Subscribe to our Newsletter | To Post On IoT Central, Click here


All Posts (949)

Sort by

Ever wanted the power of the all new Raspberry Pi 4 Single Board Computer, but in a smaller form factor? With more options to expand the I/Os and its functions? Well, The Raspberry Pi Compute Module 4 (a.k.a. CM4) got you covered! In this article, we’ll be taking a deep dive into the all-new CM4, see what’s new and how different the latest iteration is from its predecessor, CM3.

Introduction - The System on Module Architecture

The CM4 can be described as a ‘stripped-down’ version of the Raspberry Pi 4 Model B, which contains the same processor, memory, eMMC flash memory and the power regulation circuitry built-in. The CM4 looks almost like a breakout board with two connectors underneath, hence the name “System on Module (SoM)”. However, what differs the CM4 (all compute modules for that matter) from the regular Raspberry Pi 4 is that the CM4 does not come equipped with any hardware I/O ports such as USB, Ethernet and HDMI, but offers access to all the useful I/O pins of the cpu to be utilized to connect external peripherals that the designers include in their circuit designs. This offers the ultimate freedom to the designers and developers to use the computing power of the Raspberry Pi 4, while reducing the overall cost of their designs by only having to use what’s necessary in their designs.

 

What’s New In The CM4?

The key difference with the CM4, at first glance, is the form factor of the module. The previous versions, including the CM3 were designed to have the DDR2-SODIMM (mechanically compatible) form factor which looked like a laptop RAM stick. The successor, CM4 comes in a smaller form factor, with 2x 100-pin High-Density connector which can be ‘popped-on’ to the receiving board.

8221226476?profile=RESIZE_710x

Key Features

The CM4 comes in 32 different variants which has varying Flash and RAM options and optional wireless connectivity. Similar to the predecessors, there is also a CM4Lite version, which does not come with a built-in eMMC memory, reducing the cost of the module to a minimum of $25. However, all the variants of CM4 are equipped with following key features:

 
  • Broadcom BCM2711, Quad Core Cortex-A72 (Arm V8) 64-bit System on Chip, running at 1.5 GHz

  • 1/2/4/8GB LPDDR4 RAM options

  • 0(CM4Lite)/8/16/32GB of eMMC storage options (upto 100MB/s bandwidth)

  • Smaller footprint of 55mm x 40mm x 4.7mm (w x l x h)

  • Supports H.265 (4Kp60 Decode); H.264 (1080p60fps Decode, 1080p30fps Encode) using OpenGL ES 3.0 graphics

  • Radio Module

  • 2.4/5GHz IEEE 802.11 b/g/n/ac Wireless (optional)

  • Bluetooth 5.0 BLE

  • On-board selector to switch between PCB trace antenna and external antenna

  • On-board Gigabit Ethernet PHY supporting IEEE 1588 standard

  • 1x PCI Express Gen2.0 lane (5Gbps)

  • 2x HDMI2.0 ports (upto 4k60fps)

  • 1x USB 2.0 port (480MBps)

  • 28x GPIO pins, with the support on both 1.8V or 3.3V logic levels along with the peripheral options:

  • 2x PWM channels

  • 3x GPCLK

  • 6x UART (Serial)

  • 6x I2C

  • 5x SPI

  • 1x SDIO interface

  • 1x DPI

  • 1x PCM

  • MIPI DSI (Serial Display)

  • 1x 2-lane MIPI DSI display port

  • 1x 4-lane MIPI DSI display port

  • MIPI CSI-2 (Serial Camera)

  • 1x 2-lane MIPI CSI camera port

  • 1x 4-lane MIPI CSI camera port

  • 1x +5V Power Supply Input (on-board regulator circuitry available)

 

The Applications - DIY? Industrial?

The CM4 can be integrated into end products, designed and prototyped using the full-size Raspberry Pi 4 SBC. This allows the removal of unused ports, peripherals and components which reduces the overall cost and complexity. Therefore application ideas are virtually limitless and range all the way from DIY projects such as the PiBoy to industrial IoT designs such as integrated home automation systems, small scale hosting servers, data exchange hubs and portable electronics which require the processing power offered by the CM4, all while maintaining the smaller form factor and power consumption. Compute Module Clusters such as the Turing Pi 2, which harnesses the power of multiple Compute Modules are also an option with this powerful, yet small System on Module, the Raspberry Pi CM4.

 

How Can I Use Upswift Solutions On My Compute Module 4 Based Design?

Upswift offers hassle-free management solutions for all Linux-based embedded systems (CM4 included), by providing you a one-click solution to monitor, control and manage all your connected devices, from one place.

Originally posted HERE.

Read more…

It’s been a long time since I performed Karnaugh map minimizations by hand. As a result, on my first pass, I missed a couple of obvious optimizations.

I’m sorry about the title of this blog, but I’m feeling a little wackadoodle at the moment. I think the problem is that I’m giddy with excitement at the thought of the forthcoming Thanksgiving holiday.

So, here’s the deal. Starting sometime in 2021, I’m going to be writing a series of columns for Practical Electronics magazine in the UK teaching digital logic fundamentals to absolute beginners.

This will have a hands-on component with an accompanying circuit board. We’re going to start by constructing some simple logic gates at the transistor level, then use primitive logic gates in 7400-series ICs to construct more sophisticated functions, and work our way up to… but I fear I can say no more at the moment.

After we’ve created some really simple combinatorial functions — like a 2:1 multiplexer — by hand, we’re going to introduce things like Boolean algebra, DeMorgan transforms, and Karnaugh maps, and then we are going to use what we’ve learned to implement more complex combinatorial functions, cumulating in a BCD to 7-segment decoder, before we progress to sequential circuits.

I was sketching out some notes this past weekend. Prior to the BCD to 7-segment decoder, we’ll already have tackled a BCD to decimal decoder, so a lot of the groundwork will have been laid. We’ll start by explaining how the segments in the 7-segment display are identified using the letters ‘a’ through ‘f’ and showing the combinations of segments we use to create the decimal digits 0 through 9.

8217684257?profile=RESIZE_710x

Using a 7-segment display to represent the decimal digits 0 through 9 (Click image to see a larger version — Image source: Max Maxfield)

Next, we will create the truth table. We’ll be using a common cathode 7-segment display, which means active-high outputs from our decoder because this is easier for newbies to wrap their brains around.

8217685658?profile=RESIZE_710x

Truth table for BCD to 7-segment decoder with active-high outputs (Click image to see a larger version — Image source: Max Maxfield)

Observe the input combinations shown in red in the truth table. We’ll point out that, in our case, we aren’t planning on using these input combinations, which means we don’t care what the corresponding outputs are because we will never actually see them (we’re using ‘X’ characters to represent the “don’t care” values). In turn, this means we can use these don’t care values in our Karnaugh maps to aid us in our logic minimization and optimization.

The funny thing is that it’s been a long time since I performed Karnaugh map minimizations by hand. As a result, on my first pass, I missed a couple of obvious optimizations. Just for giggles and grins, I’ve shown the populated maps below. Before you look at my solutions, why don’t you take a couple of minutes to perform your own minimizations to see how much you remember?

 8217691254?profile=RESIZE_710x

Use these populated maps to perform your own minimizations and optimizations (Click image to see a larger version — Image source: Max Maxfield)

I should point out that I’m a bit rusty at this sort of thing, so you might want to check that I’ve correctly captured the truth table and accurately populated these maps before you leap into the fray with gusto and abandon.

Remember that we’re dealing with absolute beginners here, so — even though I will have recently introduced them to Karnaugh map techniques, I think it would be a good idea to commence this portion of the discussions by walking them through the process for segment ‘a’ step-by-step as illustrated below.

8217692064?profile=RESIZE_710x

Karnaugh map minimizations for 7-segment display (Click image to see a larger version — Image source: Max Maxfield)

Next, I extracted the Boolean equations corresponding to the Karnaugh map minimizations. As shown below, I’ve color-coded any product terms that appear multiple times. I don’t recall seeing this done before, but I think it could be a useful aid for beginners. Once again, I’d be interested to hear your thoughts about this.

8217692289?profile=RESIZE_710x

Boolean equations for 7-segment display (Click image to see a larger version — Image source: Max Maxfield)

Actually, I’d love to hear your thoughts on anything I’ve shown here. Do you think the way I’ve drawn the diagrams is conducive to beginners understanding what’s going on? Can you spot anything I’ve missed or could do better? I can’t wait for you to see what we have planned with regards to the circuit board and the “hands-on” part of this forthcoming series (I will, of course, be reporting back further in the future). Until then, as always, I welcome your comments, questions, and suggestions.

Originally posted HERE.

Read more…

 

by Sam Kingsley

The "internet of things" will provide cyber criminals with new ways to exploit faults in personal security systems.

As the number of online devices surges and superfast 5G connections roll out, record numbers of companies are offering handsome rewards to ethical hackers who successfully attack their cybersecurity systems.

The fast-expanding field of internet-connected devices, known as the "nternet of things" (IoT) which includes smart televisions and home appliances, are set to become more widespread once 5G becomes more available—posing one of the most serious threats to digital security in future.

At a conference hosted by Nokia last week, "friendly hacker" Keren Elazari said that co-opting hackers—many of whom are amateurs—to hunt for vulnerabilities "was looked at as a trendy Silicon Valley thing six to eight years ago".

But "bug bounty programmes" are now offered by organisations ranging from the Pentagon and banks such as Goldman Sachs to airlines, tech giants and thousands of smaller businesses.

The largest bug-bounty platform, HackerOne, has 800,000 hackers on its books and said its organisations paid out a record $44 million (38.2 million euros) in cash rewards this year, up 87 percent on the previous 12 months.

"Employing just one full-time security engineer in London might cost a company 80,000 pounds (89,000 euros, $106,000) a year, whereas we open companies up to this global community of hundreds of thousands of hackers with a huge diversity in skills," Prash Somaiya, security solutions architect at HackerOne, told AFP.

8212233260?profile=RESIZE_710x

"We already know from what has happened in the past five years that the criminals find very clever ways to utilise digital devices," a friendly hacker told AFP

"We're starting to see an uptick in IoT providers taking hacking power seriously," Somaiya said, adding that HackerOne now regularly ships internet-connected toys, thermostats, scooters and cars out to its hackers for them to try to breach.

"We already know from what has happened in the past five years that the criminals find very clever ways to utilise digital devices," Elazari told AFP.

A sobering example was the 2016 "Mirai" cyberattack, during which attackers took control of 300,000 unsecured devices, including printers, webcams and TV recorders, and directed them to flood and disable websites of media, companies and governments around the world.

"In the future of 5G we're talking about every possible device having high-bandwidth connections, it's not just your computer or your phone," Elazari warned.

