Automated Water Quality Survey and Evaluation Using an IoT Platform with Mobile Sensor Nodes

Area of Science:

• Environmental Science
• Sensor Networks
• Water Quality Monitoring

Background:

• Remote aquatic environmental monitoring faces challenges in data acquisition and real-time analysis.
• Existing methods for water quality assessment are often offline and resource-intensive.
• The need for efficient, spatiotemporal surface water quality evaluation is critical.

Purpose of the Study:

• To design and develop an Internet of Things (IoT) platform for remote aquatic environmental monitoring.
• To enable spatiotemporal quality evaluation of surface water using mobile sensor nodes.
• To introduce an Online Water Quality Index (OLWQI) for interpreting large volumes of real-time data.

Main Methods:

• A cellular decomposition approach using hexagonal cells to define Sampling Locations of Interest (SLoIs).
• A survey planner utilizing a spanning tree approach to generate efficient paths for Mobile Sensor Nodes (MSNs).
• Development of an Online Water Quality Index (OLWQI), adapted from the Canadian Council of Ministers of Environment Water Quality Index (CCME WQI).

Main Results:

• The proposed IoT platform effectively supports remote aquatic environmental monitoring with Mobile Sensor Nodes (MSNs).
• The survey planner successfully generated optimized paths for MSNs to visit SLoIs within energy and time constraints.
• The Online Water Quality Index (OLWQI) demonstrated reliable performance in real-time indexing of extensive water quality parameter measurements.

Conclusions:

• The developed IoT platform provides a robust solution for spatiotemporal surface water quality evaluation.
• The integration of a survey planner and an online indexing method enhances the efficiency and effectiveness of aquatic monitoring.
• The field deployment and performance validation confirm the platform's practical applicability and reliability.