Security Issues and Privacy Concerns in Industry 4.0 Applications. Группа авторов
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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
Various literature surveys on SWMS have been summarized in Table 1.1 [19-25]. Monitoring the water quality and classifying them according to the contamination level is performed through ML methods along with IoT. When the water is classified under impure category, the level of contamination has to be tested. In water parameters such as chloride, sulphate and alkalinity contents were analyzed. With the presence of these chemicals, water quality is predicted through neural networks [19]. Big data and Artificial Intelligence (AI)–based Support Vector Machine (SVM) play an important role in categorization of water. When drinking water is analyzed, an ML-based prediction method is implemented and also IoT sensors are deployed in video-surveillance for the classification of polluted water and clean water [20]. In 2020, an intelligent water management system has been designed and implemented through Thingspeak cloud platform, where the water leakage has been detected via Blynk application [21]. Also, water metering is attached through which the amount of water consumed can be measured in real time.
Table 1.1 Research on IoT-based 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.
1.4 Conclusion
This chapter has contributed a deep dive into the review of existing research works on SWMS. A systematic framework on review of Industry 4.0 with a smart water management system is explained in the introductory part in Section I and then followed by IoT and SC. Among the applications of SC, importance of conservation of water resources is discussed in detail. Section 1.2 discusses preliminaries on three parts namely, Internet to Intelligent World, Architecture of IoT and Architecture of SC. Under subsection 3, a literature survey on SWMS is focused on water quality parameters related to SWMS, SWMS in agriculture, and SWMS in smart grids and ML models on SWMS. Finally, a summarized table of review on IoT-based SWMS research is presented. Overall, the paper focuses on what SWMS means and what are the possible research directions on which future researchers can focus. As there is a lack of well efficient real-time tests in water resources, more research has to be performed in the water management system in collaboration with cross-disciplinary sectors. The future scope of SWMS relies on integration of Big Data, IoT and Cloud technologies to maintain the sustainability of water resources. It will also assist in adding the value chain which will help stakeholders to become well-versed in understanding Industry 4.0 and come up with the solutions.
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