Internet of Things Handbook 2016

April 2016

Sensors drive IIoT innovations Page 16

Batteries for the Industrial Internet of Things Page 40

INTERNET of THINGS

HANDBOOK

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Internet of Things

Volume 2 Number 2

40 06 The fooled-by-data effect 08

Research in the IoT

10

Delivering reliable IoT

Here’s a simple explanation of how to build a hard-wired IoT network that doesn’t get clogged by network traffic.

16

Sensors drive IIoT innovations

21

Powering the Cloud for an energy efficient Internet of Everything

Industrial networks will increasingly make use of sensors that operate from energy they harvest themselves.

As the Internet-of-Everything puts pressure on Cloud services, software-optimized power conversion will help save energy and let operators keep on top of costs.

25

10

The benefits of the Industrial Internet of Things

The Industrial Internet of Things (IIoT) will improve the end user experience and create new OEM revenue streams.

Design and test for the IoT

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Proliferating wireless standards put heavy demands on instrumentation.

Here are some factors to consider when gearing up for IoT development work.

HMIs double as edge-of-network gateways

Industrial networks can use operator terminals to handle communications and analytical duties associated with IoT tasks.

52

Harvesting power for IoT devices

Sometimes energy for IoT devices comes from the likes of thermoelectric generators and thermopiles thanks to power management ICs that double as energy misers.

55 The challenges of the Industrial

Internet of Things and communications at the edge

The Industrial Internet of Things (IIoT) promises to let everything within an industrial environment connect to get complete visibility into operations and allow the best real-time decisions—with or without human intervention.

IoT connectivity for next-generation automotive apps is made possible by new software devised with audio and video networking in mind.

31

Batteries for the Industrial Internet of Things

Not all battery chemistries are the same when it comes to powering devices designed for the IIoT. A few guidelines help field cells able to handle rugged surroundings for long periods.

Embedded software enables IoT in today’s vehicles

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2

40

60

62

The new era of design for the IIoT

The IIoT promises more operational efficiency and lower costs thanks to close coupling of machines and systems.

How to ensure network works with the IoT

Test beds will ensure new network infrastructures support the Industrial Internet of Things.

65

SWARM Intelligence for Industrial IoT

Cover Photo Courtesy of Miles Budimer

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The power to

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Internet of Things

GRAPHICS

EDITORIAL

VIDEO

MARKETING

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Traffic Manager Mary Heideloff mheideloff@wtwhmedia.com

Senior Editor Miles Budimir mbudimir@wtwhmedia.com @dw_Motion

WITH US!

CONNECT

Follow the whole team on twitter @DesignWorld

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Internet of Things

The fooled-by-data effect LELAND TESCHLER EXECUTIVE EDITOR

ABOUT

a year ago, everyone in my office got a fitness watch. This was at the height of the IoT wearables craze.

Fitness watches were supposed to do wonderful things such as measure steps taken, distance traveled, stairs climbed, active minutes, elapsed time, calories burned, pace, elevation and improve sleep quality.

An informal poll of my colleagues revealed that after about six months, roughly half those fitness watches sat unused in bedroom drawers.

The problem for most of them was data overload.

They just weren't all that interested in continually monitoring their calorie burn or steps. And all the data the watch generated seemed overwhelming.

It might be just as well. There are those who argue that having too much data about medical conditions can be more harmful than having too little. One in that camp is Nassim Nicholas Taleb. Taleb is a professor of risk engineering at N.Y. University's Polytechnic Institute. But he made his fortune trading options on equities and futures contracts. His strategy was to buy cheap options that paid off big when widely unanticipated events occurred—he is said to have cleaned up in the dot-com crash around Y2K. He outlined his philosophy in a best-seller he authored called Fooled By Randomness.

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Taleb has little good to say about the Big Data coming out of IoT endeavors. Judging by what he said in his most recent book, Antifragile, he thinks even less of the reams of data about vital signs generated by items such as fitness watches. "If you want to accelerate someone's death, give him a personal doctor. I don't mean provide him with a bad doctor: just pay for him to choose his own. Any doctor will do," wrote Taleb. "Did you ever wonder why heads of state and wealthy people with access to all this medical care die just as easily as regular persons? Well, it looks like this is because of overmedication and excessive medical care," he posits.

Taleb's criticism of Big Data, and particularly big medical data, is that most of it should be ignored. The reason: Medical data is noisy. Looking at data more frequently is likely to increase the chance you are merely looking at the noise in a signal rather than at the signal itself. So it is easy to end up reacting to spurious readings.

"More data means more information, perhaps, but it also means more false information. … Someone looking at history from the vantage point of a library will necessarily find many more spurious relationships than one who sees matters in the making, in the usual sequences one observes in real life," Taleb said.

The IoT is worsening the situation. The same kind of noisiness that describes medical data can be found in numerous other disciplines that are increasingly the subject of analytics made possible by pervasive connected sensors. "The fooled-by-data effect is accelerating," wrote Taleb. "There is a nasty phenomenon called Big Data in which researchers have brought cherry picking to an industrial level. Modernity provides too many variables (but too little data per variable), and the spurious relationships grow much, much faster than real information, as noise is convex and information is concave," he said.

Those are points to consider before betting your business on an IoT Big Data initiative.

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e r s a n d o u r n s , w h ic h p r o d uc t s .

t e r y h o l d e r s . c o m

All product names, logos, and brands are property of their respective owners.

MPD trade marks, All other registered trademarks or trademarks are property of their respective owners.

Use of these names does not imply any co-operation or endorsement.

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THE R E S E A R C H IN

s g n i h T f o t e n r Inte LELAND TESCHLER EXECUTIVE EDITOR

Researchers ink nanocrystal FETs onto plastic substrate

A plastic substrate containing several FETs with an enlarged portion shows the geometry involved in creating the device.

A model of the FET the U. of Penn. group created on a plastic substrate illustrates the gate region formed by silver ink, the aluminum oxide insulator, the channel formed by the cadmiumselenide semiconductor ink, topped with source and drain contacts formed by indium/silver ink.

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A group at the University of Pennsylvania has patterned nanocrystal-based field effect transistors onto flexible plastic backings. The group used ordinary spin coating to create the FETs but say the devices could eventually be constructed by 3D printers.

The researchers devised four different inks for the work: a conductor with silver nanoparticles, an insulator containing aluminum oxide, a semiconductor of cadmium The patterning of the FETs proceeded according selenide, and a conductor to the sequence illustrated here. Researchers formulated the inks involved so they would have combined with a silver and special surface chemistry that would allow them indium dopant for doping the to stay in place and not lose their electrical semiconductor layer of the qualities once they were deposited. transistor with impurities.

To make FETs, researchers first deposited the conductive silver nanocrystal ink on a flexible plastic surface treated with a photolithographic mask, then rapidly spun the surface to spin coat it. The mask was then removed to leave the silver ink in the shape of the transistor’s gate electrode. Next came a spin-coated layer of the aluminum oxide nanocrystalbased insulator, then a layer of the cadmium selenide nanocrystal-based semiconductor, and finally another masked layer for the indium/silver mixture, which forms the transistor’s source and drain electrodes.

Application of relatively low heat caused the indium dopant to diffuse from those electrodes into the semiconductor component.

Researchers note it was a bit tricky to add a layer without washing off the layer beneath it. So there were special treatments added both when the nanocrystals were in solution and when they had been deposited so they had the right electrical properties and would stick together. The ink-based fabrication process works at temperatures much lower than those of existing vacuum-based methods.

“Making transistors over larger areas and at lower temperatures have been goals for an emerging class of technologies, when people think of the Internet of things, large area flexible electronics and wearable devices,” said lead researcher Cherie Kagan, the Stephen J. Angello Professor in the School of Engineering and Applied Science. “We haven’t developed all of the necessary aspects so they could be printed yet, but because these materials are all solution-based, it demonstrates the promise of this materials class and sets the stage for additive manufacturing.”

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RESEARCH IN THE IoT

Hybrid inks for flexible electronics need no sintering Researchers at Rensselaer Polytechnic Institute (RPI) are building tools and developing a framework that developers can use to easily perform data analytics over a multitude of devices.

Rather than having developers design custom algorithms for each network of devices, the framework of software RPI has in mind sits on all the network devices. The cloud will automatically manage communication between the devices and deal with device and network failures. Said lead researcher Stacy Patterson, RPI Clare Boothe Luce Assistant Professor of Computer Science, “Now the developer only

needs to provide a little bit of code to say ‘this is how I want it to work,’ and this framework will take care of the rest.”

The project, “Toward a Machine Learning Framework for the Internet of Things,” is an extension of Patterson’s current research into enhancing the utility of sensors embedded in automobiles, by creating real-time networks that allow automobiles to pool their individual information into a larger shared picture of driving conditions in the area. Current approaches for Big Data analytics require full data transfer to a platform with large computational power, such as the cloud. Given the projected explosion in the number of devices and the resulting data generation rate, this is not feasible.

Patterson said she has three goals in IoT research. The first is to develop a computational framework that reduces the problem to an abstraction, anticipating considerations like the type and quality of data, the number of devices, and how the data are related across devices. “What kinds of relationships are people interested in with this data, and how does that embed down to the physical world? This is ultimately about searching for a pattern in how I would solve the kinds of problems that interest people,” Patterson said.

The second goal is to provide a stable platform that masks the differences between devices and compensates for failed devices or computers and lost data. Finally, she will build tools to enforce a standard for speed and accuracy of the framework.

Wearable electronics could make use of open-source microprocessors

The PULPino open-source microcontroller-like system uses a small 32-bit RISC-V core developed at ETH Zurich. The core supports a base integer instruction set (RV32I), compressed instructions (RV32C) and partially supports a multiplication instruction set extension (RV32M).

It implements extensions for hardware loops, post-incrementing load and store instructions, ALU and MAC operations. To allow embedded operating systems such as FreeRTOS to run, a subset of the privileged specification is supported. When the core is idle, the platform can be put into a low power mode, where only a simple event unit is active and wakes up the core in case an event/interrupt arrives. The PULPino platform is available for RTL simulation, FPGA and the first ASIC (called Imperio) was taped out in January 2016.

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Scientists at ETH Zurich and the University of Bologna have open-sourced the design of one of their microprocessor systems. The arithmetic instructions that the microprocessor can perform are also open source: developers made the processor compatible with the open-source instruction set RISC-V developed at the University of California in Berkeley.

The new processor is called PULPino. It is designed for batterypowered devices with extremely low energy consumption (PULP stands for parallel ultra-low power). ETH researchers say the processor could be used for small devices such as smartwatches, for wearable electronics monitoring heart rates and similar parameters, or sensors for the Internet of Things. For demonstration purposes, the group is developing a smartwatch equipped with the PULPino processor and a camera. It can analyze visual information and use it to determine the user’s whereabouts.

For now, the main interest in PULPino comes from academics. ETH scientists want to work with other groups to jointly develop academically interesting extensions to PULPino; these would also be open source, thus allowing the number of the hardware’s functional components to steadily grow.

Development costs are reduced considerably with the open-source royalty-free design, said ETH Professor and project leader Luca Benini. “It could result in new research and development partnerships with industry to jointly develop novel chip components on the basis of PULPino.” So PULPino's developers are planning to make their microprocessor more widely known to the open-source hardware community this year.

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Internet of Things

Delivering reliable IoT JASON TOLLEFSON, IAN SATURLEY

Here’s a simple explanation of how to

USB AND NETWORKING GROUP, MICROCHIP TECHNOLOGY

build a hard-wired IoT network that doesn’t get clogged by network traffic.

MOST

people understand what the word reliable means. If you are driving across the Arizona desert, you want to be sure that the vehicle you choose is reliable so you don’t end up stranded. When you buy a new appliance, you will likely pay attention to reliability so you don’t replace it sooner than expected.

However, many people seem to have settled for unreliable internet connections for their internetconnected things. When it comes to making our devices work, shouldn’t reliability be just as important as the devices themselves? Consider the show you’ve been binge-watching online. Is there anything more frustrating than the spinning wheel or stalled progress bar?

Wireless technology is supremely convenient, but reliable is not the first adjective that many would select to describe it.

The Internet of Things (IoT) already consists of more than 5 billion end nodes and is going to 20 billion by 2020 (Gartner 2015). In addition, its applications will span several categories according to the Spanish wireless sensor maker Libelium Comunicaciones Distribuidas S.L. Though IoT nodes are generally associated with low data rates, many IoT applications will still fight for limited bandwidth. A wireless router may be able to increase the reliability of streaming media with WiFi Multi-Media (WMM), but as more applications get on WiFi, bandwidth maxes out quickly.

For maximum reliable bandwidth, there’s nothing better than a wired connection.

An IoT network built from four Raspberry Pi single-board computers. The Raspberry Pi connect to other nodes on the network by 10/100 fast Ethernet. Two connect directly to a five-port switch, while the others are daisy-chain-connected through threeport switches with local MCUs to the five-port switch.

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ELEMENTS OF A WIRED IoT SYSTEM Consider an IoT network built from four Raspberry Pi single-board computers. The Raspberry Pi are connected to other nodes on the network by 10/100 fast Ethernet.

Two connect directly to a five-port switch, while the others daisy-chain-connect through three-port switches with local microcontrollers (MCUs) to the five-port switch.

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DELIVERING IoT

This system is representative of a high-performance IoT system as might be found in a manufacturing environment, in homes or automated buildings, or in security/safety systems, to name a few applications.

The five-port switch has a gigabit Ethernet uplink. This switch configuration allows for superior bandwidth by providing a highspeed link to the resident PC. The PC connects to the gigabit Ethernet port using a bridge device. The bridge takes in gigabit Ethernet and converts it to Universal Serial Bus (USB) 3.1, communicating at 5 Gb/ sec, and is then connected to the PC’s USB port.

Such a network could be replicated many times over by adding additional switches to the gigabit network.

This topology offers maximum reliable bandwidth by supplying adequate data rates across the entire network. Each Raspberry Pi is only capable of 100 Mb/ sec. If the switch was merely a 10/100 switch, there would be a loss of bandwidth because of packet priority and collisions. With a gigabit switch, there is no loss of bandwidth. This topology also provides a cost-optimized solution as 10/100 networks are inherently less expensive.

