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Unmanned aerial vehicles (UAVs): practical aspects, applications, open challenges, security issues, and future trends

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Abstract

Recently, unmanned aerial vehicles (UAVs) or drones have emerged as a ubiquitous and integral part of our society. They appear in great diversity in a multiplicity of applications for economic, commercial, leisure, military and academic purposes. The drone industry has seen a sharp uptake in the last decade as a model to manufacture and deliver convergence, offering synergy by incorporating multiple technologies. It is due to technological trends and rapid advancements in control, miniaturization, and computerization, which culminate in secure, lightweight, robust, more-accessible and cost-efficient UAVs. UAVs support implicit particularities including access to disaster-stricken zones, swift mobility, airborne missions and payload features. Despite these appealing benefits, UAVs face limitations in operability due to several critical concerns in terms of flight autonomy, path planning, battery endurance, flight time and limited payload carrying capability, as intuitively it is not recommended to load heavy objects such as batteries. As a result, the primary goal of this research is to provide insights into the potentials of UAVs, as well as their characteristics and functionality issues. This study provides a comprehensive review of UAVs, types, swarms, classifications, charging methods and regulations. Moreover, application scenarios, potential challenges and security issues are also examined. Finally, future research directions are identified to further hone the research work. We believe these insights will serve as guidelines and motivations for relevant researchers.

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References

  1. Grlj CG, Krznar N, Pranjić M (2022) A decade of UAV docking stations: a brief overview of mobile and fixed landing platforms. Drones 6(1):17

    Article  Google Scholar 

  2. Rovira-Sugranes A, Razi A, Afghah F, Chakareski J (2022) A review of AI-enabled routing protocols for UAV networks: trends, challenges, and future outlook. Ad Hoc Netw 130:102790

    Article  Google Scholar 

  3. Noor F, Khan MA, Al-Zahrani A, Ullah I, Al-Dhlan KA (2020) A review on communications perspective of flying ad-hoc networks: key enabling wireless technologies, applications, challenges and open research topics. Drones 4(4):65

    Article  Google Scholar 

  4. Dronova I, Kislik C, Dinh Z, Kelly M (2021) A review of unoccupied aerial vehicle use in wetland applications: emerging opportunities in approach, technology, and data. Drones 5(2):45

    Article  Google Scholar 

  5. Kim J, Kim S, Jeong J, Kim H, Park JS, Kim T (2018) CBDN: cloud-based drone navigation for efficient battery charging in drone networks. IEEE Trans Intell Transp Syst 20(11):4174–4191

    Article  Google Scholar 

  6. Nourmohammadi A, Jafari M, Zander TO (2018) A survey on unmanned aerial vehicle remote control using brain–computer interface. IEEE Trans Hum-Mach Syst 48(4):337–348

    Article  Google Scholar 

  7. Kanellakis C, Nikolakopoulos G (2017) Survey on computer vision for UAVs: current developments and trends. J Intell Rob Syst 87(1):141–168

    Article  Google Scholar 

  8. Zhang S, Qian Z, Wu J, Kong F, Lu S (2016) Optimizing itinerary selection and charging association for mobile chargers. IEEE Trans Mob Comput 16(10):2833–2846

    Article  Google Scholar 

  9. Aldhaher S, Mitcheson PD, Arteaga JM, Kkelis G, Yates DC (2017) Light-weight wireless power transfer for mid-air charging of drones. In: 2017 11th European conference on antennas and propagation (EUCAP). IEEE, pp 336–340

  10. Lu M, Bagheri M, James AP, Phung T (2018) Wireless charging techniques for UAVs: a review, reconceptualization, and extension. IEEE Access 6:29865–29884

    Article  Google Scholar 

  11. Raciti A, Rizzo SA, Susinni G (2018) Drone charging stations over the buildings based on a wireless power transfer system. In: 2018 IEEE/IAS 54th industrial and commercial power systems technical conference (I&CPS). IEEE, pp 1–6

  12. Rohan A, Rabah M, Talha M, Kim SH (2018) Development of intelligent drone battery charging system based on wireless power transmission using hill climbing algorithm. Appl Syst Innov 1(4):44

    Article  Google Scholar 

  13. Shin M, Kim J, Levorato M (2019) Auction-based charging scheduling with deep learning framework for multi-drone networks. IEEE Trans Veh Technol 68(5):4235–4248

    Article  Google Scholar 

  14. Pham H, Smolka SA, Stoller SD, Phan D, Yang J (2015) A survey on unmanned aerial vehicle collision avoidance systems. arXiv preprint arXiv:1508.07723

  15. Hayat S, Yanmaz E, Muzaffar R (2016) Survey on unmanned aerial vehicle networks for civil applications: a communications viewpoint. IEEE Commun Surv Tutor 18(4):2624–2661

