![Water Consumption Rate Per Capita Smart water management is one of the many challenges of a Smart City plan that can be solved through ICT based technology. Figure 3 shows various problems that are needed to be addressed to achieve an effective smart water management system. However, water consumption monitoring is the prime focus of this study as consumption monitoring can help the inefficient use of water and conservation [32-33]. Information collected about water consumption of a household from the smart water meter can be used for consumption monitoring [34-35].](https://www.researchgate.net/profile/Rudra-Nayak/publication/344587414/figure/fig2/AS:946043147526144@1602565671805/Water-Consumption-Rate-Per-Capita-Smart-water-management-is-one-of-the-many-challenges-of_Q320.jpg)
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Sefali Surabhi Rout et al., International Journal of Emerging Trends in Engineering Research, 8(9), September 2020, 5335 – 5343
5335
ABSTRACT
Conservation of water in urban areas is an ongoing challenge
in which technology like IoT and WSN is playing a very
crucial role. Studies show that 54% of India is facing absolute
water scarcity or high economic water stress. To address this
challenge the forecasting and monitoring of water
consumption along with effective management and
distribution are important. This paper implements the
seasonal threshold constraint on water distribution which
conserves a significant amount of water loss over uniform
supply around the year by considering end-user behavior
changes according to the season. The results have been
confirmed through simulation of the proposed algorithm
Weather-based Smart Water Monitoring (WSWM). This
extensive study only suggests a possible alternative approach
to design the water distribution system to conserve water.
However, more work is required for achieving an optimized
approach for sustainable urban water management.
Key words: Smart Water, Wireless Sensor Network, Water
Sustainability, Water Grid.
1. INTRODUCTION
Water is a resource that is essential for the survival of
mankind. Though 70 per-cent of Earth surface is covered with
water, availability for human use is very less [1-2]. Drastic
climatic change and explosive population growth has led us to
critical problems like water scarcity and water pollution.
Approximately 2.7 billion people are living in water shortage
already [2-4]. The increasing water demand and depleting
water resources gives rise to challenges such as sustainable
smart water management systems [5-6].
1.1. Water Consumption Scenario in India
India which is home to 16 percent of the world population has
only 2.5 percent of landmass and only 4 percent of water
resources. Though an approximate of 4,000 trillion liters of
precipitation is received every year only 1,869 trillion liters
are retained by the inland water bodies and man-made
reservoir [7-8]. Out of the total amount of water available
1,122 trillion liters can be exploited due to topological
constraint and inefficient distribution networks. In 2010
consumption of the country was 581 trillion liters out of
which the domestic demand is around 41 trillion liters [9-10].
With the growth of population and an increase in demand the
per capita availability of water has come down and is likely to
decrease further in the future [11-12]. By 2025 it is expected
to lower by 36 percent and by 2050 the forecast is a decrease of
60 percent in per capita water availability. While water
scarcity is ever increasing, a huge amount of water is lost in
the process of distribution in India [13-14]. These scenarios
further justify the need for an effective water management
system that monitors consumption and achieves proper
distribution which can be attained through IoT and ICT
[15-16].
