Embedded Computing Design February 2015 by OpenSystems Media

FeBRUARY 2015 VOLUME 13

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IoT Issue PG. 31

The Internet of Things: It's a connected world

Sensor-enabled nodes support the IoT for smart buildings and smart transport pg. 26

PLUS

Silicon

Balancing power and performance in wearables pg. 12

Strategies

Can connected cars be secure cars? pg. 21

Mini-ITX

COM Express Module

PICMG SBC

www.portwell.com info@portwell.com 1-877-278-8899

Small Form Factor System

Network Security Appliance

FEBRUARY 2015 VOLUME 13

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Departments

5 Tracking Trends IoT: Revolutionising home energy management IoT Insider 7

Rory Dear, Technical Contributor

Brandon Lewis, Assistant Managing Editor

Deconstructing the hype machine: Data analytics key differentiator for IoT

DIY Corner 8 IoT DIY with Bluetooth Low Energy and Arduino 9 Research Review

31 10

Interview with Steve Whalley, MEMS Industry Group (MIG)

Software Balancing power and performance in wearables

12 16 18

Internet of Things Special Section

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When one cyberattack becomes a thousand: Protecting the IoT By Ken McLaurin, Red Hat Inc.

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Connecting devices to the Internet of Things with Wi-Fi

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Encryption 101: Choosing the right scheme

By Kristen Maglia, Rogue Wave Software

Updating car software: Why delta technology is better than compression

Web Wire

Special Features

By Curt Schwaderer, Editorial Director

21 24 26

Monique DeVoe, Managing Editor

Global Internet governance and the IoT

Can connected cars be secure cars?

Editor's Choice

By Becky Oh and Andrew Taylor, PNI Sensor Corporation

Strategies

Monique DeVoe, Managing Editor

Building the clouds of the future

Silicon Open source MEMS initiative seeks to reduce barriers to sensor development

By Nikos Vokas, Econais

By Paul Dillien, Algotronix Ltd.

By Yoram Berholtz, Red Bend Software

Sensor-enabled nodes support the IoT for smart buildings and smart transport

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By Roger Grace, Roger Grace Associates, and Alessandro Bassi, Alessandro Bassi Consulting Embedded Computing Design | February 2015

TRACKING TRENDS

IoT: Revolutionising home energy management By Rory Dear, Technical Contributor

rdear@opensystemsmedia.com

We’re privileged to live in an era that is truly heralding a revolution in the way we can take ownership of our home’s energy consumption. Of course a key driver is the spiraling cost of energy and focus on our own environmental impact and carbon footprint – but this isn’t purely a cost cutting exercise, this revolution also promises a substantial functionality increase that simplifies our ever-hectic lives.

consist of paranoid listening or gazing at a baby monitor, then conceding I’d better physically check just to be sure!

Today, most homes’ energy management capabilities consist of appliance standby modes and CT based “per home” amperage monitors. Both technologies, whilst offering some benefit, have inherent flaws that don’t quite make the grade – enter the smart, connected home. Let’s start with energy efficiency opportunities. The obvious function and the first we’re seeing hitting the UK market is “smart boiler” control. Functions available today are predominantly manual control via smartphones, be that within the home or remotely. The next generation will see the introduction of habit learning intelligence, reviewing for example your daily hot water usage statistics, adjusting boiler activation times and duration accordingly to reduce wasted energy. Let’s face it, I’m sure all of us would admit we purposefully over specify this “to be safe” and invariably have plenty of wasted hot water remaining at the end of each day as a result. We should also expect to see cross device integration, for example setting your burglar alarm when leaving the house automatically deactivates any active central heating – and any individual appliances that clearly offer no benefit remaining powered within an empty house. Appliances are already following suit in “self” management. For those enjoying a hot drink as they wake, expect to see coffee machines and kettles activated by the deactivation of your smartphone alarm. An environmental group calculated 1/3 ($2 billion) of lighting in the U.S. is wasted each year. “Smart lighting” offers not only the PIR type activation many of us are used to in our offices, but also the ability to configure individual lights’ brightness and activation times, monitor status remotely, and deactivate all lighting in an “empty home” scenario. Now for user functionality improvements. Having recently become a first-time father myself, monitoring a baby can www.embedded-computing.com

I want to instantly see environmental information such as temperature from my smartphone and don’t require a constant AV stream of every murmur falsely demanding my attention, an intelligent alarm would be infinitely better. Interestingly such advances are equally attractive to those caring for the elderly, increasingly wanting to stay in situ rather than seek residential care in their twilight years. With the advent of online shopping and smart fridges, once the food packaging industry introduces RFID tagging your fridge can actively monitor “best before” dates and even automatically reorder essentials that are no longer detected. What’s in this for the energy companies? Paradoxically, it may seem, those profiting most from soaring bills are heavily driving this revolution. Worldwide governments are applying pressure to these conglomerates to reduce household bills either by lowing the price per, or quantity of, kWh – the latter costing the energy company far less. It’s also true that as energy bills soar, the levels of payment default do too, which negatively affects cash flow. Pressure on reducing carbon footprints internationally also drives future taxation levels for energy companies, another key consideration. What risks are suppressing this innovation? As with most technological revolutions the infrastructure costs are high; a typical homeowner will not encompass this cost alone as a business can usually provide a better ROI case due to significantly higher savings – though as we’ve seen already a keenness exists from the energy companies to support funding this. Ease of installation is another challenge, particularly when perhaps less technologically savvy householders find themselves at the front end. Securing any cloud-based remote access, as always, demands “security” high up on the agenda too. The cross platform support of Wi-Fi and web servers is obviously critical, though I worry, as per the HD-DVD/Blu-ray type wars of the past, that those major players are again deriving their own proprietary formats. From a business perspective these players are understandably aiming to secure market share, though that’s unfortunate for innovation and technology – a frustrating obstacle indeed.

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Advertiser Index

Advertiser Information

11 ACCES I/O Products, Inc. — USB embedded I/O solutions: Rugged, industrial strength USB 2 American Portwell Technology — Portwell empowers intelligent solutions 19 Anaren — Join the evolution 35 Bluegiga Technologies, Inc. — Bluetooth modules

ECD Editorial/Creative Staff Rich Nass, Brand Director rnass@opensystemsmedia.com Curt Schwaderer, Editorial Director cschwaderer@opensystemsmedia.com

Rory Dear, Technical Contributor rdear@opensystemsmedia.com

Monique DeVoe, Managing Editor mdevoe@opensystemsmedia.com

Konrad Witte, Senior Web Developer kwitte@opensystemsmedia.com

37 BZ Media - Wearables TechCon — Learn how to design, build and develop apps for the wearable technology revolution

Brandon Lewis, Assistant Managing Editor blewis@opensystemsmedia.com

15 COMMELL Systems Corporation — Intel Celeron J1900, N2930 and Atom E3845 SBC

Sales Group

27 Datalight — Driven to preserve and protect critical data for the lifespan of your automotive design 31 Datalight — Industrial IoT devices demand enduring, decision-quality data 34 Datalight — Revenue-grade data for industrial Internet of Things devices 28 Digital Voice Systems, Inc. — New AMBE+2 Vocoder chip delivers high quality voice at low cost 3 Embedded World — The gathering of the embedded community 32 Kontron — Hyperconnecting the Internet of Things 35 Kontron — IoT ready KBOX A-201

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17 Micro Digital, Inc. — SMX RTOS is IoT ready 39 Mobile World Congress — The edge of innovation 35 Pocket Soft, Inc. — RTPatch: Binary diff patch software solution 29 Rogue Wave Software — Can you quickly find the code defect? 34 Rogue Wave Software — Deliver safe, secure mission critical software, faster

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OpenSystems Media Editorial/Creative Staff

27 Security Innovation — Security Innovation ACE Labs 34 Toradex — Experience the difference between community support and committed support 33 Vitesse Semiconductor Corp. — IoT security done right 40 WinSystems, Inc. — Thinking beyond the board

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Embedded Computing Design | February 2015

IoT INSIDER

Deconstructing the hype machine: Data analytics key differentiator for IoT By Brandon Lewis, Assistant Managing Editor

When I pulled up a chair next to Gartner’s Paul O’Donovan at the 2015 International CES earlier this year, one of the first things he said to me was, “You know what really angers me, Brandon? When companies add a Wi-Fi chip to a washing machine and claim it’s an Internet of Things device. That’s not the IoT.” By now I’m sure you’ve heard reports about the IoT hitting the top of the “hype cycle,” as well as the projections that IoT services alone are projected to generate in the neighborhood of $260 billion a year by 2020. But as Paul rightly points out, much of the buzz surrounding the IoT hype machine to date has centered around adding connectivity to previously “dumb” devices, rather than focusing on the data processing and analytics that will actually provide intelligence to those devices and make the IoT truly transformative. For instance, while a washing machine you can run remotely is cool, leveraging analytics about a washer that is running outside normal operating limits and diagnosing a problem with the machine’s motor can save you time, a huge mess, and open up additional service-based revenue streams for manufacturers; a smart home that informs you of power consumption is great, but a smart home that tracks usage patterns over time and adjusts the run cycles of your appliances to off-peak hours is a game changer that saves you money and presents utilities with the opportunity to tweak service plans. So why isn’t more attention being paid to data analytics? www.embedded-computing.com

Big Data structures and IoT analytics Outside of the large investment in backend infrastructure required to get a data processing system off the ground, one of the major problems for Big Data analysis in the IoT is that data from different sensors is often generated in different formats. The reason for this is that developers of the initial data logging infrastructure didn’t put much thought into formatting the logs of data producers (such as sensor devices) because humans were the primary consumers of log data. For us, parsing through different logs and extracting information from them isn’t much of a challenge, so loosely structured log formats sufficed. But today, humans are not the foremost consumers of log data, not even by a long shot. We now increasingly rely on machines to perform the bulk of the processing and analysis on data generated by other machines, and, unfortunately, machines aren’t as adept as humans at parsing through the diverse, semi-structured data sets associated with the IoT. Recently, however, there have been efforts in the data science community to fix this data log illiteracy, notably through the open source Fluentd project (www.fluentd.org). Fluentd is a data collection software that attempts to reconcile the log formats of data sources with those of the backend systems responsible for processing and analysis. It is able to achieve this through what is called a Unified Logging

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Layer interface, which restructures data logs from both the source and destination in JSON format. Combined with a set of community-contributed plugins that make Fluentd compatible with numerous data sources and outputs, the Unified Logging Layer provides a mechanism for quickly collecting, filtering, and outputting log data from various inputs into a consistent schema that is suitable for analysis. Treasure Data, Inc. (treasuredata.com) has been a major contributor to the Fluentd project, and uses a commercial version, the Treasure Agent, as part of its Big Data solutions. Using the Treasure Agent, the company can capture data logs from a wide range of sources in the IoT, telecom, retail, and advertising sectors before securely storing it in a cloud backend, from which clients can run SQL queries. In addition, business intelligence can also be integrated to automate massive IoT deployments and create new business opportunities, such as with the Pioneer telematics service currently under development (see opsy.st/TreasureDataFig).

