IoT Sensors in Water Quality Course
About our
The IoT Sensors in Water Quality course explores the application of the Internet of Things (IoT) in water resource monitoring and management. It focuses on the implementation of remote sensors to measure crucial parameters such as pH, conductivity, dissolved oxygen, and turbidity. It integrates with wireless communication technologies and data platforms for real-time analysis, providing valuable information for decision-making and environmental protection. The course includes hands-on experience with microcontrollers and the development of IoT solutions tailored to the sector’s needs. The program offers knowledge of communication protocols, data analysis, and visualization, preparing participants to design and implement water quality monitoring systems. It addresses the use of renewable energy for sensor operation, as well as the importance of cybersecurity in data protection. This training is key for professionals seeking roles in environmental engineering, water resource management, and sustainable technology development.
IoT Sensors in Water Quality Course
- Format: Online
- Duration: 3 months
- Hours: 300 H
- Language: English / Spanish
- Registration date: 23-10-2026
- Start date: 12-12-2026
- Available places: 3
550 $
Competencies and results
What you will learn
1. **IoT Sensor Domain for Advanced Water Quality Monitoring**
- **Fundamentals of IoT and Sensors:** Understand the architecture of the Internet of Things (IoT) and its application in environmental monitoring. Study the different types of IoT sensors (physical, chemical, biological) and their operating principles.
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**Sensor Selection and Configuration for Water Quality:** Learn to select the most appropriate sensors to measure specific water quality parameters (pH, dissolved oxygen, turbidity, conductivity, temperature, contaminants). Master sensor configuration and calibration.
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**Sensor Network Implementation:** Design and implement IoT sensor networks for water quality monitoring. Study wireless communication protocols (Wi-Fi, Bluetooth, LoRaWAN, NB-IoT) and their application in aquatic environments.
- **Data Acquisition and Processing:** Learn to acquire data from IoT sensors and store it on cloud storage platforms. Master data processing and analysis techniques, including noise filtering, error correction, and anomaly detection.
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**Data Analysis and Modeling:** Use data analysis tools to identify trends, patterns, and relationships in water quality data. Develop predictive models for water quality using machine learning techniques.
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**Data Visualization and Presentation:** Create interactive dashboards and visualizations to display water quality data in real time. Learn to communicate data analysis results effectively.
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**Practical Applications and Case Studies:** Explore practical applications of water quality monitoring with IoT sensors in different environments (rivers, lakes, oceans, treatment plants). Analyze real-world case studies and learn from best practices.
Regulatory and Ethical Aspects: Understand the regulatory aspects related to water quality monitoring and data privacy. Consider the ethical aspects related to the use of IoT technology in water management.
Sensor Maintenance and Calibration: Learn how to perform preventive maintenance and calibration of IoT sensors to ensure their long-term accuracy and reliability.
Future Trends and Technological Advancements: Explore future trends in IoT technology and its application in water quality monitoring. Investigate technological advances in sensors, communication, and data analysis.
2. **Expert IoT Implementation for Comprehensive Water Quality Assessment**
- Understand the fundamentals of the **Internet of Things (IoT)** and its specific application in water quality monitoring.
- Design and implement **IoT sensor** systems for real-time data collection on key water quality parameters (pH, temperature, dissolved oxygen, turbidity, contaminants, etc.).
- Select and configure appropriate **IoT hardware and software** for aquatic environments, considering factors such as water resistance, durability, and measurement accuracy.
- Develop **communication protocols** and IoT networks for the secure and efficient transmission of data from sensors to a centralized platform.
- Apply **data analysis** techniques to process, interpret, and visualize the information collected by IoT sensors, identifying trends, anomalies, and patterns in water quality.
- Utilize **artificial intelligence** tools (AI) and machine learning to predict and prevent water quality problems, such as pollution or harmful algal blooms.
Integrate IoT data with Geographic Information Systems (GIS) to map and visualize water quality in specific geographic areas.
Evaluate and select different calibration and verification methods for IoT sensors to ensure data accuracy and reliability.
Understand environmental regulations and standards related to water quality and how IoT systems can help meet them.
Apply the acquired knowledge in practical projects and case studies related to comprehensive water quality assessment using IoT.