1.2. IoT and ICT
IoT is the concept where everything that is network-enabled
be connected to form a network. The information collected
from a network are needed to be converted, stored, protected,
processed, transmitted and retrieved [17-18]. The technology
involved in this information processing is ICT. The
application of these technologies is extensive and plays an
important role in the planning of smart cities [19-20]. The
various applications of IoT and ICT in designing smart cities
Smart Water Solution for Monitoring of Water Usage Based
on Weather Condition
Sefali Surabhi Rout1, Hitesh Mohapatra2*, Rudra Kalyan Nayak3, Ramamani Tripathy4, Dhiraj Bhise5,
S.P.Patil6, Amiya Kumar Rath7
1,2*,7 Department of CS&E, Veer Surendra Sai University of Technology, Burla, Sambalpur-768018, OD, India
2*,3Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation,
Vaddeswaram-522502, Guntur, AP, India, hiteshmahapatra@gmail.com*
4Dept.of Master of Computer Application, United School of Business Management, Bhubaneswar, OD, India
5Department of Information Technology, SVKM’s NMIMS, Shirpur, MH, India
6Department of Computer Science and Engineering, VTU, Belagavi-590018
ISSN 2347 - 3983
Volume 8. No. 9, September 2020
International Journal of Emerging Trends in Engineering Research
Available Online at http://www.warse.org/IJETER/static/pdf/file/ijeter71892020.pdf
https://doi.org/10.30534/ijeter/2020/71892020
Sefali Surabhi Rout et al., International Journal of Emerging Trends in Engineering Research, 8(9), September 2020, 5335 – 5343
5336
are shown in Figure 1.This paper focuses on the improvement
of an energy-efficient clustering algorithm by extending the
existing algorithms LEACH and SEP. For our work, we have
considered homogeneous energy level among sensor nodes
and the nature of sensor nodes are static [21-22]. Our
proposed algorithm is a mimic of the chameleon attack which
executes in two phases. The first phase implemented the
calculation of residual energy of SNs and sorting them in
descending order. From the sorted value, the top 10% of High
residual energy-based nodes clubbed in a set called CH-set.
And the second part of execution is responsible for cluster
formation by measuring the INN gap which is based on
nature-inspired phenomena [23-25] . Our proposed procedure
advocates against the adopted sub-operations by our existing
traditional algorithms LEACH and SEP. The simulation and
evaluation of REACH are compared against the above said
traditional algorithms individually. In our clustering process,
we also paid the same level of attention to the energy-saving
scheme [26-27].
Figure 1: Application of IoT and ICT in Smart Cities
1.3. Household Water Consumption
Water consumption of a household is always affected by
various factors like the size of the family, topology, income,
etc. Season is also one of the factors affecting household water
consumption [28-29]. An average Indian uses about 150 liters
of water a day but water requirement may vary from 48 to 74
liters per person per day in winter to 66 to 104 liters per
person per day in summer. Figure 2 represents the domestic
water consumption per household and capita per day of major
cities in India [30-31].
Figure 2: Water Consumption Rate Per Capita
Smart water management is one of the many challenges of a
Smart City plan that can be solved through ICT based
technology. Figure 3 shows various problems that are needed
to be addressed to achieve an effective smart water
management system. However, water consumption
monitoring is the prime focus of this study as consumption
monitoring can help the inefficient use of water and
conservation [32-33]. Information collected about water
consumption of a household from the smart water meter can
be used for consumption monitoring [34-35].
Figure 3: Components of a Smart Water Management
System
The rest of the paper is organized as follows; Section 2,
discusses the recent developments in the field of smart water
management using IoT or ICT briefly. Section 3, shows the
proposed model for water consumption monitoring and
supply distribution to conserve water. Section 4, discusses the
algorithm for threshold detection and the results of the
simulation. Section 5 concludes the paper and highlights
some of the future issues that are to be addressed.
Sefali Surabhi Rout et al., International Journal of Emerging Trends in Engineering Research, 8(9), September 2020, 5335 – 5343
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2. LITERATURE REVIEW
Optimization at the water distribution stage is considered as
an important step for saving water and capital in the urban
water cycle. Some studies show early leak detection [36] and
determination of peak demand for pumping schedules [37]
can achieve this goal. The methodology followed in this study
starts with the installation of a high-resolution Smart water
meter. The end-use consumption patterns are developed by
analyzing data collected from the Smart water meter and
normalization of these patterns is done for each end-use.
Estimation of indoor and out-door consumption of water
splits is carried out leading to the development of final AD
patterns and peak demand curves. Some studies also show
that understanding customer demand can achieve better water
efficiency [38] and making the customer aware of their
consumption can help in water-saving [39]. Most of these
studies are paper-based interventions like leak notification
letters, feedback postcards, In-home displays, and online
portals [40] and suggest customer’s awareness as a key factor
in sustainable management of water [41]. However, the
domestic water consumption is influenced by various weather
conditions, economic and socio-demographic factors [42].