Deconstructing the hype machine Discussions around connectivity are important, especially as we continue to roll out the IoT infrastructure rollout. But that being said, there’s a clear difference between a “connected” device and an IoT device, and the differentiator is data analytics. Before the hype machine moves any further, I hope that becomes more central to the conversation.

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DIY CORNER

IoT DIY with Bluetooth Low Energy and Arduino

By Monique DeVoe, Managing Editor

mdevoe@opensystemsmedia.com

Connecting yourself into the Internet of Things (IoT) is becoming increasingly accessible as DIY platform features and capabilities expand. DIY board and peripheral developers are coming out with more connected products all the time. Bluetooth Smart is one connectivity technology that shows a lot of promise for the IoT and small, low-power maker projects – plus the ubiquity of Bluetooth-enabled devices doesn’t hurt.

“Bluetooth Low Energy was only introduced in 2010 – it is still evolving; it took classic Bluetooth more than 10 years before it became stable and mature,” Ma says.

Bluetooth Low Energy (BLE) includes additional functionality on a Classic Bluetooth controller, including lower power consumption, AES-128 encryption using CCM for strong packet encryption and authentication, and extended range from 30 feet to 200 feet – good for home-wide automation and sensing projects.

As for Arduino as an IoT platform, RedBearLab finds it to be a user-friendly, widely used platform.

RedBearLab (www.redbearlab.com) is a maker board company who was an early adopter to BLE on maker platforms, specifically Arduino. The company focuses on IoT applications with special interests in embedded to mobile/portable devices and embedded to embedded technologies utilizing BLE and/or Wi-Fi. In 2012 they launched their first BLE shield for Arduino. “At the time, there was a lot of interest in trying out the latest Bluetooth technology, but no ‘user-friendly’ development option was available – most of the BLE development tools still required very low-level embedded programming skills,” says Ma Chi Hung, CEO at RedBearLab. For Apple product support, BLE was a great addition to a maker’s connected device development options. “Before the launch of BLE, developing hardware that could work with iOS was limited to Apple’s Made for iPod (MFi) licensees,” Ma says. “Although you could use Wi-Fi instead of BLE, BLE is cheaper and more power efficient.” The biggest advantages of BLE are its low energy consumption compared to Wi-Fi, and its better mobile device and PC support compared to ZigBee, Ma says. BLE does have some drawbacks as well: Support on Android and other mobile and desktop OSs are still under development, Ma says, among other issues. For example, before BLE v4.2, BLE couldn’t connect to TCP/IP networking directly, limiting its IoT usefulness. Even though that issue has been addressed, it’ll take time for the specification to become widely adopted.

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In all, BLE excels at certain applications and struggles with others. RedBearLab sees a mix of all existing and upcoming connectivity technologies being the most useful in the future IoT space.

“A lot of existing users are familiar with Arduino and there is a big community of Arduino users sharing their BLE projects online,” Ma says. Arduino boards will celebrate their 10th year in 2015 – plenty of time to build a strong community and project resources. On the arduino.cc forums, “Home Automation and Networked Objects” is one of the largest topical boards (second to robotics at the time of posting – it’s hard to beat robots in project coolness), not to mention the various other communities that focus on the platform. However, Ma says the Arduino’s Atmel MCU could case some scalability issues for BLE projects – running the library for BLE on the Arduino takes up a lot of resources quickly. Arduino can also be less than ideal for low power processing as it lacks a stand-by mode, but this could be addressed in future board versions. Developing firmware can be a challenge based on your skill level. RedBearLab tries to make this more accessible to makers familiar with the Arduino IDE with their open source Arduino library for Nordic nRF51822 IC (github.com/RedBearLab/ nRF51822-Arduino), an ARM-M0 SoC with BLE capability. More advanced users can still use KEIL, GCC, or mbed.org. With a maker board, some development skills, and good community support, you could be on your way to the next big IoT thing. “We believe that DIYers and makers are the driving force for the adoption as well as innovation in the IoT space,” Ma says. “The majority of IoT successes so far are from new startups with strong maker, open source, and crowd-sourcing backgrounds.” Embedded Computing Design | February 2015

RESEARCH REVIEW

Building the clouds of the future By Monique DeVoe, Managing Editor

mdevoe@opensystemsmedia.com

– to look at the clouds that we have now, and to think about how we can change them at fundamental levels,” says Robert Ricci, Research Assistant Professor, School of Computing at the University of Utah, which is leading the CloudLab project. “To do that work, you need to not just work within the cloud, you need to be able to control and instrument it at very low levels, and you need to be able to do that at a reasonably large scale.”

Figure 1

The University of Utah’s Downtown Data Center. Photo by Chris Coleman, School of Computing, University of Utah.

The cloud has been a great addition to computing – bringing many benefits with its added computing power – but it still has a lot of room for improvement. The ability to make discoveries about and advance the cloud is difficult for typical cloud users, especially when system details such as network topologies and storage system design are intentionally hidden from users. National Science Foundation (NSF)-funded CloudLab (www.cloudlab.us) aims to allow researchers to build their own clouds to make discoveries about cloud architecture and potential new applications. “The goal of building the CloudLab infrastructure is to enable researchers to do transformative science on the architecture and applications of cloud computing www.embedded-computing.com

Ricci cites a few current challenges among many that hold the cloud back: security and privacy, predictability and real-time performance, and power efficiency. Encrypting data is a start, but there’s a long way still to go before cloud users’ data and the data collected about them can be considered truly private and secure. The ability to isolate cloud tenants’ performance isn’t perfect yet either, and the isolation and virtualization layers typically used add overheads and introduce hard-to-predict performance variability. And as with many computing areas, power efficiency, integrating variable power sources like solar, and addressing cooling issues present many challenges. These challenges are part of what CloudLab is set up to help researchers address by providing a tool to build clouds with maximum flexibility. Researchers have access to CloudLab’s hardware and software stack configuration components to get their custom clouds up and running in about 10 minutes. Hardware includes typical x86based servers in addition to hardware like ARM-based servers and OpenFlow switches that may have an impact on future cloud development. A fully programmable Layer 2 (L2) network between data centers is provided

through national research and education network Internet2. Popular software stack profiles are available, such as pre-built installations of OpenStack and Hadoop. If they choose, researchers can use these pre-built stacks, build their own, or use bare metal. Users have full control and visibility and don’t have to share resources with other users. Three universities are hosting server clusters designed to handle different cloud computing challenges. The University of Utah’s server-class 64-bit ARM cores, built in partnership with HP on its Moonshot platform, emphasize powerefficient computing (Figure 1). Next to be built are the University of WisconsinMadison and Clemson University clusters, emphasizing high bisection bandwidth/ storage and high memory, respectively. Current plans call for the system to grow to around 15,000 cores. Additions will include rolling out bare metal access to network resources and providing specialized hardware such as FPGAs and specialized switching equipment. The project is poised to give current and future engineers a head start in revolutionizing cloud technology, as it is free for use by research and education communities. Ricci says it’ll help level the playing field across all institution types and sizes. The tool will be available for teaching classes, and in this role provide students with access to a level of handson experience that’s hard to come by. Ricci hopes CloudLab can be as transformative as the cloud itself. “The cloud has been transformative because it has taken infrastructure that used to be time-consuming and expensive to produce and install, and made it easy for anyone to get it with almost zero effort and time,” Ricci says. “CloudLab aims to do the same for the cloud itself; that is, in CloudLab it’s as easy to build your own cloud as it is to set up a virtual machine in a traditional cloud. We hope that will similarly inspire people to come up with bold ideas about what the future of the cloud itself can be.”

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MEMS

Open source MEMS initiative seeks to reduce barriers to sensor development Steve Whalley Chief Strategy Officer MEMS Industry Group (MIG) hat are the biggest challenges W in sensor development, be it a single sensor or handling sensor fusion?

