Training Course on GIS and IoT for Real-Time Monitoring - GIS Software

Module 1: Introduction to GIS and IoT

Theory:

Overview of GIS and IoT concepts.

IoT components: sensors, actuators, gateways, connectivity protocols.

Benefits of IoT-GIS integration.

Applications: Smart cities, environmental monitoring, transportation tracking.

Hands-on: Identify IoT devices and explore GIS platforms that support real-time data integration.

Case Study: Real-time air quality monitoring using GIS and IoT sensors.

Module 2: IoT Sensors and Data Acquisition

Theory:

Types of sensors: temperature, humidity, GPS, motion, environmental.

Data acquisition methods and IoT protocols (MQTT, HTTP, CoAP).

Sensor calibration and data quality.

Hands-on: Configure sensors and simulate data streaming to a test environment.

Case Study: Weather station sensor network for environmental monitoring.

Module 3: Connecting IoT to GIS Platforms

Theory:

GIS platforms for real-time data (ArcGIS, QGIS, Web GIS).

Streaming protocols and data connectors.

Data formats: JSON, CSV, GeoJSON, XML.

Hands-on: Stream simulated IoT data into ArcGIS Online or QGIS.

Case Study: Real-time traffic sensor integration for urban mobility analysis.

Module 4: Real-Time Data Processing and Storage

Theory:

Processing pipelines for sensor data.

Databases for real-time IoT data: PostgreSQL/PostGIS, InfluxDB.

Data cleaning, aggregation, and transformation.

Hands-on: Store and process IoT streams in PostGIS or a cloud database.

Case Study: Monitoring water levels in rivers for flood alert systems.

Module 5: Spatial Analysis of Real-Time IoT Data

Theory:

Applying spatial analysis to sensor streams.

Hotspot detection, spatial interpolation, and temporal analysis.

Geofencing and proximity alerts.

Hands-on: Perform spatial analysis on live sensor data and generate actionable insights.

Case Study: Detecting high-traffic areas in a city using GPS sensors.

Module 6: Visualization of Real-Time GIS Data

Theory:

Real-time mapping and dynamic dashboards.

Visualization tools: ArcGIS Dashboards, Power BI, web mapping frameworks.

Layering multiple real-time data streams.

Hands-on: Create a live dashboard integrating IoT sensor feeds.

Case Study: Smart city dashboard showing energy consumption, traffic flow, and air quality.

Module 7: Alerts, Notifications, and Automated Responses

Theory:

Setting thresholds and triggers for sensor data.

Automating alerts via email, SMS, or app notifications.

Integration with IoT actuators for automated responses.

Hands-on: Configure alert rules based on sensor thresholds and visualize triggers in GIS.

Case Study: Automated flood warning system using river-level sensors.

Module 8: Data Quality and Security in IoT-GIS Systems

Theory:

Ensuring accuracy, consistency, and reliability of sensor data.

Security challenges in IoT-GIS integration (encryption, authentication).

Managing data privacy and access control.

Hands-on: Implement basic security measures and validate incoming sensor data streams.

Case Study: Securing real-time environmental monitoring data from unauthorized access.

Module 9: Applications of GIS and IoT

Theory & Discussion:

Smart cities: traffic management, energy monitoring, public safety.

Environmental monitoring: air quality, water quality, pollution tracking.

Agriculture: soil moisture, crop health, irrigation monitoring.

Disaster management: early warning systems, emergency response.

Hands-on: Simulate multiple IoT applications and visualize results in GIS dashboards.

Case Study: Multi-sensor integration for disaster response and resource allocation.

Module 10: Capstone Project

Project Examples:

Smart city traffic monitoring dashboard integrating GPS and environmental sensors.

Real-time flood monitoring and alert system with river-level sensors.

Agricultural field monitoring using soil moisture and temperature sensors.

Hands-on: End-to-end project: configure sensors → stream data → process → analyze → visualize → set alerts.

Presentation: Demonstrate real-time monitoring system, workflow, and dashboard insights.