Water consumption varies significantly with variation in
temperature. Higher temperature results to increase in water
consumption [43]. Precipitation also affect the water
consumption. Water demand forecast with various
explanatory variables of consumption [44]. Re-engineering of
some traditional urban water management processes by
application of smart water meter[45] and advanced data
analytic tools was also suggested and use of a software tool
that automatically dis-aggregates and synthesizes higher
consumption rate water end-use data of costumer into reports
using a hybrid combination of data mining techniques and
pattern recognition algorithms for better water use
monitoring has also been proposed [46]. Optimization of pipe
network modeling, Improvisation in water demand forecast,
and development of more targeted conservation programs
during the time of water scarcity can be achieved by the use
the pro-posed software [47]. Along with the Smart metering
technology, this software can lead to better detection of leaks,
reduction of peak demand, pumping schedule optimization,
and improvisation in the management of wastewater. for
water management has been attempted by researchers [47].
Some researchers have used weather conditions in the
construction of water demand models using a non-linear
climatic effect based on monthly time series and annual
time-series.
2. PROPOSED METHOD
After an extensive study, it was found that it is necessary to
supply water to consumers based on the seasonal threshold. A
model is proposed to implement it effectively. This model
suggests the collection of consumption data from the user and
calculates the threshold for each season. Water is supplied
based on the threshold.
3.1 Protocol Architecture
The first step towards designing an efficient monitoring
algorithm is protocol de-sign which chalks out the overall
study flow and highlights the requirements for the algorithm.
Data about water consumption behavior collected from
various sources used to set an approximate threshold for every
season. The algorithm WSWM is designed based on the
architecture model shown in Figure 4. The purpose of the
algorithm is to monitor water consumption and to keep a
check on excess water supplies. The realization of the
architecture and WSWM is achieved by simulation along
with the data evaluation after seasonal channelization to show
that water can be conserved by following this model. Figure.4
shows the protocol architecture of the proposed model.
Figure 4: Protocol Architecture
Sefali Surabhi Rout et al., International Journal of Emerging Trends in Engineering Research, 8(9), September 2020, 5335 – 5343
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3.2 System Model
Each node in the network is a smart water meter along with a
data logger or sensor which sends data to the cluster head
[34-36]. The cluster head sends accumulated in-formation to
the base station. The base station maintains a log table that is
accessed by the WUC (Water Utility Centre). The log table
contains an identifier for each node and water consumed at
that node per month is updated. The WUC fixes the threshold
for water uses and has the authority to change it based on
weather and availability. The node where the reading of the
meter exceeds the predetermined threshold is charged with an
extra amount or the supply is stopped. Figure 4 shows the
proposed system model. It is a combaination of 4 phases such
as; protocol design, intital data accumulation, architecture
design and water flow management. The water flow
management unit is assisted by data analysis section. This
section monitors water usages during the different times of a
year. These collected data from several monitoring sections,
forwarded for further evaluation. The analysis on same
collected data, helps to bring a clear picture for efficient
decision making process. The decisions like tariff plans,
water consumption monitoring, water supply monitoring,
generation of water wastage and water usage reort.
Figure 5: System Model
4. PROPOSED ALGORITHM
Weather based Smart Water Monitoring (WSWM)
Algorithm
The algorithm which is used here to determine the threshold
for each season and the action that is to be taken with
variation in weather is named Weather-based Smart Water
Monitoring (WSWM) algorithm.
Algorithm: WSWM
Input:
Per head Water consumption (wp)
Member in a family (n)
Per family Water consumption (wf) = wp × n
Threshold Water consumption (Thw)
Process:
1. Initialize Water supply from Utility Centre (U.C.)
2. Initialize Thw based on weather
3. Initialize Calendar
4. Check(month) & fix (Thw)
5. Compute(wp) & Compute(wf)
6. If (wf ≥ Thw × n)
Stop (Water supply from U.C.)
Go to (Extra water demand phase)
Apply (Extra Charges)
Else
Abort (Water Supply)
7. Repeat Steps (1- 6) for each day at each node (where each
node represents a house).
