Water and wastewater infrastructure is one of the most important utility systems in modern society.

Drinking-water treatment plants, pumping stations, reservoirs, distribution networks, wastewater treatment plants, sewer systems, retention ponds, industrial wastewater facilities, and remote pumping stations must operate continuously while maintaining water quality, environmental compliance, equipment reliability, and energy efficiency.

At the same time, many water and wastewater operators still depend heavily on:

periodic manual inspections,
operator rounds,
isolated instruments,
standalone PLC systems,
manual meter readings,
laboratory sampling,
and reactive maintenance.

These methods remain necessary, but they provide only snapshots of system condition.

Industrial Internet of Things, or IIoT, monitoring can provide another layer of continuous visibility.

By connecting field sensors, pumps, power meters, analyzers, PLCs, edge gateways, communication networks, databases, dashboards, and analytics platforms, water utilities can monitor important operating parameters continuously and remotely.

A typical architecture can be represented as:

Sensor or Existing Instrument

↓

PLC / RTU / Edge Gateway

↓

Industrial Communication Network

↓

Time-Series Database

↓

Dashboard and Analytics

↓

Alerts

↓

Operational or Maintenance Action

The objective is not simply to collect more data.

The objective is to convert water-system data into information that supports better decisions.

What Is IoT Monitoring for Water and Wastewater Systems?

IoT monitoring for water and wastewater systems refers to the use of connected sensors, industrial instruments, gateways, communications infrastructure, software platforms, and analytics to continuously observe the condition and performance of water-related assets.

Parameters may include:

water level,
pressure,
flow,
temperature,
pH,
conductivity,
turbidity,
dissolved oxygen,
oxidation-reduction potential,
chlorine concentration,
total dissolved solids,
pump vibration,
bearing temperature,
motor current,
energy consumption,
rainfall,
tank level,
sewer level,
and many others.

The exact parameters depend on the application.

For example, a drinking-water reservoir may primarily require:

level,
inlet flow,
outlet flow,
chlorine residual,
pressure,
and pump status.

Meanwhile, a wastewater aeration basin may require:

dissolved oxygen,
pH,
temperature,
airflow,
blower power,
and process-related measurements.

The main value comes from connecting these measurements so operators can understand the complete system rather than individual devices in isolation.

Why Continuous Monitoring Matters

Water infrastructure can extend across very large geographic areas.

A utility may operate:

several treatment plants,
dozens of reservoirs,
hundreds of pumps,
thousands of kilometers of pipeline,
remote booster stations,
wastewater lift stations,
sewer networks,
and environmental monitoring locations.

Continuous manual inspection of all these assets can become expensive and inefficient.

IoT monitoring provides remote visibility.

Instead of asking:

Has someone inspected Pump Station 14 today?

operators can view:

reservoir level,
pump status,
pressure,
motor current,
flow,
communication condition,
and alarm status

from a centralized dashboard.

This does not eliminate physical inspection.

It helps prioritize where inspections are actually needed.

1. Water Intake Monitoring

Raw-water intake is the beginning of many drinking-water treatment systems.

Sources may include:

rivers,
reservoirs,
lakes,
groundwater wells,
and industrial raw-water systems.

Important parameters can include:

raw-water level,
intake flow,
pump status,
turbidity,
temperature,
conductivity,
pH,
rainfall,
and upstream environmental conditions.

Continuous monitoring can identify changes before they affect the treatment process.

For example:

Heavy Rainfall

↓

Increasing River Turbidity

↓

Higher Raw-Water Solids

↓

Increased Treatment Demand

Operators can prepare chemical dosing or treatment capacity before water quality deteriorates significantly.

This is much more useful than viewing rainfall and turbidity data separately.

2. Water Treatment Plant Monitoring

A water treatment plant may include:

coagulation,
flocculation,
sedimentation,
filtration,
disinfection,
chemical dosing,
storage,
and pumping.

Each stage generates valuable operational data.

Typical measurements include:

flow,
turbidity,
pH,
chlorine residual,
pressure,
level,
conductivity,
dosing rate,
filter differential pressure,
pump current,
and power consumption.

IoT platforms can consolidate these parameters into plant-wide dashboards.

ISO 24512:2024 provides current international guidance for managing drinking-water utilities and assessing drinking-water services across treatment facilities, distribution networks, and other components of water-supply systems.

3. Pump Condition Monitoring

Pumps are among the most important assets in water and wastewater systems.

