* [47] M.-A. Lahmeri, M. A. Kishk, and M.-S. Alouini, “Artificial intelligence for uav-enabled wireless networks: A survey,” _IEEE Open Journal of the Communications Society_ , vol. 2, pp. 1015–1040, 2021.
  * [48] Q. Zhang, M. Mozaffari, W. Saad, M. Bennis, and M. Debbah, “Machine learning for predictive on-demand deployment of uavs for wireless communications,” in _2018 IEEE Global Communications Conference (GLOBECOM)_ , 2018, pp. 1–6.
  * [49] J. Chen, U. Yatnalli, and D. Gesbert, “Learning radio maps for uav-aided wireless networks: A segmented regression approach,” in _2017 IEEE International Conference on Communications (ICC)_ , 2017, pp. 1–6.
  * [50] J. Kim, C. Park, J. Ahn, Y. Ko, J. Park, and J. C. Gallagher, “Real-time uav sound detection and analysis system,” in _2017 IEEE Sensors Applications Symposium (SAS)_ , 2017, pp. 1–5.
  * [51] Y. Zhang, J. Wen, G. Yang, Z. He, and X. Luo, “Air-to-air path loss prediction based on machine learning methods in urban environments,” _Wireless Communications and Mobile Computing_ , vol. 2018, pp. 1–9, 06 2018.
  * [52] W. Xia, S. Rangan, M. Mezzavillla, A. Lozano, G. Geraci, V. Semkin, and G. Loianno, “Generative neural network channel modeling for millimeter-wave uav communication,” 2020.
  * [53] M. Chen, W. Saad, and C. Yin, “Deep learning for 360∘ content transmission in UAV-Enabled Virtual Reality,” in _ICC 2019 - 2019 IEEE International Conference on Communications (ICC)_ , 2019, pp. 1–6.
  * [54] S. Wang, J. Chen, Z. Zhang, G. Wang, Y. Tan, and Y. Zheng, “Construction of a virtual reality platform for uav deep learning,” in _2017 Chinese Automation Congress (CAC)_ , 2017, pp. 3912–3916.
  * [55] K. Mukadam, A. Sinh, and R. Karani, “Detection of landing areas for unmanned aerial vehicles,” in _2016 International Conference on Computing Communication Control and automation (ICCUBEA)_ , 2016, pp. 1–5.
  * [56] N. Imanberdiyev, C. Fu, E. Kayacan, and I.-M. Chen, “Autonomous navigation of uav by using real-time model-based reinforcement learning,” in _2016 14th International Conference on Control, Automation, Robotics and Vision (ICARCV)_ , 2016, pp. 1–6.
  * [57] X. Liu, Y. Liu, and Y. Chen, “Reinforcement learning in multiple-uav networks: Deployment and movement design,” 2019.
  * [58] G. Faraci, A. Raciti, S. A. Rizzo, and G. Schembra, “Green wireless power transfer system for a drone fleet managed by reinforcement learning in smart industry,” _Applied Energy_ , vol. 259, p. 114204, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0306261919318914
  * [59] N. I. Mowla, N. H. Tran, I. Doh, and K. Chae, “Federated learning-based cognitive detection of jamming attack in flying ad-hoc network,” _IEEE Access_ , vol. 8, pp. 4338–4350, 2020.
  * [60] Y. Liu, J. Nie, X. Li, S. H. Ahmed, W. Y. B. Lim, and C. Miao, “Federated learning in the sky: Aerial-ground air quality sensing framework with uav swarms,” _IEEE Internet of Things Journal_ , pp. 1–1, 2020.
  * [61] A. Razi, C. Wang, F. Almaraghi, Q. Huang, Y. Zhang, H. Lu, and A. Rovira-Sugranes, “Predictive routing for wireless networks: Robotics-based test and evaluation platform,” in _2018 IEEE 8th Annual Computing and Communication Workshop and Conference (CCWC)_ , Jan 2018, pp. 993–999.
  * [62] J. Brown, “Types of military drones: The best technology available today,” Feb 2020. [Online]. Available: https://www.mydronelab.com/blog/types-of-military-drones.html
  * [63] “4 military sensor technologies that drones are transporting to the commercial market,” Jan 2019. [Online]. Available: https://militaryethernet.com/4-military-sensor-technologies-drones-transporting-commercial-market/
  * [64] J. Wilson, “The future of artificial intelligence and quantum computing,” Aug 2020\. [Online]. Available: https://www.militaryaerospace.com/computers/article/14182330/future-of-artificial-intelligence-and-quantum-computing
  * [65] B. Knight, “A guide to military drones,” Jun 2017. [Online]. Available: https://www.dw.com/en/a-guide-to-military-drones/a-39441185
  * [66] A. Sharma, “Everything you need to know about industrial grade drones,” Jul 2020\. [Online]. Available: https://jungleworks.com/everything-you-need-to-know-about-industrial-grade-drones/
  * [67] Flyability, “Commercial drones: Industries that use drones, deliverables, and our list of the top models on the market.” [Online]. Available: https://www.flyability.com/commercial-drones
  * [68] Dec 2020. [Online]. Available: https://www.faa.gov/uas/recreational_fliers/
  * [69] R. Yin, W. Li, Z.-q. Wang, and X.-x. Xu, “The application of artificial intelligence technology in uav,” in _2020 5th International Conference on Information Science, Computer Technology and Transportation (ISCTT)_ , 2020, pp. 238–241.
  * [70] R. Hof, “Ai-on-a-chip soon will make phones, drones and more a lot smarter,” May 2016. [Online]. Available: https://www.forbes.com/sites/roberthof/2016/05/07/ai-on-a-chip-soon-will-make-phones-drones-and-more-a-lot-smarter/?sh=75b2d2ba7eef
  * [71] T. Hwang, “Computational power and the social impact of artificial intelligence,” 2018.
  * [72] C. Dilmegani, “Ai chips in 2021: Guide to cost-efficient ai training & inference,” Jan 2021. [Online]. Available: https://research.aimultiple.com/ai-chip/#what-are-its-components
  * [73] K. Freund, “Ai hardware: Harder than it looks,” _Forbes_ , Oct 2019. [Online]. Available: https://www.forbes.com/sites/moorinsights/2019/10/07/ai-hardware-harder-than-it-looks/?sh=23dc41e5471f
  * [74] Freund, “Intel shows off its ai chips and chops,” _Forbes_ , Jun 2018. [Online]. Available: https://www.forbes.com/sites/moorinsights/2018/06/01/intel-shows-off-its-ai-chips-and-chops/?sh=272064ad6643
  * [75] I. Insider, “Drone market outlook in 2021: industry growth trends, market stats and forecast,” Feb 2021. [Online]. Available: https://www.businessinsider.com/drone-industry-analysis-market-trends-growth-forecasts#:~:text=InsiderIntelligencedefinesenterprisedrones,annualgrowthrate(CAGR)
  * [76] L. Schroth, “Drone manufacturer market shares: Dji leads the way,” Jan 2021. [Online]. Available: https://droneii.com/drone-manufacturer-market-shares-dji-leads-the-way-in-the-us
  * [77] “Geospatial data analytics for the enterprise.” [Online]. Available: https://www.precisionhawk.com/
  * [78] “Dronehunter: Net gun drone capture: Products,” Mar 2021. [Online]. Available: https://fortemtech.com/products/dronehunter/?gclid=Cj0KCQjwl9GCBhDvARIsAFunhskjqqROz4pMBmxT_bxGxRK5KMyievKr4lH3kejZtH_gLS1qQt0X68kaAsEfEALw_wcB
  * [79] “Top 100 drone companies to watch in 2020,” Jan 2021. [Online]. Available: https://uavcoach.com/drone-companies/
  * [80] C. Insights, “How drones will impact society: From fighting war to forecasting weather, uavs change everything,” Jun 2020. [Online]. Available: https://www.cbinsights.com/research/drone-impact-society-uav/
  * [81] W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge computing: Vision and challenges,” _IEEE Internet of Things Journal_ , vol. 3, no. 5, pp. 637–646, 2016.
  * [82] A. V. Dastjerdi and R. Buyya, “Fog computing: Helping the internet of things realize its potential,” _Computer_ , vol. 49, no. 8, pp. 112–116, 2016.