    Article  Google Scholar 

  16. Ebeid E, Skriver M, Jin J (2017) A survey on open-source flight control platforms of unmanned aerial vehicle. In: 2017 euromicro conference on digital system design (DSD). IEEE, pp 396–402

  17. Geraci G, Garcia-Rodriguez A, Giordano LG, López-Pérez D, Björnson E (2018) Understanding UAV cellular communications: from existing networks to massive MIMO. IEEE Access 6:67853–67865

    Article  Google Scholar 

  18. Fotouhi A, Qiang H, Ding M, Hassan M, Giordano LG, Garcia-Rodriguez A, Yuan J (2019) Survey on UAV cellular communications: practical aspects, standardization advancements, regulation, and security challenges. IEEE Commun Surv Tutor 21(4):3417–3442

    Article  Google Scholar 

  19. Mozaffari M, Saad W, Bennis M, Nam YH, Debbah M (2019) A tutorial on UAVs for wireless networks: applications, challenges, and open problems. IEEE Commun Surv Tutor 21(3):2334–2360

    Article  Google Scholar 

  20. Li B, Fei Z, Zhang Y (2018) UAV communications for 5G and beyond: recent advances and future trends. IEEE Internet Things J 6(2):2241–2263

    Article  Google Scholar 

  21. Zhang L, Zhao H, Hou S, Zhao Z, Xu H, Wu X et al (2019) A survey on 5G millimeter wave communications for UAV-assisted wireless networks. IEEE Access 7:117460–117504

    Article  Google Scholar 

  22. Ullah Z, Al-Turjman F, Mostarda L (2020) Cognition in UAV-aided 5G and beyond communications: a survey. IEEE Trans Cogn Commun Netw 6(3):872–891

    Article  Google Scholar 

  23. Oubbati OS, Atiquzzaman M, Ahanger TA, Ibrahim A (2020) Softwarization of UAV networks: a survey of applications and future trends. IEEE Access 8:98073–98125

    Article  Google Scholar 

  24. Zhi Y, Fu Z, Sun X, Yu J (2020) Security and privacy issues of UAV: a survey. Mob Netw Appl 25(1):95–101

    Article  Google Scholar 

  25. Skorobogatov G, Barrado C, Salamí E (2020) Multiple UAV systems: a survey. Unmanned Syst 8(02):149–169

    Article  Google Scholar 

  26. Jiang X, Sheng M, Zhao N, Xing C, Lu W, Wang X (2021) Green UAV communications for 6G: a survey. Chin J Aeronaut

  27. Song Q, Zeng Y, Xu J, Jin S (2021) A survey of prototype and experiment for UAV communications. Sci China Inf Sci 64(4):1–21

    Article  Google Scholar 

  28. Srivastava S, Narayan S, Mittal S (2021) A survey of deep learning techniques for vehicle detection from UAV images. J Syst Arch 117:102152

    Article  Google Scholar 

  29. Haider SK, Nauman A, Jamshed MA, Jiang A, Batool S, Kim SW (2022) Internet of drones: routing algorithms. Tech Chall Math 10(9):1488

    Google Scholar 

  30. Poudel S, Moh S (2022) Task assignment algorithms for unmanned aerial vehicle networks: a comprehensive survey. Veh Commun 100469

  31. Tahir A, Böling J, Haghbayan MH, Toivonen HT, Plosila J (2019) Swarms of unmanned aerial vehicles—a survey. J Ind Inf Integr 16:100106

    Google Scholar 

  32. Mairaj A, Baba AI, Javaid AY (2019) Application specific drone simulators: recent advances and challenges. Simul Model Pract Theory 94:100–117

    Article  Google Scholar 

  33. kalpa Gunarathna J, Munasinghe R (2018) Development of a quad-rotor fixed-wing hybrid unmanned aerial vehicle. In: 2018 Moratuwa engineering research conference (MERCon). IEEE, pp 72–77

  34. Sheng WEN, Jie HAN, Yubin LAN, Xuanchun YIN, Yuhua LU (2018) Influence of wing tip vortex on drift of single rotor plant protection unmanned aerial vehicle. Nongye Jixie Xuebao/Trans Chin Soc Agric Mach 49(8)

  35. Lee D, Zhou J, Lin WT (2015) Autonomous battery swapping system for quadcopter. In: 2015 international conference on unmanned aircraft systems (ICUAS). IEEE, pp 118–124

  36. de Souza BJO, Endler M (2015) Coordinating movement within swarms of UAVs through mobile networks. In: 2015 IEEE international conference on pervasive computing and communication workshops (PerCom Workshops). IEEE, pp 154–159

  37. Pestana J, Sanchez-Lopez JL, de la Puente P, Carrio A, Campoy P (2014) A vision-based quadrotor swarm for the participation in the 2013 international micro air vehicle competition. In: 2014 international conference on unmanned aircraft systems (ICUAS). IEEE, pp 617–622

