IEEE INFOCOM 2022 - IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS) | 978-1-6654-0926-1/22/$31.00 ©2022 IEEE | DOI: 10.1109/INFOCOMWKSHPS54753.2022.9798212
IEEE INFOCOM WKSHPS: A4E 2022: AI/ML for Edge/Fog Networks
Machine learning model for IoT-Edge device based
Water Quality Monitoring
Yogendra Kumar, Siba K Udgata*
School of Computer and Information Sciences
University of Hyderabad, Hyderabad, India
email: yogendrak348@gmail.com, udgata@uohyd.ac.in
Abstract—The aim of this work is to intelligently detect alarming
events in the water quality using machine learning techniques at
the edge device, which is adaptive to localities, applications and
also time. There are four objectives of this work; (1) To develop an
edge device for sensing the water quality parameters (2) to detect
changes in the water quality with respect to base line parameter
using a machine learning approach at the edge device itself (3) to
generate the alarm signals when water quality parameters go beyond
its threshold value and (3) to classify different types of contamination
and analyze them for identifying possible contamination types. For
the experimentation, three water quality indicative methods are used
to calculate the water quality, namely (a) Weighted Arithmetic Index,
(b) NSF Water Quality Index and (c) User feedback of the water
quality. Water quality is determined using water quality indexes
(WQI) on the basis of six physico-chemical sensor parameters like
biological oxygen demand, dissolved oxygen, pH, total hardness,
total dissolved solids and turbidity. With the help of WQI of these
methods, a light weight machine learning model which is suitable
for the edge device, has been developed using the Support Vector
Machine (SVM) algorithm. We also clustered the alarming events
to find out different types of alarming events.
Index Terms—Edge Intelligence, Water Quality Sensors, Water
Quality Index (WQI), Alarm Events, Machine Learning, Event
Clustering.
I. I NTRODUCTION
Water is essential to human life, health and equally important
for the environment. In our daily lives, we use water for many
purposes like drinking, cooking, bathing, cleaning, agriculture,
irrigation, among a few others. We require quality water which is
different for different purposes mentioned above. Quality water
is not only important for human health, but also for farming
fish, agriculture success, wildlife habitats and contributes to the
health of the mother earth. If the water quality is not monitored
and maintained, it will be harmful in various ways to human
life and the environment. In [1], authors have proposed different applications of the wireless sensor network using machine
learning models. Intelligence at the edge is very crucial in these
types of applications where the decision can be taken at the edge
and required follow-up action can be initiated. In the literature,
many models are proposed which are cloud and fog based
architectures and mostly suffer from large latency. In [2], authors
have proposed a sensor node placement method for detecting the
contamination source in a wireless sensor network based water
quality monitoring system,
There are some methods as Weighted Arithmetic Index, NSF
Water Quality Index, CCME Water Quality Index etc, which
can detect the water quality based on the value of the Water
Quality Index and in this work we propose to have a provision
for user feedback of the water quality which can be considered
as another method. In this work, we propose a machine learning
approach to detect the changes in water quality and also the type
of contamination. Support Vector Machine (SVM) is used to build
a machine learning (ML) model using alarming events which are
generated with the help of given WQI values. The workflow is
shown in figure 1. The training data set is used to create a model
and the test data set is used to validate the model.
A water quality index provides one number (like grade) that
shows the overall quality of water at a certain time and location
based on several parameters of water quality. The objective of
an index is to change complex data of water quality into usable
and reasonable information. Among the scientists of water quality
use of an index to ”grade” water quality is a debatable issue. It
is not possible to express the whole description of water quality
by a single number and many other parameters of water quality
are there, which are not included in the water quality index. The
index we are presenting here is not especially intended for aquatic
life regulations or human health. However, a water quality index
can give a meaningful indicator/ signal of water quality which is
based on some relevant and important parameters. It provides the
public a general idea of the probable problems with the water in
the region.
