Spending Surge Predicted for Factory Data Management

By George Leopold

As big data goes, the industrial sector is among the largest producers, with sensors collecting data along assembly lines on everything from the status of manufacturing equipment to product inspection cameras.

Industrial Internet of Things deployments are therefore expected to boost manufacturers’ already hefty investments in data management and analytics tools as producers seek to up their game from merely collecting to organizing and gleaning insights from industrial data.

That trend is seen pushing industrial spending to new heights. For example, ABI Research last week forecast that manufacturers and industrial firms will spend $19.8 billion in 2026 on data management, data analytics and related digital services. Those investments will target operations ranging from predictive equipment maintenance to production line optimization.

A return on those investments requires upfront planning in terms of priorities, whether the goal is scaling production, improving quality or reducing downtime. Setting priorities also requires closer ties with suppliers, the market researcher said.

“For many manufacturers, there is an appreciation that operational decisions need to be based on empirical evidence rather than guesswork. The challenges are not necessarily capturing and analyzing data, rather what to analyze in the first place,” said Michael Larner, principal analyst at ABI Research. “The findings need to have a meaningful impact on operations and so manufacturers need to take a step back and devise precise objectives.”

Hence, a supplier ecosystem is emerging to help ease manufacturers’ digital transition, or what has become known as Industry 4.0. That transformative approach combines advanced manufacturing techniques with cloud and edge computing, AI and machine learning, robotic process automation and vision systems along with augmented and virtual reality platforms.

Other analysts using roughly the same timeframe as ABI Research report that smart factories that devise workflows for leveraging big data are just around the corner. For example, business consultant Deloitte recently reported that 86 percent of U.S. manufacturers think smart factories will emerge as the main driver of competition by 2025. Meanwhile, 83 percent said the industrial IoT will “transform the way products are made.”

That transformation is being driven in part by the emergence of machine learning tools that allow factory managers to move beyond reporting manufacturing data to recommending actions and predicting outcomes. As big data is democratized, other tools like data visualizations allow production managers to analyze data on the factory floor without the aid of data scientists.

Hence, ABI’s Larner said manufacturers must move beyond mere data collection. “While manufacturers have spent decades refining their physical production lines, today they need to expend effort in optimizing their processes for collecting and analyzing data,” he said.

Another factor shaping the factory of the future is the potential for “re-shoring” manufacturing operations as the novel coronavirus exposes vulnerabilities and reconfigures global supply chains. Among the platforms in the expanding digital production ecosystem are “manufacturing execution systems” designed to help producers manage and leverage IoT data.

Recent items:

Big Data Challenges for Industry 4.0

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Edge Impulse makes TinyML available to millions of Arduino developers

By Arduino Team

This post is written by Jan Jongboom and Dominic Pajak.

Running machine learning (ML) on microcontrollers is one of the most exciting developments of the past years, allowing small battery-powered devices to detect complex motions, recognize sounds, or find anomalies in sensor data. To make building and deploying these models accessible to every embedded developer we’re launching first-class support for the Arduino Nano 33 BLE Sense and other 32-bit Arduino boards in Edge Impulse.

The trend to run ML on microcontrollers is called Embedded ML or Tiny ML. It means devices can make smart decisions without needing to send data to the cloud – great from an efficiency and privacy perspective. Even powerful deep learning models (based on artificial neural networks) are now reaching microcontrollers. This past year great strides were made in making deep learning models smaller, faster and runnable on embedded hardware through projects like TensorFlow Lite Micro, uTensor and Arm’s CMSIS-NN; but building a quality dataset, extracting the right features, training and deploying these models is still complicated.

Using Edge Impulse you can now quickly collect real-world sensor data, train ML models on this data in the cloud, and then deploy the model back to your Arduino device. From there you can integrate the model into your Arduino sketches with a single function call. Your sensors are then a whole lot smarter, being able to make sense of complex events in the real world. The built-in examples allow you to collect data from the accelerometer and the microphone, but it’s easy to integrate other sensors with a few lines of code.

