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Can AI Replace Firmware?

Scott Rosenthal and I go back about a thousand years; we've worked together, helped midwife the embedded field into being, had some amazing sailing adventures, and recently took a jaunt to the Azores just for the heck of it. Our sons are both big data people; their physics PhDs were perfect entrees into that field, and both now work in the field of artificial intelligence.

At lunch recently we were talking about embedded systems and AI, and Scott posed a thought that has been rattling around in my head since. Could AI replace firmware?

Firmware is a huge problem for our industry. It's hideously expensive. Only highly-skilled people can create it, and there are too few of us.

What if an AI engine of some sort could be dumped into a microcontroller and the "software" then created by training that AI? If that were possible - and that's a big "if" - then it might be possible to achieve what was hoped for when COBOL was invented: programmers would no longer be needed as domain experts could do the work. That didn't pan out for COBOL; the industry learned that accountants couldn't code. Though the language was much more friendly than the assembly it replaced, it still required serious development skills.

But with AI, could a domain expert train an inference engine?

Consider a robot: a "home economics" major could create scenarios of stacking dishes from a dishwasher. Maybe these would be in the form of videos, which were then fed to the AI engine as it tuned the weighting coefficients to achieve what the home ec expert deems worthy goals.

My first objection to this idea was that these sorts of systems have physical constraints. With firmware I'd write code to sample limit switches so the motors would turn off if at an end-of-motion extreme. During training an AI-based system would try and drive the motors into all kinds of crazy positions, banging destructively into stops. But think how a child learns: a parent encourages experimentation but prevents the youngster from self-harm. Maybe that's the role of the future developer training an AI. Or perhaps the training will be done on a simulator of some sort where nothing can go horribly wrong.

Taking this further, a domain expert could define the desired inputs and outputs, and then a poorly-paid person do the actual training. CEOs will love that. With that model a strange parallel emerges to computation a century ago: before the computer age "computers" were people doing simple math to create tables of logs, trig, ballistics, etc. A room full all labored at a problem. They weren't particularly skilled, didn't make much, but did the rote work under the direction of one master. Maybe AI trainers will be somewhat like that.

Like we outsource clothing manufacturing to Bangladesh, I could see training, basically grunt work, being sent overseas as well.

I'm not wild about this idea as it means we'd have an IoT of idiots: billions of AI-powered machines where no one really knows how they work. They've been well-trained but what happens when there's a corner case?

And most of the AI literature I read suggests that inference successes of 97% or so are the norm. That might be fine for classifying faces, but a 3% failure rate of a safety-critical system is a disaster. And the same rate for less-critical systems like factory controllers would also be completely unacceptable.

But the idea is intriguing.

Original post can be viewed here

Feel free to email me with comments.

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Theoratical Embedded Linux requirements

Hardware

SoC

A System on Chip (SoC), is essentially an integrated circuit that takes a single platform and integrates an entire computer system onto it. It combines the power of the CPU with other components that it needs to perform and execute its functions. It is in charge of using the other hardware and running your software. The main advantage of SoC includes lower latency and power saving.

It is made of various building blocks:

  • Core + Caches + MMU – An SoC has a processor at its core which will define its functions. Normally, an SoC has multiple processor cores. For a “real” processor, e.g. ARM Cortex-A9. It’s the main thing kept in mind while choosing an SoC. Maybe co-adjuvanted by e.g. a SIMD co-processor like NEON.
  • Internal RAM – IRAM is composed of very high-speed SRAM located alongside the CPU. It acts similar to a CPU cache, and generally very small. It is used in the first phase of the boot sequence.
  • Peripherals – These can be a simple ADC, DSP, or a Graphical Processing Unit which is connected via some bus to the Core. A low power/real-time co-processor helps the main Core with real-time tasks or handle low power states. Examples of such IP cores are USB, PCI-E, SGX, etc.

External RAM

An SoC uses RAM to store temporary data during and after bootstrap. It is the memory an embedded system uses during regular operation.

Non-Volatile Memory

In an Embedded system or single-board computer, it is the SD card. In other cases, it can be a NAND, NOR, or SPI Data flash memory. It is the source of data the SoC reads and stores all the software components needed for the system to work.

External Peripherals

An SoC must have external interfaces for standard communication protocols such as USB, Ethernet, and HDMI. It also includes wireless technology protocols of Wi-Fi and Bluetooth.

Software

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First of all, we introduce the boot chain which is the series of actions that happens when an SoC is powered up.

Boot ROM: It is a piece of code stored in the ROM which is executed by the booting core when it is powered-on. This code contains instructions for the configuration of SoC to allow it to execute applications. The configurations performed by Boot ROM include initialization of the core’s register and stack pointer, enablement of caches and line buffers, programming of interrupt service routine, clock configuration.

Boot ROM also implements a Boot Assist Module (BAM) for downloading an application image from external memories using interfaces like Ethernet, SD/MMC, USB, CAN, UART, etc.

1st stage bootloader

In the first-stage bootloader performs the following

  • Setup the memory segments and stack used by the bootloader code
  • Reset the disk system
  • Display a string “Loading OS…”
  • Find the 2nd stage boot loader in the FAT directory
  • Read the 2nd stage boot loader image into memory at 1000:0000
  • Transfer control to the second-stage bootloader

It copies the Boot ROM into the SoC’s internal RAM. Must be tiny enough to fit that memory usually well under 100kB. It initializes the External RAM and the SoC’s external memory interface, as well as other peripherals that may be of interest (e.g. disable watchdog timers). Once done, it executes the next stage, depending on the context, which could be called MLO, SPL, or else.

2nd stage bootloader

This is the main bootloader and can be 10 times bigger than the 1st stage, it completes the initialization of the relevant peripherals.

  • Copy the boot sector to a local memory area
  • Find kernel image in the FAT directory
  • Read kernel image in memory at 2000:0000
  • Reset the disk system
  • Enable the A20 line
  • Setup interrupt descriptor table at 0000:0000
  • Setup the global descriptor table at 0000:0800
  • Load the descriptor tables into the CPU
  • Switch to protected mode
  • Clear the prefetch queue
  • Setup protected mode memory segments and stack for use by the kernel code
  • Transfer control to the kernel code using a long jump

Linux Kernel

The Linux kernel is the main component of a Linux OS and is the core interface between hardware and processes. It communicates between the hardware and processes, managing resources as efficiently as possible. The kernel performs following jobs

  • Memory management: Keep track of memory, how much is used to store what, and where
  • Process management: Determine which processes can use the processor, when, and for how long
  • Device drivers: Act as an interpreter between the hardware and the processes
  • System calls and security: Receive requests for the service from processes

To put the kernel in context, they can be interpreted as a Linux machine as having 3 layers:

  • The hardware: The physical machine—the base of the system, made up of memory (RAM) and the processor (CPU), as well as input/output (I/O) devices such as storage, networking, and graphics.
  • The Linux kernel: The core of the OS. It is a software residing in memory that tells the CPU what to do.
  • User processes: These are the running programs that the kernel manages. User processes are what collectively makeup user space. The kernel allows processes and servers to communicate with each other.

Init and rootfs – init is the first non-Kernel task to be run, and has PID 1. It initializes everything needed to use the system. In production embedded systems, it also starts the main application. In such systems, it is either BusyBox or a custom-crafted application.

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CLICK HERE TO DOWNLOAD

This complete guide is a 212-page eBook and is a must read for business leaders, product managers and engineers who want to implement, scale and optimize their business with IoT communications.

Whether you want to attempt initial entry into the IoT-sphere, or expand existing deployments, this book can help with your goals, providing deep understanding into all aspects of IoT.

CLICK HERE TO DOWNLOAD

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Edge Products Are Now Managed At The Cloud

Now more than ever, there are billions of edge products in the world. But without proper cloud computing, making the most of electronic devices that run on Linux or any other OS would not be possible.

And so, a question most people keep asking is which is the best Software-as-a-service platform that can effectively manage edge devices through cloud computing. Well, while edge device management may not be something, the fact that cloud computing space is not fully exploited means there is a lot to do in the cloud space.

Product remote management is especially necessary for the 21st century and beyond. Because of the increasing number of devices connected to the internet of things (IoT), a reliable SaaS platform should, therefore, help with maintaining software glitches from anywhere in the world. From smart homes, stereo speakers, cars, to personal computers, any product that is connected to the internet needs real-time protection from hacking threats such as unlawful access to business or personal data.

Data being the most vital asset is constantly at risk, especially if individuals using edge products do not connect to trusted, reliable, and secure edge device management platforms.