  * [83] G. Baldini, S. Karanasios, D. Allen, and F. Vergari, “Survey of wireless communication technologies for public safety,” _IEEE Communications Surveys Tutorials_ , vol. 16, no. 2, pp. 619–641, 2014.
  * [84] V. Sharma, I. You, G. Pau, M. Collotta, J. Deok Lim, and J. Kim, “Lorawan-based energy-efficient surveillance by drones for intelligent transportation systems,” _Energies_ , vol. 11, 03 2018.
  * [85] V. Delafontaine, F. Schiano, G. Cocco, A. Rusu, and D. Floreano, “Drone-aided localization in lora iot networks,” in _2020 IEEE International Conference on Robotics and Automation (ICRA)_ , 2020, pp. 286–292.
  * [86] O. Saraereh, A. Alsaraira, I. Khan, and P. Uthansakul, “Performance evaluation of uav-enabled lora networks for disaster management applications,” _Sensors_ , vol. 20, pp. 1–18, 04 2020.
  * [87] S. Chandrasekharan, K. Gomez, A. Al-Hourani, S. Kandeepan, T. Rasheed, L. Goratti, L. Reynaud, D. Grace, I. Bucaille, T. Wirth, and S. Allsopp, “Designing and implementing future aerial communication networks,” _IEEE Communications Magazine_ , vol. 54, no. 5, pp. 26–34, 2016.
  * [88] “Different wi-fi protocols and data rates.” [Online]. Available: https://www.intel.com/content/www/us/en/support/articles/000005725/network-and-io/wireless.html
  * [89] “xfold spy.” [Online]. Available: http://www.xfoldrig.com/xfold-spy/
  * [90] X. Lin, V. Yajnanarayana, S. D. Muruganathan, S. Gao, H. Asplund, H. Maattanen, M. B. A, S. Euler, and Y. E. Wang, “The sky is not the limit: LTE for unmanned aerial vehicles,” _CoRR_ , vol. abs/1707.07534, 2017. [Online]. Available: http://arxiv.org/abs/1707.07534
  * [91] Y. Zeng, J. Lyu, and R. Zhang, “Cellular-connected UAV: potentials, challenges and promising technologies,” _CoRR_ , vol. abs/1804.02217, 2018\. [Online]. Available: http://arxiv.org/abs/1804.02217
  * [92] T. Lagkas, V. Argyriou, S. Bibi, and P. Sarigiannidis, “Uav iot framework views and challenges: towards protecting drones as “things”,” _Sensors_ , vol. 18, no. 11, p. 4015, 2018.
  * [93] S. Sekander, H. Tabassum, and E. Hossain, “Multi-tier drone architecture for 5g/b5g cellular networks: Challenges, trends, and prospects,” _IEEE Communications Magazine_ , vol. 56, no. 3, pp. 96–103, 2018.
  * [94] S. A. R. Naqvi, S. A. Hassan, H. Pervaiz, and Q. Ni, “Drone-aided communication as a key enabler for 5g and resilient public safety networks,” _IEEE Communications Magazine_ , vol. 56, no. 1, pp. 36–42, 2018.
  * [95] M. Y. Selim and A. E. Kamal, “Post-disaster 4g/5g network rehabilitation using drones: Solving battery and backhaul issues,” in _2018 IEEE Globecom Workshops (GC Wkshps)_. IEEE, 2018, pp. 1–6.
  * [96] W. Saad, M. Bennis, and M. Chen, “A vision of 6g wireless systems: Applications, trends, technologies, and open research problems,” _IEEE Network_ , vol. 34, no. 3, pp. 134–142, 2020.
  * [97] M. Mozaffari, X. Lin, and S. Hayes, “Towards 6g with connected sky: Uavs and beyond,” _CoRR_ , vol. abs/2103.01143, 2021. [Online]. Available: https://arxiv.org/abs/2103.01143
  * [98] S. Aggarwal, N. Kumar, and S. Tanwar, “Blockchain-envisioned uav communication using 6g networks: Open issues, use cases, and future directions,” _IEEE Internet of Things Journal_ , vol. 8, no. 7, pp. 5416–5441, 2021.
  * [99] U. Challita, A. Ferdowsi, M. Chen, and W. Saad, “Machine learning for wireless connectivity and security of cellular-connected uavs,” _IEEE Wireless Communications_ , vol. 26, no. 1, pp. 28–35, 2019.
  * [100] J. Park, Y. Kim, and J. Seok, “Prediction of information propagation in a drone network by using machine learning,” in _2016 International Conference on Information and Communication Technology Convergence (ICTC)_ , 2016, pp. 147–149.
  * [101] A. Shamsoshoara, F. Afghah, A. Razi, S. Mousavi, J. Ashdown, and K. Turk, “An autonomous spectrum management scheme for unmanned aerial vehicle networks in disaster relief operations,” _IEEE Access_ , vol. 8, pp. 58 064–58 079, 2020.
  * [102] A. Shamsoshoara, M. Khaledi, F. Afghah, A. Razi, and J. Ashdown, “Distributed cooperative spectrum sharing in uav networks using multi-agent reinforcement learning,” in _2019 16th IEEE Annual Consumer Communications & Networking Conference (CCNC)_. IEEE, 2019, pp. 1–6.
  * [103] A. Jalali and L. Schiff, “Beam forming and pointing in a network of unmanned aerial vehicles (uavs) for broadband access,” Jul. 18 2017, uS Patent 9,712,228.
  * [104] A. Muralidharan and Y. Mostofi, “Energy optimal distributed beamforming using unmanned vehicles,” _IEEE Transactions on Control of Network Systems_ , vol. 5, no. 4, pp. 1529–1540, 2017.
  * [105] A. Rovira-Sugranes and A. Razi, “Predictive routing for dynamic uav networks,” in _2017 IEEE International Conference on Wireless for Space and Extreme Environments (WiSEE)_ , Oct 2017, pp. 43–47.
  * [106] A. Rovira-Sugranes, F. Afghah, and A. Razi, “Optimized compression policy for flying ad hoc networks,” in _2019 16th IEEE Annual Consumer Communications Networking Conference (CCNC)_ , 2019, pp. 1–2.
  * [107] A. Rovira-Sugranes and A. Razi, “Optimizing the age of information for blockchain technology with applications to iot sensors,” _IEEE Communications Letters_ , vol. 24, no. 1, pp. 183–187, 2020.
  * [108] M. C. Jeffrey, S. Subramanian, C. Yan, J. Emer, and D. Sanchez, “A scalable architecture for ordered parallelism,” in _2015 48th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)_ , 2015, pp. 228–241.
  * [109] Z. Xiaoning, “Analysis of military application of uav swarm technology,” in _2020 3rd International Conference on Unmanned Systems (ICUS)_ , 2020, pp. 1200–1204.
  * [110] J. Peng, Z. Zhang, Q. Wu, and B. Zhang, “Anti-jamming communications in uav swarms: A reinforcement learning approach,” _IEEE Access_ , vol. 7, pp. 180 532–180 543, 2019.
  * [111] L. Ruetten, P. A. Regis, D. Feil-Seifer, and S. Sengupta, “Area-optimized uav swarm network for search and rescue operations,” in _2020 10th Annual Computing and Communication Workshop and Conference (CCWC)_ , 2020, pp. 0613–0618.
  * [112] M. Campion, P. Ranganathan, and S. Faruque, “A review and future directions of uav swarm communication architectures,” in _2018 IEEE International Conference on Electro/Information Technology (EIT)_ , 2018, pp. 0903–0908.
  * [113] A. Tahir, J. Böling, M.-H. Haghbayan, H. T. Toivonen, and J. Plosila, “Swarms of unmanned aerial vehicles — a survey,” _Journal of Industrial Information Integration_ , vol. 16, p. 100106, 2019. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S2452414X18300086
  * [114] X. Chen, J. Tang, and S. Lao, “Review of unmanned aerial vehicle swarm communication architectures and routing protocols,” _Applied Sciences_ , vol. 10, no. 10, 2020. [Online]. Available: https://www.mdpi.com/2076-3417/10/10/3661
  * [115] I. Bekmezci, O. K. Sahingoz, and S. Temel, “Flying ad-hoc networks (fanets): A survey,” _Ad Hoc Networks_ , vol. 11, no. 3, pp. 1254 – 1270, 2013. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S1570870512002193
  * [116] A. Sivakumar and C.-Y. Tan, “Uav swarm coordination using cooperative control for establishing a wireless communications backbone,” _9th International Joint Conference on Autonomous Agents and Multiagent Systems 2010_ , pp. 1157–1164, 2010.
