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Industrial Prototyping for IoT

I-Pi SMARC.jpg

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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Helium Expands to Europe

Helium, the company behind one of the world’s first peer-to-peer wireless networks, is announcing the introduction of Helium Tabs, its first branded IoT tracking device that runs on The People’s Network. In addition, after launching its network in 1,000 cities in North America within one year, the company is expanding to Europe to address growing market demand with Helium Hotspots shipping to the region starting July 2020. 

Since its launch in June 2019, Helium quickly grew its footprint with Hotspots covering more than 700,000 square miles across North America. Helium is now expanding to Europe to allow for seamless use of connected devices across borders. Powered by entrepreneurs looking to own a piece of the people-powered network, Helium’s open-source blockchain technology incentivizes individuals to deploy Hotspots and earn Helium (HNT), a new cryptocurrency, for simultaneously building the network and enabling IoT devices to send data to the Internet. When connected with other nearby Hotspots, this acts as the backbone of the network. 

“We’re excited to launch Helium Tabs at a time where we’ve seen incredible growth of The People’s Network across North America,” said Amir Haleem, Helium’s CEO and co-founder. “We could not have accomplished what we have done, in such a short amount of time, without the support of our partners and our incredible community. We look forward to launching The People’s Network in Europe and eventually bringing Helium Tabs and other third-party IoT devices to consumers there.”  

Introducing Helium Tabs that Run on The People’s Network
Unlike other tracking devices,Tabs uses LongFi technology, which combines the LoRaWAN wireless protocol with the Helium blockchain, and provides network coverage up to 10 miles away from a single Hotspot. This is a game-changer compared to WiFi and Bluetooth enabled tracking devices which only work up to 100 feet from a network source. What’s more, due to Helium’s unique blockchain-based rewards system, Hotspot owners will be rewarded with Helium (HNT) each time a Tab connects to its network. 

In addition to its increased growth with partners and customers, Helium has also seen accelerated expansion of its Helium Patrons program, which was introduced in late 2019. All three combined have helped to strengthen its network. 

Patrons are entrepreneurial customers who purchase 15 or more Hotspots to help blanket their cities with coverage and enable customers, who use the network. In return, they receive discounts, priority shipping, network tools, and Helium support. Currently, the program has more than 70 Patrons throughout North America and is expanding to Europe. 

Key brands that use the Helium Network include: 

  • Nestle, ReadyRefresh, a beverage delivery service company
  • Agulus, an agricultural tech company
  • Conserv, a collections-focused environmental monitoring platform

Helium Tabs will initially be available to existing Hotspot owners for $49. The Helium Hotspot is now available for purchase online in Europe for €450.

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I recently joined the Embedded Online Conference thinking I was going to gain new insights on embedded and IoT techniques. But I was pleasantly surprised to see a huge variety of sessions with a focus on modern software development practices. It is becoming more and more important to gain familiarity with a more modern software approach, even when you’re programming a constrained microcontroller or an FPGA.

Historically, there has been a large separation between application developers and those writing code for constrained embedded devices. But, things are now changing. The embedded world intersecting with the world of IoT, data science, and ML, and the deeper co-operation between software and hardware communities is driving innovation. The Embedded Online Conference, artfully organised by Jacob Beningo, represented exactly this cross-section, projecting light on some of the most interesting areas in the embedded world - machine learning on microcontrollers, using test-driven development to reduce bugs and programming an FPGA in Python are all things that a few years ago, had little to do with the IoT and embedded industry.

This blog is the first part of a series discussing these new and exciting changes in the embedded industry. In this article, we will focus on machine learning techniques for low-power and cost-sensitive IoT and embedded Arm-based devices.

Think like a machine learning developer

Considered for many year's an academic dead end of limited practical use, machine learning has gained a lot of renewed traction in recent years and it has now become one of the most interesting trends in the IoT space. TinyML is the buzzword of the moment. And this was a hot topic at the Embedded Online Conference. However, for embedded developers, this buzzword can sometimes add an element of uncertainty.

The thought of developing IoT applications with the addition of machine learning can seem quite daunting. During Pete Warden’s session about the past, present and future of embedded ML, he described the embedded and machine learning worlds to be very fragmented; there are so many hardware variants, RTOS’s, toolchains and sensors meaning the ability to compile and run a simple ‘hello world’ program can take developers a long time. In the new world of machine learning, there’s a constant churn of new models, which often use different types of mathematical operations. Plus, exporting ML models to a development board or other targets is often more difficult than it should be.