  38. Fotouhi A, Ding M, Hassan M (2017) Understanding autonomous drone maneuverability for internet of things applications. In: 2017 IEEE 18th international symposium on a world of wireless, mobile and multimedia networks (WoWMoM). IEEE, pp 1–6

  39. Al-Hourani A, Gomez K (2017) Modeling cellular-to-UAV path-loss for suburban environments. IEEE Wirel Commun Lett 7(1):82–85

    Article  Google Scholar 

  40. Saggiani G, Persiani F, Ceruti A, Tortora P, Troiani E, Giuletti F, et al. (2007) A UAV system for observing volcanoes and natural hazards. In: AGU fall meeting abstracts, vol 2007, pp GC11B-05

  41. Berman ES, Fladeland M, Liem J, Kolyer R, Gupta M (2012) Greenhouse gas analyzer for measurements of carbon dioxide, methane, and water vapor aboard an unmanned aerial vehicle. Sens Actuators, B Chem 169:128–135

    Article  Google Scholar 

  42. Khan A, Schaefer D, Tao L, Miller DJ, Sun K, Zondlo MA et al (2012) Low power greenhouse gas sensors for unmanned aerial vehicles. Remote Sens 4(5):1355–1368

    Article  Google Scholar 

  43. Watai T, Machida T, Ishizaki N, Inoue G (2006) A lightweight observation system for atmospheric carbon dioxide concentration using a small unmanned aerial vehicle. J Atmos Ocean Tech 23(5):700–710

    Article  Google Scholar 

  44. McGonigle AJS, Aiuppa A, Giudice G, Tamburello G, Hodson AJ, Gurrieri S (2008) Unmanned aerial vehicle measurements of volcanic carbon dioxide fluxes. Geophys Res Lett 35(6)

  45. Hill AC, Laugier EJ, Casana J (2020) Archaeological remote sensing using multi-temporal, drone-acquired thermal and Near Infrared (NIR) Imagery: a case study at the Enfield Shaker Village. New Hamps Remote Sens 12(4):690

    Article  Google Scholar 

  46. Miyoshi GT, Arruda MDS, Osco LP, Marcato Junior J, Gonçalves DN, Imai NN et al (2020) A novel deep learning method to identify single tree species in UAV-based hyperspectral images. Remote Sens 12(8):1294

    Article  Google Scholar 

  47. Lin YC, Cheng YT, Zhou T, Ravi R, Hasheminasab SM, Flatt JE et al (2019) Evaluation of UAV LiDAR for mapping coastal environments. Remote Sens 11(24):2893

    Article  Google Scholar 

  48. Liu Z, Zhang Y, Yu X, Yuan C (2016) Unmanned surface vehicles: an overview of developments and challenges. Annu Rev Control 41:71–93

    Article  Google Scholar 

  49. Ding M, Wang P, López-Pérez D, Mao G, Lin Z (2015) Performance impact of LoS and NLoS transmissions in dense cellular networks. IEEE Trans Wirel Commun 15(3):2365–2380

    Article  Google Scholar 

  50. Hempe D (2006) Unmanned aircraft systems in the United States. In: US/Europe international safety conference

  51. EASA UAS Workshop [Online]. https://www.easa.europa.eu/sites/default/files/dfu/ws_prod-g-doc-Events-2008-February-1-Overview-of-the-UAV-Industry-(UVS).pdf

  52. ArcturusUAV. Jump 20 [Online]. https://arcturus-uav.com/product/jump-20

  53. AlphaUnmmanedSystems. Alpha 800 UAV Helicopter [Online]. https://alphaunmannedsystems.com/alpha-800-uav/

  54. DJI. DJI Agras MG-1P Series [Online]. https://www.dji.com/mg-1p/infor#specs

  55. AgEagle Aeriel Systems Inc. AgEagle RX-60 Taking Agriculture Intelligence to the Next Level [Online]. https://docs.wixstatic.com/ugd/89e3c5_e3de865b41b644fbb68adea13706723c.pdf?index=true

  56. Technical Specification Group Radio Access Network (2017) Study on Enhanced LTE Support for Aerial Vehicles (Release 15), 3GPP Standard TS 36.777

  57. Use cases and spectrum considerations for UAS (unmanned aircraft systems) (2018). ETSI, Sophia Antipolis, France, Rep. 103 373

  58. Functional architecture for unmanned aerial vehicles and unmanned aerial vehicle controllers using IMT-2020 networks (2017). ITU-T, Geneva

  59. Lieb J, Volkert A (2020) Unmanned aircraft systems traffic management: a comparsion on the FAA UTM and the European CORUS ConOps based on U-space. In: 2020 AIAA/IEEE 39th digital avionics systems conference (DASC). IEEE, pp 1–6

