), New Orleans, LA, USA."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"3981","DOI":"10.1021\/acs.jcim.9b00387","article-title":"Predicting drug\u2013target interaction using a novel graph neural network with 3D structure-embedded graph representation","volume":"59","author":"Lim","year":"2019","journal-title":"J. Chem. Inf. Model."},{"key":"ref_24","unstructured":"Ma, Q., Ge, S., He, D., Thaker, D., and Drori, I. (2019). Combinatorial optimization by graph pointer networks and hierarchical reinforcement learning. arXiv."},{"key":"ref_25","unstructured":"Shen, Y., Zhang, J., Song, S., and Letaief, K.B. (2022). Graph neural networks for wireless communications: From theory to practice. arXiv."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"He, H., Kosasihy, A., Yu, X., Zhang, J., Song, S., Hardjawanay, W., and Letaief, K.B. (2022). Graph neural network enhanced approximate message passing for MIMO detection. arXiv.","DOI":"10.1109\/WCNC55385.2023.10118792"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1109\/TII.2020.3047843","article-title":"A graph neural network-based digital twin for network slicing management","volume":"18","author":"Wang","year":"2020","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1109\/LCOMM.2020.3025298","article-title":"Combining deep reinforcement learning with graph neural networks for optimal VNF placement","volume":"25","author":"Sun","year":"2020","journal-title":"IEEE Commun. Lett."},{"key":"ref_29","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Mozaffari, M., Saad, W., Bennis, M., and Debbah, M. (2015, January 6\u201310). Drone small cells in the clouds: Design, deployment and performance analysis. Proceedings of the 2015 IEEE Global Communications Conference (GLOBECOM), San Diego, CA, USA.","DOI":"10.1109\/GLOCOM.2015.7417609"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Saif, A., Dimyati, K., Noordin, K.A., Shah, N.S.M., Alsamhi, S., and Abdullah, Q. (2021, January 10\u201312). Energy-efficient tethered UAV deployment in B5G for smart environments and disaster recovery. Proceedings of the 2021 IEEE 1st International Conference on Emerging Smart Technologies and Applications (eSmarTA).","DOI":"10.1109\/eSmarTA52612.2021.9515754"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Galkin, B., Kibilda, J., and DaSilva, L.A. (2016, January 23\u201325). Deployment of UAV-mounted access points according to spatial user locations in two-tier cellular networks. Proceedings of the 2016 Wireless Days (WD), Toulouse, France.","DOI":"10.1109\/WD.2016.7461487"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Huang, H., and Savkin, A.V. (2018, January 12\u201315). Reactive deployment of flying robot base station over disaster areas. Proceedings of the 2018 IEEE International Conference on Robotics and Biomimetics (ROBIO), Kuala Lumpur, Malaysia.","DOI":"10.1109\/ROBIO.2018.8664865"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2638","DOI":"10.1109\/TII.2018.2875041","article-title":"A Method for Optimized Deployment of Unmanned Aerial Vehicles for Maximum Coverage and Minimum Interference in Cellular Networks","volume":"15","author":"Huang","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Cicek, C.T., Gultekin, H., Tavli, B., and Yanikomeroglu, H. (2019, January 5\u20137). UAV base station location optimization for next generation wireless networks: Overview and future research directions. Proceedings of the 2019 1st International Conference on Unmanned Vehicle Systems-Oman (UVS), Muscat, Oman.","DOI":"10.1109\/UVS.2019.8658363"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Sabzehali, J., Shah, V.K., Fan, Q., Choudhury, B., Liu, L., and Reed, J.H. (2022). Optimizing Number, Placement, and Backhaul Connectivity of Multi-UAV Networks. IEEE Internet Things J., 1.","DOI":"10.1109\/JIOT.2022.3184323"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Kang, Z., You, C., and Zhang, R. (2020, January 7\u201311). Placement learning for multi-UAV relaying: A Gibbs sampling approach. Proceedings of the ICC 2020\u20132020 IEEE International Conference on Communications (ICC), Dublin, Ireland.","DOI":"10.1109\/ICC40277.2020.9149409"},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Ko\u0161merl, J., and Vilhar, A. (2014, January 10\u201314). Base stations placement optimization in wireless networks for emergency communications. Proceedings of the 2014 IEEE International Conference on Communications Workshops (ICC), Sydney, Australia.","DOI":"10.1109\/ICCW.2014.6881196"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Kalantari, E., Yanikomeroglu, H., and Yongacoglu, A. (2016, January 18\u201321). On the number and 3D placement of drone base stations in wireless cellular networks. Proceedings of the 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall), Montreal, QC, Canada.","DOI":"10.1109\/VTCFall.2016.7881122"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"11454","DOI":"10.1109\/ACCESS.2019.2892564","article-title":"Joint Positioning of Flying Base Stations and Association of Users: Evolutionary-Based Approach","volume":"7","author":"Plachy","year":"2019","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Alsamhi, S.H., Shvetsov, A.V., Kumar, S., Hassan, J., Alhartomi, M.A., Shvetsova, S.V., Sahal, R., and