IoT Design Guide 2017

OCTOBER 2017 | VOLUME 4

EMBEDDED-COMPUTING.COM/IOT

MARKET SPOTLIGHT Wearables PG 22

IOT INSIDER

The Internet of Tomatoes PG 5

embedded-computing.com/designs/iot_dev_kits

Development Kit Selector

2017 Design Guide

PG 25

AVNET Easy IoT Starter Kit PG 28

AVNET

AVNET AT&T LTE Starter Kit II PG 37

Visible Things 2 Starter Kit PG 26 AND 29

AVNET BCM4343W IoT Starter Kit PG 27

Design a Solar-Powered Bluetooth Sensor Node PG 12

Advertiser Index PAGE ADVERTISER 14 ACCES I/O Products, Inc. – PCI Express Mini Card & mPCIe embedded I/O solutions 40 Arena Solutions – Product problems? Put them in our hands 1 Avnet – Visible Things – The Edge to Enterprise Platform 1 Avnet – Visible Things – Industrial Starter Kit 1 Avnet – AT&T LTE Starter Kit II 1 Avnet – BCM4343W IoT Starter Kit TO COME 1 Avnet – Easy IoT – Development Kit for Qseven Modules with Intel E3800 1 Digikey – IoT Development Kit Selector 3 Embedded World – Immerse yourself in the world of embedded systems and discover innovations for your success 15 Micro Digital, Inc. – SMX RTOS – Ideal for your project 11 Microchip Technology, Inc. – The world’s first dual-port INIC 11 Microchip Technology, Inc. – Complete system solutions for your wireless design 24 Toradex – Designed for Industrial IoT and embedded applications

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IoT Design Guide 2017

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October 2017 | Volume 4

CONTENTS

opsy.st/IoTDesign

FEATURES

COVER

6 How to develop tiny solar-powered wireless sensor nodes

By Eiji Fukawa, Cypress 12 Identifying secure firmware update mechanisms and open source options for embedded Linux devices

@iot_guide

By Alex Gonzalez, Digi International

Industry development kits provide the fastest on-ramp to IoT prototypes and production. Avnet carries an IoT dev kit for every use case, with the Easy IoT Starter Kit (page 28), the BCM4343W IoT Starter Kit (page 27), the Visible Things 2 Starter Kit (pages 26 and 29), and the AT&T LTE Starter Kit (page 37) featured in this year’s IoT Design Guide.

WEB EXTRAS  Cross-industry semantic interoperability: Towards a common data format and API

12 16 Machine Learning and Industrial IoT –

16

By Michael Schmidt, Technologic Systems http://bit.ly/NodeJS4embedded

By Dinesh Nair, National Instruments

 Defrosting coal wagons with wireless technology

20 The birth of 30 million digital twins: Manufacturing feedback

By Jan Pešl, IQRF Alliance http://bit.ly/DefrostingCoalWagons

loops and paying it forward

 Raspberry Pi smart home solutions

By Brandon Lewis, Technology Editor

By Jeremy Cook, Engineering Consultant http://bit.ly/RaspberryPiSmartHome

22 Wearables development in an IoT world

By Curt Schwaderer, Editorial Director

26 2017 Design Guide Dev Kits Industrial IOT RTOS & Tools, Security Wireless, Cloud

COLUMN 5

IOT INSIDER – Using the IIoT to yield better tomatoes

By Rich Nass, Brand Director

http://bit.ly/SemanticInterop5

 Sample project: Interfacing IoT with embedded using Node.js

Now and in the future

By representatives from Zigbee, ARTS, GS1, VeriSign, OMG, ControlBEAM, SkyFoundry, and Shaw Communications

22

 Check these boxes before deploying IoT devices

By Donald Schleede, Digi International http://bit.ly/IoTBoxChecking

Published by:

2017 OpenSystems Media® © 2017 Embedded Computing Design All registered brands and trademarks within Embedded Computing Design magazine are the property of their respective owners. iPad is a trademark of Apple Inc., registered in the U.S. and other countries. App Store is a service mark of Apple Inc. ISSN: Print 1542-6408 Online: 1542-6459 enviroink.indd 1

4

IoT Design Guide 2017

10/1/08 10:44:38 AM

www.embedded-computing.com/iot

IoT INSIDER

Using the IIoT to yield better tomatoes

By Rich Nass, Brand Director rnass@opensystemsmedia.com

Last year, as part of our IoT Roadshow, I interviewed Michael Murray, General Manager of Analog Devices’ Industrial Sensing Group. The interview was titled “Get into the application to really understand it.” At the time, we were talking about autonomous sensing, ­basically describing what it is and how it works. The part that Michael and I discussed that didn’t get included in the video is the farming application that the company is involved with.

To be honest, I had forgotten about this very cool application until recently when an announcement crossed my desk that said, “Analog Devices’ monitoring initiative aims to improve crop quality and yields and boost profitability of local farmers.”

According to the announcement, Analog Devices has “entered into a collaboration with the Cornucopia Project and ripe.io to explore the local food supply chain and use this work as a vehicle for educating students at ConVal Regional High School in Peterborough, N.H., and local farmers on 21st century agriculture skills.”

“STUDENTS WILL USE THE IOT TO TRACK THE CONDITIONS AND MOVEMENT OF PRODUCE FROM THE TOMATO PLANT TO THE END LOCATION, WITH A GOAL OF IMPROVING THE QUALITY, YIELDS, AND PROFITABILITY OF THAT PRODUCE.” Here at IoT Design and Embedded Computing Design, we talk a lot about Industrial IoT. In most cases, we refer to manufacturing, building automation, robotics, smart cities, and so on.

But the IoT for agriculture topic is extremely popular in many parts of the country. I’ve heard examples of tomato farming and grape harvesting. This announcement from Analog Devices shows that the proof is in the pudding … well, maybe the tomato paste.

Students will use the IoT to track the conditions and movement of produce from the tomato plant to the end location, with a www.embedded-computing.com/iot

goal of improving the quality, yields, and profitability of that produce. Analog Devices will provide a prototype version of its sensor-to-cloud crop monitoring solution, which measures environmental factors that can affect a crop such as water, fertilization, ripeness, and so on. That solution is combined with a near infrared (NIR) miniaturized spectrometer that conducts further analysis.

You have to love it when a project like this comes to fruition (ouch!).

MORE INDUSTRY/UNIVERSITY COLLABORATIONS:  Competitions prepare students for changing technology landscapes

By Sebastian Castro, Mathworks http://bit.ly/StudentCompetitionLandscape

 Linking connected cars with city infrastructure to help improve driver awareness in Las Vegas

By Steve Crumb, GENIVI Alliance http://bit.ly/SmartCars-Cities

 Your hardware is not secure

By Craig Ramsay, University Of Strathclyde http://bit.ly/InsecureHardware

 Student Competitions Fuel Next-Gen Engineers

By Sandeep Hiremath, Mathworks http://bit.ly/Next-GenEngineers IoT Design Guide 2017

5

Wireless Sensor Nodes

How to develop tiny solar-powered wireless sensor nodes

By Eiji Fukawa, Cypress

Wireless sensor nodes promise to reduce implementation costs by reducing the size of sensors, simplifying maintenance issues, and maximizing battery life. However, even greater growth will be possible with an emphasis on battery-free implementations.

A best practice for designing battery-free devices is to reduce the average power consumption of the wireless sensor system. This can be done using technologies such as Bluetooth Low Energy (BLE) for both communications and energy harvesting.

Figure 1 shows the architecture of a tiny wireless sensor. This sensor is built around a microcontroller (MCU) with an integrated BLE radio that can operate entirely on power from an energy harvesting power management integrated circuit (PMIC).

To operate using only energy harvesting power, a sensor must optimize its BLE implementation to minimize power consumption. To achieve this, designers must first understand the implementation details of the BLE subsystem. Next, firmware needs Solar BLE Sensor (Top View) Mode Select Switch

Solar BLE Sensor (Bottom View) Energy Harvesting PMIC S6AE103A

to be written to match the requirements of each operating/power mode. Then, designers must analyze actual power consumption to confirm assumptions and further improve the power efficiency of the system.

The following illustrates techniques for reducing power consumption using the CYALKIT-E02 Solar-Powered BLE Sensor Beacon Reference Design Kit (RDK) from Cypress Semiconductor (Figure 1). This RDK includes the Cypress PSoC 4 BLE and S6AE10xA Energy Harvesting PMIC.

Optimizing Bluetooth Low Energy 25 mm

0.2-F Supercapacitor Connector for Debugging

15 mm x 15 mm Solar Cell

FIGURE 1 6

Temperature and Humidity Sensor

100-µF x 4 pcs Storage Capacitor

EZ-BLE PRoC Module CYBLE-022001-00

The CYALKIT-E02 is a tiny wireless sensor built around an MCU with an integrated BLE radio. It is optimized to operate using power only from an energy-harvesting power management IC.

IoT Design Guide 2017

First, let’s consider a simple BLE design without power optimization. The BLE radio is configured as a beacon in nonconnectable advertisement mode. A BLE beacon is a one-way communication method that broadcasts at a regular interval. It consists of small packets of data (30 bytes) that are sent as part of the advertisement packet. Beacons that want to be discovered can be used for a www.embedded-computing.com/iot

Cypress Semiconductor www.cypress.com

TWITTER

@CypressSemi

LINKEDIN

FACEBOOK

www.linkedin.com/company/ cypress-semiconductor

YouTube

www.facebook.com/CypressSemi

www.youtube.com/user/cypresssemi

“A BLE BEACON IS A ONE-WAY COMMUNICATION

Access Address (32)

Preamble (8)

METHOD THAT BROADCASTS

Header (16)

AT A REGULAR INTERVAL. IT CONSISTS OF SMALL PACKETS OF DATA (30 BYTES)

PDU Type (4)

Rx Add (1)

Tx Add (1)

RFU (2)

Length (6)

AdvA Advetiser's Address (48) Public device Address

Company_ Company_ assigned id (24) (24)

() means “Bit”

hash (24)

FIGURE 2

variety of smartphone or computer applications to trigger actions such as push messages, app actions, and prompts.

•• “Preamble” must set “10101010b” •• “Access Address” must be set to “1000111010001001101111101101 0110b (0x8E89BED6)” •• “PDU” has “Header” and “Payload” The packet structure of a BLE beacon belongs to “Advertising Data” in “Payload.”

Figure 3 shows the BLE Beacon packet format, while Table 1 shows the setting values.

We can calculate the average consumption current using the voltage and current waveform to determine the efficiency of the BLE design. Figure 4 (page 8) shows the energy results of the non-power-optimized design.

The average current is about 5 mA, and total consumed energy from startup to wait is 34.76 mJ. To operate using environmental energy, we will need to reduce the consumption current. www.embedded-computing.com/iot

AdvData Advertising Data (0-248) AD Structure 1 Length

AD type

AD Structure 2 AD data

Length

AD type

AD data

Random device Address

OF THE ADVERTISEMENT

Figure 2 shows a link layer format of the BLE for Advertising channel packet format. The link layer of BLE has “Preamble,” “Access Address,” “Protocol Data Unit (PDU),” and “Cyclic Redundancy Code (CRC).” Note that the following information does not include “Data Channel Packets.”

Payload (as per the Length field in the Header)

RFU (2)

THAT ARE SENT AS PART

PACKET.”

CRC (24)

PDU (Protocol Data Unit) (16 to 312)

Link layer format of BLE for Advertising channel packet format.

AdvData Advertising Data [30]

[ ] means “Byte”

Length [1]

AD type [1]

AD data [1]

Length [1]

AD type [1]

Length 1 [1]

AD type 1 [1]

AD data [1]

Length 2 [1]

AD type 2 [1]

FIGURE 3

random (24)

AD data [25]

Company ID

[2]

Device Length 3 Type [1] [1]

Major [2]

UUID [16]

RS SI

Minor [2]

[1]

BLE beacon packet format

Length1

0x02

AD type1

0x01

AD data

0x04

Length2

0x1A

AD type2

0xFF

Company

ID 0x0131 (Cypress Semiconductor Corporation)

Device type

0x02

Length3

0x15

UUID*

00050001-0000-1000-8000-00805F9B0131 (hex)

Major**

Your Own ID

Minor***

0x0001

RSSI****

0xC3 (-61 dBm)

Transmitter power

0 dBm

Advertise interval

1500 ms

* This is a 16-byte string used to differentiate a large group of related beacons ** This is a 2-byte string used to distinguish a smaller subset of beacons within the larger group *** This is a 2-byte string meant to identify individual beacons **** Received Signal Strength Indication – This is used to determine proximity (distance) from the beacon

TABLE 1

BLE beacon format IoT Design Guide 2017

7

Wireless Sensor Nodes

“THE USER DESIGN SHOULD MANAGE THE

Optimizing firmware for low power We can reduce the average current consumption of the BLE design by optimizing the following four functions: 1. Low-power startup 2. Deep-Sleep 3. Internal main oscillator (IMO) clock setting 4. Debug select To do so, we need to utilize a watchdog timer (WDT) to wake up the system when the system is in low power mode.

Low-power startup After a power-on-reset (POR), the BLE system initializes various components by calling their respective start functions.

