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Link to original content: https://doi.org/10.1007/s11235-021-00870-2
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Survey on the application of deep learning in the Internet of Things

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Abstract

The Internet of Things (IoT) is a network of physical instruments, software, and sensors connected to the Internet. The IoT produces massive data, where this enormous volume of data allows the use of deep learning algorithms. The recent upgrade of the hardware boosting the computational power has resulted in utilizing deep learning alongside the IoT. Therefore, the present research aims to review the relevant conference and journal articles in IoT and deep learning from 2012 to August 2021. A composition of Systematic Mapping Study and Systematic Literature Review has been employed to review the publications for creating a survey paper. Accordingly, some questions have been raised; 36 studies have been investigated to answer these questions. The studies have been categorized into four sections, focusing on data management, network, computing environment, and applications, each being examined and analyzed. This article would be beneficial for researchers who want to investigate the field of deep learning and IoT.

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References

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

    Article  Google Scholar 

  2. Alabaa, F. A., Othmana, M., Hashema, I. A. T., & Alotaibib, F. (2017). Internet of Things security: a survey. J Netw Comput Appl, 88, 10–28.

    Article  Google Scholar 

  3. Makhdoom, I., Abolhasan, M., Abbas, H., & Ni, W. (2019). Blockchain’s adoption in IoT: the challenges, and a way forward. J Netw Comput Appl, 125, 251–279.

    Article  Google Scholar 

  4. Shadroo, Sh., & Rahmani, A. M. (2018). Systematic survey of big data and data mining in internet of things. Comput Netw, 139, 19–47.

    Article  Google Scholar 

  5. Usak, M., Kubiatko, M., Shabbir, M. S., Viktorovna Dudnik, O., Jermsittiparsert, K., & Rajabion, L. (2019). Health care service delivery based on the Internet of things: A systematic and comprehensive study. Int J Commun Syst, 32(14), 4179.

    Google Scholar 

  6. Ning, H., & Sha, Hu. (2012). Technology classification, industry, and education for future Internet of Things. Int J Commun Syst, 25(9), 1230–1241.

    Article  Google Scholar 

  7. Yanming Guo, Yu., Liu, A. O., Lao, S., Song, Wu., & Lew, M. S. (2016). Deep learning for visual understanding: a review. Neurocomputing, 187, 27–48.

    Article  Google Scholar 

  8. Mohammadi, M., Al-Fuqaha, A., Guizani, M., & Oh, J. S. (2018). Semisupervised deep reinforcement learning in support of IoT and smart city services. IEEE Internet Things J, 5(2), 624–635.

    Article  Google Scholar 

  9. Cruz-Benito J (2016) Systematic literature review and mapping. Nov 2016. [Online]. Available: https://doi.org/10.5281/zenodo.165773.

  10. Shojaiemehr, B., Rahmani, A. M., & Qader, N. N. (2018). Cloud computing service negotiation: a systematic review. Comput Stand Interfaces, 55, 196–206.

    Article  Google Scholar 

  11. Engström, E., & Runeson, P. (2011). Software product line testing—a systematic mapping study. Inf Softw Technol, 53(1), 2–13.

    Article  Google Scholar 

  12. Petersen, K., Vakkalanka, S., & Kuzniarz, L. (2015). Guidelines for conducting systematic mapping studies in software engineering: an update. Inf Softw Technol, 64, 1–18.

    Article  Google Scholar 

  13. Ghomi, E. J., Rahmani, A. M., & Qader, N. N. (2017). Load-balancing algorithms in cloud computing: a survey. J Netw Comput Appl, 88, 50–71.

    Article  Google Scholar 

  14. Breivold, H. P., Crnkovic, I., & Larsson, M. (2012). A systematic review of software architecture evolution research. Inf Softw Technol, 54(1), 16–40.

    Article  Google Scholar 

  15. Patel, A., Taghavi, M., Bakhtiyari, K., & Júnio, J. C. (2013). An intrusion detection and prevention system in cloud computing: a systematic review. J Netw Comput Appl, 36(1), 25–41.

