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Applications of Big Data Analytics and Machine Learning in the Internet of Things

  • Shamim Yousefi
  • Farnaz Derakhshan
  • Hadis KarimipourEmail author
Chapter
  • 40 Downloads

Abstract

Nowadays, the efficiency of Machine Learning (ML) mechanisms in the Internet of Things (IoT) prompts the researchers and developers to use these emerging technology in different academic and real-world applications. IoT systems could be integrated with the ML-based approaches to map the real-world challenges into the artificial intelligence world. Machine learning mechanisms have been applied to several types of IoT applications, including data analysis, wireless communication, healthcare systems, industrial systems, and security. However, the extensive use of ML-based approaches on the internet of things has posed different challenges on systems, including lack of standard datasets, trust, and resource limitation. In this chapter, we review recent ML-based approaches on IoT systems, in which a set of common issues and challenges are discussed. Our review might provide new research directions about machine learning mechanisms on the internet of things for interested researchers and developers.

References

  1. 1.
    S. Li, L. Da Xu, S. Zhao, 5G internet of things: a survey. J. Ind. Inf. Integr. 10, 1–9 (2018)Google Scholar
  2. 2.
    J. Lin, W. Yu, N. Zhang, X. Yang, H. Zhang, W. Zhao, A survey on internet of things: architecture, enabling technologies, security and privacy, and applications. IEEE Internet Things J. 4(5), 1125–1142 (2017)CrossRefGoogle Scholar
  3. 3.
    A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari, M. Ayyash, Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Commun. Surv. Tutor. 17(4), 2347–2376 (2015)CrossRefGoogle Scholar
  4. 4.
    S. Li, L. Da Xu, S. Zhao, The internet of things: a survey. Inf. Syst. Front. 17(2), 243–259 (2015)CrossRefGoogle Scholar
  5. 5.
    H.L.H. Karimipour, S. Geris, A. Dehghantanha, Intelligent anomaly detection for large-scale smart grids, in 2019 IEEE Canadian Conference of Electrical and Computer Engineering (CCECE) (IEEE, 2019), pp. 1–4Google Scholar
  6. 6.
    Z. Li, W. Zhang, D. Qiao, Y. Peng, Lifetime balanced data aggregation for the internet of things. Comput. Electr. Eng. 58, 244–264 (2017)CrossRefGoogle Scholar
  7. 7.
    L. Li, S. Li, S. Zhao, QoS-aware scheduling of services-oriented internet of things. IEEE Trans. Ind. Inform. 10(2), 1497–1505 (2014)CrossRefMathSciNetGoogle Scholar
  8. 8.
    U.S. Shanthamallu, A. Spanias, C. Tepedelenlioglu, M. Stanley, A brief survey of machine learning methods and their sensor and IoT applications, in 2017 8th International Conference on Information, Intelligence, Systems & Applications (IISA) (IEEE, 2017), pp. 1–8Google Scholar
  9. 9.
    H. HaddadPajouh, A. Dehghantanha, R. Khayami, K.K.R. Choo, A deep recurrent neural network based approach for internet of things malware threat hunting. Futur. Gener. Comput. Syst. 85, 88–96 (2018)CrossRefGoogle Scholar
  10. 10.
    M.S. Mahdavinejad, M. Rezvan, M. Barekatain, P. Adibi, P. Barnaghi, A.P. Sheth, Machine learning for internet of things data analysis: a survey. Digit. Commun. Netw. 4(3), 161–175 (2018)CrossRefGoogle Scholar
  11. 11.
    K. Ashton, That ‘internet of things’ thing. RFiD J. 22(7), 1 (2011)Google Scholar
  12. 12.
    W. Li, H. Song, F. Zeng, Policy-based secure and trustworthy sensing for internet of things in smart cities. IEEE Internet Things J. 5(2), 716–723 (2018)CrossRefGoogle Scholar
  13. 13.
    F. Firouzi et al., Internet-of-things and big data for smarter healthcare: from device to architecture, applications and analytics. Futur. Gener. Comput. Syst. 78, 583–586 (2018)CrossRefGoogle Scholar
  14. 14.
    P.A. Laplante, N. Laplante, The internet of things in healthcare: potential applications and challenges. IT Prof. 18(3), 2–4 (2016)CrossRefGoogle Scholar
  15. 15.
    B.L. Risteska Stojkoska, K.V. Trivodaliev, A review of internet of things for smart home: challenges and solutions. J. Clean. Prod. 140, 1454–1464 (2017)CrossRefGoogle Scholar
  16. 16.
