# Usage of IoT Framework in Water Supply Management for Smart City in Nepal
## 4 Results and Discussion  
The experiment using the setup as shown in Figure 3 was conducted for 360 days for 6 spots in Kathmandu valley but the data were generated for only 85 days. The status of consumption and leakage amount of water in these spots is shown in Figure 5. Problemswere not reported by the house owners; they were delighted with having such a system installed at their house because this IoT application prevented the overflow of water and identified the leakage pertinent in all these six spots. The experiment tested in the IoT laboratory has been regarded as a baseline for the IoT framework development shown in Figure 3. The result of the system and the expectation of the house owners were almost similar. However, there were some problems regarding the mechanical part while installing the system in the water tanks of the owner in particular, while mounting the sensor in the cover of the tank. The observation of the consumption and leakage amount of water in each of the six spots is depicted in Figure 5. In some spots, the leakage was insignificant whereas in some it was highly significant. The plot also gives information that when leakage was reported automatically, the leakage area was identified by lessening the amount of leakage. After observation of the data acquired, we can easily use it to observe the various trends among each of the household under experimentation. For example: the average consumption of Spot 1 over 85 days is found to be 17.19 L, Spot 2 is 33.275 L, Spot 3 is 40.94 L, Spot 4 is 37.25 L, Spot 5 is 54.75 L and Spot 6 is 33.95 L. Similarly, the analysis can be expanded as mentioned in our framework for analysis of an entire city or province using big data analytics. Also, various methods can be implemented to forecast consumption in the near future. This forecast can help the water supply authority to predict the water resource needed in the future and plan for it accordingly. Consumers can use the data to predict and monitor their own water usage. Nevertheless, after applying the billing formula, the total cost required for water can be predicted which can help in budgeting for consumers as well as the authority. Also, along with the consumption, leakage monitoring was undertaken. The aforementioned algorithm to detect leakage was used in the experimental spots. During the experiment, Spot 1 showed almost no leakage. During the time of Day 20 to Day 29, an average of 5.34 L of water is reported as leakage by the system. This was not alerted to the household though the system because it is still under the threshold which was defined to be 10 L per day although this threshold can vary. The threshold is set by speculative analysis only and it can be calculated by observing the various leakages after data from huge number of households is generated. In the case of Spot 2 and Spot 3, there was almost no leakage. Figure 5: Monitoring the consumption and leakage pattern in all six spots where the IoT application was installed. At Spot 4, the water usage at nighttime was observed to go over our threshold but there was no consistency and our system requires at least a week of consistency so that it was also not reported. Such inconsistency is expected because there are times when people intentionally use water but the volume is generally very small and large volumes are also inconsistent over a period of time. In Spot 6, large volumes of usage are observed (greater than our threshold), but still due to inconsistency, it is also marked as human use. Although the data is seen in Figure 4 as leakage, it is actually the measure of leakage and does not necessarily mean that the leakage was reported. At Spot 5 though, during Day 22 to Day 36, an average of 10.38 L of leakage was observed which was considered a leakage by our system and the issue was reported to the people there.