Bridges the Gap Between Complicated Software And End Users

Cloud computing is the new frontier through which SaaS platforms help manage edge devices in real-time. But something even more noteworthy is the increasing number of complicated software that now run edge devices at homes and in workplaces.

Edge device management, therefore, ensures everything runs smoothly. From fixing bugs, running debugging commands to real-time software patch deployment, cloud management of edge products bridges a gap between end-users and complicated software that is becoming the norm these days.

Even more importantly, going beyond physical firewall barriers is a major necessity in remote management of edge devices. A reliable Software-as-a-Service, therefore, ensures data encryption for edge devices is not only hackproof by also accessed by the right people. Moreover, deployment of secure routers and access tools are especially critical in cloud computing when managing edge devices. And so, developers behind successful SaaS platforms do conduct regular security checks over the cloud, design and implement solutions for edge products.

Reliable IT Infrastructure Is Necessary

Software-as-a-service platforms that manage edge devices focus on having a reliable IT infrastructure and centralized systems through which they can conduct cloud computing. It is all about remotely managing edge devices with the help of an IT infrastructure that eliminates challenges such as connectivity latency.

Originally posted here

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Introducing Profiler, by Auptimizer: Select the best AI model for your target device — no deployment required.

Profiler is a simulator for profiling the performance of Machine Learning (ML) model scripts. Profiler can be used during both the training and inference stages of the development pipeline. It is particularly useful for evaluating script performance and resource requirements for models and scripts being deployed to edge devices. Profiler is part of Auptimizer. You can get Profiler from the Auptimizer GitHub page or via pip install auptimizer.

The cost of training machine learning models in the cloud has dropped dramatically over the past few years. While this drop has pushed model development to the cloud, there are still important reasons for training, adapting, and deploying models to devices. Performance and security are the big two but cost-savings is also an important consideration as the cost of transferring and storing data, and building models for millions of devices tends to add up. Unsurprisingly, machine learning for edge devices or Edge AI as it is more commonly known continues to become mainstream even as cloud compute becomes cheaper.

Developing models for the edge opens up interesting problems for practitioners.

  1. Model selection now involves taking into consideration the resource requirements of these models.
  2. The training-testing cycle becomes longer due to having a device in the loop because the model now needs to be deployed on the device to test its performance. This problem is only magnified when there are multiple target devices.

Currently, there are three ways to shorten the model selection/deployment cycle:

  • The use of device-specific simulators that run on the development machine and preclude the need for deployment to the device. Caveat: Simulators are usually not generalizable across devices.
  • The use of profilers that are native to the target device. Caveat: They need the model to be deployed to the target device for measurement.
  • The use of measures like FLOPS or Multiply-Add (MAC) operations to give approximate measures of resource usage. Caveat: The model itself is only one (sometimes insignificant) part of the entire pipeline (which also includes data loading, augmentation, feature engineering, etc.)

In practice, if you want to pick a model that will run efficiently on your target devices but do not have access to a dedicated simulator, you have to test each model by deploying on all of the target devices.

Profiler helps alleviate these issues. Profiler allows you to simulate, on your development machine, how your training or inference script will perform on a target device. With Profiler, you can understand CPU- and memory-usage as well as run-time for your model script on the target device.

How Profiler works

Profiler encapsulates the model script, its requirements, and corresponding data into a Docker container. It uses user-inputs on compute-, memory-, and framework-constraints to build a corresponding Docker image so the script can run independently and without external dependencies. This image can then easily be scaled and ported to ease future development and deployment. As the model script is executed within the container, Profiler tracks and records various resource utilization statistics including Average CPU UtilizationMemory UsageNetwork I/O, and Block I/O. The logger also supports setting the Sample Time to control how frequently Profiler samples utilization statistics from the Docker container.

Get Profiler: Click here

How Profiler helps

Our results show that Profiler can help users build a good estimate of model runtime and memory usage for many popular image/video recognition models. We conducted over 300 experiments across a variety of models (InceptionV3, SqueezeNet, Resnet18, MobileNetV2–0.25x, -0.5x, -0.75x, -1.0x, 3D-SqueezeNet, 3D-ShuffleNetV2–0.25x, -0.5x, -1.0x, -1.5x, -2.0x, 3D-MobileNetV2–0.25x, -0.5x, -0.75x, -1.0x, -2.0x) on three different devices — LG G6 and Samsung S8 phones, and NVIDIA Jetson Nano. You can find the full set of experimental results and more information on how to conduct similar experiments on your devices here.

The addition of Profiler brings Auptimizer closer to the vision of a tool that helps machine learning scientists and engineers build models for edge devices. The hyperparameter optimization (HPO) capabilities of Auptimizer help speed up model discovery. Profiler helps with choosing the right model for deployment. It is particularly useful in the following two scenarios:

  1. Deciding between models — The ranking of the run-times and memory usages of the model scripts measured using Profiler on the development machine is indicative of their ranking on the target device. For instance, if Model1 is faster than Model2 when measured using Profiler on the development machine, Model1 will be faster than Model2 on the device. This ranking is valid only when the CPU’s are running at full utilization.
  2. Predicting model script performance on the device — A simple linear relationship relates the run-times and memory usage measured using Profiler on the development machine with the usage measured using a native profiling tool on the target device. In other words, if a model runs in time x when measured using Profiler, it will run approximately in time (a*x+b) on the target device (where a and b can be discovered by profiling a few models on the device with a native profiling tool). The strength of this relationship depends on the architectural similarity between the models but, in general, the models designed for the same task are architecturally similar as they are composed of the same set of layers. This makes Profiler a useful tool for selecting the best suited model.

Looking forward

Profiler continues to evolve. So far, we have tested its efficacy on select mobile- and edge-platforms for running popular image and video recognition models for inference, but there is much more to explore. Profiler might have limitations for certain models or devices and can potentially result in inconsistencies between Profiler outputs and on-device measurements. Our experiment page provides more information on how to best set up your experiment using Profiler and how to interpret potential inconsistencies in results. The exact use case varies from user to user but we believe that Profiler is relevant to anyone deploying models on devices. We hope that Profiler’s estimation capability can enable leaner and faster model development for resource-constrained devices. We’d love to hear (via github) if you use Profiler during deployment.

Originaly posted here


Authors: Samarth Tripathi, Junyao Guo, Vera Serdiukova, Unmesh Kurup, and Mohak Shah — Advanced AI, LG Electronics USA

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Industrial IoT Revolution

Why the Nvidia Jetson Nano is responsible for the biggest industrial IoT revolution these days

 
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It feels like yesterday when the Raspberry Pi foundation released the first-in-line Single Board Computer (SBC) to the market. Back in 2012, Raspberry Pi wasn't alone in the SBC growing market, however, it was the first to make a community-based product that brings the hardware and the software eco-system to a beautiful harmony on the internet. Before those days, embedded Linux based SBC's and SOM's were a place for Linux kernel and embedded hardware experts, no easy-to-use tools, ready Linux based distros, or most importantly without the enormous amount of questions and answers across the internet on anything related.

Today, 8 years later, the "2012 revolution" happens again

This time, it took a year to understand the impact of the new 'kid' in the market, but now, there are a few indications that defiantly build the route to a revolution.

The Raspberry Pi was the first to make embedded Linux easy while keeping the advantages of reliability and flexibility in terms of fitting to different kinds of industries applications. It's almost impossible to ignore the variety of industries where Raspberry Pi is in its hurt of products to save time-to-market and costs. The power of this magical board leans on the software side: The Raspberry Pi foundation and their community, worked hard across the years to improve and share their knowledge, but, at the same time, without notice or targeting, they brought the Pi development to an extremely "serverless" level.

The Nvidia Jetson Nano

Let's stop talking about the Raspberry Pi and focus on today's industry needs to understand better why the new kid in the town is here to change the market of IoT and smart products forever.

 
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 Why do we need to thanks Nvidia and the Jetson Nano?
 

The market is going forward. AI, Robotics, amazing-looking screen app Gui's, image processing, and long data calculations are all become the new standard of smart edge products.

If a few years ago, you would only want to connect your product to the cloud and receive anything valuable, today, product managers and developers compete in a much tougher industry era. This time, the Raspberry Pi can't be the technology hero again, its resources are limited and the eco-system starts to squint to a better-fit solution.

 
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NVIDIA Jetson devices in Upswift.io device management platform

The Jetson Nano is the first SBC to understood the necessary combination that will drive new products to use it. It's the first SBC designed in the mind of industrial powerful use cases, while not forgetting the prototyping stage and the harmony that gave the Raspberry Pi their success. It's the first solution to bring the whole package for developers and for hardware engineers with a "SaaS" feel: The OS is already perfect thanks to Ubuntu, there is plenty of software instructions by Nvidia and open-source ready-to-use tools custom made for the Jetson family, and for the hardware engineers: they are free to go with the System On Module (SOM) that is connected to a carrier board which includes all the necessary outputs and inputs to make the development stage even faster.