  60. Dronehub. Autonomous drones-in-a-Box. https://dronehub.ai

  61. HIVE. Autonomous Drone Port. https://hive.aero

  62. Skycharge. SKYPORT DP5 drone box hangar. https://www.skycharge.de/drone-box-hangar

  63. Percepto. Percepto Base. https://percepto.co/air-mobile/

  64. Security, N. https://www.nightingalesecurity.com/specs-faqs/

  65. Campi T, Cruciani S, Feliziani M (2018) Wireless power transfer technology applied to an autonomous electric UAV with a small secondary coil. Energies 11(2):352

    Article  Google Scholar 

  66. Wireless power transmission: patent landscape analysis [Online]. https://www.wipo.int/edocs/plrdocs/en/lexinnova_plr_wireless_power.pdf

  67. AirMed&Rescue [Online]. https://www.airmedandrescue.com

  68. Drone Market Report (2024) Drone Ind. Insights UG, Germany

    Google Scholar 

  69. Xu J, Zeng Y, Zhang R (2018) UAV-enabled wireless power transfer: trajectory design and energy optimization. IEEE Trans Wireless Commun 17(8):5092–5106

    Article  Google Scholar 

  70. Hu Y, Yuan X, Jie Xu, Schmeink A (2019) Optimal 1D trajectory design for UAV-enabled multiuser wireless power transfer. IEEE Trans Commun 67(8):5674–5688

    Article  Google Scholar 

  71. Song C, Kim H, Kim Y, Kim D, Jeong S, Cho Y et al (2018) EMI reduction methods in wireless power transfer system for drone electrical charger using tightly coupled three-phase resonant magnetic field. IEEE Trans Ind Electron 65(9):6839–6849

    Article  Google Scholar 

  72. Jawad AM, Jawad HM, Nordin R, Gharghan SK, Abdullah NF, Abu-Alshaeer MJ (2019) Wireless power transfer with magnetic resonator coupling and sleep/active strategy for a drone charging station in smart agriculture. IEEE Access 7:139839–139851

    Article  Google Scholar 

  73. Li J, Yin F, Wang L, Cui B, Yang D (2019) Electromagnetic induction position sensor applied to anti-misalignment wireless charging for UAVs. IEEE Sens J 20(1):515–524

    Article  Google Scholar 

  74. Liu CH, Piao C, Tang J (2020) Energy-efficient UAV crowdsensing with multiple charging stations by deep learning. In: IEEE INFOCOM 2020-IEEE conference on computer communications. IEEE, pp 199–208

  75. Hassija V, Chamola V, Krishna DNG, Guizani M (2020) A distributed framework for energy trading between UAVs and charging stations for critical applications. IEEE Trans Veh Technol 69(5):5391–5402

    Article  Google Scholar 

  76. Qin C, Li P, Liu J, Liu J (2021) Blockchain-enabled charging scheduling for unmanned vehicles in smart cities. J Internet Technol 22(2):327–337

    Google Scholar 

  77. Zhu K, Yang J, Zhang Y, Nie J, Lim WYB, Zhang H, Xiong Z (2022) Aerial refueling: scheduling wireless energy charging for UAV enabled data collection. IEEE Trans Green Commun Netw

  78. Oubbati OS, Lakas A, Guizani M (2022) Multi-agent deep reinforcement learning for wireless-powered UAV networks. IEEE Internet Things J

  79. Fazelpour F, Vafaeipour M, Rahbari O, Shirmohammadi R (2013) Considerable parameters of using PV cells for solar-powered aircrafts. Renew Sustain Energy Rev 22:81–91

    Article  Google Scholar 

  80. Woźniak W, Jessa M (2021) Selection of solar powered unmanned aerial vehicles for a long range data acquisition chain. Sensors 21(8):2772

    Article  Google Scholar 

  81. Thipyopas C, Sripawadkul V, Warin N (2019) Design and development of a small solar-powered UAV for environmental monitoring application. In: 2019 IEEE Eurasia conference on IOT, communication and engineering (ECICE). IEEE, pp 316–319

  82. Wu J, Wang H, Huang Y, Su Z, Zhang M (2018) Energy management strategy for solar-powered UAV long-endurance target tracking. IEEE Trans Aerosp Electron Syst 55(4):1878–1891

    Article  Google Scholar 

  83. Gao XZ, Hou ZX, Guo Z, Chen XQ (2015) Reviews of methods to extract and store energy for solar-powered aircraft. Renew Sustain Energy Rev 44:96–108

    Article  Google Scholar 

  84. Elkunchwar N, Chandrasekaran S, Iyer V, Fuller SB (2021) Toward battery-free flight: duty cycled recharging of small drones. In: 2021 IEEE/RSJ international conference on intelligent robots and systems (IROS). IEEE, pp 5234–5241

  85. Dhingra D, Chukewad YM, Fuller SB (2020) A device for rapid, automated trimming of insect-sized flying robots. IEEE Robot Autom Lett 5(2):1373–1380

    Article  Google Scholar 

  86. Keennon M, Klingebiel K, Won H (2012) Development of the nano hummingbird: a tailless flapping wing micro air vehicle. In: 50th AIAA aerospace sciences meeting including the new horizons forum and aerospace exposition, p 588