Hawbani, A. (2022). Computing in the Sky: A Survey on Intelligent Ubiquitous Computing for UAV-Assisted 6G Networks and Industry 4.0\/5.0. Drones, 6.","DOI":"10.3390\/drones6070177"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Chaudhri, S.N., Rajput, N.S., Alsamhi, S.H., Shvetsov, A.V., and Almalki, F.A. (2022). Zero-padding and spatial augmentation-based gas sensor node optimization approach in resource-constrained 6G-IoT paradigm. Sensors, 22.","DOI":"10.3390\/s22083039"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"50023","DOI":"10.1109\/ACCESS.2022.3168549","article-title":"Smart Packet Transmission Scheduling in Cognitive IoT Systems: DDQN Based Approach","volume":"10","author":"Salh","year":"2022","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1109\/TNN.2008.2005605","article-title":"The Graph Neural Network Model","volume":"20","author":"Scarselli","year":"2009","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","article-title":"LSTM: A Search Space Odyssey","volume":"28","author":"Greff","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"602","DOI":"10.1016\/j.neunet.2005.06.042","article-title":"Framewise phoneme classification with bidirectional LSTM and other neural network architectures","volume":"18","author":"Graves","year":"2005","journal-title":"Neural Netw."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Yan-e, D. (2011, January 28\u201329). Design of intelligent agriculture management information system based on IoT. Proceedings of the Fourth International Conference on Intelligent Computation Technology and Automation (ICICTA), Shenzhen, China.","DOI":"10.1109\/ICICTA.2011.262"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Dan, L., Xin, C., Chongwei, H., and Liangliang, J. (2015, January 19\u201320). Intelligent agriculture greenhouse environment monitoring system based on IOT technology. Proceedings of the 2015 International Conference on Intelligent Transportation, Big Data and Smart City, Halong Bay, Vietnam.","DOI":"10.1109\/ICITBS.2015.126"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MCOMSTD.2017.1700023","article-title":"Bringing 5G into rural and low-income areas: Is it feasible?","volume":"1","author":"Chiaraviglio","year":"2017","journal-title":"IEEE Commun. Stand. Mag."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Maluleke, H., Bagula, A., and Ajayi, O. (2020, January 12\u201314). Efficient airborne network clustering for 5G backhauling and fronthauling. Proceedings of the 2020 16th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob), Thessaloniki, Greece.","DOI":"10.1109\/WiMob50308.2020.9253390"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2714","DOI":"10.1109\/COMST.2018.2841996","article-title":"Space-air-ground integrated network: A survey","volume":"20","author":"Liu","year":"2018","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"1123","DOI":"10.1109\/COMST.2015.2495297","article-title":"Survey of Important Issues in UAV Communication Networks","volume":"18","author":"Gupta","year":"2016","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"2109","DOI":"10.1109\/TWC.2017.2789293","article-title":"Joint Trajectory and Communication Design for Multi-UAV Enabled Wireless Networks","volume":"17","author":"Wu","year":"2018","journal-title":"IEEE Trans. Wirel. Commun."},{"key":"ref_54","unstructured":"Mittal, S., Bengio, Y., and Lajoie, G. (2022). Is a modular architecture enough?. arXiv."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1090\/qam\/102435","article-title":"On a routing problem","volume":"16","author":"Bellman","year":"1958","journal-title":"Q. Appl. Math."},{"key":"ref_56","unstructured":"Schulman, J., Moritz, P., Levine, S., Jordan, M., and Abbeel, P. (2016, January 2\u20134). High-dimensional continuous control using generalized advantage estimation. Proceedings of the 4th International Conference on Learning Representations (ICLR), San Juan, Puerto Rico."},{"key":"ref_57","unstructured":"Hendrycks, D., Mazeika, M., Kadavath, S., and Song, D. (2019, January 8\u201314). Using self-supervised learning can improve model robustness and uncertainty. Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019 (NeurIPS 2019), Vancouver, BC, Canada."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"1872","DOI":"10.1007\/s11431-020-1647-3","article-title":"Pre-trained models for natural language processing: A survey","volume":"63","author":"Qiu","year":"2020","journal-title":"Sci. China Technol. Sci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1038\/scientificamerican0792-66","article-title":"Genetic algorithms","volume":"267","author":"Holland","year":"1992","journal-title":"Sci. Am."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4377\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T00:22:48Z","timestamp":1760142168000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/17\/4377"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,9,3]]},"references-count":59,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2022,9]]}},"alternative-id":["rs14174377"],"URL":"https:\/\/doi.org\/10.3390\/rs14174377","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,9,3]]}}}