Forests are home to more than 80 percent of all terrestrial species of animals, plants and insects and around 1.6 billion people depend on forests for their livelihoods. Every year, over 20 million acres of forests are lost or otherwise destroyed. Detecting chainsaws and trucks can curb illegal logging in protected forests. Predicting illegal logging with AI is even better.
IoT + AI Blueprint:
Assemble the collection of components displayed below to address the use case of reducing deforestation from illegal logging.
Sensor(s)
Acoustic: Listen for Chainsaws
Cameras: See Unauthorized Vehicles and Personnel
Motion: Detect Unexpected Movement
Panic Button: Person in the Area Witnesses Illegal Logging
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 Trees in Protected Forest Areas
Logging Roads
Data Processing + Storage Location(s)
Edge: In Forest
Cloud: Filtered Data Relayed from Edge to Monitor Broader Areas of Forests
Streaming 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 Unauthorized Vehicles or Personnel are Detected at Greater Distances
Red KPI: Alert Notification Forest Rangers or Authorities to take Action When Chainsaws, Unauthorized Vehicles or Personnel are Present
People
Deploy and Maintain Solution
SMEs Define KPIs and Actions
Forest Rangers and Authorities
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 and Identify Unauthorized Vehicles and Personnel
Machine Learning Time Series Forecasting Model to Predict Future Occurrence of Chainsaw Usage
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.
