An IoT-Based Water Management System for Smart Cities
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1. The signal from the sensors of the tank.
2. The requirement calculated from the data.
6.1 Signal from the Sensors
As mentioned in section IV, the water tank placed on top of every house is fitted with
different sensors. These sensors keep sending the data to the control center. This data
is used to determine when the water should be supplied and when not. When signals
are received from the sensor that the water level is low, the motorized valve at the
outlet of the pipe is opened and water is sent in. In case the sensor shows that enough
water is present in the tank adequate for the consumption of the household (obtained
from the historic data collected), then the water is not sent to the house. This helps
to divert that water to another area or block where it is actually required. Following
this method will reduce the wastage or unwanted supply of water to the houses in
the city.
6.2 Supply on Demand
The water supply to the houses as mentioned above also depends on another factor,
i.e., supply on demand. This means that the water is supplied only to an extent
which the household requires. This requirement is calculated by the analysis of the
historic data [15]. Extrapolating from usage patterns already recorded YF-S201 Hall
effect sensor, future requirement can be predicted. This would allow regulated water
supply in exact quantity as required by a household and prevent wastage. In case
of excess requirement, special requests can be arranged for individual household
delivery. The obtained value and the actual value are kept and later the error is used
to tune the model better. Storage of such information and excess supply needed by
individual households would help in tuning the predictive models for better analysis
including error margins in prediction. The same information can also be utilized for
taxing individuals for excess use beyond set limits. This application of data analysis
differentiates this method of supply from the regular distribution methods. Regular
tuning of the model can keep the prediction in range and can help optimize the
distribution better. Even seasonal variations in usage patterns can be tracked and
included in the model. Administrative decisions taken at times of water scarcity
caused by lack of rain can also be handled through such models.
257
1. The signal from the sensors of the tank.
2. The requirement calculated from the data.
6.1 Signal from the Sensors
As mentioned in section IV, the water tank placed on top of every house is fitted with
different sensors. These sensors keep sending the data to the control center. This data
is used to determine when the water should be supplied and when not. When signals
are received from the sensor that the water level is low, the motorized valve at the
outlet of the pipe is opened and water is sent in. In case the sensor shows that enough
water is present in the tank adequate for the consumption of the household (obtained
from the historic data collected), then the water is not sent to the house. This helps
to divert that water to another area or block where it is actually required. Following
this method will reduce the wastage or unwanted supply of water to the houses in
the city.
6.2 Supply on Demand
The water supply to the houses as mentioned above also depends on another factor,
i.e., supply on demand. This means that the water is supplied only to an extent
which the household requires. This requirement is calculated by the analysis of the
historic data [15]. Extrapolating from usage patterns already recorded YF-S201 Hall
effect sensor, future requirement can be predicted. This would allow regulated water
supply in exact quantity as required by a household and prevent wastage. In case
of excess requirement, special requests can be arranged for individual household
delivery. The obtained value and the actual value are kept and later the error is used
to tune the model better. Storage of such information and excess supply needed by
individual households would help in tuning the predictive models for better analysis
including error margins in prediction. The same information can also be utilized for
taxing individuals for excess use beyond set limits. This application of data analysis
differentiates this method of supply from the regular distribution methods. Regular
tuning of the model can keep the prediction in range and can help optimize the
distribution better. Even seasonal variations in usage patterns can be tracked and
included in the model. Administrative decisions taken at times of water scarcity
caused by lack of rain can also be handled through such models.