  * [117] D. F. Vránics, M. Palik, and B. Zs, “Electronic administration of unmanned aviation with public key infrastructure (pki),” _Security & Future_, vol. 3, no. 4, pp. 152–155, 2019.
  * [118] O. K. Sahingoz, “Networking models in flying ad-hoc networks (FANETs): Concepts and challenges,” _Journal of Intelligent & Robotic Systems_, vol. 74, no. 1-2, pp. 513–527, oct 2013. [Online]. Available: https://doi.org/10.1007\%2Fs10846-013-9959-7
  * [119] Y. Zhou, J. Li, L. Lamont, and C. Rabbath, “Modeling of packet dropout for uav wireless communications,” in _2012 International Conference on Computing, Networking and Communications (ICNC)_ , 2012, pp. 677–682.
  * [120] H. Shariatmadari, R. Ratasuk, S. Iraji, A. Laya, T. Taleb, R. Jäntti, and A. Ghosh, “Machine-type communications: current status and future perspectives toward 5g systems,” _IEEE Communications Magazine_ , vol. 53, no. 9, pp. 10–17, 2015.
  * [121] F. Boccardi, R. W. Heath, A. Lozano, T. L. Marzetta, and P. Popovski, “Five disruptive technology directions for 5g,” _IEEE Communications Magazine_ , vol. 52, no. 2, pp. 74–80, 2014.
  * [122] M. Agiwal, A. Roy, and N. Saxena, “Next generation 5g wireless networks: A comprehensive survey,” _IEEE Communications Surveys Tutorials_ , vol. 18, no. 3, pp. 1617–1655, 2016.
  * [123] P. Demestichas, A. Georgakopoulos, D. Karvounas, K. Tsagkaris, V. Stavroulaki, J. Lu, C. Xiong, and J. Yao, “5g on the horizon: Key challenges for the radio-access network,” _IEEE Vehicular Technology Magazine_ , vol. 8, no. 3, pp. 47–53, 2013.
  * [124] G. Beni and J. Wang, “Swarm intelligence in cellular robotic systems,” in _Robots and Biological Systems: Towards a New Bionics?_ , P. Dario, G. Sandini, and P. Aebischer, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993, pp. 703–712.
  * [125] S. Awasthi, B. Balusamy, and V. Porkodi, “Artificial intelligence supervised swarm uavs for reconnaissance,” in _Data Science and Analytics_ , U. Batra, N. R. Roy, and B. Panda, Eds. Singapore: Springer Singapore, 2020, pp. 375–388.
  * [126] J. Johnson, “Artificial intelligence, drone swarming and escalation risks in future warfare,” _The RUSI Journal_ , vol. 165, no. 2, pp. 26–36, 2020. [Online]. Available: https://doi.org/10.1080/03071847.2020.1752026
  * [127] J. Kusyk, M. U. Uyar, K. Ma, J. J. Wu, W. Ruan, D. K. Guha, G. Bertoli, and J. Boksiner, “Ai based flight control for autonomous uav swarms,” in _2018 International Conference on Computational Science and Computational Intelligence (CSCI)_ , 2018, pp. 1155–1160.
  * [128] J. Kusyk, M. U. Uyar, K. Ma, E. Samoylov, R. Valdez, J. Plishka, S. E. Hoque, G. Bertoli, and J. Boksiner, “Artificial intelligence and game theory controlled autonomous uav swarms,” _Evolutionary Intelligence_ , pp. 1–18, 2020.
  * [129] T. Camp, J. Boleng, and V. Davies, “A survey of mobility models for ad hoc network research,” _Wireless communications and mobile computing_ , vol. 2, no. 5, pp. 483–502, 2002.
  * [130] J. Broch, D. A. Maltz, D. B. Johnson, Y.-C. Hu, and J. Jetcheva, “A performance comparison of multi-hop wireless ad hoc network routing protocols,” in _Proceedings of the 4th annual ACM/IEEE international conference on Mobile computing and networking_. ACM, 1998, pp. 85–97.
  * [131] W. Wang, X. Guan, B. Wang, and Y. Wang, “A novel mobility model based on semi-random circular movement in mobile ad hoc networks,” _Information Sciences_ , vol. 180, no. 3, pp. 399 – 413, 2010. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0020025509004265
  * [132] J. Yoon, M. Liu, and B. Noble, “Sound mobility models,” in _Proceedings of the 9th annual international conference on Mobile computing and networking_. ACM, 2003, pp. 205–216.
  * [133] M. C. Gonzalez, C. A. Hidalgo, and A.-L. Barabasi, “Understanding individual human mobility patterns,” _Nature_ , vol. 453, no. 7196, pp. 779–782, 2008\.
  * [134] K. Kumari, B. Sah, and S. Maakar, “A survey: different mobility model for fanet,” _International Journal of Advanced Research in Computer Science and Software Engineering_ , vol. 5, no. 6, 2015.
  * [135] I. Bekmezci, M. Ermis, and S. Kaplan, “Connected multi uav task planning for flying ad hoc networks,” in _Communications and Networking (BlackSeaCom), 2014 IEEE International Black Sea Conference on_. IEEE, 2014, pp. 28–32.
  * [136] S.-J. Lee, M. Gerla, and C.-C. Chiang, “On-demand multicast routing protocol,” in _Wireless Communications and Networking Conference, 1999. WCNC. 1999 IEEE_ , vol. 3. IEEE, 1999, pp. 1298–1302.
  * [137] O. Bouachir, A. Abrassart, F. Garcia, and N. Larrieu, “A mobility model for uav ad hoc network,” in _2014 International Conference on Unmanned Aircraft Systems (ICUAS)_ , May 2014, pp. 383–388.
  * [138] E. Kuiper and S. Nadjm-Tehrani, “Mobility models for uav group reconnaissance applications,” in _Wireless and Mobile Communications, 2006. ICWMC’06. International Conference on_. IEEE, 2006, pp. 33–33.
  * [139] E. Kieffer, G. Danoy, P. Bouvry, and A. Nagih, “Hybrid mobility model with pheromones for uav detection task,” in _2016 IEEE Symposium Series on Computational Intelligence (SSCI)_ , Dec 2016, pp. 1–8.
  * [140] “Vehicular mobility trace of the city of cologne, germany,” 2016. [Online]. Available: http://kolntrace.project.citi-lab.fr/
  * [141] S. Uppoor, O. Trullols-Cruces, M. Fiore, and J. M. Barcelo-Ordinas, “Generation and analysis of a large-scale urban vehicular mobility dataset,” _IEEE Transactions on Mobile Computing_ , vol. 13, no. 5, pp. 1061–1075, 2014.
  * [142] B. Boghossian and S. Velastin, “Image processing system for pedestrian monitoring using neural classification of normal motion patterns,” _Measurement and Control_ , vol. 32, no. 9, pp. 261–264, 1999.
  * [143] O. Ossama, H. M. Mokhtar, and M. E. El-Sharkawi, “An extended k-means technique for clustering moving objects,” _Egyptian Informatics Journal_ , vol. 12, no. 1, pp. 45–51, 2011.
  * [144] M. Bennewitz, W. Burgard, G. Cielniak, and S. Thrun, “Learning motion patterns of people for compliant robot motion,” _The International Journal of Robotics Research_ , vol. 24, no. 1, pp. 31–48, 2005.
  * [145] J. Harri, F. Filali, and C. Bonnet, “Mobility models for vehicular ad hoc networks: a survey and taxonomy,” _IEEE Communications Surveys & Tutorials_, vol. 11, no. 4, 2009.
  * [146] S. Atev, O. Masoud, and N. Papanikolopoulos, “Learning traffic patterns at intersections by spectral clustering of motion trajectories,” in _Intelligent Robots and Systems, 2006 IEEE/RSJ International Conference on_. IEEE, 2006, pp. 4851–4856.