In October Nokia announced it had detected a 100 percent increase in malware infections on IoT devices in the previous year, noting in its threat report that each new application of 5G offers criminals "more opportunities for inflicting damage and extracting ransom".

8212234673?profile=RESIZE_710x

"Bug bounty programmes" are now offered by organisations ranging from the Pentagon and banks such as Goldman Sachs to airlines, tech giants and thousands of smaller businesses.
 

Breaker mindset

The rewards for hackers can be high: 200 of HackerOne's bug-hunters have now claimed more than $100,000 in prizes, while nine have breached the million-dollar earnings mark.

Apple, which advertises its own bug bounty programme, increased its maximum reward to more than $1 million at the end of last year, for a hacker able to demonstrate "zero click" weaknesses that would allow someone to access a device without any action by the user.

"A big driver is of course the financial incentive, but there's this element of a breaker mindset, to figure out how something is built so you can break it and tear it apart," Somaiya said.

"Being one individual who's able to hack multibillion-dollar companies is a real thrill, there's a buzz to it."

The rush of companies shifting to remote working during the pandemic has also led to "a surge in hacktivity", HackerOne said, with a 59 percent increase in hackers signing up and a one-third increase in rewards paid out.

The French and UK governments are among those to have opened up coronavirus tracing apps to friendly hackers, Somaiya added.

8212235461?profile=RESIZE_710x

"I see a lot of risk for misconfiguration and improper access control, these glitches are one of the main risks," Silke Holtmanns, head of 5G security research for cybersecurity firm AdaptiveMobile, told AFP
 

Incentive to act

While 5G internet systems will have new security features built into the network infrastructure—something absent before—the new technology is vastly more complex than its predecessors, leaving more potential for human error.

"I see a lot of risk for misconfiguration and improper access control, these glitches are one of the main risks," Silke Holtmanns, head of 5G security research for cybersecurity firm AdaptiveMobile, told AFP.

But companies are being motivated to act as security moves up the agenda, Holtmanns believes.

The EU, along with governments around the world, has begun tightening cybersecurity demands on organisations, and fines for data breaches have been increasing.

"Before now it's been hard for companies to justify higher investment in security," Holtmanns, who sits on the EU cybersecurity advisory group Enisa, said.

But she added, "If they can say: 'With that security level we can attract a higher level of customer, or lower insurance premiums,' people start thinking in this direction, which is a good thing."

Originally posted HERE.

Read more…

Everybody Needs a ShieldBuddy

 

Arduino Mega footprint; three 32-bit cores all running at 200 MHz; 4 Mbytes of Flash and 500 Kbytes of RAM; works with the Arduino IDE; what’s not to love?

I tend to have a lot of hobby projects on the go at any particular time. Occasionally, I even manage to finish one. More rarely, one actually works.

maxncb-0102-01-awesome-audio-reactive-artifact-300x218.jpg?profile=RESIZE_400x

 Awesome Audio Reactive Artifact (Click image to see a larger version — Image source: Max Maxfield)

I also have a soft spot for 8-bit microprocessors and microcontrollers. Thus, many of my hobby projects are based on the Arduino Nano, Uno, or Mega platforms.

Take my Awesome Audio Reactive Artifact, for example. This little rascal is currently powered using an Arduino Uno, which is driving 145 tricolor NeoPixels. In turn, these NeoPixels are mounted under 31 defunct vacuum tubes (see also Awesome Audio-Reactive Artifact meets BirmingHAMfest).

The Awesome Audio Reactive Artifact also includes an ADMP401-based MEMS Microphone breakout board (BOB), which costs $10.95 from the guys and gals at SparkFun. In turn, this feeds a cheap-and-cheerful MSGEQ7 audio spectrum analyzer chip, which relieves the Arduino of a lot of processing pain (see also Using MSGEQ7s In Audio-Reactive Projects).

 

maxncb-0102-02-countdown-timer-300x200.jpg?profile=RESIZE_400x Countdown Timer (Click image to see a larger version — Image source: Max Maxfield) 

Sad to relate, 8-bit Arduinos sometimes run out of steam. Consider my Countdown Timer, for example, whose task it is to display the years (YY), months (MM), days (DD), hours (HH), minutes (MM), and seconds (SS) to my 100th birthday (see also Yes! My Countdown Timer is Alive!).

This little scamp employs 12 Lixie displays, each of which contains 20 NeoPixels, which gives us 240 NeoPixels in all. As the sophistication of the effects I was trying to implement increased, so did my processing requirements. Thus, I decided to use a Teensy 3.6, which features a 32-bit 180 MHz ARM Cortex-M4 processor with a floating-point unit. Furthermore, the Teensy 3.6 boasts 1 Mbyte of Flash memory for code, along with 256 Kbytes of RAM for dynamic data and variables.

maxncb-0102-03-inamorata-prognostication-engine-152x300.jpg?profile=RESIZE_180x180 Prognostication Engine (Click image to see a larger version — Image source: Max Maxfield)

All of which brings us to the pièce de résistance in the form of my Pedagogical and Phantasmagorical Prognostication Engine (see also The Color of Prognostication). This bodacious beauty sports two knife switches, eight toggle switches, ten pushbutton switches, five motorized potentiometers, six analog meters, and a variety of sensors (temperature, barometric pressure, humidity, proximity). All of this requires a bunch of analog and digital general-purpose input/output (GPIO) pins.

Furthermore, in addition to a wealth of weird, wonderful, and wide-ranging sound effects, the engine is equipped with 354 NeoPixels. These could potentially be daisy-chained from a single pin, although I ended up partitioning them into five strands. More importantly, the various effects require a lot of processing and memory.

When things finally started to come together on this project, I was initially thinking of using an Arduino Mega to power the beast, mainly because it has 54 digital pins and 16 analog inputs. On the downside, we have to remember that this is only an 8-bit processor gamely running at 16 MHz with a scant 256 Kbytes of Flash memory and 8 Kbytes of RAM. Furthermore, the Mega doesn’t have a floating-point unit (FPU), which means that if you need to use floating-point operations, this will really impact the performance of your programs.

maxncb-0102-04-hitex-shieldbuddy-300x155.jpg?profile=RESIZE_400x The tri-core ShieldBuddy (Click image to see a larger version — Image source: Hitex)

But turn that frown upside down into a smile, because the boffins at Hitex (hitex.com) have taken the Arduino Mega form factor and slapped an awesome Infineon Aurix TC275 processor down on it.

These processors are typically found only in state-of-the-art embedded systems. they rarely make it into the maker world (like the somewhat disheveled scientist who is desperately in need of a haircut says in the movie Independence: “They don’t let us out very often”).

The result is called the ShieldBuddy. As you can see in this video, I just took delivery of my first ShieldBuddy, and I’m really rather excited (I say “first” because I have no doubt this is going to be one of many).

So, what makes the ShieldBuddy so special? Well, how about the fact that the TC275 boasts three independent 32-bit cores, all running at 200 MHz, each with its own FPU, and all sharing 4 Mbytes of Flash and 500 Kbytes of RAM (actually, this is a bit of a simplification, but it will suffice for now). 

There’s no need for you to be embarrassed — I’m squealing in excitement alongside you. Now, if you are a professional programmer, you’ll be delighted to hear that the main ShieldBuddy toolchain is the Eclipse-based “FreeEntryToolchain” from HighTec/PLS/ Infineon. This is a full-on C/C++ development environment with source-level debugger and suchlike. 

But how about if — like me — you aren’t used to awesomely powerful (and correspondingly complicated) Eclipse-based toolchains? Well, there’s no need to worry, because the guys and gals at Hitex also have a solution for the Arduino’s integrated development environment (IDE). 

Sit up straight and pay attention, because this is where things start to get really clever. In addition to any functions you create yourself, an Arduino sketch (program) always contains two functions: setup(), which runs only one time, and loop(), which runs over and over again. 

Now, remember that the ShieldBuddy has three processor cores, which we might call Core 0, Core 1, and Core 2. Well, you can take your existing sketches and compile/upload them for the ShieldBuddy, and — by default — they will run on Core 0. 

You could achieve the same effect by renaming your setup() function to be setup0(), and renaming your loop() function to be loop0(), which explicitly tells the compiler to target these functions at Core 0. 

The point is that you can also create setup1() and loop1() functions, which will automatically be compiled to run on Core 1, and you can create setup2() and loop2() functions, which will automatically be compiled to run on Core 2. Any of your remaining functions will be compiled in such a way as to run on whichever of the cores need to use them. 

Although each core runs independently, they can communicate between themselves using techniques like shared memory. Also, you can use interrupts to coordinate and communicate between cores. 

And things just keep on getting better and better, because it turns out that a NeoPixel library is already available for the ShieldBuddy. 

I’m just about to start experimenting with this little beauty. All I can say is that everybody needs a ShieldBuddy and my ShieldBuddy is my new best friend (sorry Little Steve). How about you? Could any of your projects benefit from the awesome processing power provided by the ShieldBuddy? 

Originally posted HERE.

Read more…

The COVID phenomenon has upended businesses in several verticals, and among the most impacted is the foodservice industry, characterized by high-touch, labor-intensive and, mostly indoor operations. However, this industry has taken the initiative to adopt modern technology to fight its way out of the crisis.

Case in point - a major US foodservice company with nearly $10B in revenue and over 1000 stores nationwide recently made the decision to partner with Ayla Networks as part of its digital transformation initiative. Why? The company’s leadership team saw that new technology, specifically IoT, was a way to improve store operations, accelerate menu innovation, and ultimately deliver a superior guest experience. The results are real and quantifiable – an estimated 25% in OpEx savings, 10% revenue uplift, and 15% fewer compliance incidents just in the first year.

thumbnail_quick-serve-restaurant.jpg


These are impressive numbers and are reflective of a broader trend in the foodservice industry despite the fact that foodservice companies have unique challenges such as a large base of deployed equipment that require retrofitting, heterogenous equipment types, and poor connectivity infrastructure. The quick-service restaurant segment is not the only one experimenting with new connected technology; other segments such as coffee shop chains, retail & convenience stores, and commercial kitchens are also adopting IoT to drive improved food safety and quality assurance, for faster new recipe rollouts or to improve equipment reliability. The trend is evident not just in the fully owned store model but also in the franchise-based model.

Some of the key IoT-driven business outcomes include:

Food Safety & Quality Assurance: Sensor-based IoT solutions can monitor food preparation procedures and ensure they are followed consistently in adherence to policy. Any anomalies can be flagged and remediated to better manage risk and food quality.

Faster Recipe Rollouts: The foodservice industry is a competitive tight margin business where the smallest differentiators can make a difference. The ability to use automated over the air recipe updates from the cloud can speed up new offerings and keep the menu fresh, driving topline growth. Most importantly an effective recipe management system enables A/B testing of new recipes across markets.

