Simulation Based Decision Intelligence Model for Multicopter Charging Stations in Smart Cities

Öz

Urban air mobility increasingly relies on autonomous multicopter fleets whose operational sustainability remains constrained by the absence of intelligent recharging infrastructures. This study introduces a simulation based decision intelligence model designed to evaluate six multicopter charging station archetypes under smart city conditions. The proposed framework integrates five normalized evaluation factors, namely Security, Infrastructure Cost, Logistics Compatibility, Smart City Integration, and Sustainability, within a transparent and auditable multi-criteria decision framework. Two complementary evaluation modes are developed to ensure analytical rigor and interpretability. The first mode, Mode A, represents a reproducible baseline configuration that employs equal weighting to retained methodological clarity. The second mode, Mode B, functions as a bounded coordination operator that establishes a controlled relationship between infrastructure capacity and logistics flow, enabling interaction informed evaluation without altering the ranking logic. Synthetic decision data are generated through Latin Hypercube Sampling, while bootstrap resampling is used to quantify uncertainty. The stability of both modes is analytically verified, showing that Kendall’s τ exceeds 0.90 and Top-k retention remains above 95 percent. These results demonstrate that introducing interaction awareness refines interpretability while maintaining analytical consistency across uncertainty ranges. The findings reveal that Last Mile and First Mile stations maintain the highest composite efficiency scores, 0.82 and 0.80 respectively, across various urban morphologies. Roof and Electric Vehicle Coupled configurations also display competitive scalability and improved performance when aligned with renewable energy scenarios. The overall framework provides a reproducible, policy aligned, and scientifically traceable foundation for the planning, deployment, and empirical calibration of urban drone charging networks. It further establishes a consistent methodological pathway for decision making in data scarce environments, ensuring that analytical transparency and operational relevance are sustained throughout future pilot implementations.

Anahtar Kelimeler

• Autonomous UAV recharging
• Intelligent decision
• Smart city logistics
• Sustainability
• Urban air mobility (UAM).

Akıllı Şehirlerdeki Multikopter Şarj İstasyonları için Simülasyon Tabanlı Karar Zekası Modeli

Öz

Kentsel hava hareketliliği giderek artan biçimde otonom çok rotorlu hava aracı filolarına dayanmaktadır. Ancak bu filoların operasyonel sürdürülebilirliği, akıllı şarj altyapılarının eksikliği nedeniyle hâlâ önemli ölçüde sınırlıdır. Bu çalışma, akıllı şehir koşulları altında altı farklı çok rotorlu şarj istasyonu arketipini değerlendirmek üzere tasarlanmış simülasyon temelli bir karar zekâsı modeli sunmaktadır. Önerilen çerçeve, Güvenlik, Altyapı Maliyeti, Lojistik Uyumluluk, Akıllı Şehir Entegrasyonu ve Sürdürülebilirlik olmak üzere beş normalleştirilmiş değerlendirme faktörünü, şeffaf ve denetlenebilir bir çok ölçütlü karar yapısı içinde birleştirmektedir. Analitik titizliği ve yorumlanabilirliği güvence altına almak amacıyla iki tamamlayıcı değerlendirme modu geliştirilmiştir. İlk mod olan Mode A, eşit ağırlıklar kullanan ve yöntemsel açıklığı koruyan tekrarlanabilir bir temel yapı sunmaktadır. İkinci mod olan Mode B ise altyapı kapasitesi ile lojistik akış arasında kontrollü bir ilişki kuran sınırlı bir koordinasyon operatörü olarak işlev görmekte ve etkileşim farkındalığını sıralama mantığını değiştirmeden sisteme dâhil etmektedir. Karar verileri Latin Hiperküp Örneklemesi yöntemiyle sentetik olarak üretilmiş, belirsizlik ise bootstrap yeniden örnekleme tekniğiyle nicel biçimde hesaplanmıştır. Her iki modun da kararlılığı analitik olarak doğrulanmış, sonuçlar Kendall’s τ değerinin 0.90’ın üzerinde, Top-k korunum oranının ise yüzde 95’in üzerinde olduğunu göstermiştir. Bu bulgular, etkileşim farkındalığının yorum gücünü artırırken analitik tutarlılığı koruduğunu kanıtlamaktadır. Elde edilen sonuçlar, Son Mil ve İlk Mil istasyonlarının farklı kentsel morfolojilerde en yüksek bileşik verimlilik puanlarını (sırasıyla 0.82 ve 0.80) koruduğunu ortaya koymuştur. Çatı ve Elektrikli Araç Entegreli istasyon konfigürasyonları da yenilenebilir enerjiyle uyumlu senaryolarda rekabetçi bir ölçeklenebilirlik ve gelişmiş performans sergilemiştir. Geliştirilen genel çerçeve, kentsel drone şarj ağlarının planlanması, uygulanması ve ampirik olarak kalibre edilmesi için tekrarlanabilir, politika açısından uyumlu ve bilimsel olarak izlenebilir bir temel sunmaktadır. Ayrıca veri eksikliği bulunan ortamlarda karar alma süreçlerine yönelik tutarlı bir yöntemsel yol haritası oluşturmakta, analitik şeffaflık ve operasyonel anlamlılığın gelecekteki pilot uygulamalar boyunca korunmasını sağlamaktadır.