    Article  Google Scholar 

  16. Tanwar, S., Kumar, N., & Rodrigues, J. J. (2015). A systematic review on heterogeneous routing protocols for wireless sensor network. J Netw Comput Appl, 53, 39–56.

    Article  Google Scholar 

  17. Cocchia, A. (2014). Smart and digital city: a systematic literature review. In R. Dameri & C. Rosenthal-Sabroux (Eds.), Smart city (pp. 13–43). Cham: Springer.

    Chapter  Google Scholar 

  18. ACM (2021) 20 1 2021. [Online]. Available: http://portal.acm.org.

  19. Institute of Electrical and Electronics Engineers (IEEE) (2021) 20 1 2021. [Online]. Available: https://ieeexplore.ieee.org/.

  20. Elsevier (2021) 20 1 2021. [Online]. Available: http://www.elsevier.com.

  21. Springer (2021) 20 1 2021. [Online]. Available: https://www.springer.com/gp.

  22. Wiley Online Library (2021) 20 1 2021. [Online]. Available: https://onlinelibrary.wiley.com/.

  23. Durga S, Nag R, Daniel E (2019) Survey on machine learning and deep learning algorithms used in Internet of Things (IoT) healthcare. In: 2019 3rd International conference on computing methodologies and communication (ICCMC)

  24. Ma X et al. (2019) A survey on deep learning empowered IoT applications. IEEE Access, vol. 7

  25. Sharma B, Sharma L, Lal C (2019) Anomaly detection techniques using deep learning in IoT: a survey. In: 2019 International conference on computational intelligence and knowledge economy (ICCIKE), 2019.

  26. Tmamna J, Ayed EB, Ayed MB (2020) Deep learning for internet of things in fog computing: survey and open issues. In: 2020 5th International conference on advanced technologies for signal and image processing (ATSIP)

  27. Idrissi I, Azizi M, Moussaoui O (2020) IoT security with deep learning-based intrusion detection systems: a systematic literature review. In: 2020 Fourth international conference on intelligent computing in data sciences (ICDS)

  28. N. Koroniotis, N. Moustafa and E. Sitnikova, "Forensics and Deep Learning Mechanisms for Botnets in Internet of Things: A Survey of Challenges and Solutions," IEEE Access, vol. 7, 2019.

  29. Babu MR, Veena KN (2021) A survey on attack detection methods for iot using machine learning and deep learning. In: 2021 3rd International conference on signal processing and communication (ICPSC)

  30. Al-Garadi, M. A., Mohamed, A., Al-Ali, A. K., Du, X., Ali, I., & Guizani, M. (2020). A survey of machine and deep learning methods for internet of things (IoT) Security. IEEE Commun Surveys Tutor, 22(3), 1646–1685.

    Article  Google Scholar 

  31. Janani K, Ramamoorthy S (2021) IoT security and privacy using deep learning model: a review. In: 2021 International conference on intelligent technologies (CONIT)

  32. Wang W, Zhang M (2018) Tensor deep learning model for heterogeneous data fusion in Internet of Things. In: IEEE Transactions on emerging topics in computational intelligence, pp. 1–10

  33. Liang SD (2018) Smart and fast data processing for deep learning in Internet of Things: less is more. In: IEEE Internet of Things Journal

  34. Fanyu, Bu., Wang, X., & Gao, Bo. (2019). A multi-projection deep computation model for smart data in Internet of. Fut Gener Comput Syst, 93, 68–76.

    Article  Google Scholar 

  35. Li, P., Chen, Z., Yang, L. T., Zhang, Q., & Jamal Deen, M. (2018). Deep convolutional computation model for feature learning on big data in Internet of Things. IEEE Trans Ind Inform, 14(2), 790–798.

    Article  Google Scholar 

  36. Yao, S., Zhao, Y., Shao, H., Zhang, C., Zhang, A., Shaohan, Hu., Liu, D., Shengzhong Liu, LuSu., & Abdelzaher, T. (2018). Deep learning for the Internet of Things. Computer, 51(5), 32–41.