  87. Förster J (2015) System identification of the Crazyflie 2.0 nano quadrocopter. Bachelor's thesis, ETH Zurich

  88. SAS PD (2018) Parrot anafi specsheet

  89. Technologies A (2015) Ascending technologies hummingbird

  90. Technologies A (2015) Payload options & accessories

  91. Systems ACUA (2017) Aerialtronics altura zenith specsheet

  92. Solar Tribune (online). https://solartribune.com/solar-powered-drones/

  93. Achtelik, M. C., Stumpf, J., Gurdan, D., & Doth, K. M. (2011, September). Design of a flexible high performance quadcopter platform breaking the MAV endurance record with laser power beaming. In: 2011 IEEE/RSJ international conference on intelligent robots and systems. IEEE, pp 5166–5172

  94. Ouyang J, Che Y, Xu J, Wu K (2018) Throughput maximization for laser-powered UAV wireless communication systems. In: 2018 IEEE international conference on communications workshops (ICC workshops). IEEE, pp 1–6

  95. Chen Q, Zhang D, Zhu D, Shi Q, Gu J, Ai Y (2015) Design and experiment for realization of laser wireless power transmission for small unmanned aerial vehicles. In: AOPC 2015: advances in laser technology and applications, vol 9671. International Society for Optics and Photonics, p 96710N

  96. Lee S, Lim N, Choi W, Lee Y, Baek J, Park J (2020) Study on battery charging converter for MPPT control of laser wireless power transmission system. Electronics 9(10):1745

    Article  Google Scholar 

  97. Kim Y, Shin HB, Lee WH, Jung SH, Kim CZ, Kim H et al (2019) 1080 nm InGaAs laser power converters grown by MOCVD using InAlGaAs metamorphic buffer layers. Sol Energy Mater Sol Cells 200:109984

    Article  Google Scholar 

  98. Zhang Q, Fang W, Liu Q, Wu J, Xia P, Yang L (2018) Distributed laser charging: a wireless power transfer approach. IEEE Internet Things J 5(5):3853–3864

    Article  Google Scholar 

  99. Jaafar W, Yanikomeroglu H (2020) Dynamics of laser-charged UAVs: a battery perspective. IEEE Internet Things J 8(13):10573–10582

    Article  Google Scholar 

  100. Zhao MM, Shi Q, Zhao MJ (2020) Efficiency maximization for UAV-enabled mobile relaying systems with laser charging. IEEE Trans Wirel Commun 19(5):3257–3272

    Article  MathSciNet  Google Scholar 

  101. Shakhatreh H, Sawalmeh AH, Al-Fuqaha A, Dou Z, Almaita E, Khalil I et al (2019) Unmanned aerial vehicles (UAVs): a survey on civil applications and key research challenges. IEEE Access 7:48572–48634

    Article  Google Scholar 

  102. Reinecke M, Prinsloo T (2017) The influence of drone monitoring on crop health and harvest size. In: 2017 1st international conference on next generation computing applications (NextComp). IEEE, pp 5–10

  103. Maes WH, Steppe K (2019) Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends Plant Sci 24(2):152–164

    Article  Google Scholar 

  104. Menouar H, Guvenc I, Akkaya K, Uluagac AS, Kadri A, Tuncer A (2017) UAV-enabled intelligent transportation systems for the smart city: applications and challenges. IEEE Commun Mag 55(3):22–28

    Article  Google Scholar 

  105. Elloumi M, Dhaou R, Escrig B, Idoudi H, Saidane LA (2018) Monitoring road traffic with a UAV-based system. In: 2018 IEEE wireless communications and networking conference (WCNC). IEEE, pp 1–6

  106. Tiansawat P, Elliott S (2020) Unmanned aerial vehicles for automated forest restoration

  107. De Almeida DRA, Broadbent EN, Ferreira MP, Meli P, Zambrano AMA, Gorgens EB et al (2021) Monitoring restored tropical forest diversity and structure through UAV-borne hyperspectral and lidar fusion. Remote Sens Environ 264:112582

    Article  Google Scholar 

  108. Moura MM, de Oliveira LES, Sanquetta CR, Bastos A, Mohan M, Corte APD (2021) Towards Amazon forest restoration: automatic detection of species from UAV imagery. Remote Sens 13(13):2627

    Article  Google Scholar 

  109. Zhang Y, Yuan X, Li W, Chen S (2017) Automatic power line inspection using UAV images. Remote Sens 9(8):824

    Article  Google Scholar 

  110. Foudeh HA, Luk PCK, Whidborne JF (2021) An advanced unmanned aerial vehicle (UAV) approach via learning-based control for overhead power line monitoring: a comprehensive review. IEEE Access

  111. Barbedo JGA (2019) A review on the use of unmanned aerial vehicles and imaging sensors for monitoring and assessing plant stresses. Drones 3(2):40

