Security Issues and Privacy Concerns in Industry 4.0 Applications. Группа авторов. Читать онлайн. Newlib. NEWLIB.NET

Автор: Группа авторов
Издательство: John Wiley & Sons Limited
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Жанр произведения: Зарубежная компьютерная литература
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isbn: 9781119776505
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aimed as a support for a strong business platform with large opportunities [18].

      1.3.4 Machine Learning Models in SWMS

Research Purpose Device/method used Models
Water Contamination [19, 24] Water Contamination Assessments ML with Fast Fourier Transform SVM and Color Layout Descriptor
Water Quality Parameters [19, 25] Water Contamination and Quality Analysis Neural Network, ML-based classification, IoT devices SVM, IoT sensor models
Drinking Water [10, 22] Drinking Water Analysis ML-based prediction and classification Decision Tree, K-Nearest Neighbour, SVM
Water Level [21, 23] Water Level Detection IoT device Raspberry Pi
Water Meter [20] Water usage measurements IoT device, WSN Arduino and NodeMCU

      1.3.5 IoT-Based SWMS

      Many contributions have been made on SWMS using ML methods. A few researches on IoT-based SWMS are summarized in Table 1.1.

       We don’t want to push our ideas on to customers; we simply want to make what they want

       – Laura Ashley

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      11. V. Radhakrishnan and W. Wu. IoT technology for smart water system. In 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE4th International Conference on Data Science and Systems (HPCC/ Smart City/DSS), pp. 1491-1496, 2018.

      12. S. Safdar, M. Mohsin, L. A. Khan, and W. Iqbal. Leveraging the internet of things for smart waters: Motivation, enabling technologies and deployment strategies for Pakistan. In 2018 IEEE SmartWorld, Ubiquitous Intelligence Computing, Advanced Trusted Computing, Scalable Computing Communications, Cloud Big Data Computing, Internet of People and Smart City Innovation (Smart World/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), pp. 2117-2124, 2018.

      13. An experimental setup of multi-intelligent control system (mics) of water management using the internet of things (IoT). ISA Transactions, 96:309-326, 2020.

      14. J. D. Gil, M. Munoz, L. Roca, F. Rodriguez, and M. Berenguel. An IoT based control system for a solar membrane distillation plant used for greenhouse irrigation. In 2019 Global IoT Summit (GIoTS), pp, 1-6, 2019.

      15. G Sushanth and S Sujatha. IoT based smart agriculture system. In 2018 International Conference on Wireless Communications, Signal Processing and Networking (Wisp NET), pp. 1-4. IEEE, 2018.

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      17. Future