Open Access
Issue
ITM Web Conf.
Volume 64, 2024
2nd International Conference on Applied Computing & Smart Cities (ICACS24)
Article Number 01013
Number of page(s) 18
DOI https://doi.org/10.1051/itmconf/20246401013
Published online 05 July 2024
  1. R. Rumba and A. Nikitenko, “The wild west of drones: A review on autonomousUAV traffic-management,” in 2020 International conference on unmanned aircraft systems (ICUAS), IEEE, (2020) [Google Scholar]
  2. A. Alioua, H. Djeghri, M. E. T. Cherif, S.-M. Senouci, and H. Sedjelmaci, “UAVs for traffic monitoring: A sequential game-based computation offloading/sharing approach,” Comp. Net., 177, 107273 (2020). [Google Scholar]
  3. F. Outay, H. A. Mengash, and M. Adnan, “Applications of unmanned aerial vehicle (UAV) in road safety, traffic and highway infrastructure management: Recent advances and challenges,” Transp Res Part A Policy Pract, 141, 116–129 (2020). [CrossRef] [Google Scholar]
  4. J. Liu, J. Wu, and M. Liu, “UAV monitoring and forecasting model in intelligent traffic oriented applications,” Comput Commun, 153, 499–506 (2020). [CrossRef] [Google Scholar]
  5. M. A. Khan, W. Ectors, T. Bellemans, Y. Ruichek, D. Janssens, and G. Wets, “Unmanned aerial vehicle-based traffic analysis: A case study to analyze traffic streams at urban roundabouts,” Procedia Comput Sci, 130, 636–643 (2018). [CrossRef] [Google Scholar]
  6. Mishra and E. Natalizio, “A survey on cellular-connected UAVs: Design challenges, enabling 5G/B5G innovations, and experimental advancements,” Comp. Net., 182, 107451 (2020). [Google Scholar]
  7. A. I. Hentati and L. C. Fourati, “Comprehensive survey of UAVs communication networks,” Comp. Stand. Interfaces, 72, 103451 (2020). [CrossRef] [Google Scholar]
  8. Z. Lv, L. Qiao, M. S. Hossain, and B. J. Choi, “Analysis of using blockchain to protect the privacy of drone big data,” IEEE Netw, 35, 1, 44–49 (2021). [CrossRef] [MathSciNet] [Google Scholar]
  9. Y. Qi, M. S. Hossain, J. Nie, and X. Li, “Privacy-preserving blockchain-based federated learning for traffic flow prediction,” Future Gen. Comp. Sys., 117, 328–337 (2021). [Google Scholar]
  10. M. Singh, G. S. Aujla, and R. S. Bali, “ODOB: One drone one block-based lightweight blockchain architecture for internet of drones,” in IEEE INFOCOM 2020-IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS), IEEE, (2020) [Google Scholar]
  11. A. Allouch, O. Cheikhrouhou, A. Koubâa, K. Toumi, M. Khalgui, and T. Nguyen Gia, “Utm-chain: blockchain-based secure unmanned traffic management for internet of drones,” Sensors, 21, 9, 3049 (2021). [CrossRef] [Google Scholar]
  12. N. A. Khan, N. Z. Jhanjhi, S. N. Brohi, R. S. A. Usmani, and A. Nayyar, “Smart traffic monitoring system using unmanned aerial vehicles (UAVs),” Comp. Comm., 157, 434–443 (2020). [Google Scholar]
  13. E. Barmpounakis and N. Geroliminis, “On the new era of urban traffic monitoring with massive drone data: The pNEUMA large-scale field experiment,” Transp Res Part C Emerg. Tech., 111, 50–71 (2020). [Google Scholar]
  14. A. Irshad, S. A. Chaudhry, A. Ghani, and M. Bilal, “A secure blockchain-oriented data delivery and collection scheme for 5G-enabled IoD environment,” Comp. Net., 195, 108219 (2021). [Google Scholar]
  15. A. Tahir, J. Böling, M.-H. Haghbayan, H. T. Toivonen, and J. Plosila, “Swarms of unmanned aerial vehicles—a survey,” J Ind Inf Integr, 16, 100106 (2019). [Google Scholar]
  16. M. Mozaffari, W. Saad, M. Bennis, Y.-H. Nam, and M. Debbah, “A tutorial on UAVs for wireless networks: Applications, challenges, and open problems,” IEEE comm. surveys & tutorials, 21, 3, 2334–2360 (2019). [CrossRef] [Google Scholar]