    Article  Google Scholar 

  112. Sharma M, Gupta A, Gupta SK, Alsamhi SH, Shvetsov AV (2021) Survey on unmanned aerial vehicle for Mars exploration: deployment use case. Drones 6(1):4

    Article  Google Scholar 

  113. Ubina NA, Cheng SC (2022) A review of unmanned system technologies with its application to aquaculture farm monitoring and management. Drones 6(1):12

    Article  Google Scholar 

  114. Pulsiri N, Vatananan-Thesenvitz R (2021) Drones in emergency medical services: a systematic literature review with bibliometric analysis. Int J Innov Technol Manag 18(04):2097001

    Article  Google Scholar 

  115. Mohsan SAH, Khan MA, Alsharif MH, Elhaty IA, Jahid A (2022) Role of drone technology helping in alleviating the COVID-19 pandemic. Micromachines 13(10):1593

    Article  Google Scholar 

  116. Alqurashi FS, Trichili A, Saeed N, Ooi BS, Alouini MS (2022) Maritime communications: a survey on enabling technologies, opportunities, and challenges. arXiv preprint arXiv:2204.12824

  117. Kourani A, Daher N (2021) Marine locomotion: a tethered UAV-Buoy system with surge velocity control. Robot Auton Syst 145:103858

    Article  Google Scholar 

  118. Swaminathan N, Reddy SRP, Rajashekara K, Haran KS (2022) Flying cars and eVTOLs-technology advancements, powertrain architectures and design. IEEE Trans Transp Electr

  119. Kasliwal A, Furbush NJ, Gawron JH, McBride JR, Wallington TJ, De Kleine RD et al (2019) Role of flying cars in sustainable mobility. Nat Commun 10(1):1–9

    Article  Google Scholar 

  120. Griffiths S, Saunders J, Curtis A, Barber B, McLain T, Beard R (2007) Obstacle and terrain avoidance for miniature aerial vehicles. In: Advances in unmanned aerial vehicles. Springer, Dordrecht, pp 213–244

  121. Winter T, Thubert P, Brandt A, Hui JW, Kelsey R, Levis P et al (2012) RPL: IPv6 routing protocol for low-power and lossy networks. RFC 6550:1–157

    Google Scholar 

  122. Kuriki Y, Namerikawa T (2014) Consensus-based cooperative formation control with collision avoidance for a multi-UAV system. In: 2014 American control conference. IEEE, pp 2077–2082

  123. Vattapparamban E, Güvenç I, Yurekli AI, Akkaya K, Uluağaç S (2016) Drones for smart cities: issues in cybersecurity, privacy, and public safety. In: 2016 international wireless communications and mobile computing conference (IWCMC). IEEE, pp 216–221

  124. He D, Chan S, Guizani M (2016) Communication security of unmanned aerial vehicles. IEEE Wirel Commun 24(4):134–139

    Article  Google Scholar 

  125. Birnbaum Z, Dolgikh A, Skormin V, O'Brien E, Muller D, Stracquodaine C (2015) Unmanned aerial vehicle security using behavioral profiling. In: 2015 international conference on unmanned aircraft systems (ICUAS). IEEE, pp 1310–1319

  126. Mansfield K, Eveleigh T, Holzer TH, Sarkani S (2013) Unmanned aerial vehicle smart device ground control station cyber security threat model In: 2013 IEEE international conference on technologies for homeland security (HST). IEEE, pp 722–728

  127. Bithas PS, Michailidis ET, Nomikos N, Vouyioukas D, Kanatas AG (2019) A survey on machine-learning techniques for UAV-based communications. Sensors 19(23):5170

    Article  Google Scholar 

  128. Yue X, Liu Y, Wang J, Song H, Cao H (2018) Software defined radio and wireless acoustic networking for amateur drone surveillance. IEEE Commun Mag 56(4):90–97

    Article  Google Scholar 

  129. Liao Q, Fischer T, Gao J, Hafeez F, Oechsner C, Knode J (2018) A secure end-to-end cloud computing solution for emergency management with UAVs. In: 2018 IEEE global communications conference (GLOBECOM). IEEE, pp 1–7

  130. Shoufan A, Al-Angari HM, Sheikh MFA, Damiani E (2018) Drone pilot identification by classifying radio-control signals. IEEE Trans Inf Forens Secur 13(10):2439–2447

    Article  Google Scholar 

  131. Zohdi T (2020) The Game of Drones: rapid agent-based machine-learning models for multi-UAV path planning. Comput Mech 65(1):217–228

    Article  MathSciNet  MATH  Google Scholar 

  132. Min M, Xiao L, Xu D, Huang L, Peng M (2018) Learning-based defense against malicious unmanned aerial vehicles. In: 2018 IEEE 87th vehicular technology conference (VTC Spring). IEEE, pp 1–5