    Article  Google Scholar 

  37. Yao, S., Zhao, Y., Shao, H., Zhang, C., Zhang, A., Hu, S., Liu, D., Liu, S., Su, L., & Abdelzaher, T. (2018). SenseGAN: enabling deep learning for internet of things with a semi-supervised framework. proc ACM Interact Mob Wear Ubiq Technol, 2(3), 1–21.

    Article  Google Scholar 

  38. Khelifi, H., Luo, S., Nour, B., Sellami, A., & Moungla, H. (2019). Bringing deep learning at the edge of information-centric internet of things. IEEE Commun Lett, 23(1), 52–55.

    Article  Google Scholar 

  39. Shadroo, S., Rahmani, A. M., & Rezaee, A. (2021). The two-phase scheduling based on deep learning in the Internet of Things. Compur Netw, 185, 107684.

    Article  Google Scholar 

  40. Lv, Z., Qiao, L., Li, J., & Song, H. (2021). Deep-learning-enabled security issues in the Internet of Things. IEEE Internet Things J, 8(12), 9531–9538.

    Article  Google Scholar 

  41. Dawoud, A., Shahristani, S., & Raun, Ch. (2018). Deep learning and software-defined networks: towards secure IoT architecture. Internet Things, 3–4, 82–89.

    Article  Google Scholar 

  42. Qiu, C., Yu, F. R., Yao, H., Jiang, C., Xu, F., & Zhao, C. (2019). Blockchain-based software-defined industrial internet of things: a dueling deep Q-Learning approach. Internet Things J, 6(3), 4627–4639.

    Article  Google Scholar 

  43. McDermott CD, Majdani F, Petrovski AV (2018) Botnet detection in the Internet of Things using deep learning approaches. In: International joint conference on neural networks (IJCNN), Rio de Janeiro

  44. Muna, A. H., Moustafa, N., & Sitnikova, E. (2018). Identification of malicious activities in industrial internet of things based on deep learning models. J Inf Secur Appl, 41, 1–11.

    Google Scholar 

  45. Ayadi MI, Saadaoui FZ, Maizatc A, Ouzzif M, Mahmoudi C (2018) Deep learning for packet forwarding with an application for real time IoT. In: 2018 International conference on selected topics in mobile and wireless networking (MoWNeT), Tangier

  46. Kim, H.-Y., & Kim, J.-M. (2017). A load balancing scheme based on deep-learning in IoT. Cluster Comput, 20(1), 873–878.

    Article  Google Scholar 

  47. Ferdowsi A, Saad W (2018) Deep learning-based dynamic watermarking for secure signal authentication in the Internet of Things. In: 2018 IEEE international conference on communications (ICC), Kansas City, MO, USA, 2018

  48. Zhu, J., Song, Y., Jiang, D., & Song, H. (2018). A new deep-q-learning-based transmission scheduling mechanism for the cognitive internet of things. Internet Things J, 5(4), 2375–2385.

    Article  Google Scholar 

  49. Jafari H, Omotere O, Adesina D, Wu H, Qian L (2018) IoT devices fingerprinting using deep learning. In: MILCOM 2018 2018 IEEE military communications conference (MILCOM), Los Angeles, CA

  50. Ferdowsi, A., & Saad, W. (2019). Deep learning for signal authentication and security in massive Internet-of-Things systems. IEEE Trans Commun, 63(2), 1371–1387.

    Article  Google Scholar 

  51. Qi X, Liu C (2018) Enabling deep learning on IoT edge: approaches and evaluation. In: 2018 IEEE/ACM Symposium on Edge Computing (SEC), Seattle, WA, 2018

  52. Wei, Y., Yu, F. R., Song, M., & Han, Z. (2019). Joint optimization of caching, computing, and radio resources for fog-enabled IoT using natural actor-critic deep reinforcement learning. IEEE Internet Things J, 6(2), 2061–2073.

    Article  Google Scholar 

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

    Article  Google Scholar 

  54. Lyu, L., Bezdek, J. C., He, X., & Jin, J. (2019). Fog-embedded deep learning for the Internet of Things. IEEE Trans Ind Inform, 15(7), 4206–4215.

