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Real-Time Smart Parking Integration in Intelligent Transportation Systems (ITS)

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Proceedings of the Future Technologies Conference (FTC) 2023, Volume 3 (FTC 2023)

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Abstract

The surge in the volume of vehicles on the roads has led to a significant increase in traffic issues. The current transportation and parking facilities are inadequate to handle this rise, causing drivers to struggle with locating parking spaces during peak times. This leads to inefficient time and energy. Therefore, intelligent parking systems are crucial for the development of smart cities to address immediate parking needs. The objectives of intelligent parking systems encompass identifying unoccupied parking spots and tallying parked vehicles. This article introduces a smart parking system proposal that employs real-time image processing methods. This system offers multiple functions, such as detecting available parking spaces, recognizing improperly parked cars, displaying open parking areas, and offering guidance to different types of parking spaces, including those designated for handicapped individuals. By utilizing the existing video surveillance setup, this system captures and analyzes sequences of images to direct and update drivers regarding vacant parking spaces.

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References

  1. Abbasspour, A., Sargolzaei, A., Victorio, M., Khoshavi, N.: A neural network-based approach for detection of time delay switch attack on networked control systems. Procedia Comput. Sci. 168, 279–288 (2020)

    Google Scholar 

  2. Aguiar, E., et al.: Classification of events in switch machines using bayes, fuzzy logic system and neural network. In: Mladenov, V., Jayne, C., Iliadis, L. (eds.) EANN 2014. CCIS, vol. 459, pp. 81–91. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11071-4_8

    Chapter  Google Scholar 

  3. Ahmed, M.U., Brickman, S., Dengg, A., Fasth, N., Mihajlovic, M., Norman, J.: A machine learning approach to classify pedestrians’ events based on IMU and GPS. Int. J. Artif. Intell. 17(2), 154–167 (2019)

    Google Scholar 

  4. Ahriz, I., Oussar, Y., Denby, B., Dreyfus, G.: Full-band GSM fingerprints for indoor localization using a machine learning approach. Int. J. Navig. Obser. 2010, 7 (2010)

    Google Scholar 

  5. Al-Garadi, M.A., Mohamed, A., Al-Ali, A.K., Du, X., Ali, I., Guizani, M.: A survey of machine and deep learning methods for internet of things (IoT) security. IEEE Commun. Surv. Tutorials 22(3), 1646–1685 (2020)

    Google Scholar 

  6. Ali, N.I.: Simulation for position control of DC motor using fuzzy logic controller. PhD thesis, Universiti Tun Hussein Onn Malaysia (2013)

    Google Scholar 

  7. Alibadi, S.H., Sadkhan, S.B.: A proposed security evaluation method for bluetooth e 0 based on fuzzy logic. In: 2018 International Conference on Advanced Science and Engineering (ICOASE), pp. 324–329. IEEE (2018)

    Google Scholar 

  8. Alippi, C., Camplani, R., Galperti, C., Roveri, M.: A robust, adaptive, solar-powered WSN framework for aquatic environmental monitoring. IEEE Sens. J. 11(1), 45–55 (2010)

    Google Scholar 

  9. Alrehan, A.M., Alhaidari, F.A.: Machine learning techniques to detect DDOS attacks on VANET system: a survey. In: 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS), pp. 1–6. IEEE (2019)

    Google Scholar 

  10. Alshathri, K., Xia, H., Lawrence, V., Yao, Y.D.: Cellular system identification using deep learning: GSM, UMTs and LTE. In: 2019 28th Wireless and Optical Communications Conference (WOCC), pp. 1–4. IEEE (2019)

    Google Scholar 

  11. Alshinina, R.A., Elleithy, K.M.: A highly accurate deep learning based approach for developing wireless sensor network middleware. IEEE Access 6, 29885–29898 (2018)

    Google Scholar 

  12. Alwakeel, S.S., Alhalabi, B., Aggoune, H., Alwakeel, M.: A machine learning based WSN system for autism activity recognition. In: 2015 IEEE 14th International Conference on Machine Learning and Applications (ICMLA), pp. 771–776. IEEE (2015)

    Google Scholar 

  13. Amanullah, M.A.: Deep learning and big data technologies for IoT security. Comput. Commun. 151, 495–517 (2020)

    Google Scholar 

  14. Amato, G., Carrara, F., Falchi, F., Gennaro, C., Vairo, C.: Car parking occupancy detection using smart camera networks and deep learning. In: 2016 IEEE Symposium on Computers and Communication (ISCC), pp. 1212–1217 (2016)

    Google Scholar 

  15. Archip, A., Botezatu, N., Şerban, E., Herghelegiu, P.-C., Zală, A.: An IoT based system for remote patient monitoring. In: 2016 17th International Carpathian Control Conference (ICCC), pp. 1–6. IEEE (2016)

    Google Scholar 

  16. Argyriou, M., Dragoni, N., Spognardi, A.: Security flows in Oauth 2.0 framework: a case study. In: Tonetta, S., Schoitsch, E., Bitsch, F. (eds.) SAFECOMP 2017. LNCS, vol. 10489, pp. 396–406. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-66284-8_33

    Chapter  Google Scholar 

  17. Badaoui, R., Al-Jumaily, A.: Fuzzy logic based human detection for CCTV recording application. In: 2010 6th International Conference on Advanced Information Management and Service (IMS) (2010)

    Google Scholar 

  18. Baroffio, L., Bondi, L., Cesana, M., Redondi, A.E., Tagliasacchi, M.: A visual sensor network for parking lot occupancy detection in smart cities. In: 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), pp. 745–750 (2015)