The Jetson Nano combination is basically providing the first world infrastructure for producing a "2020" product with complex software while working in a minimal budget and time-to-market. The Jetson Nano enables developers and product managers to imagine further without compromises, bringing tough software missions to the edge easily.

Originally posted here

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by Dan Carroll, Carnegie Mellon University, Department of Civil and Environmental Engineering

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Credit: Pixabay/CC0 Public Domain
 
Across the U.S., there has been some criticism of the cost and efficacy of emissions inspection and maintenance (I/M) programs administered at the state and county level. In response, Engineering and Public Policy (EPP) Ph.D. student Prithvi Acharya and his advisor, Civil and Environmental Engineering's Scott Matthews, teamed up with EPP's Paul Fischbeck. They have created a new method for identifying over-emitting vehicles using remote data transmission and machine learning that would be both less expensive and more effective than current I/M programs.
 

Most states in America require passenger vehicles to undergo periodic emissions inspections to preserve air quality by ensuring that a vehicle's exhaust emissions do not exceed standards set at the time the vehicle was manufactured. What some may not know is that the metrics through which emissions are gauged nowadays are usually measured by the car itself through on-board diagnostics (OBD) systems that process all of the vehicle's data. Effectively, these emissions tests are checking whether a vehicle's "check engine light" is on. While over-emitting identified by this system is 87 percent likely to be true, it also has a 50 percent false pass rate of over-emitters when compared to tailpipe testing of actual emissions.

With cars as smart devices increasingly becoming integrated into the Internet of Things (IoT), there's no longer any reason for state and county administrations to force drivers to come in for regular I/M checkups when all the necessary data is stored on their vehicle's OBD. In an attempt to eliminate these unnecessary costs and improve the effectiveness of I/M programs, Acharya, Matthews, and Fischbeck published their recent study in IEEE Transactions on Intelligent Transportation Systems.

Their new method entails sending data directly from the vehicle to a cloud server managed by the state or county within which the driver lives, eliminating the need for them to come in for regular inspections. Instead, the data would be run through machine learning algorithms that identify trends in the data and codes prevalent among over-emitting vehicles. This means that most drivers would never need to report to an inspection site unless their vehicle's data indicates that it's likely over-emitting, at which point they could be contacted to come in for further inspection and maintenance.

Not only has the team's work shown that a significant amount of time and cost could be saved through smarter emissions inspecting programs, but their study has also shown how these methods are more effective. Their model for identifying vehicles likely to be over-emitting was 24 percent more accurate than current OBD systems. This makes it cheaper, less demanding, and more efficient at reducing vehicle emissions.

This study could have major implications for leaders and residents within the 31 states and countless counties across the U.S. where I/M programs are currently in place. As these initiatives face criticism from proponents of both environmental deregulation and fiscal austerity, this team has presented a novel system that promises both significant reductions to cost and demonstrably improved effectiveness in reducing vehicle emissions. Their study may well redefine the testing paradigm for how vehicle emissions are regulated and reduced in America.

 
Originally posted here on Tech Xplore
 
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Summary: Know How Businesses Are Leveraging Their Business Power with the Help of the Internet of Things (IoT). They Are Paying Attention to It to Enhance Their Business Process and Ensuring Gain Long Term Success for Their Business in This Fiercely Competitive Market. 

In this IT era, the latest technology is making its way to our day to day life. It has influenced our life to a great extent and has also affected the way we work. Now we use different gadgets and modern equipment that ease our work and helps us to complete it more smoothly and accurately than ever before. The latest technology like Machine Learning, Big Data Analytics, and Artificial Intelligence has slowly established its command across different industries. Apart from all these technologies one technology that gained significant importance is the internet of things (IoT), it has affected the different areas of various sectors to a great extent. 

The use of IoT enabled devices has enhanced the way people live their lives. According to Gartner's prediction, more than 25 billion IoT devices will be present in the market by 2021. The use of IoT will introduce new innovation for businesses, customers, and society. 

The potential growth in usage of IoT has resulted in improvement in various sectors like healthcare, education, entertainment, and many more. Now it has become possible to track assert in real-time, monitoring the ups and downs in the human body, home automation, environmental monitoring, etc have become easy and all thanks go to the internet of things (IoT). 

Internet of Things: Know Why Businesses Need It for Their Business?

As per the report by Cisco, more than 500 billion devices will be connected with the Internet by 2030. Each device that will be connected by the internet will include sensors that collect data by interacting with the environment and will communicate over a network very accurately. 

And all this will become possible through the Internet of Things (IoT) as it's the network of all these connected devices. These smart devices which are developed using this latest technology will generate data that IoT applications use to accomplish various tasks like deliver insight, analyze, aggregate which helps to respond much accurately as per user's actions. 

The internet of things is one such latest technology that is continuously improving with each passing second. As this technology connects multiple things with each other, it becomes possible for businesses to get real-time access to all the information on the network and thus it has been proved to be beneficial for them to improve their business processes. It provided multiple benefits to the businesses who adopt it, go through the list of benefits that IoT offers for your business. There are various advantages to explore when it comes to implementing the internet of things for your business. 

1. Offers a Large Amount of Data

Almost all businesses these days have realized the power of the internet of things and have started opting for the same for their business. As more and more businesses are stepping ahead to opt for this technology it is predicted that the total market value of IoT will grow rapidly and will reach $3 trillion by 2026

IoT enabled devices are able to collect huge data from the network with the help of added sensors. This information can be beneficial for businesses as they can easily know what their customers really want from them, how can they fulfill their demands in the best possible way, and much more. 

2. Better Customer Service 

Every business these days boil down to satisfy their customers and offer the best to them on their demand. The combo of IoT based devices with an app like spoitify can provide quick access to customers' behaviors. It helps businesses to analyze all the data which includes customers' preference, the time they spent on making a particular purchase, the language they prefer, and much more. 

All this information can help businesses to enhance their customer support and come up with an advanced solution that satisfies all their needs. Using this information you can diversify your business according to new market trends and grab all the opportunities that come your way. 

3. Ability to Monitor and Track Things

IoT enabled devices will allow all businesses to track and monitor each and every activity of their employees. They can easily know what their employees are working, how many tasks they have completed, what progress that has made, and much more. They can even share information with their employees in real-time about the current project on which they are working and can also get information from them whenever they want.  

4. Save Money and Resource

There is no doubt that machine to machine communication is growing dramatically in recent years. It is estimated that the total number of M2M connections will grow speedily from 5 billion to 27 billion from 2014 to 2024.

Machines have taken the place of the human in most of the business sector which save a huge amount of money and resources of businesses which they used to spend on human labor. Nowadays work like answering customers' queries, managing accounts, keeping other business records, and much more work in the business environment is performed by the latest application and software that has been developed using the latest technology like the internet of things or any other. 

5. Automation 

IoT helps businesses to find the best way to make their business process faster and better. They can let them know which areas to be automated so that they can reduce the task of the employees and can save a huge amount of time and resources of their business. If as a business entrepreneur if you feel that your business needs to be automated then IoT will analyze each and every area of your business and will let you know which can be automated and don't need human interaction. 

6. Helps to offer Personalized Experiences

As stated above, businesses can get all the information related to their ideal customers with the help of IoT enabled devices. They can know their purchase preferences, likes, dislikes, and much more and can try to provide a personalized experience. 

As per New Epsilon research, 80% of consumers like to make a purchase from a particular brand if and only if they offer personalized experiences to them. For example, businesses can develop accurate bills keeping in mind the analyzed IoT data and can provide various discounts and offers to the customers as more than 74% of customers expect that they will get automatic crediting for coupons and loyalty points. 

Wonders of the Internet of Things Have a Long Way to Go!

There are certain areas that are still untapped by businesses as they are unable to implement IoT technology in every aspect of their business environment. And even some of the businesses have yet not opted for this modern technology, due to which they are missing various opportunities that are in their success. There are various ways in which IoT works wonders for every business sector. As technology is evolving continually due to research and efforts of brilliant minds, there are certain changes that IoT will have much to offer to the businesses in the nearby future. 

When businesses implement the internet of things in their business they will experience enhancement in their employee's productivity, speed, and efficiency which will directly affect the business profit. Hence work on your business niche and find out whether you can implement IoT in your business environment or not. It’s the demand of time to stand out from others and you can do it using IoT, implement this technology in a basic way for your business if possible.