A few months ago I did a kind of MEMS 101 class with a bunch of startups, sort of an incubator unit out in Chandler, Ariz., and they had some very basic questions. One of them was, “Where can I get an accelerometer? Where do I buy one?” So it’s simple things like that. You can go to the MEMS site – the MEMS Industry Group (MIG) is a good place to start to get some resources. Then it’s going to the vendor sites like Freescale, ST Microelectronics, Bosch, etc., and then you start to understand what you need for your particular application. That’s the sensor side, picking a magnetometer, a gyroscope, or an accelerometer. Then it comes down to how do I really bring these things to life by fusing the data that comes from them together with multiple sensors? That’s sensor fusion. Those are the algorithms that if you have a pedometer or a sleep analysis application that lets you know

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Whether working with a single input or fusing data from multiple sources, getting sensors to work in real-world systems can be a daunting task for the inexperienced. Steve Whalley, Chief Strategy Officer, MEMS Industry Group (MIG) discusses some of the challenges of sensor development and explains how MIG’s newly formed Accelerated Innovation Community (AIC) is amassing industry expertise to empower the next generation of sensor-enabled applications. if you’re walking or running or sitting to understand the different states you’re in. The algorithms are essentially 80 percent math and working out the equations of what you need to do when things start

“What if we could do the basic stuff just to get people going?” to move, and the other 20 percent is getting it to work in a real system. It’s the 80/20 rule – sometimes that last 20 percent can take 80 percent of the time – because what you’re doing is taking something that might work in isolation because of the mathematics of the algorithm, but when you put it in a fairly

harsh environment like a smartphone or a tablet, you’ve got a lot of other interactions going on with other electromagnetic forces. You’ve got speakers and magnets around, you’ve got the traces on the circuit boards that affect fields, and so on. That all starts to get fairly complicated if you haven’t done it before. It’s not so much writing your first algorithm as it is getting it to work in a real system. It’s possible to get these algorithms from algorithm companies, and there used to be quite a few, but they’ve all been swallowed up now by hardware companies. So there are no longer independent algorithm companies except for the likes of PNI in Santa Rosa, Calif., and Hillcrest Labs in the Midwest. You can obviously go to the hardware companies and they will license the algorithms to you, but one of the things I wanted to do with this open-source algorithm initiative, or what we call the Accelerated Innovation Community (AIC), was give these startup companies a foundation they could start from. Basically open-source, free software

Embedded Computing Design | February 2015

algorithms that allowed them to get their first product going, even if it only got them a prototype or a proof of concept design that they could get in front of an angel investor just to give them a demo. What we’re trying to do is rely on some of the expertise of the people who have gone before us and worked on these kinds of applications and usage models and environments for many years and have tweaked those algorithms to get you started. ow did the AIC come about, H and what is currently available to developers?

such as an accelerometer, gyroscope, or magnetometer could make very good use of them. There are also some basic filters like Kalman filters, Quaternion filters, etc., and PNI has put up some heart rate monitoring capability and a step counter as well.

What the end user has now is a choice: you can take some very basic stuff and get started, and then either build on top of that in house or go license it from one of the sensor vendors or algorithm vendors that are still out there. So it’s giving you a little bit more choice.

This is not to try and take business away from the algorithm companies. Essentially, they don’t really get paid for these low-level algorithms – an OEM like Samsung or Apple isn’t going to pay for a basic step counter, they want the algorithm companies to focus on more of the complex, differentiated algorithms.

MEMS Industry Group (MIG)  memsindustrygroup.site-ym.com/ ?AIC @MEMSgroup opsy.st/MEMSIndustryGroupLinkedIn  youtube.com/user/ MEMSindustryGroup

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I actually started these discussions with Freescale over lunch, and they were willing to put in their basic algorithms. These are algorithms that probably most of the vendors have and they’ve all done their own version of them, and when a startup came along before the AIC they could try to license them for a cost or try to develop them themselves – for a basic pedometer, a step counter some, basic filters, whatever. What we discussed was that people are reinventing the wheel every time for this very basic stuff, and it impacts cost, it impacts time to market, and we said, “What if we could do the basic stuff just to get people going?” That would allow them to focus more on their value add – on what the product is really about. Like on a smartphone, you’ve got to have something that does portrait landscape as you flip the phone around, but your real value add is then adding health applications or environmental monitoring applications. If I’m a startup, that’s where I want to focus. That’s how the AIC was born. Freescale was the first to put their algorithms in, PNI has put some in, we’re getting algorithms from some of the universities like UC Berkeley and Carnegie Mellon, Analog Devices is going to be putting algorithms up there, and Kionix as well. Those are the main companies we launched with back at MEMS Executive Congress, and there are more coming aboard. There are some things up there, from a sensor fusion point of view, such as 3-axis, 6-axis, and 9-axis sensor fusion. So a design that uses basic inertial sensors www.embedded-computing.com

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MEMS

Balancing power and performance in wearables By Becky Oh and Andrew Taylor

In consumer products, replacing or recharging a battery isn’t a mission-critical operation. It’s just an annoyance. If that’s the case, why is everyone so focused on power and cost for wearables and IoT devices? One reason is that today’s wearables/IoT devices hold marginal benefit for consumers, at least relative to the product price. So component suppliers feel pressured to drive down costs while improving the performance and power consumption of their parts. Today most wearables rely solely on accelerometers for activity measurements. But the problem is that all accelerometer-only based wearables do not deliver the same results. I put this to the test last year when I wore both Jawbone UP and Fitbit simultaneously in order to compare the data.

Accelerometers measure acceleration, and in a simple implementation, by setting a threshold trigger on the accelerometer reading, one can extract the number of steps. Hence, most accelerometer-only based systems can be easily spoofed. Shake an accelerometer-based wearable device with periodicity, and it will pick up false positive steps and soft steps, which register below the set accelerometer threshold and may not be picked up. This causes either too few or too many steps to be counted. Today, every manufacturer’s accelerometers are essentially the same. It is actually the performance of the manufacturer’s algorithms that determine how wearables capture data. Clever algorithms can be developed to achieve higher-accuracy accelerometer-only step counting. PNI developed accelerometer-only based step counting algorithms optimizing both power and

What’s up with Jawbone UP and Fitbit? When I compared Jawbone UP and Fitbit, I found that both provided a solid overview of my daily activities and largely reported the same activity trends. While I did get a detailed breakdown on number of steps, distance traveled, and calories burned, I didn’t feel all that enlightened about my daily activity levels. What’s more, I was puzzled that the data from two devices were off by 10-20 percent.

Discrepancy demystified From a technical perspective, the discrepancy between the two devices was not surprising since both Jawbone UP and Fitbit are accelerometer-only based devices, and each company uses their own algorithms to determine step count and intensity.

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Figure 1

Jawbone Up and Fitbit dashboards for January 4, 2015.

Embedded Computing Design | February 2015

performance. This algorithm applies both biomechanical and heuristics-based filtering on threshold crossing features extracted over a 4-deep step buffer to accurately identify false or missing steps. In extracting the number of steps, the accelerometer-only algorithms proved to be more than 98 percent accurate while consuming less than 60 µA.

Hacked phone time PNI’s accelerometer-only step counting algorithm outputs step frequency, which the user can use in combination with leg length to relate the step count to step distance. However, adding a gyro input to the sensor fusion algorithm is an even better solution. It allows for accurate distance traveled without user input (calibration) and reduces false and missed steps.

That’s because the gyro lets us accurately maintain both the instantaneous and long-term Earth frame reference for gravitational and linear accelerations. We wanted to put the gyro addition to the test so we took two Nexus 5 phones and modified the hardware to include PNI’s M&M modules, which include a SENtral coprocessor running motion

Testing algorithms for accuracy In order to test the accuracy of the algorithms, we used 194 test vectors — including Brajdic’s “unconstrained smartphone“ open source data[1][2], which include both slow and fast walking profiles in each file — as well as data captured by PNI, including 30+ minutes of driving data with zero steps. The total log time was 305.25 minutes with 16,726 truth steps. Our algorithm reported 16,770 step counts resulting in a step count accuracy of 100.26 percent. The algorithm produced less than 3 percent false positive (additional false steps – noted as Fp) and less than 0.5 percent false negative (missed steps – noted as Fn), with a resulting median error of 1.46 percent. The distribution of Fp and Fn are shown in Figures 2 and 3. 90.2 percent of the test vectors had 1 Fn or less while 73.7 percent had 2 Fp or less (Table 2). The step counting algorithm is processed in the SENtral coprocessor, and the total average power consumption — including the 3-axis accelerometer — was less than 60 µA, which is equivalent to 17,000 hours on an alkaline AAA battery. These results look very good, and seem more than sufficient as a pedometer.

January 4

Jawbone UP

Fitbit

Delta

Steps

18,362

22,011

3,649 (16.6 percent)

Miles

8.7 miles

9.52 miles

0.82 miles (8.6 percent)

Total Calorie burn

1,792 cals

2,304 cals

512 (22 percent)

Steps

14,978

14,803

175 (1 percent)

Miles

7.28 miles

6.63 miles

0.65 miles (9.8 percent)

Total Calorie burn

1,698 cals

1,505 cals

193 (12.8 percent)

January 6

Table 1

Data comparison between Jawbone UP and Fitbit.

Total Files

194

Total Steps

16770

Total Truth

16726

Total Fp

488

Total Fn

Total log time (minutes)

305.26

The power-cost question

% Accuracy

100.26

Knowing that consumer products such as wearables are in cost-sensitive markets, is it worth it to include additional sensors even if it increases power consumption and cost? If a device is over $100, requires set-up, and needs to be charged once a week, I would want it to be more than just a pedometer. As long as it can maintain an acceptable power-consumption level and cost about the same as an accelerometer-only product, adding more sensors and functionality makes good sense. With MEMS gyroscopes (gyros) proliferating in smartphones, making them small, reasonably low power, and affordable, adding a gyro to a wearable could be an ideal solution.

% Median Error

1.46%

% 0 Fp

42.78%

% 0 Fn

70.10%

% 1 or less Fp

61.86%

% 1 or less Fn

90.21%

% 2 or less Fp

73.71%

% 2 or less Fn

97.94%

Mean F1 score

98.30%

Mean Recall

99.43%

Mean Precision

97.30%

Latency

3 steps

www.embedded-computing.com

Table 2

Summary of algorithm results for 194 test vectors.

