TECHNICAL ARTICLE| Share on Twitter| Share on LinkedIn| EmailIntelligence at the Edge Part 1:
The Edge Node
Ian Beavers

Analog Devices, Inc.

The industrial Internet of Things (IoT) encompasses the broad transformation underway that will make pervasive sensing across connected
machines not just a competitive advantage, but an essential fundamental
service. The industrial IoT starts with the edge node, which is the sensing and measurement entry point of interest. This is where the physicalworld interacts with computational data analytics. Connected industrial
machines can sense a wide array of information that will be used to makekey decisions. This edge sensor is likely far removed from the cloud serverthat stores historical analysis. It must connect through a gateway that
aggregates edge data into the internet. Ideally, the edge sensor node isunobtrusive within a small nominal form factor to easily deploy in spaceconstrained environments.

Sense, Measure, Interpret, Connect
In this first of a multipart industrial IoT series, we will break down andexplore the fundamental aspects of the edge node sense and measurement capabilities within the larger IoT framework: sensing, measuring,interpreting, and connecting data, with additional consideration for power
management and security. Each portion presents a unique set of challenges. Smart partitioning of the edge node can be key to a successful
implementation. In some cases, ultralow power (ULP) is the most important performance metric. The vast majority of potential data may be filteredwhen the sensor wakes from sleep mode during key events.

Sensors form the front-end edge of the industrial IoT electronics ecosystem. Measurements transform the sensed information into something
meaningful such as a quantifiable value of pressure, displacement, or
rotation. The interpretation stage is where edge analytics and processing
transforms the measured data into an actionable event.1 Only the mostvaluable information should be connected beyond the node into the cloudfor predictive or historical processing. All along the signal chain, the data
can be rejected or filtered based on initial limits of acceptability. Ideally, thesensor node should only send information that is absolutely necessary andshould make critical decisions as soon as key data is available.
The edge node must be connected to the outside network, either througha wired or wireless sensor node (WSN). Data integrity remains key in thisblock of the signal chain. Optimum sensed and measured data is of little
value if the communication is inconsistent, lost, or corrupted. Missing
data via communication cannot be an option. Electrically noisy industrial
environments can be harsh and unforgiving, especially for radio frequency
communication in the presence of high metal content. Therefore, a robust
communication protocol must be designed as a forethought during systemarchitecture design.
Power management for ULP systems starts with regulator component
selection for maximum efficiency. But, as edge nodes may also wake andsleep with a rapid duty cycle, the power-up and power-down time should
also not be ignored. An external trigger or wake-up command aids in theability to quickly alert the edge node to begin sensing and measuring data.

Edge Node
MeasureSecurity

Analog Domain
AC Signal

Video

Current

Magnetic

DC Voltage

Pressure

PSI

MotionCapacitance

Rotation

Temperature

Light

Particulate

Moisture

Audio

AMP

ADC

Interpret

Connect

Digital Domain

Signal
Processing
and Analytics

Security

Sense

SecurityCloud
RF TxRx

Gateway

Firmwareand Control

Power Management
Security

Figure 1. An edge node device provides the intelligence to sense, measure, interpret, and connect to an internet gateway to the cloud. The data can be preprocessed withsome form of analytics before it is transmitted for deeper data mining intelligence.

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2

Industrial IoT Sensing and Measurement: the Edge Node

Data security must also be a consideration for an industrial IoT system.
Not only does the data protection within the edge need to be secure, but
its access to the network gateway must also be protected from malicious
intent. An edge node must not be allowed to be spoofed to gain network
access for nefarious activity.