Typical applications include:

intake pumps,
transfer pumps,
booster pumps,
high-lift pumps,
sludge pumps,
chemical pumps,
drainage pumps,
wastewater lift pumps,
and dewatering pumps.

Pump failures can cause:

loss of water supply,
tank overflow,
sewer overflow,
production disruption,
environmental incidents,
and increased operating costs.

IoT condition monitoring may include:

vibration,
bearing temperature,
motor current,
voltage,
power,
suction pressure,
discharge pressure,
flow,
operating hours,
number of starts,
and pump status.

Example: Detecting Pump Problems

Consider a booster pump.

Under normal operation:

discharge pressure is stable,
flow is stable,
current remains within normal range,
vibration remains low.

Over time the platform observes:

vibration increasing,
current increasing,
flow gradually decreasing.

That combination may indicate developing mechanical or hydraulic problems.

Possible causes could include:

impeller deterioration,
bearing degradation,
blockage,
misalignment,
or operation away from the pump’s preferred operating region.

The system does not necessarily diagnose the exact cause automatically.

It helps identify which equipment requires engineering investigation.

4. Pump Efficiency Monitoring

A pump can continue running while operating inefficiently.

That condition can remain unnoticed for months.

By combining:

Flow + Pressure + Electrical Power

operators can estimate pump performance.

A useful indicator may be expressed as:

Energy Consumption per Unit Volume Pumped

For example:

kWh / m³

If the value gradually increases while the operating duty remains comparable, possible reasons include:

hydraulic degradation,
valve throttling,
pipe restriction,
worn impeller,
poor pump selection,
or motor deterioration.

This converts simple electrical monitoring into an operational-efficiency tool.

5. Reservoir and Tank Monitoring

Water reservoirs and tanks are excellent candidates for remote IoT monitoring.

Typical parameters include:

water level,
inlet flow,
outlet flow,
pressure,
chlorine residual,
water temperature,
pump status,
valve status,
and security-related conditions.

Remote monitoring allows operators to visualize tank inventory across an entire water network.

A dashboard could show:

Reservoir	Level	Inlet Flow	Outlet Flow	Status
R-01	83%	Normal	Normal	Healthy
R-02	42%	Low	High	Warning
R-03	91%	Normal	Low	High Level

This immediately tells operators where attention is required.

6. Distribution Network Pressure Monitoring

Maintaining appropriate pressure is essential in drinking-water distribution networks.

Pressure that is too low can cause service problems.

Excessive pressure can contribute to:

leakage,
pipe stress,
valve damage,
and increased pumping energy.

Distributed pressure sensors can provide continuous visibility across the network.

The data can be used to identify:

unusually low-pressure zones,
excessive pressure,
abnormal pressure drops,
pressure transients,
and possible leakage.

ISO 24516-1 specifically addresses asset management for drinking-water distribution networks and emphasizes long-term management of network assets. A 2025 amendment added further material on performance indicators, monitoring, and review.

7. Water Leakage Detection

Water loss is a major operational challenge for many utilities.

No single IoT sensor can identify every leak, but connected data can support leak detection.

Useful parameters include:

flow,
pressure,
acoustic signals,
reservoir levels,
district-metered-area consumption,
and historical demand patterns.

Consider a distribution zone where:

Night Flow Normally = 25 m³/h

but gradually becomes:

Night Flow = 45 m³/h

while customer demand remains unchanged.

This may indicate increasing leakage.

Analytics can flag the zone for investigation.

8. District Metered Area Monitoring

A District Metered Area, or DMA, divides a distribution system into measurable zones.

IoT monitoring can collect:

inlet flow,
outlet flow,
pressure,
minimum night flow,
consumption,
and reservoir level.

This enables operators to compare network behavior between zones.

Potential KPIs include:

water supplied,
estimated consumption,
minimum night flow,
pressure stability,
and non-revenue-water indicators.

Instead of treating the distribution network as one large system, operators can investigate specific geographic areas.

9. Smart Water Metering

Connected meters can provide more detailed consumption information than periodic manual readings.

Possible applications include:

customer metering,
industrial users,
commercial buildings,
public facilities,
irrigation,
and internal plant consumption.

Benefits may include:

faster meter reading,
consumption trending,
leak identification,
unusual-usage detection,
and improved water balance.

For industrial facilities, smart metering can identify which processes consume the most water.

This supports water-conservation programs.

10. Wastewater Collection Network Monitoring

Wastewater systems present different challenges.