Perform the following steps at initialization for low-power operation (Figure 5): 1. Shut down the 24 MHz external clock oscillator (ECO) to reduce power consumption while the 32.768 kHz watch crystal oscillator (WCO) is starting 2. Enable the WDT to wake up the system after 500 ms (WCO startup time) 3. Configure the MCU in deepsleep mode for the 500 ms WCO startup time 4. After the WCO is enabled, restart the ECO to enable the BLE subsystem (BLESS) interface 5. Set the WCO to low-power mode and change the lowfrequency clock (LFCLK) source from the 32 kHz internal low-speed oscillator (ILO) to the WCO 6. Enable the WDT to wake up the system 7. Configure the MCU in deep-sleep mode

Deep-Sleep The user design should manage the system clocks, system power modes, and BLESS power modes to implement lowpower operation in BLE-enabled MCUs (Figure 6).

The following steps are recommended to implement Deep-Sleep between BLE intervals:

8

IoT Design Guide 2017

SYSTEM CLOCKS, SYSTEM POWER MODES, AND BLESS POWER MODES TO IMPLEMENT LOW-POWER OPERATION IN ­BLE-ENABLED MCUS”

3.3 V

Time Wave1: 1 s/div Wave2: 1 ms/div 500 ms

19.8 mW

9.9 mJ

3.3 V

0.3 ms

23.1 mW

0.00693 mJ 0.0132 mJ

Power Mode

Stage

Current 5 mA/div

Voltage 1 V/div

Startup

A

6 mA

B

7 mA

BLE Transmit

Power

Energy

C

4 mA

3.3V

1 ms

13.2 mW

D

14 mA (typ)

3.3V

2 ms

46.2 mW

0.092 mJ

Wait

E

5 mA

3.3V

1500 ms

16.5 mW

24.75 mJ

Total

-

-

-

2003.3 ms

-

34.76 mJ

Wave2: Zoomed BLE Current Consumption

Wave1: Non-Optimized Design A

E

1 sec

B

C

1 ms

D

BLE Voltage (1 V/div)

BLE Voltage (1 V/div) Zoomed BLE Transmit

1.5 sec Time Interval of BLE Transmit

BLE Current (5 mA/div)

BLE Current (5 mA/div)

FIGURE 4

Current consumption of a BLE design without power optimization

POR

Transmit BLE

Power 0.5 sec

3

BLE Activity

1 2

ECO ILO

6 7

Deep-Sleep Shut down ECO Enable WDT

2

BLE Event

0.5 sec Deep-Sleep

4

Restart ECO

5

Change LFCLK to WCO

5

Wait WCO stable

Start ECO

Stop ECO

WCO low power mode

WCO

This waveform shows the steps to reduce current consumption at BLE system initialization.

FIGURE 5

5

ECO Stable

ECO ON Deep-Sleep BLE Activity Active or Sleep Active or Deep-Sleep Deep-Sleep

3 A

System Activity ECO

1.5 sec

BLE Event

B

A. B.

1 2

Deep-Sleep

Application pre-processing Application post-processing

Shut down ECO Enable WDT

Transmit BLE

BLE Event

4

A

B

Restart ECO

WCO

FIGURE 6

This waveform shows the steps to reduce current consumption at BLE system operation. www.embedded-computing.com/iot

1. Shut down the ECO to reduce power consumption 2. Enable the WDT to wake up the system after 1.5 s (BLE event interval) 3. Configure the MCU in Deep-Sleep mode 4. After 1.5 s, restart the ECO to enable the BLESS interface 5. Transmit the BLE advertisement data 6. Repeat from step 1

IMO clock setting The 3 MHz to 48 MHz IMO is the primary source of internal clocking. The IMO default frequency is 48 MHz and can be adjusted between 3 MHz and 48 MHz in steps of 1 MHz. The IMO tolerance of the RDK in this example has a default calibration setting of ±2 percent.

IMO Frequency

Maximum Current

48 MHz

1000

Processing Time

A

Voltage

2

1 sec

3.3 V

Total Energy 3300

J

24 MHz

325

A

2 sec

3.3 V

2145

J

12 MHz

225

A

4 sec

3.3 V

2970

J

6 MHz

180

A

8 sec

3.3 V

4752

J

3 MHz

150

A

16 sec

3.3 V

7920

J

IMO DC specifications and example total energy (* – Example processing time based on 48 MHz = 1 s)

FIGURE 7

Time Wave1: 1 s/div __ Wave2: 1 ms/div 1000 ms

Power Mode

Stage

Current 5 mA/div

Voltage 1 V/div

Startup

A

0.0015 mA

3.3 V

B

2 mA

3.3 V

0.3 ms

C

3 mA

3.3V

D

12.5 mA (typ)

Standby

E

0.0015 mA

Total

–

BLE Transmit

Power

Energy

0.00495 mW

0.00495 mJ

6.6 mW

0.00198 mJ

1 ms

9.9 mW

0.0099 mJ

3.3V

2.2 ms

41.25 mW

0.0825 mJ

3.3V

1500 ms

0.00495 mW

0.00743 mJ

–

2503.5 ms

–

0.106 mJ

–

Wave1: Optimized Second Design A

Wave2: Zoomed BLE Current Consumption 1 sec

E

B

C

1 ms

D

BLE Voltage (1 V/div)

BLE Voltage (1 V/div)

Figure 7 shows an example of total energy by changing IMO frequency.

Zoomed BLE Transmit

1.5 sec Time Interval of BLE Transmit

Debug select The serial wire debug (SWD) pins are used for run-time firmware debug during development stages. Configuring the SWD pins for debugging increases current consumption. Therefore they should have been switched to general-purpose I/O (GPIO) mode in the production release. This way they are still available for device programming on chip reset.

FIGURE 8

Current consumption of a BLE design with power optimization

CYALKIT-E04

Solar Module

Door Sensor

+

330 m F

FIGURE 9

Now we need to confirm that the system can operate using energy harvesting technology at this level of average current and total energy consumption.

Figure 9 shows a block diagram of the energy harvesting system. This system utilizes the S6AE10xA Energy Harvesting (EH) PMIC family and can operate for an entire day using the CYALKIT-E04 S6AE102A and S6AE103A evaluation kit www.embedded-computing.com/iot

F

PRoC BLE

Kit Prog

USB Port

BLE Blue LED

Header

300

BLE-USB Bridge

PSoC 4 BLE

Energy Harvesting PMIC S6AE103A

Header

Pioneer Baseboard USB Port

KitProg

Energy harvesting system block diagram

EH PMIC

The average current is about 1.5 µA, and total consumed energy from startup to wait is 0.106 mJ.

How to operate for an entire day using energy harvesting

BLE Connection

CY8CKIT-042-BLE

Backup Battery

WDT

Light Sensor

To confirm how much we have optimized the design, calculate the average consumption current of the optimized BLE design using the voltage and current waveform. Figure 8 shows the energy results of the power-optimized design.

BLE Current (5 mA/div)

BLE Current (5 mA/div)

SW

SW

VDD

3.3–2.1 V VOUT

PSoC 4 BLE LDO WDT

BLE

VST1 Solar Cell

300

F

Wave1: BLE Transmit Under 100 lx Charge Charge Discharge

Wave2: Zoomed BLE Current Consumption 1 ms

1 sec VOUT Voltage (1 V/div)

1.5-sec Time Interval of BLE Transmit

BLE Current (5 mA/div)

FIGURE 10

VVOUTH = 3.3 V VVOUTL = 2.1 V

Discharge

VOUT Voltage (1 V/div)

Zoomed BLE Transmit

BLE Current (5 mA/div)

Simple energy harvesting IoT Design Guide 2017

9

Wireless Sensor Nodes (EVK) in conjunction with the CY8CKIT042-BLE Pioneer Kit. The block diagram in Figure 10 (page 9) shows simple energy harvesting operation with a PSoC 4 BLE, which is supported by the S6AE102A and S6AE103A boards.

Wave1 highlights BLE operation using solar energy and Wave2 shows BLE current consumption while transmitting.

The PMIC first stores solar energy in a storage device on VSTORE1 (VST1), which is a 300 μF ceramic capacitor.

When VST1 achieves VVOUTH, the energy is sent to the MCU for BLE operation.

The S6AE10xA automatically changes the power source according to the voltage level of VST1. If the VST1 voltage reaches VVOUTL, energy is supplied from the VBAT power source to provide energy in the absence of ambient light.

Eiji Fukawa is a senior systems application engineer in the Analog Business Unit of Cypress Semiconductor, formerly Spansion. He currently focuses on the development of energy harvesting products.

Charging

Discharging EH PMIC

Figure 12 shows the waveforms detailing the charge profile for storing energy in VST2. The S6AE10xA stores energy to both VST1 (a small capacitor) and VST2 (a large capacitor). The energy stored in VST1 is used to operate the system and any remaining energy is used to recharge the sub-storage device on VST2. The energy in VST2 is supplied to the system continuously so that the system can continue to operate for some time even when there is no ambient light.

The block diagram in Figure 13 shows hybrid power input control. Wave1 shows how the PMIC controls two power sources (solar and battery). The PMIC converts the two power sources to operate the system under various conditions. An ambient light source is generally constant, but some locations do not have constant light. The PMIC can convert two power sources automatically and continue to supply energy without ambient light.

 10

IoT Design Guide 2017

EH PMIC

To BLE

300 F

VST2

VST1 300 F

0.33 F

Wave1: Charging Waveform

To BLE

Control Circuit

No Ambient Light

VST1

VST2 0.33 F

VOUT SW

VDD

Control Circuit

Solar Cell

Wave2: Discharging Waveform 100 sec

Charge

However, such simple energy harvesting cannot operate for an entire day without a backup capacitor (such as during periods where there is no light).

The block diagrams and waveforms in Figure 11 illustrate hybrid storage-­ control functionality. The energy needed to operate the system is stored in VST1 and the remaining energy is used to recharge VSTORE2 (VST2). The energy in VST2 is supplied to the system continuously, even when there is no ambient light.

VOUT SW

VDD

100 sec

Discharge

VST1 Voltage (1 V/div)

VST1 Voltage (1 V/div)

Discharge

VST2 Voltage (1 V/div)

Charge VST2 Voltage (1 V/div)

FIGURE 11

Hybrid storage-control function

Charging Operation CHRGE

CHRGE

VOUTH ≈ 3.30V

CHRGE

CHRGE

CHRGE

V OUTM ≈ 3.14V

(VOUTHx0.95)

VSTORE1 300μF 0V

VSTORE2 0.33 F

VOUTH ≈ 3.30V Full charging

CHRGE

CHRGE

CHRGE

CHRGE

CHRGE

0V START

VOUT

ON

OFF

0V

FIGURE 12

ON

Waveform illustrating surplus energy storage

Hybrid Power Input Coin Battery

Wave:1 Ability to Change the Power Source

EH PMIC

VBAT (2 V/div)

SW

VBAT

VOUT SW

VDD

To BLE VDD (2 V/div)

Solar

Control Circuit VST2 VST1

VST1

Solar Cell 0.33 F

10 sec

Battery

VSTORE1 (2 V/div)

VOUT 300

VOUT (2 V/div)

F

VOUT Usage Battery

Solar

Solar

Battery

Solar

Battery

FIGURE 13

Hybrid power input control www.embedded-computing.com/iot

EXECUTIVE SPEAKOUT

OS81119, the World’s First Dual-Port INIC

By Carmelo De Mola, Manager Product Marketing AIS

The OS81119 is the world’s first dual-port, highly integrated Intelligent Network Interface Controller (INIC) for the 150 Mbps MOST® Network (MOST150) with support for both optical physical layers (oPHY) and coaxial (coax) physical layers (cPHY) that can efficiently handle audio, video, packet, and control data for infotainment and Advanced Driver Assistance Systems (ADAS).

On cPHY, it enables daisy chain on MOST150 networks with the support of full-duplex communication. With a full-duplex

daisy-chained network, a single cable segment is sufficient to connect two adjacent devices in the network, reducing cables and connectors for the back channel on each network connection. It also eliminates the return wire connecting the last node of the network to the first. This reduces wiring and component count resulting in lower system costs as well as potential weight savings that can impact Corporate Average Fuel Economy (CAFE) goals and other fuel efficiency regulations.

The classic ring topology with dual-simplex communication is supported by oPHY and cPHY as well.

The OS81119 further allows designers to implement creative hybrid networks, mixing physical layers and topologies. This helps customers to protect their investment in MOST150 technology, enabling them to expand their existing MOST150 networks with innovative applications that are based on costefficient cPHY daisy chain topologies with little hardware and software design effort.

The OS81119 offers the following state-of-the-art industry standards as user interfaces: I2C, Streaming Port, MediaLB®, SPI, and USB2.0 hi-speed port. On the software side, it is compatible with MOST Linux® Driver, MOST NetServices, and Microchip’s Unified Centralized Network Stack (UNICENS).

The OS81119 is offered in a QFN88 package; OS81119 ICs are available today in volume production. To purchase these, for obtaining additional information, or requesting samples, please contact any Microchip sales representative or authorized worldwide distributor.