    A. Alkhamisi, M.S.H. Nazmudeen, S.M. Buhari, A cross-layer framework for sensor data aggregation for IoT applications in smart cities, in 2016 IEEE International Smart Cities Conference (ISC2) (IEEE, 2016), pp. 1–6Google Scholar
  17. 17.
    W.T. Hartman, A. Hansen, E. Vasquez, S. El-Tawab, K. Altaii, Energy monitoring and control using internet of things (IoT) system, in 2018 Systems and Information Engineering Design Symposium (SIEDS) (IEEE, 2018), pp. 13–18Google Scholar
  18. 18.
    P.M. Kumar, U. Devi G, G. Manogaran, R. Sundarasekar, N. Chilamkurti, R. Varatharajan, Ant colony optimization algorithm with internet of vehicles for intelligent traffic control system. Comput. Netw. 144, 154–162 (2018)CrossRefGoogle Scholar
  19. 19.
    K.-H.N. Bui, J.J. Jung, Internet of agents framework for connected vehicles: A case study on distributed traffic control system. J. Parallel Distrib. Comput. 116, 89–95 (2018)CrossRefGoogle Scholar
  20. 20.
    P.A. Pico Valencia, J.A. Holgado-Terriza, D. Herrera-Sánchez, J.L. Sampietro, Towards the internet of agents: An analysis of the internet of things from the intelligence and autonomy perspective. Ing. e Investig. 38(1), 121–129 (2018)CrossRefGoogle Scholar
  21. 21.
    S. Luthra, S.K. Mangla, D. Garg, A. Kumar, Internet of things (IoT) in agriculture supply chain management: a developing country perspective, in Emerging Markets from a Multidisciplinary Perspective. Advances in Theory and Practice of Emerging Markets, ed. By Y. Dwivedi et al. (Springer, Cham, 2018), pp. 209–220Google Scholar
  22. 22.
    N. Khatri, A. Sharma, K.K. Khatri, G.D. Sharma, An IoT-based innovative real-time pH monitoring and control of municipal wastewater for agriculture and gardening, in Proceedings of First International Conference on Smart System, Innovations and Computing. Smart Innovation, Systems and Technologies, vol. 79 (Springer, Singapore, 2018), pp. 353–362Google Scholar
  23. 23.
    Statista, Technology & Telecommunication, Consumer Electronics (Source: IHS, 2019), https://www.statista.com/statistics/471264/iot-numberof-connected-devices-worldwide/
  24. 24.
    S. Smith, Internet of things’ connected devices to almost triple to over 38 billion units by 2020 (2015), https://www.juniperresearch.com/press/press-releases/iot-connected-devices-to-triple-to-38-bn-by-2020
  25. 25.
    C.V. Forecast, Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2016–2021 White Paper (Cisco Public Inf., 2017)Google Scholar
  26. 26.
    J. Granjal, E. Monteiro, J. Sa Silva, Security for the internet of things: A survey of existing protocols and open research issues. IEEE Commun. Surv. Tutor. 17(3), 1294–1312 (2015)CrossRefGoogle Scholar
  27. 27.
    J. Lloret, J. Tomas, A. Canovas, L. Parra, An integrated IoT architecture for smart metering. IEEE Commun. Mag. 54(12), 50–57 (Dec. 2016)CrossRefGoogle Scholar
  28. 28.
    J. Ju, M.-S. Kim, J.-H. Ahn, Prototyping business models for IoT service. Procedia Comput. Sci. 91, 882–890 (2016)CrossRefGoogle Scholar
  29. 29.
    T. Yashiro, S. Kobayashi, N. Koshizuka, K. Sakamura, An internet of things (IoT) architecture for embedded appliances, in 2013 IEEE Region 10 Humanitarian Technology Conference (IEEE, 2013), pp. 314–319Google Scholar
  30. 30.
    M.A.A. da Cruz, J.J.P.C. Rodrigues, P. Lorenz, P. Solic, J. Al-Muhtadi, V.H.C. Albuquerque, A proposal for bridging application layer protocols to HTTP on IoT solutions. Futur. Gener. Comput. Syst. 97, 145–152 (2019)CrossRefGoogle Scholar
  31. 31.
    J. Ceron, K. Steding-Jessen, C. Hoepers, L. Granville, C. Margi, Improving IoT botnet investigation using an adaptive network layer. Sensors 19(3), 727 (2019)CrossRefGoogle Scholar
  32. 32.
    A. Azmoodeh, A. Dehghantanha, K.-K.R. Choo, Big data and internet of things security and forensics: challenges and opportunities, in Handbook of Big Data and IoT Security (Springer International Publishing, Cham, 2019), pp. 1–4Google Scholar
  33. 33.