Intelligence Starts at the Edge
There are a legion of sensing solutions at the edge, which may not just bea single discrete device. The edge may be a plurality of various concurrent
unrelated data acquisitions. Temperature, sound, vibration, pressure,humidity, motion, pollutants, audio, and video are just some of the variables
that can be sensed, processed, and sent to the cloud through a gateway
for further historic and predictive analysis.
It is not a hyperbole to say that sensors are the backbone of industrial
IoT.2 But it might be more accurate to say they’re the central nervous
system for extracting insights. The edge node sense and measurement
technology is the birthplace for the data of interest. If bad or incorrect
data is faithfully recorded at this stage in the solution chain, no amount of
postprocessing in the cloud can reclaim the lost value.
Mission critical systems, such as healthcare and factory line-down
monitoring with high stakes outcomes, require robust integrity of quality
data measurements. Data quality is paramount. False positives or omissions can be costly, time consuming, and potentially life threatening.
Costly errors eventually result in unplanned maintenance, inefficientlabor use, or having to disable the IoT system entirely. Intelligence starts
at the edge node where avoidance of the old adage still applies—
garbage in, garbage out.

Optimized Smart Partitioning Maximizes
Cloud Value
Only the most important measured information needs to be sent throughthe gateway to the cloud for final processing. In some cases, a majorityof the data is completely unimportant.5 However, system data that is time
critical with a need for a local real-time decision should be acted uponlong before it is aggregated at a distant point with remote access. In contrast, information that leverages historical value with prediction models to
influence long-term insights makes an ideal application for cloud processing. Archiving data into mass databases for retroactive processing anddecisions plays to the strengths of powerful cloud processing and storage.6
Legacy:No EdgeIntelligence

SampleAll Data

Full Data
Sampling

Send
All Data

Full Data
BandwidthStore
All Data

ReducedData Set
Sense
CriticalData

MeasureCritical
Data

FilterInterpret
Process

Triggers
AlertsProfilesDecisions
Reduced Latency

Parse and
Analyze

Delayed Latency

Edge Node

Edge Node:
Smart
Partitioning

Full Data
Storage

Connect

Gateway
ReducedBandwidth

Cloud
Predictive
Analysis
ReducedStorage

Figure 3. Smart partitioning at the edge node solves new challenges that could nototherwise be solved before. Leaner processing and intelligence further ahead in
the signal chain allow for a more efficient total IoT solution.

Living on the Edge with Real-Time Decisions
Edge
Node #2

Gateway
Node

EdgeNode #3

Edge
Node #1

Edge
Node #4

Edge
Node #5

Figure 2. Many edge node outputs, both wired and wireless, can autonomously
connect to a gateway node to be aggregated before transmission to a cloud server.

With Access to Troves of Data,
Comes Great Responsibility
In legacy signal chain solutions without edge node intelligence, data stays
data. An unintelligent node never helps generate wisdom and knowledge
to make actionable decisions.1 There can be large quantities of raw, low
quality data that have no impact on the system performance of interest.3
It can be power hungry and bandwidth intensive to convert and send allof this data to an eventual cloud storage destination.
By contrast, intelligent smart partitioning edge node sensing and measuring
turns data into actionable information. An intelligent node lowers overall
power consumption, lowers latency, and reduces bandwidth waste.4 This
enables a move from a reactive IoT with a long latency to both a realtime and predictive IoT model. Basic analog signal chain circuit design
philosophy still applies to the IoT. For complex systems, deep application
expertise is often needed to interpret the processed data.