EXECUTIVE SPEAKOUT

Complete System Solutions for Your Wireless Design

By Clayton Pillion, Director of Marketing WSG

Adding wireless connectivity to your product is exciting, and Microchip has you covered every step of the way with each piece of the design. Whether you want to add a Wi-Fi® sensor to your water tank or you just want the ease and control of connecting your product to a smartphone via Bluetooth®, we have the complete solution for your design. With a broad wireless portfolio, the industry’s largest microcontroller selection, and software stacks with built-in security that enable cloud connectivity, Microchip is a one-stop-shop for a complete wireless solution.

To speed up development, we offer multiple reference designs and design ideas to start building from. These reference designs are great tools for showing you the right components to start with and real-life use cases for further inspiration and ease of design. And with our free software available on MPLAB® Harmony and Atmel Studio, development has never been easier.

Firmware Updates

Identifying secure firmware update mechanisms and open source options for embedded Linux devices

By Alex Gonzalez, Digi International

The rapid growth of the Internet of Things (IoT) has triggered the appearance of billions of Internet-connected, wireless embedded devices. From medical devices to tank sensors, smart thermostats, intelligent streetlights, water monitors, and more, the IoT is in more places than ever.

What could possibly go wrong?

Most of these devices were not designed as if they were the target of malicious attacks. Embedded systems have traditionally been considered stable products, but costly to implement, with the return on investment (ROI) planned over a fairly long lifecycle. Once shipped, they seldom require updating. The desire to develop updates and actually apply them in the field only became common with the eruption of smart mobile phones and operating systems (OSs) like Android.

Suddenly, malicious hackers now have as potential targets all these vulnerable ­connected devices, which run on inadequate, outdated OSs and Linux kernels with well-known vulnerabilities that have not been patched. They are able to be remotely exploited!

There is also the question of atomicity.

The Linux server world is used to performing package-based updates, and everything seems to work just fine. But an embedded device is not a server.

A server usually lives in a controlled environment, probably secure, and has a guaranteed power and network connection.

It also resides in a monitored, accessible location, so user intervention for recovery is possible, even if undesired.

Not a very attractive prospect.

Secure updates: Embedded versus server Today, a new class of field software updates has been fueled by security concerns, but also allows engineers to add new features and fix bugs.

With regards to embedded devices, a firmware update mechanism must be not only secure, but also reliable in that it either succeeds in the update or fails to a recoverable state. In no way should the software update brick a device, and it should be able to happen unattended. Most updates must also preserve the previous device state, although on some occasions recovering a device could involve resetting to a default state.

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IoT Design Guide 2017

Linux servers usually rely on package management, typically rpm-based (Yellowdog Updater Modified, or YUM) or deb-based (apt), with dependency resolution that performs non-atomic, incremental updates. Updates are driven by package version updates, each with a set of complex pre/post installation scripts that could leave the system in an undefined, even non-working state. www.embedded-computing.com/iot

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Unfortunately, embedded devices might be inaccessible, may be in low-power modes most of the time, have very long life times, and could suffer from power or network outages that could interrupt a firmware update.

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•• User partitions: Storage locations for user applications and persistent data are the partitions most frequently in need of updates.

Basically, image firmware updates can range from the whole system – that is the kernel, root, and user partitions – to just some of them.

There are two types of image updates possible: symmetric and asymmetric.

Ultimately, package manager-based updates are not atomic, making it difficult to test and support them. This usually results in losing track of the actual state of the firmware on devices, as well as the dreaded “What did I update on this device the last time?” question. This isn’t suitable for embedded systems that are expected to always perform consistently.

Image updates The traditional, and best-until-provenotherwise way of updating an embedded device is performing a whole image update. In traditional baremetal embedded devices, this would be a whole image with all the firmware on the device. In embedded Linux devices, this usually translates to partition updates, so the partitioning scheme is an important consideration as it will impact the type of software update that can be performed.

Embedded Linux devices usually divide media into different partitions that can be updated separately:

•• Bootloader partitions: Rarely, if ever, updated, updating the bootloader of an embedded device in the field will result in the eventual bricking of a device.

Therefore, well-thought-through update mechanisms avoid this as much as possible.

•• Boot/kernel partition: Linux kernel and related artifacts, such as device trees and initramfs images, usually happen for security more than functional reasons.

Updates are not usually required.

•• Root filesystem partitions: Where the OS files are stored are commonly read-only immutables. This is also seldom updated but could happen more often if the application relies on libraries from here. www.embedded-computing.com/iot

•• Symmetric: Symmetric updates require a dual copy of the partition images being updated so that one can be updated while the other is running.

This typically requires two boot/kernel partitions, two root filesystems, as well as two user partitions. The bootloader then tracks which partitions to use for a given boot. Symmetric updates have minimal downtime (a usually only the reboot time) and allow for update cancellation.

•• Asymmetric: Asymmetric updates use a recovery OS that usually runs from memory, with a Linux kernel and an initramfs image. This reduces the number of partitions needed, as the recovery mode lives in just one extra partition and can update any of the others. If the update fails, the recovery can be re-tried.

Asymmetric updates have longer downtimes when updating and do not allow for user cancellation.

The user space updater application Typically, updates are performed by a user space application that fetches the software update package, applies it, and informs the bootloader of the update. It also needs to allow for post-installation operations to take place. The bootloader then starts a hardware watchdog and tries to boot. If the boot succeeds, the hardware watchdog is deactivated; if not, it is triggered and the bootloader tries booting again. After a number of tries it either swaps back to the known-good partitions or into recovery mode.

An important consideration is that the user space update firmware must be updatable with the firmware update process. Another risk is it’s possible to update to a bootable system that has a broken firmware update mechanism. Unfortunately, it is then necessary to fall back to the bootloader or other recovery mechanisms to update the firmware.

Some systems use the bootloader to perform the update. This has some serious drawbacks. If the firmware update code must be updated (for example because of a partition change), it’s the bootloader that needs to be updated, and this is very risky. The bootloader is also very limited in the number of drivers, tools, libraries, and network protocols it supports, so the update occurs in a resource-limited environment.

There are two main options for signed image-based open source software updates with support for both symmetric and asymmetric updates:

•• swupdate [1] (under a GPLv2 license) – swupdate is supported in Yocto through the meta-swupdate layer (but is limited to symmetric updates). It also contains a Mongoose web server (the Suricatta daemon) to pull updates from a remote server with a REST client to Eclipse HawkBit [2] (a backend solution to roll out software updates to end devices). It currently only works with the U-Boot bootloader.

•• RAUC [3], under a LGPLv2.1 license – RAUC is supported in Yocto through the meta-ptx layer with support for Grub or Barebox bootloaders.

Remote image updates A firmware update process must be capable of updating both from local sources (for example, flash, USB, µSD, or UART), as well as remotely in what is commonly known as an over-the-air (OTA) update. OTA updates use a remote server to push updates to a client running on the device.

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Firmware Updates Some options for open source remote OTA firmware updates include:

•• Mender.io [4], under an Apache 2 license – Mender.io is used for both client and server. It is supported in Yocto through the meta-mender layer. The server can act as a deployment server and build artifact manager, but also contains a device management console.

•• Digi International Remote Manager [5], under the MPLv2

license – The Digi Remote Manager has a proprietary cloudbased server and an open source client. It is supported in Yocto through the meta-digi layer. The server can act as a deployment server, build artifact manager, and also contains a device management console that features device reporting and monitoring.

•• Eclipse HawkBit [2], under the Eclipse Public license – Eclipse HawkBit is an Eclipse Public license

server that also acts as deployment server, build artifact manager, and device manager with device reporting and monitoring features.

Full image updates have the problem of usually being large in size, which can be problematic for resource-limited applications where bandwidth is not free or broad, such as cellular. Differential image firmware updates are a good compromise for this problem, where only the deltas from the previous version are actually transmitted.

Containerized updates The use of containerized applications simplifies the software update use case, as applications can then be updated separately.

Container updating is built on an immutable distribution (possibly a read-only filesystem) where applications only exist in containers upgradable by container deltas.

Some examples of open source projects that use container-based firmware updates are:

•• Resin.io [6] – Resin.io is Docker-based with a proprietary OTA update server and Apache 2-licensed client. It is supported in Yocto through the meta-resin layer.

•• Ubuntu “Snappy” Core [7] – Ubuntu Core is a minimal Ubuntubased OS that provides enough to update container-like applications with deltas.

There are also new OSs designed to host Docker applications that could end up being used in the embedded space, such as CoreOS [8] (on arm64) and Project Atomic [9] (a Fedora/CentOS/ RHEL-based OS that uses rpm-ostree (based on libostree) instead of rpm).

Incremental binary atomic OS updates Finally, an upcoming trend in the embedded market is incremental per-file atomic updates that can be quickly deployed or rolled back on demand while keeping a complete history of deployments.

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www.embedded-computing.com/iot

“... AN UPCOMING TREND IN THE EMBEDDED MARKET

Alex Gonzalez is software engineering supervisor at Digi International and one of the maintainers of the Digi Embedded Yocto distribution.

IS INCREMENTAL PER-FILE

References:

ATOMIC UPDATES THAT CAN BE QUICKLY DEPLOYED OR ROLLED BACK ON DEMAND WHILE KEEPING A COMPLETE HISTORY OF DEPLOYMENTS.” Some open source example projects:

•• libOSTree [10] – libOSTree is composed of a library and command line tools defined as “Git for operating systems binaries.” It uses git-like objects to store and deploy OS deltas, each with a copy of persistent data. There is a metaupdater layer for Yocto that makes use of it. It is also used for OSs like Project Atomic.

•• swupd [11] – swupd was originally part of ClearLinux and sponsored by Intel. It is very similar to libOSTree, and supported in Yocto through the meta-swupd layer.

1. “SWUpdate: Software update for embedded system.” SWUpdate: software update for embedded system – Embedded Software Update Documentation 2017.07 documentation. Accessed September 14, 2017. https://sbabic.github.io/swupdate/ swupdate.html.

2. Beaton, Wayne. “Eclipse hawkBit.” Projects.eclipse.org. October 20, 2016.

Accessed September 14, 2017. https://projects.eclipse.org/projects/iot.hawkbit.

3. “Safe and Secure Updating!” Safe and secure embedded Linux updates | RAUC. Accessed September 14, 2017. http://rauc.io/.

4. “Over-the-air software updates for embedded Linux devices.” Open source OTA software updates for embedded Linux | Mender. Accessed September 14, 2017. https://mender.io/.

5. “Digi Remote Manager®.” Monitor and Manage Remote Connected Devices Digi International. Accessed September 14, 2017. https://www.digi.com/products/cloud/ digi-remote-manager.

6. “Embedded software deployment done right.” Home | Resin.io. Accessed September 14, 2017. https://resin.io/.

7. Canonical. “Ubuntu Core.” Ubuntu Core | Ubuntu. Accessed September 14, 2017. https://www.ubuntu.com/core.

8. “CoreOS.” CoreOS Blog ATOM. Accessed September 14, 2017. https://coreos.com/.

9. “Building the Next Generation Container OS.” Project Atomic. Accessed September 14, 2017. http://www.projectatomic.io/.

10. OSTree. Accessed September 14, 2017. https://ostree.readthedocs.io/en/latest/.

11. “Software update.” Clear Linux Project. April 25, 2016. Accessed September 14, 2017. https://clearlinux.org/features/software-update.

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Narrowing the options With the emergence of IoT devices, firmware updates are not simply nice to have, but a required feature for new product development. The choice of a firmware update strategy needs to be considered early on as it will affect product design decisions down the road. As with all early decisions, bad choices can place a significant burden on development time.

Projects that are on a tight time-to-market schedule will probably lean towards more traditional, tried-and-tested, fullimage firmware update strategies. These include the one provided via the Yocto Project’s meta-swupdate layer, and enterprise-ready OTA update solutions like Digi International’s Remote Manager.

Projects on the bleeding edge, however, could extend the whole system firmware update approach through a containerized application design that allows applications to be isolated from system updates. www.embedded-computing.com/iot

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 15

Machine Learning for IIoT

Machine Learning and Industrial IoT – Now and in the future

By Dinesh Nair, National Instruments

To serve target customers better than their competition, embedded design teams today are looking into new technologies such as machine learning (ML) and deep learning (DL). ML and DL allow these designers to develop and deploy complex machines and equipment faster and with limited resources. With these technologies, design teams can build complex models of a system or systems using a data-driven approach.

Instead of using physics-based models to describe the behavior of the system, ML and DL algorithms infer the model of a system from data. Traditional ML algorithms are useful when the amount of data to be processed is relatively small and the complexity of the problem is low.

But what about larger problems with a lot more data, like the autonomous vehicle?

This challenge requires DL techniques. This emerging technology will push us into the next era of control design and Industrial Internet of Things (IIoT) applications.

Current applications of machine learning for industrial assets First, consider the application of ML technology for condition-based monitoring of industrial assets. ML has helped transition condition-based monitoring applications from an era of reactive and preventive maintenance to one of predictive maintenance.

These techniques are being used to detect anomalous behavior, diagnose problems, and, to some extent, predict the remaining useful life of industrial assets such as motors, pumps, and turbines.

FIGURE 1 ď„ 16  

Analytics workflow with machine learning

IoT Design Guide 2017

The workflow for developing and deploying models based on ML is shown in Figure 1.