  * [147] J.-G. Lee, J. Han, and X. Li, “A unifying framework of mining trajectory patterns of various temporal tightness,” _IEEE Transactions on Knowledge and Data Engineering_ , vol. 27, no. 6, pp. 1478–1490, 2015.
  * [148] C. Larsen, M. Zawodniok, and S. Jagannathan, “Route aware predictive congestion control protocol for wireless sensor networks,” in _Intelligent Control, 2007. ISIC 2007. IEEE 22nd International Symposium on_. IEEE, 2007, pp. 13–18.
  * [149] H. Tan, Y. Wu, B. Shen, P. J. Jin, and B. Ran, “Short-term traffic prediction based on dynamic tensor completion,” _IEEE Transactions on Intelligent Transportation Systems_ , vol. 17, no. 8, pp. 2123–2133, 2016.
  * [150] P. P. Choi and M. Hebert, “Learning and predicting moving object trajectory: a piecewise trajectory segment approach,” _Robotics Institute_ , p. 337, 2006\.
  * [151] G. Aoude, J. Joseph, N. Roy, and J. How, “Mobile agent trajectory prediction using bayesian nonparametric reachability trees,” in _Infotech@ Aerospace 2011_ , 2011, p. 1512.
  * [152] G. Lee, R. Mallipeddi, and M. Lee, “Identification of moving vehicle trajectory using manifold learning,” in _International Conference on Neural Information Processing_. Springer, 2012, pp. 188–195.
  * [153] M. S. Grewal, _Kalman filtering_. Springer, 2011.
  * [154] K. Lee, S. Hong, S. J. Kim, I. Rhee, and S. Chong, “Slaw: A new mobility model for human walks,” in _INFOCOM 2009, IEEE_. IEEE, 2009, pp. 855–863.
  * [155] Z. X. Ming, Z. Yue, Y. Fan, and A. Vasilakos, “Interference-based topology control algorithm for delay-constrained mobile ad hoc networks, in mobile computing,” _IEEE Trans_ , vol. 14, no. 4, pp. 742–754, 2015.
  * [156] T.-E. Lu, K.-T. Feng _et al._ , “Predictive mobility and location-aware routing protocol in mobile ad hoc networks,” in _GLOBECOM’05: IEEE Global Telecommunications Conference, Vols 1-6: DISCOVERY PAST AND FUTURE_ , 2005, pp. 899–903.
  * [157] R. Groenevelt, E. Altman, and P. Nain, “Relaying in mobile ad hoc networks: The brownian motion mobility model,” _Wireless Networks_ , vol. 12, no. 5, pp. 561–571, 2006.
  * [158] M. Owen, R. W. Beard, and T. W. McLain, “Implementing dubins airplane paths on fixed-wing uavs,” in _Handbook of Unmanned Aerial Vehicles_. Springer, 2015, pp. 1677–1701.
  * [159] J. Wiest, M. Höffken, U. Kreßel, and K. Dietmayer, “Probabilistic trajectory prediction with gaussian mixture models,” in _Intelligent Vehicles Symposium (IV), 2012 IEEE_. IEEE, 2012, pp. 141–146.
  * [160] K. Chandrashekar, M. R. Dekhordi, and J. S. Baras, “Providing full connectivity in large ad-hoc networks by dynamic placement of aerial platforms,” in _IEEE MILCOM 2004. Military Communications Conference, 2004._ , vol. 3. IEEE, 2004, pp. 1429–1436.
  * [161] W. T. L. Teacy, J. Nie, S. McClean, and G. Parr, “Maintaining connectivity in uav swarm sensing,” in _2010 IEEE Globecom Workshops_ , Dec 2010, pp. 1771–1776.
  * [162] M. Rosalie, M. R. Brust, G. Danoy, S. Chaumette, and P. Bouvry, “Coverage optimization with connectivity preservation for uav swarms applying chaotic dynamics,” in _2017 IEEE International Conference on Autonomic Computing (ICAC)_ , July 2017, pp. 113–118.
  * [163] R. Costa, D. RosÃ¡rio, E. Cerqueira, and A. Santos, “Enhanced connectivity for robust multimedia transmission in uav networks,” in _2014 IFIP Wireless Days (WD)_ , Nov 2014, pp. 1–6.
  * [164] S. Alsamhi, O. Ma, and M. S. Ansari, “Convergence of machine learning and robotics communication in collaborative assembly: mobility, connectivity and future perspectives,” _Journal of Intelligent & Robotic Systems_, pp. 1–26, 2019.
  * [165] E. W. Dijkstra, “A note on two problems in connexion with graphs,” _Numer. Math._ , vol. 1, no. 1, pp. 269–271, Dec. 1959. [Online]. Available: http://dx.doi.org/10.1007/BF01386390
  * [166] L. Ying, S. Shakkottai, A. Reddy, and S. Liu, “On combining shortest-path and back-pressure routing over multihop wireless networks,” _IEEE/ACM Transactions on Networking_ , vol. 19, no. 3, pp. 841–854, June 2011.
  * [167] O. K. Sahingoz, “Networking models in flying ad-hoc networks (fanets): Concepts and challenges,” _Journal of Intelligent & Robotic Systems_, vol. 74, no. 1, pp. 513–527, Apr 2014. [Online]. Available: https://doi.org/10.1007/s10846-013-9959-7
  * [168] T. H. Clausen and P. Jacquet, “Optimized link state routing protocol (olsrp),” vol. 3626, 10 2003.
  * [169] A. Alshabtat, L. Dong, J. Li, and F. Yang, “Low latency routing algorithm for unmanned aerial vehicles ad-hoc networks,” vol. 6, pp. 48–54, 08 2011.
  * [170] S. Y. Dong, “Optimization of olsr routing protocol in uav ad hoc network,” in _2016 13th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP)_ , Dec 2016, pp. 90–94.
  * [171] N. Coutinho, R. Matos, C. Marques, A. Reis, S. Sargento, J. Chakareski, and A. Kassler, “Dynamic dual-reinforcement-learning routing strategies for quality of experience-aware wireless mesh networking,” _Elsevier Computer Networks_ , vol. 88, pp. 269–285, Sep. 2015.
  * [172] C. E. Perkins and P. Bhagwat, “Highly dynamic destination-sequenced distance-vector routing (dsdv) for mobile computers,” in _Proceedings of the Conference on Communications Architectures, Protocols and Applications_ , ser. SIGCOMM ’94. New York, NY, USA: ACM, 1994, pp. 234–244. [Online]. Available: http://doi.acm.org/10.1145/190314.190336
  * [173] J. Chroboczek, “The babel routing protocol.” IETF RFC 6126, 04 2011. [Online]. Available: https://tools.ietf.org/html/rfc6126
  * [174] M. Al-Ghazal, A. El-Sayed, and H. Kelash, “Routing optimlzation using genetic algorithm in ad hoc networks,” in _2007 IEEE International Symposium on Signal Processing and Information Technology_ , Dec 2007, pp. 497–503.
  * [175] S. A. N. Shaha, V. Pai, and U. K. Shenoy, “Comparison of wireless routing protocols over ftp traffic in mobile and non mobile nodes,” in _2017 International Conference on Intelligent Computing, Instrumentation and Control Technologies (ICICICT)_ , July 2017, pp. 349–353.
  * [176] B. Bellur and R. G. Ogier, “A reliable, efficient topology broadcast protocol for dynamic networks,” in _INFOCOM ’99. Eighteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE_ , vol. 1, Mar 1999, pp. 178–186 vol.1.
  * [177] A. Neumann, C. Aichele, M. Lindner, and S. Wunderlich, “Ietf better approach to mobile ad hoc networking (b.a.t.m.a.n.).” IETF Internet Draft draft-wunderlich-openmesh-manet-routing-00, 04 2008. [Online]. Available: https://tools.ietf.org/html/draft-wunderlichopenmesh-manet-routing-00
  * [178] D. B. Johnson and D. Maltz, “Dynamic source routing in ad hoc wireless networks,” vol. 353, 05 1999.
