Thousands of species of animals have been illegally traded via wildlife trafficking and over 45,000 species are threatened with extinction. Establishing protected areas of flora and fauna while utilizing real-time wildlife monitoring can detect a decline in population from trafficking and poaching to assist in conservation efforts. Predicting when trafficking typically occurs with AI is even better.
IoT Blueprint:
Assemble the collection of components displayed below to address the use case of protecting endangered species.
Sensor(s)
Cameras: Computer Vision to Visually Detect Illegal Hunting or Trafficking
Acoustic: Listen for Unexpected Vehicles and Personnel as well as Gunshots
GPS and RFID Tags to Track the Movement of Endangered Species
Panic Button: Person in the Area Witnesses Illegal Hunting or Trafficking
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
Collection of Flora and Fauna in a Protected Area
Individual Endangered Plants and Animals
Data Processing + Storage Location(s)
Edge: In Protected Area
Cloud: Filtered Data Relayed from Edge to Monitor Broader Areas Containing Endangered Fauna and Flora
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 an Illegal Hunting or Trafficking Scenario
Red KPI: Alert Notification for Forest Rangers, Police, and Conservation Officers to take Action on Illegal Hunters or Traffickers
People
Deploy and Maintain Solution
SMEs Define KPIs and Actions
Forest Rangers, Conservation Officers, and Police
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
Computer Vision to Perform Object Detection to Detect Unauthorized Personnel
Machine Learning Time Series Forecasting Model to Predict Future Occurrence of Illegal Hunting or Trafficking
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.