Improve Equipment Reliability: Foodservice enterprises have a large base of deployed assets including ovens, ranges, fryers, coolers, soda machines among others - complex equipment with unpredictable failure patterns. Using IoT analytics to improve uptime means minimizing lost revenue and less business disruption. Equally, understanding the true cost of support for different products and product types can streamline decisions related to purchasing, warranties, and when to fix vs. replace.

Screen Shot 2020-08-13 at 4.00.13 PM.png


Overall, IoT can play a powerful role in enhancing business performance and guest satisfaction in foodservice organizations. By leveraging the power of sensors, cloud, analytics, and mobile applications, foodservice companies can gain an unfair competitive advantage and realize sustainable growth for the long term. However, one shouldn’t expect this innovation to come from the OEMs that supply the operators, it’s the equivalent of the fox guarding the henhouse since these equipment makers have much service & maintenance revenue at stake. The key to success is choosing a neutral platform provider that can reliably scale to managing millions of devices (equipment) across thousands of distributed locations, have the ability to work seamlessly across appliances from a variety of OEMs, and possess the analytical edge to transform the ‘big data’ from stores to perform advanced analysis for descriptive and prescriptive purposes.

Originally posted here

Read more…

On-chip UHD SS–MSCs as a device-unitized power source. Credit: Professor Sang-Young Lee, UNIST

by Ulsan National Institute of Science and Technology

A tiny microsupercapacitor (MSC) that is as small as the width of a person's fingerprint and can be integrated directly with an electronic chip has been developed. This has attracted major attention as a novel technology to lead the era of Internet of Things (IoT) since it can be driven independently when applied to individual electronic components.

 

Through the study, Professor Sang-Young Lee and his research team in the School of Energy and Chemical Engineering at UNIST have unveiled a new class of ultrahigh areal number density solid-state MSCs (UHD SS–MSCs) on a chip via electrohydrodynamic (EHD) jet printing. According to the research team, this is the first study to exploit EHD jet printing in the MSCs.

A supercapacitor (SC), also known as an ultracapacitor, can store much more energy than ordinary capacitors. The benefits of supercapacitors include having high power delivery and longer cycle life compared to lithium-based secondary batteries. In particular, it can be produced as small as the width of a person's fingerprint via semiconductor manufacturing process, and thus can be also applicable for wearables and internet of things (IoT) devices.

However, becuase the heat produced in manufacturing process may cause deterioration of the electrical characteristics of the supercapacitor, it has been difficult to connect them directly to electronic components. In addition, the fabrication method that combines supercapacitors with electronic components via inkjet printing technique has also the disadvantage of lower precision.

The research team solved this issue using EHD jet printing, a high-resolution patterning technique in microelectronics. EHD jet printing uses the electrode and electrolyte for printing purpose similar to that of conventional inkjet printing, yet it can control printed liquid with an electric field.

"We were able to produce up to 54.9 unit cells per square centimeter (cm2) via electro-hydrodynamic jet printing technique, and thus the output of 65.9 volts (V) was achieved in the same area," says Kwonhyung Lee (Combined M.S/Ph.D. of Energy and Chemical Engineering, UNIST), the first author of the study.

The team also succeeded in fabricating 36 unit cells on a chip (area = 8.0 mm × 8.2 mm, 54.9 cells cm−2) and areal operating voltage (65.9 V cm−2) that lie far beyond those of previously reported MSCs fabricated by printing techniques. Besides, upon exposure to hot temperature (80 degrees C), these cells maintained normal cyclic voltammetry (CV) profiles, and thus has proven they can withstand excessive heat generated during the operation of actual electronic component. In addition, these batteries can provide customized powere supplies, as they can be connected either in series or parallel.

"In this study, we have demonstrated on-chip UHD SS–MSCs fabricated via EHD jet printing," says Professor Lee. "The on-chip UHD SS–MSCs presented here hold great promise as a new platform technology for miniaturized monolithic power sources with customized design and tunable electrochemical properties."

Originaly posted HERE

 
Read more…

In order to form proper networks to share data, the Internet of Things (IoT) needs reliable communications and connectivity. Because of popular demand, there’s a wide range of connectivity technologies that operators, as well as developers, can opt for.

IoT Connectivity Groups

The IoT connectivity technologies are currently divided into two groups. The first one is cellular-based, and the second one is unlicensed LPWAN. The first group is based around a licensed spectrum, something which offers an infrastructure that is consistent and better. This group supports larger data rates, but it comes with a cost of short battery life and expensive hardware. However, you don’t have to worry about this a lot as its hardware is becoming cheaper.

Cellular-Based IoT

Because of all this, cellular-based IoT is only offered by giant operators. The reason behind this is that acquiring licensed spectrum is expensive. But these big operators have access to this licensed spectrum, as well as expensive hardware. The cellular IoT connectivity also has its own two types. The first one being the narrowband IoT (NB-IoT) and category M1 IoT (Cat-M1).

Although both are based on cellular standards, there is one big difference between the two. That NB-IoT has a smaller bandwidth than Cat-M1, and thus offers a lower transmission power. In fact, its bandwidth is 10x smaller than that of Cat-M1. However, both still have a very long range with NB-IoT offering a range of up to 100 Km.

The cellular standard based IoT connectivity ensure more reliability. Their device operational lifetimes are longer as compared to unlicensed LPWAN. But when it comes to choosing, most operators prefer NB-IoT over Cat-M1. This is because Cat-M1 provides higher data rates that are not usually necessary. In addition to this, the higher costs of it prevent operators from choosing it.

Cat-M1 is mostly chosen by large-scale operators because it provides mobility support. This is something suitable for transportation and traffic control-based network. It can also be useful in emergency response situations as it offers voice data transfer.

The hardware (module) used for cellular IoT is relatively more expensive compared to LPWAN. It can cost around $10, compared to $2 LPWAN. However, this cost has been dropping rapidly recently because of its popular demand. 

Unlicensed LPWAN

As for the unlicensed LPWANs, they are used by those who don’t have the budget to afford cellular-based IoT. They are designed for customized IoT networks and offer lower data rates, but with increased battery life and long transmission range. They can also be deployed easily. At the moment, there are two types of unlicensed LPWANs, LoRa (Long Range) and SigFox.

Both types are amazing as they designed for devices that have a lower price, increased battery life, and long range. Their coverage range can be up to 10 Km, and their connectivity cost is as low as $2 per module. Not only this, but the cost is even lower than this sometimes. Therefore, they are ideal for local areas.

Weightless LPWAN

Although there are many variants of the LPWAN, Weightless is considered to be the most popular one. This is because the Weightless Special Interest Group, or the SIG, currently offers three different protocols. These include the Weightless-N, the Weightless-W, and the Weightless-P. All three work in a different way as they have different modalities.

Weightless-W

First off, we have the Weightless-W open standard model. This one is designed to operate in TV white space (TVWS). TV Whitespace (TVWS) is the inactive or unoccupied space found between channels actively used in UHF and VHF spectrum its frequency spans from 470 MHz – 790 MHz. For those who don’t know, this is similar to what Neul was developing before getting acquired by Huawei. Now, while using TVWS can be great as it uses ultra-high frequency spectrum, it has one downside. In theory, it seems perfect. But in practice, it is difficult because the rules and regulations for utilizing TVWS for IoT vary greatly.

In addition to this, the end nodes of this model don’t work like they are supposed to. They are designed to operate in a small part of the spectrum. As is difficult to design an antenna that can cover a such wide band of spectrum. This is why TVWS can be difficult when it comes to installing it. The Weightless-W is considered a good option in:

  • Smart Oil sector.
  • Gas sector.

Weightless-N

Second up we have the ultra-narrowband system, the Weightless-N. This model is similar to SigFox as both have a lot in common. The best thing about it is it is made up of different networks instead of being an end-to-end enclosed system. Weightless-N uses differential binary phase shift keying (DBPSK) digital modulation scheme same as of used in SigFox.

The Weightless-N line is operated by Nwave, a popular IoT hardware and software developer. However, while is model is best for sensor-based networks, temperature readings, tank level monitoring, and more, there are some problems with it. For instance, Nwave has a special requirement for TCXO, that is the temperature compensated crystal oscillator.

 In addition to this, it has an unbalanced link budget. The reason behind why this is bad is that there will be much more sensitivity going up to the base station compared to what will be coming down. 

Weightless-P

Finally, we have the Weightless-P. This model is the latest one in the group as it was launched some time after the above two. What people love the most about this one is that it has two-way features. In addition to this, it has a 12.5 kHz channel that is pretty amazing. The Weightless-P doesn’t require a TXCO, something which makes it different from Weightless-N and -W.

The main company behind Weightless-P is Ubiik. The only downside about this model is that it is not ideal for wide-area networks as it offers a range of around 2 Km. However, the Weightless-P is still ideal for:

  • Private Networks
  • Extra sophisticated use cases.
  • Areas where uplink data and downlink control are important.

Capacity

Because of the fact that the Weightless protocols are based on SDR, its base station for narrowband signals is much more complex. This is something that ends up creating thousands of small binary phase-shift keying channels. Although this will let you get more capacity, it will become a burden on your wallet.

In addition to this, since the Weightless-N end nodes require a TXCO, it will be more expensive. The TXCO is used when there is a threat of the frequency becoming unstable when the temperature gets disturbed at the end node.

Range

Talking about the ranges, the Weightless-N and -W has a range of around 5 Km in Urban environments. As for the Weightless-P, it can go up to 2 Km.

Comparison

Weightless and SigFox

If we take the technology into consideration, then the Weightless-N and SigFox are pretty similar. However, they are different when it comes to go-to-market. Since Weightless is a standard, it will require another company to create an IoT based on it. However, this is not the case with SigFox as it is a different type of solution.

Weightless and LoRa

In terms of technology, the Weightless and LoRa. Lorawan are different. However, the functionally of the Weightless-N and LoRaWAN is similar. This is because both are uplink-based systems. Weightless is also sometimes considered as the very good alternative when LoRa is not feasible to the user.

Weightless and Symphony Link

The Symphony Link and Weightless-P standards are more similar to each other. For instance, both focus on private networks. However, Symphony Link has a much more better range performance because it uses LoRa instead of Minimum-shift keying modulation MSK.

Originaly posted here

Read more…

PYNQ is great for accelerating Python applications in programmable logic. Let's take a look at how we can use it with OpenMV camera.

Things used in this project

Hardware:

  • Avnet Ultra96-V2 (Can also use V1 or V3)
  • OpenMV Cam M7
  • Avnet Ultra96 (Can use V1 or V2)

Software:

  • Xilinx PYNQ Framework

Introduction

Image processing is required for a range of applications from vision guided robotics to machine vision in industrial applications.