3. Comprehensive user-oriented design and validation (from modeling to manufacturing)
You will learn to integrate the entire product development process, from initial model conception to final validation, applying user-centered methodologies. You will develop skills in parametric design, ergonomics, simulation, sustainable materials, 3D visualization, and manufacturing management, ensuring efficient, safe solutions that meet current industry standards.
4. **IoT Strategies for Optimizing Water Quality Monitoring and Control**
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Identify and understand the fundamental principles of the Internet of Things (IoT) and its applicability to water quality monitoring and control.
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Analyze the key components of IoT systems for water management, including sensors, communication devices, data platforms, and analytical software.
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Design and implement real-time monitoring strategies using IoT sensors for contaminant detection, measurement of physicochemical parameters, and water quality assessment.
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Develop IoT-based automated control systems for efficient water management, including adjusting treatment processes, optimizing resource use, and responding to adverse events.
- Use data platforms and analytical tools to process and visualize data collected by IoT systems, identifying trends, patterns, and anomalies in water quality.
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Develop skills in the selection and configuration of IoT sensors, device programming, and systems integration, enabling the creation of customized solutions for water quality monitoring and control.
Evaluate the efficiency and impact of IoT strategies on optimizing water quality monitoring and control, considering aspects such as cost reduction, improved accuracy, and environmental sustainability.
Explore case studies and practical examples of the implementation of IoT technologies in water management globally, including applications in industry, agriculture, and drinking water supply.
Understand the challenges and security considerations associated with implementing IoT systems for water management, including data protection, cybersecurity, and device reliability.
Become familiar with relevant regulations and standards regarding water quality and the use of IoT technologies, ensuring legal compliance and environmental protection.
5. **Deep Integration of IoT Sensors for Water Quality Monitoring and Analysis**
5. **In-Depth Integration of IoT Sensors for Water Quality Monitoring and Analysis**
Understand the fundamentals of IoT technology and its applications in environmental monitoring.
Become familiar with various IoT sensors for detecting critical water quality parameters, such as pH, dissolved oxygen, turbidity, temperature, conductivity, and the presence of specific contaminants.
Learn to select, configure, and calibrate IoT sensors suitable for different aquatic environments (rivers, lakes, oceans, water treatment systems).
Acquire knowledge of IoT system architectures for the acquisition, processing, and transmission of sensor data (wireless sensor networks, gateways, communication protocols).
Master the use of platforms and tools for managing and storing IoT sensor data (cloud databases, visualization dashboards).
Develop skills in analyzing water quality data using statistical and machine learning techniques to identify trends, patterns, and anomalies.
Apply predictive models for water quality forecasting and early problem detection.
Study the impact of environmental factors (climate, human activity) on water quality and their relationship to IoT sensor data.
Learn about implementing early warning systems and making decisions based on IoT sensor data.
Explore security and privacy considerations in the collection and use of IoT sensor data.
Understand regulations and standards related to water quality and environmental monitoring.
Complete practical projects for implementing water quality monitoring systems using IoT sensors.
Analyze case studies on the use of IoT sensors for water management in different contexts.
Acquire skills for the effective communication of results and the presentation of technical reports.
6. **Optimizing Water Quality Through the Strategic Implementation of IoT Sensors**
6. **Optimizing Water Quality Through the Strategic Implementation of IoT Sensors**
Identifying Key Parameters for Water Quality: You will learn about the critical factors that influence water quality (pH, conductivity, dissolved oxygen, turbidity, etc.) and their impact in different contexts.
Selecting and Configuring IoT Sensors: You will learn to choose the most suitable IoT sensors to monitor specific water quality parameters, considering their technical specifications, accuracy, range, and application environment.
Designing IoT Monitoring Systems: You will develop skills to design IoT system architectures, including the selection of hardware (sensors, microcontrollers, communication modules), software (firmware, data platforms), and communication protocols (MQTT, LoRaWAN).
Sensor Implementation and Calibration: You will gain hands-on experience installing, configuring, and calibrating IoT sensors in real-world environments, ensuring the accuracy and reliability of measurements.
Data Management and Analysis: You will learn to use data storage and analysis platforms (cloud, dashboards) to visualize, process, and analyze the data collected by IoT sensors.
Development of Analysis and Alerting Algorithms: You will develop skills to create algorithms that detect anomalies, trends, and patterns in data, and design early warning systems to respond to changes in water quality.
IoT Integration with Treatment Systems: You will explore integrating IoT monitoring systems with existing water treatment systems, such as water treatment plants or irrigation systems, to optimize their operation and efficiency.