The rest of the paper is organized as follow: in section II the
related works and background is discussed. Section III gives explanations on Conventional WQI Detection methods and Machine
Learning algorithm used in the proposed work. In section IV we
explained the design and development of our proposed Algorithm
and also discussed the results and experimental analyses. In
section V we have described the conclusions of the paper and
followed by an outline of future works in section VI.
II. R ELATED W ORK
As water pollution is increasing day by day, the requirement of
quality water and the consequences of polluted water have drawn
the attention of researchers. Although a good amount of quality
work has been done using the latest technologies and applying the
recent algorithms in the recent decade by the researchers [1][2],
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Fig. 1: Workflow for detection and classification of alarming events of water quality using machine learning approach.
still there is a requirement to design and develop some improved
and optimal solutions for detecting the water quality index and
water quality class. Most of the proposed methods are based on
a wide area sensor network and a cloud based analysis model
for monitoring the water quality which has its own limitations.
An independent and intelligent IoT-edge device that can collect
the data in its IoT-layer and do the intelligent computation and
analysis in the edge-layer still remains a challenge.
The supervised machine learning based approach to predict the
water quality was proposed by Ahmed et al.[10]. The parameter used in the proposed model, namely total dissolved solid,
turbidity, temperature and pH value of water. To retrieve the
water quality indicators Hafeez et al.[11] presented a study based
on different Machine Learning Algorithms in Hong Kong over
the coastal waters. Yung et al.[12] discussed and proposed an
approach using Decision Tree to predict the water quality index
on Klang River water in Malaysia. Jing et al.[13] designed an
integrated approach using a firefly meta-heuristic algorithm with
Support Vector Machine and presented a hybrid evolutionary
model for determining the indicator of water quality. An indirect
methodology presented by Granata et al.[14] for the estimation
of quality indicators of the main wastewater based on Regression
Trees and Support Vector Machine. Xusong et al.[15] proposed
a technique for monitoring the quality of wastewater and Hoon
et al.[16] used GOCI satellite data for monitoring coastal water
quality based on the Machine Learning approaches. Arunima et.al
[17] proposed a Soft sensor model for Chemical Oxygen Demand
(COD) estimation and Sahoo et.al. [18] proposed a model for
accurate estimation of water level using ultrasonic sensors in a
direction for providing safe and sufficient water. Das et. al. [2]
proposed a method for optimal placement of sensor nodes in a
water channel network for detecting the source of contamination.
In most of the above-proposed methods, recent machine learning algorithms and statistical methods are used, but there is
no involvement of well established and acceptable methods for
finding the WQI. In this work for training the machine learning
model, the traditional and popular methods, namely Weighted
Arithmetic Index, NSF Water Quality Index and User feedback
are also used, which makes the proposed method more efficient,
adaptive and acceptable. In addition, we propose to integrate the
complete model and integrate it with the IoT-edge device for
independent and immediate decision making.
III. C ONVENTIONAL WQI METHODS AND M ACHINE
L EARNING A LGORITHMS IN THE PROPOSED WORK
Water quality is the condition of the water body. It can be
defined in two terms (i)Quantitative and (ii)Qualitative. In this
work, we have focused on the term Qualitative. The quality
of water is being determined by various methods based on
different qualitative parameters like pH, Dissolved Oxygen (DO),
Oxidation Reduction Potential (ORP), Turbidity BOD, COD,
Salinity, Arsenic, Heavy Metals, Bacteria concentration, Fluoride,
Nitrogen, Total hardness etc. There are many methods proposed
in the literature to determine the quality of the water based on
the above quality parameters.
Water Quality Index based methods are mainly used to find
a quality index of the water based on the value of the sample
parameters. Based on the index value (a single number) the water
is classified as very good, good, average, bad, worst, etc. In
our study, we have considered only two types of quality like
good or bad. Some of the popular WQI based methods are (i)
Weighted Arithmetic Index and (ii) NSF Water Quality Index.
These methods detect the water quality in terms of Water Quality
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IEEE INFOCOM WKSHPS: A4E 2022: AI/ML for Edge/Fog Networks
Index. The methodologies used for calculating different water
quality indexes are given as follows.