In this project we are going to look at how we can fuse the OpenMV camera with the Ultra96 running PYNQ. This will allow out PYNQ application to offload some image processing to the camera. Doing so will provide a higher performance system and open the Ultra96 using PYNQ to be able to work with the OpenMV ecosystem.

 

What Is the OpenMV Camera 

The OpenMV camera is a low cost machine vision camera which is developed using Python. Thanks to this architecture of the OpenMV Camera we can therefore offload some of the image processing to the camera. Meaning the image frames received by our Ultra96 already have faces identified, eyes tracked or Sobel filtering, it all depends on how we set up the OpenMV Camera.

As the OpenMV camera has been designed to be extensible it provides 10 external IO which can be used to drive external sensors. These 10 are able to support a range of interfaces from UART to SPI, I2C and PWM. Of course the PWM is very useful for driving servos.

On very useful feature of the OpenMV camera is its LEDs mine (OpenMV M7) provides a tri-colour LED which can be used to output Red, Green, Blue and a separate IR LED. As the sensor is IR sensitive this can be useful for low light performance.

8100406101?profile=RESIZE_400xOpenMV Camera

How Does the OpenMV Camera Work

OpenMV Cam uses micro python to control the imager and output frames over the USB link. Micro python is intended for use on micro controllers and is based on Python 3.4. To use the OpenMV camera we need to first generate a micro python script which configures the camera for the given algorithm we wish to implement. We then execute this script by uploading and running it over the USB link.

This means we need some OpenMV APIs and libraries on a host machine to communicate with the OpenMV Camera.

To develop the script we want to be able to ensure it works, which is where the OpenMV IDE comes into its own, this allows us to develop and test the script which we later use in our Ultra96 application.

We can develop this script using either a Windows, MAC or Linux desktop.

 

Creating the OpenMV Script using the OpenMV IDE

To get started with the OpenMV IDE we frist need to download and install it. Once it is installed the next step is to connect our OpenMV camera to it using the USB link and then running a script on it.

To get started we can run the example hello world provided, which configures the camera to outputs standard RGB image at QVGA resolution. On the right hand side of the IDE you will be able to see the images output from the camera.

 

We can use this IDE to develop scripts for the OpenMV camera such as the one below which detects and identifies circles in the captured image.

Note the frame rate is lower when the camera is connected to the IDE.

 

We can use the scripts developed here in our Ultra96 PYNQ implementation let's take a look at how we set up the Ultra96 and PYNQ

Setting Up the Ultra96 PYNQ Image

The first thing we need to do if we have not already done it, is to download and create a PYNQ SD Card so we can run the PYNQ framework on the Ultra96.

As we want to use the Xilinx image processing overlay we should download the Ultra96 PYNQ v2.3 image.

Once you have this image creating a SD Card is very simple, extract the ISO image from the compressed file and write it to a SD Card. To write the ISO image to the SD Card we need a program such a etcher or win32 disk imager.

With a SD Card available we can then boot the Ultra96 and connect to the PYNQ framework using either

  • Use a USB Ethernet connection over the MicroUSB (upstream USB connection).
  • Connect via WiFi.
  • Use the Ultra96 as a single-board computer and connect a monitor, keyboard and mouse.

For this project I used the USB Ethernet connection.

The next thing to do is to ensure we have the necessary overlays to be able to accelerate image processing functions into the programmable logic. To to this we need to install the PYNQ computer vision overlay. 

Downloading the Image Processing Overlay

Installing this overlay is very straight forward. Open a browser window and connect to the web address of 192.168.3.1 (USB Ethernet address). This will open a log in page to the Jupyter notebooks, the password is Xilinx

 

Upon log in you will see the following folders and scripts

 

Click on new and select terminal, this will open a new terminal window in a browser window. To download and use the PYNQ Computer Vision overlays we enter the following command

sudo pip3 install --upgrade git+https://github.com/Xilinx/PYNQ-ComputerVision.git
 

Once these are downloaded if you look back at the Jupyter home page you will see a new directory called pynqOpenCV.

 

Using these Jupyter notebooks we can test the image processing performance when we accelerate OpenCV functions into the programmable logic.

 

Typically the hardware acceleration as can be seen in the image above greatly out performs implementing the algorithm in SW.

Of course we can call this overlay from our own Jupyter notebooks

 

Setting Up the OpenMV Camera in PYNQ

The next step is to configure the Ultra96 PYNQ instance to be able to control the OpenMV camera using its APIs. We can obtain these by downloading the OpenMV git repo using the command below in a terminal window on the Ultra96.

git clone https://github.com/openmv/openmv
 

Once this is downloaded we need to move the file pyopenmv.py

From openmv/tools

To /usr/lib/python3.6

This will allow us to control the OpenMV camera from within our Jupyter applications.

To be able to do this we need to know which serial port the OpenMV camera enumerates as. This will generally be ttyACM0 or ttyACM1 we can find this out by doing a LS of the /dev directory

 

Now we are ready to begin working with the OpenMV camera in our applications let's take a look at how we set it up our Jupyter Scripts

 

Initial Test of OpenMV Camera

The first thing we need to do in a new Jupyter notebook is to import the necessary packages. This includes the pyopenmv as we just installed.

We will alos be importing numpy as the image is returned as a numpy array so that we can display it using numpy functionality.

import pyopenmvimport timeimport sysimport numpy as np 

The first thing we need to do is define the script we developed in the IDE, for the "first light" with the PYNQ and OpenMV we will use the hello world script to obtain a simple image.

script = """

# Hello World Example

#

# Welcome to the OpenMV IDE! Click on the green run arrow button below to run the script!

import sensor, image, time

import pyb

sensor.reset()                      # Reset and initialize the sensor.

sensor.set_pixformat(sensor.RGB565) # Set pixel format to RGB565 (or GRAYSCALE)

sensor.set_framesize(sensor.QVGA)   # Set frame size to QVGA (320x240)

sensor.skip_frames(time = 2000)     # Wait for settings take effect.

clock = time.clock()                # Create a clock object to track the FPS.

red_led = pyb.LED(1)

red_led.off()

red_led.on()

while(True):

   clock.tick() 

   img = sensor.snapshot()         # Take a picture and return the image.

"""

Once the script is defined the next thing we need to do is connect to the OpenMV camera and download the script.

 

portname = "/dev/ttyACM0"

connected = False

pyopenmv.disconnect()

for i in range(10):

   try:

       # opens CDC port.

       # Set small timeout when connecting

       pyopenmv.init(portname, baudrate=921600, timeout=0.050)

       connected = True

       break

   except Exception as e:

       connected = False

       sleep(0.100)

if not connected:

   print ( "Failed to connect to OpenMV's serial port.\n"

           "Please install OpenMV's udev rules first:\n"

           "sudo cp openmv/udev/50-openmv.rules /etc/udev/rules.d/\n"

           "sudo udevadm control --reload-rules\n\n")

   sys.exit(1)

# Set higher timeout after connecting for lengthy transfers.

pyopenmv.set_timeout(1*2) # SD Cards can cause big hicups.

pyopenmv.stop_script()

pyopenmv.enable_fb(True)

pyopenmv.exec_script(script)

Finally once the script has been downloaded and is executing, we want to be able to read out the frame buffer. This Cell below reads out the framebuffer and saves it as a jpg file in the PYNQ file system.

 

running = True

import numpy as np

from PIL import Image

from matplotlib import pyplot as plt

while running:

   fb = pyopenmv.fb_dump()

   if fb != None:

       img = Image.fromarray(fb[2], 'RGB')

       img.save("frame.jpg")

       img = Image.open("frame.jpg")

       img

       time.sleep(0.100)

 

When I ran this script the first light image below was received of me working in my office.

 

Having achieved this the next step is to start working with advanced scripts in the PYNQ Jupyter notebook. using the same approach as above we can redefine scripts which can be used for different processing including

script = """

import sensor, image, time

sensor.reset() # Initialize the camera sensor.

sensor.set_pixformat(sensor.GRAYSCALE) # or sensor.RGB565

sensor.set_framesize(sensor.QQVGA) # or sensor.QVGA (or others)

sensor.skip_frames(time = 2000) # Let new settings take affect.

sensor.set_gainceiling(8)

clock = time.clock() # Tracks FPS.

while(True):

   clock.tick() # Track elapsed milliseconds between snapshots().

   img = sensor.snapshot() # Take a picture and return the image.

   # Use Canny edge detector

   img.find_edges(image.EDGE_CANNY, threshold=(50, 80))

   # Faster simpler edge detection

   #img.find_edges(image.EDGE_SIMPLE, threshold=(100, 255))

   print(clock.fps()) # Note: Your OpenMV Cam runs about half as fast while

"""

For Canny edge detection when imaging a MiniZed Board

 

Alternatively we can also extract key points from images for tracking in subsequent images.

script = """

import sensor, time, image

# Reset sensor

sensor.reset()

# Sensor settings

sensor.set_contrast(3)

sensor.set_gainceiling(16)

sensor.set_framesize(sensor.VGA)

sensor.set_windowing((320, 240))

sensor.set_pixformat(sensor.GRAYSCALE)

sensor.skip_frames(time = 2000)

sensor.set_auto_gain(False, value=100)

def draw_keypoints(img, kpts):

   if kpts:

       print(kpts)

       img.draw_keypoints(kpts)

       img = sensor.snapshot()

       time.sleep(1000)

kpts1 = None

# NOTE: uncomment to load a keypoints descriptor from file

#kpts1 = image.load_descriptor("/desc.orb")

#img = sensor.snapshot()

#draw_keypoints(img, kpts1)

clock = time.clock()

while (True):

   clock.tick()

   img = sensor.snapshot()

   if (kpts1 == None):

       # NOTE: By default find_keypoints returns multi-scale keypoints extracted from an image pyramid.

       kpts1 = img.find_keypoints(max_keypoints=150, threshold=10, scale_factor=1.2)

       draw_keypoints(img, kpts1)

   else:

       # NOTE: When extracting keypoints to match the first descriptor, we use normalized=True to extract

       # keypoints from the first scale only, which will match one of the scales in the first descriptor.

       kpts2 = img.find_keypoints(max_keypoints=150, threshold=10, normalized=True)

       if (kpts2):

           match = image.match_descriptor(kpts1, kpts2, threshold=85)

           if (match.count()>10):

               # If we have at least n "good matches"

               # Draw bounding rectangle and cross.

               img.draw_rectangle(match.rect())

               img.draw_cross(match.cx(), match.cy(), size=10)

           print(kpts2, "matched:%d dt:%d"%(match.count(), match.theta()))

           # NOTE: uncomment if you want to draw the keypoints

           #img.draw_keypoints(kpts2, size=KEYPOINTS_SIZE, matched=True)

   # Draw FPS

   img.draw_string(0, 0, "FPS:%.2f"%(clock.fps()))

"""

Circle Detection

 

import sensor, image, time

sensor.reset()

sensor.set_pixformat(sensor.RGB565) # grayscale is faster

sensor.set_framesize(sensor.QQVGA)

sensor.skip_frames(time = 2000)

clock = time.clock()

while(True):

   clock.tick()

   img = sensor.snapshot().lens_corr(1.8)

   # Circle objects have four values: x, y, r (radius), and magnitude. The

   # magnitude is the strength of the detection of the circle. Higher is

   # better...

   # `threshold` controls how many circles are found. Increase its value

   # to decrease the number of circles detected...

   # `x_margin`, `y_margin`, and `r_margin` control the merging of similar

   # circles in the x, y, and r (radius) directions.

   # r_min, r_max, and r_step control what radiuses of circles are tested.

   # Shrinking the number of tested circle radiuses yields a big performance boost.

   for c in img.find_circles(threshold = 2000, x_margin = 10, y_margin = 10, r_margin = 10,

           r_min = 2, r_max = 100, r_step = 2):

       img.draw_circle(c.x(), c.y(), c.r(), color = (255, 0, 0))

       print(c)

   print("FPS %f" % clock.fps())

 

 

 

This fusion of ability to offload processing to either the OpenMV camera or the Ultra96 programmable logic running Pynq provides the system designer with maximum flexibility.