    Google Scholar 

  19. Belkhala, S., Benhadou, S., Boukhdir, K., Medromi, H.: Smart parking architecture based on multi agent system. Int. J. Adv. Comput. Sci. Appl. 10, 378–382 (2019)

    Google Scholar 

  20. Bhattacharyya, D.K., Kalita, J.K.: Network Anomaly Detection: a Machine Learning Perspective. CRC Press, Boca Raton (2013)

    Google Scholar 

  21. Bibi, R., et al.: Edge AI-based automated detection and classification of road anomalies in VANET using deep learning. Comput. intell. Neurosci. 2021, 1–16 (2021)

    Google Scholar 

  22. Blissett, R.J., Stennett, C., Day, R.M.: New techniques for digital CCTV processing in automatic traffic monitoring. In: Proceedings of VNIS’93-Vehicle Navigation and Information Systems Conference, pp. 137–140. IEEE (1993)

    Google Scholar 

  23. Bong, D.B.L., Ting, K.C., Lai, K.C.: Integrated approach in the design of car park occupancy information system (COINS). IAENG Int. J. Comput. Sci. 35(1), 7–14 (2008)

    Google Scholar 

  24. Browning, E., Bolton, M., Owen, E., Shoji, A., Guilford, T., Freeman, R.: Predicting animal behaviour using deep learning: GPS data alone accurately predict diving in seabirds. Methods Ecol. Evol. 9(3), 681–692 (2018)

    Google Scholar 

  25. Buess, C., Pietsch, P., Guggenbuhl, W., Koller, E.A.: A pulsed diagonal-beam ultrasonic airflow meter. J. Appl. Physiol. 61(3), 1195–1199 (1986)

    Google Scholar 

  26. Cai, B.Y., Alvarez, R., Sit, M., Duarte, F., Ratti, C.: Deep learning-based video system for accurate and real-time parking measurement. IEEE Internet Things J. 6(5), 7693–7701 (2019)

    Google Scholar 

  27. Čakić, S., Šandi, S., Nedić, D., Krčo, S., Popović, T.: Human activity detection using deep learning and bracelet with bluetooth transmitter. In: 2021 29th Telecommunications Forum (TELFOR), pp. 1–4. IEEE (2021)

    Google Scholar 

  28. Chen, W., Yeo, C.K.: Unauthorized parking detection using deep networks at real time. In: 2019 IEEE International Conference on Smart Computing (SMARTCOMP), pp. 459–463 (2019)

    Google Scholar 

  29. Choo, S.H., Amin, S.H., Fisal, N., Yeong, C.F., Bakar, J.A.: Using bluetooth transceivers in mobile robot. In: Student Conference on Research and Development, pp. 472–476. IEEE (2002)

    Google Scholar 

  30. Czajka, J.J., Oyetunde, T., Tang, Y.J.: Integrated knowledge mining, genome-scale modeling, and machine learning for predicting yarrowia lipolytica bioproduction. Metabolic Eng. 67, 227–236 (2021)

    Google Scholar 

  31. Di, M., Joo, E.M.: A survey of machine learning in wireless sensor netoworks from networking and application perspectives. In: 2007 6th International Conference on Information, Communications & Signal Processing, pp. 1–5. IEEE (2007)

    Google Scholar 

  32. Dimou, A., Medentzidou, P., Garcia, F.A., Daras, P.: Multi-target detection in CCTV footage for tracking applications using deep learning techniques. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 928–932. IEEE (2016)

    Google Scholar 

  33. Dsouza, K.B., Mohammed, S., Hussain, Y.: Smart parking-an integrated solution for an urban setting. In: 2017 2nd International Conference for Convergence in Technology (I2CT), pp. 174–177. IEEE (2017)

    Google Scholar 

  34. Elhoseny, M., Hassanien, A.E.: Secure data transmission in WSN: an overview. In: Dynamic Wireless Sensor Networks, pp. 115–143 (2019)

    Google Scholar 

  35. Elias, A.R., Golubovic, N., Krintz, C., Wolski, R.: Where’s the bear?-automating wildlife image processing using IoT and edge cloud systems. In: 2017 IEEE/ACM Second International Conference on Internet-of-Things Design and Implementation (IoTDI), pp. 247–258. IEEE (2017)

    Google Scholar 

  36. Felix, C., Raglend, I.J.: Home automation using GSM. In: 2011 International Conference on Signal Processing, Communication, Computing and Networking Technologies, pp. 15–19. IEEE (2011)

    Google Scholar 

  37. Frank, A., Al Aamri, Y.S.K., Zayegh, A.: IoT based smart traffic density control using image processing. In: 2019 4th MEC International Conference on Big Data and Smart City (ICBDSC), pp. 1–4. IEEE (2019)

    Google Scholar 

  38. Ftaimi, S., Mazri, Tomader T.: A comparative study of machine learning algorithms for vanet networks. In: Proceedings of the 3rd International Conference on Networking, Information Systems & Security, pp. 1–8 (2020)

    Google Scholar 

  39. Fu, C.-Z., et al.: Research on a detection and recognition algorithm for high-voltage switch cabinet based on deep learning with an improved yolov2 network. In: 2018 11th International Conference on Intelligent Computation Technology and Automation (ICICTA), pp. 346–350. IEEE (2018)

    Google Scholar 

  40. Fuentes, L.M., Velastin, S.A.: Tracking-based event detection for CCTV systems. Pattern Anal. Appl. 7(4), 356–364 (2004)

    MathSciNet  Google Scholar 

  41. Gao, J., Liu, Y.: Applications of remote sensing, GIS and GPS in glaciology: a review. Prog. Phys. Geogr. 25(4), 520–540 (2001)