    Article  Google Scholar 

  55. Diro, A. A., & Chilamkurti, N. (2018). Distributed attack detection scheme using deep learning approach for Internet of Things. Fut Gener Comput Syst, 82, 761–768.

    Article  Google Scholar 

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

    Article  Google Scholar 

  57. Zhu, H., Cao, Y., Wei, X., Wang, W., Jiang, T., & Jin, S. (2019). Caching transient data for internet of things: a deep reinforcement learning approach. IEEE Internet Things J, 6(2), 2074–2083.

    Article  Google Scholar 

  58. Zhang, Q., Yang, L. T., Chen, Z., Li, P., & Bu, F. (2019). An adaptive dropout deep computation model for industrial IoT big data learning with crowdsourcing to cloud computing. IEEE Trans Ind Inform, 15(4), 2330–2337.

    Article  Google Scholar 

  59. Sundaravadivel, P., Kesavan, K., Kesavan, L., Mohanty, S. P., & Kougianos, E. (2018). Smart-log: a deep-learning based automated nutrition monitoring system in the IoT. IEEE Trans Consum Electron, 64(3), 390–398.

    Article  Google Scholar 

  60. Vellappally, S., Al Kheraif, A. A., Anil, S., & Wahba, A. A. (2019). IoT medical tooth mounted sensor for monitoring teeth and food level using bacterial optimization along with adaptive deep learning neural network. Measurement, 135, 672–677.

    Article  Google Scholar 

  61. Yao, C., Shuodong, Wu., Liu, Z., & Li, P. (2019). A deep learning model for predicting chemical composition of gallstones with big data in medical Internet of Things. Fut Gener Comput Syst, 94, 140–147.

    Article  Google Scholar 

  62. Sun Y, Xu L, Li L, Xu B, Yin C, Cai H (2018) Deep learning based image cognition platform for IoT applications. In: 2018 IEEE 15th International conference on e-business engineering (ICEBE), Xi'an

  63. Aruul Mozhi Varman S, Baskaran AR, Aravindh S, Prabhu E (2017) Deep learning and iot for smart agriculture using WSN. In: 2017 IEEE International conference on computational intelligence and computing research (ICCIC), Coimbatore

  64. Wang, X., Wang, X., & Mao, S. (2018). rf sensing in the Internet of Things: a general deep learning framework. IEEE Commun Magaz, 56(9), 62–67.

    Article  Google Scholar 

  65. Liu, Z., Yao, C., Yu, H., & Wu, T. (2019). Deep reinforcement learning with its application for lung cancer detection in medical Internet of Things. Fut Gener Comput Syst, 97, 1–9.

    Article  Google Scholar 

  66. Wang, W., Liu, F., Zhi, X., Zhang, T., & Huang, C. (2021). An integrated deep learning algorithm for detecting lung nodules with low-dose CT and its application in 6G-enabled internet of medical things. IEEE Internet Things J, 8(7), 5274–5284.

    Article  Google Scholar 

  67. Kharkovyna OL (2019) Top 10 Best Deep Learning Frameworks in 2019. 3 Jun 2019. [Online]. Available: https://towardsdatascience.com/top-10-best-deep-learning-frameworks-in-2019-5ccb90ea6de. [Accessed 24 July 2019]

  68. Comparison of deep-learning software (2019) [Online]. Available: https://en.wikipedia.org/wiki/Comparison_of_deep-learning_software. [Accessed 24 July 2019]

  69. Makadia M (2019)·Dzone. 29 March 2018. [Online]. Available: https://en.wikipedia.org/wiki/Comparison_of_deep-learning_software. [Accessed 24 July 2019]

  70. Lang, S., Bravo-Marquez, F., Beckham, Ch., Hall, M., & Frank, E. (2019). WekaDeeplearning4j: a deep learning package for Weka based on Deeplearning4j. Knowl-Based Syst, 178, 48–50.