 

Wrap Up

The ability to use the OpenMV camera, coupled with the PYNQ computer vision libraries along with other overlays such as the klaman filter and base overlays. We can implement algorithms which can be used to enable us to implement vision guided robotics. Using the base overlay and the Input Output processors also enables us to communicate with lower level drives, interfaces and other sensors required to implement such a solution.

Originaly posted here.

 

Read more…

Arm DevSummit 2020 debuted this week (October 6 – 8) as an online virtual conference focused on engineers and providing them with insights into the Arm ecosystem. The summit lasted three days over which Arm painted an interesting technology story about the current and future state of computing and where developers fit within that story. I’ve been attending Arm Techcon for more than half a decade now (which has become Arm DevSummit) and as I perused content, there were several take-a-ways I noticed for developers working on microcontroller based embedded systems. In this post, we will examine these key take-a-ways and I’ll point you to some of the sessions that I also think may pique your interest.

(For those of you that aren’t yet aware, you can register up until October 21st (for free) and still watch the conferences materials up until November 28th . Click here to register)

Take-A-Way #1 – Expect Big Things from NVIDIAs Acquisition of Arm

As many readers probably already know, NVIDIA is in the process of acquiring Arm. This acquisition has the potential to be one of the focal points that I think will lead to a technological revolution in computing technologies, particularly around artificial intelligence but that will also impact nearly every embedded system at the edge and beyond. While many of us have probably wondered what plans NVIDIA CEO Jensen Huang may have for Arm, the Keynotes for October 6th include a fireside chat between Jensen Huang and Arm CEO Simon Segars. Listening to this conversation is well worth the time and will help give developers some insights into the future but also assurances that the Arm business model will not be dramatically upended.

Take-A-Way #2 – Machine Learning for MCU’s is Accelerating

It is sometimes difficult at a conference to get a feel for what is real and what is a little more smoke and mirrors. Sometimes, announcements are real, but they just take several years to filter their way into the market and affect how developers build systems. Machine learning is one of those technologies that I find there is a lot of interest around but that developers also aren’t quite sure what to do with yet, at least in the microcontroller space. When we hear machine learning, we think artificial intelligence, big datasets and more processing power than will fit on an MCU.

There were several interesting talks at DevSummit around machine learning such as:

Some of these were foundational, providing embedded developers with the fundamentals to get started while others provided hands-on explorations of machine learning with development boards. The take-a-way that I gather here is that the effort to bring machine learning capabilities to microcontrollers so that they can be leveraged in industry use cases is accelerating. Lots of effort is being placed in ML algorithms, tools, frameworks and even the hardware. There were several talks that mentioned Arm’s Cortex-M55 architecture that will include Helium technology to help accelerate machine learning and DSP processing capabilities.

Take-A-Way #3 – The Constant Need for Reinvention

In my last take-a-way, I eluded to the fact that things are accelerating. Acceleration is not just happening though in the technologies that we use to build systems. The very application domain that we can apply these technology domains to is dramatically expanding. Not only can we start to deploy security and ML technologies at the edge but in domains such as space and medical systems. There were several interesting talks about how technologies are being used around the world to solve interesting and unique problems such as protecting vulnerable ecosystems, mapping the sea floor, fighting against diseases and so much more.

By carefully watching and listening, you’ll notice that many speakers have been involved in many different types of products over their careers and that they are constantly having to reinvent their skill sets, capabilities and even their interests! This is what makes working in embedded systems so interesting! It is constantly changing and evolving and as engineers we don’t get to sit idly behind a desk. Just as Arm, NVIDIA and many of the other ecosystem partners and speakers show us, technology is rapidly changing but so are the problem domains that we can apply these technologies to.

Take-A-Way #4 – Mbed and Keil are Evolving

There are also interesting changes coming to the Arm toolchains and tools like Mbed and Keil MDK. In Reinhard Keil’s talk, “Introduction to an Open Approach for Low-Power IoT Development“, developers got an insight into the changes that are coming to Mbed and Keil with the core focus being on IoT development. The talk focused on the endpoint and discussed how Mbed and Keil MDK are being moved to an online platform designed to help developers move through the product development faster from prototyping to production. The Keil Studio Online is currently in early access and will be released early next year.

(If you are interested in endpoints and AI, you might also want to check-out this article on “How Do We Accelerate Endpoint AI Innovation? Put Developers First“)

Conclusions

Arm DevSummit had a lot to offer developers this year and without the need to travel to California to participate. (Although I greatly missed catching up with friends and colleagues in person). If you haven’t already, I would recommend checking out the DevSummit and watching a few of the talks I mentioned. There certainly were a lot more talks and I’m still in the process of sifting through everything. Hopefully there will be a few sessions that will inspire you and give you a feel for where the industry is headed and how you will need to pivot your own skills in the coming years.

Originaly posted here

Read more…

Will We Ever Get Quantum Computers?

In a recent issue of IEEE Spectrum, Mikhail Dyakonov makes a pretty compelling argument that quantum computing (QC) isn't going to fly anytime soon. Now, I'm no expert on QC, and there sure is a lot of money being thrown at the problem by some very smart people, but having watched from the sidelines QC seems a lot like fusion research. Every year more claims are made, more venture capital gets burned, but we don't seem to get closer to useful systems.

Consider D-Wave Systems. They've been trying to build a QC for twenty years, and indeed do have products more or less on the market, including, it's claimed, one of 1024 q-bits. But there's a lot of controversy about whether their machines are either quantum computers at all, or if they offer any speedup over classical machines. One would think that if a 1K q-bit machine really did work the press would be all abuzz, and we'd be hearing constantly of new incredible results. Instead, the machines seem to disappear into research labs.

Mr. Duakonov notes that optimistic people expect useful QCs in the next 5-10 years; those less sanguine expect 20-30 years, a prediction that hasn't changed in two decades. He thinks a window of many decades to never is more realistic. Experts think that a useful machine, one that can do the sort of calculations your laptop is capable of, will require between 1000 and 100,000 q-bits. To me, this level of uncertainty suggests that there is a profound lack of knowledge about how these machines will work and what they will be able to do.

According to the author, a 1000 q-bit machine can be in 21000 states (a classical machine with N transistors can be in only 2N states), which is about 10300, or more than the number of sub-atomic particles in the universe. At 100,000 q-bits we're talking 1030,000, a mind-boggling number.

Because of noise, expect errors. Some theorize that those errors can be eliminated by adding q-bits, on the order of 1000 to 100,000 additional per q-bit. So a useful machine will need at least millions, or perhaps many orders of magnitude more, of these squirrelly microdots that are tamed only by keeping them at 10 millikelvin.

A related article in Spectrum mentions a committee formed of prestigious researchers tasked with assessing the probability of success with QC concluded that:

"[I]t is highly unexpected" that anyone will be able to build a quantum computer that could compromise public-key cryptosystems (a task that quantum computers are, in theory, especially suitable for tackling) in the coming decade. And while less-capable "noisy intermediate-scale quantum computers" will be built within that time frame, "there are at present no known algorithms/applications that could make effective use of this class of machine," the committee says."

I don't have a dog in this fight, but am relieved that useful QC seems to be no closer than The Distant Shore (to quote Jan de Hartog, one of my favorite writers). If it were feasible to easily break encryption schemes banking and other systems could collapse. I imagine Blockchain would fail as hash algorithms became reversable. The resulting disruption would not be healthy for our society.

On the other hand, Bruce Schneier's article in the March issue of IEEE Computing Edge suggests that QC won't break all forms of encryption, though he does think a lot of our current infrastructure will be vulnerable. The moral: if and when QC becomes practical, expect chaos.

I was once afraid of quantum computing, as it involves mechanisms that I'll never understand. But then I realized those machines will have an API. Just as one doesn't need to know how a computer works to program in Python, we'll be insulated from the quantum horrors by layers of abstraction.

Originaly posted here

Read more…

SSE Airtricity employees Derek Conty, left, Francie Byrne, middle, and Ryan Doran, right, install solar panels on the roof of Kinsale Community School in Kinsale, Ireland. The installation is part of a project with Microsoft to demonstrate the feasibility of distributed power purchase agreements. Credit: Naoise Culhane

by John Roach

Solar panels being installed on the roofs of dozens of schools throughout Dublin, Ireland, reflect a novel front in the fight against global climate change, according to a senior software engineer and a sustainability lead at Microsoft.

The technology copmpany partnered with SSE Airtricity, Ireland's largest provider of 100% green energy and a part of FTSE listed SSE Group, to install and manage the internet-connected solar panels, which are connected via Azure IoT to Microsoft Azure, a cloud computing platform.

The software tools aggregate and analyze real-time data on energy generated by the solar panels, demonstrating a mechanism for Microsoft and other corporations to achieve sustainability goals and reduce the carbon footprint of the electric power grid.

"We need to decarbonize the global economy to avoid catastrophic climate change," said Conor Kelly, the software engineer who is leading the distributed solar energy project for Microsoft Azure IoT. "The first thing we can do, and the easiest thing we can do, is focus on electricity."

Microsoft's $1.1 million contribution to the project builds on the company's ongoing investment in renewable energy technologies to offset carbon emissions from the operation of its datacenters.