Look at how this workflow is used to monitor the health of a motor. Data is collected from multiple types of sensors, such as accelerometers, thermocouples, and current transducers attached to the motor. The feature engineering step usually consists of two parts: feature extraction and feature reduction (Figure 2).

Feature extraction is used to derive useful information from the raw data (or waveform) to understand the health of the asset. For example, the frequency spectrum of the current signal from the motor has information embedded in it that can be used to detect failures, as shown in Figure 3. The average amplitude across different frequency bands in the spectrum can serve as features that are extracted from the current signal. Features extracted from multiple www.embedded-computing.com/iot

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sensors may have redundant information in them.

for the feature engineering step. Data acquired from the sensors (raw measurements) can be directly input into the DL algorithms, as shown in Figure 4.

A feature reduction method, such as principal component analysis (PCA), can be used to reduce the number of features that are finally used to build a model. Reducing the number of features reduces the complexity of the ML model that is to be used. The reduced set of features is represented as a vector (or array) and input into the ML algorithm used in the model creation step.

DL algorithms are based on artificial neural networks. Artificial neural network learning algorithms are inspired by the structure and functional aspects of biological neural networks. These algorithms are structured in the form of an interconnected group of computational nodes (artificial neurons) organized in layers. The first layer is referred to as the input layer, which interfaces to the input signal or data. The last layer is the output layer, and the neurons in this layer output the final prediction or decision.

Types of machine learning Model creation and validation is an iterative process through which you experiment with several ML algorithms and pick the one that works best for your application. An unsupervised ML algorithm (such as the Gaussian mixture model (GMM)), can be used to model the normal behavior of the motor and detect when the motor starts to deviate from its baseline. Unsupervised methods are good for discovering hidden patterns in the data without the need for labeling the data.

Whereas unsupervised techniques can be used to detect anomalies in the motor, supervised algorithms are required to detect the cause of the anomaly. In supervised methods, the algorithm is presented with pairs of the input data and the desired output. This data is referred to as labeled data. The algorithm learns the function that maps the inputs to the outputs. The data used to train the ML algorithm comprises features extracted under normal and faulty conditions. The features are clearly identified using a label that signifies the state of the motor. Support vector machines (SVM), logistic regression, and artificial neural networks are commonly used supervised ML algorithms.

FIGURE 2

Feature engineering

FIGURE 3

Feature extraction from a motor current signal

FIGURE 4

Deep learning workflow

A challenge with traditional ML techniques is the feature extraction process. It is a brittle process that requires a domain expert’s knowledge, and is usually the point of failure in the ML workflow.

A move towards deep learning workflows DL algorithms have recently gained popularity because they remove the need www.embedded-computing.com/iot

IoT Design Guide 2017

 17

Machine Learning for IIoT In between the input and the output layer, there are one or more hidden layers (Figure 5). The outputs of one layer are connected to nodes in the next layer by weighted connections. A network learns a mapping between the input and output by modifying these weights. By using multiple hidden layers, DL algorithms learn the features that need to be extracted from the input data without the need to explicitly input the features to the learning algorithm. This is referred to as “feature learning.”

Designing with deep learning technology DL has seen recent success in IIoT applications mainly because of the coming of age of technological components, such as more compute power in hardware, large repositories of labeled training data, breakthroughs in learning algorithms and network initialization, and the availability of open source software frameworks.

What follows are some main considerations for designing systems with DL technology.

Topologies DL is an evolving field and numerous network topologies are currently in use[1]. A few of these networks that show promise for control and monitoring IIoT applications are:

•• Deep neural networks (DNNs) are fully connected artificial neural networks with many hidden layers (hence deep). These networks are excellent function approximators and, for example, can be used in the application of power electronics control.

A simulation model of the system you are trying to control can be used to build a controller using deep networks, as well as to generate the training data.

With this, you can explore states (boundary/corner conditions) that are usually difficult to control using traditional methods.

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IoT Design Guide 2017

FIGURE 5

Feed-forward artificial neural network

•• Convolutional neural networks (CNNs) are designed to take advantage of the two-dimensional structure of input signals, such as an input image or a speech signal. A convolutional network is composed of one or more convolutional layers (filtering layers), followed by a fully connected multilayer neural network.

These networks are successful in problems such as defect detection in imaging and object recognition. They are also used for scene understanding in advanced driver assistance systems (ADAS).

•• Recurrent neural networks (RNNs) are based on algorithms that make use of sequential (or historical) information to make predictions. These networks are good for time-series analysis. A traditional neural network assumes that all inputs (and outputs) are independent of each other in time or order of arrival. RNNs record state information, which stores information about the past and uses the information calculated so far to make the next prediction.

In IIoT applications, RNNs are good for learning historical behavior and using that to predict events in the future, such as the remaining useful life (RUL) of an asset. The long short-term memory (LSTM) network is good for these kinds of applications[2].

•• Deep reinforcement learning (DRL) is good for designing adaptive control systems that operate in complex dynamic environments.

Consider controlling robots deployed in warehouse operations where the robots must dynamically adapt to new tasks. Reinforcement learning–based controllers learn a task by the reward they receive for performing an action that moves them closer to the goal. For example, the controller receives an image from a camera that shows the current position of a robot arm and uses the information in the image to learn how to move the arm closer to the target (Figure 6)[3]. The DL-based controller can be trained using a robot simulator or by observing the real robot in action.

Training DNNs require large amounts of training data that preferably include data from all the different states or conditions that the network needs to learn. For most applications, the available data is predominantly from the normal working condition of the system with a small sampling of data from other states. www.embedded-computing.com/iot

Data augmentation is a technique used to improve this imbalance in data, where you can start with the existing small sample set and create additional synthetic versions by transforming the data.

You can also use simulation models of the system to create training data.

Another challenge is the difficulty in collecting the large amounts of data required to train these networks. Transfer learning is one approach you can use to mitigate this problem. Using transfer learning, you can start with a pre-trained neural network (most DL software frameworks provide fully trained models that you can download) and fine-tune it with data from your application.

Hardware Training deep networks has enormous processing requirements. GPUs have emerged as the primary option for training deep networks. GPUs are attractive and almost a necessity for training because of high computation performance, large memory, high memory bandwidth, and a choice of programming tools.

FPGAs, furthermore, are good targets for deploying trained networks. FPGAs provide lower latency, better power efficiency, and determinism, especially for deployment of these networks on embedded devices for control systems that operate in a tight loop with I/O.

FIGURE 6

Deep reinforcement learning for robotic control applications

A quick way to get started with DL is by downloading one of the open source frameworks mentioned previously and experimenting with the tutorial examples. Start with an example that is similar to your application and use transfer learning to get operational quickly.

Dinesh Nair is chief architect at National Instruments.

References: 1. Veen, Fjodor Van. “The Neural Network Zoo.” The Asimov Institute. October 28, 2016.

Accessed September 20, 2017. http://www.asimovinstitute.org/neural-network-zoo/.

2. “Long short-term memory.” Wikipedia. August 27, 2017. Accessed September 20, 2017. https://en.wikipedia.org/wiki/Long_short-term_memory.

3. Zhang, Fangyi, Jürgen Leitner, Michael Milford, Ben Upcroft, and Peter Corke. “Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control.” [1511.03791] Towards Vision-Based Deep Reinforcement Learning for Robotic Motion Control.

November 13, 2015. Accessed September 20, 2017. https://arxiv.org/pdf/1511.03791.pdf.

4. “TensorFlow.” TensorFlow. Accessed September 20, 2017. https://www.tensorflow.org/.

5. “Caffe.” Caffe | Deep Learning Framework. Accessed September 20, 2017. http://caffe.berkeleyvision.org/.

6. “Keras: The Python Deep Learning library.” Keras Documentation. Accessed September 20, 2017. https://keras.io/.

7. “Video: Unlock deeper learning with the new Microsoft Cognitive Toolkit.”

Microsoft Cognitive Toolkit. Accessed September 20, 2017. https://www.microsoft.com/ en-us/cognitive-toolkit/.

NEWSLETTER

Software A reason for the rapid adoption and success of DL is the availability of mature software frameworks. Some of the common ones are TensorFlow, Caffe, Keras, and the Computational Network Toolkit (CNTK)[4, 5, 6, 7]. These frameworks support different operating systems such as Windows and Linux, and languages such as Python and C++.

Most of these frameworks have support or examples for implementing the latest DL networks. They also support learning on GPUs.

Deep learning: A new direction in IIoT control design DL is an exciting new direction in artificial intelligence and a promising technology to solve next-generation problems in industrial control design applications. www.embedded-computing.com/iot

The Internet of Things has reached the top of nearly every buzz chart, but it still faces some tough real-world questions. IoT Design Weekly goes beyond the hype to provide practical coverage on Development Kits, MCUs and MPUs, Sensors, Operating Systems and Tools, Security, Wireless, Cloud, Industrial, Smart Home, the Connected Car, and more.

Subscribe to IoT Design Weekly at: http://bit.ly/SubscribeIoTDesignWeekly IoT Design Guide 2017

 19

Digital Twins

The birth of 30 million digital twins: Manufacturing feedback loops and paying it forward

By Brandon Lewis, Technology Editor

Digital twin technology is combining with product line engineering (PLE) software to transform the manufacturing industry. However, the process for mapping these technologies to the engineering lifecycle is still in its infancy.

The “digital twin” was first introduced to mainstream technology by GE back in 2015, and has been assimilating into Industrial Internet of Things (IIoT) hier­ archies ever since.

Digital twins are virtual representations of physical products that, when combined with connectivity, enable device makers to interact with their equipment long after it has been deployed in the field.

From an engineering perspective, digital twins can be used to monitor the impact of operational or environmental wear and tear on system components, as well as simulate failures before they occur. From a business standpoint, on the other hand, digital twins allow companies to track customer behavior and usage patterns in an effort to provide improved products, features, and services.

Although the digital twin concept is still in its relative infancy, large-scale manufacturing has been quick to adopt

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the technology. In the automotive market, for example, digital twins are being integrated with product line engineering (PLE) platforms to help manufacturers manage the immense variation in makes, models, and features of vehicles across their fleets.

Today this helps ease the burden of events like recalls, as digital twins based on PLE data can automatically indicate whether or not a particular unit has been equipped with an affected feature. Tomorrow’s objective, however, is a true digital twin for every vehicle that leaves the dealership.

Using digital twins, it is possible for manufacturers to extract detailed diagnostic data and match it against the precise options of a specific vehicle to determine if maintenance is required or how an over-the-air (OTA) firmware upgrade will influence performance based on a given feature set. Dr. Charles Krueger, founder and CEO of PLE company BigLever Software, Inc., foreshadows this as “the birth of 30 million digital twins a year.”

“As we get more complex systems out in the world, it’s not like the old days where you put together a mechanical thing, sold the car to the consumer, and the cord was cut at the dealership,” Krueger says. “With the complexity of systems and the way they interact, companies are trying to keep track of their products as they are out in the world. They’d like products to ‘phone home’ and provide data through the IoT so that the manufacturer can still remain connected to the consumer and the product.

“In order to do that there’s a physical product in the world that the manufacturer is trying to keep a sufficient representation of in the office,” he continues. “As the product evolves and has maintenance issues, upgraded features, etc., [the digital www.embedded-computing.com/iot

twin] mirrors the real product as it exists in the digital world. It should provide sufficient representation in a digital form to stay connected to a product, understand its current state, and communicate with the owner of that product to provide the best user experience based on that continuing knowledge.

“The end game is that the manufacturers want a true digital twin for every product,” Krueger continues. “If you’re getting close to an oil change or there is some diagnostic information coming back from your car, they want that level of detail.”

Feedback loops and evolutionary design To fully appreciate how digital twins can transform the manufacturing lifecycle, it’s first important to understand how large-scale manufacturers like car companies are able to efficiently produce fleets with massive amounts of variation.

Figure 1 shows the standard systems engineering V model, which spans from requirements, design, and test, to user documentation. The upper tiers of the V represent the modeling and design phases where engineers build out various features based on product requirements, which waterfall down to subsequent phases of the development and manufacturing lifecycle.

Using software such as BigLever’s Gears PLE Lifecycle Framework, individual features of a given product are represented as pieces of source code that can be turned on and off, allowing assets to be generated based on a particular feature set. These accumulations of source code-based assets provide the foundation for creating physical products, but can also be leveraged simultaneously to create a corresponding digital twin. The physical product and digital twin can be bound using component serial numbers once the physical device is manufactured, and remain in contact throughout the product’s life to ensure that the twin is updated accordingly.

While digital twins have thus far been positioned as a reactionary monitoring mechanisms, their integration with PLE www.embedded-computing.com/iot

FIGURE 1

The Gears PLE Lifecycle Framework maps with the systems engineering V model of large-scale manufacturing to enable the simultaneous creation of physical products and their digital twins.]

technology places them early enough in the development lifecycle that they can be used as part of a feedback loop to inform ongoing product engineering decisions.

Data from digital twins can be leveraged proactively to show how assets based on a certain feature set perform under different conditions, and then be mapped back into earlier stages of the engineering lifecycle to enable dynamic, evolving system designs. Organizations can model and simulate these progressive systems before creating physical prototypes, and, in fact, even create digital twins for systems that are no longer being produced.