Anahtar Kelimeler

• Otonom İHA şarjı
• Akıllı karar
• Akıllı şehir lojistiği
• Sürdürülebilirlik
• Kentsel hava mobilitesi.

Kaynakça

1. H. Shakhatreh et al., “Unmanned aerial vehicles (UAVs): A survey on civil applications and key research challenges,” IEEE Access, vol. 7, pp. 48572–48634, 2019.
2. S. Melo, F. Silva, M. Abbasi, P. Ahani, and J. Macedo, “Public acceptance of the use of drones in city logistics: A citizen-centric perspective,” Sustainability, vol. 15, p. 2621, 2023.
3. J. C. Chaudemar, O. Aïello, P. de Saqui-Sannes, and O. Poitou, “Mission-based design of UAVs,” Syst. Eng., vol. 27, pp. 850–868, 2024.
4. R. Alyassi et al., “Autonomous recharging and flight mission planning for battery-operated autonomous drones,” IEEE Trans. Autom. Sci. Eng., vol. 20, pp. 1034–1046, 2022.
5. E. Pastor, J. Lopez, and P. Royo, “UAV payload and mission control hardware/software architecture,” IEEE Aerosp. Electron. Syst. Mag., vol. 22, pp. 3–8, 2007.
6. Y. Cao et al., “An optimized EV charging model considering TOU price and SOC curve,” IEEE Trans. Smart Grid, vol. 3, pp. 388–393, 2011.
7. Z. Liu, F. Wen, and G. Ledwich, “Optimal planning of electric-vehicle charging stations in distribution systems,” IEEE Trans. Power Del., vol. 28, pp. 102–110, 2012.
8. M. Yilmaz and P. T. Krein, “Review of battery charger topologies, charging power levels, and infrastructure for plug-in electric and hybrid vehicles,” IEEE Trans. Power Electron., vol. 28, pp. 2151–2169, 2012.