    Google Scholar 

  42. Gao, Y., et al.: ihear food: eating detection using commodity bluetooth headsets. In: 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE), pp. 163–172. IEEE (2016)

    Google Scholar 

  43. Gecgel, S., Goztepe, C., Kurt, G.K.: Transmit antenna selection for large-scale MIMO GSM with machine learning. IEEE Wirel. Commun. Lett. 9(1), 113–116 (2019)

    Google Scholar 

  44. Gelana, F., Yadav, A.: Firearm detection from surveillance cameras using image processing and machine learning techniques. In: Tiwari, S., Trivedi, M.C., Mishra, K.K., Misra, A.K., Kumar, K.K. (eds.) Smart Innovations in Communication and Computational Sciences. AISC, vol. 851, pp. 25–34. Springer, Singapore (2019). https://doi.org/10.1007/978-981-13-2414-7_3

    Chapter  Google Scholar 

  45. Geng, Y., Cassandras, C.G.: A new “smart parking” system infrastructure and implementation. Procedia - Soc. Behav. Sci. 54, 1278–1287 (2012). Proceedings of EWGT2012 - 15th Meeting of the EURO Working Group on Transportation, September 2012, Paris

    Google Scholar 

  46. Ghorbanzadeh, O., et al.: Spatial prediction of wildfire susceptibility using field survey GPS data and machine learning approaches. Fire 2(3), 43 (2019)

    Google Scholar 

  47. Ghorbel, O., Ayedi, W., Jmal, M.W., Abid, M.: Images compression in WSN: performance analysis. In: 2012 IEEE 14th International Conference on Communication Technology, pp. 1363–1368. IEEE (2012)

    Google Scholar 

  48. Gondalia, A., Dixit, D., Parashar, S., Raghava, V., Sengupta, A., Sarobin, V.R.: IoT-based healthcare monitoring system for war soldiers using machine learning. Procedia Comput. Sci. 133, 1005–1013 (2018)

    Google Scholar 

  49. Grichi, H., Mosbahi, O., Khalgui, M., Li, Z.: RWiN: new methodology for the development of reconfigurable WSN. IEEE Trans. Autom. Sci. Eng. 14(1), 109–125 (2016)

    Google Scholar 

  50. Grover, J., Prajapati, N.K., Laxmi, V., Gaur, M.S.: Machine learning approach for multiple misbehavior detection in VANET. In: Abraham, A., Mauri, J.L., Buford, J.F., Suzuki, J., Thampi, S.M. (eds.) ACC 2011. CCIS, vol. 192, pp. 644–653. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22720-2_68

    Chapter  Google Scholar 

  51. Guo, J., Liu, Y., Yang, Q., Wang, Y., Fang, S.: GPS-based citywide traffic congestion forecasting using CNN-RNN and c3d hybrid model. Transp. Transp. Sci. 17(2), 190–211 (2021)

    Google Scholar 

  52. Gupta, I., Riordan, D., Sampalli, S.: Cluster-head election using fuzzy logic for wireless sensor networks. In: 3rd Annual Communication Networks and Services Research Conference (CNSR 2005), pp. 255–260. IEEE (2005)

    Google Scholar 

  53. Haripriya, A.P., Kulothungan, K.: Secure-MQTT: an efficient fuzzy logic-based approach to detect dos attack in MQTT protocol for internet of things. EURASIP J. Wirel. Commun. Netw. 2019(1), 1–15 (2019)

    Google Scholar 

  54. He, X., Arabnia, H.R.: Scalable switch for bi-directional multiring network. In: Proceedings of the Fourth IEEE International Symposium on Signal Processing and Information Technology, pp. 279–282. IEEE (2004)

    Google Scholar 

  55. Hussain, F., Hussain, R., Hassan, S.A., Hossain, E.: Machine learning in IoT security: current solutions and future challenges. IEEE Commun. Surv. Tutorials 22(3), 1686–1721 (2020)

    Google Scholar 

  56. Indaco, A., Ortega, F.: Adapting to climate risk? local population dynamics in the united states (2023)

    Google Scholar 

  57. Ineneji, C., Kusaf, M.: Hybrid weapon detection algorithm, using material test and fuzzy logic system. Comput. Electr. Eng. 78, 437–448 (2019)

    Google Scholar 

  58. Hafis, M., Ishak, I., et al.: Bluetooth-based home automation system using an android phone. Jurnal Teknologi 70(3), 57–61 (2014)

    Google Scholar 

  59. Jain, R.: A congestion control system based on VANET for small length roads. preprint arXiv:1801.06448 (2018)

  60. Jaiswal, V., Sharma, V., Varma, S.: Comparative analysis of CCTV video image processing techniques and application: a survey. IOSR J. Eng. (IOSRJEN) 8(10), 38–47 (2018)

    Google Scholar 

  61. Jayaramireddy, C.S., Naraharisetti, S.V.V.S.S., Nassar, M., Mekni, M.: A survey of reinforcement learning toolkits for gaming: applications, challenges and trends. In: Arai, K. (eds.) Proceedings of the Future Technologies Conference (FTC) 2022, Volume 1. FTC 2022 2022. LNNS, vol. 559, pp. 165–184. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-18461-1_11

  62. Jiao, Y., Hall, J.J., Morton, Y.T.: Automatic equatorial GPS amplitude scintillation detection using a machine learning algorithm. IEEE Trans. Aerospace Electron. Syst. 53(1), 405–418 (2017)

    Google Scholar 

  63. Jindal, M., Gupta, J., Bhushan, B.: Machine learning methods for IoT and their future applications. In: 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), pp. 430–434. IEEE (2019)