Sewer networks can be affected by:

blockages,
excessive infiltration,
stormwater ingress,
pump failures,
overflow,
sediment accumulation,
and rapidly changing flows.

Remote level sensors can be installed at selected locations.

Parameters may include:

sewer level,
flow,
rainfall,
pump station status,
gas concentration,
and communication health.

ISO 24516-3 provides specific guidance for asset management of wastewater collection networks and remains a current standard following its latest ISO confirmation.

11. Sewer Overflow Monitoring

An unexpected increase in sewer level may indicate:

blockage,
downstream restriction,
pump failure,
or excessive stormwater entry.

A simple IoT architecture could be:

Ultrasonic or Radar Level Sensor

↓

Edge Gateway

↓

Cellular Network

↓

Monitoring Platform

If the level exceeds a defined threshold, an alert is generated.

However, effective alarm logic should go beyond a simple threshold.

For example:

Sewer level above 80% for more than 10 minutes and rising faster than predefined rate.

This provides more useful information than repeated instantaneous alerts.

12. Wastewater Lift Station Monitoring

Lift stations are often distributed across municipalities or industrial sites and may operate without permanent staff.

Typical monitoring parameters include:

wet-well level,
pump running status,
discharge pressure,
flow,
motor current,
number of starts,
operating hours,
vibration,
temperature,
and power supply condition.

A monitoring system can identify situations such as:

High Wet-Well Level + Pump Not Running

which may indicate an urgent failure.

Another example:

Pump Running + No Discharge Flow

may indicate:

blockage,
pump damage,
valve problem,
or other hydraulic failure.

Such logic creates much greater value than simply displaying pump ON/OFF status.

13. Wastewater Treatment Plant Monitoring

Wastewater treatment plants may contain:

screening systems,
sedimentation tanks,
equalization basins,
biological treatment,
aeration tanks,
clarifiers,
sludge handling,
chemical dosing,
disinfection,
pumping,
and effluent monitoring.

ISO 24516-4 provides guidelines for managing assets in wastewater treatment plants, sludge-treatment facilities, pumping stations, and retention and detention facilities.

IoT monitoring can consolidate data across all these process areas.

14. Aeration System Monitoring

Aeration is often one of the largest electricity consumers in biological wastewater treatment.

Important parameters include:

dissolved oxygen,
airflow,
blower pressure,
blower power,
aeration valve position,
basin temperature,
and process loading.

A conventional strategy may operate blowers at fixed capacity.

A data-driven strategy can adjust aeration according to process demand.

Conceptually:

High DO + Low Biological Demand

may indicate excessive aeration.

This means energy may be wasted.

Conversely:

Low DO + High Process Load

may indicate inadequate aeration.

Monitoring enables operators to identify both conditions.

15. Blower Condition Monitoring

Blowers are critical rotating assets.

Monitoring can include:

vibration,
bearing temperature,
discharge temperature,
inlet pressure,
outlet pressure,
motor current,
power,
airflow,
and operating hours.

If multiple blowers operate in parallel, analytics can compare their performance.

For example:

Blower A = 0.19 kWh/m³ air

Blower B = 0.24 kWh/m³ air

This may prompt investigation of Blower B.

16. Dissolved Oxygen Monitoring

Dissolved oxygen, or DO, is an important biological-treatment parameter.

If DO becomes too low, treatment performance may decline.

If DO is maintained unnecessarily high, blower energy may be wasted.

Continuous DO monitoring can therefore support both:

treatment quality,
and energy efficiency.

Historical trends can also reveal:

diurnal loading,
changes in biological demand,
instrumentation drift,
and aeration-system limitations.

17. pH Monitoring

pH monitoring is important across:

water treatment,
industrial wastewater,
biological treatment,
neutralization systems,
chemical dosing,
and final discharge.

An IIoT platform can provide:

current pH,
historical trends,
alarm events,
rate-of-change information,
comparison with chemical dosing,
and compliance reporting.

However, online instruments require calibration and maintenance.

An IoT dashboard cannot compensate for a poorly maintained sensor.

Reliable analytics requires reliable instrumentation.

18. Turbidity Monitoring

Turbidity can be monitored in applications such as:

raw water,
treated water,
filter outlet,
final effluent,
and surface-water discharge.

Trending can help identify process changes.

For example:

Filter Differential Pressure Increasing

plus

Outlet Turbidity Increasing

may indicate declining filter performance.

This illustrates the importance of combining process variables rather than viewing measurements separately.

19. Conductivity and TDS Monitoring

Conductivity can provide information about dissolved ionic material in water.