9. M. S. K. Asnaz and B. Özdemir, “Elektrikli araç şarj istasyonlarının çok kriterli karar verme yöntemleri ile optimal konumlandırması,” Akıllı Ulaşım Sist. Uygul. Derg., vol. 4, pp. 175–187, 2021.
10. M. Valenti, D. Dale, J. How, D. Pucci de Farias, and J. Vian, “Mission health management for 24/7 persistent surveillance operations,” in Proc. AIAA Guid., Navig. Control Conf. Exhib., 2007, p. 6508.
11. H. Üçgün, “Döner kanatlı İHA’lar için otonom şarj istasyonu = Autonomous charging station for rotary wing UAVs,” M.S. thesis, 2022.
12. Z. A. Filipovic, G.-W. Wang, and W. Wang, “Handy base station system, device and method,” Google Patent, 2020.
13. J. Leng, “Using a UAV to effectively prolong wireless sensor network lifetime with wireless power transfer,” M.S. thesis, Comput. Sci. Eng., Univ. Nebraska–Lincoln, 2014.
14. L. Grando, J. F. G. Jaramillo, J. R. E. Leite, and E. L. Ursini, “Systematic literature review methodology for drone recharging processes in agriculture and disaster management,” Drones, vol. 9, p. 40, 2025.
15. H. Huang and A. V. Savkin, “Deployment of charging stations for drone delivery assisted by public transportation vehicles,” IEEE Trans. Intell. Transp. Syst., vol. 23, pp. 15043–15054, 2021.
16. A. Raciti, S. A. Rizzo, and G. Susinni, “Drone charging stations over the buildings based on a wireless power transfer system,” in Proc. IEEE/IAS Ind. Commercial Power Syst. Tech. Conf. (I&CPS), 2018, pp. 1–6.
17. A.-M. Muñoz-Gómez, J.-M. Marredo-Píriz, J. Ballestín-Fuertes, and J.-F. Sanz-Osorio, “A novel charging station on overhead power lines for autonomous unmanned drones,” Appl. Sci., vol. 13, p. 10175, 2023.
18. Y. Qin, M. A. Kishk, and M.-S. Alouini, “Performance analysis of charging infrastructure sharing in UAV and EV-involved networks,” IEEE Trans. Veh. Technol., vol. 72, pp. 3973–3988, 2022.
19. M. ElSayed, A. Foda, and M. Mohamed, “Autonomous drone charging station planning through solar energy harnessing for zero-emission operations,” Sustain. Cities Soc., vol. 86, p. 104122, 2022.
20. Q. Ren and M. Sun, “Predicting the spatial demand for public charging stations for EVs using multi-source big data: An example from Jinan City, China,” Sci. Rep., vol. 15, p. 6991, 2025.
21. H. Eskandaripour and E. Boldsaikhan, “Last-mile drone delivery: Past, present, and future,” Drones, vol. 7, p. 77, 2023.
22. A. Vilkki, A. Tikanmäki, and J. Röning, “Automatic jig-assisted battery exchange for lightweight drones,” Machines, vol. 12, p. 818, 2024.
23. Y. Wang, Z. Su, N. Zhang, and R. Li, “Mobile wireless rechargeable UAV networks: Challenges and solutions,” IEEE Commun. Mag., vol. 60, pp. 33–39, 2022.
24. P. K. Chittoor, B. Chokkalingam, and L. Mihet-Popa, “A review on UAV wireless charging: Fundamentals, applications, charging techniques and standards,” IEEE Access, vol. 9, pp. 69235–69266, 2021.
25. A. B. Junaid, Y. Lee, and Y. Kim, “Design and implementation of autonomous wireless charging station for rotary-wing UAVs,” Aerosp. Sci. Technol., vol. 54, pp. 253–266, 2016.
26. M.-A. Lahmeri, M. A. Kishk, and M.-S. Alouini, “Stochastic geometry-based analysis of airborne base stations with laser-powered UAVs,” IEEE Commun. Lett., vol. 24, pp. 173–177, 2019.
27. M. Sadrani, A. Najafi, R. Mirqasemi, and C. Antoniou, “Charging strategy selection for electric bus systems: A multi-criteria decision-making approach,” Appl. Energy, vol. 347, p. 121415, 2023.
28. R. Carli, M. Dotoli, and R. Pellegrino, “Multi-criteria decision-making for sustainable metropolitan cities assessment,” J. Environ. Manag., vol. 226, pp. 46–61, 2018.
29. M. Hamurcu and T. Eren, “Selection of unmanned aerial vehicles by using multicriteria decision-making for defence,” J. Math., vol. 2020, p. 4308756, 2020.
30. R. Santin, L. Assis, A. Vivas, and L. C. Pimenta, “Matheuristics for multi-UAV routing and recharge station location for complete area coverage,” Sensors, vol. 21, p. 1705, 2021.
31. S. A. H. Mohsan, N. Q. H. Othman, Y. Li, M. H. Alsharif, and M. A. Khan, “Unmanned aerial vehicles (UAVs): Practical aspects, applications, open challenges, security issues, and future trends,” Intell. Serv. Robot., vol. 16, pp. 109–137, 2023.
32. I. Anagnostis, P. Kotzanikolaou, and C. Douligeris, “Understanding and securing the risks of uncrewed aerial vehicle services,” IEEE Access, vol. 13, pp. 47955–47995, 2025.
33. K. Dorling, J. Heinrichs, G. G. Messier, and S. Magierowski, “Vehicle routing problems for drone delivery,” IEEE Trans. Syst., Man, Cybern.: Syst., vol. 47, pp. 70–85, 2016.
34. H. Bany Salameh and Y. Jararweh, “Efficient charging station deployment in unmanned aerial vehicle systems for enhanced mission efficiency,” Cluster Comput., vol. 28, pp. 1–12, 2025.