“THE END GAME IS THAT THE MANUFACTURERS WANT A TRUE DIGITAL TWIN FOR EVERY PRODUCT. IF YOU’RE GETTING CLOSE TO AN OIL CHANGE OR THERE IS SOME DIAGNOSTIC INFORMATION COMING BACK FROM YOUR CAR, THEY WANT THAT LEVEL OF DETAIL.”

Standardization the PLE-driven digital twin: Paying it forward Despite their success in corners of the industry, PLE and digital twins still face challenges on the road to widespread adoption. Both technologies recently emerged from the world of academia into the commercial space, and as a result suffer from a lack of uniform implementation and widespread adoption that serve to drive costs down and instill confidence in users.

In response, a working group at the International Council on Systems Engineering (INCOSE) has been established to create an ISO standard around PLE that defines technology, process, and workflow best practices for implementing PLE in broad manufacturing environments. The working group currently consists of members from GM, Air Bus, Boeing, General Dynamics, Raytheon, and BigLever Software, along with others who have a vested interest in reducing manufacturing and design complexity through the use of PLE and digital twins.

“The ISO standard is based on those companies who have been successful,” Krueger says. “We’re trying to create a clear and concise set of terminology and concepts to help organizations go down a lower risk path.”

With the birth of 30 million digital twins on the horizon, the pressure is on.

IoT Design Guide 2017

 21

Market Spotlight: Wearables

Wearables development in an IoT world

By Curt Schwaderer, Editorial Director

Microcontroller (MCU) innovations are quietly packing more intelligence in smaller form factors. These advancements are enabling operating systems (OSs) to be utilized in small form factor sensor devices, such as wearables.

Wearables, IoT sensor trends Intelligence is increasing in smaller form factor wearables and sensor devices. Today’s MCUs are now small and cost-effective enough to be used in a wide variety of sensor applications where price and size requirements previously excluded them.

As MCUs proliferate in these applications, so too have the OSs that run on them.

Adding an OS to a wearable opens new doors in terms of features, functions, and time-to-market benefits. The question becomes whether it is enough to just use any OS that targets the MCU, or if the OS must be tuned for these devices?

RoweBots recently announced availability of a “WearableOS” at the Wearables Technology Conference that is specifically tuned for wearable and Internet of Things-enabled (IoT-enabled) devices.

Emergence of the “WearableOS” RoweBots’ history includes the Unison RTOS used in military systems where security and reliability are critical. This history includes traditional embedded system practices involving porting and running the OS on a specific processor architecture, adding drivers for board support packages (BSPs), and providing APIs for embedded application development.

Over the last 10 years RoweBots has been working in the IoT space as well. “Our OS products target MCUs and small microprocessors with a goal of creating an efficient design,” says Kim Rowe, CEO of RoweBots. “Real-time requirements have

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IoT Design Guide 2017

diminished over time. Flash and RAM sizes have become a critical cost factor for embedded and IoT designs.”

Rowe mentions two key areas of focus for their WearableOS:

•• Being able to do more in a small package. Support for lean product development (LPD) is a cornerstone of RoweBots’ IoT OSs (Figure 1).

•• Open APIs based on embedded Linux allow RoweBots OS products to integrate libraries that match the functions needed and available resources on the platform.

“We took a more packaged approach to delivering a real-time operating system (RTOS) when developing the WearableOS,” Rowe explains. “We didn’t stop at a chip support package for a controller. We added packages that deal with specific vertical markets. Wearables is one of these targets. The three key pillars for this effort were hardware support for wearable products, optimized software design, and enabling faster time to market.”

The world of wearables needs a complete package of OS/­ processor support, connectivity, storage, and cloud connectivity. In addition, security requirements are important, and RoweBots’ history with the Unison RTOS in the military industry gave them a leg up in understanding security considerations.

Much of this has also been incorporated in to the WearableOS. Rowe mentions the importance of not stopping development at an MCU OS and driver support for the sensors that a manufacturer provides. “Sensor support needs to be general,” www.embedded-computing.com/iot

he says. “It’s important to allow the developer to utilize any number of temperature sensors, accelerometers, or whatever peripherals are required to allow choice and maintain hardware independence and abstraction.”

Power management Power management is another critical component. Some MCUs are simply designed as very low power with little to no power modes. Therefore, power management of most MCUs is simple when compared to the capabilities of an ARM processor, for example.

“MCU manufacturers typically try to maximize all-around power efficiency,” Rowe says. “However, in some of the more advanced processors, power management has taken a leap.

There are a lot more options in terms of what you can do to go to sleep and wake up again.”

Some of the options Rowe alludes to involve integrated power modes in hardware that can be updated, as well as certain silicon events that are capable of waking up software.

Wearable connectivity Wireless connectivity is another important feature of the WearableOS. Rowe mentions some interesting applications in the sports industry where different radio types are needed.

“Our OS needs to support three different types of radios depending on the sport,” Rowe says. “One is for individual operation, like bike racing. Wearables are used to provide metrics on speed, vitals, and crash information. These kinds of things use Bluetooth Low Energy (BLE). Another extension is road races or marathons where you don’t want to be carrying around a phone. These applications need LoRa radios. Football or basketball applications are a third example. These applications use 802.14, which allows group communication capabilities among teammates.”

Rowe adds that RoweBots has provided LTE and Wi-Fi solutions to the traditional embedded markets for a while. In the past they have used proprietary connectivity as well, like satellite phones.

Wearable security Two key aspects for wearables are communications/­information security and software update security and authentication. The Unison RTOS has shipped with complete over-the-air (OTA) update capabilities for the past six years, with security a focus throughout. These features were adopted in their WearableOS environment.

“Everyone wants to talk about security, no one wants to pay for it yet,” says Rowe. “We decided about 5 years ago that it was unacceptable to have fielded systems with our OS that weren’t secure. So we bundled in the important security features at no cost.

“For example, TLS communications comes standard, along with secure SFTP for file transfer and SSH for remote control (login and command execution),” he adds. www.embedded-computing.com/iot

FIGURE 1

Lean product development (LPD) is a development methodology for reducing product engineering cycles and cost that is central to RoweBots’ WearableOS.

Secure boot is also critical for wearables. This is important to ensure OTA update security. Depending on the processor features, it can be incorporated in hardware or emulated in software.

For example, i.MX6 (NXP/Freescale) and RZ (Renesas) secure boot technology and other similar mechanisms embed keys in hardware and provide embedded encryption unique to that silicon, which is used to sign and encrypt software images. This allows platforms with secure boot technology to authenticate the source of the image and know that the image itself has not been corrupted. It also provides a mechanism to roll back to the previous release if the update doesn’t happen successfully or if there are errors in operation. All this results in a more reliable, secure system.

Rowe further acknowledges that the company continues to work towards enhancing the security of its products, including the recent completion of a Microsoft Azure package that includes secure communications over the HTTP, MQTT, and AMQP protocols, as well as for the cloud platform itself.

Graphics and user interfaces Perhaps the most interesting challenges relating to wearables and smart sensors are the unique graphics and user interfaces (UIs). On larger systems there are lots of capabilities and screen area for fancy graphics and user interaction. These systems also tend to be very power hungry.

IoT Design Guide 2017

 23

Market Spotlight: Wearables On the smaller end, for watch and wrist wearables, Rowe notes that the WearableOS typically uses vendor-recommended packages or a third-party vendor for ultra-low-power graphics and UI components. Not surprisingly, programming wearables is a lot like traditional embedded system programming using application libraries and the C/C++ programming language with an Eclipse variant or embedded integrated development environment.

allows people with sight problems to see normally and has literally changed lives.

“... PROGRAMMING WEARABLES IS A LOT LIKE TRADITIONAL EMBEDDED SYSTEM PROGRAMMING USING APPLICATION LIBRARIES AND THE C/C++ PROGRAMMING

BSD network sockets, file I/O, and embedded Linux and POSIX-compliant APIs speed development in these builds.

Rowe cites an embedded Linux application example that was ported, run, and tested on the OS in 2 days using these tools.

Upon porting, benchmarking showed that the same application running on their OS had a 50 percent increase in frame rate due to the significantly lower overhead of the WearableOS versus embedded Linux.

Summary

Another exciting example of miniaturizing wearables with ­similar capabilities involves an eSight technologies product.

These are glasses that help people with specific types of peripheral or myopic blindness conditions see normally. eSight puts a camera on the bridge of your nose to do auto exposure and compensation for your specific sight deficiency. On your hip you have zoom and pan controls. The wearable puts the image up on a tiny screen in front of your eye. This technology

Wearable and IoT sensor and device development requires a combination of hardware, software, and the ability to reuse them in order to decrease time to market while not sacrificing performance and reliability. Whether you’re developing a medical, factory floor, or wearable device that must be integrated into a larger IoT environment, building with a vertical OS can improve reliability and security with more finished, tested code, while also decreasing time to market and maintaining flexibility for component and hardware vendors.

 24

IoT Design Guide 2017

LANGUAGE WITH AN ECLIPSE VARIANT OR EMBEDDED INTEGRATED DEVELOPMENT ENVIRONMENT.”

www.embedded-computing.com/iot

IoT Design Guide

2017 Design Guide Profile Index Page Advertiser

Category

26-29 Avnet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Development Kits 37 Avnet . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wireless, Cloud 32

congatec, Inc. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Industrial

38

Copper Mountain Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wireless, Cloud

33

Digi International . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . IoT

30 Ellisys . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Development Kits 36

Micro Digital, Inc. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Software & Operating Systems

39

Microchip Technology, Inc. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Wireless, Cloud

30-31

Technologic Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Industrial

31, 33

WinSystems, Inc. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Industrial

34-35 wolfSSL . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . RTOS & Tools, Security

OpenSystems Media works with industry leaders to develop and publish content that educates our readers.

Hearables get longer life with SIMO

By Maxim Integrated Hearable devices are an emerging market at the intersection of wireless stereo earbuds and fitness monitoring. However, the small form factor escalates the miniaturization challenge for electronics and presents additional challenges for battery life.

This article presents an innovative power management system that delivers power with high efficiency in a very small space, while enabling longer battery life for miniaturized hearable devices.

www.embedded-computing.com/white-paper-library/hearables-get-longer-life-with-simo

www.embedded-computing.com/iot

Check out our white papers at www.embedded-computing.com/ white-paper-library IoT Design Guide 2017

  ď„ 25

IoT Design Guide

Dev Kits

Visible Things 2 Starter Kit Visible Things simplifies the complexity of secure edge to enterprise IoT technology. It delivers a complete evaluation and reference platform to connect smart devices right through to the cloud and enterprise software. Avnet supports an ever increasing range of sensors, connectivity, gateway and security technologies, together with cloud, analytics, mobile and enterprise integration services. We regularly add new features, provide updates and deliver training on our Visible Things platform.

Visible Things is delivered in the form of many different boards which can be taken depending on the functionality required. The very nature of IoT technology requires the features delivered by these boards to be pre-integrated and tested end to end wherever possible. This fundamental principle of Visible Things allows the users to concentrate on their applications to deliver the target IoT business outcomes.

This product brief aims to describe the features delivered and provide guidance to the most appropriate board or kit to use.

Sensor to server security layer on top of network security Quick evaluation of end application Highest degree of flexibility Reduces development time significantly – time saving Optimized power consumption Integrated and tested communication path from Edge to Enterprise ĄĄ Cloud ready ĄĄ ĄĄ ĄĄ ĄĄ ĄĄ ĄĄ

Examples of Target Applications ĄĄ ĄĄ ĄĄ ĄĄ ĄĄ ĄĄ ĄĄ ĄĄ ĄĄ

Remote monitoring Predictive maintenance of motors and drives Room control in homes and buildings Lighting and shading (indoor & outdoor) Security and surveillance Home appliances Smart energy (metering & in home displays) Health care & infrastructure Industrial automation, inspection, drives monitoring, sensor hub

Edge (Avnet & Partner)

Motion HMI Vision

Drive Motor Valve Light

Enterprise

LPWAN

Connected Sensor Data Generation Quantity of Data

Environment

Cloud Infrastructure & Platform

- LoRaWAN - Sigfox Cellular

Enterprise Integration - ERP - CRM - Customer Portal

Services

- 3G - 4G Connected Actuator Data Generation Quantity of Data

Scope of the Visible Things Platform

features

- Remote Monitoring - Natural User I/F - Mobile UI (for Control) - Predictive Analytics - Prescriptive Analytics - Automation

GW/Fog

- BLE - Thread - ZigBee - WMBUS - 6LoWPAN - WiFi

Building Blocks - Generation - Communication - Storage - Analytics - Presentation - Action

- ETH - KNX - BACnet

Communication, Security & Compliance, Application Access.

See more at: www.avnet.com/wps/portal/us/products/new-products/visible-things-platform iotdesign.embedded-computing.com/p374454

Avnet Inc.

www.avnet.com

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IoT Design Guide 2017



iot@avnet.com

 www.linkedin.com/company/2572

 480-643-2000

 twitter.com/avnet www.embedded-computing.com/iot

BCM4343W IoT Starter Kit This versatile development platform for the Wi-Fi and BT/BLE Avnet BCM4343W SoC module, enables easy prototyping of cloudconnected IoT designs as well as facilitating rapid transition to volume production using the pre-certified SoC module.