The Raspberry Pi and MCUs in the system generate data on the network. In this example, the four Raspberry Pi each run a network performance measurement tool called “iperf.”

The data generated includes bandwidth and packet loss.

Daisy-chained to the network are two MCUs. The two MCUs each run code provided

by Interniche, an embedded networking software company.

The code provides device management services, like Managed Information Base (MIB) and Object Identifiers (OID), that can be shared on the network with the Simple Network Management Protocol (SNMP).

Also running is MQTT (formerly the MQ Telemetry Transport Example real-time statistics generated by a protocol). Created well before network performance measurement tool called the concept of IoT, MQTT is a “iperf.” The data generated includes bandwidth lightweight publish/subscribe and packet loss. messaging protocol suitable for IoT thanks to its minimal bandwidth requirement.

Once the network generates data on performance, the data should be displayed in a way that helps identify important characteristics. A weather map can depict the loading of the IoT network.

The weather map depicts full duplex conditions across the network. A color spectrum coding can provide an instant understanding of network load percentage.

Orange and red can indicate high loading conditions.

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Internet of Things

In the accompanying diagram, the five-port switch resides in the middle of the network. In this representation, you can see that a high amount of data is sent to each network leg. However, the light blue and white colors of the upstream gigabit port show the perfect balance of this network as the loading is minimal. This shows that the bandwidth of each IoT node is maximized while the resident PC has ample bandwidth. If this network were hooked to a larger gigabit network, it would not flood the larger installation with data, so it provides maximum reliable bandwidth.

WIRING UP AN IoT APPLICATION If we were actually adding wired Ethernet to the network in this example, the end device would take the place of either one of the Raspberry Pi boards or the PIC32 Ethernet Starter Kit II. There are three ways to add Ethernet to an application if it is not already present: adding an

Ethernet controller, a bridge, or a physical layer (PHY). Ethernet controllers are used in applications where the MCU does not have an onboard Media Access Controller (MAC). There are several varieties that use Serial Peripheral Interfaces (SPI) or parallel interfaces to the MCU. Bridges are typically used with a System on Chip (SoC) or Microprocessor Unit (MPU). Bridges use a USB or Peripheral Component Interconnect express (PCIe) interface to the processor and convert to Ethernet.

An Ethernet PHY or transceiver is used in applications with an MCU, SoC or MPU that has a MAC on board. The PHY is the physical Ethernet interface and requires a special processor interface.

To extend the reach of the network, daisy chaining is a great option. A network switch is a device with two or more ports to allow for daisy chaining.

Look for features such as virtual PHY, which lets the switch appear like a regular PHY in the system.

This removes burden and risk when adding ports. An example can be found in Microchip’s LAN9303, where customers with a proven single port design can scale to a dual-port platform.

Hardware design has a lot to do with getting the maximum reliable bandwidth available using wired IoT. The speed of communications can be dramatically affected by board layout and the components used. It can be helpful to use an Ethernet supplier that provides design-check services.

These services are often free and can result in significantly better results. The service should check your design schematic, provide printed circuit board (PCB) guidelines, and analyze your

A weather map depicting the loading of the example IoT network. The map depicts full-duplex conditions across the network. Color spectrum coding helps grasp network load percentage. Orange and red indicate high loading conditions.

This representation shows there is a high amount of data going to each leg of the network. But the light blue and white coloring of the upstream gigabit port shows the loading is minimal.

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Internet of Things

PCB layout and routing.

These design checks can prevent multiple board design spins.

Diagnostics is another necessity. They are a feature of the network switch. The switch incorporates counters that the host processor can read. The illustration of a network weather map in the accompanying figure was generated using these counters. Diagnostics information can even be used as a service that generates revenue.

A Signal Quality Indicator (SQI) is a metric that can show the quality of the link between devices. It can also give an indication of a possible fault condition before an actual error is detected.

Often, the failure mechanism is related to some environmental condition. Features such as SQI and switch counters can generate a long-term data set. This information helps anticipate failure, guide maintenance and minimize downtime.

All in all, it is not difficult to connect any processor basedapplication to the IoT using a wired connection. Supplier design checks when adding Ethernet by means of a PHY, bridge or controller can yield a highly reliable IoT application. When configured in a network using properly balanced sub-nets, like the example of the five-port switch, traffic delays are avoided and costs stay under control.

When it comes to making our devices work, shouldn’t reliability be just as important as the devices themselves?

RESOURCES: Gartner, Inc. gartner.com/newsroom/id/3165317 Libelium Comunicaciones Distribuidas S.L. libelium.com/top_50_iot_sensor_ applications_ranking Microchip Technology microchip.com SNMP basics at Paessler AG kb.paessler.com/en/topic/653-howdo-snmp-mibs-and-oids-work

E X AM P L E : I o T S Y S T E M C OM PON EN TS

Raspberry Pi (4)

Intel Haswell Industrial Computer

PIC32 Ethernet Starter Kit II

Microchip KSZ8795 5-port Switch

LAN9303 PHY Switch Daughter Board

EVB-LAN7800 USB3.1 to Gigabit Ethernet Board

ME T H O D S F O R AD D I N G ETHER N ET I N TER FA CES

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METHOD

PROCESSOR INTERFACE

DRIVER REQUIRMENTS PROCESSOR

Ethernet Controllers

SPI, Parallel, 8-/16-/32-bit

Requires Driver

MCU

Ethernet Brige

USB2.0/USB3.1/PCle

Requires Driver

SoC/MPU

PHY/Transceiver

RMII/MII/RGMII/GMII

Requires Driver

MCU/SoC/MPU

DESIGN WORLD — EE Network

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Internet of Things

Sensors drive IIoT innovations Industrial networks will increasingly make use of sensors that operate from energy they harvest themselves.

WIREN PERERA

ON SEMICONDUCTOR

THE IoT

has spawned the concept of the Industrial Internet of Things (IIoT).The IIoT includes the concept of greatly enhanced connectivity in areas such as industrial automation, security and surveillance and building automation. The IIoT uses sensors and actuators embedded in equipment and objects, linked via wired or wireless networks, to improve productivity, and adjust to varying conditions in real-time.

The key evolutionary trends in the IIoT depend highly on innovative enabling technologies and solutions from semiconductor manufacturers. These will help ensure that the transition from the conceptual designs to real-world working implementations happens.

The IoT is here and now, and has been for some time -albeit under different names, such as M2M and embedded connectivity. As the consumer-oriented IoT shows burgeoning growth and adoption, the manufacturing and service industries are looking at ways the IoT can be harnessed and exploited to drive productivity into their manufacturing systems. Enter the IIoT—a hugely important and rapidly growing subset of the IoT that is advancing an ever-closer linkage between Operational Technology and Information Technology in businesses. Ultimately, the IIoT will greatly expand the number of ‘things’ that comprise the IoT.

The typical building blocks of the IIoT include sensors, a processor, a power source, connectivity to other resources, and connections to industrial assets such as motors and actuators.

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SENSORS

Attributed to Peter Drucker, the phrase “If you can’t measure it, you can’t improve it” is at the heart of the IIoT philosophy. The driving trends are more measurements, a need for rapid and thorough analysis of data, and faster improvements to processes. But, to measure more, we must be able to sense more — more parameters, with more accuracy and more often.

Sensors are fundamental to the IIoT. Overlaying software on existing technologies can bring incremental gains, but for a true step forward we need to bring more parameters into the fold. Every new parameter we sense brings significant opportunity and makes the system smarter.

Sensors are the ‘eyes, ears and hands’ that are enabling the expansion of the IIoT and its capabilities.

Traditional sensors continue to evolve; we can measure temperature, light, position, level, humidity, pressure and many other parameters better than ever.

But, even as they become smaller, less expensive and more embedded, each of these sensors is dedicated and, therefore, limited in functionality and adaptability.

Vision-based sensing removes these limitations; once a machine can truly ‘see,’ almost anything is possible.

With vision sensing, programmability brings flexibility, enabling a single vision system to sense missing or misplaced components, or detect a subtle color change that indicates a process is drifting out of control. The IIoT will continue to add more basic sensors to measure the fundamentals, but the trend toward visionbased sensing—both still and video— makes smarter systems more flexible and more valuable.

As production lines and factories are reconfigured to move from one product to another, or to build variants of a complex product, vision systems need no manual repositioning or reconfiguration. They only require a simple change from one control program to the next, and the system is ready to run—lowering costs, saving time and manpower, and eliminating opportunities for the mistakes that humans are prone to making.

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Internet of Things

An example of a wireless sensor that can harvest its energy from the environment is that based around a Magnus-S sensing block from RF Micron. It uses an antenna to convert an environmental variable to be sensed -- such as moisture, temperature, or some other parameter -- into an impedance change that is then converted into a sensor code as it dynamically matches the antenna impedance to the die impedance.

In many ways, data is the key to this revolution.

Sensors bring data that, through checks, balances and redundancy, we can trust. But post-processing brings the valuable information and ability to control our factories and processes, and to improve them.

One of the biggest challenges and potential stumbling blocks for the IIoT is power. The distributed nature of the IIoT and the need to place sensors where the ‘action’ is makes the reliable delivery of power challenging. And the spiraling energy costs associated with powering this plethora of IIoT sensors can be significant too.

Successful sensors, particularly those intended for the IIoT, have four basic attributes. They need to be self powered, collect data, broadcast their status and have the ability to connect. Energy harvesting (EH) wireless sensors are exactly what is needed to drive the IIoT forward.

Fundamentally, this new breed of sensors needs to be able to measure multiple physical parameters, such as temperature, moisture, pressure and proximity, and communicate the data without the use of a direct power source. For example, ON Semiconductor recently introduced a battery and microcontroller-free, wireless sensor family.

These ultra-thin devices can be used to sense in places where space is constrained or in areas that cannot be accessed by traditional sensors. Similar advances in the acquisition and processing of bio sensor data will fuel the growth of the IoT in the healthcare industry.

It is interesting to explore these new sensors more deeply because they use a single mechanism – antenna detuning – to perform multiple types of sensing. In this 18

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regard, they are probably a harbinger of sensing trends to come.

The sensors use RFMicron’s Magnus S2 ICs. They are used to comprise a wireless passive RFID tag composed of an antenna and a Magnus S sensor die.

ON Semiconductor’s battery-free wireless sensor tags perform temperature, moisture, pressure, or proximity sensing. The underlying antenna self-tuning technology automatically adjusts the input impedance of the IC to optimally tune the tag every time it is accessed.

Tags based on conventional chips can be detuned by numerous external factors, most commonly by proximity to liquids or metals. Such factors can change the impedance of a tag’s antenna. When the tag chip has a fixed impedance, there’s a mismatch between the chip and the antenna. The self-tuning technology maintains the chip-antenna match as conditions change.

The Magnus S die includes a bank of tuning capacitors between the antenna ports and an RFID engine. The engine dynamically adjusts the chip’s input impedance by switching capacitors in or out of the circuit to maximize the power harvested from the antenna.

Because the tag antenna can respond to a change in environment in a known way, the sensor code can provide a quantized measurement of the change in the environment. This lets the tag become a wireless passive sensor.

Moisture isn’t the only parameter these chips could be used to sense. Solid-state films react to a variety of gases with a change in resistance. So it should be feasible to construct sensor tags that respond to gases such as CO, CO2, NOX, H2S, O2 and Cl2. Thin

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SENSORS

films deposited on an interdigitated capacitor can produce sufficient change in circuit Q to build wireless passive sensors readable through the sensor code. Proximity at micron resolution is detectable through inductive changes created by eddy currents on nearby metal surfaces. These can be used to detect movement or to build pressure sensors.

However, referring back to the key basic required attributes, the real breakthrough is that these sensors, through energy harvesting, acquire power themselves. The principles are not new and share a lot with large-scale renewable energy initiatives such as wind power or harnessing the tides.

Being able to scavenge and accumulate free ambient background energy will allow sensors to work autonomously.

The sources are many and varied,

and include the obvious solar and wind power but, increasingly, other areas such as kinetic energy, thermal gradients, body temperature and even acoustic noise are being explored.

It’s also important for IIoT platforms to support a broad array of communication standards, including Thread, SIGFOX, EnOcean (used primarily in building automation and security systems), M-BUS (European standard for remote reading of gas or electricity meters), KNX (for building automation), ZigBee and proprietary protocols. The adoption of a softwaredefined radio approach allows a single platform to support multiple protocols.

ZigBee and Thread are complementary, and the alliance of the industry organizations behind these protocols are likely to drive their broad adoption within the smart home.

Thread is an IP-based (IPv6) networking protocol with 6LoWPAN adaptation built on open standards for low-power 802.15.4 mesh networks that can connect hundreds of devices to each other and directly to the cloud.

Security and in.teroperability are two of Thread’s key capabilities.

Conversely, SIGFOX enables widearea networks that provide relatively low bandwidth communication with fixed or mobile smart objects or sensors that are deployed over a large area. Example applications include the nationwide tracking of shipping containers or vehicles and communication with geographically dispersed assets such as oil pumps and pipelines.

For the IIoT to reach its full potential, we have to do something with the information gathered. The feedback loop needs to be completed and binary

An IoT Development Kit for energy harvesting wireless sensors Each IoT Platform Development Kit incorporates ON Semiconductor’s battery-free wireless sensor tags, which use RFMicron’s Magnus S2 Sensor IC, and can perform temperature, moisture, pressure, or proximity sensing functions. The platform also features a UHF RFID reader module with 32 dBm power rating and an 860 MHz to 960 MHz frequency range. Localized data processing is performed by the ARM Cortex-A8 based AM335x SoC. The platform can transfer captured data either wirelessly (via WLAN, Zigbee, Z-Wave, UHF Gen 2, etc.) or using wireline infrastructure (via KNX, CAN, SPI, Ethernet. Etc.).

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Internet of Things

data needs to translate into physical action.

Simple on-off, or ‘bang-bang,’ control is easily realized through the application of semiconductor switches, but many industrial controls are more sophisticated and require proportional control or careful and often rapid positioning. This could mean anything from a fan to control the environment or cool a critical piece of equipment, through to a motor or servo to adjust a valve, or a sophisticated stepper motor to position a robot arm to complete a precision task.