13 

SILICOn

MEMS

sensor fusion algorithms, and inertial sensors from either ST or Bosch and AKM. Although the Nexus phones have gyros, accelerometers, and magnetic sensors, we needed to hack the phone to include the M&M modules so we could easily control the sensors

in Android and run low-power step counting algorithms from SENtral. We used two Nexus 5 phones, one with accelerometer-only step counting algorithms and the other with an accelerometer- and gyroscope-based pedestrian dead reckoning (PDR)

Anderson-Darling Normality Test

A-Squared P-Value <

22.68 .0005

Mean StDev Variance Skewness Kurtosis N

2.5155 4.0684 16.5516 2.38929 6.21937 194

Minimum 1st Quartile Median 3rd Quartile Maximum

0.0000 0.0000 1.0000 3.0000 21.0000

95% Confidence Interval for Mean 1.9394 3.0916 95% Confidence Interval for Median 0.8685 1.0000 95% Confidence Interval for StDev 3.6998 4.5191

algorithms. Holding these two phones on top of each other, we went around a test bench in the lab multiple times, walking 101 steps. Figure 4 shows the result after 101 steps. It is interesting to note that both algorithms report 101 steps, but the device to the right running PDR algorithms — using both a gyroscope and accelerometer — reported distance traveled without having to calibrate or input the users stride length. It was also able to trace the user’s path of travel. As we know from earlier tests, an accelerometer-only based system has limitations. It cannot track the user’s path of travel and cannot automatically calculate distance traveled unless the user inputs their average stride length. And using average stride length to calculate distance traveled is not as accurate as measuring the distance traveled for each step via a gyroscope with sensor fusion algorithms.

Worth the investment

Figure 2

False positives for 194 test vectors.

Anderson-Darling Normality Test A-Squared 31.16 P-Value < .0005 Mean StDev Variance Skewness Kurtosis N Minimum 1st Quartile Median 3rd Quartile Maximum

0.44845 0.86971 0.75640 3.0011 12.6153 194 0.0000 0.0000 0.0000 0.0000 6.0000

95% Confidence Interval for Mean 0.32530 0.57161 95% Confidence Interval for Median 0.0000 0.0000 95% Confidence Interval for StDev 0.79093 0.96607

Figure 3

 14

False negatives for 194 test vectors.

Adding a gyro to a wearable device is a logical choice. The results from an accurate PDR algorithm could allow us to create more compelling wearable applications, such as tracking lost children in a mall or monitoring elders. That’s more compelling than mere activity monitoring, for example, and it’s just the tip of the iceberg as to where these applications will ultimately go. It’s true that there are additional costs and power to consider. The incremental bill of materials (BOM) cost to add a gyroscope would be $1-$2 for the gyro and another $1 for added processing. The increase in power would be an additional 1-2 mA for the gyro and about 400 µA to run the PDR algorithm. That’s just $2-$3 in incremental hardware costs. While the increased system power is more than 20 times that of an accelerometer-only based step counter, the total system would run for about 12 hours. By opening up new classes of applications to wearables, that’s a trade-off I am willing to make. Manufacturers looking for that competitive edge in a fastmoving marketplace will see it the same

Embedded Computing Design | February 2015

Figure 4

Nexus phone on the left has accelerometer-only based step counting and Nexus phone on the right has accelerometer- and gyroscope-based pedestrian dead reckoning (PDR).

way. If adding a gyro requires only incremental costs/power consumption, but brings them vast numbers of new customers, then gyro- and accelerometerbased wearables are in our near future. References [1] Brajdic, Agata, and Robert Harle. “Walk detection and step counting on unconstrained smartphones.” In Proceedings of the 2013 ACM International Joint Conference on Pervasive and ubiquitous computing, 2013, pp. 225-234. [2] www.cl.cam.ac.uk/~ab818/ubicomp2013.html

More on…

Items

Recommendation

System-wide

Plan for a full product lifecycle – design, deploy, maintain, update, retire, and replace. Achieve as much consistency as possible by addressing security at the lowest possible level of infrastructure: operating system, network protocols, disk.

Devices

Design security and access policies for each device type or deployment environment.

Applications

Follow authentication best practices.

Data sets

Design security policies for each type of data for various stations where data is at rest or in flight.

Connections

Use secure network infrastructure, a secure protocol, and encryption of data.

Table 1

Guidelines for securing an Internet of Things solution.

The same approach to measuring risk and impact will guide decisions about how much protection is needed for data at rest, that is data written to disk on a device or server, and how much is needed when data moves between components.

Security and the Cloud

In some instances, encryption is not enough so data is transmitted in ways that context is difficult to reconstruct if some of the packets are intercepted. For example, one can separate credit card numbers from identifying information and send them in different transmissions. Some organizations use algorithms for “jumbling” and re-assembling data streams in addition to encryption.

First steps

Blog Security threats: The Dark Side of the IoT By David Somo, ON Semiconductor  opsy.st/IoTSecurityONSemiBlog

Blog A VPN may not be the right tool for IoT security By Bob McIlvride, Skkynet Cloud Systems, Inc.  opsy.st/VPNSecuritySkkynetBlog

Blog The hidden world of the IIoT: Between sensors and the cloud By Ron Sege, Echelon Corp.  opsy.st/IIoTSensorsCloudEchelonBlog

Article Connecting devices to the Internet of Things with Wi-Fi By Nikos Vokas, Econais  opsy.st/IoTWiFiEconais

 20

Is security for the IoT complex? Yes, because the attack surface is huge, the risk can be very high, and the consequences severe. The good news is that the tools at your disposal are familiar to most IT organizations and well proven. The challenging part is providing the right level of security at each device, gateway, or server and then surveying all the connection points, assessing the risk posed at each one, and choosing the best-suited protection method (Table 1). Ken McLaurin is Senior Manager, Product Strategy at Red Hat Inc. Red Hat Inc.  www.redhat.com @RedHatNews linkedin.com/company/red-hat plus.google.com/+RedHat  youtube.com/user/RedHatVideos

  

Embedded Computing Design | February 2015

Connected Cars

Can connected cars be secure cars? The growing concern over software security in the automobile industry By Kristen Maglia

Recent statistics about automobile safety are hard to miss these days. Attention-grabbing headlines have exploded both in mainstream and social media, and you can hardly read a blog without seeing one of them: “Hands-Free Driving is Not Trouble Free.” “Toyota Widens Recall of Cars with Takata Airbags.” “Hacked Driverless Cars Could Cause Chaos in London.”

So we’ve come to the “connected car.” While nebulous, this term is appropriate in describing this phenomenon. Most new cars coming off the production line today really are connected; they can easily communicate with other devices both inside and outside the vehicle. Smart devices sync to deliver in-car infotainment, to provide diagnostic information for the mechanic, and to

As sensational as these headlines are, the concern is real. Technology is moving faster than the government’s attempts to regulate, and nobody wants to stifle innovation, much less slow the consumer’s access to more and better bells and whistles on their cars. Although it seems like an overnight development, automotive manufacturing has followed a long road of innovation since the dawn of the electro-mechanical era of the 1970s through to today. Only now we’re at a tipping point. Innovation no longer starts and ends with a car’s mechanical components; software has now taken over as the kingpin of the automobile industry, not because software in cars is a new development, but because of the sheer volume of code powering cars and the resulting complexity. Estimates are that 60-70 percent of vehicle recalls are due to software glitches[1]. Cars are run by networks of computers, wireless connections, and electronic control units (ECU), offering the potential for hackers to access critical car controls such as steering and braking. Cars today also can easily connect to smart devices and the Internet, so it’s easy to see how those critical systems can be exposed. Exposed systems could mean scenarios including drivers losing control of cruise control mechanisms, braking systems, and other safety-critical operations. www.embedded-computing.com

21 

Strategies

Connected Cars

enable extra convenience controls such as navigation, roadside assistance, and parking apps.

be written in a matter of hours that jammed the message from the key fob to the car, disabling the locking system.[4]

It’s not just new models either. Older cars are increasingly connected using new systems, like O2’s Car Connection solution, which links drivers to their cars via smartphones, providing diagnostic information directly to the phone and to tools like a vehicle finder.

Securing the supply chain

So what controls the functionality in today’s cars? We’ve heard that today’s average high-end car has 100 million lines of software code[2], and anyone can appreciate the magnitude of that number, at least on the surface. (Especially when it’s contrasted with the space shuttle which, according to NASA, only contains 400,000 lines of code.[3]) But what does that number really mean? What does it mean to the consumer? To the automotive software supply chain?

It’s important to remember that the development process has evolved. Once a single developer or team of developers created code to solve a problem. Now, software development is very much akin to an art form, as developers assemble parts from various sources and skillfully coordinate their functions to create a cohesive, working product in the end.

It means that all of those millions of lines of code – regardless of where they come from – need to be bulletproof. Stakes are high. And, in vehicles, when software doesn’t work the way it’s intended, it’s serious.

The new role of the automobile manufacturer: Software security experts The business of keeping automotive software secure is a dicey one. Today’s connected car is assembled from pieces, parts, and code from various companies that make up the supply chain to the manufacturer, and the end result is what ultimately ends up on the showroom floor. For manufacturers with roots in mechanics, it’s increasingly difficult to get their processes up to date around the vastly different needs of hardware and software. The complexity that comes with the shift to the Internet of Things, devices, and communications networks is additive to existing processes and systems. Now, managers at car manufacturers need to ensure security within everything that makes up their cars. These same managers are also tasked with quickly adding the latest and greatest features to stay competitive. Security often takes a back seat when financial pressure mounts. It begs the question: How much thought has actually gone into the software security of automobiles before they are released? Security has not always been part of the day-to-day workflow in the development world. Developers might not even know what they should be doing as individuals to ensure the code they’re writing does not have security problems. And, typical development team leads may have not implemented the proper software tools, education on standards, and how to comply and production processes to make the job of ensuring security seamless. Manufacturers need to recognize that they are not only supplying cars, they are now cyber security managers as well. Although automobile hacks have yet to become commonplace, they do happen. Recently in Canada, authorities attributed “phantom” car break-ins to hacking, and found that a simple program could

 22

From the computer screen to the assembly line, manufacturers should now consider themselves attack vectors who are responsible for everything that goes into their products, not just what’s directly within their own development groups.