IoT sensors are predominantly analog. The specific industrial application
requirements will dictate the dynamic range and bandwidth of the sensor
that will be required at the front end of the edge node. The front end of
the signal chain will be within the analog domain before the signal is converted to a digital representation and transmitted outside the edge. Eachcomponent in the analog signal chain has the potential to limit the overall
performance of the edge node if not selected properly. The dynamic range
will be the delta between the full-scale sensor of interest relative to thenoise floor or next highest undesired signal.
Since IoT sensors are typically looking for both known and unknown
activities, an analog filter does not always make sense. Digital filtering
is performed after the signal is sampled. Unless an analog filter is used at
the front end of the sensor, harmonics of the fundamental or other spurious signals can fold into the sensed information and compete in powerwith the signal of interest. Therefore, planning for unanticipated sensed
signals in both the time and frequency domain during the design phasewill prevent unwanted artifacts from showing up in the measured data.
The sensed information is typically measured with an ADC that is next inthe signal chain. If the IoT edge node is designed using discrete components, care should be taken to choose a measurement ADC that does
not reduce the dynamic range of the sensor. An embedded ADC’s input
full-scale range is usually matched well to the sensor output amplitude.
Ideally, the sensor output should consume nearly the entire ADC input
range, within 1 dB, without saturating the ADC and getting clipped at therange limits. However, an amplifier stage may also be used to gain or
attenuate the sensor output signal to maximize the ADC’s own dynamic
range. The ADC full-scale input, sample rate, resolution in bits, input
bandwidth, and noise density will all contribute to the signal measurement
performance of the edge node.

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A front-end amplifier can either be embedded in the measurement of
the node or added as a discrete component ahead of the ADC. The gain,
bandwidth, and noise of the amplifier can also enhance the performanceof the edge node.
The measurement ADC after the sensor in the signal chain is often oneof two sampling architecture types: Nyquist rate or a continuous timeΣ-Δ (CTSD), with the latter being more prevalent with embedded ADCs.
A Nyquist rate ADC will have a nominal flat noise floor equal to half thesample rate frequency, or fs/2. A CTSD uses an oversampling rate with anotched pass-band that pushes the noise outside the bandwidth of interest for increased dynamic range. The measurement ADC architecture andits resolution are key to understanding the analog bandwidth and dynamic
range of the edge node.

The dynamic range of the edge node will be composed of the dynamic
range of the sensor, the amplification of the signal, if needed, and the ADC
full-scale dynamic range. If the full-scale sensor output signal does notreach within 1 dB of the ADC full-scale range input, then some portionof the ADC dynamic range will be left unused. Conversely, an overranged
ADC input from the sensor will distort the sampled signal. Amplifier
bandwidth, gain, and noise will also be part of the consideration for the
dynamic range of the edge node. The electrical noise of the sensor, amplifier, and the ADC combined will be the square root of the square sum ofeach rms component.7
Dynamic
Range Lost

Full-Scale
ADC RangeADC

FFT of Nyquist Rate ADC>Full-Scale =Clipped Signal

1st Nyquist Zonend Nyquist

2

Figure 5. An example of a sensor signal output amplitude that does not match the
input full scale of the ADC and dynamic range is lost (blue). An amplifier is needed
to maximize the dynamic range of the sensor while preventing ADC saturation(red). Signal matching must consider the bandwidth, dynamic range, and noiseof the entire edge node signal chain.

Zone

3rd Nyquist Zone
Noise Floor
fs/2

Continuous Time Σ-Δ ADC
Signal of Interest

Noise Shaping

fMOD/2
Band ofInterest

Figure 4. Without a front-end analog filter on an IoT sensor, a Nyquist rate ADC will
fold higher-order frequencies beyond the 1st Nyquist zone back into the bandwidthof interest. By contrast, a CTSD ADC architecture with an oversampling modulation
clock uses noise shaping to allow high dynamic range within a band of interest.
The CTSD is less sensitive to signal aliasing as it provides inherent filtering.

For example, in the frequency domain, the noise density per unit bandwidth of 1 Hz will be based upon the SNR of the ADC and how wide thenoise is spread across the sampled spectrum of the ADC. In a Nyquist
rate ADC, the noise spectral density (per 1 Hz bandwidth) = 0 dB – ADC
signal-to-noise ratio (SNR) – 10 × log(fs/2) where fs/2 is the sample rate
divided by two or a single Nyquist zone of the ADC. The ideal SNR canbe calculated as SNR = 6.02 × N + 1.76 dB, where N is the number of
ADC bits. However, the actual SNR of an ADC includes the nonidealitiesof transistor and semiconductor processing, including electrical noise
and transistor level component imperfections. These nonlinearities will
degrade the SNR performance below the ideal, so check the ADC data
sheet for the SNR performance of interest.