Alongside the rapid development of the IIoT, these actuators and their controllers are seeing similar advancements. The discrete solutions to motor control are fast disappearing and new, advanced integrated power modules are taking their place.

Offering complete systems—including power stages, drivers, protection and control

are based on new embedded solutions, allowing rapid adoption.

Open-source support is also important, since a broad ecosystem and interoperability are crucial for the IoT’s success.

Take the all-important vision capability as an example. From a hardware perspective, this requires video processing skills to implement; not to mention the image-processing software to do something useful with the data stream. To accelerate design cycles in this area, ON Semiconductor is sharing its knowledge with customer engineers through tools such as the MatrixCamTM video development kit (VDK). Similarly, the recently introduced IoT Development Kit for EH wireless sensors allows the movement of sensor data to the cloud for applications development. These are a few of the many examples where companies are sharing their specialist knowledge through development kits or reference designs, which is contributing significantly to the rapid growth of the IIoT. The true autonomy of factories and manufacturing processes is getting ever nearer. The ability to remotely identify, monitor and control every individual device on a network offers

The IIoT uses sensors and actuators embedded in equipment and objects, linked via wired or wireless networks, to improve productivity and adjust to varying conditions in real-time. logic in a single module—these new, highly integrated solutions are lighter, smaller, cheaper and easier to implement.

The breadth of sensing technologies needed for an integrated manufacturing process brings its own challenges. The wide-ranging expertise needed to build a smart factory full of sensors and actuators is beyond the breadth of many engineers, and this is where we see leading component suppliers providing value.

Fully integrated hardware and software development environments are crucial to facilitating the customization of specific functions for adoption into end products.

Modularity makes these platforms extensible to new IoT/IIoT functions and devices that

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unparalleled opportunities—especially in industrial applications.

Critical to the IIoT’s success is the effective meshing and connection of sensing, computing and control technologies in a resilient and energy-efficient manner.

The rapid development and innovation occurring in the IIoT arena is driving clear business benefits, in terms of management efficiency, reduced operating costs and more resilient, selflearning, processes. Leading semiconductor companies are at the forefront in enabling this IIoT future, with their wide breadth of capabilities and technologies.

It won’t be long before smart factories are sending messages to mobile devices the world over saying ‘There was a problem, it’s fixed and everything is back on schedule.’ Welcome to the brave new world of the Industrial Internet of Things!

ON Semiconductor onsemi.com

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POWERING THE CLOUD

Powering the cloud for an energy efficient Internet of Everything MARK ADAMS CUI INC.

As the Internet-of-Everything puts pressure on Cloud services, software-optimized power conversion will help save energy and let operators keep on top of costs.

THE INTERNET

of Everything (IoE) will capture data continuously from activities such as retail, transportation, infrastructure management, manufacturing, mining, food production, security and many others. Figures from Cisco say a large retail store collects about 10 GB of data per hour and transmits 1 GB of that to a data center. An automated manufacturing site can generate about 1 TB of data per hour, of which about 5 GB may be stored, and a large mining operation can generate up to 144 TB per hour.

Cloud services will hold the key to transforming the data collected into useful information. But they will face enormous pressure to keep up with the explosion in data received from more and more IoE applications.

Energy is the most significant resource consumed by the data centers residing at the heart of today’s Cloud services. Over a typical lifetime of three years, the cost to power a server actually exceeds the equipment purchase price. It is also costly to run the cooling systems that maintain safe equipment-operating temperatures. Data center operators must minimize these expenses. The need to minimize

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these costs is driving major industry trends, such as siting new data centers in cooler climates and placing locations close to plentiful renewable sources of energy such as hydroelectric plants. Current favored locations include the U.S. Pacific Northwest and Scandinavia. Operators are also trying to establish higher maximum equipment operating temperatures to save on cooling costs.

However, operators also recognize the importance of treating the cause of heat generation: poor energy efficiency of data center equipment.

There is now a push to maximize energy efficiency of servers, power supplies and system-management software. It is worth noting, however, that peak power consumption is rising to meet demands for more computing capability. Typical server board consumption has risen from a few hundred watts to 2 or 3 kW today, and could reach 5 kW or more in the future. As a result, there is a growing difference between the server’s minimum power at light load and maximum full-load power. Power distribution architectures are becoming more flexible, with real-time adaptive capabilities, to maintain optimal efficiency under all operating conditions.

A typical distributed power architecture comprises a front-end ac-todc converter that delivers a 48-Vdc input to an Intermediate Bus Converter (IBC). The IBC provides a 12-V intermediate bus that supplies low-voltage, dc-to-dc point-of-load (POL) converters positioned close to major powerconsuming components on the board, such as processors, system-on-chips or FPGAs. Multiple POLs may be used to supply core, I/O and any other voltage domains.

The 48-Vdc front-end output and 12-V intermediate bus voltage have been chosen to minimize down-conversion losses and losses proportional to current and distance when supplying typical server boards. But these

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fixed voltages are less suited to maintaining optimal efficiency given the changes in core voltage, current draw, maximum power and difference between full-load and no-load power. The ability to set different voltages, and change these dynamically in real-time, lets the system adapt continuously to optimize efficiency.

TAKE CONTROL WITH PMBus The PMBus protocol provides an industrystandard framework for communicating with connected, digitally-controllable-power, front-end, intermediate, and point-of-load converters. A host controller can monitor the status of the converters and can send commands to optimize input and output voltages. This controller can also manage other aspects such as enable/disable, voltage margining, fault management, sequencing, rampup and tracking.

As system designers develop more effective ways to exploit the control PMBus brings, power architectures are becoming software defined and respond in real time to optimize efficiency. Some of today’s most powerful techniques for optimizing efficiency include Dynamic Bus Voltage (DBV) optimization, Adaptive Voltage Scaling (AVS), and multicore activation on demand.

DBV offers a means of adjusting the intermediate bus voltage dynamically to suit prevailing load conditions. At higher levels of server-power demand, PMBus instructions can command a higher output voltage from the IBC to reduce the output current and minimize distribution losses.

AVS is a technique used by high-performance microprocessors to optimize both the supply voltage and clock frequency. The point is to ensure processing demands are always satisfied with the lowest possible power consumption.

AVS also enables automatic compensation for the effects of silicon process variations and changes in operating temperature. To support AVS, the PMBus specification has recently been revised to define the AVSBus, which allows a POL converter to respond to AVS requests from an attached processor.

Multicore activation-on-demand provides a means of activating or powering-down individual cores of a multicore processor in response to changes in load. Clearly, de-activating unused cores at times of low processing load can help gain valuable energy savings.

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The traditional fixed distributed power architecture. This IBC and POL structure is optimized for earlier generations of servers.

Modern PMBus-compatible converters allow digital optimization on the fly to optimize energy efficiency.

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Internet of Things

These are the first adaptive features to be implemented as power supply developers begin introducing software-defined power architectures. Many additional, powerful techniques are expected to emerge, assisted by the arrival of PMBus-compatible, front-end, ac-to-dc power supplies, such as the CUI PSE-3000 and PSA-1100 and Novum digital IBCs and non-isolated dc-to-dc digital POLs. The continuous optimization of power-conversion architectures and bus voltages will yield better converters. Consider a power supply comprising a frontend ac-to-dc converter with average efficiency of 95%, an IBC operating at 93% and a POL operating at 88%. Here, an improvement of just 1% in each stage can reduce the power dissipated from 22.2% of the input power to 19.6%. This not only represents a 12% reduction in power losses, but also relieves the load on the datacenter cooling system to deliver extra energy savings.

Underlying all these developments is an understanding of how data centers use power. Software can then intelligently provision and manage that power to realize significant energy savings. Such virtualization of the power infrastructure makes power an elastic resource and can improve usage by up to 50% within the existing power footprint. This not only means more efficient power consumption, but also avoids the capital expense of bringing additional, and unnecessary, resources into play. Virtual Power Systems, a company that is championing a concept called Software Defined Power, has recently partnered with CUI to extend its software into the hardware domain with an Intelligent Control of Energy (ICE) Block. The ICE will help manage power sources within data centers and similar ecosystems.

All in all, the Internet of Everything will feed huge quantities of data into the Cloud. This data must be processed quickly and stored for later reference. At the board level, energy lost during power conversion can be reduced by adjusting bus voltages as load conditions change. PMBuscompatible converters and real-time software-based control help stem these losses. At the system-level, optimized hardware and software will greatly improve power usage in data centers as capacity demands rise.

CUI Inc. cui.com

Architectures employing software defined power have several advantages for powering data centers.

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AUTOMOTIVE CONNECTIVITY

Embedded software enables IoT in today’s vehicles IoT connectivity for next-generation automotive apps is made possible by new software devised with audio and video networking in mind.

ANIL KHANNA, ANDREW PATTERSON MENTOR AUTOMOTIVE

THERE’S

a shifting landscape when it comes to today’s modern automobiles.

Automakers face the challenge of satisfying consumer expectations for rich, multimedia experiences within the vehicle cabin. Meanwhile, security has become a big issue as regulations and requirements have begun to apply to external vehicle connectivity.

Today, the electronification of the car generates large amounts of data within and across the vehicle. This data must be integrated, processed and presented, usually in real time, in a format the occupants of the vehicle find actionable.

And finally, cost issues are as critical as ever. The challenge is to innovate while keeping research and development costs down. Embedded systems have taken a lead role in meeting such challenges.

There has been a steady progression in vehicle electronics, from simple electronic control units (ECUs) that had no need for an embedded operating system (OS), to today’s complex multi-function ECUs, which

can require multiple operating systems. Once upon a time, an embedded operating system was treated as a separate isolated entity, but for performance and security reasons, this can no longer be the case. The operating system directly influences safety, security and connectivity to devices both inside the vehicle and to the roadside infrastructure, cloud or other vehicles externally.

Connectivity inside the vehicle encompasses communications across a wide range of physical networks. Traditional in-vehicle networking technologies such as CAN, FlexRay, MOST, and LIN are being supplemented by more capable technologies such as Ethernet, Ethernet Audio Video Bridging (eAVB), Automotive Audio Bus (A2B), and wireless solutions. The design of individual networks is typically driven by application needs, with gateway ECUs connecting different vehicle domains. The combination of powerful SoCs and software systems let automakers consider new consolidated system

A high-level view of the Connected OS software platform and development tools.

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architectures. One example of this would be a joint in-vehicle infotainment (IVI) and driver information cockpit that not only displays infotainment choices, but also overlays vehicle operation data such as speed, engine status, safety pointers, and data from lane departure warning systems (LDWS) and other kinds of warning systems.

ECU AND MODULE CONSOLIDATION Luxury vehicles now carry more than 100 ECUs. And ECUs are migrating from 8- and 16-bit microcontrollers to 32-bit microprocessor-based system SoCs and into multicore architectures.

As vehicles sport more electronic features, it becomes obvious that electronic functions need to consolidate into modules. Therein a number of issues arise. The vehicle wiring harness grows more complex and heavier. The growing number of ECUs in the vehicle also forces designers into more standardization.

There’s a challenge to reengineer the software and perhaps re-architect the system to move or consolidate functions among modules.

Partnership efforts, such as the Automotive Open System Architecture (Autosar), have done The simplified network an exceptional job of creating schematic for a typical eAVB and establishing open standards network includes IVI and ADAS applications. for various automotive software architectures to address these types of issues. Automotive OEMs, electrical suppliers, chip manufacturers and software companies are all Autosar members.

A modern infotainment system is the cockpit through which the driver and passenger command and control data across the vehicle. The infotainment system must connect with the vehicle network to collect data from multiple ECUs and report on its own status. Externally, there are connections to smart devices, increasingly fulfilled through apps and technologies for car-smartphone connectivity, such as Apple CarPlay, Google Android Auto and MirrorLink.

With the advent of autonomous cars, the infotainment functions must also connect to other vehicles and the outside world. It is not surprising that the OS of the infotainment system, typically in the head-unit, has become the proverbial brain of the car.

Consequently, the embedded system forming the cockpit is a crucial piece of technology. One concept addressing this role is called Connected OS. Devised by Mentor Graphics, it covers the integration and connectivity needed for next-generation in-car experiences.

The Connected OS software employs a modular, GENIVI-based Linux platform with a board support package (SuperBSP) and a middleware layer (OPTstack). Out of the box, the software platform delivers technologies such as fast boot, instant-on, and optimized audio/video—all necessary for cuttingedge automotive applications. One example of its use might be enabling a rapid system start-up along with quick turn-on of audio and video, necessary for infotainment systems with back-up cameras.

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Allegro’s solutions are extremely robust, handling wide ambient temperature ranges and input/output operating conditions. Design focus is applied to fault mode survival and recovery. It produces industry-leading packaging for enhanced thermal performance.

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Internet of Things

A mixed-criticality instrument cluster of electronics allows certified safety-critical graphics indicators to combine with 3D graphics on a single display. The safety-critical graphics operate in a secure hardware zone and run on a stand-alone safety-certified Nucleus SafetyCert RTOS.

In addition, Connected OS offers middleware support for emerging networking technologies such as eAVB and A2B. The preintegration of the eAVB software stack is especially helpful in developing applications that require low-latency, real-time communications, such as those found in Advanced Driver Assistance Systems (ADAS).

Beyond this, the combination of support for protocols, such as eAVB with video processing expertise, allows Connected OS based systems to deliver features

such as Rear Seat Entertainment (RSE). The eAVB stack in Connected OS is developed to IEEE AVB standards and complies with AVnu Alliance deterministic networking standards.

Supported IEEE implementations include IEEE 802.1AS (to ensure synchronization for time-sensitive applications such as audio and video); 802.1Qat (protocols, procedures and managed objects that let network resources be reserved for specific traffic streams traversing a bridged LAN); 802.1Qav (lets bridges provide guarantees for time-sensitive, loss-sensitive, real-time AV data transmission); 1722.1 (provides the audio video discovery, enumeration, connection management, and control protocol for AVB devices); and 1733 (protocol, data encapsulations, connection management, and presentation time procedures for interoperability between AV-based stations). Similarly, support for the A2B software stack in Connected OS lets automakers develop less expensive audio networks that still deliver exceptional in-car audio.