A typical approach to power datacenters with renewable energy is for companies such as Microsoft to sign so-called power purchase agreements with energy companies.The agreements provide financial guarantees needed to build industrial-scale wind and solar farms and connections to the power grid.

The new project demonstrates the feasibility of agreements to install solar panels on rooftops distributed across towns with existing grid connections and use internet of things, or IoT, technologies to aggregate the accumulated energy production for carbon offset accounting.

"It utilizes existing assets that are sitting there unmonetized, which are roofs of buildings that absorb sunlight all day," Kelly said.

New Business Model

The project is also a proof-of-concept, or blueprint, for how energy providers can adapt as the falling price of solar panels enables distributed electric power generation throughout the existing electric power grid.

Traditionally, suppliers purchase power from central power plants and industrial-scale wind and solar farms and sell it to consumers on the distribution grid. Now, energy providers like SSE Airtricity provide renewable energy solutions that allow end consumers to generate power, from sustainable sources, using the existing grid connection on their premises.

"The more forward-thinking energy providers that we are working with, like SSE Airtricity, identify this as an opportunity and industry changing shift in how energy will be generated and consumed," Kelly noted.

The opportunity comes in the ability to finance the installation of solar panels and batteries at homes, schools, businesses and other buildings throughout a community and leverage IoT technology to efficiently perform a range of services from energy trading to carbon offset accounting.

Kelly and his team with Azure IoT are working with SSE Airtricity to develop the tools and machine learning models necessary to unlock this opportunity.

"Instead of having utility scale solar farms located outside of cities, you could have a solar farm at the distribution level, spread across a number of locations," said Fergal Ahern, a business energy solutions manager and renewable energy expert with SSE Airtricity.

For the distributed power purchase agreement, SSE Airtricity uses Azure IoT to aggregate the generation of all the solar panels installed across 27 schools around the provinces of Leinster, Munster and Connacht and run it through a machine learning model to determine the carbon emissions that the solar panels avoid.

The schools use the electricity generated by the solar panels, which reduces their utility bills; Microsoft receives the renewable energy credits for the generated electricity, which the company applies to its carbon neutrality commitments.

The panels are expected to produce enough energy annually to power the equivalent of 68 Irish homes for a year and abate more than 2.1 million kilograms, which is equivalent to 4.6 million pounds, of carbon dioxide emissions over the 15 years of the agreement, according to Kelly.

"This is additional renewable energy that wouldn't have otherwise happened," he said. "Every little bit counts when it comes to meeting our sustainability targets and combatting climate change."

Every little bit counts

Victory Luke, a 16 year old student at Collinstown Park Community College in Dublin, has lived by the "every little bit counts" mantra since she participated in a "Generation Green" sustainability workshop in 2019 organized by the Sustainable Energy Authority of Ireland, SSE Airtricity and Microsoft.

The workshop was part of an education program surrounding the installation of solar panels and batteries at her school along with a retrofit of the lighting system with LEDs. Digital screens show the school's energy use in real time, allowing students to see the impact of the energy efficiency upgrades.

Luke said the workshop captured her interest on climate change issues. She started reading more about sustainability and environmental conservation and agreed to share her newfound knowledge with the younger students at her school.

"I was going around and talking to them about energy efficiency, sharing tips and tricks like if you are going to boil a kettle, only boil as much water as you need, not too much," she explained.

That June, the Sustainable Energy Authority of Ireland invited her to give a speech at the Global Conference on Energy Efficiency in Dublin, which was organized by the International Energy Agency, an organization that works with governments and industry to shape sustainable energy policy.

"It kind of felt surreal because I honestly felt like I wasn't adequate enough to be speaking about these things," she said, noting that the conference attendees included government ministers, CEOs and energy experts from around the world.

At the time, she added, the global climate strike movement and its youth leaders were making international headlines, which made her advocacy at school feel even smaller. "Then I kind of realized that it is those smaller things that make the big difference," she said.

SSE Airtricity and Microsoft plan to replicate the educational program that inspired Luke and her classmates at dozens of the schools around Ireland that are participating in the project.

"When you've got solar at a school and you can physically point at the installation and a screen that monitors the power being generated, it brings sustainability into daily school life," Ahern said.

Proof of concept for policymakers

The project's education campaign extends to renewable energy policymakers, Kelly noted. He explained that renewable energy credits—a market incentive for corporations to support renewable energy projects—are currently unavailable for distributed power purchase agreements.

For this project, Microsoft will receive genuine renewable energy credits from a wind farm that SSE Airtricity also operates, he added.

"And," he said, "we are hoping to use this project as an example of what regulation should look like, to say, 'You need to award renewable energy credits to distributed generation because they would allow corporates to scale-up this type of project.'"

For her part, Luke supports steps by multinational corporations such as Microsoft to invest in renewable energy projects that address global climate change.

"It is a good thing to see," she said. "Once one person does something, other people are going to follow.

Originaly posted HERE

Read more…

An edge device is the network component that is responsible for connecting a local area network to an external or wide area network, which can be accessed from anywhere. Edge devices offer several new services and improved outcomes for IoT deployments across all markets. Smart services that rely on high volumes of data and local analysis can be deployed in a wide range of environments.

Edge device provides the local data to an external network. If protocols are different in local and external networks, it also translates this information, and make the connection between both network boundaries. Edge devices analyze diagnostics and automatic data populating; however, it is necessary to make a secure connection between the field network and cloud computing. In the event of loss of internet connection or cloud crash edge device will store data until the connection is established, so it won’t lose any process information. The local data storage is optional and not all edge devices offer local storage, it depends on the application and service required to implement on the plant.

How does an edge device work?

An edge device has a very straightforward working principle, it communicates between two different networks and translates one protocol into another. Furthermore, it creates a secure connection with the cloud.

An edge device can be configured via local access and internet or cloud. In general, we can say an edge device is a plug-and-play, its setup is simple and does not require much time to configure.

Why should I use an edge device?

Depending on the service required in the plant, the edge devices will be a crucial point to collect the information and create an automatic digital twin of your device in the cloud. 

Edge devices are an essential part of IoT solutions since they connect the information from a network to a cloud solution. They do not affect the network but only collect the data from it, and never cause a problem with the communication between the control system and the field devices. by using an edge device to collect information, the user won’t need to touch the control system. Edge is one-way communication, nothing is written into the network, and data are acquired with the highest possible security.

Edge device requirements

Edge devices are required to meet certain requirements that are to meet at all conditions to perform in different secretions. This may include storage, network, and latency, etc.

Low latency

Sensor data is collected in near real-time by an edge server. For services like image recognition and visual monitoring, edge servers are located in very close proximity to the device, meeting low latency requirements. Edge deployment needs to ensure that these services are not lost through poor development practice or inadequate processing resources at the edge. Maintaining data quality and security at the edge whilst enabling low latency is a challenge that need to address.

Network independence

IoT services do not care for data communication topology.  The user requires the data through the most effective means possible which in many cases will be mobile networks, but in some scenarios, Wi-Fi or local mesh networking may be the most effective mechanism of collecting data to ensure latency requirements can be met.

Good-Edge-IOT-Device-1024x576.jpg

Data security

Users require data at the edge to be kept secure as when it is stored and used elsewhere. These challenges need to meet due to the larger vector and scope for attacks at the edge. Data authentication and user access are as important at the edge as it is on the device or at the core.  Additionally, the physical security of edge infrastructure needs to be considered, as it is likely to hold in less secure environments than dedicated data centers.

Data Quality

Data quality at the edge is a key requirement to guarantee to operate in demanding environments. To maintain data quality at the edge, applications must ensure that data is authenticated, replicated as and assigned into the correct classes and types of data category.

Flexibility in future enhancements

Additional sensors can be added and managed at the edge as requirements change. Sensors such as accelerometers, cameras, and GPS, can be added to equipment, with seamless integration and control at the edge.

Local storage

Local storage is essential in the event of loss of internet connection or cloud crash edge device will store data until the connection is established, so it won’t lose any process information. The local data storage is optional and not all edge devices offer local storage, it depends on the application and service required to implement on the plant

Originaly Posted here

Read more…

by Singapore University of Technology and Design

Internet-of-Things (IoT) such as smart home locks and medical devices, depend largely on Bluetooth low energy (BLE) technology to function and connect across other devices with reduced energy consumption. As these devices get more prevalent with increasing levels of connectivity, the need for strengthened security in IoT has also become vital.

A research team, led by Assistant Professor Sudipta Chattopadhyay from the Singapore University of Technology and Design (SUTD), wit team members from SUTD and the Institute for Infocomm Research (I2R), designed and implemented the Greyhound framework, a tool used to discover SweynTooth—a critical set of 11 cyber vulnerabilities.

Their study was presented at the USENIX Annual Technical Conference (USENIX ATC) on 15 to 17 July 2020 and they have been invited to present at the upcoming Singapore International Cyber Week (SICW) in October 2020.

These security lapses were found to affect devices by causing them to crash, reboot or bypass security features. At least 12 BLE based devices from eight vendors were affected, including a few hundred types of IoT products including pacemakers, wearable fitness trackers and home security locks.

The SweynTooth code has since been made available to the public and several IoT product manufacturers have used it to find security issues in their products. In Singapore alone, 32 medical devices reported to be affected by SweynTooth and 90% of these device manufacturers have since implemented preventive measures against this set of cyber vulnerabilities.

Regulatory agencies including the Cyber Security Agency and the Health Sciences Authority in Singapore as well as the Department of Homeland Security and the Food and Drug Administration in the United States have reached out to the research team to further understand the impact of these vulnerabilities.

These agencies have also raised public alerts to inform medical device manufacturers, healthcare institutions and end users on the potential security breach and disruptions. The research team continues to keep them updated on their research findings and assessments.

Beyond Bluetooth technology, the research team designed the Greyhound framework using a modular approach so that it could easily be adapted for new wireless protocols. This allowed the team to test it across the diverse set of protocols that IoTs frequently employ. This automated framework also paves new avenues in the testing security of more complex protocols and IoTs in next-generation wireless protocol implementations such as 5G and NarrowBand-IoT which require rigorous and systematic security testing.

"As we are transitioning towards a smart nation, more of such vulnerabilities could appear in the future. We need to start rethinking the device manufacturing design process so that there is limited reliance on communication modules such as Bluetooth to ensure a better and more secure smart nation by design," explained principal investigator Assistant Professor Sudipta from SUTD.

Originally posted HERE.

Read more…

When you’re in technology, you have to expect change. Yet, there’s something to the phrase “the more things change, the more they stay the same.” For instance, I see in the industrial internet of things (IIoT) a realm that’ll dramatically shape the future - how we manufacture, the way we run our factories, workforce needs – but the underlying business goals are the same as always.