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In the era of digitalization, IoT is fostering the upcoming revolution in mobile apps. The ways companies used to provide mobile app development are changing because of IoT. After helping thousands of corporates to deliver extraordinary user experiences, IoT is all set with some new and advanced mobile app development trends. 

The tech world is the one that is continuously evolving. Every year and each day, innovations come to light. Each of them is revolutionizing our lives in one or the other ways. From the first wheel to smart cities, humans have come a long way.

The evolution and foundation of smart cities is the result of IoT or the Internet of Things. IoT has definitely stirred quite an uproar in the digital world with the mass potential it has. It can bring everything and everyone online. 

As per the latest mobile app development stats, IoT will become a more significant player in the mobile app development industry. The market share of IoT is going to increase more than double in 2021 with a staggering amount of 520 billion USD. While four years back in 2017, this number was 235 billion USD. 

Soon the IoT mobile app development will face new trends in the coming year and beyond.

Let us take a look at the top IoT mobile app development trends.

IoT App Trend #1: Cybersecurity for IoT

With an increase in the number of devices online, cybersecurity is the top priority for all businesses as IoT gains popularity. The network is expected to expand in the coming years, and so the data volume will also increase. All this draws attention to more information to protect.

IoT security will see an exponential rise as more users will store their data over the cloud. From banking details to home security, everything is easily breached if the security firewall is weak in IoT applications. 

Therefore mobile app development companies need to work upon the up-gradation of their IoT enabled mobile apps. 

IoT App Trend #2: Roaring Popularity of Smart Home Devices

When smart home devices were launched, many mocked them by calling them unrealistic toys for lazy youngsters. Now, the same people are finding it increasingly difficult to resist the charm of IoT devices. 

IoT devices are expected to be very popular in 2021 and the years to come. The reason behind their growing popularity is that the IoT devices are becoming highly intuitive and innovative. They are extended not only to the comfort of home automation but also to home security and the safety of your family.

Another great advantage of implementing smart IoT development adoption is the need to save energy. The intelligent lights or intelligent thermostats help in conserving energy, reducing bills. These reasons will lead to more and more people to adopt smart home devices.

IoT App Trend #3: Backed by AI and ML

Artificial Intelligence and Machine Learning both are thriving technologies. Both of these are the facilitators of automation. We all know how Artificial Intelligence has touched millions of lives around the globe. 

Together with IoT, AI and ML are unique data-driven technologies shaping the future of human-machine interactions. The developers set up a combination of IoT and Artificial Intelligence that helps automate the routine tasks, simplifies work, and gets the most accurate information.

IoT App Trend #4: IoT and Healthcare

With the revolution in the health-tech industry, healthcare companies are turning towards mobile platforms. IoT enabled apps to open up new opportunities to improve the medical sector.

IoT has immense applications that are already running in the healthcare field and is expected to increase by 26.2% 

Healthcare apps featuring IoT technology are expected to reform the world of medical sciences. These IoT mobile apps can even help doctors and medical professionals treat their patients even from a distance.

Smart wearables and implants will be able to record diverse parameters to keep the patient’s health in check. By integrating sensors, portable devices, and all kinds of medical equipment, real-time updates of a patient’s health can be recorded and sent to the concerned person. 

IoT App Trend #5: Edge Computing to Overtake Cloud Computing

This is a change where we have to be careful. For the past many years, IoT devices have been storing their data on cloud storage. However, the IoT developers, development services, and manufacturers have started thinking about the utility of storing, calculating, and analyzing data to the limit.

So basically this means, in place of sending the entire data from IoT devices to the cloud, the data is first transmitted to a local or nearer storage device located close to the IoT device or on the edge of the network. 

This local storage device then analyzes, sorts, filters and calculates the data and then sends all or only a part of the data to the cloud, reducing the traffic on the network avoiding any bottleneck situation.

Known as “edge computing”, this approach has several advantages if used correctly. Firstly, it helps in the better management of the large amount of data that each device sends. Second, the reduced dependency on cloud storage allows devices and applications to perform faster and also reduce latency.

Being able to collect and process data locally, the IoT application is expected to consume lesser bandwidth and work even when connectivity to the cloud is affected. After seeing these positive aspects, state-of-the-art computing is looking forward to better innovation and broad adoption in IoT, both consumer and industrial.

Reduced connectivity to the cloud will also result in fewer security costs and facilitate better security practices. 2021 will see better state-of-the-art IT in IoT.

IoT App Trend #6: Are You Excited About Smart Cities?

Well, all of us are super excited to witness smart cities. Smart cities are one of the significant accomplishments of IoT and modernization. Integrated with IoT-powered devices, smart cities promise improved efficiency and security for the common folk on the streets and inside their homes.

With superfast data transfer supported by 5G, public transportation will also see a massive change in the way they work. 

By now, we know that IoT will focus on developing smart parking lots, street lights, and traffic controls. To add up to this, with IoT and fast internet, we will live inside a world where our refrigerators will be aware of what food we have inside.

IoT will impact traffic congestion and security. It will also help in the development of sustainable cities leading us to a green future.

IoT App Trend #7: Blockchain for IoT Security

Many financial and governmental institutions, entrepreneurs, consumers as well as industrialists will be decentralized, self-governing, and be quite smart. Most of the new companies are seen building their territory on the entanglement of IOTA to develop modules and other components for firms without the cost of SaaS and Cloud.

IOTA is a distributed ledger especially designed to record and execute transactions between devices in the IoT ecosystem.

If you are in this industry, then you should prepare to see the centralized and monolithic computer models that are separated in the jobs and microservices. All this will be distributed to decentralized machines and devices. 

In the coming future, IoT will penetrate the disciplines of health, government, transactions, and others that we cannot think of right now. Such types of IoT technology trends will create significant effective differences.

IoT App Trend #8: IoT for Retail Apps

The eCommerce industry will also get benefited from IoT integration. Retail supply change will be more efficient after the incorporation of IoT mobile apps. It is expected to improve the online shopping experience for individuals across the globe.

Also, IoT will make the retail experience more personalized for each customer with in-app advertisements based on the user’s shopping history. We already get notifications once we purchase a product from a particular eStore. With IoT enabled mobile apps, the app will guide us to our favorite store using in-site maps.

IoT App Trend #9- Will IoT Boost Predictive Maintenance?

Yes, it will. In 2021 and beyond, the smart home system will notify the owner about plumbing leaks, appliance failures, or any other problem so that the house owner can avoid any disaster. Soon these intelligent sensors will enter our houses.

In response to these predictive skills of IoT, we can expect to see home care offers as a contractor service. If there will be a need for any emergency action, your presence in the house will not be necessary. 

IoT App Trend #10: Easy and Better Commuting

IoT mobile applications are expected to make commuting easier for students, the elderly, the business person, and many more. Today, due to heavy traffic, commuting is a significant issue for most of us. With major innovations in technology and integration of IoT, mobile applications will make traveling a breeze for everyone.

Here are some of the conventional ways that commuting will change:

  • Smart street lights will make walking on the road safe for pedestrians
  • Finding parking spaces will be a lot easier and seamless with data-driven parking apps. 
  • In-app navigation and public transportation will definitely make public transit more reliable 
  • IoT powered mobile apps will also improve routing between different modes of transfer.

With so many innovative ideas and benefits for iOS and android based IoT mobile apps, the mobile app development market will see an influx of transportation apps in the years to come.

IoT App Trend #11: Sustainable-as-a-Service Becomes the Norm.

While talking about the IoT trends, SaaS or Sustainable-as-a-Service is considered as one of the hot topics for the estimated market. Because of the low cost of entry, SaaS is quickly getting to the top list for being the favorite firm in the IT gaming sector. 

Out of these emerging technological IoT trends, Software-as-a-service will make the lives of people better than ever.

IoT App Trend #12- Energy and Resource Management 

Do you know what affects energy management the most? Well, energy management majorly depends on the acquisition of a better understanding of how to consume resources. IoT mobile app-based electronics are expected to play a significant role in the conservation of energy. 

All of these IoT trends can be integrated into resource management, making lives more accessible, more comfortable, and responsible.

Automatic notifications can also be added to the mobile app in order to send information to the owner in case the power threshold exceeds. Various other fancy features can also be added to these IoT mobile apps such as sprinkler control, in-house temperature management, etc.

Conclusion

We all know that IoT has great potential to bring revolutionary changes in the present mobile app development industry trends. It is expected to open up immense possibilities for every business or individual related to this field. Directly or indirectly, IoT will drive the future of almost every industry.