  17. K. S. Sowmya, A. Shivani, M. L. S. Charan, and S. Swaminathan, “Exploring VANETs and Their Applications with Blockchain,” in International Conference on Information and Communication Technology for Competitive Strategies, Springer, 381–390 (2023) [Google Scholar]
  18. X. Du, S. Tao, K. Yuan, Y. Li, and Y. Zhou, “A blockchain authentication scheme for UAV-aided fog computing,” Complex & Intelligent Sys., 10, 2, 1689–1702 (2024). [Google Scholar]
  19. A. Keith, T. Sangarapillai, A. Almehmadi, and K. El-Khatib, “A Blockchain-Powered Traffic Management System for Unmanned Aerial Vehicles,” Appl. Sci., 13, 19, 10950 (2023). [Google Scholar]
  20. Y. Zhang, L. Meng, J. Gan, and Z. Huang, “A Novel and Efficient Authentication Scheme Based on UAV-UAV Environment,” Wirel Com. Mob Comp., (2023). [Google Scholar]
  21. J. Wang, Z. Jiao, J. Chen, X. Hou, T. Yang, and D. Lan, “Blockchain-aided secure access control for UAV computing networks,” IEEE Trans Netw Sci Eng, (2023). [Google Scholar]
  22. S. Namane, M. Ahmim, A. Kondoro, and I. Ben Dhaou, “Blockchain-Based Authentication Scheme for Collaborative Traffic Light Systems Using Fog Computing,” Elect.s (Basel), 12, 2, 431 (2023). [Google Scholar]
  23. A. Chriki, H. Touati, H. Snoussi, and F. Kamoun, “FANET: Communication, mobility models and security issues,” Comp. Net., 163, 106877 (2019). [Google Scholar]
  24. T. Alladi, G. Bansal, V. Chamola, and M. Guizani, “SecAuthUAV: A novel authentication scheme for UAV-ground station and UAV-UAV communication,” IEEE Trans Veh Technol, 69, 12, 15068–15077 (2020). [CrossRef] [Google Scholar]
  25. R. Rumba and A. Nikitenko, “The missing link of UTM,” Rural Sustainability Research, 47, 342, 87–92 (2022). [CrossRef] [Google Scholar]
  26. Z. Ning, J. Huang, and X. Wang, “Vehicular fog computing: Enabling real-time traffic management for smart cities,” IEEE Wirel Commun, 26, 1, 87–93 (2019). [CrossRef] [Google Scholar]
  27. F. Ho et al., “Decentralized multi-agent path finding for UAV traffic management,” IEEE Trans. on Intell. Trans. Sys., 23, 2, 997–1008 (2020). [Google Scholar]
  28. F. Ho et al., “Pre-flight conflict detection and resolution for UAV integration in shared airspace: Sendai 2030 model case,” IEEE Access, 7, 170226–170237 (2019). [CrossRef] [Google Scholar]
  29. M. Torabbeigi, G. J. Lim, and S. J. Kim, “Drone delivery scheduling optimization considering payload-induced battery consumption rates,” J Intell Robot Syst, 97, 471–487 (2020). [CrossRef] [Google Scholar]
  30. L. P. Osco et al., “A review on deep learning in UAV remote sensing,” International Journal of Appl. Earth Obs. and Geo., 102, 102456 (2021). [Google Scholar]
  31. D. Cvetek, M. Muštra, N. Jelušić, and L. Tišljarić, “A survey of methods and technologies for congestion estimation based on multisource data fusion,” Appl. Sci., 11, 5, 2306 (2021). [Google Scholar]
  32. R. Tashakkori, A. S. Hamza, and M. B. Crawford, “Beemon: An IoT-based beehive monitoring system,” Comp. Electr. Agric, 190, 106427 (2021). [CrossRef] [Google Scholar]
  33. T. Tristono, S. D. Cahyono, S. Aji, P. Utomo, and J. Triono, “Traffic lights time strategy for T-junctions of toll road gate”, (2023). [Google Scholar]
  34. V. Kulyukin and S. Mukherjee, “On video analysis of omnidirectional bee traffic: Counting bee motions with motion detection and image classification,” Applied Sci., 9, 18, 3743 (2019). [Google Scholar]