Applications include:

raw water,
reverse-osmosis systems,
industrial wastewater,
cooling systems,
boiler systems,
and effluent monitoring.

Changes may indicate:

contamination,
process breakthrough,
salt concentration changes,
chemical dosing issues,
or membrane deterioration.

20. Chlorine Monitoring

Drinking-water systems may monitor disinfectant residual at several stages.

Possible locations include:

treatment plant outlet,
reservoir,
remote distribution points,
and network extremities.

Combining:

chlorine residual,
water age,
temperature,
flow,
and location

can provide much more useful information than occasional manual measurements alone.

Continuous online monitoring should complement—not necessarily replace—required laboratory and field testing programs.

21. Industrial Wastewater Monitoring

Manufacturing, petrochemical, mining, food processing, pulp and paper, and other industries may generate complex wastewater.

Relevant parameters may include:

pH,
temperature,
conductivity,
flow,
turbidity,
dissolved oxygen,
oxidation-reduction potential,
total dissolved solids,
and specialized online analyzers.

IoT can provide continuous operational visibility between laboratory analyses.

For example, the system could identify:

Rapid Conductivity Increase

at the wastewater equalization tank.

That may indicate an abnormal process discharge upstream.

Operators can investigate before the abnormal water reaches later treatment stages.

22. Effluent Monitoring

Final effluent is often one of the most important locations in wastewater facilities.

Monitoring can include:

flow,
pH,
turbidity,
temperature,
conductivity,
dissolved oxygen,
and other parameters required for the specific facility.

IoT platforms can display:

current conditions,
historical trends,
alarm events,
daily averages,
and compliance-related summaries.

Regulatory compliance must always follow applicable legal requirements and validated measurement methods.

IoT monitoring should provide operational intelligence—not substitute legally required certified sampling or laboratory analysis unless regulations explicitly allow it.

23. Stormwater Monitoring

Rainfall can rapidly affect water and wastewater networks.

Possible parameters include:

rainfall intensity,
accumulated rainfall,
drainage level,
retention pond level,
sewer level,
and pump condition.

A useful system could combine:

Weather Station

Drainage Level Sensors

Pump Station Monitoring

This allows operators to see whether rainfall is likely to create capacity problems.

24. Flood and Retention Pond Monitoring

Remote monitoring can support:

retention ponds,
detention basins,
flood channels,
drainage systems,
industrial stormwater ponds,
and water reservoirs.

A solar-powered monitoring station could include:

radar level sensor,
rainfall sensor,
weather sensor,
camera where appropriate,
cellular gateway,
and battery monitoring.

The architecture may be:

Level Sensor + Weather Station

↓

Solar Edge Gateway

↓

4G / LTE

↓

Cloud Platform

↓

Dashboard and Alert

This is especially useful when locations are remote.

25. Chemical Dosing Monitoring

Water and wastewater facilities may use:

chlorine,
coagulants,
polymers,
pH-adjustment chemicals,
disinfectants,
and other treatment chemicals.

IoT monitoring can collect:

tank level,
dosing pump status,
dosing rate,
flow,
chemical consumption,
and process-response variables.

The system can calculate:

Chemical Consumption per m³ Treated Water

This creates a useful efficiency KPI.

If chemical consumption suddenly increases without a corresponding water-quality change, the dosing system may need investigation.

26. Filter Performance Monitoring

Filtration systems can be monitored using:

inlet pressure,
outlet pressure,
differential pressure,
flow,
turbidity,
and operating time.

An increasing pressure differential usually indicates increasing resistance.

Combining differential pressure with outlet quality helps determine whether cleaning or backwashing is required.

Instead of:

Backwash every eight hours,

a condition-based strategy may eventually use:

Backwash when hydraulic or quality conditions justify it.

This can reduce unnecessary cleaning cycles where the process design permits.

27. Membrane System Monitoring

Reverse osmosis, ultrafiltration, and other membrane processes can generate large amounts of useful data.

Parameters may include:

feed pressure,
permeate pressure,
concentrate pressure,
flow,
conductivity,
temperature,
differential pressure,
and energy consumption.

Analytics can calculate:

recovery ratio,
specific energy consumption,
normalized flow,
pressure differential,
and membrane performance trends.

A gradual decline can indicate:

fouling,
scaling,
membrane degradation,
or pretreatment issues.

28. Electrical Power Monitoring

Water and wastewater systems consume significant electrical energy, primarily through:

pumps,
blowers,
aerators,
mixers,
compressors,
and treatment equipment.