For instance, a company provides a manufacturer with the software that controls airbags. Developers for the airbag company may have incorporated open source software to visualize testing data, or they may have grabbed some prefab code to create reports. Some code controlling the airbag was written from scratch – but that could up as little as one percent of a total application. Another nine percent comes from the rest of the development team, and as much as 90 percent of any application could come from other sources – commercial software packages, outsourced development, open source, and legacy custom code. “It just doesn’t fly anymore to pass responsibility for security to another party – whether it’s the manufacturer to the supplier, or the supplier to the manufacturer,” says Stephane Raynaud, automotive account liaison for Rogue Wave Software. “It makes sense to leverage prebuilt functionality; every participant on the supply chain has to make sure every bit of it is safe and secure.”

Protecting companies and consumers: Know what’s in your code – all of it. How can companies protect themselves – their reputations, their financial stability, and their customers? They can do it by knowing, understanding, and taking responsibility for all of the code that makes up their product – not just what their own developers have written. The automobile industry could take a page from the playbook of telecom companies who faced a similar challenge several years ago when their devices suddenly became the only thing standing between the consumer and complex, embedded software code. These companies learned quickly that they could not pass all of the responsibility to the companies that supply the features in their product. They also learned that their product was only as strong as their weakest supplier’s code. The bottom line is that companies need to open the aperture of what they’re securing, and they need to do it before they become the subject of dramatic news headlines. And, those who are part of the supply chain need to tighten processes. Putting security first means three things: Embedded Computing Design | February 2015

1. Policies Organizations need to implement policies to take the guesswork out of how to ensure the security of its code. Successful policies are easy to follow, easily accessible, and properly educate the workforce so that developers know and understand security issues and how it is applies to their workflows.

will determine if they get used, and if used, can encourage good coding practices. Developers may have some fear about processes cutting into their creativity, so managers need to demonstrate how easy certain tools are to use, empowering them even more toward innovation. In short, providing the right tools means better, more secure software with less effort by the individual developer.

Management should implement these two types of policies:

õõ

Operations policies – These are documented policies outlining the tools that are approved for use in an organization, the agreed upon processes, and testing practices and test suites – all of which are designed to ensure optimal code security. Typically these decisions and policies are managed by an enterprise architecture group. Open source software policies – Designed to outline how an organization manages the open source in its code base, these policies cover how open source is used and when it is considered appropriate in the development process. More and more organizations are implementing groups within existing company functions to create and manage policies around open source code.

2. Processes Clear processes are key to secure software development. Teams from both the automobile manufacturers and the companies within their supply chains need to agree that security processes are important, and then mandate consistent application as nonnegotiable. Organizations can start by educating their own workforces on the importance of security and defining how each how each individual plays an important role in releasing secure products. Though top-level management may direct the need for processes, front line development managers should deploy processes that bring security into developers’ existing workflows and manage them ongoing. Processes should be seamlessly integrated with builds to ensure important steps aren’t forgotten, overlooked intentionally, or are too difficult to maintain. Processes should include:

õõ õõ õõ õõ

Building automated test suites Teaching secure coding practices Putting processes in place for acquisition and monitoring of open source Making tools readily available and updated regularly

And, anything developed by internal teams should apply to the supply chain. When accepting code from suppliers, manufacturers have a right to ask what processes are in place, and even require contractually that clean practices be applied. 3. Tools Knowledge goes a long way, but developers can only do so much to ensure secure code. Human error and hidden threats need advanced tools built that expose issues to the developer. Management should provide their development teams with automated, easy-to-use tools that operationalize policies and procedures. These tools should be built into processes, and automate the detection of critical security issues. Ease of use www.embedded-computing.com

Development managers can help ensure secure software development:

õõ

Open source scanning and support – As open source has become a large component of virtually any application, the first step is to discover what and where OSS is across all code lines. Also, ask these questions: Can the OSS be supported during any failures? Which packages have security vulnerabilities? How can we better manage our OSS use? Static code analysis – Static code analysis is the process of analyzing the health of source code without actually executing it. Developers should be able to identify and correct problems with code before it is ever checked in, saving time earlier in the development process. Dynamic code analysis – As a complement to static code analysis, dynamic code analysis is the process of executing code in real time to find security errors while it is running. Developers in complex environments using extensive memory and compute resources should have a dynamic code analysis tool at their disposal to perform simultaneous debugging of many processes and threads at once.

Setting the bar higher As cars become more connected, and our dependency on the software that powers them grows, the need for advanced tools to ensure security in code will grow too. Stand out organizations in the automotive industry will set the tone for other companies by not only creating higher standards for their developers, but by demanding the same level of excellence from their entire software supply chain. Progressive development managers today are already taking steps to defend their companies against worst case scenarios by providing expert knowledge, policies, processes, and tools to their developer workforce. References [1] http://www.automotiveworld.com/megatrends-articles/connected-carsconnected-era [2] http://www.motorauthority.com/news/1026505_modern-luxury-vehiclesclaimed-to-feature-more-software-than-a-fighter-jet [3] http://www.nasa.gov/mission_p+ages/shuttle/flyout/flyfeature_ shuttlecomputers.html [4] http://www.cbc.ca/player/News/Canada/Montreal/ID/2642436500

Kirsten Maglia is Automotive Campaign Director at Rogue Wave Software Rogue Wave Software  www.roguewave.com @roguewaveinc opsy.st/RogueWaveSoftwareGooglePlus linkedin.com/company/rogue-wave-software  youtube.com/user/roguewavesoftware

  

23 

Connected Cars

Updating car software: Why delta technology is better than compression By Yoram Berholtz

There is a lot of talk about software revolutionizing the automotive industry and the conversation is growing because of how software management will impact the whole business of recalls. The outrageous amount of good money going after bad is the reason why car manufacturers and Tier 1 suppliers are looking for an optimized and alternative way to reduce the amount of time it takes to deliver a software update, reducing the cost associated with recalls and improving customer satisfaction. If the same method for performing automotive software updates in production, at the dealer, or at home continue, so will the inefficiencies that are causing car manufacturers to pay hundreds of millions of dollars every year.

Download Size in Bytes 4,500,000 4,000,000 3,500,000 3,000,000 2,500,000 2,000,000 1,500,000 1,000,000 500,000 0

Figure 1

ď&#x201E; 24

-37%

-97% Full Image

Compressed V1-V2 Delta

Total Programming Time in Seconds 250 200 150 100 50 0

-43% -71% Full Download Compresion + Pipelining

V1-V2 Delta

Download size and programming time comparison between a full file download, a compressed file, and using the delta updating method.

Embedded Computing Design | February 2015

When doing a software update either over-the-air or via a cable, one goal is to deliver the smallest update package possible, reducing update time and cost. There are several methods to reduce the update file size but the two most notable are compression and delta (differential) updates – only sending the code that is different between the old software that needs to be updated and the updated software. With both technologies the goal is to reduce the number of bytes that are being delivered to: Reduce the download time – The new software needs to quickly get to the car’s gateway (e.g., head-unit) in order to start the update process Decrease the amount of needed memory – After the new version is delivered, there needs to be room to

õõ

store it before the update is started Decrease the transport time between the gateway and the target ECU – In case of ECU update, the new version needs to go through the CAN/LIM/NOST bus, which is limited in bandwidth Reduce the update time – The update time depends in some cases on the amount of changes that exist in the new version

There are tests conducted by leading automotive companies and scientific research that show in detail the comparison between compression solutions and delta update technologies. Vector, an embedded software testing company, worked with Red Bend on a proof-of-concept testing the efficiency of the delta technology. Vector chose an NXP chipset that is common

ECU Software Size

r -MRAM -Differential File Update - Network optimizations

Ethernet access e e -- Direct Ethernet VCI-bus/backbone

- FlexRay schedule optimization - Protocol optimizations

Data size reduction w w -(compression, partitioning etc.) q q CAN bus system today t

Today

Figure 1

Max. allowed programming time

As ECU software size increases, delta or differential updates can help address the challenge of increasing reprogramming times.