    G. Manogaran, R. Varatharajan, D. Lopez, P.M. Kumar, R. Sundarasekar, C. Thota, A new architecture of internet of things and big data ecosystem for secured smart healthcare monitoring and alerting system. Futur. Gener. Comput. Syst. 82, 375–387 (2018)CrossRefGoogle Scholar
  34. 34.
    J. Zhang, S. Rajendran, Z. Sun, R. Woods, L. Hanzo, Physical layer security for the internet of things: authentication and key generation. IEEE Wirel. Commun. 26(5), 92–98 (2019)CrossRefGoogle Scholar
  35. 35.
    A. Kumar, M. Zhao, K.-J. Wong, Y.L. Guan, P.H.J. Chong, A comprehensive study of IoT and WSN MAC protocols: research issues, challenges and opportunities. IEEE Access 6, 76228–76262 (2018)CrossRefGoogle Scholar
  36. 36.
    S. Yousefi, F. Derakhshan, A. Bokani, Mobile agents for route planning in internet of things using markov decision process, in 2018 IEEE International Conference on Smart Energy Grid Engineering (SEGE) (2018), pp. 303–307Google Scholar
  37. 37.
    H. Zhang, J. Li, B. Wen, Y. Xun, J. Liu, Connecting intelligent things in smart hospitals using NB-IoT. IEEE Internet Things J. 5(3), 1550–1560 (Jun. 2018)CrossRefGoogle Scholar
  38. 38.
    M. Ammar, G. Russello, B. Crispo, Internet of things: a survey on the security of IoT frameworks. J. Inf. Secur. Appl. 38, 8–27 (2018)Google Scholar
  39. 39.
    S. Grooby, T. Dargahi, A. Dehghantanha, A bibliometric analysis of authentication and access control in IoT devices, in Handbook of Big Data and IoT Security (Springer International Publishing, Cham, 2019), pp. 25–51Google Scholar
  40. 40.
    M.A. Khan, K. Salah, IoT security: review, blockchain solutions, and open challenges. Futur. Gener. Comput. Syst. 82, 395–411 (2018)CrossRefGoogle Scholar
  41. 41.
    C. Stergiou, K.E. Psannis, B.-G. Kim, B. Gupta, Secure integration of IoT and cloud computing. Futur. Gener. Comput. Syst. 78, 964–975 (2018)CrossRefGoogle Scholar
  42. 42.
    E. Ahmad, M. Alaslani, F.R. Dogar, B. Shihada, Location-aware, context-driven QoS for IoT applications. IEEE Syst. J., 1–12 (2019).  https://doi.org/10.1109/JSYST.2019.2893913
  43. 43.
    S. Najjar-Ghabel, S. Yousefi, L. Farzinvash, Reliable data gathering in the internet of things using artificial bee colony. Turk. J. Electr. Eng. Comput. Sci. 26(4), 1710–1723 (2018)CrossRefGoogle Scholar
  44. 44.
    M.R. Begli, F. Derakhshan, H. Karimipour, A layered intrusion detection system for critical infrastructure using machine learning, in 2019 IEEE 7th International Conference on Smart Energy Grid Engineering (SEGE) (IEEE, 2019), pp. 1–5Google Scholar
  45. 45.
    H. Li, K. Ota, M. Dong, Learning IoT in edge: deep learning for the internet of things with edge computing. IEEE Netw. 32(1), 96–101 (2018)CrossRefGoogle Scholar
  46. 46.
    O. Osanaiye, H. Cai, K.K.R. Choo, A. Dehghantanha, Z. Xu, M. Dlodlo, Ensemble-based multi-filter feature selection method for DDoS detection in cloud computing. EURASIP J. Wirel. Commun. Netw. 2016, 130 (2016)CrossRefGoogle Scholar
  47. 47.
    Y. Chen, L. Lu, X. Yu, X. Li, Adaptive method for packet loss types in IoT: an naive Bayes distinguisher. Electronics 8(2), 134 (2019)CrossRefGoogle Scholar
  48. 48.
    G. Song, J. Rochas, L. El Beze, F. Huet, F. Magoules, K nearest neighbour joins for big data on MapReduce: a theoretical and experimental analysis. IEEE Trans. Knowl. Data Eng. 28(9), 2376–2392 (Sep. 2016)CrossRefGoogle Scholar
  49. 49.
    F. Alam, R. Mehmood, I. Katib, A. Albeshri, Analysis of eight data mining algorithms for smarter internet of things (IoT). Procedia Comput. Sci. 98, 437–442 (2016)CrossRefGoogle Scholar
  50. 50.