The above mentioned are some of the trends that will dominate the IoT app development ecosystem in the years to come. Amid all these predictions and trends, the future is promising and worth the wait. 

 

 

 

 

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It feels like yesterday when the Raspberry Pi foundation released the first-in-line Single Board Computer (SBC) to the market. Back in 2012, Raspberry Pi wasn't alone in the SBC growing market, however, it was the first to make a community-based product that brings the hardware and the software eco-system to a beautiful harmony on the internet. Before those days, embedded Linux based SBC's and SOM's were a place for Linux kernel and embedded hardware experts, no easy-to-use tools, ready Linux based distros, or most importantly without the enormous amount of questions and answers across the internet on anything related.

Today, 8 years later, the "2012 revolution" happens again

This time, it took a year to understand the impact of the new 'kid' in the market, but now, there are a few indications that defiantly build the route to a revolution.

The Raspberry Pi was the first to make embedded Linux easy while keeping the advantages of reliability and flexibility in terms of fitting to different kinds of industries applications. It's almost impossible to ignore the variety of industries where Raspberry Pi is in its hurt of products to save time-to-market and costs. The power of this magical board leans on the software side: The Raspberry Pi foundation and their community, worked hard across the years to improve and share their knowledge, but, at the same time, without notice or targeting, they brought the Pi development to an extremely "serverless" level.

 

The Nvidia Jetson Nano

Let's stop talking about the Raspberry Pi and focus on today's industry needs to understand better why the new kid in the town is here to change the market of IoT and smart products forever.

Why do we need to thanks Nvidia and the Jetson Nano?

The market is going forward. AI, Robotics, amazing-looking screen app Gui's, image processing, and long data calculations are all become the new standard of smart edge products.

If a few years ago, you would only want to connect your product to the cloud and receive anything valuable, today, product managers and developers compete in a much tougher industry era. This time, the Raspberry Pi can't be the technology hero again, its resources are limited and the eco-system starts to squint to a better-fit solution.

The Jetson Nano is the first SBC to understood the necessary combination that will drive new prodcuts to use it. It's the first SBC designed in the mind of industrial powerful use cases, while not forgetting the prototyping stage and the harmony that gave the Raspberry Pi their success. It's the first solution to bring the whole package for developers and for hardware engineers with a "SaaS" feel: The OS is already perfect thanks to Ubuntu, there is plenty of software instructions by Nvidia and open-source ready-to-use tools custom made for the Jetson family, and for the hardware engineers: they are free to go with the System On Module (SOM) that is connected to a carrier board which includes all the necessary outputs and inputs to make the development stage even faster.

The Jetson Nano combination is basically providing the first world infrastructure for producing a "2020" product with complex software while working in a minimal budget and time-to-market. The Jetson Nano enables developers and product managers to imagine further without compromises, bringing tough software missions to the edge easily.

ORIGINALLY POSTED HERE ON UPSWIFT

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Embedded Linux or RTOS: For IoT

by Tirichlabs

Embedded Linux utilizes Linux kernel for an embedded device, but it is quite different from the standard Linux OS. Its application to embedded systems is motivated by the availability of device support, file-systems, network connectivity, and UI support. It is a customized version of Linux for embedded systems, consequently having a much smaller size and minimal features and requires less processing power. Based on embedded system requirements, the Linux kernel is modified and optimized. Such embedded Linux can only run device-specific purpose-built applications.

The Real-Time Operating System (RTOS) with minimal code is used for such applications where least and fix processing time is required. RTOS is a time-sharing system based on clock interrupts that implement priority sequences to execute a process. In the event of a high priority, interrupt is generated by the system, the running low priority processes are stopped and the interrupt is served. The real-time operating system requires less operational memory and synchronizes the processes in such a way they can communicate with each other hence resources can be used efficiently without wastage of time.

 

COMPARISON

Size

The major difference between Embedded Linux and RTOS is in their sizes. RTOS running on an AVR requires approximately 4.4 kilobytes of ROM. Embedded Linux, on the other hand, is relatively larger. The kernel can be stripped of which are not required and even with that, the footprint is generally measured in megabytes.

Embedded Linux RAM requirement is in order of few megabytes. In practical applications, it requires more than that because some other tasks run under these Linux kernels. RTOS has much smaller memory requirements than Linux. A very simple setup, running two tasks, a scheduler, a queue for communication and a semaphore on an 8-bit architecture would use in the vicinity of 200 bytes.

Scheduler

The scheduler in an RT-system is important to ensure that tasks complete in a fixed time. Compared to a regular scheduler for a general-purpose system, it is not the main task of the scheduler to ensure ’fair’ distribution of CPU-time. A common technique is simply to let the task with the highest priority run before all tasks with lower priority. It works fine for a soft real-time system but for hard real-time, the system must provide a better guarantee.

RTOS scheduler

RTOS uses the highest priority first scheduler. It means that the task having the highest priority is always running. This is achieved by having a preemptive scheduler that at a tick-interrupt decides if the currently running task is allowed to continue executing or it needs to be switched for another task based on priority. The scheduler uses the priority to schedule the task with the highest priority. Tasks having the same priority are given a “fair” process time. This schedular allows us to achieve soft real-time but it is difficult to achieve hard real-time by not having any kind of deadline-based scheduling.

For this purpose, there are choices of having a preemptive or a cooperative scheduler. In preemptive mode, a task can be preempted unlike in cooperative mode where it’s up to all tasks to give away the CPU “often” enough so higher priority tasks get to run. Typical RTOS real-time kernel achieves scheduler latencies from zero to a few microseconds.

Embedded Linux scheduler

In Embedded Linux, there are more choices to choose the scheduler. The modular of Embedded Linux allows to change different parts of the system. A simple insmod gives the possibility to change the scheduler. There are a couple of schedulers designed for different things.

First of all, it has a basic highest priority first scheduler that uses the priority of a task and schedules it first. Embedded Linux also implements the Earliest deadline first which uses the periodic feature of Embedded Linux. Assuming that the deadline for every task is when it is next to be run again one can implement a fast EDF. In theory, it is optimal since it can schedule tasks to 100% CPU-usages. In practice, it is not the same due to some overheads. As in idle process Embedded Linux runs a usual Linux kernel and when there are no rt-tasks that can run, Linux gets to run. which can lead to starvation of Linux and thus effectively disabling Linux. But the importance of a real-time system is to run the real-time tasks this is not a big problem for the system. Typical latencies in real-time Linux schedular are in the order of tens to hundreds of microseconds.

CPU resource

Embedded Linux requires a significant amount of CPU resources, perhaps >200MIPS, 32bit processor, ideally with an MMU, 4Mb of ROM and 16MB of RAM and boot may take several seconds.

An RTOS, on the other hand, runs in less than 10Kb, on microcontrollers from 8-bit up and boot in milliseconds.

IoT Implementation of OS

Embedded Linux is often preferred for extremely low-power applications, such as sensors, run for months on batteries. The low-power nature often precludes direct IP connectivity which serves as a gateway for Internet connectivity. The gateway communicates the low-power protocol to the sensors and would translate them to IP. Linux may have an existing protocol to fulfill the requirements.

The basic requirement of an IoT device is network connectivity, typically in the form of IP via a web server. An RTOS can offer IP connectivity but have a risk to be buggy unless you examine it. For example, usually, RTOSs do not isolate the IP stack user from the IP stack itself. Network connectivity requires potentially dealing with low speed or congested links which can lead to obscure and hard-to-debug buffer handling issues when the stack is intermingled with other code. On the other hand, an embedded Linux leverages hardware separation and a widely utilized IP stack that probably has been exposed to corner cases.

Security is essential in IoT devices, which are often exposed to open Internet. A system compromise on the Internet interface is prone to intruders and information or control of the device can be hijacked. Developers can leverage native, embedded Linux features—multiuser, SELinux, and containers—to contain and limit the damage.

Linux certainly is a robust and secure OS and the system has matured in an embedded operating system. Yet one of the drawbacks is its Memory footprint when compared to a real-time operating system even though it can be trimmed down by removing tools and system services that are not required in embedded systems, it still is a large software. It simply cannot run on 8 or 16-bit MCUs and requires more onboard RAM for the Linux kernel. For example, ARM Cortex-M architecture based MCUs, which typically have only a few hundred kilobytes of RAM, and Linux cannot run on these chips.

A common engineering solution for networked systems is to use two processors in the device. In this arrangement, an 8 or 16-bit MCU is used for the sensor or actuator, while a 32-bit processor is used for the network interface which runs an RTOS. Sales of 32-bit MCUs have exploded in the last several years, and have become the largest segment of the MCU market.