Simply put, while industrial enterprise initiatives may change, financial objectives don’t – and they’re still what matter most. That’s why IIoT is so appealing. While the possibilities of smart and connected operations, sites and products certainly appeal to the dreamer and innovator, the clear payoff ensures that it’s a road even the most pragmatic decision-maker will eagerly follow.

The big three
When it comes to industrial enterprises, IIoT addresses the “big three” financial objectives head on. The technology maximizes revenue growth, reduces operating expense and increases asset efficiency.

IIoT does this in numerous ways. It yields invaluable operational intelligence, like real-time performance management data, to reduce manufacturing costs, increase flexibility and enable agility. When it comes to productivity, connected digital assets can empower a workforce with actionable insights to improve productivity and quality, even prevent safety and compliance issues.

For example, recognizing defects in a product early on can save time, materials, staff hours and possibly even a company’s reputation.

Whether on or off the factory floor, IIoT can be used to optimize asset efficiency. With real-time monitoring, diagnostics and analytics, downtime can be reduced or avoided. Asset utilization can also be evaluated and maximized. Think applications like equipment health monitoring, predictive maintenance, the ability to provide augmented 3D instructions for complex repairs. And, you can also scale production more precisely via better control over processes and inventory.

All of this accelerates time to market; another key benefit of IIoT and long held business goal.

Why is 5G important for IIoT and augmented reality (AR)?
As we look at the growing need to connect more devices, more sensors and install things like real-time cameras for doing analytics, there is growing stress and strain that is brought into industrial settings. We have seen the need to increase connectivity while having greater scalability, performance, accessibility, reliability, and broader reach with a lower cost of ownership become much more important. This is where 5G can make a real difference.

Many of our customers have seen what we are doing with augmented reality and the way that PTC can help operators service equipment. But in the not so distant future, the way that people interact with robotics, for example, will change. There will be real-time video to do spatial analytics on the way that people are working with man and machines and we’ll be able to unlock a new level of intelligence with a new layer of connectivity that helps drive better business outcomes.

Partner up
It sounds nice but the truth is, a lot of heavy lifting is required to do IIoT right. The last thing you want to do is venture into a pilot, run into problems, and leave the C-suite less than enthused with the outcome. And make no mistake, there’s a lot potential pitfalls to be aware of.

For instance, lengthy proof of concept periods, cumbersome processes and integrations can slow time to market. Multiple, local integrations can be required when connectivity and device management gets siloed. If not done right, you may only gain limited visibility into devices and the experience will fall short. And, naturally, global initiatives can be hindered by high roaming costs and deployment obstacles.

That said, you want to harness best of breed providers, not only to realize the full benefits of Industry 4.0, but to set yourself up with a foundation that’ll be able to harness 5G developments. You need a trusted IoT partner, and because of the sophistication and complexity, it takes an ecosystem of proven innovators working collaboratively.

That’s why PTC and Ericsson are partners.

Doing what’s best
Ericsson unlocks the full value of global cellular IoT connectivity and provides on-premise solutions. PTC offers an industrial IoT platform that’s ready to configure and deploy, with flexible connectivity and capabilities to build solutions without manual coding.

Drilling down a bit further, Ericsson’s IoT Accelerator can connect and manage billions of devices and millions of applications easily, seamlessly and globally. PTC’s IoT solutions digitalize processes and products, combining the physical and digital worlds seamlessly.

And with wireless connectivity, we can deploy a lot of new technology – from augmented reality to artificial intelligence applications – without having to think about the time and cost of creating fixed infrastructures, running wires, adding network capacity and more.

According ABI Research, organizations that embrace Industry 4.0 and private cellular have the potential to improve gross margins by 5-13% in factory and warehouse operations. Manufacturers can expect a 10x return on their investment. And with 4.3 billion wireless connections in smart factories anticipated by 2030, it’s clear where things are headed.

By focusing on what we each do best, PTC and Ericsson is able to do what’s best for our customers. We can help them build and scale global cellular IoT deployments faster and gain a competitive advantage. They can reap the advantages of Industry 4.0 and create that path to 5G, future-proofing their operations and enjoying such differentiators as network slicing, edge computing and high-reliability, low latency communications.

Further, with our histories of innovation, customers are assured they’ll be supported in the future, remaining out front with the ability to adapt to change, grow and deliver on financial objections.

Editor's Note: This post was originally published by Steve Dertien, Chief Technology Officer for PTC, on Ericsson's website, and is part of a joint content effort with Kiva Allgood, head of IoT for Ericsson. To view Steve's original, please click here. To read Kiva's complementary post, please click here.

Read more…

A scientist from Russia has developed a new neural network architecture and tested its learning ability on the recognition of handwritten digits. The intelligence of the network was amplified by chaos, and the classification accuracy reached 96.3%. The network can be used in microcontrollers with a small amount of RAM and embedded in such household items as shoes or refrigerators, making them 'smart.' The study was published in Electronics.

Today, the search for new neural networks that can operate on microcontrollers with a small amount of random access memory (RAM) is of particular importance. For comparison, in ordinary modern computers, random access memory is calculated in gigabytes. Although microcontrollers possess significantly less processing power than laptops and smartphones, they are smaller and can be interfaced with household items. Smart doors, refrigerators, shoes, glasses, kettles and coffee makers create the foundation for so-called ambient intelligece. The term denotes an environment of interconnected smart devices. 

An example of ambient intelligence is a smart home. The devices with limited memory are not able to store a large number of keys for secure data transfer and arrays of neural network settings. It prevents the introduction of artificial intelligence into Internet of Things devices, as they lack the required computing power. However, artificial intelligence would allow smart devices to spend less time on analysis and decision-making, better understand a user and assist them in a friendly manner. Therefore, many new opportunities can arise in the creation of environmental intelligence, for example, in the field of health care.

Andrei Velichko from Petrozavodsk State University, Russia, has created a new neural network architecture that allows efficient use of small volumes of RAM and opens the opportunities for the introduction of low-power devices to the Internet of Things. The network, called LogNNet, is a feed-forward neural network in which the signals are directed exclusively from input to output. Its uses deterministic chaotic filters for the incoming signals. The system randomly mixes the input information, but at the same time extracts valuable data from the information that are invisible initially. A similar mechanism is used by reservoir neural networks. To generate chaos, a simple logistic mapping equation is applied, where the next value is calculated based on the previous one. The equation is commonly used in population biology and as an example of a simple equation for calculating a sequence of chaotic values. In this way, the simple equation stores an infinite set of random numbers calculated by the processor, and the network architecture uses them and consumes less RAM.

7978216495?profile=RESIZE_584x

The scientist tested his neural network on handwritten digit recognition from the MNIST database, which is considered the standard for training neural networks to recognize images. The database contains more than 70,000 handwritten digits. Sixty-thousand of these digits are intended for training the neural network, and another 10,000 for network testing. The more neurons and chaos in the network, the better it recognized images. The maximum accuracy achieved by the network is 96.3%, while the developed architecture uses no more than 29 KB of RAM. In addition, LogNNet demonstrated promising results using very small RAM sizes, in the range of 1-2kB. A miniature controller, Atmega328, can be embedded into a smart door or even a smart insole, has approximately the same amount of memory.

"Thanks to this development, new opportunities for the Internet of Things are opening up, as any device equipped with a low-power miniature controller can be powered with artificial intelligence. In this way, a path is opened for intelligent processing of information on peripheral devices without sending data to cloud services, and it improves the operation of, for example, a smart home. This is an important contribution to the development of IoT technologies, which are actively researched by the scientists of Petrozavodsk State University. In addition, the research outlines an alternative way to investigate the influence of chaos on artificial intelligence," said Andrei Velichko.

Originally posted HERE.

by Russian Science Foundation

Image Credit: Andrei Velichko

 

 

 

 

Read more…

Impact of IoT in Inventory

Internet of Things (IoT) has revolutionized many industries including inventory management. IoT is a concept where devices are interconnected via the internet. It is expected that by 2020, there will be 26 billion devices connected worldwide. These connections are important because it allows data sharing which then can perform actions to make life and business more efficient. Since inventory is a significant portion of a company’s assets, inventory data is vital for an accounting department for the company’s asset management and annual report.

Inventory solutions based on IoT and RFID, individual inventory item receives an RFID tag. Each tag has a unique identification number (ID) that contains information about an inventory item, e.g. a model, a batch number, etc. these tags are scanned by RF reader. Upon scanning, a reader extracts its IDs and transmits them to the cloud for processing. Along with the tag’s ID, the cloud receives location and the time of reading. This data is used for updates about inventory items’, allowing users to monitor the inventory from anywhere, in real-time.

Industrial IoT

The role of IoT in inventory management is to receive data and turn it into meaningful insights about inventory items’ location, status, and giving users a corresponding output. For example, based on the data, and inventory management solution architecture, we can forecast the number of raw materials needed for the upcoming production cycle. The output of the system can also send an alert if any individual inventory item is lost.

Moreover, IoT based inventory management solutions can be integrated with other systems, i.e. ERP and share data with other departments.

RFID in Industrial IoT

RFID consist of three main components tag, antenna, and a reader

Tags: An RFID tag carries information about a specific object. It can be attached to any surface, including raw materials, finished goods, packages, etc.

RFID antennas: An RFID antenna receives signals to supply power and data for tags’ operation

RFID readers: An RFID reader, uses radio signals to read and write to the tags. The reader receives data stored in the tag and transmits it to the cloud.

Benefits of IoT in inventory management

The benefits of IoT on the supply chain are the most exciting physical manifestations we can observe. IoT in the supply chain creates unparalleled transparency that increases efficiencies.

Inventory tracking

The major benefit of inventory management is asset tracking, instead of using barcodes to scan and record data, items have RFID tags which can be registered wirelessly. It is possible to accurately obtain data and track items from any point in the supply chain.

With RFID and IoT, managers don’t have to spend time on manual tracking and reporting on spreadsheets. Each item is tracked and the data about it is recorded automatically. Automated asset tracking and reporting save time and reduce the probability of human error.

Inventory optimization

Real-time data about the quantity and the location of the inventory, manufacturers can reduce the amount of inventory on hand while meeting the needs of the customers at the end of the supply chain.

The data about the amount of available inventory and machine learning can forecast the required inventory which allows manufacturers to reduce the lead time.

Remote tracking

Remote product tracking makes it easy to have an eye on production and business. Knowing production and transit times, allows you to better tweak orders to suit lead times and in response to fluctuating demand. It shows which suppliers are meeting production and shipping criteria and which needs monitoring for the required outcome.

It gives visibility into the flow of raw materials, work-in-progress and finished goods by providing updates about the status and location of the items so that inventory managers see when an individual item enters or leaves a specific location.

