# Usage of IoT Framework in Water Supply Management for Smart City in Nepal
## 4 Results and Discussion
Being completely based on the experiment and concept, the IoT framework was developed as shown in Figure 3. The experiment focuses only on implementing IoT on water supply management but we generalized it to scale it for different devices and metrics of every household. The framework expands the usage to different sectors and generalizes the various components in different parts while concentrating more on the technological infrastructure layer. However, this is one of the core parts of the IoT framework and this paper elucidates the required components as well as workflow. Several opportunities can be unlocked by running a government based on data. This helps to discard the face-to-face delivery model and promulgate the policies in the interest of the public thereby creating much value to the people living around. For this reason, real-time data provided by the sensors in the flexible and scalable IoT-enabled system plays a significant role [65]. The primary concern of the environment-friendly IoT is user satisfaction and it is directly related to the quality of experience. Therefore, it is necessary to manage the network traffic in the core computing system of IoT because the demand of IoT services is increasing day by day [66]. The framework as developed is scalable to many houses and the IoT structure does not change; the same devices are installed in all the houses but the variable i.e., size of the tank varies according to the need of the household and it is addressed in the software section. Assigning the radius of the tank is a one-time effort that has to be made while installing the device/system. Each individual house is uniquely identified through an id given during the installation. The framework as shown in Figure 4 has been tested for the experimental section by the setups shown in Figure 3. In the context of the conceptual section, it is expected to be implemented after the collection of a large amount of data and expanding the experimentation to a relatively large sample as compared to the 6 spots given in Figure 5. The conceptual section implements various techniques to gather meaningful information from the data collected from its previous counterpart. Big data analysis can be used on the humongous data which can be sent through machine learning for prediction or data mining to gain meaningful insights. This information can be used in various information systems for effective planning for the future, focused mainly on the water supply management in current experiment. The framework focuses on the technical aspects required for generation of data as well as its collection and storage. It then expands its scope onto visualization for each consumer as well as central departments and analysis of the data for future planning by related authority. It incorporates both the hardware units for effective measurement of various data along with its processing after appropriate transmission and storage. In order to assess the usability of the framework and the experiment in water supply management, a survey was undertaken to find out the total number of water supplies provided by the department of water supply and sanitation. The Table 4 is presented by classifying the data based on the province and districts in the entire country. The usability and the impact of the framework are wide since the number of water supplies all over the country (Nepal) is 4925831 (according to the survey conducted by the Department of Water Supply and Sanitation, 2017) and this number is increasing. Nepal is still using traditional metering technique to know the consumed amount of water and the usage of this system will transform the country in managing the water supply sector. The tabulated data is shown in the map of Nepal in Figure 6.  
Present day teamwork and initiatives taken throughout the world to encourage IoT in aspect of smart cities are shown by current open source IoT platforms for understanding smart city applications followed by many ideal case studies [67].