    Google Scholar 

  64. Johari, A., et al.: Image processing of moving object captured and received by GPRS/GSM modem. In: 2013 Third World Congress on Information and Communication Technologies (WICT 2013), pp. 176–182. IEEE (2013)

    Google Scholar 

  65. Jwo, D.-J., Wang, S.-H.: Adaptive fuzzy strong tracking extended kalman filtering for GPS navigation. IEEE Sens. J. 7(5), 778–789 (2007)

    Google Scholar 

  66. Kamble, S.J., Kounte, M.R.: Machine learning approach on traffic congestion monitoring system in internet of vehicles. Procedia Comput. Sci. 171, 2235–2241 (2020). Third International Conference on Computing and Network Communications (CoCoNet’19)

    Google Scholar 

  67. Karmokar, P., et al.: A novel IoT based accident detection and rescue system. In: 2020 Third International Conference on Smart Systems and Inventive Technology (ICSSIT), pp. 322–327 (2020)

    Google Scholar 

  68. Khan, I., et al.: Automatic management of n\(\times \) n photonic switch powered by machine learning in software-defined optical transport. IEEE Open J. Commun. Soc. 2, 1358–1365 (2021)

    Google Scholar 

  69. Khatri, S., et al.: Machine learning models and techniques for vanet based traffic management: implementation issues and challenges. Peer-to-Peer Networking Appl. 14(3), 1778–1805 (2021)

    Google Scholar 

  70. Khekare, G.S., Sakhare, A.V.: A smart city framework for intelligent traffic system using vanet. In: 2013 International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing (iMac4s), pp. 302–305. IEEE (2013)

    Google Scholar 

  71. Kianpisheh, A., Mustaffa, N., Limtrairut, P., Keikhosrokiani, P.: Smart parking system (SPS) architecture using ultrasonic detector. Int. J. Softw. Eng. Appl. 6(3), 55–58 (2012)

    Google Scholar 

  72. Kianpisheh, A., Mustaffa, N., Mei Yean See, J., Keikhosrokiani, P.: User behavioral intention toward using smart parking system. In: Abd Manaf, A., Zeki, A., Zamani, M., Chuprat, S., El-Qawasmeh, E. (eds.) ICIEIS 2011. CCIS, vol. 252, pp. 732–743. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-25453-6_61

    Chapter  Google Scholar 

  73. Kochláň, M., et al.: WSN for traffic monitoring using raspberry pi board. In: 2014 Federated Conference on Computer Science and Information Systems, pp. 1023–1026. IEEE (2014)

    Google Scholar 

  74. Kotrotsios, K., Orphanoudakis, T.: Accurate gridless indoor localization based on multiple bluetooth beacons and machine learning. In: 2021 7th International Conference on Automation, Robotics and Applications (ICARA), pp. 190–194. IEEE (2021)

    Google Scholar 

  75. Kumar, S., Swaroop, S.: Collateral development of invasive pulmonary aspergillosis (IPA) in chronic obstructive pulmonary disease (COPD) patients. In: Gupta, A., Singh, N.P. (eds.) Recent Developments in Fungal Diseases of Laboratory Animals. FB, pp. 111–118. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-18586-2_7

    Chapter  Google Scholar 

  76. Shiv Kumar, S., et al.: Deep learning-based automated detection of sewer defects in CCTV videos. J. Comput. Civil Eng. 34(1), 04019047 (2020)

    Google Scholar 

  77. Lan, K.-C., Shih, W.-Y.: An intelligent driver location system for smart parking. Expert Syst. Appl. 41(5), 2443–2456 (2014)

    Google Scholar 

  78. Le, L.V., Sinh, D., Tung, L.P., Lin, B.S.P.: A practical model for traffic forecasting based on big data, machine-learning, and network KPIs. In: 2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC), pp. 1–4. IEEE (2018)

    Google Scholar 

  79. Lee, K.-S., Lee, S.-R., Kim, Y., Lee, C.-G.: Deep learning-based real-time query processing for wireless sensor network. Int. J. Distrib. Sens. Netw. 13(5), 1550147717707896 (2017)

    Google Scholar 

  80. Lee, K.B., Shin, H.S.: An application of a deep learning algorithm for automatic detection of unexpected accidents under bad CCTV monitoring conditions in tunnels. In: 2019 International Conference on deep learning and machine learning in emerging applications (Deep-ML), pp. 7–11. IEEE (2019)

    Google Scholar 

  81. Lee, L.K., Zachariah, M., Everett, P.: CCTV camera site selection: a field experience. In: Pacific Rim TransTech Conference. 1995 Vehicle Navigation and Information Systems Conference Proceedings. 6th International VNIS. A Ride into the Future, pp. 21–27. IEEE (1995)

    Google Scholar 

  82. Lei, F., Cai, J., Dai, Q., Zhao, H., Han, J.: Deep learning based proactive caching for effective WSN-enabled vision applications. Complexity 2019, 1–12 (2019). https://doi.org/10.1155/2019/5498606

    Article  Google Scholar 

  83. Lewandowski, M., Płaczek, B., Bernas, M., Szymała, P.: Road traffic monitoring system based on mobile devices and bluetooth low energy beacons. Wirel. Commun. Mobile Comput. 2018, 1–12 (2018). https://doi.org/10.1155/2018/3251598

    Article  Google Scholar 

  84. Li, H., Ota, K., Dong, M.: Learning IoT in edge: deep learning for the internet of things with edge computing. IEEE Network 32(1), 96–101 (2018)