Safety and security are always high on car maker priority lists. With the advent of autonomous vehicles, more wireless “attack surfaces” have appeared for hackers to exploit. Security must be at every level of the vehicle architecture, from the hardware to embedded software, through to applications and even human factors. Embedded software protection techniques—such as key-exchange protocols, public-key cryptography, data hashing algorithms, and symmetric-key encryption—can help improve vehicle data security. Further, ECUs that must communicate with each other can exchange manufacturer-assigned security keys before proceeding with any data exchange, so only necessary and authorized data transmits.

Security must be at every level of the vehicle architecture, from the hardware to embedded software, through to applications and even human factors.

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Internet of Things

On the safety side, minimization of software defects is a must. Strategies for exhaustive testing of safety-critical software continue to evolve. Through careful partitioning, safety-critical elements can be isolated and certified separately from more complex systems that are harder to fully validate line by line.

Mentor Graphics has developed a mixed-criticality instrument cluster solution that combines certified safety-critical graphics indicators with rich 3D graphics on a single display. The safety-critical graphics operate in a secure hardware zone. They run on a stand-alone safety-certified Nucleus SafetyCert RTOS for security from external interference and denial-of-service attacks.

The Connected OS concept covers more than just a foundation Linux operating system. New multicore architectures allow for the hosting of multiple operating systems with tight communication among them.

These include, for example, the Autosar

basic software (BSW) operating system, real-time operating systems such as Nucleus RTOS, and even links to Android running natively or in a Linux container (LXC).

Once multiple operating systems are used, secure communication is enabled using protocols such as Remote Processor Messaging (RPMsg) and VirtIO, so information generated in one domain can transmit to another. An example could be a phone status message, needed on the secure driver information cluster display. Separating domains on multicore frameworks, or through use of embedded hypervisors, helps manage security and separation while simultaneously optimizing performance.

Mentor Graphics mentor.com/solutions/automotive Autosar autosar.org

Multi-domain communication in a vehicular automotive system might use RPMsg and VirtIO protocols to handle infotainment, instrument cluster and engine control tasks. The software standards involved might map out in a way similar to that depicted here.

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BENEFITS OF THE IoT

The benefits of the Industrial Internet of Things The Industrial Internet of Things will improve the end user experience and create new OEM revenue streams.

ALICIA BOWERS

PRODUCT MANAGER, AUTOMATION SOFTWARE, GE DIGITAL

THOUGHT

leaders around the world say the most valued companies will be those that blend digital capabilities and industrial assets. Digital capabilities are required to drive productivity and efficiency to new levels across an organization or environment.

Through the IIoT, automation apps will guide predictive maintenance so equipment never goes offline. And equipment and systems will seamlessly adjust to market demands.

There will be a “digital twin,” a digital model, or twin, of every machine – from a jet engine to a locomotive. Data from these digital twins can be analyzed to help create and grow new business and service models.

In addition, this digital industrial era will bring OEMs and customers closer together.

OEMs will use data and analytics across the complete product life cycle. Such a digital thread will enable better initial design, smoother operation, and efficient maintenance in a closed loop.

FILTERING AND SERVING INFORMATION Today, fourth-generation automation software is helping proactive OEMs make use of the Industrial Internet. Real-time data and advanced analytics algorithms are helping them take advantage of new business opportunities.

Prior to the IIoT, machine data pretty much just went to a screen for an operator to take an action. The operator might see a list of alarms, for example, and react to them. The goal now is to shift from this kind of reactionary response and move to predictive responses. The result will be better

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Operators can be in a noisy factory and use the geointelligence and navigation to have the right information at their fingertips based on their specific location.

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Internet of Things

Fourth-generation automation software helps design engineers leverage the Industrial Internet, combining real-time data, advanced algorithms for analytics, data models, and cloud technologies to help users connect, analyze and optimize.

machine performance, less equipment downtime and fewer inefficiencies.

Innovative software apps and mobile technologies help OEMs drive real-time operational intelligence where the right operator can receive the right information at the right time and place. It sounds idealistic – yet, it is happening with today’s mobile devices and software apps. The same way these devices and apps have changed our personal lives, they will interact with the IIoT and change our industrial world.

Technology allows us to be smarter about how we filter and serve information.

Data can be driven to the device and to the appropriate operator. In some cases, this data could consist of the display tags associated with a particular piece of equipment, or an electric power demand or temperature.

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OEMs can create new revenue streams with this real-time end-user data. As an example, a refrigeration OEM offers new services to improve customer experience. In this OEM’s industry uptime can be critical.

Recently, this international OEM turned to GE automation apps to reduce its high warranty costs and provide a way to warn customers of predictable failure. The new system runs diagnostics against real-time performance data from its machines installed at remote end user facilities.

With predictive capabilities, the OEM can respond quickly and send parts, as well as provide timely, critical remote support. These actions cut the costs associated with on-site engineering visits. Asset availability has improved along with end user uptime. In addition to 24/7 monitoring and predicting failures, the GE software provides insight into how to improve system performance – which this OEM has turned into a new revenue stream. Armed with real-time process intelligence, the OEM helps end users reduce energy consumption and minimize water use. This is just one way that OEMs can embrace the IIoT to grow their businesses.

GEO-INTELLIGENCE TECHNOLOGY Additionally, engineers can make use of end-user geographical information to inform the right user at the right location.

Geo-intelligence technology takes data, puts context to assets, and then applies a geo-location to that asset. Thus, an OEM can automatically serve the right information quickly on the mobile device closest to the equipment.

For example, the geo-intelligent mobile device knows that the equipment is Pump 2 in the South River Pump Station. The appropriate screen instantly displays the data, such as KPIs. In addition, the mobile device can make use of an adjustable radius – or field of view, and can display all of the pumps located within three miles.

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BENEFITS OF THE IoT

In a manufacturing environment, geo signals are even more accurate thanks to Wi-Fi technology. Operators can be in a noisy factory and use the geo-intelligence and navigation to obtain the right information at their fingertips based on their location. This technology can speed response and reduce troubleshooting time.

The benefits of geo-intelligence multiply when applied to alarms and analytics. For example, OEMs can send an alarm to an operator, engineer or manager based on physical location. As an example, an engineer standing on Floor 4 might hear an alarm related to a machine on Floor 1, which is 25 minutes away. The geo-intelligent system determines that a colleague is 100 feet away from the machine and sends the signal there for a faster response.

Engineers can also deploy IIoT technology to filter alarms for more efficiency. According to analysts, 75% of all alarms are noise, and many companies wish to reduce that number. OEMs can deploy a system that captures all the raw alarms and sorts them based on analytics. The system

delivers the right alarm, perhaps even a derived or intelligent alarm, to the operator interface – whether stationary or mobile – rather than delivering several warnings that end up just being confusing.

PREDICTIVE KNOWLEDGE AND ACTION With an IIoT foundation, design engineers can add a layer of proactive analysis for predictive intelligent alarming. For example, if a machine monitors a temperature which exceeds the upper control limit, an alarm activates. Traditionally, an operator would react to the alarm. Analytics make it possible to predict when the event will happen and to take steps in advance of it. Either the OEM can supply analyzed information as a value-added service or it can be a feature of the equipment.

As an example, software on food processing equipment can monitor a temperature, run an analytic on it and predict temperature scenarios based on a statistical model. The OEM can design equipment that sends operator alarms to ensure action takes place quickly, before a batch is ruined. The same could be true for an OEM running a remote monitoring and predictive service for customers related to critical end user operations.

The application of predictive knowledge, delivered as an intelligent alarm in a geo-aware context is far reaching. It offers new ways of consistently optimizing operations – a high value that design engineers can provide to their end users.

OEMs can take the mass of raw machine data, turn it into better information, and deliver the key performance indicators that make sense for an asset– such as electric demand or temperature.

Steps to take in the Digital Era The Industrial Internet of Things has many implications. OEMs can consider several steps to get the true value of the IIoT and maximize the benefits of these new technologies.

GIVE STRUCTURE TO DATA There is no shortage of data, and it is largely unstructured. The first step is to map data to a structured model. This helps capture data and begins the process of transforming it. An equipment model drives structured navigation. From this structure, users easily configure data to the level of entry that makes sense.

DELIVER CONTEXT Once the data is in a navigable structure, users can apply analytics that create a context for action. A perfect example is the application of analytics to alarms. Most industrial assets have a variety of alarms – and usually an overwhelming amount on a daily basis. With an advanced alarming platform, OEMs can apply analytics to the alarms, take away the noise, and deliver relevant alarms to the appropriate person by role and location. The costand time savings can deliver an estimated 20% or more boost in operator efficiency.

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MAKE TRANSITIONS SEAMLESS TO DRIVE ACTION Once there is a navigable structure and analytics for context, the next step is to drive the appropriate action. OEMs can deliver seamless transitions on any device, getting the user the right information quickly, at the right place and time. For example, if an operator receives a critical alarm, a mobile device can immediately show the right information to guide the user through the appropriate response steps.

LEVERAGE SECURE-BY-DESIGN METHODOLOGIES Finally, OEMs and end users must implement IIoT technology using secure-by-design methodologies. The confidentiality, integrity and availability of systems and data are critical. In our IIoT world, OEM must consider how to deliver information in a staged fashion, how to limit control, and how to expose data for accessing information, anytime, anywhere for secure agility.

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Internet of Things

Design and Test for the IoT Proliferating wireless standards put heavy demands on instrumentation.

Here are some factors to consider when gearing up for IoT development work.

YOU

can say one thing about the Internet of Things scenarios being trumpeted in the media. All the applications and services being envisioned rely on networks of sensors and actuators, often linked by radio. And because IoT applications are so diverse, no single radio technology can effectively address all the needs of this evolving industry.

The range of wireless technologies available for use in the IoT is diverse and growing more so every day. Currently, there are more than 60 legacy and new RF formats in use for M2M- and IoT–related applications. Near-field communication (NFC) will handle mobile payments; geosynchronous satellites will handle communication with unattended remote weather stations. Bluetooth, wireless LAN (WLAN), ZigBee, point-to-point radio, cellular, and other technologies will all have IoT roles.

An IoT network will need to cope with all kinds of special devices having different communication requirements. At one end will be simple wireless devices, such as battery-powered sensors and actuators that will transmit minimal data while operating unattended for several years. At the other end of the spectrum will be mission-critical services and devices that require constant, reliable and super-secure connections.

Key to uniquely identifying each device is a vast IP address space. One problem is the current Internet Protocol version 4 (IPv4) addressing space is too limited, so it requires the use of concentrators (for example, routers and gateways). The most recent version of the Internet Protocol is IPv6,

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MARTHA ZEMEDE

KEYSIGHT TECHNOLOGIES

which will be a key enabler for IoT devices. IPv6 uses a 128-bit address, theoretically allowing 2128, or about 3.4×1038 addresses. The total number of possible IPv6 addresses is more than 7.9×1028 times as many as with IPv4, which uses 32-bit addresses and provides approximately 4.3 billion addresses.

The two protocols are not designed to interoperate, but several IPv6 transition mechanisms have been devised to let IPv4 and IPv6 hosts talk to each other.

IPv6 provides other technical benefits in addition to a larger addressing space. Device mobility, security and configuration aspects have been considered in the design of the protocol.

Server/cloud-based big-data analytics and machine learning play a role in the majority of IoT business models. IoT devices at the end nodes connect to the cloud or server for intelligence and analytics. Some connect directly, but often with gateways.

Gateways aggregate traffic from less trafficked networks onto higher capacity LANs and WANs. They typically include greater power supply and computing resources than end-nodes (things). Edge or fog applications running in gateways offload processing from both cloud and end-node sensors and actuators. End-nodes are often designed to have a long battery life, necessitating the efficient use of embedded computers and radio transmission.

Intelligent threshold triggers in gateway applications make traffic more efficient by passing actionable information to central cloud servers.

Gateways interface with the cloud and endnodes through a heterogeneous mix of wireless

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II o T

MQTT Protocol

Maple Systems HMIs: Your Edge Gateway

Information is Power.

Maple Systems edge gateway HMIs utilize MQTT protocol to unlock valuable data from existing machine processes. Monetize that data today using the power of information technology.

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Internet of Things

There are many pathways and gateways available for access to the cloud.

technologies, both cellular and non-cellular. Radio interfaces address varying application needs depending on coverage, latency, throughput, energy efficiency and cost.

As an example, some homeautomation applications use smartphones as a gateway. The wide availability of WiFi makes it the first choice for many IoT applications. When WiFi links are unavailable, cellular protocols are frequently substituted. In wearable applications, Bluetooth is often the choice. NFC is the natural choice when security is aided by proximity.

ZigBee, Z-Wave and Thread offer robust, low-power mesh networks for home automation and smart energy devices.

It is useful to consider IoT technologies grouped by operating

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range. NFC is a short-range system based on ISO 14443 at 13.56MHz. Perhaps best known for use in mobile payment systems, NFC devices can behave as terminals, also called proximity-coupling devices (PCD) or readers. They may also behave as cards, also known as proximity inductive-coupling cards (PICCs) or tags; cards are often powered by the RF field generated by the terminal.

In the IoT space, Bluetooth low energy (BLE) is getting a lot of interest. Designed for lower data throughput, it consumes significantly less power than Bluetooth devices and operates for years using coin-cell batteries.

It supports simplified models for device discovery, service discovery and data exchange in ways that use little airtime and consume little

power. This lets BLE serve in small devices such as watches, health monitors and battery-powered appliances.

A number of short-range wireless technologies use a standard called IEEE 802.15.4 as the physical (PHY) and media access control (MAC) layers. For protocols that include ZigBee, Thread, WirelessHART and ISA100.11a, the developer of the higher layers specifies the higherlevel protocol appropriate for the target application. This low-rate wireless personal area network (LRWPAN) supports rates that range from 20 to 250 kbps. It is designed for home networking, industrial control and building automation, all of which need low data rates, low complexity and, in many cases, long battery life.

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DESIGN & TEST

ZigBee devices can connect, exchange information and disconnect quickly before returning to sleep mode.