Power monitoring can collect:

voltage,
current,
active power,
reactive power,
power factor,
energy consumption,
demand,
harmonics,
and motor loading.

Operators can then determine which processes consume the most energy.

29. Energy Intensity Monitoring

Total electricity consumption alone provides limited operational insight.

More useful metrics include:

Drinking Water

kWh / m³ Water Produced

Wastewater

kWh / m³ Wastewater Treated

Pumping

kWh / m³ Water Pumped

Monitoring these indicators over time helps identify declining efficiency.

For example:

If a pumping station historically requires:

0.18 kWh/m³

but gradually rises to:

0.25 kWh/m³

the engineering team should investigate.

30. Transformer and Electrical Distribution Monitoring

Large treatment facilities may operate:

MV/LV transformers,
switchgear,
motor-control centers,
VFDs,
generators,
UPS systems,
and emergency power.

Monitoring can include:

transformer temperature,
load,
switchgear temperature,
breaker status,
bus voltage,
harmonic distortion,
generator status,
battery condition,
and energy consumption.

Water services are critical infrastructure.

Electrical reliability therefore deserves the same monitoring attention as hydraulic equipment.

31. Remote Sites and Edge Gateways

One major challenge is that water infrastructure is distributed.

Some locations may not have:

Ethernet,
fiber,
reliable Wi-Fi,
or local control rooms.

An edge gateway can connect:

RS485 Modbus sensors,
4–20 mA instruments,
digital inputs,
pulse meters,
and other industrial devices.

The gateway can then transmit data through:

4G/LTE,
Ethernet,
Wi-Fi,
LoRaWAN,
private radio,
or other suitable communications.

32. RS485 Modbus for Water Monitoring

RS485 Modbus RTU is particularly useful for water-monitoring applications.

A single RS485 bus can communicate with multiple compatible instruments using unique device addresses.

For example:

Edge Gateway

↓

RS485 Bus

├── Flow Meter ID 1
├── Pressure Sensor ID 2
├── pH Analyzer ID 3
├── Conductivity Sensor ID 4
└── Energy Meter ID 5

The gateway polls each device and collects data.

This can reduce wiring and simplify integration.

Proper engineering still requires consideration of:

termination,
biasing,
cable type,
topology,
grounding,
baud rate,
device addressing,
and maximum practical network length.

33. Edge Computing

Not every remote water facility has reliable internet connectivity.

Edge gateways can therefore provide:

local data buffering,
protocol conversion,
calculations,
local alarm logic,
data filtering,
timestamping,
and store-and-forward functions.

Suppose communication is unavailable for three hours.

Instead of losing data:

Sensor → Gateway Buffer

When connectivity returns:

Stored Data → Central Database

This is essential for reliable industrial monitoring.

34. Cloud, On-Premises, or Hybrid?

Water utilities can use different architectures.

Cloud

Useful for:

remote sites,
centralized dashboards,
multi-location monitoring,
analytics,
and reporting.

On-Premises

Useful where:

local data ownership is critical,
network policies restrict cloud services,
or operations must remain independent of external connectivity.

Hybrid

Often the best practical approach.

For example:

PLC / SCADA

handles local process control.

Meanwhile:

Selected Operational Data

is securely transferred to a monitoring and analytics platform.

Critical control remains local.

Remote monitoring gains broader visibility.

35. Integration with PLC and SCADA

Most established water facilities already have PLC and SCADA systems.

IIoT should normally complement these systems.

It should not automatically replace them.

A practical architecture may be:

Field Layer

Sensors and instruments.

Control Layer

PLC / RTU.

Supervisory Layer

SCADA.

Data Layer

Historian / time-series database.

Analytics Layer

IoT dashboards, reporting, predictive analytics.

Possible data interfaces include:

Modbus TCP,
OPC UA,
MQTT,
APIs,
and approved database interfaces.

36. Why Control Should Remain Local

Cloud or remote monitoring is useful, but critical control should generally remain within appropriately engineered local systems.

Examples include:

pump interlocks,
high-high tank protection,
chemical dosing safeguards,
motor protection,
emergency shutdown,
and critical treatment sequences.

The design principle is:

Control Locally. Monitor Broadly. Analyze Centrally.

An internet outage should not prevent a treatment plant from operating safely.

37. Alarm and Notification Management

An IoT platform can generate alerts through:

dashboards,
email,
mobile notifications,
messaging integrations,
or maintenance systems.

However, excessive alarms can create alarm fatigue.