  * [179] S. Murthy and J. J. Garcia-Luna-Aceves, “An efficient routing protocol for wireless networks,” _Mobile Networks and Applications_ , vol. 1, no. 2, pp. 183–197, Jun 1996. [Online]. Available: https://doi.org/10.1007/BF01193336
  * [180] Y. Wang and J. Liu, “A backup multipath routing protocol for ad hoc networks with dynamic topology,” in _2012 3rd International Conference on System Science, Engineering Design and Manufacturing Informatization_ , vol. 1, Oct 2012, pp. 237–241.
  * [181] P. M. Carthy and D. Grigoras, “Multipath associativity based routing,” in _Second Annual Conference on Wireless On-demand Network Systems and Services_ , Jan 2005, pp. 60–69.
  * [182] R. Dube, C. D. Rais, K.-Y. Wang, and S. K. Tripathi, “Signal stability-based adaptive routing (ssa) for ad hoc mobile networks,” _IEEE Personal Communications_ , vol. 4, no. 1, pp. 36–45, Feb 1997.
  * [183] S. S. Naik and A. U. Bapat, “Message priority based routing protocol in manets,” in _2015 International Conference on Pervasive Computing (ICPC)_ , Jan 2015, pp. 1–5.
  * [184] Y.-H. Wang and C.-F. Chao, “Dynamic backup routes routing protocol for mobile ad hoc networks,” _Information Sciences_ , vol. 176, no. 2, pp. 161 – 185, 2006. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0020025504003275
  * [185] D. Dimitrova, M. Brogle, T. Braun, G. Heijenk, and N. Meratnia, _Joint ERCIM eMobility and MobiSense Workshop_. University of Bern, 6 2012.
  * [186] J. Hope Forsmann, R. Hiromoto, and J. Svoboda, “A time-slotted on-demand routing protocol for mobile ad hoc unmanned vehicle systems - art. no. 65611p,” 05 2007.
  * [187] Z. J. Haas, M. R. Pearlman, and P. Samar, “The zone routing protocol (zrp) for ad hoc networks.” IETF Internet Draft draft-ietf-manet-zonezrp-04, 07 2002. [Online]. Available: https://www.ietf.org/proceedings/55/I-D/draft-ietf-manet-zone-zrp-04.txt
  * [188] V. Park and S. Corson, “Temporally-ordered routing algorithm (tora).” IETF Internet Draft draft-ietf-manet-tora-spec-04, 07 2001. [Online]. Available: http://www.ietf.org/proceedings/52/I-D/draftietf-manet-tora-spec-04.txt
  * [189] B. Karp and H. T. Kung, “Gpsr: Greedy perimeter stateless routing for wireless networks,” in _Proceedings of the 6th Annual International Conference on Mobile Computing and Networking_ , ser. MobiCom ’00. New York, NY, USA: ACM, 2000, pp. 243–254. [Online]. Available: http://doi.acm.org/10.1145/345910.345953
  * [190] C. Liu and J. Wu, “Efficient geometric routing in three dimensional ad hoc networks,” in _IEEE INFOCOM 2009_ , April 2009, pp. 2751–2755.
  * [191] R. Flury and R. Wattenhofer, “Randomized 3d geographic routing,” in _IEEE INFOCOM 2008 - The 27th Conference on Computer Communications_ , April 2008.
  * [192] Y. Li, S. Xie, and Y. Yu, “Analysis of greedy forwarding in vehicular ad hoc networks,” in _2011 International Conference on System science, Engineering design and Manufacturing informatization_ , vol. 2, Oct 2011, pp. 344–347.
  * [193] S. Li, H. Gao, and D. Wu, “An energy-balanced routing protocol with greedy forwarding for wsns in cropland,” in _2016 IEEE International Conference on Electronic Information and Communication Technology (ICEICT)_ , Aug 2016, pp. 1–7.
  * [194] J. Zhou, Y. Chen, B. Leong, and P. S. Sundaramoorthy, “Practical 3d geographic routing for wireless sensor networks,” in _Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems_ , ser. SenSys ’10. New York, NY, USA: ACM, 2010, pp. 337–350. [Online]. Available: http://doi.acm.org/10.1145/1869983.1870016
  * [195] D. Rosario, Z. Zhao, T. Braun, E. Cerqueira, A. Santos, and I. Alyafawi, “Opportunistic routing for multi-flow video dissemination over flying ad-hoc networks,” in _Proceeding of IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks 2014_ , June 2014, pp. 1–6.
  * [196] W. Qingwen, L. Gang, L. Zhi, and Q. Qian, “An adaptive forwarding protocol for three dimensional flying ad hoc networks,” in _2015 IEEE 5th International Conference on Electronics Information and Emergency Communication_ , May 2015, pp. 142–145.
  * [197] R. Shirani, M. St-Hilaire, T. Kunz, Y. Zhou, J. Li, and L. Lamont, “Combined reactive-geographic routing for unmanned aeronautical ad-hoc networks,” in _2012 8th International Wireless Communications and Mobile Computing Conference (IWCMC)_ , Aug 2012, pp. 820–826.
  * [198] Y. Li, M. St-Hilaire, and T. Kunz, “Enhancements to reduce the overhead of the reactive-greedy-reactive routing protocol for unmanned aeronautical ad-hoc networks,” in _2012 8th International Conference on Wireless Communications, Networking and Mobile Computing_ , Sept 2012, pp. 1–4.
  * [199] D. Rosario, Z. Zhao, A. Santos, T. Braun, and E. Cerqueira, “A beaconless opportunistic routing based on a cross-layer approach for efficient video dissemination in mobile multimedia iot applications,” _Computer Communications_ , vol. 45, pp. 21 – 31, 2014. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0140366414001388
  * [200] S. Temel and I. Bekmezci, “Lodmac: Location oriented directional mac protocol for fanets,” _Computer Networks_ , vol. 83, pp. 76 – 84, 2015. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S138912861500081X
  * [201] S. Biswas and R. Morris, “Exor: Opportunistic routing in multi-hop wireless networks[c],” pp. 133–143, 01 2005.
  * [202] Y. Ko and N. H. Vaidya, “Location-aided routing (lar) in mobile ad hoc networks,” _Wireless Networks_ , vol. 6, no. 4, pp. 307–321, Sep 2000. [Online]. Available: https://doi.org/10.1023/A:1019106118419
  * [203] J. Mingliang, L. Jinyang, and T. Y. Chiang, “Cluster based routing protocol(cbrp) functional specification,” Aug 1998. [Online]. Available: https://tools.ietf.org/id/draft-ietf-manet-cbrp-spec-00.txt
  * [204] K. Devarajan and V. Padmathilagam, “An enhanced cluster gateway switch routing protocol (ecgsr) for congestion control using aodv algorithm in manet,” _International Journal of Computer Applications_ , vol. 123, no. 3, pp. 37–42, 2015.
  * [205] K. Obraczka, K. Viswanath, and G. Tsudik, “Flooding for reliable multicast in multi-hop ad hoc networks,” _Wireless Networks_ , vol. 7, no. 6, pp. 627–634, Nov 2001. [Online]. Available: https://doi.org/10.1023/A:1012323519059
  * [206] Y. Sasson, D. Cavin, and A. Schiper, “Probabilistic broadcast for flooding in wireless mobile ad hoc networks,” in _2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003._ , vol. 2, March 2003, pp. 1124–1130 vol.2.
  * [207] H. Tian, H. Shen, and T. Matsuzawa, “Randomwalk routing for wireless sensor networks,” in _Sixth International Conference on Parallel and Distributed Computing Applications and Technologies (PDCAT’05)_ , Dec 2005, pp. 196–200.
  * [208] X. Li, X. Tao, and N. Li, “Energy-efficient cooperative mimo-based random walk routing for wireless sensor networks,” _IEEE Communications Letters_ , vol. 20, no. 11, pp. 2280–2283, Nov 2016.
  * [209] Z. Zheng, A. K. Sangaiah, and T. Wang, “Adaptive communication protocols in flying ad hoc network,” _IEEE Communications Magazine_ , vol. 56, no. 1, pp. 136–142, Jan 2018.
  * [210] S. Rosati, K. Kruzelecki, L. Traynard, and B. R. Mobile, “Speed-aware routing for uav ad-hoc networks,” in _2013 IEEE Globecom Workshops (GC Wkshps)_ , Dec 2013, pp. 1367–1373.
