Buildings are responsible for 27% of operational carbon emissions due to their energy usage. Connecting office building occupancy to lighting can cut that usage by as much as 40%. Motion detectors can reduce wasteful energy usage by illuminating buildings, floors, and spaces only when people are detected. Additionally, light sensors can adjust brightness based on the amount of natural sunlight present to save even more energy. Predicting when and where people will be within buildings with AI is even better.
IoT + AI Blueprint:
Assemble the collection of components displayed below to address the use case of reducing energy usage in buildings with smart lighting.
Sensor(s)
Motion: Detect People
Light: Detect Intensity of Outdoor Light Entering Building Through Windows
Single Board Computer (SBC): Raspberry Pi, NVIDIA Jetson, Orange Pi (If AC Power Available)
Power Options
Lithium-Ion Polymer (LiPo) Batteries
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
Digital Twin Modeling
Buildings
Floors
Spaces (Offices, Conference Rooms, Common Areas, Hallways)
Data Processing + Storage Location(s)
Edge: In Building
Cloud: Filtered Data Relayed from Edge to Monitor More Than One Building or Property
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 People, Turn Off Lights. Outside Light Intensity Low, No Action.
Yellow KPI: Outside Light Intensity Medium, Dim Indoor Lights Slightly.
Red KPI: People Present, Turn On Lights. Outside Light Intensity High, Dim Indoor Lights More.
People
Deploy and Maintain Solution
SMEs Define KPIs and Actions
Facilities Management Personnel, Lighting Experts
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 People are Most Likely to be in Buildings, Floors, or Specific Spaces
Machine Learning Time Series Forecasting Model to Predict When Various Levels of Outdoor Light Intensity will Affect Indoor Lighting
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.