Case Studies and Practical Applications: You will study successful case studies of IoT system implementations for water quality optimization in different industries and contexts (agriculture, aquaculture, wastewater treatment, etc.).
Regulatory Compliance and Best Practices: You will learn about the relevant regulations and standards for water quality monitoring and best practices to ensure compliance and project sustainability.
Who this program is for:
IoT Sensors in Water Quality Course
- Environmental engineers, chemists, biologists, or related professionals with an interest in water quality monitoring.
- Technical professionals and specialists in the water sector (drinking water, wastewater, industrial water) seeking to integrate IoT technologies into their operations.
- Staff from water treatment companies, analytical laboratories, and regulatory bodies who wish to improve the efficiency and accuracy of their monitoring.
- Data scientists and software engineers interested in developing IoT solutions for water resource management.
- Standards-driven curriculum: you will work with CS-27/CS-29, DO-160, DO-178C/DO-254, ARP4754A/ARP4761, ADS-33E-PRF from the first module.
- Accreditable laboratories (EN ISO/IEC 17025) with rotor bench, EMC/Lightning pre-compliance, HIL/SIL, vibrations/acoustics.
- Evidence-oriented TFM: safety case, test plan, compliance dossierand operational limits.
- Mentored by industry: teachers with experience in rotorcraft, tiltrotor, eVTOL/UAM and flight test.
- Flexible modality (hybrid/online), international cohorts and support from SEIUM Career Services.
- Ethics and security: safety-by-design approach, cyber-OT, DIH and compliance as pillars.
Module 1 — IoT Fundamentals in Water Quality
1.1 Introduction to IoT and its relevance in water management.
1.2 Basic principles of IoT sensors and their operation.
1.3 Key components of an IoT system for water monitoring: sensors, gateways, platforms.
1.4 Types of IoT sensors for measuring key water parameters (pH, dissolved oxygen, temperature, turbidity).
1.5 IoT communication networks: technologies and protocols (WiFi, LoRaWAN, NB-IoT).
1.6 IoT data management platforms: storage, processing, and visualization.
1.7 Fundamentals of data analysis and information visualization.
1.8 Ethics and privacy in IoT data collection and use.
1.9 Case studies: Basic IoT applications in water quality monitoring.
1.10 Introduction to regulations and standards related to water quality and IoT.
2.2 IoT Fundamentals and the Digital Revolution in Aquaculture
2.2 Introduction to Aquatic Sensors: Types and Applications
2.3 Operating Principles and Selection of IoT Sensors
2.4 Key Components of an IoT Monitoring System
2.5 IoT Communication Networks for Aquatic Environments
2.6 IoT Communication Protocols: MQTT, LoRaWAN, and More
2.7 Architecture of an IoT Monitoring System
2.8 Case Studies: Initial IoT Applications in Water
2.2 IoT Sensor Design: Component and Material Selection
2.2 Sensor Implementation: Calibration and Configuration
2.3 Sensor Integration with Microcontrollers
2.4 Microcontroller Programming for Data Acquisition
2.5 Sensor Housing Design and Environmental Protection
2.6 Implementation of Wireless Sensor Networks
2.7 Testing and Validation IoT Sensors in Real-World Environments
2.8 Best Practices for Sensor Installation and Maintenance
3.2 IoT Data Collection and Storage: Databases
3.2 Data Analysis: Statistical Techniques and Visualization
3.3 Data Interpretation: Water Quality Indicators
3.4 Use of Data Analysis Platforms: Cloud vs. On-Premise On-Premise
3.5 Identifying Patterns and Trends in Data
3.6 Time Series Analysis for Monitoring
3.7 Applying Machine Learning Algorithms for Analysis
3.8 Generating Reports and Dashboards for Decision Making
4.2 Strategies for Monitoring Water Quality
4.2 Designing IoT-Based Early Warning Systems
4.3 Optimizing Water Use: Irrigation and Precision Agriculture
4.4 Process Automation: Remote Control and Actuators
4.5 Integrating IoT with Existing Water Management Systems
4.6 IoT Applications in the Water Industry: Drinking Water Treatment
4.7 Environmental Impact: Reducing Consumption and Pollution