Smart Factory
One application that will be important within the industrial IoT is machine
vibration condition monitoring. New or legacy machine equipment canhave key mechanical components, such as rotating shafts or gears,
mounted with high dynamic range MEMS accelerometers.8 These multiaxis sensors sample the vibratory displacement of machinery in real time.
Vibration signatures can be measured, processed, and compared to an
ideal machine profile.9 In a factory, the analysis of this information aides
increased efficiency, reduces line-down situations, and can predictmechanical failures in advance. In extreme cases, a machine with arapidly deteriorating mechanical component, that would otherwise induce
further damage, can be immediately shut down.

Machine Condition MetricSignal Power (dB)

Signal of Interest

Satisfactory
Operating
SystemRoutineMaintenance(Time Based)

Machine Health Signature
Based on Vibration Analytics

Predictive
Maintenance

Run-to-BreakPoint of
FailureOperating Life

Figure 6. Although routine machine maintenance can be performed at regular timeintervals, it is often not done with intelligence about the condition of the machine.10
By analyzing the vibration performance of specific machine operations, a predictive
point of failure and maintenance milestone can be alerted at the edge node.

3

Industrial IoT Sensing and Measurement: the Edge Node

Decision time latency can be drastically reduced by enabling edge node
analytics. An example of this can be seen in Figure 7, where a MEMS
sensor warning threshold limit is exceeded and an alert is immediately
sent. If the event is extreme enough to be deemed critical, the node may
be given authority to automatically disable the offending equipment toprevent a time sensitive catastrophic mechanical breakdown.
Alternately, a trigger signal may be invoked to enable another sense andmeasurement node, such as one on a secondary machine component, tobegin interpreting data based on the 1st event. This reduces the total data
set of sampled data from the edge nodes. In order to determine any vibration abnormality from nominal, the front-end node must be designed withthe required performance for detection. The dynamic range, sample rate,and input bandwidth of the sense and measure circuit should be morethan sufficient to identify any excursion event.
Waiting for Trigger

Sampling

K
g
in O
rk =
Pa oneo
N re Z
Fi

Histo

rical

Path

Car (x, y)Moving at 5 mph

Human(x, y)Dog (x, y)
Not a Human
Historical

Acceleration (g Force)

4

Path

Figure 8. Using edge node video analytics, object type detection, trajectory, andboundary crossing can be determined in a low power system without sending fullbandwidth video data to the cloud for analysis. Only a timestamp with breadcrumb
object coordinates and type need to be communicated.

Trigger Threshold

Machine Vibration
SignalTime

Figure 7. A time domain representation of sampled machine vibration data wherea comparator threshold can determine whether or not the sensed and measured
data is communicated beyond the edge. A lower power state can be maintained
to filter a majority of the information until data preponderance is achieved by athreshold crossing event.

Smart City
A different industrial IoT edge node application is that of a smart city
industrial camera with embedded video analytics. Smart city defines theurban mission to integrate a myriad of information and communication
points into a cohesive system to enable management of a city’s assets.
A common application is to provide parking space vacancy alerts andoccupancy detection. At the time of commissioning, each camera has apredetermined field of view. Boundary edge detection can be defined andused within the analytics to identify a variety of objects and their motion.
Not only can historical object movement be analyzed, but due to object
trajectory, a predicted path can also be computed at the edge using digital
signal processing (DSP) algorithms.