Key items on the module are Cypress® CYW4343W 802.11 b/g/n Wi-Fi and Bluetooth® 4.1 combo SoC, plus companion STM32F411 ARM® Cortex™ M4 Microcontroller (with 512KB Flash, 128KB RAM), as well as 8Mb SPI Serial Flash This IOT Starter Kit facilitates access from the MCU peripherals to Arduino-compatible headers, as well as a Pmod™compatible 2x6 peripheral connector, allowing connection to a range of expansion boards.

features ĄĄ

ĄĄ

Application software development is well supported by Cypress WICED™ SDK and ZentriOS SDK toolchains An extensive range of reference designs are available for download (for Amazon AWS, IBM Bluemix, ThingSpeak, plus other cloud-connected services) to accelerate cloudconnected product development.

For more details, or to purchase IoT Starter Kits and modules, visit:

http://cloudconnectkits.org/product/ avnet-bcm4343w-iot-starter-kit

Starter Kit: AES-EVB-BCM4343W-G Module: AES-BCM4343W-M1-G

ĄĄ

ĄĄ

Based on pre-certified Avnet BCM4343W SoC Module with the following features: • 802.11 b/g/n Wi-Fi plus Bluetooth 4.1 • STM32F411 ARM® Cortex™ M4 MCU • 512KB Flash, 128KB SRAM, 8Mb SPI Flash • Dual fractal PCB antennas for diversity • 20 x 35 mm easy-pitch 45 pin LGA package • FCC, IC, CE, RoHS certifications Arduino™ compatible shield expansion connectors support the following interfaces: • GPIO (4) • Analog inputs (3) • Two I2C ports (1 shared) • SPI port • Two UARTs (1 shared) Pmod™-compatible peripheral expansion connector, supports: • I2C port (shared) Combo USB interface provides • JTAG debugger/programmer • UART Console communications • +5V DC power

ĄĄ

Reset push-button switch

ĄĄ

User push-button switch

ĄĄ

Two User LEDs

ĄĄ

Four Status LEDs (UART, JTAG, WLAN, 3V3 Power)

ĄĄ

Ambient light sensor

ĄĄ

Onboard high capacity 3.3V DC regulator iotdesign.embedded-computing.com/p374456

Avnet Inc.

www.avnet.com www.embedded-computing.com/iot

 480-643-2000

 twitter.com/avnet

 www.linkedin.com/company/2572 IoT Design Guide 2017

 27

IoT Design Guide

Dev Kits

IoT Design Guide

Dev Kits

Easy IoT Starter Kit Starter Kit Hardware is certified for Microsoft Azure The Qseven Rev. 2.0 Starter Kit for Qseven modules with the Intel® Atom™ E3800 SOCs is based on a 3.5" Qseven Rev. 2.0 carrier board MSC Q7-MB-EP6 and contains all necessary products to quickly enable the user to operate and evaluate the Q7-BT module range.

The kit does not contain a Qseven module in order to give the user greater flexibility as to which particular module version and CPU speed variant is desired. As part of the Qseven Rev. 2.0 Starter Kit, the appropriate heatsink and a power supply with cable kit will be supplied.

Assembly only takes a few moments and is well documented in the “Getting Started” manual, then the Qseven Starter Kit will be ready to run.

If used with Avnet’s “Easy IoT” suite, the hardware will be usable for access to the Microsoft® Azure™ Cloud.

features Enabling Customers ĄĄ Easy IoT Dev tools ĄĄ SW consulting ĄĄ Azure subscription management Gateways ĄĄ Broad portfolio ĄĄ Industrial Specifications ĄĄ Azure Certified

Easy IoT Hardware ĄĄ Industrial Hardware ĄĄ ARM to X86 ĄĄ Nano RISC to Systems ĄĄ Azure Certified

Easy IoT: Enabling customers to easily access Azure services, Avnet has a global Cloud Services Provider (CSP) agreement. This removes the complexities of the set-up and commercial billings procedures relating to the consumption of Azure services, and presents customers with the right licensing model to sell services to their end customers. Being CSP partners, Avnet Integrated Solutions and MSC Technologies are able to create value for customers by offering advice on cloud architecture to ensure that customers build scalable, robust and future-proof implementations regardless of their deployment models.

The Avnet Azure on-boarding tool (AAoT) from Avnet Integrated Solutions and MSC Technologies is designed to give customers a plug-and-play testbed and set of building blocks on which to build or commission their own IoT solution. It comprises a Linux image, part of the Linux development kit for MSC Technologies’ manufactured boards and gateways, and access to a joint-tenant cloud portal within the Microsoft Azure suite. It is offered free of charge to customers, subject to usage conditions.

Out of the box, the AAoT makes use of attributes of the Microsoft Azure IoT Hub to provide customers with a secure means to measure, monitor and interact dynamically with MSC’s manufactured hardware.

Once the cloud and device applications are started, keys need to be exchanged between the device and the cloud. And once entered into the demo application on the device, the two are securely and irrevocably paired. The AAoT is designed to stimulate new thinking and discussion around the ‘art of the possible’. The only limit is your imagination.

Avnet Inc.

www.avnet.com

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IoT Design Guide 2017



info_integrated@avnet.eu

 www.linkedin.com/company/2572

iotdesign.embedded-computing.com/p374455

 480-643-2000

 twitter.com/avnet www.embedded-computing.com/iot

Visible Things – Industrial Starter Kit Ready-to-use kit with smart sensor Visible Things simplifies the complexity of secure edge to enterprise IoT technology. It delivers a complete evaluation and reference platform to connect smart devices right through to the cloud and enterprise software. Avnet Silica supports an ever increasing range of sensor, connectivity, gateway and security technologies, together with cloud, analytics, mobile and enterprise integration services. We regularly add new features, provide updates and deliver training on our Visible Things platform.

Visible Things is delivered in the form of many different boards which can be taken depending on the functionality required. The very nature of IoT technology requires the features delivered by these boards to be pre-integrated and tested end to end wherever possible. This fundamental principle of Visible Things allows the users to concentrate on their applications to deliver the target IoT business outcomes.

ĄĄ

Sensor to server security layer on top of network security

ĄĄ

Quick evaluation of end application

ĄĄ

Highest degree of flexibility

ĄĄ

Reduces development time significantly – time saving

ĄĄ

Optimized power consumption

ĄĄ

ĄĄ

Integrated and tested communication path from Edge to Enterprise Cloud ready

Secure Bluetooth Smart Sensor

Edge (Avnet & Partner)

Motion HMI Vision

Drive Motor Valve Light

Enterprise

LPWAN

Connected Sensor Data Generation Quantity of Data

Environment

Cloud Infrastructure & Platform

- LoRaWAN - Sigfox Cellular

Services

- 3G - 4G Connected Actuator Data Generation Quantity of Data

Scope of the Visible Things Platform

features

Enterprise Integration - ERP - CRM - Customer Portal

- Remote Monitoring - Natural User I/F - Mobile UI (for Control) - Predictive Analytics - Prescriptive Analytics - Automation

GW/Fog

- BLE - Thread - ZigBee - WMBUS - 6LoWPAN - WiFi

Building Blocks - Generation - Communication - Storage - Analytics - Presentation - Action

- ETH - KNX - BACnet

Communication, Security & Compliance, Application Access.

See more at: www.avnet.com/wps/portal/silica/solutions/iot/internet-of-things/visible-things/vt-kit2 iotdesign.embedded-computing.com/p374453

Avnet Inc.

www.silica.com www.embedded-computing.com/iot



visible-things@avnet.eu

 www.linkedin.com/company/2572

 480-643-2000

 twitter.com/avnet IoT Design Guide 2017

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IoT Design Guide

Dev Kits

IoT Design Guide

Dev Kits

Ellisys Bluetooth Tracker Ultra-Portable BLE and Wi-Fi Protocol Analyzer The pocket-sized (7.5 x 7.5 x 1.7 cm), bus-powered Bluetooth Tracker supports concurrent capture and analysis of Bluetooth Low Energy and Wi-Fi communications, as well as a wide variety of wired interfaces, including logic signals, host controller interface (HCI) protocols (UART and SPI), Audio I2S, and WCI-2, all visualized over the widely adopted Ellisys software suite.

With its innovative reconfigurable radio, the Ellisys sniffer can uniquely be updated by software to support changes in the specification, without any change to the hardware, and even without any interaction from the user. The Tracker comes with free lifetime software updates, so all customers can benefit from these great additions free-of-charge!

The Ellisys Bluetooth Tracker sniffer supports one-click concurrent capture of Bluetooth Low Energy, Wi-Fi 1x1 802.11 a/b/g/n, 2.4 GHz Spectrum, UART HCI and SPI HCI (2 ports), logic signals, and Wireless Coexistence Interface 2 (WCI-2).

Ellisys



FEATURES ĄĄ All-in-One: concurrent capture of Bluetooth Low Energy, Wi-Fi 1x1, raw

spectrum, HCI and logic, all synchronized to sub-microsecond precision

ĄĄ Wideband Capture: rock-solid capture of all Bluetooth Low Energy

channels

ĄĄ Reprogrammable Digital Radio: support for new specifications with a

simple software update, without hardware changes

ĄĄ Wi-Fi: debug your Wi-Fi a/b/g/n and BLE connections simultaneously,

as well as coexistence

ĄĄ Raw 2.4 GHz Spectrum Capture: characterize the wireless environment

and visualize interferences

ĄĄ Professional Software: use the acclaimed, widely adopted and highly

flexible Ellisys multi-protocol analysis software.

ĄĄ Logic Analysis: visualize digital signals such as GPIOs, interrupts, debug

ports, etc, concurrently and perfectly synchronized with your BLE and Wi-Fi traffic

iotdesign.embedded-computing.com/p374436

sales@ellisys.com

 www.linkedin.com/company/Ellisys

www.ellisys.com

 866-724-9185

 @Ellisys1

Industrial

TS-4900 i.MX6 Computer on Module The TS-4900 is a high performance TS-SOCKET Computer on Module based on the NXP i.MX6 CPU which implements the ARM® Cortex™-A9 architecture clocked at 1 GHz (Single or Quad Core) and paired with 1GB or 2GB of DDR3 RAM. Several industry standard interfaces and connections such as Gigabit Ethernet, WiFi and Bluetooth, USB, SATA II, PCI Express, and more make the TS-4900 a great fit for nearly any embedded systems application, especially those needing wireless connections like an industrial internet of things gateway.

The TS-4900 boots from the onboard Flash or microSD card to Linux, Android, QNX Neutrino RTOS, or Windows Embedded Compact operating systems via U-Boot bootloader. The TS-4900 comes standard with Yocto Linux distribution running the 3.10 kernel version, specially customized to take full advantage of the i.MX6 CPU 3D graphics acceleration capabilities, and boots to shell prompt in under 6 seconds.

A wide variety of software platforms are available including Linux, Ubuntu Core, Android, Windows Embedded Compact 2013, and QNX for flexibility in matching your embedded system requirements.

Technologic Systems

www.embeddedARM.com

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IoT Design Guide 2017



FEATURES 1 GHz Quad Core Cortex A9 ARM CPU ĄĄ 2 GB DDR3 RAM ĄĄ Bluetooth 2.1+EDR (4.0 BLE) and WiFi 802.11b/g/n onboard radios ĄĄ 4GB MLC eMMC flash storage ĄĄ SATA II port ĄĄ Up to 70x DIO, 2x I2C, 1x I2S, 2x SPI, 2x CAN ĄĄ Industrial temperature range (-40°C to 85°C) ĄĄ

sales@embeddedARM.com

iotdesign.embedded-computing.com/p374425

 www.linkedin.com/company/556283/

 480-837-5200 @ts_embedded



www.embedded-computing.com/iot

TS-7553-V2 Industrial IoT Gateway This versatile embedded single board computer hits on all the main points for a low power, cost effective, Internet-of-Things (IoT) capable, and ready-to-deploy OEM board with an emphasis on data integrity.

Not only does the TS-7553-V2 have flexible connectivity options like cellular modem, XBee, WiFi, and Bluetooth, it also has eMMC flash configurable as pSLC or MLC and on-board power reserve via optional TS-SILO technology. The TS-SILO option provides up to 30 seconds of backup power when the power goes out, ensuring opportunity to run your shutdown scripts to avoid data corruption. The eMMC flash configured in a pseudo-SLC mode provides maximum flash data storage reliability.

Each component on the TS-7800-V2 has been carefully chosen to ensure reliable operation in the field. Data is reliably stored in the onboard eMMC flash, configurable as pSLC for further reliability or MLC for more capacity. The fanless design of the TS-7800-V2 paired with the low cost enclosure is able to withstand high vibration, high debris, and a wide temperature range of -40 °C to 85 °C.

