In e na ional Jou nal on AdHoc Ne wo king Sys ems (IJANS) Vol. 15, No.1/2, Ap il 2025
DOI:10.5121/ijans.2025.15201 1
DESIGN AND ANALYSIS OF THE PERFORMANCE
OF THE IOT-BASED WATER PURITY
IDENTIFICATION SYSTEM
Md. Ash a ul Islam and Md. Ma iqul Islam
Depa men o In o ma ion and Communica ion Enginee ing, Rajshahi Uni e si y,
Rajshahi, Bangladesh
ABSTRACT
In ecen yea s, millions o ou is s ha e isi ed Bangladesh o expe ience i s na u al beau y, unde sco ing
he c i ical need o access o po able wa e . Con amina ion o wa e sou ces poses signi ican isks o
human heal h, pa icula ly in high- a ic ou is egions. This s udy p oposes an In e ne o Things (IoT)
based wa e quali y moni o ing sys em designed o assess d inking wa e sa e y in Bangladesh’s ou is
a eas. The sys em is in eg a ed wi h a Wi-Fi-capable ESP32 mic ocon olle and ou senso s o collec
ou wa e pa ame e alues such as pH, u bidi y, o al dissol ed solids (TDS), and empe a u e wi h a.
Da a om 1,000 wa e samples collec ed ac oss di e se ou is loca ions a e ansmi ed wi elessly o a
dedica ed IoT se e o eal- ime analysis, aligning wi h wa e quali y h esholds es ablished by he Wo ld
Heal h O ganiza ion (WHO) and Bangladesh s anda ds. Resul s indica e ha 36% o samples ully
complied wi h sa e y s anda ds, 58% we e mode a ely pu e (posing minimal isk o ulne able
popula ions), and 6% we e pollu ed. These indings highligh he sys em’s po en ial o iden i y unsa e wa e
sou ces, sa egua d public heal h, and p omo e en i onmen al sus ainabili y. By o e ing scalable, cos -
e ec i e moni o ing, his IoT amewo k add esses egional challenges while con ibu ing o global e o s
o clean wa e access.
KEYWORDS
IoT, wa e pu i y, EPS32,TDS senso , pH senso , Tu bidi y senso
1. INTRODUCTION
Access o clean and sa e d inking wa e is a undamen al human igh , ye billions o people
wo ldwide s ill lack access o po able wa e . Acco ding o he Wo ld Heal h O ganiza ion
(WHO), o e 2 billion people globally ely on wa e sou ces con amina ed wi h eces, leading o
se e e heal h isks such as chole a, dia hea, dysen e y, and o he wa e bo ne diseases [1]. In
Bangladesh, a coun y known o i s ich na u al beau y and cul u al he i age, he issue o wa e
pollu ion is pa icula ly acu e. Millions o ou is s isi Bangladesh annually, d awn o i s scenic
landscapes, i e s, and his o ical si es.
Howe e , he a ailabili y o sa e d inking wa e emains a c i ical conce n o bo h locals and
ou is s, as wa e sou ces a e inc easingly con amina ed due o indus ial was e, ag icul u al
uno , and inadequa e sani a ion in as uc u e [2]. Fo ou is s, who o en ely on na u al wa e
sou ces o local supplies, he isk o consuming con amina ed wa e is high, leading o po en ial
heal h haza ds. This si ua ion unde sco es he u gen need o e ec i e wa e quali y moni o ing
sys ems ha can p o ide eal- ime da a o ensu e d inking wa e sa e y, pa icula ly in ou is
a eas [3-4].
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In ecen yea s, In e ne o Things (IoT) echnologies ha e e olu ionized wa e quali y
moni o ing by enabling eal- ime da a collec ion, emo e sensing, and au oma ed analysis [5-6].
IoT-based sys ems o e a cos -e ec i e and scalable solu ion o con inuous wa e quali y
moni o ing, making hem pa icula ly sui able o de eloping coun ies like Bangladesh, whe e
esou ces a e limi ed and he need o e icien wa e managemen is c i ical. In elligen sys ems
models a e now being de eloped o p edic wa e sa e y and enhance wa e quali y, signi ican ly
ad ancing o e adi ional me hods [7-8]. Wa e pu i y le el is de e mined by he alue o he
pa ame e s based on he wa e quali y equi emen s speci ied by Bangladesh and he guidelines
supplied by he WHO [9-10] ha a e sa e o d inking. Se e al s udies ha e explo ed he use o
IoT o wa e quali y moni o ing, bu many o hese sys ems ha e signi ican limi a ions. Fo
ins ance, [11] p oposed a GSM-based sys em o moni o ing wa e pu i y pa ame e s such as pH,
empe a u e, and conduc i i y. Howe e , i was limi ed by i s inabili y o s o e la ge amoun s o
da a o u he analysis. Simila ly, [12] de eloped an au oma ed wa e quali y moni o ing sys em
ha only measu ed wo pa ame e s, which is insu icien o a comp ehensi e assessmen o
wa e sa e y.