ORIGINALLY POSTED HERE ON TIRICH LABS

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When I work on a development project, I’ve become a big fan of using development boards that have the Arduino headers on them. The vast number of shields that easily connect to these headers is phenomenal. The one problem that I’ve always had though was that there is always a need to use a breadboard to test a circuit or integrate a sensor that just isn’t in an Arduino header format. The result is a wiring mess that can result in loose or missing connections.

I was recently talking with Max Maxfield and he pointed me to a really cool adapter board designed to remove these wiring jumpers to a breadboard. Max wrote about this board here but I’m so excited about this that I thought I’d add my two cents as well.

The BreadShield, which can be purchased at https://www.crowdsupply.com/loser/breadshield, adapts the Arduino headers to a linear set of header pins designed to be plugged into a breadboard. You can see in the image below that this completely removes all the extra jumpers that one would normally require which has the potential to remove quite a few jumper wires.

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When I heard about these, I purchased three assembled units for about $28 which saves me the time from having to assemble the adapter myself. DIY assembly runs for about $15 for a set of three boards. Either way, a great price to remove a bunch of wires from the workbench.

Now I’m still waiting for mine to arrive, but from the image, you can see that the one challenge to using these adapters might be adapting the height of your breadboard to your hardware stack. While this could be an issue, I keep various length spacers around the office so that I can adapt board heights and undoubtedly there will be a length that will ensure these line up properly.

You can view the original post here

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In-Circuit Emulators

Does anyone remember in-circuit emulators (ICEs)?

Around 1975 Intel came out with the 8080 microprocessor. This was a big step up from the 8008, for the 8080 had a 64k address space, a reasonable ISA, and an honest stack pointer (the 8008 had a hardware stack a mere 7 levels deep). They soon released the MDS 800, a complete computer based on the 8080, with twin 8" floppy drives. An optional ICE was available; this was, as I recall, a two-board set that was inserted in the MDS. A ribbon cable from those boards went to a small pod that could be plugged into the 8080 CPU socket of a system an engineer was developing.

The idea was that the MDS could act as the device's under test (DUT) CPU. It was rather like today's JTAG debuggers in that one could run code on the DUT, set breakpoints, collect trace data, and generally debug the hardware and software. For there was no JTAG then.

We had been developing microprocessor-based products using the 8008, but quickly transitioned to the 8080 for the increased computational power and address space. I begged my boss for the money for an MDS, which was $20k (about $100k in today's dollars), and to my surprise he let us order one. Despite slow floppies that stored only 80 KB each this tool greatly accelerated our work.

Before long ICEs were the standard platform for embedded work. Remember: this was before PCs so there were no standard desktop computers. The ICE was the computer, the IDE (such as it was) and the debugger.

In the mid-80s I was consulting and designed a, uh, "data gathering" system for our friends in Langley, VA, using multiple NSC-800 CPUs. There were few tools available for this part so I created a custom ICE that let me debug the code. Then a light bulb went on: why not sell the thing? There was practically no market for NSC-800 tools so I came up with versions for the Z80 and 8085 and slapped a $695 label on it. Most ICEs at the time cost many thousands so sales spiked.

Back then we still drew schematics on large D-size (17" x 22") vellum with a pencil. I laid out the PCBs on mylar with black tape for the tracks, as was the norm at the time.

This ICE is perhaps the design I'm most proud of in my career. It was only 17 ICs but was the epitome of an embedded system. Software replaced the usual gobs of hardware. On a breakpoint, for instance, the hardware switched from using the DUT stack to a stack on the emulator, but since the user's stack pointer could point anywhere, and the RAM in the ICE was only a few KB, the hardware masked off the upper address bits and lots of convoluted code reconstructed the user environment.

At the time ICEs advertised their breakpoints; most supported no more than a few as comparators watched the address bus for the breakpoint. My ICE used a 64k by one bit memory that mirrored the user bus. Need a breakpoint at, say, address 0x1234? The emulator set that bit in the memory true. Thus, the thing had 65K breakpoints. One of my dumbest mistakes was to not patent that, as all ICE vendors eventually copied the approach.

The trouble with tools is support. An ICE replaces the DUT CPU, and interfaces with all sorts of unknown target hardware. Though the low clock rates of the Z80 meant we initially had few problems, as we expanded the product line support consumed more and more time. Eventually I learned it was equally easy to sell a six-thousand-dollar product as a six-hundred-dollar version, so those simple first emulators were replaced by much more complex many-hundred chip versions with vast numbers of features.

But the market was changing. By the mid-90s SMT CPUs were common. These were challenging to connect to. Clock rate soared making every connection a Maxwell Law nightmare. I sold the business in 1997 and went on to other endeavors. Eventually the ICE market disappeared.

One regret from all those years is that I didn't save any of the emulator's firmware or schematics. In this business everything is ephemeral. We should make an effort to preserve some of that history.

You can view the original post on TEM here

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Industrial Prototyping for IoT

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ADLINK is a global leader in edge computing driving data-to-decision applications across industries. The company recently introduced I-Pi SMARC for Industrial IoT prototyping.

-       AdLInk I-Pi SMARC consists of a simple carrier paired with a SMARC Computer on Module

-       SMARC Modules are available from entry level PX30 Rockchip to top of the line Intel Apollo Lake.

-       SMARC modules are specifically designed for typical industrial embedded applications that require long life, high MTBF and strict revision control.

-       Use popular off the shelve sensors and create prototypes or proof of concepts on short notice.

Additional information can be found here

 

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By: Kelly McNelis

We have faced unprecedented disruption from the many challenges of COVID-19, and PTC’s LiveWorx was no exception. The definitive digital transformation event went virtual this year, and despite the transition from physical to digital, LiveWorx delivered.

Of the many insightful virtual keynotes, one that caught everyone’s attention was ‘Digital Transformation: The Technology & Support You Need to Succeed,’ presented by PTC’s Executive Vice President (EVP) of Products, Kevin Wrenn, and PTC’s EVP and Chief Customer Officer, Eduarda Camacho.

Their keynote focused on how companies should be prioritizing the use of best-in-class technology that will meet their changing needs during times of disruption and accelerated digital transformation. Wrenn and Camacho highlighted five of our customers through interactive case studies on how they are using PTC technology to capitalize on digital transformation to thrive in an era of disruption.

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Below is a summary of the five customers and their stories that were highlighted during the keynote.

1. Royal Enfield (Mass Customization)

Royal Enfield is an Indian motorcycle company that has been manufacturing motor bikes since 1901. They have British roots, and their main customer base is located in India and Europe. Riders of Royal Enfield wants their bikes to be particular to their brand, so they worked to better manage the complexities of mass customization and respond to market demands.

Royal Enfield is a long time PTC customer, but they were on old versions of PTC technology. They first upgraded Creo and Windchill to the latest releases so they could leverage the new capabilities. They then moved on to transform their processes for platform and variant designs, introduced simulation much earlier by using Creo Simulation Live, and leveraged generative design by bringing AI into engineering and applying it to engine and chassis complex custom forged components. Finally, they retrained and retooled their engineering staff to fully leverage the power of new processes and technologies.

The entire Royal Enfield team now has digital capabilities that accelerate new product designs, variants, and accessories for personalization; as a result, they are able to deliver a much-shortened design cycle. Royal Enfield is continuing their digital transformation trend, and will invest in new ways to create value while leveraging augmented reality with PTC's Vuforia suite.

2. VCST (Manufacturing Efficiency, Quality, and Innovation)

VCST is part of the BMT Group and are a world-class automotive supplier of precision-machined power train and brake components. Their problem was that they had high costs for their production facility in Belgium. They either needed to improve their cost efficiency in their plant or face the potential of needing to shut down the facility and relocate it to another region. VCST decided to implement ThingWorx so that anyone can have instant visibility to asset status and performance. VCST is also creating the ability to digitize maintenance requests and the ability to acquire about spare parts to improve the overall efficiency in support of their costs reduction goals.

Additionally, VCST has a goal to reach zero complaints for their customers and, if any quality problems appear to their customers, they can be required to do a 100% inspection until the problem is solved. Moreover, as cars have gotten quieter with electrification, the noise from the gears has become an issue, and puts pressure on VCST to innovate and reduce gear noise.

VCST has again relied on ThingWorx and Windchill to collect and share data for joint collaborative analysis to innovate and reduce gear noise. VCST also plans to use Vuforia Expert Capture and Vuforia Chalk to train maintenance workers to further improve their efficiency and cost effectiveness. The company is not done with their digital transformation, and they have plans to implement Creo and Windchill to enable end-to-end digital thread connectivity to the factory.