Description

File Size (Data to transmit)

Original File (complete)

Data Transfer Time on CAN

Unit

125

500

1000

Kbps

32 MB

4127.2

1031.8

515.9

Compression (-25%)

24 MB

3095.4

773.8

386.9

2 Partitions

16 MB

2063.6

515.9

257.9

Partitioning and Compression

12 MB

1547.7

386.9

193.5

Differential File

1 KB

0.1

0.031

0.016

Table 1

Theoretical case study of file size reduction methods.

www.embedded-computing.com

in ECUs – such as the powertrain – and connected it to vFlash via the CAN bus. The vFlash functions as the off-board tester for managing the reflash process. Vector ran an ECU reflash three times – one with a software full image, one with a compressed image and the third with using Red Bend’s delta technology combined with Vector’s bootloader. The efficiency of the delta technology is much greater than any compression technology (LZ77 in this case) (Figure 1). Using compression, the file went from 4.1 MB to 2.5 MB. Using delta technology, the file went down to 128 KB. There are interesting results that also support delta technology when comparing programming time associated with different processes and technologies. For the full download, programming time was 215 seconds; compression and pipelining was 124 seconds; in comparison, a delta program time was 63 seconds. Dr. Ralf Schmidgall in his thesis “Automotive Embedded Systems Software Reprogramming” (opsy. st/SchmidgallThesis) analyzed the methods of reducing the size of the version when doing software updates. In Table 1, Dr. Schmidgall summarizes the results of a theoretical case study to compare the approaches. The delta technology results in a much smaller file than any method of compression, and the impact on the update time is dramatic, even if the speed of the CAN bus is increased to 1000 Kbps also in this case the advantages of delta is clear. In his summary Dr. Schmidgall wrote, “Differential file update provides the best theoretical results of all researched approaches ... If the increase of ECU software sizes continues in the future, this approach might be the only sustainable one to solve the problem of increasing reprogramming times” (Figure 2). Yoram Berholtz is Director of Business Line Management at Red Bend Software. Red Bend Software  www.redbend.com @redbend linkedin.com/company/red-bend-software

 

25 

Connected Cars

Sensor-enabled nodes support the IoT for smart buildings and smart transport By Roger Grace, Roger Grace Associates, and Alessandro Bassi, Alessandro Bassi Consulting

Gartner Research values the aggregate number of IoT sensors to reach $10.1 billion by 2020 from $1.3 billion in 2014, with a compound annual growth rate (CAGR) of 41.7 percent. IDTechEx has estimated the market value for IoT IP-addressed sensing nodes to grow from less than $1 billion in 2015 to greater than $48 billion by 2025 (Figure 1). Sensing capabilities are significant in all fields, but smart buildings and smart transportation, referred to as “built infrastructure,” will represent markets of primary importance. In both fields, there’s a need for many different devices that can span from nodes providing basic monitoring to active nodes with a high computational capability. The rationales for the adoption of IoT in these fields are several, from social to environmental to economical. Energy conservation, environmental control, traffic optimization, infrastructure monitoring, accident prevention, and disaster containment are just some of the fields that can benefit from interconnected sensing devices.

60 50

■ Other ■ Telemedicine ■ Servers

■ Vehicles ■ Smart meters/smart ■ Security

cities

US$ billions

40 30

The global Internet of Things (IoT) phenomenon is opening unparalleled opportunities for sensor technology. A presentation at the 2014 IDTechEx Conference claimed that the bill of materials (BOM) for an IoT node is split evenly between sensors and transceiver (at 45 percent each), with a small part left for the microprocessor (5 percent) and other functions (5 percent). Internet of Things numbers can make heads spin: Cisco IBSG predicts 25 billion IoT devices by 2015 and 50 billion by 2020;

 26

20 10 0

2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Figure 1

IDTechEx is forecasting the value of IP-addressed sensor nodes to increase from $0.68 billion in 2015 to $48 billion in 2025, constituting a 47 percent compound annual growth rate (CAGR). Embedded Computing Design | February 2015

Besides a thorough knowledge of sensing capability, understanding the different communication characteristics of IoT nodes is of primary importance. Tradeoffs must be made when developing a solution, and a proper architectural study will enable the minimization of costs, maximizing system performances at the same time.

SIGNAL POWER/CONTROL BACKEND CONDITIONING ELECTRONICS COMMUNICATIONS ELECTRONICS ELECTRONICS • Sensor(s) • ASICS • Energy Harvesting • Wireless • Actuator(s) • DSP • Battery • Non-wireless • Structure(s) • Microcontroller • Networked Monolithic/Heterogeneous

MEMS FRONT END

FUNCTIONS

Characteristic of IoT nodes

PACKAGING/INTERCONNECTS

Design for Manufacturing Test

DESIGN The major characteristics of IoT nodes Co-Design PRINCIPLES (Figure 2) include a sensor front-end, lowSystems Engineering power signal conditioning electronics (typically an ASIC including a microcontroller with embedded algorithms), power MEMS-based Systems Solutions (MBSS) integrate critical elements of the Internet of Things supply/storage/management, and back(IoT) including sensing, computing, and communications to provide valuable measurements Figure 2 end, low-power communications, usually capability for smart building/smart transport monitoring and control applications. wireless and enclosed in a package (see microelectromechanical systems-based (MEMS-Based) Systems Solutions for more information). The Constrained terminals show important limitations with respect technological challenge for the implementation of such devices to unconstrained terminals: a reduced transmission capais limited to the integration and packaging of different existing bility, smaller than 1 Mbps; a limited energy reserve; a limcomponents, as well as the availability of energy harvesters to ited data storage capability (typical values are 10 KB for RAM make the node self sufficient. and 100 KB for ROM); and a limited computational power (less than 100 MHz). Finally, tag-type terminals show extreme In the IoT domain, networks can be classified as unconstrained limitations in computing power, memory storage, and energy (NTU) – characterized by high-speed communication links, storage. offering transfer rates in the Megabit per second (Mbps) range – and constrained (NTC) – characterized by relatively low Smart buildings transfer rates, typically smaller than 1 Mbps. Smart buildings provide a quality and comfortable environment, and increased safety and security while operating in The network taxonomy is also dependent on the type of terminal an energy-efficient fashion. A typical example is the Nest used. Unconstrained terminals have high computational power “learning” thermostat. It consists of seven non-MEMS senand a theoretically unlimited energy reserve, allowing them to sors measuring not only temperature and humidity but also implement complex tasks such as strong cryptography, HTTP presence, allowing temperature control based on occupant traffic, and high transmission rates typical of NTU networks. usage history.

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Security Innovation ACE Labs

• Power failsafe reliability ensures complete data integrity • Improve flash memory endurance to extend the working life of data storage • Fast performance to capture data quickly and completely • Pre-ported to Linux/Android, VxWorks, Windows Embedded Compact; i.MX and OMAP processor families • Ask us about support for other operating systems and processor families • Transactional file system field-proven by the leading producers of fleet telematics and IVI systems

Security Innovation’s Automotive Centers of Excellence Labs in Boston and Seattle provide automotive OEMs, Tier 1 and after-market suppliers with research and guidance to help secure their platforms and applications. • SECURE SDLC GAP ANALYSIS & OPTIMIZATION: We identify key points within the process to incorporate security at each phase, streamline activities, map development activities to compliance, and improve security tool usage. • ARCHITECTURE & DESIGN REVIEW: We provide mitigation recommendations via a detailed Threat Model, a summary of potential architectural issues, and a Risk Mitigation Plan. • VEHICLE ATTACK SIMULATION: Our security engineers will conduct attacks on your vehicle or subsystem (WiFi, Cellular, Bluetooth, RFID/NFC, 802.11p (V2X), OBD2, USB, CAN bus, CD/DVD, and more.), disclosing which hardware and software applications are putting you at risk. • PENETRATION TESTING: Our experts identify the highest risk areas and provide a detailed report that includes severity rating and prescriptive remediation guidance. • Aerolink™ Vehicle to Vehicle (V2V) Integration Services: Our software engineers can integrate Aerolink with your hardware for the US and European markets.

www.datalight.com/solutions/industries/ automotive

www.embedded-computing.com

www.securityinnovation.com

27 

Strategies

Connected Cars

The Bob and Betty Beyster Computer Science Building at the University of Michigan was recently instrumented by Professor Jerry Lynch of the Center for Wireless Integrated MicroSensing and Systems (WIMSS) with 15 Martlet wireless sensor nodes consisting of 45 channels of temperature, humidity, and CO2 sensors. The objective of the project, states Professor Lynch, is to “deploy a sensor network and model the environmental conditions as they relate to heating, ventilation, and air conditioning (HVAC) performance. The next steps include monitoring occupant’s behavior/presence and connecting the network directly to the control system of the HVAC system to achieve optimum performance versus cost.”

system is mounted on lampposts and uses CO2 sensors to measure air quality. Sensys Networks has developed a similar magnetic sensorbased system for use in traffic intersections. The system consists of a three-axis magnetometer, signal conditioning electronics with embedded software, and a radio in a 3" x 3" x 3" package that gets embedded at traffic roadways and intersections. This is clearly a lower cost solution to today’s large, 6' diameter magnetic loops. This package will be enhanced with a low noise floor, high-sensitivity accelerometer to determine vehicle classification based on axle counts and spacing using vibration signature analysis.

Smart transport Major drivers for IoT adoption in transport are safety, convenience, fuel efficiency, and environmental pollution. Libelium has developed a system of sensor platforms measuring the presence of parked vehicles in Santander, Spain. This 400node monitoring system includes magnetic sensors, signal conditioning electronics, a 7- to 10-year battery life, and a radio in a 12 cm diameter package. Data is transmitted to an access point on a nearby lamppost and relayed to the parking department headquarters where it gets analyzed and then sent to displays on the street. It can also be accessed by Internet-connected devices to direct vehicles to the appropriate available parking spots. Additionally, another 600-node

Sensys has also introduced “micro radar” sensor systems, installed at intersections and bike lanes. Consisting of a highly directive radar antenna operating at 6.3 GHz, the system can determine the presence of bicycles in a range from 1.2 to 3.0 m. The radar approach was adopted because a magnetic sensor can’t adequately detect the presence of people and composite materials of bicycles. Similar functionalities including signal processing, battery, and a radio are employed. The U.S. highway system is a prime example of how a valuable asset has been permitted to slowly deteriorate to the point where several bridges have collapsed, notably, the I-35W bridge over the Mississippi River in Minneapolis, resulting in 13 casualties. Many of the original highway roads and bridges constructed from the 1950s to the 1970s as part of the interstate highway system have exceeded their design life and traffic expectancy. Public funding has been limited to support adequate maintenance and repair. A recent study, Federal National Bridge Inventory, showed that 65,605 of 607,380 bridges were classified as “structurally deficient” (in need of rehabilitation or replacement because at least one of the major components of the span has advanced deterioration), and 20,808 were classified as “fracture critical” (without redundant protections and at risk of collapse if a single, vital component fails). To directly address this severe situation, Michigan’s Professor Lynch has instrumented two bridges – the Monroe Michigan Telegraph Road Bridge and the New Carquinez California Bridge – with sensor nodes to determine the bridge’s structural status under dynamic conditions. Built in 2003, the New Carquinez Bridge has 31 wireless sensor nodes deployed across the 1.056 km structure. A total of 87 channels of triaxis accelerometers, strain gages, wind velocity, temperature, and potentiometer displacement sensors are measured using a proprietary Narada 4" by 4" printed circuit board platform that can accept up to four channels of sensor data. Professor Lynch states that the purpose of the implementation was to determine the cost-effective deployment and robustness of the Narada sensor nodes and their remote sensors. Installed in 2011, the system is currently collecting data and is supported by the California Department of Transportation.

