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Opportunities for ML Analytics at the Sensor Endpoint
Chris Rogers, CEO SensiML Corporation
MAKING SENSOR DATA SENSIBLE
IoT Smart DevicesHow Many Qualify as Truly Smart?
The Majority of IoT Endpoint Devices…
What’s missing is useful adaptable algorithms embedded in the device
• Incorporate sensors
• Connected but dumb
• Defer analytics elsewhere
• Network constrained
• Not real-time
• Stream unfiltered sensitive data
• Static algorithms
So What’s the Big Deal With Having Dumb IoT Sensors?
Conventional “Dumb” Sensor IoT Network
Simple Sensor- Temperature Sensors- Limit Switches- Counters
Complex Sensor- Cameras, imaging sensors- Audio, microphones- Motion, accelerometers, IMUs- Vibration, piezo sensors- Passive IR- Current, voltage, electrodes- RF signals
Key Challenges:
• Bandwidth
• Power
• Latency
• Security
Conventional IoT Sensor Network: Bandwidth / Power
Raw Payload Motion Vibration Audio Video
Sample Rate 1 kHz 5 kHz 20 kHz 30 Hz
Resolution 16 bit 16 bit 16 bit 24 bit
Channels 9 (x,y,z) 3 (x,y,z) 2 (stereo) 4 MPixel
Throughput Req’d
140 kbps 234 kbps 625 kbps 2.9 Gbps
Network Throughput
LPWAN(LoRA)
LTE IoT(Cat-M1 R13)
ZigBee BLE 4.2(BT Smart)
BT 5.0 WLAN (802.11ac)
Payload < 8 kbps < 375 kbps < 250 kbps <21 kbps* < 1.4 Mbps
< 125 kbps** 200Mbps
***
*** 802.11ac @ 40Mhz channel, 1x1 (embedded STA)
** BT5 2x long range operation
* BLE4.2 7.5ms CI, 20 byte MTU
Conventional IoT Sensor Network: Latency
"In cases where sensors generate a lot of telemetry, but only sporadic data that's actionable, you want to discern the signal from the noise without overwhelming the ingestion processes at the core …you don't want a 100 millisecond loop to the internet and back."
- Jeffrey Hammond, Forrester Research Analyst
Conventional IoT Sensor Network: Security
AmazonAVS Server
Local Wake WordEvent Detection
“Alexa”…ALLAudio
Specific Query
ALL AudioAmazon
AVS Server
The Role for Sensors in Intelligent IoT Networks
Our Own Brains: A Distributed Processing Architecture
As Applied to IoT Analytics Processing…
A High-Performance Distributed IoT Network Application
Cloud Analytics(offline data mining,business intelligence)
Edge ANN/CNN(vision, spatial and image processing)
Cloud NLP(speech recognition, automated assistant)
Endpoint Rich Sensing ML(audio processing,motion and vibration classification)
Local Critical Insight ML(network failure independent,mission critical feedback/control)
Endpoint Real-Time ML(machine control, robotics)
Sensed Property Acquired SignalPhysical World
Smart Sensors: Combine Rich Signals with Expert Insight…
Machine vibration
Conventional Sensor
… to provide local inferencing of meaningful events
Event Detection Expert Training Meaningful Insight
CauseEvent
Extruder #3
Excess Vibration
Flange Bearing Fail
Obstruction
OK
Smart Sensor
Signal Conditioning Communication
Filtering, down-sampling,averaging, etc.
Packetization, data compression,
error correction
Feature Engineering Classification
A Distributed Smart Sensor Endpoint IoT Network
Key Challenges:
•Processing
• Learning
•Data Loss
Smart Sensing: Processing Limitations
Algorithm Suitability to IoT Endpoint Device Processors
Hand-coded
Classic numerical methods(i.e. regression, heuristics,sorting, linear programming)
Efficient Execution
Costly Development
Inflexible / Static
Rules Based
Expert systems(Knowledge base collection of rules)
Code/Rules Separation
Brittle Logic
Inefficient code
AI / Deep Learning
ANN / CNN(Neuron arrays trained by backpropagation)
Overall Performance
HW Requirements
Large Training Datasets
Machine Learning
Classification(i.e. SVM, kNN, random forest, clustering)
Efficient Execution
Adaptive Learning
Training Intervention
Smart Sensing: On-Device Learning Challenge
• If sensor nodes “see” only their own input data…they can learn only from what they are exposed
• Cloud capable of seeing data from ALL sensors…but then training is centralized not distributed
Dilemma:
Smart Sensing: A Flexible Learning ArchitectureLevel 1: Algorithm Tuning and Personalization• Local model reconfiguration and parameter tuning• Improved classifier performance over time• No cloud required
Example: Tailoring generic model to a specific user or device
Level 2: Neuron Remapping• Same event triggers and features, new classifier configuration• Cloud invoked for redefinition of classifier stage (harnessing training data from all available sensors)• On-the-fly model change initiated by cloud
Example: Learn a new gesture or activity
Level 3: Algorithm Reprogramming• All new event triggers, features, and classifier• Full algorithm reconstruction via the cloud (harnessing training data from all available sensors)• Over-the-air sensor firmware update
Example: Provide an entirely new application
Throw away this stuff!?!? I never know when I might find a use for it!
A Cloud Centric ‘Big Data’ Analyst
Smart Sensing: Data Retention Limitations
Mitigations to the Need for Data Hoarding
Classifier Driven Sampling – Capture and store sampled anomalous raw data
Parameterized Sampling – Reduction of raw data to feature vectors
Localized Model Personalization – Per device customization of algorithm parameters (e.g. classifier weight factors)
Cloud Analytics(offline data mining,business intelligence)
Edge ANN/CNN(vision, spatial and image processing)
Cloud NLP(speech recognition, automated assistant)
Endpoint Rich Sensing ML(audio processing,motion and vibration classification)
Local Critical Insight ML(network failure independent,mission critical feedback/control)
Endpoint Real-Time ML(machine control, robotics)
The Role of Sensor Endpoints in IoT Analytics Processing
Endpoint: Localized Sensory Insight
Cloud: Broad Contextual Insight
Q & A
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