4.8 Sustainability and Profitability of IoT Solutions
5.2 Integrating Multiple Sensors: Types and Functions
5.2 Designing Heterogeneous Sensor Networks
5.3 Advanced Communication Systems: 5G and Satellite
5.4 Synchronization and Calibration of Multiple Sensors
5.5 Data Management in Distributed Environments
5.6 Advanced Analysis: Anomaly Detection and Prediction
5.7 Integration with SCADA Systems and Centralized Control
5.8 Case Studies: Complex IoT Implementations in Water Resources
6.2 Optimizing IoT System Design
6.2 Sensor Selection for Energy Efficiency
6.3 Cost Reduction: Implementation and Maintenance
6.4 Performance Improvement: Scalability and Reliability
6.5 Optimizing Bandwidth Usage in Networks
6.6 Applying Artificial Intelligence for Optimization
6.7 Designing Sustainable Energy Systems for IoT Sensors
6.8 Case Studies: System Optimization Existing
7.2 Analysis of Measured Water Quality Parameters
7.2 Selection of Specific Sensors for Applications
7.3 Data Analysis: Advanced Techniques
7.4 Applications in Environmental Monitoring and Industry
7.5 Interpretation of Results and Decision Making
7.6 Practical Case Studies: Analysis of Real Data
7.7 Development of an IoT Monitoring System: Final Project
7.8 Presentation of Results and Conclusions
8.2 Architecture of Integrated Monitoring Systems
8.2 Data Integration: Sources and Formats
8.3 Visualization Platforms and Advanced Analysis
8.4 Implementation of Control Panels and Alerts
8.5 Data Analysis: Trend and Anomaly Detection
8.6 Predictive Maintenance and Asset Management
8.7 Security of IoT Systems in Critical Environments
8.8 Projects Comprehensive Monitoring: Success Stories
3.3 Introduction to IoT Data Analysis in Water Management
3.2 Data Collection and Management: IoT Platforms and Databases
3.3 Data Cleaning and Preprocessing: Techniques and Tools
3.4 Descriptive and Exploratory Data Analysis in Water Quality
3.5 Predictive Analysis: Water Quality Modeling and Forecasting
3.6 Data Visualization: Dashboards and Interactive Reports
3.7 Machine Learning Applications in Water Management
3.8 Integrating IoT Data with Geographic Information Systems (GIS)
3.9 Case Study: Data Analysis in a Real-World Monitoring System
3.30 Data-Driven Decision Making: Strategies and Tools
4.4 Designing IoT Strategies for Water Sensor Selection
4.2 Implementing IoT Networks for Water Data Collection
4.3 Analyzing IoT Data for Contaminant Detection
4.4 Optimizing Water Quality Monitoring Frequency
4.5 Developing Real-Time Alerts and Notifications
4.6 Integrating IoT with Water Quality Management Systems
4.7 Cybersecurity Strategies for IoT Monitoring Networks
4.8 Case Studies: Implementing Successful IoT Strategies
4.9 Scalability and Adaptability of IoT Solutions
4.40 Future Trends in Water Quality Monitoring with IoT
5.5 Environmental Legislation and Water Regulation
5.5 Fundamental Concepts of the Internet of Things (IoT)
5.3 Architecture and Components of IoT Systems
5.4 Sensors and Actuators in Water Quality Applications
5.5 IoT Communication Protocols and Networks
5.6 Platforms and Platforms for IoT Data Management
5.7 Cybersecurity in IoT Systems for Water Management
5.8 Case Studies: IoT Applications in Water Management
5.5 Selection and Specification of IoT Sensors for Water Quality
5.5 Design and Installation of Sensor Networks
5.3 Sensor Calibration and Maintenance
5.4 Power and Supply Considerations for Sensors
5.5 Integration of Sensors with IoT Platforms
5.6 Implementation of Real-Time Monitoring Systems
5.7 Practical Examples of Sensor Installation and Configuration
5.8 Troubleshooting Common Problems in IoT Sensor Implementation
3.5 Introduction to Data Analysis in Water Quality Water Resources
3.5 IoT Data Collection and Storage
3.3 Data Cleaning and Preprocessing
3.4 Exploratory Data Analysis (EDA)
3.5 Time Series Analysis and Anomaly Detection
3.6 Data Visualization and Report Generation
3.7 Use of Data Analysis Tools (e.g., Python, R)
3.8 Interpretation of Results and Decision Making
4.5 Design of IoT-Based Monitoring Strategies
4.5 Definition of Water Quality Objectives and Metrics
4.3 Selection of Sampling Locations and Frequency
4.4 Design of Alerts and Notifications