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Embedded Computing Design | February 2015

Strategies

Connected Cars

Data taken by the system will be used to validate the models developed by the WIMSS team and will be used to better understand the response of the bridge under conditions including high wind loading and earthquakes. Professor Bill Spencer of the University of Illinois UrbanaChampagne and his team have instrumented the Jindo Island Bridge in Korea with 113 nodes (the largest deployment of its type for bridge monitoring) over the 344 m span. The 659 data channels are comprised of sensors including accelerometers to measure vibration in the bridges stay cables, strain gauges, anemometers for wind speed and direction, and temperature and light level sensors. The system was installed in 2010 and operated until 2012. Professor Spencer stated, “In conjunction with our colleagues at Seoul National University, we have demonstrated that we can deploy a wireless autonomous measurement solution that’s robust and significantly lower in cost at about $100 per channel. This project has returned results as expected, and we’ve been able to better understand the wind loading algorithms and validate our models.”

New developments A solution for smart buildings currently being developed by Innoveering uses accelerometers and strain gages connected to a node/access point. The Enhanced Structural Collapse Awareness and Prediction Equipment (ESCAPE) application measures the structural integrity of a building during a fire and warns first responders of the building’s condition to keep them from harm’s way. This program is in the early stages of hardware and algorithm development.

More on…

Professors Babak Moaveni and Usman Khan of Tufts University are developing drone-based optical systems for the inspection of bridges. Such inspection is currently conducted by engineers and maintenance personnel using visual methods. The Tufts researchers are exploring the instrumentation of drones with HD and IR camera to take pictures of the structures, store the information on the drone’s memory system, and download the information when the drone returns to base. Using the drone pictures to detect cracks in the structure coupled with vibration signature analysis is expected to achieve higher accuracy assessments of deteriorating structures. A major advantage of this approach is that it uses a historically and highly acceptable approach of determining bridge structural deterioration – visual – which is expected to facilitate its acceptance by the maintenance community.

A cost/benefit analysis of IoT sensor nodes Wireless autonomous sensor networks/IoT nodes have two main components: sensors and communication modules. It’s possible to classify communication devices according to their capabilities: unconstrained, constrained, and tags and system architectures must integrate various IoT nodes seamlessly. They’ve been in operation over the past years in different domains, but mainly in pilot projects. Based on research results, the major barrier to their widespread adoption is funding. Although many studies have established the aging nature of our roadways and bridges and their constant deterioration, this isn’t sufficient to motivate government agencies to address these problems structurally. These conditions exist in Japan, China, and Vietnam, as well as the U.S. We believe that a cost/benefit ratio should be used as a primary index for developing IoT nodes and monitoring systems. The replacement cost of the I-35W Mississippi Bridge was $234 million, which is enough to instrument more than 20,000 bridges.

Automotive

E-mag Automotive E-mag Issue 2  opsy.st/AutoEmag2

E-cast Three ways to ensure your automotive developers deliver secure, compliant, defect-free software Presented by Rogue Wave Software  ecast.opensystemsmedia.com/515

News GENIVI Alliance to provide Android Auto interface By GENIVI  opsy.st/GENIVIAndroidAuto

Roger Grace is the president of Roger Grace Associates, a strategic marketing consulting firm specializing in high technology. Alessandro Bassi is currently an independent consultant, working on topics related to the Internet of Things (IoT), cloud storage, and smart cities, and has been a keynote speaker in more than 100 events. Roger Grace Associates  www.rgrace.com  rgrace@rgrace.com Alessandra Bassi Consulting  www.bassiconsulting.eu  info@bassiconsulting.eu @bassiconsulting

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Embedded Computing Design | February 2015

Internet of Things

Innovation and collaboration in the Internet of Things With the rapid development of an emerging area of embedded computing like the Internet of Things (IoT) comes the dreaded fragmentation that can hold it back from further growth. Companies are always thinking about how to make the IoT bigger and better, as you will see in the following executive speakouts and product spotlights, but interoperability is still a challenge. “We don’t want to have smart people solving it one way over here and another way over there, and then have 15 different incompatible versions of the framework,” says David McCall, Senior Strategic Planner, Communication Frameworks, Intel corporation. “We’ve created an organization made up of some of the leading industry players and smaller companies who are interested in participating in this space, and we all come together and solve the problem once.” The 50-member-and-growing OIC takes a unique approach by working on both open source code solutions and established

connectivity standards to combat fragmentation with scalability and interoperability across verticals. “Everyone is developing really, really cool apps within verticals,” says Guy Martin, Senior Strategist, Open Source Group at Samsung. “We think that the next great thing in IoT and the future of IoT are those apps that go cross-vertical?” As part of their work, the Linux Foundation is hosting developer collaboration through the IoTivity Open Source Project (www. iotivity.org), an open source software framework for IoT connectivity. The IoTivity preview release launched in mid-January and serves as a reference implementation of future OIC standards. For more on the IoTivity preview release, see opsy.st/IoTivityRelease, and for the full interview with David McCall and Guy Martin from the Consumer Electronics Show, see opsy.st/OICQandA.

Internet of Things Executive Speakout

Industrial IoT Devices Demand Enduring, Decision-Quality Data By Datalight

www.datalight.com

As data storage demands for Industrial IoT (IIoT) edge devices such as intelligent sensors increase, new requirements for storage software are emerging. Datalight is bringing its expertise to these highly resource-constrained, hard real-time systems through purpose-built products leveraging our data storage technology and expertise. Many market-leading OEMs in the ruggedized segment of the industry have adopted Datalight’s flash memory and file system technology to create end-user products that are winning reliability awards and delighting their customers. Many of these products have complex use cases and multiple applications with products like handheld terminals, fleet management computers, industrial automation controllers, and medical diagnostic and treatment devices, so data storage has been a priority. www.embedded-computing.com

As designers make IIoT devices smarter, a common design goal is for the intelligent device to operate autonomously and adjust its behavior based on data-driven decisions. The goal is to save time, money, and even lives. The ability of the IIoT device to accomplish this mission hinges upon reliable availability of decision-quality data. These emerging require reliable storage over the entire lifetime of the device that won’t require a complex and risky software update or a trip to Mars to work around a failing flash memory device. In the brave new world of IIoT, data can be rapidly collected and has potential to be transmitted up to the cloud for storage. But this doesn’t mean designers should neglect the integrity of their primary, on-device data storage. You might be thinking, “I don’t have to store it locally,” but is there a connection available all the time? Will network data traffic cause an unrecoverable timeout? Have you accounted for degradation of hardware performance over time? If the data on your device can’t be reliably saved or retrieved, it doesn’t matter how good you make your application or the hardware you produce. Reliable data storage needs to be treated as an asset and or it becomes a liability.

31 

Internet of Things Executive Speakout

Hyperconnecting the Internet of Things By Jens Wiegand, CTO at Kontron

www.kontron.com

Harnessing the Internet of Things (IoT) and making it a reality promises immense opportunities to companies worldwide, but it is not without some serious challenges. Today, the market is fragmented and characterized by incompatible systems and stovepiped solutions. To deliver a viable end-to-end IoT implementation requires an approach that removes the barriers between traditional information technology (IT) and operational technology (OT) to form a hyperconnected infrastructure. IoT applications such as those supporting predictive maintenance, analytics and big data requires a holistic methodology so there is ample cohesiveness between hardware and software suppliers, service providers and communication infrastructure vendors.

One-size-fits-all won’t work

Few suppliers are offering a broad breadth of IoT solutions today. Only solving a part of the problem has led to a lack of necessary interoperability. Individual islands of automation don’t provide the capabilities needed unless suppliers can integrate with the enterprise aligning the entire solution. Taking this type of global approach allows companies to share data and analysis with partners or consumers, or take in data from other IoT sources.

õõ

Most organizations do not have the expertise to develop an end-to-end IoT solution. Suppliers end up supporting just a subset of capabilities rather than providing crucial technologies that meet multiple protocol requirements, the diverse set of application development strategies, trusted device insertion and management needs and real-time capabilities combined with enterprise scale and cloud-based services.

 32

A good example of the challenges facing designers is in the development of industrial IoT (IIoT) that need to connect beyond one cluster of devices. Compounding the issue is these developers rarely have a “greenfield” to work with. For developers to deploy applications that enable an enduring IoT transformation mandates that they do more than plug devices into a common network protocol or use a single hardware and software development model for a set of pre-defined services. The complexity involved in a typical IIoT deployment consists of:

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Multiple connectivity protocols, both wired and wireless, call for gateway and protocol conversion capability. Hardware requirements range from tiny, power-efficient microcontrollers to single-board computers (SBCs) and systems to massive, workloadoptimized datacenter infrastructure. Software frameworks vary in development approaches, programming languages, standards compliance, completeness, robustness and openness. Services need to blend traditional information technology (IT) with operational technology (OT), integrating end-to-end capability from real-time performance to analytics.