    Y. Alsouda, S. Pllana, A. Kurti, IoT-based urban noise identification using machine learning, in Proceedings of the International Conference on Omni-Layer Intelligent Systems - COINS ’19 (ACM, 2019), pp. 62–67Google Scholar
  51. 51.
    X. Kong, Z. Meng, N. Nojiri, Y. Iwahori, L. Meng, H. Tomiyama, A HOG-SVM based fall detection IoT system for elderly persons using deep sensor. Procedia Comput. Sci. 147, 276–282 (2019)CrossRefGoogle Scholar
  52. 52.
    A. Dehghantanha, K.R.C.A. Azmoodeh, Robust malware detection for internet of (battlefield) things devices using deep eigenspace learning. IEEE Trans. Sustain. Comput. 4, 88–95 (2019)CrossRefGoogle Scholar
  53. 53.
    I. Lee, K. Lee, The internet of things (IoT): Applications, investments, and challenges for enterprises. Bus. Horiz. 58(4), 431–440 (2015)CrossRefGoogle Scholar
  54. 54.
    A. Alabdulkarim, M. Al-Rodhaan, T. Ma, Y. Tian, PPSDT: A novel privacy-preserving single decision tree algorithm for clinical decision-support systems using IoT devices. Sensors 19(1), 142 (2019)CrossRefGoogle Scholar
  55. 55.
    S. Geris, H. Karimipour, A feature selection-based approach for joint cyber-attack detection and state estimation, in IEEE Int. Conf. on Smart Energy Grid Engineering (SEGE) (IEEE, 2019), pp. 1–5Google Scholar
  56. 56.
    M. Domb, E. Bonchek-Dokow, G. Leshem, Lightweight adaptive random-forest for IoT rule generation and execution. J. Inf. Secur. Appl. 34, 218–224 (2017)Google Scholar
  57. 57.
    A.D. Shah, J.W. Bartlett, J. Carpenter, O. Nicholas, H. Hemingway, Comparison of random forest and parametric imputation models for imputing missing data using MICE: a CALIBER study. Am. J. Epidemiol. 179(6), 764–774 (2014)CrossRefGoogle Scholar
  58. 58.
    Z. Xuanxuan, Multivariate linear regression analysis on online image study for IoT. Cogn. Syst. Res. 52, 312–316 (2018)CrossRefGoogle Scholar
  59. 59.
    C. Ioannou, V. Vassiliou, An intrusion detection system for constrained WSN and IoT nodes based on binary logistic regression, in Proceedings of the 21st ACM International Conference on Modeling, Analysis and Simulation of Wireless and Mobile Systems - MSWIM ’18 (2018), pp. 259–263Google Scholar
  60. 60.
    H. Emami, F. Derakhshan, Integrating fuzzy K-means, particle swarm optimization, and imperialist competitive algorithm for data clustering. Arab. J. Sci. Eng. 40(12), 3545–3554 (2015)CrossRefGoogle Scholar
  61. 61.
    G. Han, H. Wang, M. Guizani, S. Chan, W. Zhang, KCLP: a k-means cluster-based location privacy protection scheme in WSNs for IoT. IEEE Wirel. Commun. 25(6), 84–90 (2018)CrossRefGoogle Scholar
  62. 62.
    J.L. Vermeulen, A. Hillebrand, R. Geraerts, A comparative study of k-nearest neighbour techniques in crowd simulation. Comput. Animat. Virtual Worlds 28(3–4), e1775 (2017)CrossRefGoogle Scholar
  63. 63.
    J.S. Kumar, M.A. Zaveri, Hierarchical clustering for dynamic and heterogeneous internet of things. Procedia Comput. Sci. 93, 276–282 (2016)CrossRefGoogle Scholar
  64. 64.
    V. Cohen-addad, V. Kanade, F. Mallmann-trenn, C. Mathieu, Hierarchical clustering. J. ACM 66(4), 1–42 (2019)CrossRefzbMATHGoogle Scholar
  65. 65.
    F. Bu, An efficient fuzzy c-means approach based on canonical polyadic decomposition for clustering big data in IoT. Futur. Gener. Comput. Syst. 88, 675–682 (2018)CrossRefGoogle Scholar
  66. 66.
    K.A. Eldrandaly, M. Abdel-Basset, L. Abdel-Fatah, PTZ-surveillance coverage based on artificial intelligence for smart cities. Int. J. Inf. Manage. 49, 520–532 (2019)CrossRefGoogle Scholar
  67. 67.
    H.K.S. Mohammadi, V. Desai, Multivariate mutual information feature selection for intrusion detection, in 2018 20th International Conference on Advanced Communication Technology (ICACT) (IEEE, 2018), pp. 1–6Google Scholar
  68. 68.