A. Water Quality Indexes
1) Weighted Arithmetic Index: The Weighted Arithmetic
Index[3] is a standard of drinking water quality followed by
the World Health Organization (WHO), the Indian Council for
Medical Research (ICMR) and the Bureau of Indian Standards
(BIS).
Based on Water Quality Index, the status of the given water
sample is calculated. Different Water Quality status is given with
their respective Water Quality Index Level in table I.
WQI Level
0-25
26-50
51-75
76-100
>100
Status of of Water Quality
Excellent water quality
Good water quality
Poor water quality
Very Poor water quality
Unsuitable for drinking
TABLE I: Weighted Arithmetic Index WQI and status of water
quality
2) NSF Water Quality Index: It is a standard index of water
quality that was designed and created by the National Sanitation
Foundation(NSF). The calculating principle of the NSF Water
Quality Index is given in [4].
IV. P ROPOSED M ETHOD
A. Proposed Algorithm
The proposed work introduces the SVM based WQI, given
as algorithm 1, for classifying the water into two categories as
normal and abnormal water quality based on traditional methods
and Machine Learning approaches. The Proposed Algorithm takes
the Training and Testing Data set, including the Water Quality
Indexes calculated using Traditional methods and returns the
classified value of WQI as normal or abnormal based on the
Machine Learning classifier.
Step 1 of the proposed algorithm calculates the WQI and corresponding alarming events of all the given traditional Methods.
Step 2 is one of the most important steps of the algorithm, in
this step, the union of WQI of all the traditional methods is
calculated and stored in the variable named Abnormalindex[],
which means if the instance is classified as abnormal by any one
of the given methods, the instance will be considered abnormal
WQI and remaining all other instances will be set as normal WQI
by taking the set-difference. Initialise the WQI with 0 and 1 for
the normal and abnormal index respectively in step 3 and assign it
to WQIClass[]. Step 4 is to append the proposed WQIClass[] to
the main dataset with their respective instances, values present
in WQIClass[] are considered as the label for the supervised
Machine Learning algorithm. Step 4 also involves the separation
of the Training and Testing dataset. In Step 5 Machine Learning
Model is trained based on Support Vector Machines followed by
analyses of the trained model on the test data set in step 6.
Algorithm 1 Proposed Algorithm
Input:
DP XN = Water Quality Data for Training with N no of water
quality parameters {x1 , x2 , ..., xN }, and P no of samples.
TQXN = Water Quality Data for Testing with N no of water
quality parameters {x1 , x2 , ..., xN }, and Q no of Examples.
M = {W QIM ethod1 , . . . W QIM ethodK }, Water Quality
Index Methods to calculate water quality where K is the
number of methods.
Output: SVM model based classification of WQI.
1. Calculate the WQI and corresponding alarming events of
all the given traditional Methods.
2. Abnormalindex[] = Union(Abnormalindex1 ,
Abnormalindex2 ,.., AbnormalindexK )
NormalIndex[] = SetDifference(Totalindex[], Abnormalindex[])
3. Initialize:
∀ Totalindex[], set 1 for Abnormalindexes
otherwise set 0.
WQIclass[] = Totalindex[]
4. Appending the WQIclass[] to main dataset and separate
the training dataset from it.
5. Build the model on training dataset using Support Vector
Machines Algorithm.
6. Use and analyze the composite SVM-Model on test data
or unseen examples.
B. Experimental set-up, results and discussions:
This work is an outcome of a funded research project for
monitoring water quality parameters in a wireless sensor zone.
We have been considering different water quality parameters and
locations for analyzing the water quality of the samples. The
different parameters considered for water quality measurements
and their acceptable range is given in the following table 2 along
with water temperature. These parameters are chosen based on
their importance, usability, availability of sensor and associated
electronics. These parameters are mainly interdependent and
change due to environmental conditions. Thus, a single parameter
based abnormality analysis always leads to improper results and
we need a multi-parameter monitoring system.