In [13], he au ho s su ey he ools and echniques used in exis ing wa e quali y moni o ing
sys ems. O he sys ems, such as hose p oposed by [14], elied on p op ie a y applica ions like
BLYNK, which es ic ed hei usabili y o speci ic use s and limi ed hei scalabili y. Simila ly,
[15] highligh ed he challenges o IoT-based wa e quali y moni o ing in domes ic applica ions
bu did no p o ide a solu ion ailo ed o he needs o ou is s o u al communi ies. While hese
sys ems [16] ha e demons a ed he po en ial o IoT o wa e quali y moni o ing, hey o en lack
he lexibili y and scalabili y needed o b oade applica ions, such as moni o ing wa e quali y in
ou is a eas. Fo example, [17] p oposed an IoT-based sys em o moni o ing wa e quali y in
i e s, bu i was limi ed by i s eliance on expensi e senso s and complex da a p ocessing
algo i hms.
Mo eo e , many exis ing sys ems ocus on moni o ing wa e quali y in con olled en i onmen s,
such as labo a o ies o indus ial se ings, and do no add ess he speci ic challenges o
moni o ing wa e in na u al o ou is a eas, whe e wa e quali y can a y signi ican ly due o
en i onmen al ac o s.
To add ess hese gaps, his pape p oposes an IoT-based wa e pu i y iden i ica ion sys em
ailo ed o ou is a eas in Bangladesh. The sys em in eg a es wi h Wi-Fi-capable ESP32
mic ocon olle and ou senso s o measu ing wa e pa ame e s such as pH, TDS, u bidi y, and
empe a u e. Da a is ansmi ed wi elessly o an IoT se e , enabling eal- ime moni o ing
wi hou p op ie a y so wa e. Unlike p io sys ems, ou s p io i izes usabili y in esou ce-limi ed
se ings: i s o es da a locally o mi iga e connec i i y issues. I classi ies wa e sa e y in o h ee
ca ego ies ( ully pu e, a e age pu e, and pollu ed) based on WHO and na ional s anda ds. This
empowe s ou is s and communi ies o make in o med decisions, educing isks o wa e bo ne
diseases. By combining a o dabili y, lexibili y, and comp ehensi e pa ame e analysis, his
sys em ad ances IoT applica ions in wa e quali y moni o ing, o e ing a p ac ical solu ion o
Bangladesh’s u gen public heal h challenges.
The emainde o his pape is o ganized as ollows: Sec ion 2 desc ibes he me hodology used in
his s udy. Sec ion 3 p esen s he p oposed IoT-based wa e pu i y iden i ica ion sys em, i s
ha dwa e componen s, and IoT se e con igu a ion. Sec ions 4 and 5 discuss he esul s and
discussion o he wa e quali y analysis and hei implica ions o public heal h and
en i onmen al sus ainabili y. Finally, Sec ion 6 concludes he pape wi h a summa y o he
indings and ecommenda ions o u u e esea ch.
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2. METHODOLOGY
This pape p oposes a eal- ime IoT-based wa e quali y iden i ica ion sys em ha in eg a es
senso s o collec and analyse wa e pu i y da a. The me hodology comp ises h ee co e
componen s: wa e pu i y e alua ion pa ame e s, da a collec ion p o ocols, and wa e pu i y
classi ica ion c i e ia.
2.1. Da a Collec ion A ea
Figu e 1 indica es he s udy a ea map o Bangladesh, showing he nume ous ou is a eas whe e
he wa e samples we e collec ed. The wide ange o geog aphical ea u es is shown in his isual
ep esen a ion. We collec ed da a om i e ou is egions labeled om G-1 o G-5 in
Bangladesh, as shown in Table 1. This s udy, 1,000 wa e samples we e collec ed om se e al
sou ces such as na u al wa e ( i e s, ube wells), ap wa e , bo led wa e , and ho el/ es au an
supplies. The p oposed sys em con inuously acks wa e quali y indica o s, including
empe a u e, pH le els, cloudiness ( u bidi y), and TDS. The collec ed in o ma ion is ansmi ed
wi elessly o a cen al p ocessing hub, enabling immedia e analysis and indica ing he wa e
pu i y le el in eal- ime.