One key attribute is the use of a mesh network topology that can include thousands of nodes. ZigBee radios operate with low duty cycles, so their applications run for years on inexpensive batteries. Target applications include smart energy, home automation, healthcare, retail and lighting control, each of which has a specific ZigBee profile and certification.

Thread technology is similar to ZigBee in that it is based on the IEEE 802.15.4 PHY and MAC, but it uses the IPv6 over low-power wireless personal

area network (6LoWPAN) protocol. It’s an encrypted mesh network designed to connect hundreds of home-automation products and devices.

The network is self-healing and is configured such that there is no single point of failure. Its short messaging conserves bandwidth and power, while a streamlined routing protocol reduces network overhead and latency.

WiFi is the most widely used wireless Internet connectivity technology, with 802.11a/b/g/n being most common. Two recent amendments to the PHY layer address the need for high throughput data rates: 802.11ac, which operates below 6 GHz and is becoming the standard in mobile phones, tablets and PCs; and 802.11ad, which operates in the 60-GHz band. An upcoming version called 802.11ah (HaLow) is intended to support low energy for IoT applications. It uses low power and low data rates. It operates in the sub-gigahertz band and has a range of up to 1 km.

802.11p is also called wireless access in vehicular environments (WAVE). It was created specifically for applications such as telematics, roadside assistance, fleet management and young-driver insurance validation. In the future, 802.11p will also enable vehicle-to-vehicle

IoT technologies grouped by operating range. Many formats are available for short-range connections between devices and gateways, but standards are also quickly forming and evolving to support connections into the IoT ecosystem. To date, there are more than 60 legacy and new RF formats in use for M2M- and IoT–related applications. More popular formats include Bluetooth, WLAN and cellular, while others like ZigBee and Thread have emerged to fill a need in specific niche markets.

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Internet of Things

Keysight Advanced Design System (ADS) software (top) is used by more than two-thirds of the world’s RF/microwave design engineers and helps solve design challenges for IoT applications. Keysight 89600 VSA software (below) enables designers to see through the complexity of today’s most advanced signals, and can be configured to support most of the wireless formats used in IoT devices: 2G/3G/4G cellular formats, WLAN, ZigBee, Bluetooth and Wi-SUN signals.

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(V2V) and vehicle-to-infrastructure (V2I) connectivity for tasks such as vehicle safety, traffic management and toll collection.

The two main wireless neighborhood area network (WNAN) technologies are Wi-SUN (IEEE 802.15.4g) and ZigBeeNAN. The aim of this standard is to provide a framework that facilitates large-scale process-control applications. Wi-SUN provides a low-rate wireless network capable of supporting large, geographically dispersed smart utility networks with minimal network infrastructure, with potentially millions of fixed endpoints.

ZigBee-NAN is a recent extension that has an operating range to 1 km and supports end-to-end IPv6. The ZigBee Alliance is currently working with the Thread Group on interoperability for home and commercial sensor networks.

A lot of innovation is happening in low power wide area (LPWA) networks. For applications with low data rates and low duty cycles, LPWA extends battery life, reduces cost and improves link budgets compared with currently deployed cellular formats. LPWA systems, such as LoRa and SIGFOX, are being rolled out nationally in some countries using lightly licensed or unlicensed spectrum. Anticipating strong growth in low-power M2M applications, 3GPP radio access network (RAN) working groups are developing cellular protocols to support LPWA in licensed spectrum.

3GPP Release 12 (Rel-12) introduced a new low-complexity device category (Cat-0) for LTE machine-type-communication (MTC).

Cat-0 improves efficiency for low-data-rate applications as a stepping stone to more significant advances. The newest 3GPP Rel-13 includes enhanced-MTC (eMTC) Cat-M1; a 1.4-MHz bandwidth optimization of LTE. Also included is Extended Coverage General Packet Radio Service (EC-GPRS); using retransmission and other protocol updates to improve link budgets. Finally, it specifies a narrow band IoT (NB-IoT) also referred to as Cat-M2; this is a new radio format optimized for LPWA applications.

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DESIGN & TEST

Tasks associated with design and test become all the more difficult given the challenges of IoT work. For example, as the IoT becomes more pervasive, design engineers must work harder to maximize power efficiency, manage electrothermal effects, and deal with the more severe electromagnetic coupling that results when designs become more compact. Additional hurdles will include evaluation and selection of the best technology mix (GaAs, GaN, SiGe/Si/ SOI, CMOS), as well as integration of subsystems

The next step is to measure and analyze the design. When choosing a test instrument for this purpose, typical selection criteria includes performance specifications, measurement speed, physical footprint, configuration scalability and cost (upfront and ongoing). No single instrument setup will be best for all needs.

The IoT will rely on simple battery-powered sensors and actuators that transmit little data while providing years of unattended operation.

The design and development of such devices

All the applications and services being envisioned rely on networks of sensors and actuators, often linked by radio. And because IoT applications are so diverse, no single radio technology can effectively address all the needs of this evolving industry. and verification of performance relative to industry standards.

And as designs become more complex, circuit simulation becomes more difficult. The electronic design automation software for designing and simulating new IoT devices must handle the challenges inherent in new communication formats. The best approach is to simulate new devices early in the development process and give system architects and algorithm developers the freedom to innovate at the PHY layer of wireless communications systems.

Instrumentation supporting this approach should also include virtual measurement tools that can attach to nodes in the simulation to provide a view of expected performance.

EXPLORING SOLUTIONS FOR DESIGN AND TEST As the design moves from simulation to hardware, physical device modules can substitute into the simulation, with real measurements or hardware-in-the-loop replacing virtual tools. This practice allows developers to compare simulated and actual performance.

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employs tools that can measure battery drain during three main conditions: sleep mode, idle mode and transmit mode. For signal creation and analysis during the design phase, the preference is for benchtop instruments that are generalpurpose (for example, swept-tuned spectrum analysis and functions supporting signal analysis and troubleshooting).

Later in the product lifecycle, criteria like test speed, flexibility and footprint are more important. Here, modular and one-box testers are better candidates. Important software includes that which can synthesize and analyze custom and standard-compliant test signals for wireless communications formats, including cellular, IEEE 802.11 variants, Bluetooth, ZigBee and WiSUN. In the best of all worlds, the solutions for simulating, designing and testing IoT devices will be integrated to enable product feedback across the entire lifecycle.

All in all, design and test instrumentation will ensure IoT device designs will allow reliable connectivity.

Keysight Technologies keysight.com

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Internet of Things

Batteries for the Industrial Internet of Things Not all battery chemistries are the same when it comes to powering devices designed for the IIoT. A few guidelines help field cells able to handle rugged surroundings for long periods.

SOL JACOBS

THE DEVELOPMENT

of electricity in the late 1800s drove the first industrial revolution by providing abundant and inexpensive power to factories. This paradigm shift led to the development of innovative machines and processes that defined the modern workplace by boosting factory production.

Slightly more than a century later, a similar revolution is underway: the Industrial Internet of Things (IIoT). It is beginning to transform the modern workplace, bringing seamless connectivity to all types of industrial applications, including machine-to-machine (M2M) and system control and device automation (SCADA) technologies.

Unlike the past, however, the IIoT will not be bound by the limitations of the old-fashioned factory floor. A new generation of wireless technology extends the IIoT to places not currently served by the national power grid.

The main limitations of traditional electric power are proximity and expense, as it costs $100/ft or more to extend hard-wired ac power to any industrial setting. This cost becomes even more problematic with the logistical, regulatory, and permitting hurdles required to extend ac power to remote, inaccessible locations that are often environmentally sensitive as well.

The emerging IIoT will not be held back by the power grid. It will thrive on battery-operated sensors that extend Big Data analytics to all types of industries, including but not limited to transportation Some batteries are now formulated specifically to handle the high current pulses that infrastructure, energy production, characterize some IoT-communication modes. An example is the PulsesPlus battery, available TADIRAN BATTERIES

in sizes from ½ AA to DD and multi-cell packs. PulsesPlus batteries feature bobbin type construction, plus a unique hermetically sealed hybrid layer capacitor.

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Keep a Hand on Precise Frequency Control

CTS places the frequency-control products you need in the palm of your hand. Serving some of the most challenging military/defense and industrial applications with compelling SWaP-C benefits, CTS offers a wide range of high-performance frequency control solutions for demanding ultra-low-power, low-phase-noise, and low-g-sensitivity requirements in extremely compact configurations. In addition to CTS miniature low-power OCXOs, Frequency Control products include flexible and configurable RF modules to 2.5 GHz with industry-best jitter and phase-noise performance as well as Hi-Rel COTS clock oscillators with stable frequencies to 800 MHz, full military temperature range, and low-jitter options. CTS offers custom solutions and quick-turnaround prototypes to meet the most demanding frequency-control requirements: • Low-power, miniature OCXOs, 10 to 250 MHz with 10-ppb stability, -40 to +85°C • Low-power, miniature OCXOs with low-g sensitivity to 2E-10/g Gamma • Ruggedized TCXO modules to beyond 1 GHz • Ruggedized RF modules to beyond 2.5 GHz • Hi-Rel COTS crystal oscillators (XOs) from 16 kHz to 800 MHz, -55 to +125°C PMS 2925

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Internet of Things

The MITE WIS remote unit is a small, extremely low-power, wireless device for extended data acquisition and recording. The units can be configured for such sensors as strain gauges, resistive thermal devices (RTDs), pressure sensors, humidity sensors, accelerometers, and so forth. A unit transmits its data in realtime to the receiver or it can store its data in non-volatile memory. This data is later downloaded via RF to the receiver and application software for display and storage.

MITE WIS units monitor repaired concrete sections of a tunnel and determine when a failure occurs by sensing strain variations across the boundaries of the concrete patches. During monthly scheduled maintenance closures, the units download data wirelessly. They are powered by bobbin-type LiSOCl 2 batteries.

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environmental monitoring, manufacturing, distribution, healthcare, smart buildings and industrial automation.

Industrial applications will need power supplies that can perform reliably even in extreme environmental conditions. This is especially true for applications characterized by complex, multi-tiered interoperability to synchronize manufacturing, supply chain logistics and product marketing.

Generally speaking, the more remote the application, the more likely the need for an industrial-grade lithium battery. Inexpensive consumer-grade batteries could suffice in certain instances, especially for easily accessible devices that operate within a moderate temperature range. However, inexpensive consumer-grade batteries can also be highly misleading: The cost of labor to replace a consumer-grade battery typically far exceeds that of the battery itself.

For example, consider what it takes to replace batteries in a seismic monitoring system sitting on the ocean floor or in a stress sensor attached to a bridge abutment.

To judge whether a short-lived consumergrade battery is a worthy investment, you must calculate the lifetime cost of the power supply.

To be accurate, the calculation has to properly account for the cost of all labor and materials associated with future battery replacements.

PRIMARY LiSOCl2 BATTERIES PREDOMINATE Remote wireless sensors designed for long-term deployment are mainly powered by bobbintype lithium thionyl chloride (LiSOCl2) batteries.

These cells offer special performance attributes that are particularly well suited for devices that draw low average daily current.

Bobbin-type LiSOCl2 cells feature high energy density, high capacity and a wide temperature range. They also have a low annual self-discharge rate, with certain bobbin-type LiSOCl2 cells able to operate for up to 40 years.

These features have made bobbin-type LiSOCl2 batteries the preferred power source for virtually all meter transmitter units (MTUs) in AMI/AMR metering for water and gas utilities.

MTUs are often buried in underground pits and see extreme temperatures that far exceed the limitations of consumer grade batteries. Extended battery life is essential to AMI/AMR applications because any large-scale

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BATTERIES

system wide battery failure could create potential chaos by disrupting billing and customer service operations. To preempt this type of problem, utility companies specify bobbin-type LiSOCl2 batteries that have been field proven to operate maintenance-free for decades.

Bobbin-type LiSOCl2 batteries are also found in electronic toll tags, another early IIoT-related application.

These batteries were chosen because they can handle the severe temperature cycles that characterize car interiors.

Heat soak can hit 113° C (according to SAE) when parked, cooling down rapidly to room temperature. In cold weather, of course, the battery must handle cold soak and a rapid temperature rise.

This same battery technology is now being adapted to other transportation applications, such as MITE-WIS wireless data acquisition systems embedded within concrete repair patches in tunnels. These self-powered units monitor concrete sections to help detect problems in repairs that have been covered by a layer of high-temperature fireproofing.

Bobbin-type LiSOCl2 batteries are also powering wireless

sensors that monitor stress and vibration on critical bridge infrastructure. These battery-powered sensors often mount to the underside of bridge abutments, a location that cannot be accessed for routine maintenance without expensive scaffolding or safety harnesses. Batteries that can operate reliably for extended periods solve this problem.

The operating life of a bobbin-type LiSOCl2 cell can vary significantly depending on its annual energy usage and annual self-discharge rate. Most remote wireless devices use a low-power communication protocol to help extend battery life. In addition, these devices operate mainly in a “sleep” mode that draws little or no current, periodically querying for the presence of data and awakening only if certain pre-set data thresholds are exceeded. It is not uncommon for more energy to be lost through annual battery self-discharge than through actual battery use.

The way a battery is manufactured and the quality of its raw materials can significantly impact its annual self-discharge rate. For example, the highest quality bobbin-type LiSOCl2 cells feature a self-discharge rate as low as 0.7% annually.

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Part Number

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SRAM Max. Freq.