  35. H. Seter, L. Hansen, and P. Arnesen, “Comparing user acceptance of integrated and retrofit driver assistance systems–A real-traffic study,” Transp Res Part F Traffic Psychol Behav, 79, 139–156 (2021). [CrossRef] [Google Scholar]
  36. C. F. Daganzo, “Traffic flow theory,” in Fundamentals of transportation and traffic operations, Emerald Group Publishing Limited, 66–160 (1997). [Google Scholar]
  37. P. Arnesen, H. Seter, Ø. Tveit, and M. M. Bjerke, “Geofencing to enable differentiated road user charging,” Transp Res Rec, 2675, 7, 299–306 (2021). [CrossRef] [Google Scholar]
  38. A. Kadkhodayi, M. Jabeli, H. Aghdam, and S. Mirbakhsh, “Artificial IntelligenceBased Real-Time Traffic Man-agement,” Journal of Elect. Elect. Eng.g, 2, 4, 368–373 (2023). [CrossRef] [Google Scholar]
  39. S. Reza, M. C. Ferreira, J. J. M. Machado, and J. M. R. S. Tavares, “A multi-head attention-based transformer model for traffic flow forecasting with a comparative analysis to recurrent neural networks,” Expert Syst Appl, 202, 117275 (2022). [CrossRef] [Google Scholar]
  40. H. K. Chaudhary, K. Saraswat, H. Yadav, H. Puri, A. R. Mishra, and S. S. Chauhan, “A real time dynamic approach for management of vehicle generated traffic, (2023). [Google Scholar]
  41. H. Dou, H. Zhang, and B. Li, “A fast traffic sign detection algorithm based on modified YOLOv3,” in Journal of Physics: Conference Series, IOP Publishing, 012025 (2021) [Google Scholar]
  42. Q.-C. Mao, H.-M. Sun, Y.-B. Liu, and R.-S. Jia, “Mini-YOLOv3: real-time object detector for embedded applications,” Ieee Access, 7, 133529–133538 (2019). [CrossRef] [Google Scholar]
  43. C. Gong, A. Li, Y. Song, N. Xu, and W. He, “Traffic sign recognition based on the YOLOv3 algorithm,” Sensors, 22, 23, 9345 (2022). [CrossRef] [Google Scholar]
  44. S.-K. Tai, C. Dewi, R.-C. Chen, Y.-T. Liu, X. Jiang, and H. Yu, “Deep learning for traffic sign recognition based on spatial pyramid pooling with scale analysis,” Appl. Sci., 10, 19, 6997 (2020). [Google Scholar]
  45. V. A. Kulyukin and A. V Kulyukin, “Accuracy vs. energy: An assessment of bee object inference in videos from on-hive video loggers with YOLOv3, YOLOv4-Tiny, and YOLOv7-Tiny,” Sensors, 23, 15, 6791 (2023). [CrossRef] [Google Scholar]
  46. S. Du, B. Zhang, and P. Zhang, “Scale-sensitive IOU loss: An improved regression loss function in remote sensing object detection,” IEEE Access, 9, 141258–141272 (2021). [CrossRef] [Google Scholar]
  47. C. Guo, X. Lv, Y. Zhang, and M. Zhang, “Improved YOLOv4-tiny network for realtime electronic component detection,” Sci Rep, 11, 1, 22744 (2021). [CrossRef] [Google Scholar]
  48. S. Tippannavar and Y. SD, “Real-time vehicle identi-fication for improving the traffic management system-a review,” Journal of Trends in Comp. Sci. and Smart Tech., 5, 3, 323–342 (2023). [Google Scholar]
  49. J. Wang, Y. Chen, Z. Dong, and M. Gao, “Improved YOLOv5 network for real-time multi-scale traffic sign detection,” Neural Comput Appl, 35, 10, 7853–7865 (2023). [CrossRef] [Google Scholar]
  50. M. Kisantal, Z. Wojna, J. Murawski, J. Naruniec, and K. Cho, “Augmentation for small object detection,” arXiv preprint arXiv:1902.07296 (2019). [Google Scholar]
  51. N. Gray et al., “GLARE: A dataset for traffic sign detection in sun glare,” IEEE Trans. on Intell. Trans. Sys., (2023). [Google Scholar]
  52. Z. Zheng, P. Wang, W. Liu, J. Li, R. Ye, and D. Ren, “Distance-IoU loss: Faster and better learning for bounding box regression,” in Proceedings of the AAAI conference on artificial intelligence, 12993–13000 (2020). [Google Scholar]
