# Usage of IoT Framework in Water Supply Management for Smart City in Nepal
## 2 Related Works
In 2020 [PERSON] and [PERSON] in the United Kingdom focused on the estimation of the water demand by selecting machine learning models and this model was the so-called gradient boosting machine (GBM). This model was also used to identifying the one that achieves the best prediction accuracy. By using the models in postcode area it was found that GBM method combines prediction accuracy with ease of implementation. The spatial scale was used to allow sufficient data to train and test the model [53]. This model was based on the machine learning as well as statistical analysis and the system has to depend on the external entities to obtain data. The consumption pattern was demonstrated by using 423 smart meters and radio frequency (RF) modules and the signal repeaters were installed at different locations. The research has not written about devices that have been used in the smart meters. However, an efficient process of obtaining the data regarding water consumption has been incorporated [54]. Although the different frameworks as presented in Table 2 and other relevant research works have their own features and limitations, they have significant importance to knowing if we are reinventing the wheel. Almost all of the frameworks have limitations of particular protocols, microcontroller, operating system (OS), network, security and scalability. However, in the IoT framework developed in this research fits for almost all of the IoT applications and can be said as a generalized IoT framework. Nevertheless, the water supply management sector has been considered to narrow down the research work. The detail of the framework is discussed in detail in the results and discussion section. Nevertheless, the framework as an output of this research work does not focus only on the model but an entire ecosystem from generation of data to its effective application. As such, the following are the core features of this framework and the experimentation which can be regarded as the novelty of the developed framework:  
1. The experimental setup uses water-proof ultrasonic sensor which is new as compared to other smart meters. The use of ultrasonic sensors means that the installation is easy as it is independent of the water path but only dependent on the reservoir. 2. The framework focuses both on data generation along with its application. The experimental setup defines a methodology of how the various hardware and software technologies can be layered such that each aspect of the system can be handled uniquely. For example, the sensor and module unit deals with the transducers along all the signal analysis for the retrieval of data. The control and processing unit can then perform further analysis for correctness of the data and further processing. 3. The framework implements cloud based technologies in IoT to make any IoT system scalable. 4. The framework also focuses on the actual use of data for forecasting or prediction, knowledge discovery as well as other Information system needs. 5. The framework can be regarded as a general data acquisition, storage and analysis framework for IoT devices which point out the major units that are needed in the IoT rather than other primitive data-focused systems.