    Google Scholar 

  85. Li, X., Huang, Y., Heng, W., Jing, W.: Machine learning-inspired hybrid precoding for mmWave MU-MIMO systems with domestic switch network. Sensors 21(9), 3019 (2021)

    Google Scholar 

  86. Li, Y., Yan, Y.: Fuzzy logic based handoff decision algorithm in GSM-r network (2007)

    Google Scholar 

  87. Liu, X., Feng, Z., Zhang, Y.Y., Liu, S.Q.: GPS positioning system design based on micro control unit. Adv. Mater. Res. 915, 1171–1174 (2014)

    Google Scholar 

  88. Małecki, K.: A computer simulation of traffic flow with on-street parking and drivers’ behaviour based on cellular automata and a multi-agent system. J. Comput. Sci. 28, 32–42 (2018)

    Google Scholar 

  89. Masek, P., et al.: A harmonized perspective on transportation management in smart cities: the novel IoT-driven environment for road traffic modeling. Sensors 16(11), 1872 (2016)

    Google Scholar 

  90. Meana-Llorián, D., et al.: IoFClime: the fuzzy logic and the internet of things to control indoor temperature regarding the outdoor ambient conditions. Future Gener. Comput. Syst. 76, 275–284 (2017)

    Google Scholar 

  91. Medhat, N., Moussa, S.M., Badr, N.L., Tolba, M.F.: A framework for continuous regression and integration testing in IoT systems based on deep learning and search-based techniques. IEEE Access 8, 215716–215726 (2020)

    Google Scholar 

  92. Mekni, M.: Sensor web deployment using informed virtual geographic environments. In: ICCGI 2013, p. 237 (2013)

    Google Scholar 

  93. Mekni, M.: An artificial intelligence based virtual assistant using conversational agents. J. Softw. Eng. Appl. 14(9), 455–473 (2021)

    Google Scholar 

  94. Mekni, M., Buddhavarapu, G., Chinthapatla, S., Gangula, M.: Software architectural design in agile environments. J. Comput. Commun. 6(1), 171–189 (2017)

    Google Scholar 

  95. Mekni, M., Jayaramireddy, C.S., Naraharisetti, S.V.V.S.S.: Reinforcement learning toolkits for gaming: a comparative qualitative analysis. J. Softw. Eng. Appl. 15(12), 417–435 (2022)

    Google Scholar 

  96. Mekonnen, Y., Namuduri, S., Burton, L., Sarwat, A., Bhansali, S.: Machine learning techniques in wireless sensor network based precision agriculture. J. Electrochem. Soc. 167(3), 037522 (2019)

    Google Scholar 

  97. Memon, J., Sami, M., Khan, R.A., Uddin, M.: Handwritten optical character recognition (OCR): a comprehensive systematic literature review (SLR), IEEE Access 8, 142642–142668 (2020)

    Google Scholar 

  98. Mohammadi, M., Al-Fuqaha, A., Sorour, S., Guizani, M.: Deep learning for IoT big data and streaming analytics: a survey. IEEE Commun. Surv. Tutorials 20(4), 2923–2960 (2018)

    Google Scholar 

  99. Muhammad, K., et al.: Fuzzy logic in surveillance big video data analysis: Comprehensive review, challenges, and research directions. ACM Comput. Surv. (CSUR) 54(3), 1–33 (2021)

    Google Scholar 

  100. Muji, A.L., Tahar, K.N.: Assessment of digital elevation model (dem) using onboard GPS and ground control points in UAV image processing. In: 2017 Intelligent Systems Conference (IntelliSys), pp. 835–842. IEEE (2017)

    Google Scholar 

  101. Nafi, N.S., Khan, J.Y.: A vanet based intelligent road traffic signalling system. In: Australasian Telecommunication Networks and Applications Conference (ATNAC) 2012, pp. 1–6. IEEE (2012)

    Google Scholar 

  102. Naranjo, J.E., González, C., García, R., de Pedro, T., Revuelto, J., Reviejo, J.: Fuzzy logic based lateral control for GPS map tracking. In: IEEE Intelligent Vehicles Symposium, 2004, pp. 397–400. IEEE (2004)

    Google Scholar 

  103. Nayak, P., Devulapalli, A.: A fuzzy logic-based clustering algorithm for WSN to extend the network lifetime. IEEE Sens. J. 16(1), 137–144 (2015)

    Google Scholar 

  104. Owen, J.I., Wells, M.: An advanced digital antenna control unit for GPS. In: Proceedings of the 2001 National Technical Meeting of The Institute of Navigation, pp. 402–407 (2001)

    Google Scholar 

  105. Page, L.D., Mekni, M., Radday, E.A.: Incorporating cybersecurity concepts in connecticut’s high school stem education. J. Comput. Sci. Colleges 38(8), 173–187 (2023)

    Google Scholar 

  106. Pasala, K.L., et al.: Smart parking system (SPS): an intelligent image-processing based parking solution. In: Nathanail, E.G., Gavanas, N., Adamos, G. (eds.) Smart Energy for Smart Transport. CSUM 2022. LNITI, pp. 291–299. Springer, Cham (2023). https://doi.org/10.1007/978-3-031-23721-8_25

  107. Patel, C., Shah, D., Patel, A.: Automatic number plate recognition system (ANPR): a survey. Int. J. Comput. Appl. 69, 21–33 (2013). https://doi.org/10.5120/11871-7665

    Article  Google Scholar 

  108. Peppa, M.V., Bell, D., Komar, T., Xiao, W.: Urban traffic flow analysis based on deep learning car detection from CCTV image series. In: SPRS TC IV Mid-term Symposium “3D Spatial Information Science–The Engine of Change”. Newcastle University (2018)