8. Return (water consumption statistics)
9. Stop
Output:
1. Monitoring water consumption
2. Reduce excess water consumption
3. Generating revenue
Sub-Algorithm: Extra Water Demand Phase
Input: Data (Water monitoring sensor data from each node)
Initialize: Excess water demand from costumer end.
Process:
Sefali Surabhi Rout et al., International Journal of Emerging Trends in Engineering Research, 8(9), September 2020, 5335 – 5343
5339
1. SN (data)=> BS
2. BS(Information)=> UC
3. UC (Decisive Operation)
Output: Decisive Action
Where, the per head water consumption is (wp), number of
members in a family is (n) and threshold (Thw) are taken as
inputs Weather based Smart Water Monitoring (WSWM).
Every day threshold for a household is calculated by
multiplying the number of members with the predetermined
threshold. The total consumption of a household is denoted as
wf. The value of the Thw varies according to the season as
Thws for summer, Thwr for rainy and Thw for winter. The per
family water consumption (wf) is compared to threshold. If wf
exceeds the household threshold decisive action is taken by
the utility center either in the form of extra charge or abortion
of water supply depending upon the availability.
4.1. Results and Discussion
The simulation is done using CupCarbon U-one 3.8.2. A
cluster of 40 sensor nodes along with a base station is
deployed and a homogeneous family structure of 4 members
along with uniform water usage patterns are considered here.
The threshold for each season is set at each node. When the
supply from the base station exceeds the seasonal threshold,
the node sends a signal to the base station to stop the supply.
Figure 6 shows the deployment of sensor nodes and base
stations for a single cluster. Here 40 sensor nodes along with a
sink node are deployed over an area of Sambalpur (Ref:
Fig.6).
Figure 6: Deployment of SN
Figure 7:(a) Simulation for Summer
Sefali Surabhi Rout et al., International Journal of Emerging Trends in Engineering Research, 8(9), September 2020, 5335 – 5343
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Figure 7: (b) Simulation for Rainy Season
Figure 7:(c) Simulation for Winter
Figure 7(a) shows the simulation for the summer season
where the temperature is 36.89-degree Celsius and the
threshold is set to 540 liters per day per node. Figure 7(b)
shows the simulation for the rainy season where the
temperature is 24.44 degrees Celsius and the threshold is set
to 462 liters per day per node. Figure 7(c) shows the
simulation for the summer season where the temperature is
14.89-degree Celsius and the threshold is set to 400 liters per
day per node.
Figure 9 represents the comparison between uniform supply
throughout the year and supply based on the seasonal
threshold. The blue curve and the red curve represent the
uniform water supply around the year and supply based on
seasonal threshold respectively, whereas the green curve
represents the amount of water that can be saved per month in
liters. The water supply can lower to 6828.24 kl from 8760 kl
in a year. An approximate of 1931.76 kl of water can be
conserved in this cluster in a year.
Figure 8: (a). Average daily water requirement per person
based on the 12-months
Sefali Surabhi Rout et al., International Journal of Emerging Trends in Engineering Research, 8(9), September 2020, 5335 – 5343
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Figure 8 :(b). District Wise Water Monitoring Statistics
Figure 9: Uniform and seasonal supply comparison
Figure.10 illustrates the water consumption statistics based
on summer and winter season. During this study data of 30
districts of Odisha state has been used. From the graph it can
be clearly understandable that the water consumption during
summer is comparatively hiogher then winter.
Figure 10: Season wise water consumption within 30
districts
5. CONCLUSION
Water is the driving force for the survival of life on Earth. The
gap between supply and demand is gradually increasing
which, is an alarming issue for survival. The uncontrolled
supply and uses are the main cause of water loss. The existing
system fails to monitor and control the wastage of water. The
proposed WSWM model regulates the water supply based on
weather conditions which save a substantial amount of water
from wastage. The deployment of smart water is a long term
and gradually evolved process. The adoption of the threshold
factor gives two major advantages such as regulated water
consumption and revenue generation. In our result, we
illustrated the clear difference in water consumption by
weather conditions. The possible future direction of
investigation is a consideration of floating water demands
from the end-user.
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APPENDICES
Table 1: District Code used for Figure 8(b)