    Y. Aït-Sahalia, D. Xiu, Principal component analysis of high-frequency data. J. Am. Stat. Assoc. 114(525), 287–303 (2019)CrossRefMathSciNetzbMATHGoogle Scholar
  69. 69.
    Q. Zhang, L.T. Yang, Z. Chen, P. Li, F. Bu, An adaptive dropout deep computation model for industrial IoT big data learning with crowdsourcing to cloud computing. IEEE Trans. Ind. Informatics 15(4), 2330–2337 (2019)CrossRefGoogle Scholar
  70. 70.
    M.A. Khan, A. Khan, M.N. Khan, and S. Anwar, A novel learning method to classify data streams in the internet of things, in 2014 National Software Engineering Conference (2014), pp. 61–66Google Scholar
  71. 71.
    W. Derguech, E. Bruke, E. Curry, An autonomic approach to real-time predictive analytics using open data and internet of things,” in 2014 IEEE 11th Intl Conf on Ubiquitous Intelligence and Computing and 2014 IEEE 11th Intl Conf on Autonomic and Trusted Computing and 2014 IEEE 14th Intl Conf on Scalable Computing and Communications and its Associated Workshops (IEEE, 2014), pp. 204–211Google Scholar
  72. 72.
    J. Shotton et al., Real-time human pose recognition in parts from single depth images. Commun. ACM 56(1), 116 (2013)CrossRefGoogle Scholar
  73. 73.
    S.K. Lakshmanaprabu, K. Shankar, M. Ilayaraja, A.W. Nasir, V. Vijayakumar, N. Chilamkurti, Random forest for big data classification in the internet of things using optimal features. Int. J. Mach. Learn. Cybern. 10, 2609–2618 (2019)CrossRefGoogle Scholar
  74. 74.
    I. Kotenko, I. Saenko, F. Skorik, S. Bushuev, Neural network approach to forecast the state of the internet of things elements, in 2015 XVIII International Conference on Soft Computing and Measurements (SCM) (IEEE, 2015), pp. 133–135Google Scholar
  75. 75.
    P.M. Kumar, S. Lokesh, R. Varatharajan, G. Chandra Babu, P. Parthasarathy, Cloud and IoT based disease prediction and diagnosis system for healthcare using fuzzy neural classifier. Futur. Gener. Comput. Syst. 86, 527–534 (2018)CrossRefGoogle Scholar
  76. 76.
    K. Panetta, Gartner’s top 10 strategic technology trends for 2017, Smarter With Gartner (2016)Google Scholar
  77. 77.
    I. Mehmood et al., Efficient image recognition and retrieval on IoT-assisted energy-constrained platforms from big data repositories. IEEE Internet Things J. 6(6), 9246–9255 (2019)CrossRefGoogle Scholar
  78. 78.
    J. Su, V. Danilo Vasconcellos, S. Prasad, S. Daniele, Y. Feng, K. Sakurai, Lightweight classification of IoT malware based on image recognition, in 2018 IEEE 42nd Annual Computer Software and Applications Conference (COMPSAC) (IEEE, 2018), pp. 664–669Google Scholar
  79. 79.
    C.-Y. Liao, R.-C. Chen, S.-K. Tai, Emotion stress detection using EEG signal and deep learning technologies, in 2018 IEEE International Conference on Applied System Invention (ICASI) (IEEE, 2018), pp. 90–93Google Scholar
  80. 80.
    M. Alhussein, G. Muhammad, M.S. Hossain, S.U. Amin, Cognitive IoT-cloud integration for smart healthcare: case study for epileptic seizure detection and monitoring. Mob. Netw. Appl. 23(6), 1624–1635 (2018)CrossRefGoogle Scholar
  81. 81.
    M. Chen, Y. Zhang, M. Qiu, N. Guizani, Y. Hao, SPHA: smart personal health advisor based on deep analytics. IEEE Commun. Mag. 56(3), 164–169 (2018)CrossRefGoogle Scholar
  82. 82.
    M.I. AlHajri, N.T. Ali, R.M. Shubair, Indoor localization for IoT using adaptive feature selection: a cascaded machine learning approach. IEEE Antennas Wirel. Propag. Lett. 18, 2306–2310 (2019)CrossRefGoogle Scholar
  83. 83.
    B. Berruet, O. Baala, A. Caminada, V. Guillet, DelFin: a deep learning based CSI fingerprinting indoor localization in IoT context, in 2018 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (IEEE, 2018), pp. 1–8Google Scholar
  84. 84.