    Article  Google Scholar 

  71. Patterson EK, Gurbuz S, Tufekci Z, Gowdy JN (2002) CUAVE: a new audio-visual database for multimodal human-computer interface research. In: 2002 IEEE International conference on acoustics, speech, and signal processing, Orlando, FL

  72. Coates A, Lee H, Ng A (2011) An analysis of single layer networks in unsupervised feature learning. In: AISTATS

  73. Rosset, S., & Inger, A. (2000). KDD-cup 99: Knowledge discovery in a charitable organization’s donor database. SIGKDD Explor Newslett, 1(2), 85–90.

    Article  Google Scholar 

  74. Tavallaee M, Bagheri E, Lu W, Ghorbani A (2009) A detailed analysis of the KDD CUP 99 data set. In: Second IEEE symposium on computational intelligence for security and defense applications (CISDA)

  75. Moustafa N, Slay J, Creech G (2017) Novel geometric area analysis technique for anomaly detection using trapezoidal area estimation on large-scale networks. In:IEEE Transactions on Big Data

  76. Stisen A, Blunck H, Bhattacharya S, Siiger Prentow Th, Baun Kjærgaard M, Dey A, Sonne T, Møller Jensen M (2015) Smart devices are different: assessing and mitigating mobile sensing heterogeneities for activity recognition. In: 13th ACM Conference on embedded networked sensor systems (SenSys 2015), Seoul, Korea

  77. Chua TS, Tang J, Hong R, Li H, Luo Zh, Zheng YT (2009) NUS-WIDE: a real-world web image database from National University of Singapore. In: ACM International conference on image and video retrieval, Greece

  78. Lane ND, Bhattacharya S, Georgiev P, Forlivesi C, Kawsar F (2015) An early resource characterization of deep learning on wearables, smartphones and Internet-of-Things devices. In: Proceedings of the 2015 international workshop on internet of things towards applications, Seoul, South Korea

  79. Krizhevsky A, Sutskever I, Hinton GE (2012) Image net classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems

  80. Lane ND et al. (2016) DeepX: a software accelerator for low-power deep learning inference on mobile devices. In: 15th ACM/IEEE international conference on information processing in sensor networks (IPSN), Vienna

  81. Netzer Y et al. (2011) Reading Digits in Natural Images with Unsupervised Feature Learning. In: NIPS workshop on deep learning and unsupervised feature learning

  82. Lane N, Georgiev P, Qendro L (2015) DeepEar: robust smartphone audio sensing in unconstrained acoustic environments using deep learning. In: UbiComp ’15

  83. Yao S, Zhao Y, Zhang A, Su L, Abdelzaher T (2017) Deepiot: Compressing deep neural network structures for sensing systems with a compressor-critic framework. In: Proc 15th ACM Conf Embed Netw Sensor Syst (SenSys)

  84. Yao, S., Zhao, Y., Shao, H., Zhang, A., Zhang, C., Li, S., & Abdelzaher, T. (2018). Rdeepsense: Reliable deep mobile computing models with uncertainty estimations. Proc ACM Interact Mob Wear Ubiq Technol, 1(4), 1–26.

    Article  Google Scholar 

  85. Anderson, C. B. (2018). The CCB-ID approach to tree species mapping with airborne imaging spectroscopy. PeerJ, 6, e5666.

    Article  Google Scholar 

  86. Zikria, Y. B., Afzal, MKh., & Kim, S. W. (2020). Deep learning for intelligent IoT: opportunities, challenges and solutions. Comput Commun, 164, 50–53.

    Article  Google Scholar 

  87. Zhang, J., & Tao, D. (2021). Empowering things with intelligence: a survey of the progress, challenges, and opportunities in artificial intelligence of things. IEEE Internrt Things J, 8(10), 7789–7817.

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Computer Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran

    Shabnam Shadroo & Ali Rezaee

  2. Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin, 64002, Taiwan

    Amir Masoud Rahmani

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  1. Shabnam Shadroo
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Correspondence to Amir Masoud Rahmani.

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Shadroo, S., Rahmani, A.M. & Rezaee, A. Survey on the application of deep learning in the Internet of Things. Telecommun Syst 79, 601–627 (2022). https://doi.org/10.1007/s11235-021-00870-2

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