4.5 Integration of Monitoring Systems with Management Platforms
4.6 Development of Response Plans for Critical Events
4.7 Optimization of the Sensor Network
4.8 Implementation of Predictive Models
5.5 Selection and Integration of Specific Sensors
5.5 Design of Complex Sensor Networks
5.3 Communication Between Sensors and Platforms
5.4 Management 5.5 Data from Multiple Sources
5.6 Data Synchronization and Time Management
5.7 Integration with Control and Automation Systems
5.8 Implementation of Advanced Analytics Systems
5.9 Deep Integration Case Studies
6.5 Water Quality Assessment
6.5 Identifying Problems and Improvement Opportunities
6.3 Sensor Selection and Control Strategies
6.4 Design of Remote Control Systems
6.5 Implementation of Corrective and Preventive Measures
6.6 Water Management Optimization
6.7 Water Quality Simulation and Modeling
6.8 Evaluation of the Impact of Optimization
7.5 Fundamentals of IoT Sensors for Water Quality
7.5 Selection of Sensors and Parameters to Measure
7.3 Sensor Calibration and Maintenance
7.4 Data Analysis and Trend Detection
7.5 Application of Sensors in Different Environments
7.6 Interpretation of Results and Decision Making
7.7 Practical Cases and Implementation Examples
7.8 Report Development and Results Presentation
8.5 Design and Planning of Comprehensive Monitoring
8.5 Sensor Selection and Installation
8.3 Data Platform Configuration
8.4 Advanced Data Analysis
8.5 Control System Integration
8.6 Risk Management and Cybersecurity
8.7 Reporting and Conclusions
8.8 Case Studies and Practical Examples
6.6 Introduction to Water Quality Optimization with IoT: Key Concepts
6.2 Selection and Configuration of IoT Sensors for Water Quality
6.3 Data Collection and Storage: IoT Platforms and Databases
6.4 Data Analysis: Interpretation and Visualization of Information
6.5 Optimization Strategies: Water Quality Control and Management
6.6 Implementation of IoT Systems: Design and Architecture
6.7 Data Integration: Sensors, Software, and Hardware
6.8 Maintenance and Calibration: Ensuring Sensor Accuracy
6.9 Case Studies: Practical Examples of Optimization
6.60 Future Trends: IoT and Sustainable Water Management
- Hands-on methodology: test-before-you-trust, design reviews, failure analysis, compliance evidence.
- Software (depending on licenses/partners): MATLAB/Simulink, Python (NumPy/SciPy), OpenVSP, SU2/OpenFOAM, Nastran/Abaqus, AMESim/Modelica, acoustics tools, planning toolchains DO-178C.
- SEIUM Laboratories: scale rotor bench, vibrations/acoustics, EMC/Lightning pre-compliance, HIL/SIL for AFCS, data acquisition with strain gauging.
- Standards and compliance: EN 9100, 17025, ISO 27001, GDPR.
Capstone-type projects
Admissions, fees, and scholarships
- Profile: Background in Computer Engineering, Mathematics, Statistics, or related fields; practical experience in NLP and valued information retrieval systems.
- Documentation: Updated CV, academic transcript, SOP/statement of purpose, project examples or code (optional).
- Process: Application → Technical evaluation of profile and experience → Technical interview → Review of case studies → Final decision → Enrollment.
- Fees:
- Single payment: 10% discount.
- Payment in 3 installments: No fees; 30% upon registration + 2 equal monthly payments of the remaining 35%.
Monthly payment: available with a 7% commission on the total; annual review.
Scholarships: based on academic merit, financial need, and promoting inclusion; agreements with companies in the sector for partial or full scholarships.
See “Calendar & Calls for Applications,” “Scholarships & Grants,” and “Fees & Financing” in the SEIUM mega-menu.
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F. A. Q
Frequently asked questions
Yes, we have international certification
Yes: experimental models, real data, applied simulations, professional environments, real case studies.
It is not mandatory. We offer leveling tracks and tutoring.
Completely. It covers e-propulsion, integration, and emerging regulations (SC-VTOL).
Recommended. There are also internal challenges and consortiums.
Yes. Online/hybrid modality with planned labs and visa support (see “Visa & Residence”).