Despite some of these challenges, change is coming. Machine learning on constrained IoT and embedded devices is being made easier by new development platforms, models that work out-of-the-box with these platforms, plus the expertise and increased resources from organisations like Arm and communities like tinyML. Here are a few must-watch talks to help in your embedded ML development: 

  • New to the tinyML space is Edge Impulse, a start-up that provides a solution for collecting device data, building a model based around it and deploying it to make sense of the data directly on the device. CTO at Edge Impulse, Jan Jongboom talks about how to use a traditional signal processing pipeline to detect anomalies with a machine learning model to detect different gestures. All of this has now been made even easier by the announced collaboration with Arduino, which simplifies even further the journey to train a neural network and deploy it on your device.
  • Arm recently announced new machine learning IP that not only has the capabilities to deliver a huge uplift in performance for low-power ML applications, but will also help solve many issues developers are facing today in terms of fragmented toolchains. The new Cortex-M55 processor and Ethos-U55 microNPU will be supported by a unified development flow for DSP and ML workloads, integrating optimizations for machine learning frameworks. Watch this talk to learn how to get started writing optimized code for these new processors.
  • An early adopter implementing object detection with ML on a Cortex-M is the OpenMV camera - a low-cost module for machine vision algorithms. During the conference, embedded software engineer, Lorenzo Rizzello walks you through how to get started with ML models and deploying them to the OpenMV camera to detect objects and the environment around the device.

Putting these machine learning technologies in the hands of embedded developers opens up new opportunities. I’m excited to see and hear what will come of all this amazing work and how it will improve development standards and transform embedded devices of the future.

If you missed the conference and would like to catch the talks mentioned above*, visit www.embeddedonlineconference.com

*This blog only features a small collection of all the amazing speakers and talks delivered at the Conference!

Part 2 of my review can be viewed by clicking here

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Drivers are the backbone of the trucking industry and driver retention is one of the biggest concerns. Are you facing issues like driver shortage or driver retention? Advanced technology offers various solutions to transportation industry for such issues. Real-time monitoring, time-saving in shifts, better communication of drivers with managers, etc. simplify the work of drivers. Job dissatisfaction and operational inefficiencies are the major reasons why drivers leave the job. But this can be reduced, let’s see how.

How to improve the job satisfaction of drivers?
Trucking industry relies on the drivers for navigating through complex routes to transport sensitive goods safely and on time. It is a hectic job already and hence it is necessary that the drivers are satisfied with their job. Or else they will leave the job which hampers the business profits. What efforts can be taken for retention of drivers?

Simplifying the Driver’s Work using Technology
Drivers are constantly on the road. Traffic, bad weather conditions, etc. can be very irritating to them. However, using GPS systems, such conditions can be tracked proactively. The drivers can be directed to other routes which are having less traffic or some shifts can be canceled if the routes show bad weathers ahead. The drivers will not be annoyed by waiting for long hours and instead can take rest if the shifts are canceled due to bad weathers.

Automation and digitalization save time and efforts of the drivers. There are mobile apps which keep track of the load on the trucks. It saves the drivers of manual checking of load and also sends messages to the owners about the load. Also, in case of any theft or adding illegal loads to the truck, the owners can get instant messages right on their smartphones.

One such eminent example is the mobile app- Appweigh. It uses Bluetooth-enabled weight sensor to keep the track of the load on your truck. It is a budget-friendly app which combines the sensor and Bluetooth technology. Throughout the shipment of the trucks, the sensors detect the pressure on the tyres and clearly display the weight through AppWeigh on the smartphone of owners or fleet managers. The drivers don’t need to keep manual watch on the weight when the load reaches a certain destination. It is automatically sent to the owners.

Using IoT in Transportation for Better Communication and Time Management
Open communication with the drivers not only ensures transparency but also makes the drivers feel like true partners in the business. It encourages and engages them. Internet of things or IoT in transportation industry is playing a crucial role in connecting technology with people for more accurate results. It connects tools like sensors, RFID systems, GPS systems, smartphones, etc. to each other to gather vital data and communicate it to drivers and owners. Using this data, they can make informed decisions for improving various processes in fleet management. With such transparency, manual errors by drivers can be avoided and small issues can be discussed proactively before they turn into bigger problems.

Technology saves the drivers from keeping manual records of loads, timings, etc. as everything is automatically recorded. It reduces the stress of the drivers and ensures loyalty to the owners.

Making the Driver Health a Priority
Drivers’ health is the most critical topic when it comes to driver retention. A trucking industry can offer health benefits like health insurance plans, nutrition programs, free health screenings, etc. to drivers. Such benefits are an investment in your drivers. Also, the incorporation of smart cameras can reduce the risks of accidents. When the drivers feel safe and cared for their lives, your company reputation improves. They themselves will ask other drivers to join your company.

Giving Performance Incentives and Engaging Drivers
When drivers are appreciated and rewarded for their good work, it inspires them to do better and also be stable with your trucking industry. Financial incentive systems can be used to reward the most productive and safest drivers. Technology can be used to evaluate the drivers’ performance fairly. Real-time coaching and user-friendly solutions to any issues will help the drivers to progress faster and feel supported. The drivers who work hard to improve their performance can be awarded with the performance incentives. This will also encourage and engage fellow drivers. 

 

Conclusion
Smart technologies are providing highly efficient solutions to transportation industry. Along with driver retention, these technologies help in real-time visibility of the processes, maintaining the vehicle health, improving warehouse and yard management, etc. which enormously boost the business profits. IoT in transportation industry provides robust security services to drivers as well as the freight. Reliable data that owners get from smart technical solutions lets them take the right decisions to maintain their workforce. It enhances driver satisfaction and retention rates.

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