# Usage of IoT Framework in Water Supply Management for Smart City in Nepal
## 2 Related Works
Some physico-chemical variables can be united with spectroscopic techniques and smart sensing systems to calculate pollution parameters like oxygen demand and the approximation is computed by using multiple sensors fusion technique and an artificial neural network (ANN) algorithm [31]. The experiment, spectroscopic techniques, and smart sensing system have been successfully tested in a city called Pierre Benito in France. This experiment was tested over 71 water samples in this city and the result showed that the estimated amount of oxygen demand was in a good state when measured against previous conventional methods [31]. The detailed study of prevailing IoT frameworks indicates significant differences between the frameworks with respect to their context of study as well as the spectra of the components used in the framework and their entire level of expansion, their mode of differentiation, and notion. Most patently, the findings of this study illustrate lack of knowledge that concentrates on the public sector and government as well as IoT frameworks. The in-depth study and examination of current IoT frameworks suggest significant alterations with other IoT frameworks. These differences are noted in termsof their factors of analysis, parts or constituent directed in the spectra of framework and their detailed information; additionally, their angle of change or abstraction has noticeably illustrated a gap between public services, public enterprises and government well informed about the IoT framework [1]. One of the studies conducted in Surrey city in Canada depicted that within the concept of the IoT used in smart homes, 6000 houses consume more than 9000 cubic meters of water. This type of system can help the authorities to estimate the water billing rates based on the use of water. This study has also found that the flow of water to several areas depending on the need of an area can also be controlled. The forecasting undertaken by the system can also help authorities to control water resources depending on their reservoirs. The water can be stored in reservoirs and distributed according to the water consumption parameters [32]. To check the authenticity of the statistics regarding water usage and predict for future need the skewness measure has been used. Univariate usage of water consumption W1, W2, W3, WN is given by the following formula of skewness:  
\[\mathrm{S_{k}}=\sum_{i=1}^{N}\frac{\left(\mathrm{W_{i}}-W^{-}\right)^{\frac{ 3}{N}}}{\mathrm{S^{3}}}\]  
where \(\mathrm{W^{-}}\) is the mean, \(\mathrm{s}\) is the standard deviation, and \(\mathrm{N}\) is the number of data points. While computing the skewness, the \(\mathrm{s}\) is computed with \(\mathrm{N}\), rather than \(\mathrm{N}-1\)[32]. Algorithms have been developed to obtain the data of the soil moisture that is essential in a smart irrigation system using IoT. Web service has been used for data collection from sensors and these data have been utilized in performing the statistical analysis to predict the soil moisture. The real-time monitoring of the soil moisture contributes in agricultural sector to a great extent [33]. WSN has also been used widely in IoT applications. The water has been computed by using float sensors in the tank. There is also consideration that the elevation difference between hub and nodes will affect the transmission, where most of the nodes will be placed near to the ground due to the height of the trough of less than 50 cm, where the hub's antenna has been placed 8 m high from the ground. With regard to this requirement and also due to the availability of free frequency, preliminary design of the system is to use the LoRa(tm) with 915 MHz center frequency. The alternative product of LoRa(tm), HopeRF RFM95 is used in this system. Atmel ATmega328 is used in the node side to read the water condition in the trough [34]. The overall topology of the system is depicted in Figure 2.  
As for the sensor hub, a Raspberry Pi 1 model B with 512 MB of random access memory (RAM) was used.