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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References
1. K Lova Raju and V Vijayaraghavan. IoT technologies in agricultural environment: A survey. WIRELESS PERSONALCOMMUNICATIONS, 2020.
2. Industrial internet consortium ii, fact sheet. http://www.iiconsortium.org/ docs/, 2013 (accessed 15 July 2020).
3. Li Da Xu, Wu He, and Shancang Li. Internet of things in industries: A survey. IEEE Transactions on industrialinformatics, 10(4):2233-2243, 2014.
4. A. Varghese and D. Tandur. Wireless requirements and challenges in industry 4.0. In 2014 International Conferenceon Contemporary Computing and Informatics (IC3I), pages 634-638, 2014.
5. Tim Stock and G Seliger. Opportunities of sustainable manufacturing in industry 4.0. Procedia Cirp, 40:536-541, 2016.
6. Vasja Roblek, Maja Mesko, and Alojz Krape_z. A complex view of industry 4.0. Sage Open, 6(2):2158244016653987, 2016.
7. BM Alhafidh and William Allen. Design and simulation of a smart home managed by an intelligent self-adaptive system.
8. Amir H Alavi, Pengcheng Jiao, William G Buttlar, and Nizar Lajnef. Internet of things-enabled smart cities: State-of-the-art and future trends. Measurement, 129:589-606, 2018.
9. Oladayo Bello and Sherali Zeadally. Toward efficient smartification of the inter-net of things (IoT) services. Future Generation Computer Systems, 92, 10 2017.
10. Silvia Liberata Ullo and GR Sinha. Advances in smart environment monitoring systems using IoT and sensors. Sensors, 20(11):3113, 2020.
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.
16. Public Singapore. Managing the water distribution network with a smart water grid. Smart Water, 1:1{13, 07 2016.
17. Future