Alerts should include context.

Instead of:

Pump Current High

use:

Booster Pump BP-03 current has remained above its normal operating range for 15 minutes while flow has decreased by 12%.

This provides better information for troubleshooting.

38. Predictive Maintenance

Water-system operators often perform preventive maintenance based on:

calendar intervals,
running hours,
OEM recommendations.

IIoT enables another dimension:

actual equipment condition.

A maturity path could be:

Reactive Maintenance

↓

Preventive Maintenance

↓

Condition-Based Maintenance

↓

Predictive Maintenance

Predictive maintenance should be introduced gradually.

Before advanced machine learning, organizations should establish:

reliable sensors,
historical data,
equipment baselines,
maintenance records,
failure labels,
and operational context.

39. Anomaly Detection

Anomaly detection can compare current system behavior against historical norms.

For example, a pump may normally operate within a relationship between:

flow,
pressure,
current,
and vibration.

If current operating data deviates significantly from historical patterns, the platform can flag the equipment for inspection.

This approach can identify problems that simple fixed alarm thresholds may miss.

40. Forecasting

Historical data can support forecasting for:

reservoir level,
water consumption,
wastewater inflow,
energy demand,
rainfall impact,
pump utilization,
and treatment load.

For example, water demand may show:

hourly patterns,
weekday/weekend patterns,
seasonal effects,
and holiday behavior.

Forecasting can help optimize:

pumping schedules,
storage levels,
energy consumption,
and operational planning.

41. Asset Management

Water systems contain assets with very long life cycles.

Pipelines, reservoirs, treatment infrastructure, pumps, and electrical systems may operate for decades.

ISO 24516-2 provides guidance for managing waterworks assets including treatment plants, sludge-treatment facilities, pumping stations, reservoirs, tanks, dosing equipment, metering, and ancillary infrastructure. The 2019 edition was reviewed and confirmed by ISO in 2025.

IoT monitoring can contribute to asset-management decisions by providing historical information about:

operating condition,
failure frequency,
utilization,
efficiency,
degradation,
and maintenance requirements.

42. Wastewater Asset Management

For wastewater infrastructure, similar principles apply.

ISO 24516-4 covers wastewater treatment plants, sludge-treatment facilities, pumping stations, and retention/detention facilities. ISO also published ISO/TR 24589-2:2025, which provides examples of good practices for applying asset-management approaches to wastewater systems.

This supports a broader philosophy:

Monitoring should not exist only to generate dashboards.

It should help organizations make better asset decisions.

43. Cybersecurity

Water infrastructure increasingly depends on connected automation and monitoring systems.

Cybersecurity therefore becomes an engineering requirement.

An IoT monitoring system should consider:

network segmentation,
firewall architecture,
device authentication,
encrypted communications,
user access control,
asset inventory,
software updates,
backup,
logging,
and incident response.

Industrial systems should not expose PLCs or critical control devices directly to the public internet merely to enable remote dashboards.

The safer concept is:

OT Control Network

↓

Controlled Integration Layer

↓

Monitoring / Analytics Environment

This creates separation between control and external access.

44. Data Quality Matters

A dashboard can look sophisticated while displaying poor-quality data.

Common problems include:

sensor drift,
fouled probes,
incorrect calibration,
missing data,
communication loss,
duplicate timestamps,
incorrect engineering units,
and faulty sensor installation.

IoT implementation must therefore include:

calibration strategy,
sensor maintenance,
communication-health monitoring,
data validation,
and quality flags.

A useful principle is:

Bad sensor data creates bad analytics faster.

45. Multi-Site Water Monitoring

A centralized monitoring platform becomes particularly valuable when an organization operates multiple facilities.

For example:

Water Plant A

Production: 14,200 m³/day
Energy intensity: 0.34 kWh/m³

Water Plant B

Production: 9,100 m³/day
Energy intensity: 0.47 kWh/m³

Pump Station C

Abnormal vibration detected

Reservoir D

Level below target

Wastewater Plant E

Aeration energy increasing

Management can quickly identify where attention is required.

46. Example IoT Architecture for a Water Utility

A scalable architecture may contain five layers.

Layer 1 — Field Instruments

Examples:

level sensors,
flow meters,
pressure transmitters,
pH analyzers,
turbidity sensors,
weather stations,
vibration sensors,
power meters.

Layer 2 — Communication

Examples:

4–20 mA,
pulse,
digital I/O,
RS485 Modbus RTU,
Modbus TCP,
Ethernet,
LoRaWAN.