Table 1. Sample dis ibu ion ac oss ou is egion
G oup
Tou is Regions
Samples
G-1
Cox’s Baza and Chi agong
200
G-2
Dhaka and Comilla
300
G-3
Rajshahi
100
G-4
Bog a and Rangpu
200
G-5
Kus ia and Mujibnaga
200
Figu e 1. Geog aphical loca ions o he s udy a ea
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2.2. Wa e Pu i y Classi ica ion
The ollowing h ee di e en condi ions a e conside ed o classi y he wa e pu i y le el such as
“Fully pu e wa e ”, “A e age pu e wa e ” and “Pollu ed wa e ”. Acco ding o WHO's speci ied
ange, Table 2 ca ego izes he alue o di e en wa e pa ame e s based on he abo e
classi ica ion. A "Fully pu e wa e " is conside ed when all pa ame e s o he collec ed wa e
sample a e wi hin he accep able sa e ange, as shown in Table 2. This wa e is ully pu e and
sa e o d inking. In he "A e age pu e wa e " le el, he ange o all pa ame e s may no be
wi hin he accep able sa e ange. In his case, one o wo pa ame e s o some samples may ha e
he accep able sa e ange, bu he emaining pa ame e alues a e el a ound he accep able sa e
ange, as shown in Table 2. Finally, in "Pollu ed wa e ," all pa ame e s o he wa e sample a e
no in he accep able sa e ange. This wa e is no sa e o us and causes se ious diseases. We
should a oid i .
Table 2. Wa e pu i y classi ica ion c i e ia
Ca ego y
pH
TDS (ppm)
Tu bidi y (NTU)
Fully pu e wa e
6.5-7.2
50-300
Tu bidi y<10
A e age pu e wa e
5.5-6.5 &7.2-8.5
300-500
11-35
Pollu ed wa e
pH>8.5
TDS>500
Tu bidi y>35
3. PROPOSED IOT-BASED WATER PURITY IDENTIFICATION SYSTEM
The p oposed IoT-based wa e pu i y iden i ica ion sys em is depic ed in he block diag am
shown in Figu e 2. This illus a ion e ec i ely ou lines he ope a ional sequence in ol ed in
e alua ing wa e pu i y h ough he use o IoT-enabled senso s and da a p ocessing echniques. In
his sys em, he ESP32 mic ocon olle uni (MCU) ac s as he cen al p ocessing hub, adep ly
con e ing analog senso da a in o a digi al o ma and ansmi ing he eadings o a dedica ed
IoT se e .
The con ol uni is designed o eal- ime da a acquisi ion, cap u ing senso eadings e e y
second and sending his in o ma ion o he IoT se e . Ou lie s such as senso e o s, ex eme
alues, we e emo ed o ensu e da ase in eg i y. Con igu ed o p ocess a se o ou senso
eadings e e y ou seconds, he IoT se e conduc s eal- ime analysis o he collec ed da a using
s a is ical me hods o u he assessmen . Based on he ca ego ized ou comes, cus ome s ecei e
p omp no i ica ions ega ding he s a us o wa e quali y, clea ly indica ing whe he he sample
complies wi h es ablished sa e y s anda ds o d inking wa e .
Figu e 2. IoT-based wa e pu i y iden i ica ion sys em
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3.1. IoT Se e Con igu a ion
Figu e 3 illus a es he con igu a ion o he IoT se e , which wi elessly acqui es da a om
senso s as p e iously ou lined. The IoT se e , ope a ed on a pe sonal compu e , is asked wi h
ecei ing, s o ing, and p ocessing he da a collec ed om hese senso s. The es ablished sys em
ga he s senso da a a one-second in e als, au oma ically logging he in o ma ion in o a CSV ile
on he se e 's PC. Once he necessa y da a poin s ha e been accumula ed, he sys em ceases
u he da a collec ion, and he da ase is hen ed in o a s a is ical model o classi ica ion. A
"NO" designa ion signi ies ha he wa e is con amina ed and deemed unsa e o consump ion.