Z32F06410AES Z32F06410AKS Z32F12811ARS Z32F12811ATS Z32F38412ALS Z32F38412ATS

Cortex-M3 Cortex-M3 Cortex-M3 Cortex-M3 Cortex-M3 Cortex-M3

64KB 64KB 128KB 128KB 384KB 384KB

8KB 8KB 12KB 12KB 16KB 16KB

48MHz 48MHz 72MHz 72MHz 72MHz 72MHz

ADC Resolution 12-bit x 2-unit 12-bit x 2-unit 12-bit x 3-unit 12-bit x 3-unit 12-bit x 2-unit 12-bit x 2-unit

Key Features: • High Performance Low-power Cortex-M3 Core • 64KB, 128KB, or 384KB Code Flash • Memory with Cache function • 8KB, 12KB, or 24KB SRAM • 3-Phase PWM with ADC triggering function (1-2 Channels) • 1.5Msps high-speed ADC with sequential conversion function • Watchdog Timer • External communication ports • Six General Purpose Timers • Industrial grade operating temperature (-40 ~ +85°C) Typical Applications: • BLDC/PMSM Motors • Outdoor Air Conditioners • Washing Machines • Refrigerators Design With Freedom

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Speed 1.5MS/s 1.5MS/s 1.5MS/s 1.5MS/s 1.5MS/s 1.5MS/s

TIMERS

UART SPI

6-16bit 6-16bit 6-16bit 6-16bit 10-16bit + FRT 10-16bit + FRT

2 2 2 4 4 4

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48 LQFP 32 LQFP 64 LQFP 80 LQFP 100 LQFP 80 LQFP

ZNEO32! Evaluation Kits Z32F0640100KITG

ZNEO32! 64K Evaluation Kit

Z32F1280100KITG

ZNEO32! 128K Evaluation Kit

Z32F06410AxS Block Diagram JTAG/SWD

POR

Cortex-M3

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For more information about the ZNEO32! Series, Evaluation Kits, or to download product collateral and software, please visit www.zilog.com.

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Internet of Things

Resensys SenSpot wireless sensors contain bobbin-type LiSOCl2 batteries that let them monitor stress and vibration on critical bridge infrastructure. These battery-powered sensors often mount to the underside of bridge abutments, a spot that cannot be accessed for routine maintenance without expensive scaffolding or safety harnesses.

This means they retain nearly 70% of their original capacity after 40 years. By contrast, a lesser quality bobbin-type LiSOCl2 cell can have a self-discharge rate of up to 3% per year. So nearly 30% of available capacity is lost every 10 years from annual self-discharge.

HIGH PULSE REQUIREMENTS Standard bobbin-type LiSOCl2 cells are not designed to deliver high pulses.

This challenge can be overcome by combining a standard bobbin-type LiSOCl2 cell with a patented hybrid layer capacitor (HLC). The standard LiSOCl2 cell delivers the low background current needed to power the device during sleep mode. The HLC works like a rechargeable battery to store and deliver the high pulses needed to initiate data interrogation and transmission.

Alternatively, supercapacitors can be used to store high pulse energy in an electrostatic field. While in wide use for consumer products, supercapacitors are generally not recommended for industrial applications because of inherent limitations, such as the ability to provide only short-duration power linear discharge qualities that do not allow for use of all the available energy, low capacity, low energy density, and high annual selfdischarge rates (up to 60% per year). Supercapacitors linked in series also require the use of cell-balancing circuits.

Bobbin-type LiSOCl2 batteries can handle the vast majority of long-life remote wireless applications. But there will be a growing number of IIoT-related applications that are well suited to be powered by energy harvesting devices, with Lithium-Ion (Li-Ion) rechargeable batteries used to store the harvested energy.

Several considerations go into the decision to deploy energy harvesting. Factors include the reliability of the device and its energy source; the expected operating life of the device; environmental parameters; size and weight restrictions; and the total cost of ownership.

Consumer-grade Li-Ion cells are candidates where the device is easily accessible and only operates for five years and 500 recharge cycles or less. They are also possibilities when the temperature range is a moderate 0 to 40° C. However, if the wireless device is slated for a remote site and could be exposed to extreme temperatures, the better choice is probably an industrial grade Li-Ion battery. These devices can operate up to 20 years and

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BATTERIES

The Hexagram STAR system provides fully automatic meter reading via narrow-band meter transmitters. The STAR MTU mounts near the utility meter and several times each day broadcasts the meter reading and account information by way of a brief radio transmission. They are powered by bobbintype LiSOCl 2 batteries.

give 5,000 full recharge cycles, with an expanded temperature range of -40 to 85° C. They also can deliver high pulses (5 A for a AA-size cell). Industrial grade Li-Ion cells are ruggedly constructed with a hermetic seal, which is superior to the crimped seals found on consumer-grade rechargeable batteries, which may leak.

A prime example of industrial grade rechargeable Li-ion batteries in an IIoT-themed application is the IPS solarpowered parking meter. This networked meter offers multiple payment options (credit/debit card, contactless payment, and so forth), access to real-time data, and a web-based management system.

It is also wireless, so it eliminates the overwhelming task of having to hard-wire millions of municipal parking meters.

IPS solar powered parking meters can connect with modules that read license plates and can notify authorities about outstanding parking violations.

Built-in photovoltaic panels harvest and store the solar energy, charging industrialgrade rechargeable Li-Ion batteries. The batteries deliver enough energy to handle the high-current pulses that arise during data communications. The batteries also provide up to 20 years of 24/7/365 system reliability.

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The next industrial revolution will be driven largely by electronic devices that are truly wireless, with bobbin-type LiSOCl2 cells and industrial grade Li-Ion rechargeable batteries combining to support technology convergence and interoperability. These long-term power supply solutions will power IIoT-connected wireless devices reliably and maintenance-free for decades.

REFERENCES Tadiran Batteries, tadiranbat.com

Li-ion batteries in IPS smart parking meters deliver enough energy to handle the high pulses that arise during data communications. The batteries also work for up to 20 years.

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Internet of Things

HMIs double as edge-ofnetwork gateways MICHAEL SHELDON, RYAN KELLEY MAPLE SYSTEMS

Industrial networks can use operator terminals to handle communications and analytical duties associated with IoT tasks.

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THE INDUSTRIAL

Internet of Things (IIoT) is here and it’s changing the way manufacturers think about business. These days, everyone wants to be wirelessly connected, synced up and remoted in. You name it, we want to be connected to it. Within the manufacturing industry, there is great need and desire for more connectivity and access to valuable data from our factory machines.

Enter the edge gateway. The edge gateway unlocks valuable data created by existing assets. In the manufacturing sphere, edge gateway devices translate data used by control applications into an IIoT-friendly format, sending that data to the Internet for use by IIoT applications.

An edge gateway device must fulfill key requirements. It must be efficient, reliable and scalable. It must also be easy to configure and put into service. Above all, it must be secure. And it must communicate with a wide range of existing equipment and support emerging IIoT protocols such as MQTT (formerly MQ Telemetry Transport). This is an ISO standard (ISO/IEC PRF 20922) publishsubscribe-based “lightweight” messaging protocol for use on top of the TCP/IP protocol. It is designed for connections with remote locations requiring a small code footprint or a limited network bandwidth.

An HMI is a good candidate for serving as the edge gateway device. The HMI, the operator’s window into the machine, can easily be extended to play the same role in the virtual world. The HMI is the place where data is aggregated, filtered and presented to the operator. This same data can easily be presented to users connecting to the HMI through the IIoT. A primary requirement of the IIoT is interoperability. The dizzying array of communication protocols used in manufacturing today present a significant barrier. Fortunately, modern HMIs possess extensive libraries of industrial protocol drivers operating on different

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Internet of Things

An HMI terminal can serve as an edge gateway device for IoT installations. A block diagram of the HMI internals shows the system facilities used for functions such as security and device communications.

periodically, lowering the bandwidth needed for connection. network architectures. This makes A fundamental advantage of MQTT is that data goes to a central HMIs a means of handling the “broker” instead of directly transmitting to multiple clients such as remote interoperability challenge.

For example, the Maple Systems interfaces or management software. The MQTT broker is responsible for maintaining client connections and sending/receiving messages. Client HMI5097DXL HMI has three serial devices, edge gateways and IT applications (or publishers/subscribers ports and a CANbus port that can in MQTT language) are freed up to focus on producing and consuming operate simultaneously using more data. This division of labor greatly enhances scalability. As overall system than 200 different protocols. In addition, two separate Ethernet ports sizes grow, the CPU resources and bandwidth requirements of the edge gateway remain static. let the device dedicate one port for In addition, MQTT is a lightweight protocol. A widely used real-time industrial control network broker implementation consumes only about 3 MB of RAM with 1,000 demands while the other is reserved for external network connections and connected clients. This small footprint means the HMI can be configured as both an edge gateway and an MQTT broker, reducing the need for IIoT functions. Distinctly separate additional hardware.

Ethernet ports offer added security, Applications engineers have a lot of flexibility using the data an because a direct network path does edge gateway produces as an MQTT publisher. Engineers can assign not exist to the machinery itself. topic names to the variables or tags they wish to publish to the broker.

Data transmitted by the Topics are the titles, or addresses, used to organize data in the MQTT edge gateway-enabled HMI must protocol. MQTT allows topics to be divided in intuitive ways. A single be presented to upstream IT data point can be assigned to multiple topics, and one topic can contain applications in a way that is flexible, more than one data point. An application can subscribe to all topics on modular and efficient. The MQTT a single HMI, creating an application monitoring one specific machine. feature available on all Maple Or, if a parameter (say temperature) exists on many machines, the Systems HMIs exemplifies the power programmer can use a topic “wild card” to instantly subscribe to the of this protocol. MQTT is organized same parameter across all machines. This enables efficient and easy into topics that can contain single detection of abnormal conditions across an entire installed base of data points or a group of related machines. data. The HMI can be configured The HMI configuration process is easy for the controls engineer to transmit data from a specific who incorporates an edge gateway into a system. The engineer simply topic whenever a change arises, or

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Internet of Things

creates an authenticated broker connection, then selects the tags to publish. The engineer organizes tags into topics, then downloads the project to the HMI. MQTT is one of many Internet-enabled applications available on edge gateway devices, such as Maple HMIs, which offer notable communication and data collection features. HMIs can send emails containing data log and alarm conditions. Remote access applications let remote users monitor and control machines through secure VPN connections. They also enable remote download of project updates. Database integration allows machines to log data directly to database servers over a LAN. Custom server/client applications enable smart device integration (Android/Apple tablets) for machine monitoring and control. Compared to machine replacement, edge gateway-enabled HMIs are an inexpensive option for upgrading machines or adding to new machine builds.

An example shows how the configuration process typically works. Company A wants to offer customers remote access/monitoring, remote email alarms, off-site troubleshooting and configuration, and similar benefits. Company A goes with the MQTT protocol for its extensive use and documentation with Facebook messenger, cloud compatibility, and ability to efficiently transport large amounts of data across networks. MQTT also allows Company A to seamlessly expand its Amazon Web Services without hogging all available network bandwidth at each manufacturing site.

The company’s engineering departments use many different PLC brands, all with different protocols. So Company A decides to use a Maple Systems HMI5097DXL dual-Ethernet HMI, which serves as both an HMI and edge gateway. The HMI5097DXL supports MQTT and more than 200 other control protocols, letting it link existing control networks with the IIoT. Dual-Ethernet ports separate networks inside the plant from external information technology networks. This secure connection allows for advanced remote access and monitoring, remote email alarms and remote programming. Dual-Ethernet HMIs let Company A maintain an isolated network dedicated to its machinery, yet still offer access to the internet through the customer’s ISP. The publish/subscribe architecture of the MQTT network means Company

In one example of using an HMI terminal for edge gateway duties, the HMI connects with an off-site server as a way to interact with distributed clients.

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EDGE GATEWAYS

An operator interface without a display can serve as an edge gateway. In this example, the operator interface is an RMI5010. It connects to PLCs on the factory floor using several different communication protocols. It uses MQTT to communicate with management offices.

A personnel won’t have to bug their customers’ IT departments.

Because their systems are widely distributed, Company A hosts its MQTT broker off-site. The new data stream connects to a web application that customers can log into for real-time machine monitoring.

Remote functions built into the HMI allow Company A to update projects (including off-site MQTT setup) to meet changing customer demands without spending time and money on-site. It can respond faster to software and data demands across deployed equipment without leaving company facilities. Company A uses predictive maintenance instead of preventative maintenance to cut costs, improve troubleshooting and reduce downtime. New machines added to the network send smart data back to the central database for analysis.

Consider another example involving a singlefacility food processing company. Like many in the manufacturing industry, Company B’s biggest obstacles for IIoT adoption are budget and interoperability. Its factory hosts both Ethernet and serial control networks unable to share data because of incompatible protocols. The company wants to centralize data without buying a new server and remote hosting. Key requirements for IIoT hardware include support for the MQTT protocol, legacy protocol support and secure remoting. Maple Systems RMI5010 fits Company B’s needs because there’s no

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requirement for new displays. This unit has a small form factor that fits into existing panels.

Company B creates both a project and the MQTT broker inside its new interfaces using EZware configuration software. They define MQTT topics to bring data from the plant floor to dashboards and analytic programs in use by management. Analytic software now offers a live data stream from operating machinery.

Remote access through the RMI5010 securely connects to laptops, Android phones or Android/ iPad tablets, giving floor managers mobile access.

The email alarm feature alerts staff immediately when something runs amiss regardless of location. DualEthernet ports plus serial communication offers a safe connection to office networks exposed to the Internet.

The centralizing of data from facility machinery boosts system awareness. Maintenance costs drop thanks to the linking of data about machine wear and usage. Software identifies data patterns that indicate likely failures, so machines can run to full life without risking costly shutdowns.

All in all, Company B can use detailed statistics on both its highest-profit machines and on its process bottlenecks. In the end, the HMI edge gateway performs local machine control while opening up new possibilities in the IIoT. Maple Systems maplesystems.com/IIoT

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Internet of Things

Harvesting power for IoT devices Sometimes energy for IoT devices comes from the likes of thermoelectric generators and thermopiles thanks to power management ICs that double as energy misers.

TONY ARMSTRONG LINEAR TECHNOLOGY

POWER

A Dephotex sample textile containing photovoltaics. The European group is devising photovoltaic textiles that can serve in applications such as curtains or coverings.

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consumption is a controversial topic when it comes to IoT devices. At the low end of the power spectrum are devices where most of the power consumed goes into the LDO (low dropout) regulator rather than into the functions of the device itself. Some wearable devices fall into this category. The power conversion ICs that handle these applications can be dealing with tens of microwatts and nanoamps of current.

The situation is similar for energy harvesting (EH) technologies as used to scavenge vibration energy and sunlight for wearables. Power levels can be on the order of milliwatts under typical operating conditions. It might seem as though it would be tough to operate for long with such low power levels. But harvesting elements, such as wireless sensor nodes (WSNs), can gather enough energy to provide power broadly comparable to what’s available from long-life primary batteries, in terms of both the amount of energy provided and the cost per energy unit provided.