Technologic Systems

www.embeddedARM.com



FEATURES ĄĄ

NXP i.MX6UL 528MHz or 696MHz ARM Cortex-A7 CPU

ĄĄ

512 MB DDR3 RAM

ĄĄ

4 GB MLC eMMC Flash

ĄĄ

Wireless and Bluetooth Module

ĄĄ

XBee Interface (can support NimbeLink cell modem and XBee)

ĄĄ

RS-485, RS-232 and CAN Interfaces iotdesign.embedded-computing.com/p374424

sales@embeddedARM.com

 www.linkedin.com/company/556283/

 480-837-5200 @ts_embedded



Industrial

PCM-C418 PC/104 VORTEX86DX3® SBC with Dual Ethernet WinSystems’ PCM-C418 single board computer (SBC) is a PC/104 form factor featuring the latest generation FEATURES DM&P® Vortex86DX3® SOC processor. Its small size, ĄĄ DMP® Vortex86DX3® Processor (Dual core) low power, rugged design, and extended operational ĄĄ 2 GB DDR3-LV System RAM (Soldered) temperature range make it a great fit for industrial ĄĄ Fanless -40°C to +85°C operation IoT applications and embedded systems in the indusĄĄ 1 MB battery-backed SRAM trial control, transportation, Mil/COTS, and energy ĄĄ Four USB 2.0 ports markets. ĄĄ Two Ethernet ports ĄĄ

24 Bidirectional GPIO with event sense iotdesign.embedded-computing.com/p374428

WinSystems, Inc.

www.winsystems.com www.embedded-computing.com/iot

sales@winsystems.com  817-274-7553 

IoT Design Guide 2017

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Industrial

conga-SKIT SMARC 2.0 Starter Kit Introducing the SMARC 2.0 Quick Starter Kit for Intel® Atom™ processors.

The kit comes with congatec’s first SMARC 2.0 module – the conga-SA5 – and everything developers need for the immediate evaluation of the new SMARC 2.0 standard and the latest Intel® Atom™ processor generation. Application engineers of connected Industry 4.0 and wireless IoT devices will love the option of the integrated wireless interfaces with standardized antenna connectors on board.

With SMARC 2.0, developers of highly integrated IoT and Industry 4.0 applications will get the next level of technology on a credit card sized, off-the-shelf module with minimum space requirements. The starter kit is an important building block for a SMARC eco system enabling a quick evaluation of this state of the art technology that satisfies various IoT connectivity requirements. The Quick Starter Kit comes with a ready to use SMARC 2.0 module that provides pre-integrated fast WLAN and Bluetooth LE support. Additional connectivity can be supported with dual Gigabit Ethernet for cloud connected appliances with redundancy or for secure firewall solutions.

FEATURES ĄĄ

ĄĄ

The Quick Starter Kit in detail The new SMARC 2.0 Quick Starter Kit from congatec integrates the conga-SA5, the high-end maximum performance computer module for the low-power class. It features the powerful quad-core Intel® Atom™ x7-E3950 processor with up to 2.0 GHz and Intel® Gen 9 graphics with 18 execution units. With 8 GB LPDDR4 RAM, the module also provides latest memory technology. Another highlight is the provision of a precertified M.2 communications module for up to 433 Mbit/s WLAN 802.11 b/g/n/ac, Bluetooth LE and optionally NFC. The starter kit also offers all interfaces provided by the new SMARC 2.0 module. These comprise of dual GbE including hardware-based and therefore especially fast real-time synchronization, MIPI CSI dual cameras, USB 3.0 plus fast 32 GB eMMC 5.0 flash memory and TPM 2.0 on the module.

The conga-SEVAL evaluation carrier board provides a broad range of interfaces including 4x PCIe x1, 1 mini PCIe, 2x USB 3.0, and 4x USB 2.0 for generic extensions. Also provided are 2x RJ45 for Gigabit Ethernet, 4x COM, 1x CAN bus and 12x GPIO. Displays can be connected via dual channel LVDS, eDP, DP and 2x MIPI DSI. Additional storage media are available via a SD/MMC socket and 1x SATA 6G. Digital and analog audio I/Os for I2S and HDA round out the interface offerings.

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conga-SEVAL – Evaluation Carrier Board for SMARC 2.0 modules conga-SA5/E3950-8G eMMC32 Wifi/TPM – SMARC 2.0 module with Intel® Atom™ E3950 quad core processor with 1.6GHz core frequency up to 2.0GHz, 2MB L2 cache, 8GB 2133MT/s LPDDR4 onboard memory and 32GB eMMC onboard flash conga-SA5/CSP-B – Passive cooling solution for SMARC 2.0 module conga-SA5 with open silicon Intel Pentium and Celeron processor. All standoffs are with 2.7mm bore hole. conga-SA5/HSP-B – Standard heatspreader for SMARC 2.0 module conga-SA5 with open silicon Intel Pentium and Celeron processor. All standoffs are with 2.7mm bore hole.

Cable Set – Complete set of cables to connect all components WLAN 802.11 ac/a/b/g/n Antenna – RP-SMA 2 dBi Omnidirectional Joint

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ATX Power Supply

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WLAN pigtail cable

The Quick Starter Kit is completed by an ATX power supply, the conga-ACA2 MIPI CSI-2 dual camera module, WLAN antenna, LVDS adapter, SATA, USB cables and a cooling solution for the module. On the software side, the congatec starter kit includes board support packages of the conga-SA5 module for the complete Windows 10 IoT family and Linux operating systems. Starting to develop SMARC 2.0 applications couldn’t be made any easier.

congatec

www.congatec.us

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

sales-us@congatec.com

 www.linkedin.com/company/congatec-ag

iotdesign.embedded-computing.com/p374449

 858-457-2600 twitter.com/congatecAG



www.embedded-computing.com/iot

SBC35-C398Q Quad-Core I.MX 6Q CORTEX®-A9 Industrial ARM® SBC WinSystems’ SBC35-C398Q quad-core single board computer combines high performance multimedia graphics with a rich mix of industrial I/O. The Freescale i.MX 6Q processor’s integrated power management provides excellent efficiency and allows operation from -40° to +85°C without active cooling. It is designed for demanding graphical applications in security, transportation, medical, and digital signage.

FEATURES i.MX 6Q Cortex-A9 ARM Processor (quad-core) ĄĄ High-Performance Video and Graphics ĄĄ Two CAN Bus ports, Five High Speed Serial ports ĄĄ 24 lines GPIO tolerant up to 30 VDC ĄĄ MiniPCIe and IO60 expansion ĄĄ Wide range DC or Power over Ethernet (PoE) ĄĄ Fanless -40°C to +85°C operation ĄĄ

iotdesign.embedded-computing.com/p373794  sales@winsystems.com  817-274-7553

WinSystems, Inc.

www.winsystems.com

IoT

Digi XBee® Cellular Digi is excited to bring together the power and flexibility of the Digi XBee® ecosystem with the latest 4G cellular technology, with the new Digi XBee Cellular embedded modem. This solution enables OEMs to quickly integrate cutting edge 4G cellular technology into their devices and applications without dealing with the painful, time-consuming, expensive FCC and carrier end-device certifications. A bundled data plan will be included with every development kit with 6 months of free data, with the Digi XBee fully pre-provisioned and ready to communicate over the cellular network right out of the box.

With the full suite of standard Digi XBee API frames and AT commands, existing Digi XBee customers can seamlessly transition to this new modem with only minor software adjustments. When OEMs add the Digi XBee Cellular modem to their design, they create a future-proof design with flexibility to switch between wireless protocols or frequencies as needed, ideal for any business with an agile roadmap.

FEATURES ĄĄ Convenient Digi data plans available for Digi XBee Cellular ĄĄ FCC certified and Carrier End-device certified ĄĄ Authentic 20-pin Digi XBee® TH form factor; Smallest end-device

certified cellular modem

ĄĄ Digi XBee Transparent and API modes simplify s/w design ĄĄ Integrated MicroPython programmability enables custom scripting

directly on the modem

ĄĄ OTA firmware updates ĄĄ Enhanced with Digi TrustFence™ security framework

Digi Data Plans are now available – starting as low as $2.80/month!

Digi International www.digi.com

www.embedded-computing.com/iot



aaron.mcnally@digi.com

 www.linkedin.com/company-beta/6245/

iotdesign.embedded-computing.com/p374262

 952-912-3444 or 877-912-3444

 @digidotcom

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RTOS & Tools, Security

wolfSSL TLS 1.3 wolfSSL was originally developed for embedded systems, IoT devices, and memory constrained devices. Due to its small footprint, wolfSSL’s TLS/SSL library is also proven to be cost-effective in highly scalable cloud environments because it allows each server to handle many more connections than if those servers were using OpenSSL. We actively maintain an OpenSSL compatibility layer to allow for simple “drop-in” replacement for any systems currently using OpenSSL. wolfSSL is dual licensed with GPLv2 license for open source projects and a commercial license option that includes dedicated engineering support and product line licensing options tailored for the client’s needs.

Now offering TLS 1.3 Support The wolfSSL lightweight SSL/TLS library now supports TLS 1.3 (Draft 20) on both the client and server side! A BETA release of wolfSSL (wolfSSL 3.11.1) is available for download from our download page. This release is strictly BETA and designed for testing and user feedback. Users and customers wanting a stable and productionready version of wolfSSL should remain on version 3.12.0.

One significant difference you will notice is the reduced number of round-trips when performing a full handshake. Older versions of the TLS protocol require two complete round-trips before the client sends the application data. With TLS v1.3 only 1 round-trip is required!

Additionally, the server can send application data in response to the client's first handshake message! This means network latency has less impact on the time required to establish a secure connection.

Another difference is the way session resumption works in TLS v1.3.

Previous versions of TLS have the client send a session id which the server has to look up in its cache. If there was a match then they used the same security parameters. This is a very simplistic mechanism that requires sharing of state on servers.

TLS v1.3 has made significant improvements by re-purposing the ticketing system tacked onto older versions of TLS. The server sends the client a new session ticket after the handshake is complete. This ticket, a blob of data to the client, can be a database lookup key like the old session id. Alternatively, it can be a self-encrypted and self-authenticated value that contains the data for the previous connection. Meaning the server can be stateless!

Finally, the specification has been evaluated by cryptographic experts in efforts to prove the security of the protocol. While no security proof is perfect, the previous attacks on renegotiation, protocol version downgrading, compression, CBC and padding have been mitigated and the protocol is generally more resistant to attack.

wolfSSL INC wolfssl.com

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info@wolfssl.com

FEATURES ĄĄ

Small size

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Portable

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One RTT handshake, 0 RTT resume

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Pruned of all legacy insecure algorithms

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Encrypted handshake messages after the ServerHello

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Redesigned key derivation functions

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Optimized handshake state machine

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ECC is the base spec and includes new signature algorithms Session resumption replaced with PSK exchange

TLS 1.3 is available – wolfssl.com/tls13 URL: www.wolfssl.com/ wolfSSL/Blog/Blog.html

iotdesign.embedded-computing.com/p374450

 425-245-8247

 www.linkedin.com/company/wolfssl www.embedded-computing.com/iot

wolfCrypt FIPS The wolfCrypt cryptography engine is a lightweight crypto library written in ANSI C and targeted for embedded, RTOS, and resourceconstrained environments – primarily because of its small size, speed, and feature set. It is commonly used in standard operating environments as well because of its royalty-free pricing and excellent cross-platform support. wolfCrypt supports the most popular algorithms and ciphers as well as progressive ones such as HC-128, RABBIT, and NTRU. wolfCrypt is stable, production-ready, and backed by an excellent support team. It is used in millions of application and devices worldwide.

The wolfCrypt Module is a comprehensive suite of FIPS Approved algorithms. All key sizes and modes have been implemented to allow flexibility and efficiency. The wolfCrypt Module was initially tested on Linux, iOS, and Android platforms.

What is FIPS 140-2?

Federal Information Processing Standards (FIPS) 140-2 is a mandatory standard for the protection of sensitive or valuable data within Federal systems.

FEATURES ĄĄ

Small Size

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Portable

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Cryptographic Module Validation Program (CMVP)

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Fips 140-2 Validated

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Default Entry Point

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DRBG Health Testing

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Cryptographic-based security systems

Why is FIPS 140-2 important?

Federal agencies purchasing cryptographic-based security systems must confirm an associated FIPS 140-2 certificate exists. This procurement “check-box” item is a deal breaker. Vendor claims of “designed for FIPS” or “FIPS ready” are not sufficient to pass this hurdle. No FIPS certificate = No sale . Many private and commercial organizations perceive an advantage in selecting a product with a FIPS 140-2 certificate over a solution that has not undergone the rigorous approval process.

Can I get a FIPS certificate in my company’s name?

Yes. You have the option of rebranding the wolfCrypt module and NIST will issue a FIPS 140-2 certificate in your company’s name. Your Sales Teams will thank you.

How can wolfSSL help me?

The wolfSSL team has the FIPS expertise you need. We will form a FIPS strategy that is best for you, optionally including on-site FIPS consulting! Before you search for a FIPS Consultant or begin calling several of the 22 FIPS Laboratories, contact us.

We can save you time, money, and effort. wolfSSL INC wolfssl.com

www.embedded-computing.com/iot



info@wolfssl.com

iotdesign.embedded-computing.com/p374452

 425-245-8247

 www.linkedin.com/company/wolfssl IoT Design Guide 2017

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RTOS & Tools, Security

IoT Design Guide

Software & Operating Systems

SMX RTOS haS whaT yOu need TO cOnnecT TO The IOT.

www.smxrtos.com

SMX RTOS ®

Security has become the main challenge to IoT development. In response, Micro Digital has developed MPU Plus®, which offers MPU protection on ARM Cortex-M processors.