  * [211] S. Rosati, K. Kruzelecki, G. Heitz, D. Floreano, and B. Rimoldi, “Dynamic routing for flying ad hoc networks,” _IEEE Transactions on Vehicular Technology_ , vol. 65, no. 3, pp. 1690–1700, March 2016.
  * [212] L. Lin, Q. Sun, J. Li, and F. Yang, “A novel geographic position mobility oriented routing strategy for uavs,” vol. 8, pp. 709–716, 02 2012.
  * [213] C. Zang and S. Zang, “Mobility prediction clustering algorithm for uav networking,” in _2011 IEEE GLOBECOM Workshops (GC Wkshps)_ , Dec 2011, pp. 1158–1161.
  * [214] G. Gankhuyag, A. P. Shrestha, and S. Yoo, “Robust and reliable predictive routing strategy for flying ad-hoc networks,” _IEEE Access_ , vol. 5, pp. 643–654, 2017.
  * [215] Y. Li, M. St-Hilaire, and T. Kunz, “Improving routing in networks of uavs via scoped flooding and mobility prediction,” in _2012 IFIP Wireless Days_ , Nov 2012, pp. 1–6.
  * [216] W. Jung, J. Yim, and Y. Ko, “Qgeo: Q-learning-based geographic ad hoc routing protocol for unmanned robotic networks,” _IEEE Communications Letters_ , vol. 21, no. 10, pp. 2258–2261, 2017.
  * [217] B. Sliwa, C. Schüler, M. Patchou, and C. Wietfeld, “Parrot: Predictive ad-hoc routing fueled by reinforcement learning and trajectory knowledge,” 2020.
  * [218] C. He, S. Liu, and S. Han, “A fuzzy logic reinforcement learning-based routing algorithm for flying ad hoc networks,” in _2020 International Conference on Computing, Networking and Communications (ICNC)_ , 2020, pp. 987–991.
  * [219] Y. Shilova, M. Kavalerov, and I. Bezukladnikov, “Full echo q-routing with adaptive learning rates: A reinforcement learning approach to network routing,” in _IEEE Conf. EIConRusNW_ , Feb 2016, pp. 341–344.
  * [220] M. Kavalerov, Y. Shilova, and Y. Likhacheva, “Adaptive q-routing with random echo and route memory,” in _Proceedings of the 20th Conference of Open Innovations Association FRUCT_. Helsinki, Finland, Finland: FRUCT Oy, 2017, pp. 20:138–20:145.
  * [221] D. Sharma, D. Kukreja, P. Aggarwal, M. Kaur, and A. Sachan, “Poisson’s probability‐based q‐routing techniques for message forwarding in opportunistic networks,” _International Journal of Communication Systems_ , vol. 31, 05 2018.
  * [222] T. Hendriks, M. Camelo, and S. Latré, “Q2-routing : A qos-aware q-routing algorithm for wireless ad hoc networks,” in _2018 14th International Conference on Wireless and Mobile Computing, Networking and Communications (WiMob)_ , Oct 2018, pp. 108–115.
  * [223] F. Wang, R. Feng, and H. Chen, “Dynamic routing algorithm with q-learning for internet of things with delayed estimator,” _IOP Conference Series: Earth and Environmental Science_ , vol. 234, p. 012048, mar 2019.
  * [224] N. Lyu, G. Song, B. Yang, and Y. Cheng, “Qngpsr: A q-network enhanced geographic ad-hoc routing protocol based on gpsr,” in _2018 IEEE 88th Vehicular Technology Conference (VTC-Fall)_ , 2018, pp. 1–6.
  * [225] J. LIU, Q. WANG, C. HE, and Y. XU, “Ardeep: Adaptive and reliable routing protocol for mobile robotic networks with deep reinforcement learning,” in _2020 IEEE 45th Conference on Local Computer Networks (LCN)_ , 2020, pp. 465–468.
  * [226] Y. Chen, N. Lyu, G. Song _et al._ , “A traffic-aware q-network enhanced routing protocol based on gpsr for unmanned aerial vehicle ad-hoc networks,” _Front Inform Technol Electron Eng_ , vol. 21, pp. 1308–1320, 2020. [Online]. Available: https://doi.org/10.1631/FITEE.1900401
  * [227] J. Liu, Q. Wang, C. He, K. Jaffrès-Runser, Y. Xu, Z. Li, and Y. Xu, “Qmr:q-learning based multi-objective optimization routing protocol for flying ad hoc networks,” _Computer Communications_ , vol. 150, pp. 304–316, 2020. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0140366419308278
  * [228] Q. Yang, S. Jang, and S. Yoo, “Q-learning-based fuzzy logic for multi-objective routing algorithm in flying ad hoc networks,” _Wireless Pers Commun_ , vol. 113, pp. 115–138, 2020. [Online]. Available: https://doi.org/10.1007/s11277-020-07181-w
  * [229] S. Jiang, Z. Huang, and Y. Ji, “Adaptive uav-assisted geographic routing with q-learning in vanet,” _IEEE Communications Letters_ , pp. 1–1, 2020\.
  * [230] J. Wu, M. Fang, and X. Li, “Reinforcement learning based mobility adaptive routing for vehicular ad-hoc networks,” vol. 101, no. 4, p. 2143–2171, Aug. 2018. [Online]. Available: https://doi.org/10.1007/s11277-018-5809-z
  * [231] O. Oubbati, N. CHAIB, A. Lakas, S. Bitam, and P. Lorenz, “U2rv: Uav‐assisted reactive routing protocol for vanets,” _International Journal of Communication Systems_ , vol. PP, pp. 1–13, 08 2019.
  * [232] A. Rovira-Sugranes, F. Afghah, J. Qu, and A. Razi, “Fully-echoed q-routing with simulated annealing inference for flying adhoc networks,” _IEEE Transactions on Network Science and Engineering_ , 2021.
  * [233] R. S. Sutton and A. G. Barto, “Reinforcement learning: An introduction,” _IEEE Transactions on Neural Networks_ , vol. 16, pp. 285–286, 1988.
  * [234] J. A. Boyan and M. L. Littman, “Packet routing in dynamically changing networks: A reinforcement learning approach,” in _Int. Conf. on Neural Info. Processing Systems_. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1993, pp. 671–678.
  * [235] S. Khodayari and M. J. Yazdanpanah, “Network routing based on reinforcement learning in dynamically changing networks,” in _ICTAI’05_ , 2005, pp. 5 pp.–366.
  * [236] S. P. M. Choi and D.-Y. Yeung, “Predictive q-routing: A memory-based reinforcement learning approach to adaptive traffic control,” in _Conf. on Neural Info. Processing Systems_. Cambridge, MA, USA: MIT Press, 1995, p. 945–951.
  * [237] S. Kumar and R. Mukkulainen, “Confidence based dual reinforcement q-routing: An adaptive online network routing algorithm,” in _IJCAI’99_. San Francisco, CA, USA: Morgan Kaufmann Publishers Inc., 1999, pp. 758–763.
  * [238] F. Tekiner, Z. Ghassemlooy, and T. R. Srikanth, “Comparison of the q-routing and shortest path routing algorithm,” 2004.
  * [239] N. Gupta, M. Kumar, A. Sharma, M. S. Gaur, V. Laxmi, M. Daneshtalab, and M. Ebrahimi, “Improved route selection approaches using q-learning framework for 2d nocs,” in _Int. Workshop on Many-core Embedded Systems_. New York, NY, USA: ACM, 2015, pp. 33–40.
  * [240] “Plane 11 flight simulator: More powerful. made usable.” Sep 2020. [Online]. Available: https://www.x-plane.com/
  * [241] A. Perry, “The flightgear flight simulator,” 01 2004.