Although primary batteries claim to have 10-year life spans, their useful life greatly depends on both the level of power extracted and the frequency with which it is withdrawn. Systems incorporating EH can typically recharge after depletion, something systems

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powered by primary batteries cannot do.

Nevertheless, most implementations will use ambient energy as the primary power source, supplemented by a primary battery that can be switched in if need be. This capability can give the system a long life—approaching that of the battery, usually about 12 years for lithium thionyl chloride chemistry.

Of course, the energy provided by the EH source depends on how long the source is in operation. Therefore, the primary metric for comparing scavenged sources is power density, not energy density. EH is generally subject to low, variable and unpredictable levels of available power. So most implementations use a hybrid structure that includes the harvester and a secondary power source.

The secondary source could be a rechargeable battery or a storage capacitor.

The harvester is the energy source while a battery or a capacitor supplies power when required, but otherwise charges up from the harvester. Thus the secondary energy reservoir supplies power when the ambient energy is not available for some reason.

One of the more interesting energy harvesting applications is wearable electronics. Although this area is sometimes equated with the FitBit, Google Glass and Apple Watch, wearable technology is not just for humans. Recent

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HARVESTING POWER

The LTC3107 dc-to-dc converter can extend the life of a primary battery by harvesting and managing surplus energy from extremely low input voltage sources, such as TEGs (thermoelectric generators) and thermopiles.

examples include ultrasound-delivering treatment patches and electronic saddle optimization for horses, as well as collars that track and identify animals.

Fabrics that can generate electricity from solar sources are advancing. One organization at the forefront of such research is the European Union-funded project Dephotex, which has developed methods to make photovoltaic material light and flexible enough to be worn. The group has devised prototype demos for applications that include jackets, pillows, car dashboards for charging iPhones, and larger surfaces like curtains or coverings able to get electricity for lighting or warming.

An obvious application for IoT is health monitoring.

The hope is that bio-stats from items such as smart watches will lead to lifestyle and behavior modifications. EH sensors may also find use in exoskeletons to facilitate mobility for paraplegics. But, from power conversion IC standpoint, partitioning and efficiently powering these types of wearables is not trivial.

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PRACTICAL POWER CONVERSION Linear Technology has developed several power conversion ICs having the necessary features and performance to enable the kind of low-level harvested power that is useful in IoT. The LTC3107 is a highly integrated dc-to-dc converter designed to extend the life of a primary battery by harvesting and managing surplus energy from extremely low input voltage sources, such as TEGs (thermoelectric generators) and thermopiles. The chip generates an output voltage that tracks that of the existing primary battery. The LTC3107, along with a small source of thermal energy, can extend battery life, in some cases up to the shelf life of the battery.

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Internet of Things

Another example is the LTC3331, which delivers up to 50 mA of continuous output current to extend battery life when harvestable energy is available. When powering a load with harvested energy, it requires no supply current from the battery. It pulls only 950 nA when operating from the battery under no-load conditions. The chip integrates a high-voltage EH power supply with a synchronous buck-boost dc-to-dc converter.

When paired with a rechargeable primary cell, it creates a single non-interruptible output for EH applications such as WSNs and IoT devices.

The LTC3331 EH power supply consists of a full-wave bridge rectifier accommodating ac or dc inputs and a high-efficiency synchronous buck converter. It harvests energy from piezoelectric, solar or magnetic sources. A 10-mA shunt enables simple charging of the battery with harvested energy,

while a low battery disconnect function protects the battery from deep discharge.

The rechargeable battery powers a synchronous buck-boost converter that operates from 1.8 to 5.5 V at its input and is used when harvested energy is not available to regulate the output whether the input is above, below or equal to the output. The LTC3331 will only charge the battery when the energy harvested supply has excess energy.

Without this logical function, the energy harvested source would get stuck at startup at some non-optimal operating point and would not be able to power the intended application through its startup. The LTC3331 automatically transitions to the battery when the harvesting source is no longer available. This lets the battery-operated WSN extend its operating life from 10 to more than 20 years if a suitable EH power source is available at least half of the time, and even longer if the EH source is more prevalent.

Finally, the LTC3335 nanopower buck-boost dc-to-dc converter, with an integrated coulomb counter, targets wireless sensor networks and general-purpose energy harvesting applications. It is a high-efficiency, low quiescent current (680 nA) converter. Its integrated coulomb counter monitors accumulated battery discharge in long-term battery powered applications. This counter stores the accumulated battery discharge in an internal register accessible with an I2C interface. The buck-boost converter can operate down to 1.8 V on its input and provides eight pin-selectable output voltages with up to 50 mA of output current. To accommodate a wide range of battery types and sizes, the peak input current can be selected from as low as 5 mA to as high as 250 mA, and the full-scale coulomb counter has a programmable range of 32,768:1.

REFERENCES Linear Technology linear.com

One of the more interesting traits of the LTC3331 chip is that it will only charge the battery when the energy harvested supply has excess energy. Without this logical function, the energy source would get stuck at some non-optimal operating point at startup. The LTC3331 automatically transitions to the battery when the harvesting source is no longer available.

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IIoT CHALLENGES

The challenges of the Industrial Internet of Things and communications at the edge IIoT promises to let everything within an industrial environment connect to get complete visibility into operations and allow the best real-time decisions—with or without human intervention.

TONY PAINE

PLATFORM PRESIDENT, KEPWARE

IN A

perfect world, the IIoT connects all hardware and software components (the Things) that comprise an automation system. These connections will bring benefits that include enabling Things to share information, learn about their surroundings, and autotune themselves for optimum throughput and minimal downtime. Operational personnel will be able to remotely assess and manipulate all aspects of the production line without the need for dedicated on-site expertise.

But these benefits are contingent upon resolving key challenges—several of which the industry has been working on for years.

An industrial automation process includes mechanical, digital and human components. At any time, one of those parts may have information that is valuable to others. Much of that information has existed within industrial environments and has been shared for some time now—just at a much smaller scale than as outlined in IIoT scenarios and under different names (SCADA, M2M, Predictive Maintenance and Process Optimization).

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Internet of Things

Today, there are several changes impacting the scale and speed of the IIoT: • New vendors are entering the market, trying to consolidate data into actionable information, unify historical solutions, and bridge the gap between the public and private operational domains.

• Our society increasingly relys on the Internet and has more connected tools available than ever before.

• Technology is no longer costprohibitive: We can networkenable anything with low-cost sensor technology, unlocking and storing data that was previously unavailable.

• Finally, the next generation of engineers has grown up with technology that is rich, easy to use, and everywhere—creating an expectation that existing control systems will be comprised of technology that plugs in and works with little effort.

At the enterprise level, multi-site awareness will provide critical insight for competitive strategic planning, as well as the opportunity to integrate beyond organizational boundaries for the purpose of leveraging a third party’s business services.

As industry builds out the IIoT, its biggest challenge will be in seamlessly Internet-enabling the Things that live at the edge of the network. Industry-wide, this edge contains trillions of Things that contain data points that may need to be analyzed and converted into information.

Unfortunately, the edge of the network is also the furthest removed from the information technology (IT) we have become accustomed to using when Internet connectivity is required.

IIoT CHALLENGES Identifying Things within the Internet For Things to communicate with each other, they must be uniquely identifiable

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on the Internet. Historically, this has taken place through the assignment of an Internet Protocol (IP) address. As industry looks ahead to the trillions of Things to be connected, focus has been on adopting the IPv6 standard, which defines a 128-bit address capable of uniquely identifying 340 undecillion (340 × 1036) addressable items (compared with only 4 billion addressable items using today’s IPv4 standard). Though this range will more than cover the requirements of IIoT, it will be difficult—if not impossible—to manage this number of addresses effectively on a global Internet scale. Typically managed by Naming and Number Authorities with the aid of network administrators, this will be an impediment as Things are added at an unprecedented rate.

Discovering Things and the data they possess Once a Thing can be identified, the next challenge is how to let other interested parties discover it exists and what data it holds.

Of course, a Thing should be able to restrict discovery of all or some of its data based on security requirements. Balancing ease-of-discovery with the rigid constraints of security will be fundamental to the success of IIoT and must be possible by personnel without PhD degrees in cybersecurity.

Managing massive amounts of data The trillions of Things will produce much more than trillions of data points (industry currently measures the number of data points in zettabytes or 1021 bytes), all of which will need to be collected, analyzed and possibly archived. Moving this much data over the Internet will consume much larger levels of bandwidth, which could result in the degradation of service as well as higher costs for Internet carriers, service providers and ultimately end users. Moreover, archiving this data for future analysis will require massive amounts of data storage and a new generation of scalable applications for honing in on points of interest in a timely manner.

The trillions of “Things” that will connect to the Industrial Internet of Things will produce something much larger than trillions of data points (industry currently measures this in zettabytes or 10 21 bytes), all of which will need to be collected, analyzed, and possibly archived.

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A new communications platform will be needed to integrate industrial data into the IIoT. This platform requires extensive knowledge of the intricate realm of OT and the state-of-the-art and rapidly changing domain of IT.

Navigating connectivity outages The Things that make up the IIoT, as well as the communication media that link them together, will not be available 100% of the time. While some downtime may be scheduled, there will be physical or environmental changes that result in intermittent or longer outages. But in some cases, data loss is unacceptable or operators need to know the criticality of variances in the data in real time.

Integrating existing infrastructure into new IIoT strategies Industrial Things have made data accessible over private networks for decades through both open and proprietary protocols. But these networks have ignored complexities like security in the interest of optimizing network operations and third-party integration. The typical lifecycle for industrial Things exceeds 20 years, so manufacturers have expectations of integrating their existing infrastructure into new IIoT strategies. Detailed security assessments will be necessary before manufacturers feel confident hooking the Internet into existing private networks and the data they contain.

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Leveraging the power of cloud computing To alleviate the preceding challenges, IIoT strategies will push data into a centralized cloud platform. Cloud computing and its multitudinous resources can handle the zettabytes of data that will be collected, analyzed and archived. Furthermore, the overall uptime of cloud platforms continues to trend higher as they become more resilient.

Communicating with devices on the edge The actual source of data pushed into the cloud comes from Things that live at the edge of the network. The edge bridges the gap between IT and operational technology (OT, simply, people and technology that support industrial processes), where the resources available in the cloud are not directly available. OT encompasses industrial networks that have their own nuances and introduce additional challenges.

Connecting disparate communications mediums Often, industrial networking technologies do not use Ethernet as their physical communications layer.

Depending on the environment and the Things that comprise a system, industrial networks can use anything from RS232/485 to modems to proprietary wiring.

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Likewise, the data protocols over these communication mediums are not likely to be IP derivatives. The result is a hodgepodge of industrial networks created with no thoughts of the possibility of being connected to the Internet.

Using nonstandard methods of identification Unlike IP addresses in the IT world, many industrial Things have their own addressing schemes for uniquely identifying themselves on the network.

These schemes vary by vendor and type and may or may not have built-in discovery mechanisms. It takes innate knowledge of an integration expert to interconnect the Things in a way that makes them function as a whole.

Determining a request/response model Industrial Things have historically followed a request/response model.

If a particular Thing is interested in a piece of data sitting in another Thing, it

IIoT CHALLENGES

will make an appropriate connection, request the data, and wait for a response containing the result. Although this pull model is fine for Things living within the same digital boundary of OT, security and scalability requirements render this model unacceptable for the outside IT world trying to look in. Instead, IIoT prefers a push model, where industrial data flows out to a cloud platform.

Enabling short-term data storage Within the context of a single industrial network, we may find thousands of Things that, together, could generate several thousand data points. Though this sounds like a small set of data, the real-time requirements of OT will demand these points be sampled at sub-millisecond rates for data change detection.

In the past, this high-frequency data would be simply analyzed, acted on accordingly and thrown away. As we move to making this data available to IIoT, we will require short-term storage to ensure it can be pushed to other parties when they are available.

IIoT EDGE SOLUTION It will take a new communications platform to seamlessly integrate industrial data into the IIoT. This platform must allow for the intricate realm of OT and the state-of-the-art and rapidly changing domain of IT. Within OT, the platform must understand the various network topologies and data protocols. It must be able to automatically discover and identify industrial Things and the data they contain, as well as handle the storage of high-frequency updates. Within IT, the platform must be able to transform the data it collects and push it into the cloud through IIoT standards.

Emerging standards include: • Asynchronous Messaging Queuing Protocol (AMQP) • Message Queueing Telemetry Transport (MQTT) • Constrained Application Protocol (COAP) • Data Description Services (DDS) These standards allow for the retransmission of data if it does not reach its destination. With the lack of computer networking infrastructure in OT, this platform must be embeddable and run within a stand-alone appliance or an edge-based switch or router where IT and OT converge.

Its flexibility will enable industrial data to be sampled cyclically or based on some event or condition and be published to the cloud independently of data collection. Data filtering should be available through basic analytics. Last, user setup should be minimalized by automating as much configuration as possible.

As industry continues to define IIoT, the concepts and realization of the optimal embedded IIoT solution will continue to evolve.

Industrial Things typically follow a request/response model, sometimes referred to as push/pull. A pull model is fine for Things living within the same digital boundary of OT, but security and scalability requirements make this model unacceptable for the outside IT world trying to look in. Instead, IIoT prefers a push model, where industrial data flows out to a cloud platform.

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Internet of Things

The new era of design for the IIoT RALF NEUBERT

SENIOR DIRECTOR OF INNOVATION AND TECHNOLOGY, INDUSTRY BUSINESS, SCHNEIDER ELECTRIC

The IIoT promises more operational efficiency and lower costs thanks to close coupling of machines and systems.

THE

advent of the Internet of Things (IoT) has brought the need for more connected components and for designing with automation in mind. The Industrial Internet of Things (IIoT), or what some like to call Industry 4.0, is characterized by greater intelligence integrated into components. This intelligence gives design engineers the ability to pinpoint sources of inefficiency and incorporate greater sustainability, speed and cost savings into machines.