To simplify these types of “brownfield” deployments, new development models are necessary. Hyperconnecting is a multifaceted vision for how the IoT should be built. It allows events distant or near to be sensed, combined, interpreted, and understood – with the ability to access

actionable information whenever and wherever it is needed. Hyperconnecting allows end-to-end IoT implementations to combine the following attributes:

õõ

õõ õõ

õõ õõ õõ

Sensor aggregation with multiple wireless protocols, including Wi-Fi, Bluetooth Low Energy (BLE) and ZigBee Scalability across hardware platforms with support for ARM and Intel architectures Co-existence of C and Java development with open application programming interfaces (APIs) and dynamic components Flexible messaging including RESTful web services and DDS or MQTT for publish/subscribe IT-style management such as trusted boot, role-based access, certificates, and authentication Integration with leading enterprise databases and predictive analytics packages

End-to-end IoT Readiness Kontron is perfecting the approach to IoT. Its hyperconnecting architectures on a foundation of standards-based solutions fully enable development flexibility and enterprise integration with openness for future requirements. Backed by two decades of embedded computing evolution, Kontron can leverage extensive experience incorporating thousands of real-world application deployments in the commercial, industrial, medical, and transportation markets, just to name a few. Kontron knows that to achieve IoT success means helping organizations develop true endto-end solutions that also reduce costs and enhance revenue streams.

Embedded Computing Design | February 2015

Internet of Things Executive Speakout

IoT Security Done Right Vitesse Semiconductor www.vitesse.com

Martin Nuss, CTO Vitesse Semiconductor

Cisco’s most recent Visual Networking Index forecasts the number of devices connected to IP networks at nearly 2x the world’s population in 2018. This translates to almost three networked devices per capita by 2018, almost a 50% increase over 2013. And clearly our world’s connectedness to the Internet of Things (IoT) will only continue, as devices become even more widespread and sophisticated. Morgan Stanley forecasts 75 billion devices will connect to the IoT by 2020. With practically daily reports of security breaches, cybersecurity will undoubtedly remain a hot topic. What many may not realize, however, is that resolving these security issues will be crucial to the IoT’s success. Think about it. Increasing the number of connected “things” multiples network endpoints exponentially, and each now represents a network vulnerability, especially given that hackers can theoretically compromise anything with an IP address. Obviously, no single security initiative can stem all potential intrusions. But embedded systems designers will need to pay close attention to securing not only applications, but networks and devices themselves. Applications security now commonly uses Authentication, Authorization and Accounting (AAA), in conjunction with data confidentiality (encryption). However, this effort is futile if networks and devices are not subject to the same AAA principles. Particularly as the number of mobile devices connecting into embedded systems like traffic control networks or smart grid systems increases, the consequences of a security breach can become deadly. Imagine a rogue device swapped into a nuclear power plant network that fails to authenticate, authorize and account for each of its diverse endpoints. The good news is that securing these last mile links is possible today with encryption technologies like IEEE 802.1AE with strong 256-bit encryption and link layer AAA. As an industry, we just need to be diligent enough to apply these technologies pervasively.

www.embedded-computing.com

33 

Internet of Things Product Spotlights

Revenue-Grade Data for Industrial Internet of Things Devices

Deliver safe, secure mission critical software, faster with Rogue Wave Software

• Power failsafe reliability ensures complete data integrity • Improve flash memory endurance to extend the working life of data storage • Fast performance to capture data quickly and completely • Pre-ported to Linux/Android, Windows Embedded Compact; i.MX and OMAP processor families • Ask us about support for other operating systems, including VxWorks! • Transactional file system field-proven by the leading producer of IoT edge intelligence platforms

• Prevent hacks and data breaches to safeguard your software applications against threats, attacks, and security vulnerabilities. • Meet safety-critical standards and compliance, whether they are government and industry standards or a company policy. • Build code confidence and have time to focus on creating innovative apps. • Klocwork puts static code analysis at the desktop, identifying critical safety, reliability, and coding standards issues in front of developers’ eyes – well before check in. • OpenLogic offers an enterprise-class set of management, scanning, and support tools designed to simplify development and minimize risk of open source software. • TotalView debugger provides unprecedented control over processes and thread execution, along with deep visibility into program states and data.

www.datalight.com/solutions/industries/ industrial-internet-of-things

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www.Roguewave.com

Embedded Computing Design | February 2015

Internet of Things Product Spotlights

IoT ready KBOX A-201

RTPatch®: Binary Diff Patch Software Solution

• Embedded Fanless BOX PC • Internet of Things (IoT) ready • From Intel® Quark™ X1020 to Intel® Atom™ E38xx • Maintenance-free • Soldered memory conception • Increases the flexibility, serviceability and cost efficiency for various applications

• COTS binary patch diff for all firmware, data and software updating, including FOTA • Proven safe and reliable with billions of updates applied since 1991 • Typical reduction 90+% • Supports any OS and hardware combination, or without OS • Small footprint, customization available, C source code included • From industry leader Pocket Soft, established in 1986

www.kontron.com/products/systems-and-platforms/ embedded-box-pcs/fanless-box-pc/kbox-a-201.html

pocketsoft.com

The IoT E-mag Bluetooth modules

The Internet of Things E-mag deconstructs the IoT with features that investigate device/network infrastructure, comprehensive cyber security, reengineering business models, and much, much more.

• Bluetooth 4.0, Classic and Smart Ready modules • Wi-Fi modules • Reliable and robust wireless solutions for any application • 15-year industry veteran • >98% customer satisfaction • First to market with Bluetooth Low Energy (BLE) modules

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http://opsy.st/IoTEmag

For more on latest news, articles, blogs, white papers, and products on the Internet of Things visit embedded-computing.com/topics/iot

www.embedded-computing.com

35 

Editor’s Choice

embedded-computing.com/editors-choice

Web Accelerator platform

Suvola | www.suvola.com

embedded-computing.com/p372560

The Suvola Web Accelerator is a secure and trusted web services appliance platform. The platform includes a low-latency HTTP server with reverse proxy, caching at the edge for content acceleration, security, and bandwidth management capabilities to provide a variety of front-end services for managing multiple application servers. The platform was designed to secure and accelerate cloud infrastructure workloads based on Freescale’s QorIQ multicore SoC products.

ARM CPU+FPGA module with dual OS monitor, TrustZone support DAVE Embedded Systems BORA is a Dual Cortex-A9 CPI code with integrated Xilinx Zynq application processor. The SafetyGate (SafeG) dual OS monitor has also been ported to the BORA platform and takes advantage of the ARM TrustZone security extensions. BORA is suitable for ruggedized applications requiring small form factor such as medical instrumentation, advanced communications systems, real-time and safety applications.

DAVE Embedded Systems | www.dave.eu/home.html

embedded-computing.com/p372565

Multicore programming solution, high performance, fast time to market

Texas Multicore | www.texasmulticoretechnologies.com

The SequenceL development environment is tailored for multicore and many-core programming applications. SequenceL provides a powerful functional programming language and auto-parallelizing tools for tuning code for multicore platforms. The platform plugs into Eclipse and Visual Studio IDEs, features an auto-parallelizing compiler, and a runtime environment that identifies available cores and allocates workloads to maximize utilization.

embedded-computing.com/p372566

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Embedded Computing Design | February 2015

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WEB ••• WIRE

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Floored: 2015 International CES Blog

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The theme of CES is connectivity – IoT, wearables, 5G mobile, and car connectivity, just to name a few specifics. See Brandon’s highlights of CES 2015 in this slide show.  opsy.st/CES2015Highlights

Blog

Some bold “embedded” predictions for 2015 By Rich Nass, Embedded Computing Brand Director

What can we expect from 2015? That’s a great question, and it’s one I posed to our esteemed Advisory Board. In no particular order, here are their responses.  opsy.st/EmbeddedPredictions2015

VIDEO

5 minutes with ... Jim Ready, Chief Technology Advisor, Cadence

Automotive E-mag E-mag

By Rich Nass, Embedded Computing Brand Director

The second installment of the Automotive E-mag shifts into gear with features covering in-vehicle MCU consolidation, the importance of independent software for auto manufacturers, ISO 26262 and MISRA coding tools and techniques, connected car tech, and more. Watch

In this weekly video series, Rich Nass talks embedded with industry heavyweights on various topics. In this installment, Rich and Jim Ready discuss the state of education and addressing business challenges.  opsy.st/5MinutesWithJimReady

 opsy.st/AutoEmag2

E-cast

Interoperability and the Internet of Things – To standardize or not to standardize?

Presented by ADLINK, RTI, ThingWorx

Top 3 strategies to reduce risk in automotive/in-vehicle software development WHITE PAPER

By Rogue Wave Software

Development teams, especially the managers who are ultimately responsible, face incredible challenges when building in-vehicle applications, and are learning that team members need to do more than just catch code defects during verification and validation testing. The new imperative: Identify and address security and compliance concerns earlier in the lifecycle, all while delivering innovative and differentiating features.

The Internet of Things (IoT) encompasses a range of technology verticals, from consumer electronics and connected cars to industrial controls and the smart grid. However, while this immense diversity has created a market opportunity valued as high as $14 trillion, it has also generated significant interoperability issues stemming from a vast amount of available communications protocols.  ecast.opensystemsmedia.com/520

 opsy.st/AutomotiveRiskStrategiesWP

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Embedded Computing Design | February 2015

The mobile communications revolution is driving the world's major technology breakthroughs. From wearable devices to connected cars and homes, mobile technology is at the heart of worldwide innovation. As an industry, we are connecting billions of men and women to the transformative power of the Internet and mobilising every device that we use in our daily lives. The 2015 GSMA Mobile World Congress will convene industry leaders, visionaries and innovators to explore the trends that will shape mobile in the years ahead. Weâ&#x20AC;&#x2122;ll see you in Barcelona at The Edge of Innovation.

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