    Google Scholar 

  109. Delicato, F., et al.: Autonomic wireless sensor networks: a systematic literature review. J. Sens. 2014, 1–13 (2014). https://doi.org/10.1155/2014/782789

    Article  Google Scholar 

  110. Pradityo, F., Surantha, N.: Indoor air quality monitoring and controlling system based on iot and fuzzy logic. In: 2019 7th International conference on information and communication technology (ICoICT), pp. 1–6. IEEE (2019)

    Google Scholar 

  111. Prakash, C.B., Sirisha, K.: Design and implementation of a vehicle theft control unit using GSM and can technology. Int. J. Innov. Res. Electron 1, 46–53 (2014)

    Google Scholar 

  112. Pranamurti, H, Murti, A., Setianingsih, C.: Fire detection use CCTV with image processing based raspberry pi. In: Journal of Physics: Conference Series, vol. 1201, p. 012015. IOP Publishing (2019)

    Google Scholar 

  113. Purkait, R., Tripathi, S.: Fuzzy logic based multi-criteria intelligent forward routing in vanet. Wirel. Pers. Commun. 111(3), 1871–1897 (2020)

    Google Scholar 

  114. Rahayu, Y., Mustapa, F.N.: A secure parking reservation system using GSM technology. Int. J. Comput. Commun. Eng. 2(4), 518 (2013)

    Google Scholar 

  115. Rahman, M.W., Islam, R., Hasan, A., Bithi, N.I., Hasan, M.M., Rahman, M.M.: Intelligent waste management system using deep learning with IoT. J. King Saud Univ.-Comput. Inf. Sci. 34(5), 2072–2087 (2020)

    Google Scholar 

  116. Rane, S., Dubey, A., Parida, T.: Design of IoT based intelligent parking system using image processing algorithms. In: 2017 International Conference on Computing Methodologies and Communication (ICCMC), pp. 1049–1053. IEEE (2017)

    Google Scholar 

  117. Rani, S., Maheswar, R., Kanagachidambaresan, G.R., Jayarajan, P. (eds.): Integration of WSN and IoT for Smart Cities. EICC, Springer, Cham (2020). https://doi.org/10.1007/978-3-030-38516-3

    Book  Google Scholar 

  118. Rao, N.P., Bhavana, G., Teja, M.L.R.: RTOS based image recognition & location finder using GPS, GSM and OpenCV. Int. Adv. Res. J. Sci. Eng. Technol. 2(12), 85–88 (2015)

    Google Scholar 

  119. Razavi, R., Fleury, M., Ghanbari, M.: Fuzzy logic control of adaptive ARQ for video distribution over a bluetooth wireless link. Adv. Multimedia 2007, 8 (2007). https://doi.org/10.1155/2007/45798

    Article  Google Scholar 

  120. Regin, R., Rajest, S., Singh, B.: Fault detection in wireless sensor network based on deep learning algorithms. ICST Trans. Scalable Inf. Syst. 8, 1–7 (2021). https://doi.org/10.4108/eai.3-5-2021.169578

    Article  Google Scholar 

  121. Rossi, A., Pappalardo, L., Cintia, P., Iaia, F.M., Fernández, J., Medina, D.: Effective injury forecasting in soccer with GPS training data and machine learning. PLoS one 13(7), e0201264 (2018)

    Google Scholar 

  122. Rovira-Mas, F., Han, S., Wei, J. and Reid, J.F.: Fuzzy logic model for sensor fusion of machine vision and GPS in autonomous navigation. In: 2005 ASAE Annual Meeting, p. 1. American Society of Agricultural and Biological Engineers (2005)

    Google Scholar 

  123. Rupani, A., Whig, P., Sujediya, G., Vyas, P.: A robust technique for image processing based on interfacing of raspberry-pi and FPGA using IoT. In: 2017 International Conference on Computer, Communications and Electronics (Comptelix), pp. 350–353. IEEE (2017)

    Google Scholar 

  124. Salpietro, R., Bedogni, L., Di Felice, M., Bononi, L.: Park here! a smart parking system based on smartphones’ embedded sensors and short range communication technologies. In: 2015 IEEE 2nd World Forum on Internet of Things (WF-IoT), pp. 18–23 (2015)

    Google Scholar 

  125. Samara, F., Ondieki, S., Hossain, A.M., Mekni, M.: Online social network interactions (OSNI): a novel online reputation management solution. In: 2021 International Conference on Engineering and Emerging Technologies (ICEET), pp. 1–6. IEEE (2021)

    Google Scholar 

  126. Sarkar, J., Mondal, M.S., Khalil, E.: Predicting fabric GSM and crease recovery angle of laser engraved denim by fuzzy logic analysis. J. Eng. Appl. Sci. 4, 52–64 (2020)

    Google Scholar 

  127. Sasiadek, J.Z., Wang, Q., Zeremba, M.B.: Fuzzy adaptive kalman filtering for ins/GPS data fusion. In: Proceedings of the 2000 IEEE International Symposium on Intelligent Control. Held jointly with the 8th IEEE Mediterranean Conference on Control and Automation (Cat. No. 00CH37147), pp. 181–186. IEEE (2000)

    Google Scholar 

  128. Savić, T., Radonjić, M.: WSN architecture for smart irrigation system. In: 2018 23rd International Scientific-Professional Conference on Information Technology (IT), pp. 1–4. IEEE (2018)

    Google Scholar 

  129. Seol, K., Lim, Y.: Implementation of novel application using bluetooth. In: SICE 2004 Annual Conference, vol. 2, pp. 1617–1620. IEEE (2004)

