The transportation sector accounts for more than 30% of carbon emissions and a third of that comes from fleet vehicles like trucks. Managing fleets can reduce fuel consumption and emissions by up to 25% through route optimization, identifying speeding, harsh braking, prolonged engine idling, and monitoring the health of the engine, transmission, and pressure in the tires to proactively deliver maintenance. Predicting deleterious vehicle behavior with AI is even better.
IoT + AI Blueprint:
Assemble the collection of components displayed below to address the use case of reducing emissions throughout a vehicle fleet.
Sensor(s)
OBD-II Can bus to Monitor the Health and Usage Every Aspect of the Vehicle and its Subsystems
GPS/GNSS to Determine Vehicle Location, Route, Speed, and Distance Covered
Bluetooth: For Short Range, Low Bandwidth Scenarios
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
Vehicle and Subsystems
Complete Fleets of Vehicles
Data Processing + Storage Location(s)
Edge: In Vehicles
Cloud: Filtered Data Relayed from Edge to Monitor Entire Fleet
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 Sensor Values are Trending Towards Anomalous Conditions
Red KPI: Alert Notification for Anomalous Conditions to Vehicle Managment and Maintenance Teams to Take Action
People
Deploy and Maintain Solution
SMEs Define KPIs and Actions
Vehicle Management Personnel and Maintenance Team
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 Likely Engine Failure
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.