Modern connected systems require protection of their mission-critical software, secret keys, and sensitive data. Effective protection can only be achieved via hardware means. The ARM Cortex-M v7 MPU is difficult to use and has significant limitations. However, it is the main means of hardware memory protection available for Cortex-M processors, which are in widespread use in small- to medium-size embedded systems. Hence, the security of many existing and new embedded systems can be greatly increased with MPU-Plus.

The primary goal of MPU-Plus is to make using the MPU as easy as possible, while offering as much protection as possible.

A key idea of our approach is to allow progressively increasing the security of a system. Our blog “Security and the Cortex-M MPU, Part 5: Step-by-Step MPU Security” briefly summarizes the steps to incrementally transform an existing nonsecure application to one with greatly enhanced protection. All MPU Plus articles are at www.smxrtos.com/mpu.

Wi-Fi is popular and offers high throughput and long range (250m), making connection convenient even when devices are not in immediate proximity to one another. smxWiFi supports 802.11 a/b/g/i/n and has drivers for many of the MediaTek/Ralink USB chipsets. USB dongles permit starting Wi-Fi development immediately on any evaluation board that has a USB host. Our approach uses TCP/IP directly vs. the proprietary protocols offered for serial interface chipset solutions. This provides more flexibility and better performance. Additional protocols supported: • Wi-Fi Peer-to-Peer (P2P) (Wi-Fi Direct TM) • Wi-Fi Simple Configuration (WSC) (Wi-Fi Protected SetupTM (WPS)) • SoftAP Communication protocols work better with strong multitasking support. smx is a hard real-time, high-performance RTOS kernel with a long list of features that are good for IoT designs: no-copy message passing, one-shot tasks, link service routines, and ultrafast heap, are just a few of its many advanced features. Duallanguage support permits writing high-level code in C++ and time-critical code in C.

MPU-Plus Features • Supports incrementally improving security.

• Adds strong security to existing and new products.

• Per-task or per-task-group isolation.

• Supports privileged ptasks and unprivileged utasks.

• Restricted Software Interrupt RTOS API for utasks.

• Unrestricted Direct RTOS API for ptasks.

• Background mode switched off for improved ptask protection.

See the smx Special Features whitepaper at www.smxrtos.com/special for discussion of these features and others.

Other security features of SMX: We offer SSL/TLS, SSH, and SNMPv3 for our TCP/IP stacks, WPA2 and EAP for Wi-Fi, and a Secure Bootloader for field updates. Personal and Enterprise security are supported for Wi-Fi. The bootloader supports image decryption during installation and bootloading to defend against hacking and reverse engineering, and to control optional software feature distribution.

Connectivity is intrinsic to IoT, and wireless is highly desirable.

› MPU-Plus ARM Cortex-M Memory Protection: › › › ›

smxNS6 is a dual IPv4/IPv6 stack that has passed IPv6 Ready testing. It supports IPv6 features such as Neighbor Discovery and Stateless Address Autoconfiguration. A dual stack allows a lower-cost IPv4 entry, with the ability to migrate to IPv6, when needed. Both stacks support Multicast DNS (mDNS), which allows a host on the network to discover services provided by other hosts. It supports zero-configuration (zeroconf) IP networking with Apple Bonjour and the Linux/BSD Avahi package. A rich set of other protocols also useful in IoT projects is offered such as: HTTPS, SMTPS, SNMPv3, and SNTP. Drivers are available for popular SoC Ethernet controllers.

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www.smxrtos.com/mpu SSL/TLS, SSH, HTTPS, SMTPS, SNMPv3 Wi-Fi WPA2 Personal and Enterprise Secure bootloader smx multitasking kernel with advanced features: www.smxrtos.com/special smxNS TCP/IPv4 and IPv6, mDNS (e.g Bonjour and Avahi), SNMPv3, SNTP, Web, and many more protocols; drivers for many SoC Ethernet controllers. smxWiFi 802.11 a/b/g/i/n with P2P, WSC, SoftAP, MediaTek/Ralink chipsets All modules are integrated and interoperable: www.smxrtos.com/rtos

› Full source code in ANSI-C, royalty free iotdesign.embedded-computing.com/p372486

Micro Digital, Inc. 800-366-2491

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sales@smxrtos.com

www.smxrtos.com/iot

www.embedded-computing.com/iot

AT&T LTE Starter Kit II Certified, next generation Avnet-engineered LTE Cat-4 IoT Starter Kits reduce cost and time-to-market!

Designed around latest Qualcomm quad-core processor technology, highly integrated WNC M18QWG or M18Q2FG-1 based LTE System boards dramatically reduce hardware engineering requirements, ensuring that your focus can remain on creating unique, differentiated end products.

features ĄĄ

LTE Cat-4 cellular connectivity, GPS and sensors (ambient light, temperature and accelerometer) are packaged in a unique, small form-factor LTE board, designed for system prototyping and cost-effective volume production.

Complex system designs are facilitated via popular plug-in peripheral boards and custom user-hardware. ĄĄ

LTE Cat-4 cellular SOM / embedded modem / Dev board

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WNC module (quad ARM A7, 1 CPU core for User Apps)

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Onboard GPS, Ambient Light and Accelerometer sensors

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“End Device Certified” for USA, Canada, Mexico

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Includes AT&T MicroSIM with 150MB data, 150 SMS, (valid for 6 months from SIM registration)

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Enabled for full Cat-4 150/50 Mbps data rate

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For development and cost-effective production!

For more details and to purchase LTE Starter Kits and modules, visit:

http://cloudconnectkits.org/ product/att-iot-starter-kit-2

Starter Kit: AES-ATT-M18Q2FG-SK-G Module: AES-ATT-M18Q2FG-M1-G

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LTE System Boards • North America and Global SOMs available • Certifications for North America and Europe • Onboard GPS location sensing • Ambient Light Sensor • 3-Axis Accelerometer and Temperature Sensor • USB Compound Device interface • Onboard 3.8V and 3.3V regulators M18Q2FG-1 WNC Module Detail • LTE Cat-4, Bands 2/4/5/12 • 3G Fallback, Bands 2/5 • GNSS (GPS, GLONASS) • Based on Qualcomm MDM9207 (ARM® Cortex™ A7 quad-core, one CPU dedicated for User Apps.)

• Strong set of peripheral interfaces, supported by software libraries and application example code LTE + GNSS Antenna (from Pulse Electronics) • 2G/3G/4G MIMO • GNSS (GPS, Glonass) • Two piece FPC assembly, foldable for tight spaces Exceptional Expansion Capabilities (facilitates system prototyping and use with custom production boards) Peripheral Module Connector • 2x6 pin Pmod™-compatible connector, 3.3V levels Expansion Connector • 60 pin Samtec ERF8 connector, 1.8V levels LTE Expansion Board (optional development board) • System prototyping using low-cost module • Dual sockets support a range of more than 300 MikroElektronika “Click modules” iotdesign.embedded-computing.com/p374457

Avnet Inc.

www.avnet.com www.embedded-computing.com/iot

 480-643-2000

 twitter.com/avnet

 www.linkedin.com/company/2572 IoT Design Guide 2017

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IoT Design Guide

Wireless, Cloud

Copper Mountain Technologies’ USB VNAs 20 kHz to 110 GHz Copper Mountain Technologies develops innovative laboratory-grade RF test and measurement solutions for design, production and field testing of electronic components and systems, from PCB antennas to automotive radar, to wearable smart devices, wireless power charging, infotainment systems, etc. USB VNAs are the right tools for IoT designers to ensure reliable, accurate performance of hardware in any IoT system at a reasonable cost.

Copper Mountain Technologies' VNAs include an RF measurement module and a software application which runs on any Windows PC, laptop or tablet, connecting to the measurement hardware via USB interface, taking advantage of the latest OS, processing power, larger display, and reliable performance of an external PC, as well as lower total cost of ownership. The result is a faster, more effective test process that fits into the modern workspace in numerous environments. This creative approach made them a leader in the industry and earned them both the 2015 Frost & Sullivan Global Leadership in Innovation Award and the 2017 Product Leadership Award.

Powered and operated through USB connection to an external PC or an external 5V charger, CMT’s hand-held 1-Port VNAs perform labquality measurements of the S11 parameter in various presentation formats. These cable and antenna analyzers operate in frequency ranges of 1 MHz to 18 GHz. These patented (US Patent 9,291,657) analyzers can be connected directly to a DUT without the need for a test cable, resulting in highly dependable performance and calibration stability. The measurement accuracy allows for a wide variety of analysis capabilities including time domain measurement as a standard feature, applicable to antennas and antenna feeders in the field as well as laboratory and production testing.

Our Compact VNAs deliver lab grade performance in a compact package, with all the features engineers have come to expect included: time domain and gating conversion, segmented frequency sweeps, linear/logarithmic sweeps, power sweeps, multiple trace formats, 16 channels max. with up to 16 traces each, marker math, and limit tests. Versatile and portable Compact analyzers can be powered by battery and are ideal for use by specialists working in the field, as well as laboratory and production testing.

CobaltFx Frequency Extension System is an innovative, cost-effective approach for mmWave S-parameter measurements. The system can be used for a variety of antenna applications including waveguide capable positioners performing live basic antenna and RCS measurements. They have dedicated waveguide bands of 50-75 GHz, 60-90 GHz, and 75-110 GHz can be anchored by one of four Cobalt VNAs in 2- or 4-port models with 9 or 20 GHz maximum frequency.

Copper Mountain Technologies www.coppermountaintech.com

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FEATURES ĄĄ

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Lab-grade instruments at a cost-effective price point for a wide option of products allowing for applications and uses ranging from field testing to R&D, lab testing, and fast production 1-Port USB VNAs offer lab-grade performance in a handheld device making it possible to do high-quality testing anywhere 1-Port USB VNAs (cable and antenna analyzers) have a patented design with no test cable necessary (Patent US 9,291,657) 1-Port USB VNAs models with frequency ranges from 1 MHz to 18 GHz Compact USB VNAs provide full-featured lab-grade performance in a compact package Compact USB VNAs models have frequency ranges from 9 kHz to 4.8 GHz Cobalt USB VNAs offer industry-leading dynamic range and sweep speed Cobalt USB VNAs have 2- and 4 port models with frequency ranges from 100 kHz to 20 GHz Some models of Cobalt USB VNAs have Direct Receiver Access and compatibility with CobaltFx Frequency Extension System CobaltFx Frequency Extension System has waveguide bands from 50-75 GHz, 60-90 GHz, and 75-110 GHz anchored by Cobalt USB VNAs CobaltFx Frequency Extension System is a cost-effective mmWave s-parameter measurement solution iotdesign.embedded-computing.com/p374433

marketing@coppermountaintech.com www.linkedin.com/company/2434419/

 317-222-5400 @CopperMtnTech



www.embedded-computing.com/iot

WINC1500

FEATURES

Microchip’s WINC1500 is an IEEE 802.11 b/g/n Internet of Things (IoT) module that is specifically optimized to help you easily add Wi-Fi® to your low-power IoT applications. It is the ideal add-on to existing microcontroller (MCU) solutions bringing Wi-Fi and network capabilities through UART or SPI-to-Wi-Fi interfaces into embedded applications. To help accelerate design development even more, Microchip offers the WINC1500 as a single-chip module for fast integration. This highly integrated module has a small form factor (21.7 × 14.7 × 2.1 mm) and is fully certified, saving considerable engineering time and certification costs.

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2.4 GHz IEEE 802.11 b/g/n IoT solution

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Ultra-low power (4 μA in Power Save Mode)

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Operating voltage: 3.0V to 4.2V

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Serial host interface: SPI

The module features a fully integrated power amplifier, LNA, switch and power management. It provides internal Flash memory as well as multiple peripheral interfaces including UART and SPI. The only external clock source needed is a high-speed crystal oscillator (26 MHz). The WINC1500 is available in a QFN package or as a certified module.

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Supports Wi-Fi security protocols (WPA/WPA2 Personal, TLS 1.2, SSL) Compact footprint SoC: 5 × 5 mm QFN Available in a fully certified module: WINC1500 (4 MB Flash) and ATWINC1510 (8 MB Flash) Secure IoT reference design available Network services: DHCP, DNS, TCP/IP (IPv4), UDP, HTTP, HTTPS Antenna Options: Chip and PCB antenna (WINC1500-MR210PB); Chip and u.FL connector (WINC1500-MR210UB)

iotdesign.embedded-computing.com/p374294

Microchip Technology Inc.

www.microchip.com/ATWINC1500

www.embedded-computing.com/iot

Contact Microchip: www.microchip.com/distributors/SalesHome.aspx  480-792-7200 @MicrochipTech www.linkedin.com/company/microchip-technology

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

IoT Design Guide 2017

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IoT Design Guide

Wireless, Cloud

Product Problems?

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ALM

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L -I N - O N E

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Over 1,000 customers (250 in IoT) and users in 125 countries. Consider Arena. You won’t be the first.

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arenasolutions.com

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