    Google Scholar 

  130. Shen, X.P., Wang, X., Jia, M.: Design and implementation of traffic information detection equipment based on bluetooth communication. In: 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), pp. 1595–1601. IEEE (2017)

    Google Scholar 

  131. Shin, H.-S., Kim, D.-G., Yim, M.-J., Lee, K.-B., Young-Sup, O.: A preliminary study for development of an automatic incident detection system on CCTV in tunnels based on a machine learning algorithm. J. Korean Tunnelling Underground Space Assoc. 19(1), 95–107 (2017)

    Google Scholar 

  132. Shu, J., Zhou, L., Zhang, W., Xiaojiang, D., Guizani, M.: Collaborative intrusion detection for vanets: a deep learning-based distributed SDN approach. IEEE Trans. Intell. Transp. Syst. 22(7), 4519–4530 (2020)

    Google Scholar 

  133. Singh, A.K., Goutele, S., Verma, S., Purohit, N.: An energy efficient approach for clustering in wsn using fuzzy logic. Int. J. Comput. Appl. 44(18), 8–12 (2012)

    Google Scholar 

  134. Singh, A.K., Purohit, N., Varma, S.: Fuzzy logic based clustering in wireless sensor networks: a survey. Int. J. Electron. 100(1), 126–141 (2013)

    Google Scholar 

  135. Singh, H., Anand, C., Kumar, V., Sharma, A.: Automated parking system with bluetooth access. Int. J. Eng. Comput. Sci 3(5), 3–8 (2014)

    Google Scholar 

  136. Slavik, M., Mahgoub, I.: Applying machine learning to the design of multi-hop broadcast protocols for VANET. In: 2011 7th International Wireless Communications and Mobile Computing Conference, pp. 1742–1747. IEEE (2011)

    Google Scholar 

  137. Smith, G.J.D.: Behind the screens: examining constructions of deviance and informal practices among CCTV control room operators in the UK. Surveill. Soc. 2(2/3), 376–395 (2004)

    Google Scholar 

  138. So, S., Sharma, P., Petit, J.: Integrating plausibility checks and machine learning for misbehavior detection in VANET. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 564–571. IEEE (2018)

    Google Scholar 

  139. MAO Song and Cheng-lin Zhao: Unequal clustering algorithm for WSN based on fuzzy logic and improved ACO. J. China Univ. Posts Telecommun. 18(6), 89–97 (2011)

    Google Scholar 

  140. Sonika, S., Sathiyasekar, K., Jaishree, S.: Intelligent accident identification system using GPS, GSM modem. Int. J. Adv. Res. Comput. Commun. Eng. 3(2), 5487–5489 (2014)

    Google Scholar 

  141. Sornalatha, K., Kavitha, V.R.: IoT based smart museum using bluetooth low energy. In: 2017 third International Conference on Advances in Electrical, Electronics, Information, Communication and Bio-Informatics (AEEICB), pp. 520–523. IEEE (2017)

    Google Scholar 

  142. Srivani, I, Siva, G., Prasad, V., Ratnam, D.V.: A deep learning-based approach to forecast ionospheric delays for GPS signals. IEEE Geosci. Remote Sens. Lett. 16(8), 1180–1184 (2019)

    Google Scholar 

  143. Sthapit, P., Gang, H.-S., Pyun, J.-Y.: Bluetooth based indoor positioning using machine learning algorithms. In: 2018 IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia), pp. 206–212. IEEE (2018)

    Google Scholar 

  144. Suanmali, L., Salim, N., Binwahlan, M.S.: Fuzzy logic based method for improving text summarization. arXiv preprintarXiv:0906.4690 (2009)

    Google Scholar 

  145. Sun, R., et al.: Improving GPS code phase positioning accuracy in urban environments using machine learning. IEEE Internet Things J. 8(8), 7065–7078 (2020)

    Google Scholar 

  146. Sun, Y.N., Horng, M.-H., Lin, X.Z., Wang, J.-Y.: Ultrasonic image analysis for liver diagnosis. IEEE Eng. Med. Biol. Mag. 15(6), 93–101 (1996)

    Google Scholar 

  147. Syed, S., Cannon, M.E.: Fuzzy logic based-map matching algorithm for vehicle navigation system in urban canyons. In: Proceedings of the 2004 National Technical Meeting of the Institute of Navigation, pp. 982–993 (2004)

    Google Scholar 

  148. Taha, M.R., Noureldin, A., El-Sheimy, N.: Improving ins/GPS positioning accuracy during GPS outages using fuzzy logic. In: Proceedings of the 16th International Technical Meeting of the Satellite Division of The Institute of Navigation (ION GPS/GNSS 2003), pp. 499–508 (2003)

    Google Scholar 

  149. Tana, H., Sazish, A.N., Ahmad, A., Sharif, M.S., Amira, A.: Efficient FPGA implementation of a wireless communication system using bluetooth connectivity. In: 2010 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1767–1770. IEEE (2010)

    Google Scholar 

  150. Tang, J., Sun, D., Liu, S., Gaudiot, J.-L.: Enabling deep learning on IoT devices. Computer 50(10), 92–96 (2017)

    Google Scholar 

  151. Tao, Y., Peng, R.: A fuzzy logic vertical handoff algorithm with motion trend decision. In: Proceedings of 2011 6th International Forum on Strategic Technology, vo. 2, pp. 1280–1283. IEEE (2011)

    Google Scholar 

  152. The World Bank. The World Bank (2022). Accessed 01 Jan 2022

    Google Scholar 

  153. Toth, Š., Janech, J., Krák, E.: Query based image processing in the VANET. In: 2013 Fifth International Conference on Computational Intelligence, Communication Systems and Networks, pp. 256–260. IEEE (2013)