  53. X. Wang, H. Wang, C. Zhang, Q. He, and L. Huo, “A sample balance-based regression module for object detection in construction sites,” Appl. Sci., 12, 13, 6752 (2022). [Google Scholar]
  54. S. B. Neamah and A. A. Karim, “Real-time Traffic Monitoring System Based on Deep Learning and YOLOv8,” aro-the sci. journal of koya university, 11, 2, 137–150 (2023). [Google Scholar]
  55. A. Osman, R. ÇÖTELİ, and D. AVCI, “TRAFİK GÖRÜNTÜLERİ KULLANARAK YOLOV8 TABANLI ARAÇ TESPİTİ VE SINIFLANDIRMASI”, Conference of UMTEB – XIV International Scientific Research Congress, (2023). [Google Scholar]
  56. Y. Du, X. Liu, Y. Yi, and K. Wei, “Optimizing road safety: advancements in lightweight YOLOv8 models and GhostC2f design for real-time distracted driving detection,” Sensors, 23, 21, 8844 (2023). [CrossRef] [PubMed] [Google Scholar]
  57. H. Lou et al., “DC-YOLOv8: small-size object detection algorithm based on camera sensor,” Electr. (Basel), 12, 10, 2323 (2023). [Google Scholar]
  58. D. Yushen, “Road Safety Monitoring Model Based on YOLOV8,” Acad. Journal of Comp.g & Inf. Sci., 7, 3, 91–96 (2024). [Google Scholar]
  59. X. Chi, H. Huang, J. Yang, J. Zhao, and X. Zhang, “Dataset and Improved YOLOV7 for Text-Based Traffic Sign Detection,” The International Archives of the Photogrammetry, Rem. Sen. and Spa. Inf. Sci., 48, 881–888 (2023). [Google Scholar]
  60. M. A. Rouf, Q. Wu, X. Yu, Y. Iwahori, H. Wu, and A. Wang, “Real-time Vehicle Detection, Tracking and Counting System Based on YOLOv7,” Emb. Self. Sys., 10, 7, 4–8 (2023). [Google Scholar]
  61. S. Li, S. Wang, and P. Wang, “A small object detection algorithm for traffic signs based on improved YOLOv7,” Sensors, 23, 16, 7145 (2023). [CrossRef] [Google Scholar]
  62. A. Kusiak, “Intelligent manufacturing,” System, Prentice-Hall, Englewood Cliffs, NJ, (1990). [Google Scholar]
  63. R. Zhang, K. Zheng, P. Shi, Y. Mei, H. Li, and T. Qiu, “Traffic sign detection based on the improved YOLOv5,” Appl. Sci., 13, 17, 9748 (2023). [Google Scholar]
  64. S. Liu, Y. Wang, Q. Yu, H. Liu, and Z. Peng, “CEAM-YOLOv7: Improved YOLOv7 based on channel expansion and attention mechanism for driver distraction behavior detection,” IEEE Access, 10, 129116–129124 (2022). [CrossRef] [Google Scholar]
  65. K. Jiang et al., “An attention mechanism-improved YOLOv7 object detection algorithm for hemp duck count estimation,” Agri., 12, 10, 1659 (2022). [Google Scholar]
  66. K. Liu, Q. Sun, D. Sun, L. Peng, M. Yang, and N. Wang, “Underwater target detection based on improved YOLOv7,” J Mar Sci Eng, 11, 3, 677 (2023). [CrossRef] [Google Scholar]
  67. N. Kumar and D. Kumar, “Analysis of Traffic Management and Application of Queuing System,” Math. Stat. and Eng.g App., 71, 4, 8053–8060 (2022). [Google Scholar]
  68. Q. Tang and X. Hu, “Deployment of Leader-Follower Automated Vehicle Systems for Smart Work Zone Applications with a Queuing-based Traffic Assignment Approach,” arXiv preprint arXiv:2308.03764 (2023). [Google Scholar]
  69. M. T. Horváth and T. Tettamanti, “Real-time queue length estimation applying shockwave theory at urban signalized intersections,” Peri. Poly. Civil Eng., 65, 4, 1153–1161 (2021). [Google Scholar]
  70. J. Parvez and M. A. Peer, “A comparative analysis of performance and QoS issues in MANETs,” Inter. Journal of Comp. and Inf. Eng., 4, 12, 1962–1973 (2010). [Google Scholar]
  71. D. S. A. Wokoma and Y.-H. Daerego, “comparison of effectiveness of service in filling station using queuing theory: a case study of nnpc port harcourt”, (2007). [Google Scholar]
