Smart connected parking lots based on secured multimedia IoT devices


In this paper, we present a smart connected parking lots solution to automatically count and notify drivers about empty parking spots in major cities. As its name implies, the proposed smart IoT system has two operating phases: (i) continuous counting of empty spots in the monitored far-apart parking lots, and (ii) instantaneous driver notification through a lightweight MQTT mechanism. This notification system relies only on information collected from the pre-installed multimedia devices (no other apparatus installation or maintenance such as ground sensors is required). To validate the proper operation of our solution, we have implemented a small-scale version of it and assessed its performance while considering different classical and lightweight deep learning techniques (MobileNetV2, ResNet-50, YOLOv3, SSD-MobileNetV2, Tiny-YOLO, SqueezeDet, and SqueezeDet pruned with \(\ell _1\)-norm). The experiments have confirmed the proper operation, efficiency, ease of deployment, and ease of extension of our system. They also confirmed that lightweight deep learning solutions are more adequate for small-sized resource-constrained embedded systems. They are more efficient in terms of inference time, size, resource consumption, and yield an accuracy that is close to that of classical solutions.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10


  1. 1.

    CNN: Convolutional Neural Network.

  2. 2.

    The gradient becomes very small, which prevents the weights from changing and slows down the learning process.

  3. 3.

    MQTT: Message Queuing Telemetry Transport.

  4. 4.

    Mosquitto implements the MQTT protocol versions 5.0, 3.1.1, and 3.1.

  5. 5.

  6. 6.

  7. 7.

    You Only Look Once.

  8. 8.

  9. 9.


  1. 1.

    Ahrnbom M, Astrom K, Nilsson M (2016) Fast classification of empty and occupied parking spaces using integral channel features. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 9–15

  2. 2.

    Alkheder SA, Al Rajab MM, Alzoubi K (2016) Parking problems in Abu Dhabi, UAE toward an intelligent parking management system “Adip: Abu Dhabi intelligent parking’’. Alex Eng J 55(3):2679–2687

    Article  Google Scholar 

  3. 3.

    Amato G, Carrara F, Falchi F, Gennaro C, Meghini C, Vairo C (2017) Deep learning for decentralized parking lot occupancy detection. Expert Syst Appl 72:327–334

    Article  Google Scholar 

  4. 4.

    Anwar S, Hwang K, Sung W (2015) Fixed point optimization of deep convolutional neural networks for object recognition. In: 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 1131–1135

  5. 5.

    Anwar S, Hwang K, Sung W (2017) Structured pruning of deep convolutional neural networks. ACM J Emerg Technol Comput Syst (JETC) 13(3):1–18

    Article  Google Scholar 

  6. 6.

    Bachani M, Qureshi UM, Shaikh FK (2016) Performance analysis of proximity and light sensors for smart parking. Proc Comput Sci 83:385–392

    Article  Google Scholar 

  7. 7.

    Belbachir AN (2010) Smart cameras, vol 2. Springer, Berlin

    Google Scholar 

  8. 8.

    Caicedo F, Blazquez C, Miranda P (2012) Prediction of parking space availability in real time. Expert Syst Appl 39(8):7281–7290

    Article  Google Scholar 

  9. 9.

    Camero A, Toutouh J, Stolfi DH, Alba E (2018) Evolutionary deep learning for car park occupancy prediction in smart cities. In: International conference on learning and intelligent optimization. Springer, pp 386–401

  10. 10.

    Database PL (2020)

  11. 11.

    De Almeida PR, Oliveira LS, Britto AS Jr, Silva EJ Jr, Koerich AL (2015) Pklot—a robust dataset for parking lot classification. Expert Syst Appl 42(11):4937–4949

    Article  Google Scholar 

  12. 12.

    Delibaltov D, Wu W, Loce RP, Bernal EA (2013) Parking lot occupancy determination from lamp-post camera images. In: 16th international IEEE conference on intelligent transportation systems (ITSC 2013). IEEE, pp 2387–2392

  13. 13.

    Ge Z, Bewley A, McCool C, Corke P, Upcroft B, Sanderson C (2016) Fine-grained classification via mixture of deep convolutional neural networks. In: 2016 IEEE winter conference on applications of computer vision (WACV). IEEE, pp 1–6

  14. 14.

    Geiger A, Lenz P, Urtasun R (2012) Are we ready for autonomous driving? The kitti vision benchmark suite. In: 2012 IEEE conference on computer vision and pattern recognition. IEEE, pp 3354–3361

  15. 15.

    Gholami A, Kwon K, Wu B, Tai Z, Yue X, Jin PH, Zhao S, Keutzer K (2018) Squeezenext: hardware-aware neural network design. In: 2018 IEEE conference on computer vision and pattern recognition workshops, CVPR workshops 2018, Salt Lake City, UT, USA, pp 1638–1647

  16. 16.

    Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of things (IoT): a vision, architectural elements, and future directions. Future Gener Comput Syst 29(7):1645–1660

    Article  Google Scholar 

  17. 17.

    Gupta S, Agrawal A, Gopalakrishnan K, Narayanan P (2015) Deep learning with limited numerical precision. In: International conference on machine learning, pp 1737–1746

  18. 18.

    He K, Sun J (2015) Convolutional neural networks at constrained time cost. In: IEEE conference on computer vision and pattern recognition, pp 5353–5360

  19. 19.

    He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE conference on computer vision and pattern recognition, pp 770–778

  20. 20.

    Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) Mobilenets: efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861

  21. 21.

    Huang Q, Zhou K, You S, Neumann U (2018) Learning to prune filters in convolutional neural networks. In: 2018 IEEE winter conference on applications of computer vision (WACV). IEEE, pp 709–718

  22. 22.

    Iandola F, Han S, Moskewicz M, Ashraf K, Dally W, Keutzer K (2019) Squeezenet: Alexnet-level accuracy with 50x fewer parameters and \(< 0.5\) mb model size. arXiv preprint arXiv:1602.07360

  23. 23.

    Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Advances in neural information processing systems, pp 1097–1105

  24. 24.

    Lan KC, Shih WY (2014) An intelligent driver location system for smart parking. Expert Syst Appl 41(5):2443–2456

    Article  Google Scholar 

  25. 25.

    LeCun Y, Boser BE, Denker JS, Henderson D, Howard RE, Hubbard WE, Jackel LD (1990) Handwritten digit recognition with a back-propagation network. In: Advances in neural information processing systems, pp 396–404

  26. 26.

    Li H, Kadav A, Durdanovic I, Samet H, Graf HP (2017) Pruning filters for efficient convnets. In: 5th international conference on learning representations

  27. 27.

    Lin D, Talathi S, Annapureddy S (2016) Fixed point quantization of deep convolutional networks. In: International conference on machine learning, pp 2849–2858

  28. 28.

    Locke D (2010) Mq telemetry transport (mqtt) v3. 1 protocol specification, vol 15. IBM developerWorks Technical Library

  29. 29.

    Malina L, Srivastava G, Dzurenda P, Hajny J, Fujdiak R (2019) A secure publish/subscribe protocol for internet of things. In: Proceedings of the 14th international conference on availability, reliability and security, pp 1–10

  30. 30.

    Malina L, Srivastava G, Dzurenda P, Hajny J, Ricci S (2019) A privacy-enhancing framework for internet of things services. In: International conference on network and system security. Springer, pp 77–97

  31. 31.

    Mathews SP, Gondkar RR (2019) Protocol recommendation for message encryption in mqtt. In: 2019 international conference on data science and communication (IconDSC). IEEE, pp 1–5

  32. 32.

    Mosquitto (2020) An open source MQTT broker, E.M.

  33. 33.

    Rajabioun T, Ioannou PA (2015) On-street and off-street parking availability prediction using multivariate spatiotemporal models. IEEE Trans Intell Transp Syst 16(5):2913–2924

    Article  Google Scholar 

  34. 34.

    Redmon J, Farhadi A (2018) Yolov3: an incremental improvement. arXiv preprint arXiv:1804.02767

  35. 35.

    Roukounaki A, Efremidis S, Soldatos J, Neises J, Walloschke T, Kefalakis N (2019) Scalable and configurable end-to-end collection and analysis of IoT security data: towards end-to-end security in IoT systems. In: 2019 global IoT summit (GIoTS). IEEE, pp 1–6

  36. 36.

    Sandler M, Howard A, Zhu M, Zhmoginov A, Chen LC (2018) Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4510–4520

  37. 37.

    SezerS (2018) T1c: Iot security: threats, security challenges and IoT security research and technology trends. In: 2018 31st IEEE international system-on-chip conference (SOCC). IEEE, pp 1–2

  38. 38.

    Shoeibi N, Shoeibi N (2019) Future of smart parking: automated valet parking using deep q-learning. In: International symposium on distributed computing and artificial intelligence. Springer, pp 177–182

  39. 39.

    Shoup DC (2006) Cruising for parking. Transp Policy 13(6):479–486

    Article  Google Scholar 

  40. 40.

    Wu B, Iandola F, Jin PH, Keutzer K (2017) Squeezedet: unified, small, low power fully convolutional neural networks for real-time object detection for autonomous driving. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 129–137

  41. 41.

    Xiang X, Lv N, Zhai M, El Saddik A (2017) Real-time parking occupancy detection for gas stations based on Haar-AdaBoosting and CNN. IEEE Sens J 17(19):6360–6367

    Article  Google Scholar 

  42. 42.

    Yang J, Portilla J, Riesgo T (2012) Smart parking service based on wireless sensor networks. In: IECON 2012-38th annual conference on IEEE industrial electronics society. IEEE, pp 6029–6034

  43. 43.

    Zhang X, Zhou X, Lin M, Sun J (2018) Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: 2018 IEEE conference on computer vision and pattern recognition, pp 6848–6856

  44. 44.

    Zhou H, Wei L, Fielding M, Creighton D, Deshpande S, Nahavandi S (2017) Car park occupancy analysis using UAV images. In: 2017 IEEE international conference on systems, man, and cybernetics (SMC). IEEE, pp 3261–3265

Download references


The authors would like to thank Mohammed Habib Allah Kechout and Sebti Tamraoui, from the Higher National School of Computer Science (Algiers), for their precious help to this work.

Author information



Corresponding author

Correspondence to Ahmed Mostefaoui.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Merzoug, M.A., Mostefaoui, A., Gianini, G. et al. Smart connected parking lots based on secured multimedia IoT devices. Computing (2021).

Download citation


  • In-city parking
  • Parking spot availability
  • Deep learning
  • Connected parking lots
  • Internet of things

Mathematics Subject Classification

  • 68T45: Machine vision and scene understanding
  • 68U10: Image processing