  * [242] Osrf, “Why gazebo?” [Online]. Available: http://gazebosim.org/
  * [243] “jmavsim simulation.” [Online]. Available: https://dev.px4.io/v1.9.0/en/simulation/jmavsim.html
  * [244] S. Shah, D. Dey, C. Lovett, and A. Kapoor, “Airsim: High-fidelity visual and physical simulation for autonomous vehicles,” _CoRR_ , vol. abs/1705.05065, 2017. [Online]. Available: http://arxiv.org/abs/1705.05065
  * [245] M. Müller, V. Casser, J. Lahoud, N. Smith, and B. Ghanem, “Ue4sim: A photo-realistic simulator for computer vision applications,” _CoRR_ , vol. abs/1708.05869, 2017. [Online]. Available: http://arxiv.org/abs/1708.05869
  * [246] “Overview - qgroundcontrol user guide.” [Online]. Available: https://docs.qgroundcontrol.com/master/en/index.html
  * [247] “Mission planner ground control station.” [Online]. Available: https://ardupilot.org/planner/docs/mission-planner-ground-control-station.html
  * [248] A.-M. Andreescu, M. Dima, A. Istrate, C. Muresan, C. Visoiu, and P. Parvu, “Autonomous system for image geo-tagging and target recognition,” 05 2014.
  * [249] S. E. . UgCS, “Leading drone control software.” [Online]. Available: https://www.ugcs.com/
  * [250] A. I. Hentati, L. Krichen, M. Fourati, and L. C. Fourati, “Simulation tools, environments and frameworks for uav systems performance analysis,” in _2018 14th International Wireless Communications Mobile Computing Conference (IWCMC)_ , 2018, pp. 1495–1500.
  * [251] C. Coopmans, M. Podhradský, and N. V. Hoffer, “Software- and hardware-in-the-loop verification of flight dynamics model and flight control simulation of a fixed-wing unmanned aerial vehicle,” in _2015 Workshop on Research, Education and Development of Unmanned Aerial Systems (RED-UAS)_ , 2015, pp. 115–122.
  * [252] Microsoft, “Microsoft airsim: Open source simulator based on unreal engine for autonomous vehicles from microsoft ai and research,” Feb 2017. [Online]. Available: https://github.com/Microsoft/AirSim
  * [253] J. Antunes, “Airsim: A simulator to help ai research for use in drones,” Mar 2017\. [Online]. Available: https://www.commercialuavnews.com/public-safety/airsim-simulator-help-artificial-intelligence-research-use-drones
  * [254] “Ns-3 tutorial,” Oct 2020. [Online]. Available: https://www.nsnam.org/docs/release/3.32/tutorial/html/index.html
  * [255] “Opnet network simulator,” Apr 2020. [Online]. Available: https://opnetprojects.com/opnet-network-simulator/
  * [256] M. Calvo-Fullana, D. Mox, A. Pyattaev, J. Fink, V. Kumar, and A. Ribeiro, “Ros-netsim: A framework for the integration of robotic and network simulators,” _IEEE Robotics and Automation Letters_ , vol. 6, no. 2, pp. 1120–1127, 2021.
  * [257] S. M. Najafabadi, N. Mastronarde, M. J. Medley, and J. D. Matyjas, “UB-ANC: an open platform testbed for software-defined airborne networking and communications,” _CoRR_ , vol. abs/1509.08346, 2015. [Online]. Available: http://arxiv.org/abs/1509.08346
  * [258] V. Marojevic, I. Guvenc, R. Dutta, M. L. Sichitiu, and B. A. Floyd, “Advanced wireless for unmanned aerial systems: 5g standardization, research challenges, and aerpaw architecture,” _IEEE Vehicular Technology Magazine_ , vol. 15, no. 2, pp. 22–30, 2020.
  * [259] M. L. Sichitiu, I. Guvenc, R. Dutta, V. Marojevic, and B. Floyd, “Aerpaw emulation overview,” in _Proceedings of the 14th International Workshop on Wireless Network Testbeds, Experimental Evaluation & Characterization_, ser. WiNTECH’20. New York, NY, USA: Association for Computing Machinery, 2020, p. 1–8. [Online]. Available: https://doi.org/10.1145/3411276.3412188
  * [260] “Nc state named a hot spot for 5g innovation,” Dec 2019. [Online]. Available: https://news.ncsu.edu/2019/12/5g-wireless-network-aerpaw/
  * [261] J. Breen, A. Buffmire, J. Duerig, K. Dutt, E. Eide, M. Hibler, D. Johnson, S. K. Kasera, E. Lewis, D. Maas, A. Orange, N. Patwari, D. Reading, R. Ricci, D. Schurig, L. B. Stoller, J. Van der Merwe, K. Webb, and G. Wong, “POWDER: Platform for open wireless data-driven experimental research,” in _Proceedings of the 14th International Workshop on Wireless Network Testbeds, Experimental Evaluation and Characterization (WiNTECH)_ , Sep. 2020.
  * [262] P. Gawłowicz and A. Zubow, “ns-3 meets OpenAI Gym: The Playground for Machine Learning in Networking Research,” in _ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems (MSWiM)_ , November 2019. [Online]. Available: http://www.tkn.tu-berlin.de/fileadmin/fg112/Papers/2019/gawlowicz19_mswim.pdf
  * [263] H. Yin, P. Liu, L. Zhang, L. Cao, Y. Gao, X. Hei, and X.-J. Hei, “Ns3-ai: Enable applying artificial intelligence to network simulation in ns-3,” 06 2020\.
  * [264] “Aerpaw equipment.” [Online]. Available: https://aerpaw.org/aerpaw-equipment/
  * [265] A. Antonini, W. Guerra, V. Murali, T. Sayre-McCord, and S. Karaman, “The blackbird dataset: A large-scale dataset for uav perception in aggressive flight,” 2018.
  * [266] N. Smith, N. Moehrle, M. Goesele, and W. Heidrich, “Uav pathplanning dataset & benchmark,” 2018. [Online]. Available: http://hdl.handle.net/10754/630159
  * [267] M. F. Vandroogenbroeck and Marc, “Mid-air dataset.” [Online]. Available: https://midair.ulg.ac.be/
  * [268] J. Delmerico, T. Cieslewski, H. Rebecq, M. Faessler, and D. Scaramuzza, “Are we ready for autonomous drone racing? the UZH-FPV drone racing dataset,” in _IEEE Int. Conf. Robot. Autom. (ICRA)_ , 2019.
  * [269] X. Pan, P. Desbarats, and S. Chaumette, “A deep learning based framework for uav trajectory pattern recognition,” in _2019 Ninth International Conference on Image Processing Theory, Tools and Applications (IPTA)_ , 2019, pp. 1–6.
  * [270] X. Liu, Y. Liu, Y. Chen, and L. Hanzo, “Trajectory design and power control for multi-uav assisted wireless networks: A machine learning approach,” _IEEE Transactions on Vehicular Technology_ , vol. 68, no. 8, pp. 7957–7969, 2019.
  * [271] H. Wang, J. Wang, J. Chen, Y. Gong, and G. Ding, “Network-connected UAV communications: Potentials and challenges,” _CoRR_ , vol. abs/1806.04583, 2018. [Online]. Available: http://arxiv.org/abs/1806.04583
  * [272] M. Cooney, “Drones still face major communications challenges getting onto us airspace,” 2013. [Online]. Available: https://www.networkworld.com/article/2224063
  * [273] M. Mozaffari, W. Saad, M. Bennis, Y. Nam, and M. Debbah, “A tutorial on uavs for wireless networks: Applications, challenges, and open problems,” _IEEE Communications Surveys Tutorials_ , 2019.
  * [274] A. Fotouhi, H. Qiang, M. Ding, M. Hassan, L. G. Giordano, A. García-Rodríguez, and J. Yuan, “Survey on UAV cellular communications: Practical aspects, standardization advancements, regulation, and security challenges,” _CoRR_ , vol. abs/1809.01752, 2018. [Online]. Available: http://arxiv.org/abs/1809.01752
  * [275] H. Shakhatreh, A. Sawalmeh, A. I. Al-Fuqaha, Z. Dou, E. Almaita, I. Khalil, N. S. Othman, A. Khreishah, and M. Guizani, “Unmanned aerial vehicles: A survey on civil applications and key research challenges,” _CoRR_ , vol. abs/1805.00881, 2018. [Online]. Available: http://arxiv.org/abs/1805.00881
  * [276] Y. Fu, S. Wang, C. Wang, X. Hong, and S. McLaughlin, “Artificial intelligence to manage network traffic of 5g wireless networks,” _IEEE Network_ , vol. 32, no. 6, pp. 58–64, 2018.