    J.H. Han et al., Machine learning-based self-powered acoustic sensor for speaker recognition. Nano Energy 53, 658–665 (2018)CrossRefGoogle Scholar
  85. 85.
    N. Sharghivand, F. Derakhshan, L. Mashayekhy, QoS-aware matching of edge computing services to internet of things, in 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC) (IEEE, 2018), pp. 1–8Google Scholar
  86. 86.
    H. Hromic et al., Real time analysis of sensor data for the internet of things by means of clustering and event processing, in 2015 IEEE International Conference on Communications (ICC) (IEEE, 2015), pp. 685–691Google Scholar
  87. 87.
    J. Xiong et al., Enhancing privacy and availability for data clustering in intelligent electrical service of IoT. IEEE Internet Things J. 6(2), 1530–1540 (2019)CrossRefGoogle Scholar
  88. 88.
    Z. Yu, Big data clustering analysis algorithm for internet of things based on K-means. Int. J. Distrib. Syst. Technol. 10(1), 1–12 (2019)CrossRefGoogle Scholar
  89. 89.
    I. Ericsson, Ericssoninterim mobility report (2018), https://www.ericsson.com/assets/local/mobility%2D%2Dr
  90. 90.
    C.V.N. Index, Global mobile data traffic forecast update 2017–2022, Cisco White Papers (2019)Google Scholar
  91. 91.
    X. Li, H. He, Y.-D. Yao, Reinforcement learning based adaptive rate control for delay-constrained communications over fading channels, in The 2010 International Joint Conference on Neural Networks (IJCNN) (IEEE, 2010), pp. 1–7Google Scholar
  92. 92.
    D.-Y. Kim, S. Kim, H. Hassan, J.H. Park, Adaptive data rate control in low power wide area networks for long range IoT services. J. Comput. Sci. 22, 171–178 (2017)CrossRefGoogle Scholar
  93. 93.
    J. Tang, Z. Zhou, J. Niu, Q. Wang, An energy efficient hierarchical clustering index tree for facilitating time-correlated region queries in the internet of things. J. Netw. Comput. Appl. 40, 1–11 (Apr. 2014)CrossRefGoogle Scholar
  94. 94.
    H.S. Aghdasi, S. Yousefi, Enhancing lifetime of visual sensor networks with a preprocessing-based multi-face detection method. Wirel. Netw. 24(6), 1939–1951 (2018)CrossRefGoogle Scholar
  95. 95.
    S. Najjar-Ghabel, S. Yousefi, Enhancing performance of face detection in visual sensor networks with a dynamic-based approach. Wirel. Pers. Commun. 97(4), 6151–6166 (Dec. 2017)CrossRefGoogle Scholar
  96. 96.
    F. Derakhshan, S. Yousefi, A review on the applications of multiagent systems in wireless sensor networks. Int. J. Distrib. Sens. Netw. 15(5), 155014771985076 (2019)CrossRefGoogle Scholar
  97. 97.
    V. Vashishth, A. Chhabra, D.K. Sharma, GMMR: A Gaussian mixture model based unsupervised machine learning approach for optimal routing in opportunistic IoT networks. Comput. Commun. 134, 138–148 (Jan. 2019)CrossRefGoogle Scholar
  98. 98.
    P.M. Kumar, U. Devi Gandhi, A novel three-tier internet of things architecture with machine learning algorithm for early detection of heart diseases. Comput. Electr. Eng. 65, 222–235 (2018)CrossRefGoogle Scholar
  99. 99.
    H.H. Nguyen, F. Mirza, M.A. Naeem, M. Nguyen, A review on IoT healthcare monitoring applications and a vision for transforming sensor data into real-time clinical feedback, in 2017 IEEE 21st International Conference on Computer Supported Cooperative Work in Design (CSCWD) (IEEE, 2017), pp. 257–262Google Scholar
  100. 100.
    B. Farahani, M. Barzegari, F. S. Aliee, Towards collaborative machine learning driven healthcare internet of things, in Proceedings of the International Conference on Omni-Layer Intelligent Systems - COINS ’19 (IEEE, 2019), pp. 134–140Google Scholar
  101. 101.
    S. Shukla, M.F. Hassan, L.T. Jung, A. Awang, M.K. Khan, A 3-tier architecture for network latency reduction in healthcare internet-of-things using fog computing and machine learning, in Proceedings of the 2019 8th International Conference on Software and Computer Applications - ICSCA ’19 (IEEE, 2019), pp. 522–528Google Scholar
  102. 102.
    S. Asthana, A. Megahed, R. Strong, A recommendation system for proactive health monitoring using IoT and wearable technologies, in 2017 IEEE International Conference on AI & Mobile Services (AIMS) (IEEE, 2017), pp. 14–21Google Scholar
  103. 103.