In a similar vein to frequency filtering, the full bandwidth of a video analytics frame is typically not needed for end processing. Often, when not used
for security purposes, only a small subset of the complete video frame isrequired. Most of the visual data from frame to frame is static on a fixedmounted camera. The static data can be filtered. In some cases, only acount of boundary crossings or the movement coordinates of the object ofinterest needs to be analyzed. The reduced subset can be communicatedas a breadcrumb coordinate to the next gateway in the signal chain.
Edge node video analytics can provide many filtered interpretations todifferentiate object types—car, truck, bicycle, human, animal, etc. Thisdecimation reduces data bandwidth and computational power that otherwise would be needed within a cloud server to analyze the full frame rate
video data sent downstream.
Indoor camera applications may identify the number of people that crossan entry boundary and adjust the lighting, heating, or cooling for a room.
In order to be visually effective in extreme lighting conditions or otherchallenging illuminations such as rain, a high dynamic range camera
may need to be used in outdoor cameras. A typical 8-bit or 10-bit per
pixel imaging sensor may not provide sufficient luminance dynamic rangethat is independent of illumination across all detection scenarios. In contrast
to viewing fast motion sports at a 240 Hz refresh rate, a slower frame rate
can be used to monitor the activity on an industrial analytics camera.

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About the Author

Figure 9. High dynamic range imagers with DSP object detection algorithms at theedge node can determine movement and boundary intrusions, even in low lighting
conditions. This example uses visual contrast to define edge detection for anindoor factory/office (left) and an outdoor parking lot (right).

Platform Level Solutions
The ADT7420 is a 4 mm × 4 mm digital temperature sensor with breakthrough performance that contains an internal 16-bit ADC with resolution
to 0.0078°C, consuming a mere 210 μA. The ADXL362 is an ultralow
power 3-axis MEMS accelerometer that consumes only 2 μA at a 100 Hz
sample rate in motion triggered wake-up mode. It does not use power
duty cycling, but rather employs a full bandwidth architecture at all data
rates, which prevents aliasing of input signals. The ADIS16229 is a dualaxis, 18 g digital MEMS vibration sensor with embedded RF transceiver.
It also provides on-board frequency domain signal processing with a
512 point digital FFT capability.
A DSP enabled Blackfin low power imaging platform (BLIP)11 allows rapid
prototyping for industrial vision designs based upon proven digital signal
processing tools. A library of optimized software deliverables allows
equipment manufacturers an out-of-the-box solution for motion sensing,people counting, and vehicle detection.

References:1

Colm Prendergast. “Smart Partitioning and Value Creation in a Connected
World.” Keynote IoT Address: IESA Vision Summit 2015.

2

Stephen Lawson. “IoT Keeps Pushing Analytics Closer to the Edge.”
PCWorld: IDG News Service, 2016.

3

Lisa Morgan. “Edge Analytics an Antidote to IoT Data Deluge.”
InformationWeek: UBM Electronics, 2016.

4

Daniel Kirsch. “The Value of Bringing Analytics to the Edge.”
Hurwitz & Associates Services, 2015.

5

Jason Stamper. Why IoT Is Driving Analytics to the Edge of the Network.
451 Research, 2015.

6

Steve Nelson. “Exploring the Internet of Things from End to End.”
Element14: Newark Electronics, 2014.

7

Umesh Jayamohan. “Understanding How Amplifier Noise Contributes toTotal Noise in ADC Signal Chains.” Analog Dialogue, February 2013.

8

Robert Randall. Vibration-Based Condition Monitoring.
New South Wales, Australia: University of New South Wales, 2010.

9Ed Spence. “Bring the Benefits of MEMS Accelerometers to Condition
Monitoring.” Electronic Design, Penton Publishing, 2016.

10

Jamie Smith. “Smart Edge Devices for the Industrial Internetof Things.” ARC Industry Forum, 201511Blackfin Low Power Imaging Platform (BLIP).
Analog Devices, Inc., 2014.

Ian Beavers is a product engineering manager for the Automation
Energy and Sensors team located at Analog Devices, Greensboro,
NC. He has worked for the company since 1999. Ian has over 19
years of experience in the semiconductor industry. Ian earned a bachelor’s degree in electrical engineering from North Carolina State
University and an M.B.A. from the University of North Carolina at
Greensboro. He can be reached at Ian.Beavers@analog.com.

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