  * [277] L. Xiao, X. Lu, D. Xu, Y. Tang, L. Wang, and W. Zhuang, “Uav relay in vanets against smart jamming with reinforcement learning,” _IEEE Transactions on Vehicular Technology_ , vol. 67, no. 5, pp. 4087–4097, 2018.
  * [278] P. Fraga-Lamas, L. Ramos, V. M. Mondéjar-Guerra, and T. Fernández-Caramés, “A review on iot deep learning uav systems for autonomous obstacle detection and collision avoidance,” _Remote Sensing_ , vol. 11, p. 2144, 09 2019.
  * [279] S. Islam, Q. Huang, F. Afghah, P. Fule, and A. Razi, “Fire frontline monitoring by enabling uav-based virtual reality with adaptive imaging rate,” in _2019 53rd Asilomar Conference on Signals, Systems, and Computers_. IEEE, 2019, pp. 368–372.
  * [280] M. Erdelj and E. Natalizio, “Uav-assisted disaster management: Applications and open issues,” in _2016 international conference on computing, networking and communications (ICNC)_. IEEE, 2016, pp. 1–5.
  * [281] L. F. Gonzalez, G. A. Montes, E. Puig, S. Johnson, K. Mengersen, and K. J. Gaston, “Unmanned aerial vehicles (uavs) and artificial intelligence revolutionizing wildlife monitoring and conservation,” _Sensors_ , vol. 16, no. 1, p. 97, 2016.
  * [282] M. Y. Arafat and S. Moh, “Location-aided delay tolerant routing protocol in uav networks for post-disaster operation,” _IEEE Access_ , vol. 6, pp. 59 891–59 906, 2018.
  * [283] B. Uragun, “Energy efficiency for unmanned aerial vehicles,” in _2011 10th International Conference on Machine Learning and Applications and Workshops_ , vol. 2, 2011, pp. 316–320.
  * [284] H. Peng, A. Razi, F. Afghah, and J. D. Ashdown, “A unified framework for joint mobility prediction and object profiling of drones in UAV networks,” _CoRR_ , vol. abs/1808.00058, 2018. [Online]. Available: http://arxiv.org/abs/1808.00058
  * [285] J. Chen, U. Mitra, and D. Gesbert, “Optimal uav relay placement for single user capacity maximization over terrain with obstacles,” in _2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC)_ , 2019, pp. 1–5.
  * [286] Z. Pan, L. An, and C. Wen, “Recent advances in fuel cells based propulsion systems for unmanned aerial vehicles,” _Applied Energy_ , vol. 240, pp. 473–485, 04 2019.
  * [287] E. F. Costa, D. A. Souza, V. P. Pinto, M. S. Araújo, A. Peixoto, and E. D. C. Júnior, “Prediction of lithium-ion battery capacity in uavs,” _2019 6th International Conference on Control, Decision and Information Technologies (CoDIT)_ , pp. 1865–1869, 2019.
  * [288] Q. Wu, G. Zhang, D. W. K. Ng, W. Chen, and R. Schober, “Generalized wireless-powered communications: When to activate wireless power transfer?” _IEEE Transactions on Vehicular Technology_ , vol. 68, no. 8, pp. 8243–8248, 2019.
  * [289] A. Kurs, A. Karalis, R. Moffatt, J. Joannopoulos, P. Fisher, and M. Soljacic, “Wireless power transfer via strongly coupled magnetic resonances,” _Science (New York, N.Y.)_ , vol. 317, pp. 83–6, 08 2007.
  * [290] M. Khonji, M. Alshehhi, C.-M. Tseng, and C.-K. Chau, “Autonomous inductive charging system for battery-operated electric drones,” 05 2017, pp. 322–327.
  * [291] M. Nguyen, L. D. Nguyen, T. Q. Duong, and H. D. Tuan, “Real-time optimal resource allocation for embedded uav communication systems,” _IEEE Wireless Communications Letters_ , vol. 8, no. 1, pp. 225–228, 2019\.
  * [292] S. Bi and R. Zhang, “Placement optimization of energy and information access points in wireless powered communication networks,” _IEEE Transactions on Wireless Communications_ , vol. 15, no. 3, pp. 2351–2364, 2016\.
  * [293] A. M. R. Tolba, “Trust-based distributed authentication method for collision attack avoidance in vanets,” _IEEE Access_ , vol. 6, pp. 62 747–62 755, 2018.
  * [294] T.-J. Yang, Y.-H. Chen, and V. Sze, “Designing energy-efficient convolutional neural networks using energy-aware pruning,” in _Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition_ , 2017, pp. 5687–5695.
  * [295] T. Johann, M. Dick, S. Naumann, and E. Kern, “How to measure energy-efficiency of software: Metrics and measurement results,” in _2012 First International Workshop on Green and Sustainable Software (GREENS)_. IEEE, 2012, pp. 51–54.
  * [296] E. García Martín, “Energy efficiency in machine learning: A position paper,” in _30th Annual Workshop of the Swedish Artificial Intelligence Society SAIS, Karlskrona_ , vol. 137. Linköping University Electronic Press, 2017, pp. 68–72.
  * [297] C. Zhang and W. Zhang, “Spectrum sharing for drone networks,” _IEEE Journal on Selected Areas in Communications_ , vol. 35, no. 1, pp. 136–144, Jan 2017.
  * [298] J. Kakar and V. Marojevic, “Waveform and spectrum management for unmanned aerial systems beyond 2025,” _CoRR_ , vol. abs/1708.01664, 2017. [Online]. Available: http://arxiv.org/abs/1708.01664
  * [299] F. Delgado, J. Carvalho, T. Coelho, and A. D. Santos, “An optical fiber sensor and its application in uavs for current measurements,” _Sensors_ , vol. 16, no. 11, p. 1800, 2016.
  * [300] W. Griethe, M. Gregory, F. Heine, and H. Kampfner, “High speed laser communications in uav scenarios,” 05 2011.
  * [301] B. O. Sadiq, “A feasibility study of using light fidelity with multiple unmanned aerial vehicles for indoor collaborative and cooperative networking,” _CoRR_ , vol. abs/1707.08627, 2017. [Online]. Available: http://arxiv.org/abs/1707.08627
  * [302] Jun Luo, J. . Hubaux, and P. T. Eugster, “Dictate: Distributed certification authority with probabilistic freshness for ad hoc networks,” _IEEE Transactions on Dependable and Secure Computing_ , vol. 2, no. 4, pp. 311–323, 2005.
  * [303] L. Eschenauer and V. D. Gligor, “A key-management scheme for distributed sensor networks,” in _Proceedings of the 9th ACM Conference on Computer and Communications Security_ , ser. CCS ’02. New York, NY, USA: Association for Computing Machinery, 2002, p. 41–47. [Online]. Available: https://doi.org/10.1145/586110.586117
  * [304] M. Gharib, E. Emamjomeh-Zadeh, A. Norouzi-Fard, and A. Movaghar, “A novel probabilistic key management algorithm for large-scale manets,” in _2013 27th International Conference on Advanced Information Networking and Applications Workshops_ , 2013, pp. 349–356.
  * [305] I. J. Jensen, D. F. Selvaraj, and P. Ranganathan, “Blockchain technology for networked swarms of unmanned aerial vehicles (uavs),” in _2019 IEEE 20th International Symposium on” A World of Wireless, Mobile and Multimedia Networks”(WoWMoM)_. IEEE, 2019, pp. 1–7.
  * [306] T. Alladi, N. Naren, G. Bansal, V. Chamola, and M. Guizani, “Secauthuav: A novel authentication scheme for uav-base station scenario,” _IEEE Transactions on Vehicular Technology_ , 2020.
  * [307] A. Couturier and M. Akhloufi, “A review on absolute visual localization for uav,” _Robotics and Autonomous Systems_ , vol. 135, p. 103666, 01 2021.
  * [308] T. Erpek, Y. E. Sagduyu, and Y. Shi, “Deep learning for launching and mitigating wireless jamming attacks,” _IEEE Transactions on Cognitive Communications and Networking_ , vol. 5, no. 1, pp. 2–14, March 2019.
  * [309] L. Abusalah, A. Khokhar, and M. Guizani, “A survey of secure mobile ad hoc routing protocols,” _IEEE Communications Surveys Tutorials_ , vol. 10, no. 4, pp. 78–93, 2008.