    A. Walinjkar, J. Woods, ECG classification and prognostic approach towards personalized healthcare, in 2017 International Conference On Social Media, Wearable And Web Analytics (Social Media) (IEEE, 2017), pp. 1–8Google Scholar
  104. 104.
    A. Suresh, R. Udendhran, M. Balamurgan, R. Varatharajan, A novel internet of things framework integrated with real time monitoring for intelligent healthcare environment. J. Med. Syst. 43(6), 165 (2019)CrossRefGoogle Scholar
  105. 105.
    R. Madeira, L. Nunes, A machine learning approach for indirect human presence detection using IOT devices, in 2016 Eleventh International Conference on Digital Information Management (ICDIM) (IEEE, 2016), pp. 145–150Google Scholar
  106. 106.
    A. Abdelaziz, A.S. Salama, A.M. Riad, A.N. Mahmoud, A machine learning model for predicting of chronic kidney disease based internet of things and cloud computing in smart cities, in Security in Smart Cities: Models, Applications, and Challenges. Lecture Notes in Intelligent Transportation and Infrastructure, ed. By A. Hassanien, M. Elhoseny, S. Ahmed, A. Singh (Springer, Cham, 2019), pp. 93–114Google Scholar
  107. 107.
    P.S. Pandey, Machine learning and IoT for prediction and detection of stress, in 2017 17th International Conference on Computational Science and Its Applications (ICCSA) (IEEE, 2017), pp. 1–5Google Scholar
  108. 108.
    J.R. Kwapisz, G.M. Weiss, S.A. Moore, Activity recognition using cell phone accelerometers. ACM SIGKDD Explor. Newsl. 12(2), 74 (2011)CrossRefGoogle Scholar
  109. 109.
    J. Cheng, W. Chen, F. Tao, C.-L. Lin, Industrial IoT in 5G environment towards smart manufacturing. J. Ind. Inf. Integr. 10, 10–19 (2018)Google Scholar
  110. 110.
    J. Park, H. Park, Y.-J. Choi, Data compression and prediction using machine learning for industrial IoT, in 2018 International Conference on Information Networking (ICOIN) (2018), pp. 818–820Google Scholar
  111. 111.
    J. Siryani, B. Tanju, T.J. Eveleigh, A machine learning decision-support system improves the internet of things’ smart meter operations. IEEE Internet Things J. 4(4), 1056–1066 (2017)CrossRefGoogle Scholar
  112. 112.
    S.S. Patil, S.A. Thorat, Early detection of grapes diseases using machine learning and IoT, in 2016 Second International Conference on Cognitive Computing and Information Processing (CCIP) (IEEE, 2016), pp. 1–5Google Scholar
  113. 113.
    W. Guo, T. Fukatsu, S. Ninomiya, Automated characterization of flowering dynamics in rice using field-acquired time-series RGB images. Plant Methods 11(1), 7 (2015)CrossRefGoogle Scholar
  114. 114.
    L. Li, K. Ota, M. Dong, Deep learning for smart industry: efficient manufacture inspection system with fog computing. IEEE Trans. Ind. Inform. 14(10), 4665–4673 (2018)CrossRefGoogle Scholar
  115. 115.
    Q. Zhang, L.T. Yang, Z. Yan, Z. Chen, P. Li, An efficient deep learning model to predict cloud workload for industry informatics. IEEE Trans. Ind. Inform. 14(7), 3170–3178 (2018)CrossRefGoogle Scholar
  116. 116.
    S. Mohammadi, H. Mirvaziri, M. Ghazizadeh-Ahsaee, H. Karimipour, Cyber intrusion detection by combined feature selection algorithm. J. Inf. Secur. Appl. 44, 80–88 (2019)Google Scholar
  117. 117.
    E.M. Dovom, A. Azmoodeh, A. Dehghantanha, D.E. Newton, R.M. Parizi, H. Karimipour, Fuzzy pattern tree for edge malware detection and categorization in IoT. J. Syst. Archit. 97, 1–7 (2019)CrossRefGoogle Scholar
  118. 118.
    E.M. Dovom, A. Azmoodeh, A. Dehghantanha, D.E. Newton, R.M. Parizi, H. Karimipour, Fuzzy pattern tree for edge malware detection and categorization in IoT. J. Syst. Archit. 97, 1–7 (2019)CrossRefGoogle Scholar
  119. 119.
    L. Xiao, X. Wan, Z. Han, PHY-layer authentication with multiple landmarks with reduced overhead. IEEE Trans. Wirel. Commun. 17(3), 1676–1687 (2018)CrossRefGoogle Scholar
  120. 120.