  133. Hoang TM, Nguyen NM, Duong TQ (2019) Detection of eavesdropping attack in UAV-aided wireless systems: unsupervised learning with one-class SVM and k-means clustering. IEEE Wirel Commun Lett 9(2):139–142

    Article  Google Scholar 

  134. Manesh MR, Kenney J, Hu WC, Devabhaktuni VK, Kaabouch N (2019) Detection of GPS spoofing attacks on unmanned aerial systems. In: 2019 16th IEEE annual consumer communications and networking conference (CCNC). IEEE, pp 1–6

  135. Xiao L, Xie C, Min M, Zhuang W (2017) User-centric view of unmanned aerial vehicle transmission against smart attacks. IEEE Trans Veh Technol 67(4):3420–3430

    Article  Google Scholar 

  136. Xiao L, Lu X, Xu D, Tang Y, Wang L, Zhuang W (2018) UAV relay in VANETs against smart jamming with reinforcement learning. IEEE Trans Veh Technol 67(5):4087–4097

    Article  Google Scholar 

  137. Li C, Xu Y, Xia J, Zhao J (2018) Protecting secure communication under UAV smart attack with imperfect channel estimation. IEEE Access 6:76395–76401

    Article  Google Scholar 

  138. Lv Z, Li Y, Feng H, Lv H (2021) Deep learning for security in digital twins of cooperative intelligent transportation systems. IEEE Trans Intell Transp Syst. https://doi.org/10.1109/TITS.2021.3113779

    Article  Google Scholar 

  139. Liu F, Zhang G, Lu J (2020) Multi-source heterogeneous unsupervised domain adaptation via fuzzy-relation neural networks. IEEE Trans Fuzzy Syst. https://doi.org/10.1109/TFUZZ.2020.3018191

    Article  Google Scholar 

  140. Zhang L, Zheng H, Cai G, Zhang Z, Wang X, et al. (2022) Power-frequency oscillation suppression algorithm for AC microgrid with multiple virtual synchronous generators based on fuzzy inference system. IET Renew Power Gener. https://doi.org/10.1049/rpg2.12461

    Article  Google Scholar 

  141. Zhang L, Gao T, Cai G, Hai KL (2022) Research on electric vehicle charging safety warning model based on back propagation neural network optimized by improved gray wolf algorithm. J Energy Storage. https://doi.org/10.1016/j.est.2022.104092

    Article  Google Scholar 

  142. Wu X, Zheng W, Chen X, Zhao Y, Yu T et al (2021) Improving high-impact bug report prediction with combination of interactive machine learning and active learning. Inf Softw Technol 133:106530. https://doi.org/10.1016/j.infsof.2021.106530

    Article  Google Scholar 

  143. Chen P, Pei J, Lu W, Li M (2022) A deep reinforcement learning based method for real-time path planning and dynamic obstacle avoidance. Neurocomputing (Amsterdam) 497:64–75. https://doi.org/10.1016/j.neucom.2022.05.006

    Article  Google Scholar 

  144. Carrio A, Sampedro C, Rodriguez-Ramos A, Campoy P (2017) A review of deep learning methods and applications for unmanned aerial vehicles. J Sensors 2017

  145. Vergouw B, Nagel H, Bondt G, Custers B (2016) Drone technology: types, payloads, applications, frequency spectrum issues and future developments. In: The future of drone use. TMC Asser Press, The Hague, pp 21–45

  146. Bejiga MB, Zeggada A, Nouffidj A, Melgani F (2017) A convolutional neural network approach for assisting avalanche search and rescue operations with UAV imagery. Remote Sens 9(2):100

    Article  Google Scholar 

  147. Cao B, Gu Y, Lv Z, Yang S, Zhao, J., et al. (2021) RFID reader anticollision based on distributed parallel particle swarm optimization. IEEE Internet Things J 8(5):3099–3107. https://doi.org/10.1109/JIOT.2020.3033473

    Article  Google Scholar 

  148. Hu Y, Qing JX, Liu ZH, Conrad ZJ, Cao JN et al (2021) Hovering efficiency optimization of the ducted propeller with weight penalty taken into account. Aerosp Sci Technol. https://doi.org/10.1016/j.ast.2021.106937

    Article  Google Scholar 

  149. Yang G, Liu J, Zhao C, Li Z, Huang Y, Yu H et al (2017) Unmanned aerial vehicle remote sensing for field-based crop phenotyping: current status and perspectives. Front Plant Sci 8:1111

    Article  Google Scholar 

  150. Yan J, Jiao H, Pu W, Shi C, Dai, J., et al. (2022) Radar sensor network resource allocation for fused target tracking: a brief review. Inf Fus 86–87:104–115. https://doi.org/10.1016/j.inffus.2022.06.009

    Article  Google Scholar 

  151. Cao B, Fan S, Zhao J, Tian S, Zheng Z, Yan Y, et al. (2021) Large-scale many-objective deployment optimization of edge servers. IEEE Trans Intell Transp Syst 22(6):3841–3849. https://doi.org/10.1109/TITS.2021.3059455