35. H. B. Salameh, A. Othman, and M. Alhafnawi, “Optimized charging-station placement and UAV trajectory for enhanced uncertain target detection in intelligent UAV tracking systems,” Int. J. Cogn. Comput. Eng., vol. 5, pp. 367–378, 2024.
36. P. W. Shaikh and H. T. Mouftah, “Edge computing-aided dynamic wireless charging and trip planning of UAVs,” J. Sens. Actuator Netw., vol. 14, p. 8, 2025.
37. S. H. Chung, B. Sah, and J. Lee, “Optimization for drone and drone-truck combined operations: A review of the state of the art and future directions,” Comput. Oper. Res., vol. 123, p. 105004, 2020.
38. J. Van Mulders, S. Boeckx, J. Cappelle, L. Van der Perre, and L. De Strycker, “UAV-based solution for extending the lifetime of IoT devices: Efficiency, design and sustainability,” Front. Commun. Netw., vol. 5, p. 1341081, 2024.
39. M. Souilem, W. Dghais, and A. Radwan, “Wirelessly powered unmanned aerial vehicles (UAVs) in smart city,” in Connected Autonomous Veh. Smart Cities, pp. 437–456, 2020.
40. F. Tlili, L. C. Fourati, S. Ayed, and B. Ouni, “Investigation on vulnerabilities, threats and attacks prohibiting UAVs charging and depleting UAVs batteries: Assessments and countermeasures,” Ad Hoc Netw., vol. 129, p. 102805, 2022.
41. R. Paul and M. Paul Selvan, “A hybrid deep learning-based intrusion detection system for EV and UAV charging stations,” Automatika, vol. 65, pp. 1558–1578, 2024.
42. [42] S. Kokkinos, C. Mourgelas, E. Micha, E. Chatzistavrakis, and I. Voyiatzis, “Design and implementation of drones charging station,” in Proc. 27th Pan-Hellenic Conf. Progress Comput. Informatics, 2023, pp. 116–122.
43. B. Galkin, J. Kibilda, and L. A. DaSilva, “UAVs as mobile infrastructure: Addressing battery lifetime,” IEEE Commun. Mag., vol. 57, pp. 132–137, 2019.
44. F. P. Kemper, K. A. Suzuki, and J. R. Morrison, “UAV consumable replenishment: Design concepts for automated service stations,” J. Intell. Robot. Syst., vol. 61, pp. 369–397, 2011.
45. B. Michini, T. Toksoz, J. Redding, M. Michini, J. How, M. Vavrina, et al., “Automated battery swap and recharge to enable persistent UAV missions,” in Infotech@Aerospace, 2011, p. 1405.
46. T. Lyu, J. An, M. Li, F. Liu, and H. Xu, “UAV-assisted wireless charging and data processing of power IoT devices,” Computing, vol. 106, pp. 789–819, 2024.
47. X. Ma, Y. Zhou, H. Zhang, Q. Wang, H. Sun, H. Wang, et al., “Exploring communication technologies, standards, and challenges in electrified vehicle charging,” arXiv preprint, arXiv:2403.16830, 2024.
48. O. Păscuţoiu and M.-D. T. Ungureanu, “UAV communication protocols and quality of service in 5G communication,” Rev. Air Force Acad., pp. 5–10, 2024.
49. A. Triviño, I. Casaucao, J. C. Quirós, P. Pérez, and A. Rojas, “Novel sustainable magnetic material to improve the wireless charging of a lightweight drone,” RSC Adv., vol. 13, pp. 10556–10563, 2023.
50. I. Hong, M. Kuby, and A. T. Murray, “A range-restricted recharging station coverage model for drone delivery service planning,” Transp. Res. Part C, vol. 90, pp. 198–212, 2018.
51. Q. Tan, J. Hou, Y. Li, R. Qu, P. Zhou, S. Zhong, et al., “Exploring noise reduction strategies: Optimizing drone station placement for last-mile delivery,” Transp. Res. Part D, vol. 133, p. 104306, 2024.
52. A. Çiçek, F. Karakuş, and O. Erdinç, “A novel concept for optimal operation of multi-swap stations serving electric metrobuses,” Energy Sustain. Dev., vol. 86, p. 101722, 2025.
53. B. Şafak, D. Özekinci, and A. Çiçek, “Üzerinde fotovoltaik panele sahip olan elektrikli araçları içeren bir şarj otoparkının çok amaçlı optimum enerji yönetim stratejisi,” Niğde Ömer Halisdemir Üniv. Müh. Bilim. Derg., vol. 13, no. 1, pp. 1–9, 2024.
54. H. Eren, Ö. Karaduman, and M. T. Gençoğlu, “Security challenges and performance trade-offs in on-chain and off-chain blockchain storage: A comprehensive review,” Appl. Sci., vol. 15, no. 6, p. 3225, 2025.
55. H. Eren, Ö. Karaduman, and M. T. Gençoğlu, “Security and privacy in the Internet of Everything (IoE): A review on blockchain, edge computing, AI, and quantum-resilient solutions,” Appl. Sci., vol. 15, no. 15, p. 8704, 2025.
56. Ö. Karaduman, Z. B. Gürbüz, M. T. Gençoğlu, and H. Eren, “Post-quantum security for blockchain and healthcare data management: A review,” in Proc. 9th Int. Symp. Innov. Approaches Smart Technol. (ISAS), IEEE, 2025, pp. 1–10.
57. K. Celik and H. Eren, “UAV fuel preferences for future cities,” in Proc. 6th Int. Istanbul Smart Grids Cities Congr. Fair (ICSG), IEEE, 2018, pp. 151–154.
58. Ü. Çelik and H. Eren, “Classification of manifold learning based flight fingerprints of UAVs in air traffic,” IEEE Trans. Intell. Transp. Syst., vol. 24, no. 5, pp. 5229–5238, 2023.
59. H. Eren and Ü. Çelik, “Risk assessment for aerial package delivery,” Int. J. Electr. Electron. Commun. Sci., vol. 10, no. 10, 2018.