  72. A. Khadhir, A. Bhaskar, L. Vanajakshi, and M. M. Haque, “Development of a theoretical delay model for heterogeneous and less lane-disciplined traffic conditions,” J Adv Transp, (2022). [Google Scholar]
  73. S. K. Kumaran, D. P. Dogra, and P. P. Roy, “Queuing theory guided intelligent traffic scheduling through video analysis using Dirichlet process mixture model,” Expert Syst Appl, 118, 169–181 (2019). [CrossRef] [Google Scholar]
  74. M. G. R. Alam et al., “Queueing theory based vehicular traffic management system through Jackson network model and optimization,” IEEE Access, 9, 136018–136031 (2021). [CrossRef] [Google Scholar]
  75. H. Hasyim and R. Rohani, “evaluasi tundaan pada simpang empat tak bersinyal di kota mataram (studi kasus: simpang jalan pejanggik dan simpang jalan caturwarga),” GANEC SWARA, 17, 4, 2117–2126 (2023). [CrossRef] [Google Scholar]
  76. M. Al Shinwan, L. Abualigah, N. D. Le, C. Kim, and A. M. Khasawneh, “An intelligent long-lived TCP based on real-time traffic regulation,” Multimed Tools Appl, 80, 16763–16780 (2021). [CrossRef] [Google Scholar]
  77. R. M. A. Latif, M. Jamil, J. He, and M. Farhan, “A Novel Authentication and Communication Protocol for Urban Traffic Monitoring in VANETs Based on Cluster Management, Sys., 11, 7, 322 (2023). [Google Scholar]
  78. C. Patra, “Improving TCP with Parameterized Forward-Retransmission Time Out”, (2014). [Google Scholar]
  79. S. Fahmy, V. Prabhakar, S. R. Avasarafa, and O. M. Younis, “TCP over wireless links: Mechanisms and implications,” (2003). [Google Scholar]
  80. V. Kovtun, K. Grochla, W. Kempa, and K. Połys, “Reliably Controlling Massive Traffic between a Sensor Network End Internet of Things Device Environment and a Hub Using Transmission Control Protocol Mechanisms,” Electr. (Basel), 12, 24, 4920 (2023). [Google Scholar]
  81. A. J. T. Segara and A. D. Ramadhani, “Performance Analysis of Mobile Ad-Hoc Networks Based on TCP and UDP Traffic on AODV Protocol for Warship Communication,” Journal of Sys. Eng. and Inf. Tech. (JOSEIT), 2, 2, 53–58 (2023). [CrossRef] [Google Scholar]
  82. Y. Hori, H. Sawashima, H. Sunahara, and Y. Oie, “Performance evaluation of UDP traffic affected by TCP flows,” IEICE Trans. on Comm., 81, 8, 1616–1623 (1998). [Google Scholar]
  83. L. Hansen et al., “GeoSence. Current state of the art and use case description on geofencing for traffic management,” (2021). [Google Scholar]
  84. R. Sarathe and S. Tiwari, “High Traffic Flow Management System Based on Queuing Theory-A Review,” (2012). [Google Scholar]
  85. M. Chaudhry, A. Datta Banik, S. Barik, and V. Goswami, “A novel computational procedure for the waiting-time distribution (in the queue) for bulk-service finitebuffer queues with poisson input,” Math., 11, 5, 1142 (2023). [Google Scholar]
  86. Y. Wang, “Research on the Queuing Theory based on M/M/1 Queuing Model,” Highlights in Sci., Eng. and Tech., 61, 80–87 (2023). [Google Scholar]
  87. K. Kim, M.-J. Kim, and J.-K. Jun, “Small queuing restaurant sustainable revenue management,” Sustainability, 12, 8, 3477 (2020). [CrossRef] [Google Scholar]
  88. S. C. Ferrari and R. Morabito, “performance analysis of a brazilian call center with impatient customers using m/g c/1+ g and m/g/c+ g queuing models,” Pesquisa Operacional, 43, 271290 (2023). [CrossRef] [Google Scholar]
  89. D. S. A. Wokoma and Y.-H. Daerego, “comparison of effectiveness of service in filling station using queuing theory: a case study of nnpc port harcourt” (2007). [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.