    Google Scholar 

  154. United Nation : Climate Action. Cities and Pollution (2022). Accessed 01 Jan 2022

    Google Scholar 

  155. Varman, S.A.M., Baskaran, A.R., Aravindh, S., Prabhu, E: Deep learning and IoT for smart agriculture using WSN. In: 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), pages 1–6. IEEE (2017)

    Google Scholar 

  156. Veres, M., Moussa, M.: Deep learning for intelligent transportation systems: a survey of emerging trends. IEEE Trans. Intell. Transp. Syst. 21(8), 3152–3168 (2019)

    Google Scholar 

  157. Viani, F., Rocca, P., Lizzi, L., Rocca, M., Benedetti, G., Massa, A.: WSN-based early alert system for preventing wildlife-vehicle collisions in alps regions. In: 2011 IEEE-APS Topical Conference on Antennas and Propagation in Wireless Communications, pp. 106–109. IEEE (2011)

    Google Scholar 

  158. VidyaSagar, B.: Green house monitoring and automatation using GSM. Int. J. Sci. Res. Publ. 4(6), 1–5 (2012)

    Google Scholar 

  159. Wan, C., Zhang, J., Huang, D.: SCPR: secure crowdsourcing-based parking reservation system. Secur. Commun. Netw. 2017, 1–9 (2017). https://doi.org/10.1155/2017/1076419

    Article  Google Scholar 

  160. Wang, Y., Menkovski, V., Ho, I.W.H. and Pechenizkiy, M.: VANET meets deep learning: the effect of packet loss on the object detection performance. In: 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), pp. 1–5. IEEE (2019)

    Google Scholar 

  161. Warr, J., Page, M., Crossen-White, H.: The appropriate use of CCTV observation in a secure unit. Bournemouth University (2005)

    Google Scholar 

  162. Xiao, L., Wan, X., Xiaozhen, L., Zhang, Y., Di, W.: IoT security techniques based on machine learning: How do IoT devices use AI to enhance security? IEEE Signal Process. Mag. 35(5), 41–49 (2018)

    Google Scholar 

  163. Xie, X., Wang, C., Chen, S., Shi, G., Zhao, Z.: Real-time illegal parking detection system based on deep learning. In: Proceedings of the 2017 International Conference on Deep Learning Technologies, pp. 23–27 (2017)

    Google Scholar 

  164. Yamada, K., Mizuno, M.: A vehicle parking detection method using image segmentation. Electron. Commun. Japan (Part III: Fundamental Electronic Science) 84(10), 25–34 (2001)

    Google Scholar 

  165. Yang, C., Ren, S., Liu, Y., Cao, H., Yuan, Q., Han, G.: Personalized channel recommendation deep learning from a switch sequence. IEEE Access 6, 50824–50838 (2018)

    Google Scholar 

  166. Yang, D., Ning, L., Xing, G., Yu, L., Yang, L.: The house intelligent switch control network based on can bus. In: 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet), pp. 2165–2167. IEEE (2012)

    Google Scholar 

  167. Yin, X., et al.: Standard closed-circuit television (CCTV) collection time extraction of sewer pipes with machine learning algorithm. In: Proceedings of the 36th International Symposium on Automation and Robotics in Construction, ISARC, pp. 107–113 (2019)

    Google Scholar 

  168. Zantalis, F., Koulouras, G., Karabetsos, S., Kandris, D.: A review of machine learning and IoT in smart transportation. Future Internet 11(4), 94 (2019)

    Google Scholar 

  169. Zeng, Y., Qiu, M., Zhu, D., Xue, Z., Xiong, J. and Liu, M.: DeepVCM: a deep learning based intrusion detection method in VANET. In: 2019 IEEE 5th International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing, (HPSC) and IEEE International Conference on Intelligent Data and Security (IDS), pp. 288–293. IEEE (2019)

    Google Scholar 

  170. Zhang, X., et al.: Deep learning for interference identification: band, training snr, and sample selection. In: 2019 IEEE 20th International Workshop on Signal Processing Advances in Wireless Communications (SPAWC), pp. 1–5. IEEE (2019)

    Google Scholar 

  171. Zhao, J., et al.: Truck traffic speed prediction under non-recurrent congestion: based on optimized deep learning algorithms and GPS data. IEEE Access 7, 9116–9127 (2019)

    Google Scholar 

  172. Zhao, S., Chandrashekar, M., Lee, Y., Medhi, D.: Real-time network anomaly detection system using machine learning. In: 2015 11th International Conference on the Design of Reliable Communication Networks (DRCN), pp. 267–270. IEEE (2015)

    Google Scholar 

  173. Zheng, J., Wang, Y., Nihan, N.L.: Quantitative evaluation of GPS performance under forest canopies. In: Proceedings. 2005 IEEE Networking, Sensing and Control, 2005, pp. 777–782. IEEE (2005)

    Google Scholar 

  174. Zhu, Z., Yan, S., Glick, M.S., Teh, M.Y., Bergman, K.: Silicon photonic switch-enabled server regrouping using bandwidth steering for distributed deep learning training. In: Optical Fiber Communication Conference, pp. Th5H–3. Optical Society of America (2021)

    Google Scholar 

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Mekni, M., Atilho, S., Greenfield, B., Placzek, B., Nassar, M. (2023). Real-Time Smart Parking Integration in Intelligent Transportation Systems (ITS). In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2023, Volume 3. FTC 2023. Lecture Notes in Networks and Systems, vol 815. Springer, Cham. https://doi.org/10.1007/978-3-031-47457-6_14

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