Con e sely, wa e ha is classi ied as sa e can ei he be comple ely pu e o mode a ely pu e.
U ilizing a dedica ed IoT se e enhances da a secu i y, as sensi i e in o ma ion emains con ined
wi hin he local ne wo k. Mo eo e , hos ing da a locally educes ansmission delays, boos s eal-
ime p ocessing e iciency, and con ibu es o a seamless ope a ing expe ience o he sys em.
Figu e 3. Real- ime da a ans e o he IoT se e
3.2. Ha dwa e Equipmen
The main ha dwa e equipmen in his expe imen is he EPS32 mic ocon olle . The ESP32 is a
mul i unc ional, inexpensi e mic ocon olle wi h in eg a ed Wi-Fi and Blue oo h capabili ies,
has made i highly popula in he ield o IoT and embedded sys ems. The ESP32 ea u es a dual-
co e X ensa LX6 mic op ocesso , capable o unning a up o 240 MHz, wi h an in eg a ed
Tensilica X ensa LX6 dual-co e o single-co e p ocesso . This p o ides subs an ial compu a ional
powe o a wide ange o applica ions. I comes wi h 520 KB o SRAM and ypically includes
up o 4 MB o lash memo y. Some a ian s also o e ex e nal SPI lash suppo . The ESP32
suppo s Wi-Fi (802.11 b/g/n) and Blue oo h 4.2 (bo h Classic Blue oo h and BLE), making i
ideal o IoT applica ions [18].
The p oposed sys em u ilizes ou ad anced senso s o wa e quali y assessmen . The Analog pH
senso measu es acidi y o alkalini y, ensu ing wa e sa e y o consump ion and en i onmen al
moni o ing. The Tu bidi y senso e alua es wa e cla i y by analyzing ligh ansmi ance, aiding
in d inking wa e moni o ing, i e assessmen s, and was ewa e ea men . The Analog TDS
senso de ec s dissol ed ion concen a ions, p o iding insigh s in o wa e pu i y and equi ing
calib a ion o accu acy. Las ly, he DS18B20 empe a u e senso o e s p ecise empe a u e
eadings, which is essen ial o en i onmen al and indus ial wa e quali y managemen . These
senso s collec i ely enhance eal- ime moni o ing and in o med decision-making.
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4. RESULT
The esul s o he wa e pu i y analysis, based on da a collec ed om 1,000 wa e samples ac oss
a ious ou is loca ions in Bangladesh, p o ide aluable insigh s in o he sa e y o d inking
wa e in hese a eas. The s a is ical analysis e ealed ha he majo i y o he wa e samples ell
wi hin accep able anges, as pe WHO and Bangladesh s anda ds. Howe e , a small pe cen age
o samples exhibi ed unsa e le els o con amina ion, highligh ing he need o con inuous
moni o ing and in e en ion.
4.1. pH Analysis
Figu e 5 p esen s pH analysis esul s ac oss ou is loca ions. Fo G-1 (Figu e 5(a)), 42% o
samples ell wi hin he accep able pH ange (sa e o d inking), 56% we e mode a ely pu e (non-
ha m ul bu no ideal), and 2% we e highly con amina ed (unsa e). Simila ends we e obse ed
o G-2 o G-5 (Figu es 5(b)–5(e)). Agg ega ed esul s (Figu e 5( )) show 48% ully complian ,
50% mode a ely pu e, and 2% con amina ed. While pH le els sugges mos wa e is sa e, his
pa ame e alone does no gua an ee o e all pu i y.
(a) (b)
(d)
(c)
(e) ( )
Figu e 5. pH analysis o wa e pu i y iden i ica ion; (a) pH analysis o G-1 da a, (b) pH analysis o G-2
da a, (c) pH analysis o G-3 da a, (d) pH analysis o G-4 da a, (e) pH analysis o G-5 da a, ( ) pH
analysis o all da a sample
4.2. TDS Analysis
Figu e 8 p esen s he indings o TDS. In G-1 (Figu e 6(a)), 45% o samples me he sa e TDS
h esholds, while 52% showed ele a ed le els ha could a ec as e and odo , and 3% we e
pollu ed (posing heal h isks). Figu es 8b–8e, ep esen ing G-2 h ough G-5, displayed simila
ends. O e all esul s indica e ha 44% o samples we e sa e, 52% we e mode a ely pu e, and
4% we e pollu ed (Figu e 6( )). The TDS esul s suppo he pH indings, sugges ing ha wa e is
gene ally sa e, albei wi h localized isks.