Bottlenecks in the operations

With the real-time data about the location and the quantity, manufacturers can reveal bottlenecks in the process and pinpoint the machine with lower utilization rates. For instance, if part of the inventory tends to pile up in front of a machine, a manufacturer assumes that the machine is underutilized and needs to be seen to.

The Outcomes

The data collected by inventory management is more accurate and up-to-date. By reducing these time delays, the manufacturing process can enhance accuracy and reduce wastage. An IoT-based inventory management solution offers complete visibility on inventory by providing real-time information fetched by RFID tags. It helps to track the exact location of raw materials, work-in-progress and finished goods. As a result, manufacturers can balance the amount of on-hand inventory, increase the utilization of machines, reduce lead time, and thus, avoid costs bound to the less effective methods. This is all about optimizing inventory and ensuring anything ordered can be sold through whatever channel necessary.

Originally posted here

Read more…

After so many years evangelizing the Internet of Things (IoT) or developing IoT products or selling IoT services or using IoT technologies, it is hard to believe that today there are as many defenders as detractors of these technologies. Why does the doubt still assail us: "Believe or Not Believe in the IoT"? What's the reason we keep saying every year that the time for IoT is finally now?

It does not seem strange to you that if we have already experienced the power of change that involves having connected devices in ourselves (wearables), in our homes, in cities, in transportation, in business, we continue with so many non-believers. Maybe, because the expectations in 2013 were so great that now in 2020 we need more tangible and realistic data and facts to continue believing.

In recent months I have had more time to review my articles and some white papers and I think I have found some reasons to continue believing, but also reasons not to believe.

Here below there are some of these reasons for you to decide where to position yourself.

Top reasons to believe

  • Mackinsey continue presenting us new opportunities with IoT
    • If in 2015 “Internet of Things: Mapping the value beyond the hype” the company estimated a potential economic impact as much as 11,1 US trillions per year in 2025 for IoT applications in 9 settings.
    • In 2019 “Growing opportunities in the Internet of Things” they said that “The number of businesses that use the IoT technologies has increased from 13 percent in 2014 to about 25 percent today. And the worldwide number of IoT connected devices is projected to increase to 43 billion by 2023, an almost threefold increase from 2018.”
  • Gartner in 2019 predicted that by 2021, there will be over 25 Billion live IoT endpoints that will allow unlimited number of IoT use cases.
  • Harbor Research considers that the market opportunity for industrial internet of things (IIoT) and industry 4.0 is still emergent.
    • Solutions are not completely new but are evolving from the convergence of existing technologies; creative combinations of these technologies will drive many new growth opportunities;
    • As integration and interoperability across the industrial technology “stack” relies on classic IT principles like open architectures, many leading IT players are entering the industrial arena;
  • IoT regulation is coming - The lack of regulation is one of the biggest issues associated with IoT devices, but things are starting to change in that regard as well. The U.S. government was among the first to take the threat posed by unsecured IoT devices seriously, introducing several IoT-related bills in Congress over the last couple of years. It all began with the IoT Cybersecurity Improvement Act of 2017, which set minimum security standards for connected devices obtained by the government. This legislation was followed by the SMART IoT Act, which tasked the Department of Commerce with conducting a study of the current IoT industry in the United States.
  • Synergy of IoT and AI - IoT supported by artificial intelligence enhances considerably the success in a large repertory of every-day applications with dominant one’s enterprise, transportation, robotics, industrial, and automation systems applications.
  • Believe in superpowers again, thanks to IoT - Today, IoT sensors are everywhere – in your car, in electronic appliances, in traffic lights, even probably on the pigeon outside your window (it’s true, it happened in London!). IoT sensors will help cities map air quality, identify high-pollution pockets, trigger alerts if pollution levels rise dangerously, while tracking changes over time and taking preventive measures to correct the situation. thanks to IoT, connected cars will now communicate seamlessly with IoT sensors and find empty parking spots easily. Sensors in your car will also communicate with your GPS and the manufacturer’s system, making maintenance and driving a breeze!. City sensors will identify high-traffic areas and regulate traffic flows by updating your GPS with alternate routes. These IoT sensors can also identify and repair broken street lamps. IoT will be our knight in shining, super-strong metallic armor and prevent accidents like floods, fires and even road accidents, by simply monitoring fatigue levels of truck drivers!. Washing machines, refrigerators, air-conditioners will now self-monitor their usage, performance, servicing requirements, while triggering alerts before potential breakdowns and optimizing performance with automatic software updates. IoT sensors will now help medical professional monitor pulse rates, blood pressure and other vitals more efficiently, while triggering alerts in case of emergencies. Soon, Nano sensors in smart pills will make healthcare super-personalized and 10x more efficient!

Top reasons not to believe

  1. Three fourths of IoT projects failing globally. Government and enterprises across the globe are rolling out Internet of Things (IoT) projects but almost three-fourths of them fail, impacted by factors like culture and leadership, according to US tech giant Cisco (2017). Businesses are spending $745 billion worldwide on IoT hardware and software in 2019 alone. Yet, three out of every four IoT implementations are failing.
  2. Few IoT projects survive proof-of-concept stage - About 60% of IoT initiatives get stalled at the Proof of Concept (PoC) stage. If the right steps aren’t taken in the beginning, say you don’t think far enough beyond the IT infrastructure, you end up in limbo: caught between the dream of what IoT could do for your business and the reality of today’s ROI. That spot is called proof-of-concept (POC) purgatory.
  3. IoT Security still a big concern - The 2019 annual report of SonicWall Caoture Labs threat researchers analyzing data from over 200,000 malicious events indicated that 217.5 percent increase in IoT attacks in 2018.
  4. There are several obstacles companies face both in calculating and realizing ROI from IoT. Very few companies can quantify the current, pre-IoT costs. The instinct is often to stop after calculating the cost impact on the layer of operations immediately adjacent to the potential IoT project.  For example, when quantifying the baseline cost of reactive (versus predictive or prescriptive) maintenance, too many companies would only include down time for unexpected outages, but may not consider reduced life of the machine, maintenance overtime, lost sales due to long lead times, supply chain volatility risk for spare parts, and the list goes on.
  5. Privacy, And No, That’s Not the Same as Security. The Big Corporations don’t expect to make a big profit on the devices themselves. the Big Money in IoT is in Big Data. And enterprises and consumers do not want to expose everything sensors are learning about your company or you.
  6. No Killer Application – I suggest to read my article “Worth it waste your time searching the Killer IoT Application?"
  7. No Interoperable Technology ecosystems - We have a plethora of IoT vendors, both large and small, jumping into the fray and trying to establish a foothold, in hopes of either creating their own ecosystem (for the startups) or extending their existing one (for the behemoths).
  8. Digital Fatigue – It is not enough for us to try to explain IoT, that now more technologies such as Artificial Intelligence, Blockchain, 5G, AR / VR are joining the party and of course companies say enough.

You have the last word

We can go on forever looking for reasons to believe or not believe in IoT but we cannot continue to deny the evidence that the millions of connected devices already out there and the millions that will soon be waiting for us to exploit their full potential.

I still believe. But you have the last word.

Thanks in advance for your Likes and Shares

Read more…

By: Tom Jeltes, Eindhoven University of Technology

The Internet of Things (IoT) consists of billions of sensors and other devices connected to each other via internet, all of which need to be protected against hackers with malicious purposes. A low-cost and energy efficient solution for the security of IoT devices uses the unique characteristics of the built-in memory chips. Ph.D. candidate Lieneke Kusters investigated how to make optimal use of the chip's digital fingerprint to generate a security key.

The higher the number of devices connected to each other via the Internet of Things, the greater the risk that malicious hackers might gain access to important information, or even take over entire systems. Quite apart from all kinds of privacy issues, it's not hard to imagine that that someone who, for example, has control over temperature sensors in a chemical or nuclear plant, could cause serious damage.

 To prevent problems like these from occurring, each IoT device needs to be able, as it were, to show an identity document—"authentication," in professional terms. Normally, speaking, this is done with a kind of password, which is sent in encrypted form to the person who is communicating with the device. The security key needed for that has to be stored in the IoT device one way or another, Lieneke Kusters explains. "But these are often small and cheap devices that aren't supposed to use much energy. To safely store a key in these devices, you need extra hardware with constant power supply. That's not very practical."

Digital fingerprint

There is a different way: namely by deducing the security key from a unique physical characteristic of the memory chip (Static Random-Access Memory, or SRAM) that can be found in practically every IoT device. Depending on the random circumstances during the chip's manufacturing process, the memory locations have a random default value of 0 or 1.

"That binary code which you can read out when activating the chip, constitutes a kind of digital fingerprint of the device," says Kusters, who gained her doctorate at the Information and Communication Theory Laboratory at the TU/e department of Electrical Engineering. This fingerprint is known as a Physical Unclonable Function (PUF). "The Eindhoven-based company Intrinsic ID sells digital security based on SRAM-PUFs. I collaborated with them for my doctoral research, during which I focused on how to generate, in a reliable way, a key from that digital fingerprint that is as long as possible. The longer, the safer."

The major advantage of security keys based on SRAM-PUFs is that the key exists only at the moment when authentication is required. "The device restarts itself to read out the SRAM-PUF and in doing so creates the key, which subsequently gets erased immediately after use. That makes it all but impossible for an attacker to steal the key."

Noise and reliability

But that's not the entire story, because some bits of the SRAM do not always have the same value during activation, Kusters explains. Ten to fifteen percent of the bits turn out not to be determined, which makes the digital fingerprint a bit fuzzy. How do you use that fuzzy fingerprint to make a key of the highest possible complexity that nevertheless still fits into the receiving lock—practically—each time?

"What you want to prevent is that the generated key won't be recognized by the receiving party as a consequence of the 'noise' in the SRAM-PUF," Kusters explains. "It's alright if that happens one in a million times perhaps, preferably less often." The probability of error is smaller with a shorter key, but such a key is also easier to guess for people with bad intentions. "I've searched for the longest reliable key, given a certain amount of noise in the measurement. It helps if you store extra information about the SRAM-PUF, but that must not be of use to a potential attacker. My thesis is an analysis of how you can reach the optimal result in different situations with that extra information."

Originaly posted here.


 
Read more…

Upcoming IoT Events

More IoT News

Arcadia makes supporting clean energy easier

Nowadays, it’s easier than ever to power your home with clean energy, and yet, many Americans don’t know how to make the switch. Luckily, you don’t have to install expensive solar panels or switch utility companies…

Continue

Answering your Huawei ban questions

A lot has happened since we uploaded our most recent video about the Huawei ban last month. Another reprieve has been issued, licenses have been granted and the FCC has officially barred Huawei equipment from U.S. networks. Our viewers had some… Continue

IoT Career Opportunities