3. BID Group Holdings (Connected Product)

BID Group Holdings operates in the wood processing industry. It is one of the largest integrated suppliers and North American leader in the field. The purpose of BID Group is to deliver a complete range of innovative equipment, digital technologies, turnkey installations, and aftermarket services to their customers. BID Group decided to focus on their areas of expertise, an rely on PTC, Microsoft, and Rockwell Automation’s combined capabilities and scale to deliver SaaS type solutions to their own industry.

Leveraging this combined power, the BID Group developed a digital strategy for service to improve mill efficiency and profitability. The solution is named OPER8 and was built on the ThingWorx platform. This allowed BID Group to provide their customers an out of the box solution with efficient time-to-value and low costs of ownership. BID Group is continuing to work with PTC and Rockwell Automation, to develop additional solutions that will reduce downtime of OPER8 with a predictive analytics module by using ThingWorx Analytics and LogixAI.

4. Hitachi (Service Optimization)

Hitachi operates an extensive service decision that ensures its customers’ data systems remain up and running. Their challenge was not to only meet their customers uptime Service Level Agreements, but to do it without killing their cost structure. Hitachi decided to implement PTC’s Servigistics Service Parts Management software to ensure the right parts are available when and where they are needed for service. With Servigistics, Hitachi was able to accomplish their needs while staying cost effective and delighting their customers.

Hitachi runs on the cloud, which allows them to upgrade to current releases more often, take advantage of new functionality, and avoid unexpected costs.

PTC has driven engagement and support for Hitachi through the PTC Community, and encourages all customers to utilize this platform. The network of collaborative spaces in a gathering place for PTC customers and partners to showcase their work, inspire each other, and share ideas or best practices in order to expand the value of their PTC solutions and services.

5. COVID-19 Response 

COVID-19 has put significant strain on the world’s hospitals and healthcare infrastructure, and hospitalization rates for COVID brought into question the capacity of being able to handle cases. Many countries began thinking of the value field hospitals could bring to safely care for patients and ease the admissions numbers of ‘regular’ hospitals. However, the complication is that field hospitals have essentially no isolation or air filtration capability that is required for treating COVID patients or healthcare workers.

As a result, the US Army Corp of Engineers has put out specifications to create self-contained isolation units, which are fully functioning hospital rooms that can be transported or built onsite. But, the assembly needed to happen fast, and a group of companies (including PTC) led by The Innovation Machine rallied to help design and define the SCIU’s.

With buy-in from numerous companies, a common platform was needed for companies to collaborate. PTC felt compelled to react, and many PTC customers and partners joined in to help create a collaboration platform, with cloud-based Windchill as the foundation. But, PTC didn’t just provide software to this collaboration; PTC also contributed with digital thread and design advice to help the group solve some of the major challenges. This design is a result of the many companies coming together to create deployments across various US state governments, agencies, and FEMA.

Final Thoughts

All of the above customers approached digital transformation as a business imperative. They all had sizeable challenges that needed to be solved and took leadership positions to implement plans that leveraged digital transformation technologies combined with new processes.

PTC will continue to innovate across the digital transformation portfolio and is committed to ensuring that customer success offerings capture value faster and provide the best outcomes.

Original Post Link: https://www.ptc.com/en/product-lifecycle-report/liveworx-digital-transformation–technology-and-support-you-need-to-succeed

Author Bio: Kelly is a corporate communications specialist at PTC. Her responsibilities include drafting and approving content for PTC’s external and social media presence and supporting communications for the Chief Strategy Officer. Kelly has previous experience as a communications specialist working to create and implement materials for the Executive Vice President of the Products Organization and senior management team members.

 

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How to Ensure IoT Cybersecurity

Today, the IoT devices are largely used by industries and households, smart bulbs can adjust the intensity of light by themselves, doctors can check the patient data remotely, IoT sensors can help in warehousing, and more, the potential is seemingly endless. There are billions of IoT devices on the field and billions more are expected in the next few years. The data that IoT devices produce are stored on the cloud, for example, a health monitor collects all the information about our health and stores it on the cloud. This information is further analyzed to provide us better services, but on the other hand if someone manages to get the data they can violate our privacy. Thus it is important to ensure the confidentiality and integrity of IoT solutions while mitigating the cybersecurity risks. There are many ways attackers can make their way into your system.

Most common IoT cyber attacks are:

Botnets

A botnet is a network of systems combined to remotely take control of distributing malware, controlled by botnet operators via Command-and-Control-Servers (C&C servers). They are used by attackers on a large scale for many things such as stealing private information, exploiting online banking data or spam, and phishing emails.

Man-in-the-middle

The man-in-the-middle concept is where an attacker is looking to interrupt and breach communication between two separate systems. It can be a dangerous attack because it is one where the attacker secretly intercepts and transmits messages between two parties when they are under the belief that they are communicating directly with each other.

Identity Theft

The main strategy of identity theft is to amass data, and with a little bit of patience, a lot of information can be fetched out. Generally, data is available on the internet, combined with social media information and data from smartwatches, fitness trackers, smart meters, smart fridges, and more. These data give a great all-around idea of your identity.

Recent research indicates that 85% of customers lack confidence in IoT device security, it is important to ensure the security of IoT devices by eliminating the IoT cybersecurity risk. 

Here are some best practices to ensure IoT cybersecurity:

Secure Boot

The secure boot helps a system to stop attacks and infections from malware, it is a feature embedded with IoT devices to detect tampering with the system. It works like a security gate as it restricts unauthorized access by validating the digital signature, detections are blocked from running before they attack the system. Deploying secure boot in the IoT ecosystem is important to ensure cybersecurity.

Secured passwords with two-factor authentication

You can activate two-factor authentication on almost any IoT device, it is important because it ensures authorized access to devices and automates trust into the system. Having two-factor authentication enabled with unusual passwords keeps IoT devices secure from being vulnerable to cyber attacks, it restricts attackers from making their way into the system.

Disabling the UPnP feature

UPnP feature allows an IoT device to get connected with other IoT devices, for example, smart bulbs can be paired with Google Home to turn it off or on via voice command. It is a feature that is convenient for users but poses cybersecurity risks at the same time. If hackers manage to make their way in one device they will easily be able to find another device that is connected. We can easily disable the UPnP feature as most of the IoT devices allow you to disable the UPnP feature from their settings.

Secure data storage

Keeping data in a large enterprise system is secured but the flash storage of a particular embedded device holds some important data from time to time that is not immediately secured or encrypted which can open you up to cybersecurity risk. Thus it is important to have system-level encryption of data for storage of sensitive information. If we do not encrypt the flash storage on the embedded device, someone can easily have their peak at your data.

Bottom Line

Securing IoT devices from cyberattacks is important for households and it is equally important for industries to ensure the confidentiality and integrity of their IoT devices and data produced by IoT devices. Researchers find that data breaches linked to IoT devices have increased rapidly in the past few years, according to a study by Ponemon, the number of cyberattacks due to unsecured connected devices have increased from 15% to 25% in the last two years. Thus securing the IoT devices can never be downplayed.

Author Bio- 

Piyush Jain is the founder and CEO of Simpalm, an app development company in Virginia. Piyush founded Simpalm in 2009 and has grown it to be a leading mobile and web development company in the DMV area. With a Ph.D. from Johns Hopkins and a strong background in technology and entrepreneurship, he understands how to solve problems using technology. Under his leadership, Simpalm has delivered 300+ mobile apps and web solutions to clients in startups, enterprises and the federal sector.

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The tinyML Foundation is excited to be offering a new activity to our community: tinyML Talks webcast series. A strong line-up of speakers making 30-minute presentations will take place twice a month on Tuesdays at 8 am Pacific time to make sure that tinyML enthusiasts worldwide will have an opportunity to watch them live. Presentations and videos will be available online the day afterwards for those that were not able to join live.

View Schedule of Upcoming Talks

If you want to re-watch all talks starting March 31 or were unable to join us live, the slides and links to our YouTube Channel of the talks are posted at our tinyML Forums. Many questions were asked during the presentations but not all could be answered in the allotted time frame. The answers to some of those can be found on the tinyML Forums as well.

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IoT RTOS Overviews

IoT RTOS Overviews

Below you’ll find links to overviews for Amazon FreeRTOS, Azure ThreadX, MbedOS, and Zephyr. Pros and cons abound for each. We see an increase in demand from developers for the simplest and fastest development path –we’re not there yet. As an industry we are definitely still living in the land of fragmentation. Please keep checking back for updates from IoT Central and the IoT Central community.

Azure RTOS ThreadX is Microsoft's advanced industrial grade Real-Time Operating System (RTOS) designed specifically for deeply embedded, real-time, and IoT applications.