Ultimately, the IIoT is changing how engineers work when designing automation components. Connected machines will give design engineers the opportunity to identify points of inefficiency, make improvements, and in turn, boost profitability. The resulting smart manufacturing enterprise will be more efficient, safer and sustainable.

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IIoT DESIGN

Today, some smart devices are fully online, driving efficiency and productivity within machines and on the factory floor. Ethernet is being built into the backbone of most products, so third parties can develop modules using the standard Ethernet protocol and hardware layer.

These moves facilitate an IT/OT convergence (where OT is operational technology, simply: people and technology that supports industrial processes) for a component that is truly ready to integrate in the IIoT architecture.

LEVERAGING INTELLIGENT PRODUCTS TO ADVANCE THE IIoT One example is Ethernet-connected motors and drives that provide operational insight to plant managers at a device level. Energy management calculations, diagnostics, pump curve information and other data become easier to monitor. These smart connected products can enable data analysis locally without overloading higher-level systems residing either on-premises or in the cloud.

Digital applications and services will also help end users realize the better business performance promised by the IIoT. Simple data collection must be extended to include analytics that deliver valuable business information. Some examples of such applications and services include installation optimization, asset management and protection, conditionbased monitoring, augmented reality applications, and OEE (overall equipment effectiveness) calculations.

HOW IIoT AFFECTS DESIGN ENGINEERS As the IIoT spreads, it forces design engineers to rethink their products and applications. The promise is that this rethinking will lead to increased operational

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efficiency and lower costs.

Furthermore, these smart connected devices operate as part of a larger system. So the plants of tomorrow will be more connected as well. For example, in the future, automation control functions may even move to cloudbased infrastructures.

As these changes arise, design engineers will orchestrate the functions of cloud-based services and control functions at the device level. Designers will have to consider the whole lifecycle of a system or product. Architecture choices like open protocols, platformindependent application program interfaces will be good routes to better efficiency.

There are challenges for designers of IIoT devices and systems. They must understand interfaces and how to access them to provide and consume the right data at run-time.

Common communication interfaces that implement and extend applications faster will help open and harmonize standards like OPC unified architecture embedded in devices.

Security will be a key concern and adopting a “secure by design” strategy will be a must in the IIoT arena. Design engineers will need to include options for encryption.

Application security and access control will define the devices connected to the network and the permissions those devices have. In addition, protocol security and application security will need to be consistent. Supply chain security will be an issue as well, starting with the coding of products, their manufacturing, delivery, installation, maintenance and disposal.

Having services in place to help customers keep systems secure will be critical in the IIoT era.

All in all, IIoT allows Big Data to be processed with new, advanced analytics tools. It also lets mobile technologies drive greater business value. Plus, it enables more efficiency and profitability, better cybersecurity, innovation, management of safety, and reduced CO2 emissions.

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Internet of Things

How to ensure a network works with the IoT Test beds will ensure new network infrastructures support the Industrial Internet of Things.

AS

vendors develop networking infrastructures able to implement the Internet of Things (IoT), the testing of those networks becomes critical to ensure operability. In that regard, several institutions engaged in the IoT now plan to collaborate on developing a test bed for what’s called the Time Sensitive Network (TSN), which is based on a new IEEE 802 local area network standard. This new standard should address some of the shortcomings of existing IEEE 802 networks relating to the time-sensitive

LESLIE LANGNAU MANAGING EDITOR

transmission of data. Examples include audio and video stream, where the delay of data packets or their arrival out of sequence can garble the content.

The organizations involved include the Industrial Internet Consortium (IIC), National Instruments, Bosch Rexroth, Cisco, Intel, KUKA, Schneider Electric and TTTech. They aim to advance the network infrastructure to support the future of the Industrial Internet of Things (IIoT) and Industry 4.0.

The goal of the TSN test bed is to display the value of the new Ethernet IEEE 802 standard in an ecosystem of manufacturing applications.

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Internet of Things 64

CONNECTING TO THE EDGE The IIoT promises to give more reliable and secure access to smart edge devices. To fulfill this promise, though, standard network technologies must evolve to meet the demanding requirements of next-generation industrial systems and improve machine operation, electrical grids and transportation systems.

“Standardized and open communication is a key feature in our drive and control automation solutions,” said Ralf Koeppe, VP of engineering and manufacturing electric drives and controls at Bosch Rexroth. “We regard the IIC TSN test bed to be an important contribution for further improvement of vendor interoperability and of exchanging data in an IIoT infrastructure.”

The TSN standard is an effort to support the new digital capabilities and the connected manufacturing enterprise. This standard encompasses an open network infrastructure with multivendor interoperability and integration, as well as guaranteed performance and delivery. The TSN will support real-time control and synchronization between motion applications and robots, for example, over a single Ethernet network. In addition, it will support other common traffic found in manufacturing applications, driving convergence between IT and operational technologies.

To ensure the TSN standard meets the needs of IIoT, the IIC is inviting vendors to use a test bed—a controlled experimentation platform. This platform will conform to the Consortium’s reference architecture. The platform

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lets vendors test their IIoT solutions in an environment that resembles real-world conditions.

The Consortium uses these test beds to explore technologies that have previously operated together. The test beds help determine requirements and priorities for standards organizations, and often culminate in new, potentially disruptive, products and services.

“TSNs are a critical attribute of a standard Internet model that enables the convergence of real-time control applications and devices onto open, interconnected networks,” said Eric Starkloff, EVP of global sales and marketing at NI. “This technology is necessary for the future of the IIoT. The IIC is providing a community, as well as enabling realworld test beds, where industry leaders can collaborate to make this a reality.”

Previously, many real-time control applications were deployed using nonstandard network infrastructure or unconnected networks that leave the devices and data hard to access, if accessible at all.

ACCELERATING INNOVATION The Testbed Working Group helps create test beds for the Industrial Internet and serves as the advisory body for test bed proposals.

“Our test beds are where the innovation and opportunities of the Industrial Internet—new technologies, new applications, new products, new services and new processes—can be initiated, thought through and rigorously tested to ascertain their usefulness and viability before coming to market,” said Richard Soley, executive director of the IIC. The TSN test bed will be an early implementation of TSN. As such, it will show the value of the technology as well as some of the challenges in implementations from vendors. It will also serve as a source of feedback to the relevant standards organizations on areas that need further clarification or improvement.

The test bed will: • Combine different critical control traffic (such as OPC UA) and best effort traffic flows on a single, resilient network based on IEEE 802.1 TSN standards • Demonstrate TSN’s real-time capability and vendor interoperability using standard, converged Ethernet • Assess the security value of TSN and provide feedback on the ability to secure initial TSN functions • Show ability for the IIoT to incorporate high-performance and latency-sensitive applications • Deliver integration points for smart, real-time edge cloud control systems into IIoT infrastructure and application.

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WHITE PAPER INTERNET OF THINGS

SWARM Intelligence for Industrial IoT PAUL FRISBIE

To understand the promise of the internet of things, it is helpful to first consider swarm intelligence in nature. Swarms of ants, bees and termites can produce highly sophisticated results. Termites build enormous mounds in which internal temperatures are regulated to within a degree. Individual ants forage at random, but the overall motion of the collective produces highly efficient search algorithms that researchers have compared to those used in Google Maps.

The same principles that make natural collectives successful can be put to work in data networking. The sum of the knowledge embedded within thousands of relatively simple devices, if communicated among network nodes, can produce benefits above and beyond those provided by the individual pieces of equipment.

With SWARM intelligence, an edge device does not have to be a single physical device, with implicit limitations on interfaces, resources and expansion. Instead, it can be made up of a number of discrete physical devices, with each one contributing its interfaces and processing capabilities to the collective. Together, these individual devices can then be viewed, in architectural and functional terms, as a single entity.

This approach solves the scalability problem which has been the “elephant in the room” when discussing previous edge architectures. Doing so results in a quantifiable reduction in the total cost of ownership of an edge SWARM compared to other current solutions.

B&B SMARTWORX

Five ways SWARM Intelligence reduces cost of ownership

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SERVICE ORIENTED Conventional edge devices are typically either relatively limited in their programmability providing, for example, simple scripting support, or may require detailed user programming that requires a high level of familiarity with the device and its underlying hardware and software structure. SWARM devices support both scripting and detailed programming, but dramatically reduce the time and risks involved in business logic development by providing fully rewireable services, coupled to an ontology engine that allows services to be broadcast throughout the SWARM. User programming becomes, to a much greater degree, an exercise in the binding of trusted services and user modules, while also allowing for the extension of the available services and modules for inclusion in the local SWARM.

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The generation of local business intelligence is further simplified by the provision of an internal continuous query engine, allowing users to filter and enrich underlying data passing through the SWARM by invoking calls using a comprehensive high level query language which includes the concepts of both time and number bound operations.

This combination of features dramatically reduces the time and risk involved in the development and deployment of the business logic, analytics or other user programming required at the edge. This shortens the overall time to revenue for systems based on SWARM. ADDING FUTURE INTERFACES AND RESOURCES In traditional edge devices it is necessary to define the characteristics of the device prior to installation. Parameters may include the number and type of physical interfaces to be provided, the bandwidth of the processor, the amount of RAM and the persistent storage required. This often leads to the deployment of devices that are more expensive than is really necessary, as a means of “future-proofing” the installation. Even then, an installation may prove to be inadequate for some future task, calling for replacement with more advanced equipment, potentially also incurring costs for retest or recertification of the installation.

A SWARM edge is already future proof. If a new interface is required, or if a new edge application calls for more processing power, memory or storage resources, additional nodes with the necessary features can simply be added to the pre-existing SWARM, with no effect on any of the existing interfaces or the applications built upon them. As new classes of device emerge, the SWARM will absorb and incorporate them, thus increasing the overall capabilities of the collective.

This means that deployed devices can be sized for the known requirements at the point of deployment, without risk to the investment being made at the time.

INSTALLATION COSTS SWARM technology can directly reduce the cost of integrating remote sensors and devices. In a traditional architecture, where the edge is a single physical device in a single location, connecting each sensor or subsystem requires another cable run. That can be made even more expensive if trenching is required, or there is a need for armored or specialist cables. In a SWARMbased system, a wireless node is connected to a sensor or device. The node then makes its data available to the SWARM, which provides a wireless path to the network gateway. This makes a cable run unnecessary.

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INTERNET OF THINGS

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REDUNDANCY The ability to add and use new interfaces and resources, along with the routing capabilities built into each SWARM device, makes SWARM incredibly flexible. It is easy to set up strategies to attach business logic to multiple interfaces, providing redundancy of outputs, or x-out-of-y voting on input data. Redundancy need only be added to those interfaces that truly require it. This is far cheaper and far less complicated than creating redundancy by duplicating the entire edge.

MANAGED DEVICE INFRASTRUCTURE Each device within the SWARM supports local configuration and management. More importantly, SWARM also supports remote management from a central location. This dramatically reduces the number of site trips required for system maintenance.

A SWARM reports the status of connected devices and allows for the download of user programs to both individual devices and groups of devices.

User programs are deployed in protected containers within the edge devices, and no user program can negatively impact services, interfaces or programs that are running outside of those containers. If a user downloads a program that contains bugs, the program itself may crash. But the edge device remains operational, and the user can remotely recover the situation.

SWARM devices also include the ability to support “zero touch” provisioning, automatically contacting a central server to obtain their initial configurations and user modules on initial power up.

To bring an unconfigured SWARM device into service, the device need only be physically installed and switched on. This reduces the number of operational spares needed to support a system, as standard SWARM devices can be substituted without any pre-configuration process. Additionally, the installer needs no special skills.

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CONCLUSION Like a beehive or an ant colony, SWARM technology lets individual devices contribute their abilities to the collective, even legacy equipment that was never designed to be a part of the Internet of Things. In doing so, SWARM technology provides massive scalability and the ability to easily integrate future, as yet undefined, interfaces and devices. SWARM can drastically reduce the costs of application development and deployment, installation, commissioning and maintenance out at the network edge.

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Internet of Things

Ad Index Acopian Technical Co........................................................ 49

KEB America, Inc. ............................................................. 63

Advantech B&B Smartworx............................................... 57

Keysight Technology, Inc. ................................................. 23

Allegro Microsystems, LLC ............................................... 27

Keystone Electronics Corp. ................................................ 5

Beckhoff Automation ........................................................ 47

Maple Systems Inc. ........................................................... 35

Chroma .............................................................................BC

Master Bond, Inc. ............................................................. 53

Coilcraft ............................................................................ 29

Memory Protection Devices, Inc. ........................................ 7

CTS Corp. ......................................................................... 41

NTE Electronics ................................................................ 17

CUI Inc. ............................................................................... 3

Renco Electronics Inc. ....................................................... 13

Digi-Key Corp. ......................................................Cover, IFC

Rogers Corp. ...................................................................IBC

GE Digital ........................................................................... 1

Tadiran Batteries ............................................................... 15

IXYS/Zilog ......................................................................... 43

SALES Mike Caruso mcaruso@wtwhmedia.com 469.855.7344 Todd Christenson tchristenson@wtwhmedia.com 440.381.9048 @wtwh_todd Jessica East jeast@wtwhmedia.com 330.319.1253 @wtwh_MsMedia Michael Ference mference@wtwhmedia.com 408.769.1188 @mrference Michelle Flando mflando@wtwhmedia.com 440.670.4772 @mflando Mike Francesconi mfrancesconi@wtwhmedia.com 630.488.9029

LEADERSHIP TEAM David Geltman dgeltman@wtwhmedia.com 516.510.6514 @wtwh_david Neel Gleason ngleason@wtwhmedia.com 312.882.9867 @wtwh_ngleason Tom Lazar tlazar@wtwhmedia.com 408.701.7944 @wtwh_Tom Jim Powers jpowers@wtwhmedia.com 312.925.7793 @jpowers_media

Publisher Mike Emich memich@wtwhmedia.com 508.446.1823 @wtwh_memich Managing Director Scott McCafferty smccafferty@wtwhmedia.com 310.279.3844 @SMMcCafferty EVP Marshall Matheson mmatheson@wtwhmedia.com 805.895.3609 @mmatheson

Courtney Seel cseel@wtwhmedia.com 440.523.1685 @wtwh_CSeel

CONNECT WITH US!

Follow the whole team on twitter @DesignWorld

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