    M. A. Aref, S. K. Jayaweera, S. Machuzak, Multi-agent reinforcement learning based cognitive anti-jamming, in 2017 IEEE Wireless Communications and Networking Conference (WCNC) (IEEE, 2017), pp. 1–6Google Scholar
  121. 121.
    S. Machuzak, S.K. Jayaweera, Reinforcement learning based anti-jamming with wideband autonomous cognitive radios, in 2016 IEEE/CIC International Conference on Communications in China (ICCC) (IEEE, 2016), pp. 1–5Google Scholar
  122. 122.
    G. Han, L. Xiao, H. V. Poor, Two-dimensional anti-jamming communication based on deep reinforcement learning, in 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (IEEE, 2017), pp. 2087–2091Google Scholar
  123. 123.
    A. Saied, R.E. Overill, T. Radzik, Detection of known and unknown DDoS attacks using artificial neural networks. Neurocomputing 172, 385–393 (2016)CrossRefGoogle Scholar
  124. 124.
    J. Sakhnini, H. Karimipour, A. Dehghantanha, Using machine learning to secure IoT systems, in 2016 14th Annual Conference on Privacy, Security and Trust (PST) (IEEE, 2016), pp. 219–222Google Scholar
  125. 125.
    S. Zhao, W. Li, T. Zia, A.Y. Zomaya, A dimension reduction model and classifier for anomaly-based intrusion detection in internet of things, in 2017 IEEE 15th Intl Conf on Dependable, Autonomic and Secure Computing, 15th Intl Conf on Pervasive Intelligence and Computing, 3rd Intl Conf on Big Data Intelligence and Computing and Cyber Science and Technology Congress (DASC/PiCom/DataCom/CyberSciTech) (IEEE, 2017), pp. 836–843Google Scholar
  126. 126.
    M.A. Alsmirat, Y. Jararweh, M. Al-Ayyoub, M.A. Shehab, B.B. Gupta, Accelerating compute intensive medical imaging segmentation algorithms using hybrid CPU-GPU implementations. Multimed. Tools Appl. 76(3), 3537–3555 (2017)CrossRefGoogle Scholar
  127. 127.
    H.H. Pajouh, R. Javidan, R. Khayami, A. Dehghantanha, K.-K.R. Choo, A two-layer dimension reduction and two-tier classification model for anomaly-based intrusion detection in IoT backbone networks. IEEE Trans. Emerg. Top. Comput. 7(2), 314–323 (2019)CrossRefGoogle Scholar
  128. 128.
    P. Mohamed Shakeel, S. Baskar, V.R. Sarma Dhulipala, S. Mishra, M.M. Jaber, Maintaining security and privacy in health care system using learning based deep-Q-networks. J. Med. Syst. 42(10), 186 (2018)CrossRefGoogle Scholar
  129. 129.
    B. Chatterjee, D. Das, S. Maity, S. Sen, RF-PUF: enhancing IoT security through authentication of wireless nodes using in-situ machine learning. IEEE Internet Things J. 6(1), 388–398 (2019)CrossRefGoogle Scholar
  130. 130.
    H. Karimipour, V. Dinavahi, Robust massively parallel dynamic state estimation of power systems against cyber-attack. IEEE Access 6, 2984–2995 (2018)CrossRefGoogle Scholar
  131. 131.
    J. Sakhnini, H. Karimipour, A. Dehghantanha, Smart grid cyber attacks detection using supervised learning and heuristic feature selection, arXiv Prepr. arXiv1907.03313 (2019)Google Scholar
  132. 132.
    H. Karimipour, A. Dehghantanha, R.M. Parizi, K.-K.R. Choo, H. Leung, A deep and scalable unsupervised machine learning system for cyber-attack detection in large-scale smart grids. IEEE Access 7, 80778–80788 (2019)CrossRefGoogle Scholar
  133. 133.
    V.D.H. Karimipour, On false data injection attack against dynamic state estimation on smart power grids, in 2017 IEEE International Conference on Smart Energy Grid Engineering (SEGE) (IEEE, 2017)Google Scholar
  134. 134.
    M. Conti, A. Dehghantanha, K. Franke, S. Watson, Internet of things security and forensics: Challenges and opportunities. Futur. Gener. Comput. Syst. 78, 544–546 (2018)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Shamim Yousefi
    • 1
  • Farnaz Derakhshan
    • 1
  • Hadis Karimipour
    • 2
    Email author
  1. 1.Department of Electrical and Computer EngineeringUniversity of TabrizTabrizIran
  2. 2.School of EngineeringUniversity of GuelphGuelphCanada

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