Layer 3 — Edge Gateway

Functions:

polling,
protocol conversion,
data buffering,
calculations,
local processing.

Layer 4 — Data Platform

Possible components:

time-series database,
PostgreSQL,
historian,
cloud platform,
APIs.

Layer 5 — Visualization and Analytics

Functions:

dashboards,
alarms,
historical trends,
forecasting,
anomaly detection,
reports.

47. Example: Remote Pumping Station

Consider a remote pumping station.

Equipment:

two pumps,
level transmitter,
discharge pressure sensor,
flow meter,
energy meter,
vibration sensors.

Architecture:

Sensors

↓

RS485 Modbus / Analog Inputs

↓

Edge Gateway

↓

4G Network

↓

Central Platform

Dashboard:

sump level,
Pump 1 status,
Pump 2 status,
discharge pressure,
total flow,
energy consumption,
vibration,
communication status.

The system can calculate:

kWh / m³ Pumped

and generate alerts for:

high water level,
pump failure,
low flow,
abnormal vibration,
excessive energy consumption,
and communication loss.

48. Example: Wastewater Treatment Dashboard

A wastewater dashboard could combine:

Influent

flow,
pH,
conductivity.

Biological Treatment

DO,
pH,
airflow,
temperature.

Blowers

power,
vibration,
temperature.

Clarifier

level,
sludge condition.

Effluent

flow,
pH,
turbidity,
conductivity.

Energy

total plant power,
blower power,
pump power,
kWh/m³ treated.

The advantage is not simply having many charts.

The advantage is seeing process and equipment condition together.

49. How to Start an IoT Monitoring Project

A successful project should begin with the operational problem.

Not the sensor.

Not the dashboard.

Not artificial intelligence.

Start by asking:

What problem are we trying to solve?

Examples include:

repeated pump failures,
excessive site inspections,
reservoir overflows,
high electricity consumption,
water leakage,
sewer overflow,
unstable treatment quality,
or insufficient environmental visibility.

Step 1 — Select the Use Case

Choose a problem with measurable operational value.

Step 2 — Identify Critical Assets

Prioritize equipment where failure has significant consequences.

Step 3 — Review Existing Instrumentation

Before installing additional sensors, determine whether the required data already exists in:

PLCs,
SCADA,
power meters,
VFDs,
analyzers,
protection devices,
or existing instruments.

Step 4 — Identify Data Gaps

Install additional sensors only where required.

Step 5 — Establish Communication

Select appropriate technologies based on:

distance,
environment,
bandwidth,
power availability,
reliability,
cybersecurity.

Step 6 — Build Baseline Data

Understand normal operating behavior.

Step 7 — Configure Useful Dashboards

Dashboards should support decisions rather than merely look impressive.

Step 8 — Implement Alerts

Begin with high-value conditions.

Step 9 — Measure Results

Possible metrics include:

downtime avoided,
energy saved,
inspection trips reduced,
leaks detected,
chemical consumption reduced,
equipment failures prevented.

Step 10 — Scale

Once value is demonstrated, expand to more sites and assets.

50. Measuring the Business Value

The return on investment should be linked to operational outcomes.

Potential benefits include:

Reduced Water Loss

Better flow and pressure visibility can support leakage management.

Reduced Energy Consumption

Pump and blower efficiency monitoring can identify energy waste.

Reduced Maintenance Cost

Condition monitoring helps prioritize maintenance.

Reduced Manual Inspection

Remote stations require fewer routine visits merely to check status.

Reduced Downtime

Early warning allows maintenance intervention before failure.

Better Environmental Visibility

Continuous monitoring provides faster awareness of abnormal conditions.

Improved Asset Planning

Historical performance supports repair-versus-replace decisions.

51. Siteplore for Water and Wastewater Monitoring

Siteplore can serve as a monitoring and analytics layer for water and wastewater infrastructure.

A Siteplore deployment can integrate:

water-quality sensors,
level sensors,
pressure instruments,
flow meters,
weather stations,
electrical meters,
vibration sensors,
edge gateways,
PLC data,
and existing industrial instruments.

Potential applications include:

RekaSense

For:

water-quality monitoring,
weather monitoring,
rainfall,
environmental parameters,
retention pond monitoring,
remote environmental stations.

PowerWatch

For:

pump energy monitoring,
blower energy,
electrical distribution,
transformer monitoring,
energy intensity,
power quality.