    Article  Google Scholar 

  152. Cao B, Sun Z, Zhang J, Gu Y (2021) Resource allocation in 5G IoV architecture based on SDN and Fog-cloud computing. IEEE Trans Intell Transp Syst 22(6):3832–3840. https://doi.org/10.1109/TITS.2020.3048844

    Article  Google Scholar 

  153. Mogili UR, Deepak BBVL (2018) Review on application of drone systems in precision agriculture. Procedia Comput Sci 133:502–509

    Article  Google Scholar 

  154. Wan S, Lu J, Fan P, Letaief KB (2017) To smart city: public safety network design for emergency. IEEE Access 6:1451–1460

    Article  Google Scholar 

  155. Cao B, Zhang W, Wang X, Zhao J, Gu Y et al (2021) A memetic algorithm based on two_Arch2 for multi-depot heterogeneous-vehicle capacitated arc routing problem. Swarm Evolut Comput 63:100864. https://doi.org/10.1016/j.swevo.2021.100864

    Article  Google Scholar 

  156. Li D, Ge SS, Lee TH (2021) Simultaneous-arrival-to-origin convergence: sliding-mode control through the norm-normalized sign function. IEEE Trans Autom Control. https://doi.org/10.1109/TAC.2021.3069816

    Article  MATH  Google Scholar 

  157. Li D, Yu H, Tee KP, Wu Y, Ge SS et al (2021) On time-synchronized stability and control. IEEE Trans Syst Man Cybern-Syst. https://doi.org/10.1109/TSMC.2021.3050183

    Article  Google Scholar 

  158. Wang J, Tian J, Zhang X, Yang B, Liu S, Yin L, et al. (2022) Control of time delay force feedback teleoperation system with finite time convergence. Front Neurorobot. https://doi.org/10.3389/fnbot.2022.877069

    Article  Google Scholar 

  159. Gong X, Wang L, Mou Y, Wang H, Wei X, Zheng W, et al. (2022) Improved four-channel PBTDPA control strategy using force feedback bilateral teleoperation system. Int J Control 20(3):1002–1017. https://doi.org/10.1007/s12555-021-0096-y

    Article  Google Scholar 

  160. Lu S, Ban Y, Zhang X, Yang B, Liu S, Yin L, Zheng W (2022) Adaptive control of time delay teleoperation system with uncertain dynamics. Front Neurorobot 16:928863. https://doi.org/10.3389/fnbot.2022.928863

    Article  Google Scholar 

  161. Mohsan SAH, Khan MA, Alsharif MH, Uthansakul P, Solyman AA (2022) Intelligent reflecting surfaces assisted UAV communications for massive networks: current trends, challenges, and research directions. Sensors 22(14):5278

    Article  Google Scholar 

  162. Mohsan SAH, Khan MA, Noor F, Ullah I, Alsharif MH (2022) Towards the unmanned aerial vehicles (UAVs): a comprehensive review. Drones 6(6):147

    Article  Google Scholar 

  163. Mohsan SAH, Othman NQH, Khan MA, Amjad H, Żywiołek J (2022) A comprehensive review of micro UAV charging techniques. Micromachines 13(6):977

    Article  Google Scholar 

  164. Khan MA, Kumar N, Mohsan SAH, Khan WU, Nasralla MM, Alsharif MH, et al. (2022) Swarm of UAVs for network management in 6G: a technical review. IEEE Trans Netw Serv Manag

  165. Regulation & Policies [Online]. https://www.faa.gov/regulations_policies

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under grant 62261009, Ministry of Education Key Laboratory of Cognitive Radio and Information Processing (CRKL200106)

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Authors and Affiliations

  1. Optical Communications Laboratory, Ocean College, Zhejiang University, Zheda Road 1, Zhoushan, 316021, Zhejiang, China

    Syed Agha Hassnain Mohsan & Yanlong Li

  2. School of Telecommunications Engineering, Xidian University, Xi’an, China

    Nawaf Qasem Hamood Othman

  3. Ministry of Education Key Laboratory of Cognitive Radio and Information Processing, Guilin University of Electronic Technology, Guilin, 541004, China

    Yanlong Li

  4. Department of Electrical Engineering, College of Electronics and Information Engineering, Sejong University, Seoul, 05006, Korea

    Mohammed H. Alsharif

  5. Department of Electrical Engineering, Hamdard Institute of Engineering & Technology, Islamabad, 44000, Pakistan

    Muhammad Asghar Khan

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Mohsan, S.A.H., Othman, N.Q.H., Li, Y. et al. Unmanned aerial vehicles (UAVs): practical aspects, applications, open challenges, security issues, and future trends. Intel Serv Robotics 16, 109–137 (2023). https://doi.org/10.1007/s11370-022-00452-4

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