Nearly one billion people lack access to safe drinking water and two billion has inadequate access to sanitation facilities. Water quality can be tested for chemicals and the presence of microorganisms from sewage, runoff or discharge from factories. Furthermore, monitoring acidity and the cloudiness of water is an indicator of localized pollution potentially caused by land-based activities. Predicting unsafe drinking water conditions with AI is even better.
IoT Blueprint:
Assemble the collection of components displayed below to address the use case of ensuring safe drinking water.
Sensor(s)
pH: Determine Water Purity to See if it’s Safe for Consumption
ORP (Oxidation-Reduction Potential): Detect Presence of Harmful Microorganisms in Water
EC (Electrical Conductivity): Detect Dissolved Solids in Water
TDS (Total Dissolved Solids): Detect Dissolved (In)organics to Indicate Contamination
Turbidity: Detect Particles to Ensure Water Meets EPA Requirements that Drinking Water Turbidity must be below 1 NTU (Nephelometric Turbidity Unit)
Single Board Computer (SBC): Raspberry Pi, NVIDIA Jetson, Orange Pi (If AC Power Available)
Power Options
Lithium-Ion Polymer (LiPo) Batteries
Solar
Power Over Ethernet (PoE)
AC Power (Utility | Mains | Wall Outlet)
Network(s)
Ethernet: If Present Within a Given Space for High Bandwidth Scenarios
Wi-Fi: If Available for Indoor, High Bandwidth Scenarios
Bluetooth: For Short Range, Low Bandwidth Scenarios
LoRa: Create Low Bandwidth Coverage Needed to Reach Internet Access
Cellular: If Coverage Available and Cost-Effective for High Bandwidth Scenarios
Satellite: When No Terrestrial Coverage Options are Available for Medium Bandwidth Scenarios
Digital Twin Modeling
Sewage Water Systems
Factory Discharge Systems
Lakes, Ponds, Rivers, and Waterways
Freshwater Pipeline Systems
Data Processing + Storage Location(s)
Edge: Near Waterways and Water Pipeline Systems
Cloud: Filtered Data Relayed from Edge to Monitor Broader Areas of Water Systems
Analytics
Compare Sensor Data Values to Defined Setpoints, KPI Value Ranges and Thresholds
Filter out Duplicate Sensor Data Value Readings
Automation
Green KPI: No Action
Yellow KPI: Warn Notification for Awareness When Sensor Values are Trending Towards Unsafe Water Quality
Red KPI: Alert Notification to Appropriate Personnel to Take Manual Actions or Automate the Closing of Water Valves When Unsafe Water is Present
People
Deploy and Maintain Solution
SMEs Define KPIs and Actions
Water Testing Personnel, Plumbing, and Water Utility Organizations
Security
Uniquely Identify Each Device
TLS 1.3 Encryption for Data in Transit
Encrypt Data at Rest
Validate Device Messages to Ensure they use Expected Data Format
Rotate Security Tokens
Limit IP Address Ranges
AI Anomaly Detection
Zero Trust: Reauthenticate Device Messages Through Every Step of the System
Artificial Intelligence
Machine Learning Time Series Forecasting Model to Predict When Drinking Water is Most Likely to be Unsafe
IoT Platform
Device SDK Captures Sensor Data from Physical Twin and Securely Sends it as a JSON Payload to the IoT Platform Along with a Unique Identifier and Security Token
IoT Platform Captures, Authenticates, and Saves Incoming Device + Sensor Data to a Message Queue
Background Process Takes Queued Data and Hydrates the Digital Twin by Saving it to a Database Table that Mimics the Structure of the Physical Twin
AI Model is Trained and Retrained with Digital Twin Dataset of Current and Historical Data Where Properties of Twin are Mapped to ML Features
Hot Path Data is Sent to Streaming Analytics to Facilitate Real Time Alerting and Automation
Same Hot Path Data is Also Sent to AI Model for Inferencing to Predict Future Occurrences
Rob is a writer, teacher, speaker, world traveller and undersea explorer. He's also a thought leader in the areas of enterprise mobility and the Internet of Things.