MachineGuard

For:

pump vibration,
motor condition,
blower monitoring,
bearing temperature,
rotating-equipment health.

These systems can be combined into a unified monitoring architecture.

52. From Water Data to Operational Intelligence

Water utilities do not necessarily suffer from a lack of data.

They often suffer from disconnected data.

A pump may have information in one PLC.

A power meter may have another system.

A laboratory has separate records.

Weather data may come from another source.

Environmental monitoring may have another dashboard.

The goal of IIoT is to connect these information flows.

A mature architecture creates a continuous cycle:

Measure

↓

Connect

↓

Store

↓

Contextualize

↓

Visualize

↓

Analyze

↓

Alert

↓

Act

↓

Improve

When this cycle works properly, IoT becomes more than remote monitoring.

It becomes part of the operational-management system.

53. The Future of IoT in Water and Wastewater

The next generation of water monitoring systems will increasingly combine:

industrial IoT,
edge computing,
artificial intelligence,
machine learning,
digital twins,
predictive maintenance,
weather forecasting,
hydraulic modeling,
and automated analytics.

Operators will move from asking:

What is the current tank level?

toward:

When will the reservoir reach its minimum level?

Instead of:

How much electricity did this pump consume?

they may ask:

Is the pump becoming less efficient?

Instead of:

Is the sewer level high?

the system may answer:

Based on rainfall and current inflow, this location may reach critical level within the next hour.

That represents the evolution from:

Monitoring

to

Understanding

to

Prediction

and eventually:

Decision Support

Relevant Standards and References

ISO 24512:2024 — Activities relating to drinking water and wastewater services — Guidelines for the management of drinking water utilities and for the assessment of drinking water services. The standard covers the overall drinking-water system and establishes guidance for utility management, service objectives, assessment criteria, and performance indicators.

ISO 24516-1:2016 — Guidelines for the management of assets of water supply and wastewater systems — Part 1: Drinking water distribution networks. It provides asset-management guidance for drinking-water networks. ISO 24516-1:2016/Amd 1:2025 adds material concerning performance indicators and monitoring and review.

ISO 24516-2:2019 — Guidelines for the management of assets of water supply and wastewater systems — Part 2: Waterworks. It covers treatment plants, pumping stations, reservoirs, tanks, dosing equipment, metering, and related infrastructure. The edition remained current following review and confirmation in 2025.

ISO 24516-3:2017 — Guidelines for the management of assets of water supply and wastewater systems — Part 3: Wastewater collection networks. It addresses technical aspects and good practices for managing wastewater-network assets.

ISO 24516-4:2019 — Guidelines for the management of assets of water supply and wastewater systems — Part 4: Wastewater treatment plants, sludge treatment facilities, pumping stations, retention and detention facilities. This standard is directly relevant to wastewater-treatment and pumping infrastructure.

ISO/TR 24589-2:2025 — Examples of good practice for the management of assets of water supply and wastewater systems — Part 2: Wastewater systems. This newer technical report provides practical examples supporting implementation of the ISO 24516 wastewater asset-management framework.

These standards reinforce a central principle:

IoT monitoring should support the reliable, efficient, and sustainable management of water-system assets rather than exist as a standalone technology project.

Conclusion

IoT monitoring can provide significant value across the entire water and wastewater lifecycle.

Applications include:

raw-water intake monitoring,
treatment-process monitoring,
pump condition monitoring,
reservoir monitoring,
pipeline pressure monitoring,
leakage detection,
smart metering,
sewer monitoring,
lift-station monitoring,
wastewater-treatment monitoring,
aeration optimization,
environmental monitoring,
stormwater monitoring,
electrical monitoring,
and predictive maintenance.

However, successful implementation should not begin by connecting everything.

The best approach is to identify the operational problem first.

Select critical assets.

Determine which measurements provide meaningful information.

Reuse existing instrumentation where possible.

Add sensors only where justified.

Use industrial communication and edge gateways to collect the data reliably.

Maintain critical control and protection functions in appropriately engineered local systems.

Then use IoT platforms for:

visualization,
historical analysis,
alarms,
remote monitoring,
condition monitoring,
forecasting,
and decision support.

For many water utilities and industrial wastewater operators, a focused pilot can provide the most practical entry point.

A single remote pumping station, reservoir, treatment process, or wastewater installation can demonstrate tangible value before the architecture is expanded across the broader system.

When implemented systematically, Industrial IoT can help water and wastewater organizations become more reliable, more energy-efficient, more responsive, and better prepared to manage increasingly complex water infrastructure.