Drinking Water standards and respective recommending agencies are given below(All values except pH and Electrical conductivity are in mg/L) We collected some samples from different
locations using the IoT-edge device developed and installed for
the purpose and also generated the data using the data augmentation approach to have a robust data set for training and testing.
The data is cleaned to remove some outliers which are there due
to some observational errors. We collected some 4000 samples
test results for the experimentation. Out of the 4000 samples
2000 samples are used for the training and 2000 for testing
the proposed model. Initially, the IoT-edge device deployed at
different locations sends the data to the server using wireless
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Fig. 2: Drinking Water standards and respective recommending
agencies.
network and cellular network. The algorithm is executed at the
server to classify the data, generate alarms and also determine the
possible nature of alarm through clustering techniques. Then, the
trained model is embedded in the IoT-edge device for independent
decision making without any cloud/ server. An IoT-edge device
with the detailed configuration as listed below to monitor the
different water quality parameters, is developed under project
AquaSense, as shown in Fig. 3 (a). the quality parameters values
at different installations (a snap shot) is shown in a google map
in Fig. 3(b).
Fig 3(a): AquaSense Sensor Node.
Fig 3(b): Water quality monitoring at different sites.
Arduino Uno Board
Turbidity : TSL 235R LFC,IR Led
pH : Polar graphic probe,LMC6001
Temperature: DS18b20
Conductivity: Platinum electrode
Dissolve Oxygen: Galvanic probe
ORP : Platinum electrode probe,TL072
Wireless protocol: XBEE S2/GSM/Bluetooth
Voltage Source :5V option to back up with solar panel
We used WQI methods for experimentation, namely Weighted
Arithmetic Index and NSF Water Quality Index, for the training
data set. The training data set is used to generate an SVM based
model for event classification (Alarming or not alarming). We also
have a provision for user feedback of the water quality which
is used as a third method. The event fingerprints (normalized
deviation of different parameter values of the alarming sample)
are captured and classified further using the K-means and Fuzzy
C-means clustering method. For testing the model, we also used
all the three methods described above.
1) Alarm Events and Event Fingerprints Using SVM based
WQI: The alarm signals and event fingerprints are generated
using SVM based WQIas shown in figure 3. The input signals
have been generated based on input parameters value, and the
SVM model is applied to these input signals which generates
the output signals; the output signals contain both normal signals
and alarm signals. SVM model calculates the water quality in the
binary format, technically it detects WQI as either 0 or 1. If the
SVM based WQI is 0 the water quality is classified as normal
and if the SVM based WQI is 1 the water quality is classified
as abnormal. The threshold value of WQI in this method is 1. A
total of 48 signals are reaching the threshold value whose values
are equal to 1, these signals are called alarm signals.
2) Detection Accuracy of Proposed Method: A total of 2000
instances of the test dataset was used to estimate and analyze the
accuracy of the proposed SVM based Machine Learning model
and it is found that out of 2000 instances, 1952 instances were
classified as normal water quality and 48 instances were classified
as abnormal water quality. Out of 2000 instances, 1988 instances
were correctly classified and the remaining only 12 instances were
wrongly classified using the proposed approach. The confusion
matrix of this approach is given in figure 4.
Based on the confusion matrix, the accuracy of the SVM based
WQI Classifier is calculated as 99.4%, which shows a promising
result of the proposed method.
3) Event Fingerprints Clustering: The common characteristics or patterns in the deviated values of parameters are easily
identified by separating the fingerprints of abnormal instances
into groups/clusters. Therefore, the main purpose of clustering
in this work is to detect, identify, analyze and visualize the
pattern of water quality parameters deviated value. Two clustering
algorithms Fuzzy c-means and K-means clustering were used
for clustering the fingerprints of abnormal class instances. Inter
cluster and intra cluster distance was used for identifying the
better clustering algorithm. The performance of the clustering
Algorithm showed minor variation according to the distribution
of parameters values. Following are the water quality parameters
used in the study:
1) Biological Oxygen Demand
2) Dissolved Oxygen
3) pH
4) Total Hardness
5) Total Dissolved Solid
6) Turbidity
Figure 5 shows the event fingerprints clustering. Fingerprints
of abnormal quality class instances are divided into four different
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IEEE INFOCOM WKSHPS: A4E 2022: AI/ML for Edge/Fog Networks
Fig. 3: Alarm Signals and Event Fingerprints using SVM based WQI.