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4.3. Tu bidi y Analysis
The u bidi y analysis esul s (Figu e 7) indica e ha 26% o samples we e ully complian , 70%
we e mode a ely pu e, and 4% we e pollu ed (as shown in Figu e 7( )). While he majo i y o he
wa e posed no immedia e heal h h ea s, he 4% deemed pollu ed (exceeding u bidi y limi s)
unde sco es con amina ion isks, pa icula ly o ulne able popula ions.
(a) (b)
(d)
(c)
(e) ( )
Figu e 6. TDS analysis o wa e pu i y iden i ica ion; (a) Tu bidi y analysis o G-1 da a, (b) Tu bidi y
analysis o G-2 da a, (c) Tu bidi y analysis o G-3 da a, (d) Tu bidi y analysis o G-4 da a, (e) Tu bidi y
analysis o G-5 da a, ( ) Tu bidi y analysis o all da a sample
(a) (b)
(d)
(c)
(e) ( )
Figu e 7. Tu bidi y analysis o wa e pu i y iden i ica ion; (a) Tu bidi y analysis o G-1 da a, (b)
Tu bidi y analysis o G-2 da a, (c) Tu bidi y analysis o G-3 da a, (d) Tu bidi y analysis o G-4 da a, (e)
Tu bidi y analysis o G-5 da a, ( ) Tu bidi y analysis o all da a sample
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5. DISCUSSION
Figu e 8 shows he s a is ical esul o wa e pu i y le els a ou is places in Bangladesh based
on he combined alues o pH, TDS, Tu bidi y, and empe a u e o each wa e sample. The
esul s indica e ha 36% o samples a e ully pu e and sa e o d inking, while 58% all in o he
a e age pu i y ca ego y, meaning hey may be sa e bu do no ully mee ideal s anda ds. Only
6% o samples a e classi ied as pollu ed, posing po en ial heal h isks. The p edominance o
mode a ely pu e wa e highligh s he need o cau ious consump ion. This IoT-based sys em
p o ides a aluable ool o ou is s o iden i y sa e d inking wa e sou ces. Table 3 summa izes
he o e all wa e pu i y classi ica ion.
Table 3.Resul summa y o o e all wa e quali y
Pa ame e
Fully pu e
wa e
A e age pu e
wa e
Pollu ed wa e
pH
49 %
50%
1%
TDS (PPM)
45%
53%
2%
Tu bidi y (NTU)
26%
70%
4%
Combine all
pa ame e s
36%
58%
6%
Figu e 8. O e all pu i y le el o d inking wa e
6. CONCLUSIONS
This s udy de eloped an IoT-based wa e pu i y iden i ica ion sys em o moni o d inking wa e
quali y in ou is a eas o Bangladesh. Using senso s o measu e pH, TDS, u bidi y, and
empe a u e, he sys em wi elessly ansmi s da a o eal- ime analysis. Resul s om 1,000 wa e
samples showed ha 36% we e ully pu e, 58% we e mode a ely pu e, and 6% we e pollu ed,
emphasizing he need o con inuous moni o ing and in e en ion o ensu e sa e d inking wa e .
Compa ed o adi ional me hods, he sys em o e s eal- ime da a collec ion, emo e moni o ing,
and p oac i e decision-making o p e en heal h isks. By p o iding imely wa e quali y upda es,
i enhances ou is sa e y and en i onmen al sus ainabili y. Addi ionally, IoT in eg a ion allows
o scalable and e icien wa e managemen .
Howe e , he cu en sys em lacks p edic i e capabili ies. Fu u e enhancemen s include
in eg a ing AI and machine lea ning o o ecas wa e quali y ends and expanding moni o ed
pa ame e s o include BOD and hea y me als o a mo e comp ehensi e assessmen . This
esea ch con ibu es o IoT-based wa e moni o ing, o e ing a scalable solu ion o sus ainable
wa e managemen . Fu u e s udies should ocus on imp o ing p edic i e capabili ies, inc easing
In e na ional Jou nal on AdHoc Ne wo king Sys ems (IJANS) Vol. 15, No.1/2, Ap il 2025
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moni o ed pa ame e s, and add essing implemen a ion challenges like cos and main enance o
maximize i s impac .
ACKNOWLEDGEMENTS
The au ho s would like o hank o o he co-au ho .
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