# Usage of IoT Framework in Water Supply Management for Smart City in Nepal
## 2 Related Works
This approach was tested in 327 households in Australia. The result revealed three main water use profiles, shower, clothes washing and irrigation. A difference between usage time and the intensity of use existed in each class. Customer segmentation and analysis approach found that a concise snapshot of recurrent water use routines from the smart meter could be used to support the demand management strategies in water supply sector [50]. A comparison of two procedures namely, top-down and bottom-up approaches, was made with the aim of generating water demand time series data at single user and nodal scales. The top-down procedure includes non-parametric disaggregation based on the K-nearest neighbors approach. After defining the water demand patterns, the disaggregation is used to generate time series at the lower levels of spatial aggregation. The bottom-up procedure adopts beta probability distribution with tunable bounds or a gamma distribution in the first phase and Copula based re-sort is applied to the demand time series to impose existing rank cross-correlations between users and temporal lags in a second phase. Two case studies were considered for comparing these two procedures and both were related to a smart water network in Italy. The result displayed that bottom-up approach procedure performs better than top-down in terms of skewness and rank cross-correlation at fine scale whereas; top-down procedure did better in terms of skewness and rank cross-correlation when aggregated demand was considered. Nevertheless, when the aggregation was considered at nodes there was affect in the performance of both top-down and bottom-up procedure [51]. With a motive of managing an urban water system, simulation was undertaken by examining three real world case studies that followed a single stochastic modeling strategy which is applicable at any fine time scale to preserve distributional and dependence properties of the process. This modeling strategy is based upon the Nataf's joint distribution model and, particularly, on the quantile mapping of an auxiliary Gaussian process to establish the processes with the target marginal distribution and correlation structure. The result of the simulation showed the efficiency of the simulation strategy in terms of reproducing the varieties of marginal and dependence properties encountered in water demand records from one minute up to one hour [52]. The anomalies like burst water pipes, leakage, and unauthorized consumption of water can be known if the demand reconstruction pattern is known. Therefore, to know the demand reconstruction two procedures; one is using smart meter at the end of life at all \begin{table}
\begin{tabular}{c c c} \hline \hline
**SN** & **IoT Framework** & **Features** & **Limitations** \\ \hline
18 & Mocana [47] & Cloud services with security & Only service orientation \\ \hline
19 & RTI [48] & Based on Connext DDS and does not require, response brokers, directory services, servers, as well as administration, unlike messaging middleware & Connext DDS built especially for smart computers and their cyber-physical systems \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._locations and the other is the data when smart meters were not present can be used. The first method uses stepwise regression that measures the demand time series aimed at both the selection of use locations and the model calibration. The second method consists of the application of criteria to identify the subset of representative locations on the basis of available data and other practical considerations like the annual bill (showing consumed amount of water) of the consumers. When both methodologies were applied in a case study in Italy, the result displayed that the accuracy of the total demand pattern reconstruction increases with the increase in sample size of the location [49]. This research focuses more on obtaining the data using smart meters an applying statistical models, regression and correlation to check the accuracy of the obtained data.