Fig. 4: Confusion Matrix obtained from the SVM model.
clusters. In the given graph, the clusters are classified by using
both Fuzzy c-means clustering and K-means clustering algorithm.
In cluster 1, all the parameter values have positively deviated
except Total Hardness. In other words, Total Hardness is the only
parameter that value is decreased and all remaining parameter
values are increased from their respective standard values in all
the instances of this cluster.
From cluster 2, it is recognized that pH and Total Dissolved
Solid values are decreased and the other four parameter values are
increased from their respective standard values in all the instances
of this cluster.
From Cluster 3, it is observed that all parameter values negatively deviate except Dissolved Oxygen and Total Dissolved
Solids. Dissolved Oxygen has positively deviated in all the
instances of this cluster and Total Dissolved Solid is positively
deviated in most of the instances of this cluster.
Cluster 4 shows pH value has positively deviated in all the
instances of the cluster, Dissolved Oxygen and turbidity values
have positively deviated in most of the instances of this cluster.
The remaining parameters values are negatively deviated in most
of the instances of this cluster.
It is analyzed and observed that all four clusters have shown
some specific patterns and characteristics of deviation with their
respective standard values. Based on the above observation, it
is concluded that any of the above combinations may result in
abnormal/bad water quality.
The inter cluster distance has been calculated using the Dunn
index. The Inter clustering distances of both clustering algorithms
are given as follows:
Using fuzzy c-means clustering algorithm = 0.3193
Using K-means clustering algorithm = 0.3534
The Intra clustering distance has been calculated using Silhouette value[6]. Silhouette is a method of interpretation and
validation of consistency within clusters of data. The technique
provides a succinct graphical representation of how well each
object lies within its cluster [6].
The quality of the clustering algorithm was estimated with the
help of the above Intra clustering and Inter clustering distance
results. The properties of good clustering are (i)Minimize intracluster distances and (ii)maximize inter-cluster distance. With the
results of both distances, it is observed that K-means clustering
shows better performance in this scenario as it has minimum
average intra-cluster and maximum inter-cluster distance in comparison to fuzzy c-means clustering.
4) Importance of Proposed Method: (a) It is a combination of
other traditional methods, So it will always give at least average
accurate results. (b) Any number of traditional methods of water
quality index can be combined and the Machine Learning model
can be applied successfully on the resultant dataset.
V. C ONCLUSIONS AND FUTURE SCOPE
In this work, we proposed an IoT-edge device embedded
with a machine learning model to detect abnormal changes in
water quality to detect alarming events. Various techniques for
detecting changes in the water quality were explored. The main
indicator and different parameters responsible for changes in
water quality were analyzed. The fingerprints of water parameters
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IEEE INFOCOM WKSHPS: A4E 2022: AI/ML for Edge/Fog Networks
Fig. 5: Event Fingerprints Clustering.
into different groups were clustered. Three water quality methods
as the Weighted Arithmetic Index, NSF Water Quality Index
and user feedback of the water quality used for detecting the
changes in water quality, and with the help of these methods the
SVM model was built to learn and detect the changes in water
quality for alarming events. Two machine learning clustering
algorithms, fuzzy c-means clustering and k-means clustering used
for analyzing the behaviour and pattern of the abnormal class of
water quality index. Intra clustering and inter clustering distance
were used for analyzing the quality of clustering algorithms. In
future, we will deploy more such IoT-edge devices and test the
proposed algorithm on more number of water samples in a water
distribution network system. We will also consider more number
of water quality parameters, other machine learning algorithms
and check their impact in the proposed algorithm.
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