Amazon FreeRTOS is an open source, real-time operating system for microcontrollers that makes small, low-power edge devices easy to program, deploy, secure, connect, and manage.  

Arm Mbed OS is a free, open-source embedded operating system designed specifically for the "things" in the Internet of Things.

The Zephyr™ Project strives to deliver the best-in-class RTOS for connected resource-constrained devices, built be secure and safe.

We encourage all community members to share their views regarding what RTOS they’re using and what they’d like to see from these projects in the future.

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If I had to choose three reasons why the adoption of the IoT it´s delayed several years, one of the three would include would be the mistake in their strategy, faith, IoT employee sales skills and poor investment in key industries by Mobile Network Operators (MNOs) in this business.

When I wrote more than 5 years ago my post “How to select your M2M/IoT Service Provider” I referenced several annual reports from analysts like Gartner and vendors like Ericsson or Cisco. All of them presented very optimistic predictions that unfortunately have not been fulfilled.

During this time Mobile Network Operators have adapted to the market crude reality of the market with sometimes erratic strategies. Despite this fact has not discouraged new entrants that have energized a market with again high growth expectations. Today Tier 1 and Tier 2 Mobile Network Operators are competing with many IoT Connectivity providers in all industries and use cases. The good news for these new entrants is that the MNOs have not known captivate their customers.

What do I think the MNOS are thinking now?

1-    The Technological Battle of LPWAN networks

I do not want to open in this article a debate on which LPWAN connectivity technology (5G, NB-IoT, LTE-M, LoRA, Sigfox, ….) is the best. Each of these technologies will likely play an important role in the IoT space depending on the use case, so understanding the features and differences of each is critical.

You must not forget other IoT connectivity technologies (Satellite, Mesh networks, WiFi, Zigbee,..). I have always championed the idea of multiple IoT network coexistence in which objects will connect to provide an IoT service or be part of an aggregated IoT service. And those services can be provided by both licensed and unlicensed cellular networks. Let's assume that we will not have a single protocol that regulates all of them in a long time. We are also not going to ask manufacturers of objects to incorporate the different connectivity possibilities in their designs for obvious reasons of cost and battery life. What would be very valuable is that all IoT devices could add a unique identifier that allow will be part of a SuperIoTNet that works like the current internet. But now is future fiction.

2-    The Connectivity Services Offering 

Ideally we should try to find in our IoT Connectivity Service Provider offering something like Telefonica, an end-to-end complete commercial IoT connectivity offer that allow design and build a tailored secure IoT solution. But this in not gold all that glitters. We must evaluate the ability of these IoT Connectivity Service Providers to make easy the adoption of IoT in Small and Medium Business (SMBs) with pre-integrated industry solutions based on a rich ecosystem.

Customers wants to receive specialised advice to solve any IoT need at a one-stop-shop, including full stack technology solutions from hardware selection to middleware, application development and SaaS operations. Not many IoT Connectivity Providers have the internal resources to provide these services, in that cases customers should involve either or a partner or better an independent consultant as myself.

For some customers an offering like “IoT connectivity as a Service” provided by Arkessa can be an advantage, for others “The 1NCE IoT Flat Rate”, an all-inclusive connectivity package that comprises all elements and features that IoT customer need while having their assets connected is more important. For experienced M2M customers, the portfolio Kore Wireless and industry specialization is attractive. Eseye for instances solve your IoT challenges from device to AWS cloud. In Europe SMBs must consider in the short list Wireless Logic with 4 million devices connected to its platforms globally. Special mention to module companies like Sierra Wireless that offers a Connectivity and Device Management service that connects to 600+ partner networks around the globe with multiple redundant routes in every country to eliminate local coverage gaps or Telit which  Connectivity Service allow companies Monitor, Manage & Monetize their assets.

I am expecting the unlimited opportunities with the Internet of Things after the announcement a few days ago by DT Deutsche Telekom to spin out IoT unit and launch a global open ‘hub.’  More info about new DT IoT offering here: “From vertical to horizontal and back to vertical: our way to the new horizon”

Sorry, I can not extend this paragraph with more companies, but in the picture there are many other companies with attractive services that must be considered for your unique Business case.

3-    eSIM: Threat or Opportunity

The SIM card has also been evolving since its creation in 1991. From the size of a credit card it went to mini-SIM or the classic SIM that began to reduce in size, first to microSIM and then to nanoSIM and finally the embedded SIM (also called eSIM or eUICC or MMF2 UICC).

Presented in the preludes of the Mobile World Congress 2016, the eSIM is still a SIM but it will be embedded in the devices, without the possibility of withdrawing it. eSIM is a global specification by the GSMA which enables remote SIM provisioning of any mobile device. The eSIM is designed to remotely ​manage multiple mobile network operator subscriptions and be compliant​ with GSMA's Remote SIM Provisioning specifications​.  Install one eSIM during manufacturing and change the carrier on the fly.

To date, 200 mobile carriers in more than 80 countries offer eSIM consumer services. The embedded UICC is expected to reach over 200 million shipments in 2019 (source: Eurosmart, November 2019).

GSMA promises not to rig the eSIM standard in favour of its members.

eSIM now allows consumers to store multiple operator profiles on a device simultaneously, and switch between them remotely, though only one can be used at a time. The specification now extends to a wider range of devices. Manufacturers and operators can now enable consumers to select the operator of their choice and then securely download that operator’s SIM application to any device.

At first glance, building or supporting a global eSIM solution presents a major challenge (integration with other service providers and guarantee customer experience is expensive) and not appear to benefit Communication Service Providers. Looks like stupid to invest in a solution that make easier for customers to leave them. That´s why they have not done much to extend its use.

Why is good for IoT?.  UICC and eSIM technology gives enterprises control of IoT connectivity, simplifies international deployments of IoT devices and the transition to mobility services. Large scale international deployments are possible using a single factory installed SIM. The user subscription can be updated when the device is in the field.

ARM white paper introduces 7 top  Innovative eSIM use cases: Automotive, Shipping and Logistics, Object tracking and site monitoring, Smart Energy, Wearables, Agriculture, Home Security.

Sources:

GSMA: https://www.gsma.com/esim/

Cisco Blog: “Manufacture there, connect anywhere: Cisco eSIM Flex enables global connectivity for enterprises and service providers”

Xataka: https://www.xatakamovil.com/conectividad/esim-que-que-ventajas-aporta-cuando-llegara-masivamente-todo-tipo-dispositivos

Thales: https://www.thalesgroup.com/en/markets/digital-identity-and-security/mobile/connectivity/esim/esim

Arkessa: https://www.arkessa.com/euicc/

ARM:  7 Top eSIM use cases

Choosing IoT Connectivity Service Providers

Choosing the right IoT Connectivity Service provider is not as easy as many can think. You can make a preselection using the lasts Gartner Magic Quadrant, also explore the local cellular Operators that have deployed a NB-IoT or LTE-M network and finally analyze other operators that maybe you never heard about them as I did.

The selection of the right IoT Connectivity Service Provider is a strategic decision for any Digital Transformation initiative, especially in enterprises adopting new resilient business models and optimizations of business processes. Some criteria you must consider selecting  your IoT CSP are:

  • Your internal capabilities
  • The offering: IoT Connectivity Services / IoT Managed Connectivity Services / IoT Connectivity Security Service / eSim Services
  • The cost of the IoT Connectivity Services and the flexibility of the tariffs
  • The type of IoT networks they have deployed and the coverage
  • The alliances with other IoT Connectivity Service Providers for global deployments
  • The types of M2M/IoT certified devices / modules and their applicability to your use cases.
  • The experience and references in your industry and vertical solution
  • The capabilities of their IoT Connectivity and Device Management Platforms
  • Open APIS for Integration with your Enterprise Systems
  • BSS/OSS systems and their applicability to your use
  • New business models eg IOTConnectivity as a Service
  • Levels of Support
  • Ecosystem of partners

Key Takeaways

It is not worth spending one minute more crying for the reasons that MNOs were unable to energize the IoT market earlier. We are where we are and the future is still bright, for those who really know how to see it.

The selection of the right IoT Connectivity Service Provider for your enterprise is a strategic decision. When my clients ask which is the best IoT Connectivity Service Provider? my first advice is: ". Let's define together your digital strategy, prioritize key uses cases, analyze new business model and your internal capabilities first and then work on the IoT Connectivity technology needed , which connectivity services comply with your requirements  and finally build a detailed business case that justify the value of your investment".

There is no best IoT connectivity Technology. It all depends on the use cases and the business model.

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