Excited? This is how you build your first deep learning model with the Arduino Nano 33 BLE Sense (there’s also a video tutorial here: setting up the Arduino Nano 33 BLE Sense with Edge Impulse):

  • Download the Arduino Nano 33 BLE Sense firmware — this is a special firmware package (source code) that contains all code to quickly gather data from its sensors. Launch the flash script for your platform to flash the firmware.
  • Launch the Edge Impulse daemon to connect your board to Edge Impulse. Open a terminal or command prompt and run:
$ npm install edge-impulse-cli -g
$ edge-impulse-daemon
  • Your device now shows in the Edge Impulse studio on the Devices tab, ready for you to collect some data and build a model.
  • Once you’re done you can deploy your model back to the Arduino Nano 33 BLE Sense. Either as a binary which includes your full ML model, or as an Arduino library which you can integrate in any sketch.
Deploy to Arduino from Edge Impulse
Deploying to Arduino from Edge Impulse
  • Your machine learning model is now running on the Arduino board. Open the serial monitor and run `AT+RUNIMPULSE` to start classifying real world data!
Keyword spotting on the Arduino Nano 33 BLE Sense
Keyword spotting on the Arduino Nano 33 BLE Sense

Integrates with your favorite Arduino platform

We’ve launched with the Arduino Nano 33 BLE Sense, but you can also integrate Edge Impulse with your favourite Arduino platform. You can easily collect data from any sensor and development board using the Data forwarder. This is a small application that reads data over serial and sends it to Edge Impulse. All you need is a few lines of code in your sketch (here’s an example).

After you’ve built a model you can easily export your model as an Arduino library. This library will run on any Arm-based Arduino platform including the Arduino MKR family or Arduino Nano 33 IoT, providing it has enough RAM to run your model. You can now include your ML model in any Arduino sketch with just a few lines of code. After you’ve added the library to the Arduino IDE you can find an example on integrating the model under Files > Examples > Your project – Edge Impulse > static_buffer.

To run your models as fast and energy-efficiently as possible we automatically leverage the hardware capabilities of your Arduino board – for example the signal processing extensions available on the Arm Cortex-M4 based Arduino Nano BLE Sense or more powerful Arm Cortex-M7 based Arduino Portenta H7. We also leverage the optimized neural network kernels that Arm provides in CMSIS-NN.

A path to production

This release is the first step in a really exciting collaboration. We believe that many embedded applications can benefit from ML today, whether it’s for predictive maintenance (‘this machine is starting to behave abnormally‘), to help with worker safety (‘fall detected‘), or in health care (‘detected early signs of a potential infection‘). Using Edge Impulse with the Arduino MKR family you can already quickly deploy simple ML based applications combined with LoRa, NB-IoT cellular, or WiFi connectivity. Over the next months we’ll also add integrations for the Arduino Portenta H7 on Edge Impulse, making higher performance industrial applications possible.

On a related note: if you have ideas on how TinyML can help to slow down or detect the COVID-19 virus, then join the UNDP COVID-19 Detect and Protect Challenge. For inspiration, see Kartik Thakore’s blog post on cough detection with the Arduino Nano 33 BLE Sense and Edge Impulse.

We can’t wait to see what you’ll build!

Jan Jongboom is the CTO and co-founder of Edge Impulse. He built his first IoT projects using the Arduino Starter Kit.

Dominic Pajak is VP Business Development at Arduino.

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Dr William Wu, Founder of One4City And Innovation Insight Lead at Cisco On Eco City & Smart City Innovation

By Hernaldo Turrillo

Dr William Wu, founder of One4City and Innovation Insight Lead at Cisco Innovation EMEAR, is the new guest in this interview series hosted by citiesabc founder Dinis Guarda. Both experts talk in-depth about trending topics in the eco city and smart city space, including new emerging technologies, the need for a holistic and international approach and collaboration between all players involved. Likewise, Dr William Wu tells us more about his new project, One4City, a comprehensive and evolving digital portal for a one-stop shop for UK Smart City solutions.

Interview Focus

1. An introduction from you – background, overview, education…
2. Career highlights
3. Eco cities: history and definition
4. Eco cities: Urban planning and development
5. The Shift to Smart Cities
6. One4City Overview: vision, focus and goals
7. The main trends in tech for cities and society
8. 5G, AI, IoT & big data analytics in Smart Cities
9. With Covid-19 how can you look at this as a way to redesign our society
10. Future vision

Quotes and Highlights

· I started my career right after I finished my Master in the Imperial College London. I later did my PhD on eco cities.

· The concept of ecological sustainability cities as a central element for master planning cities.

· How to look at urbanisation and quality of lives – framework for cities, KPIs… carbon zero approaches.

· Green cities – eco cities all over the world – they put sustainability in the centre.

· About ecological cities. The traditional way to do city planning is to follow the rules: the land is defined by master plans. This piece of land is for industry, etc. What is different about ecological planning is taking a different approach and taking a holistic look at how things are planned to be focused on sustainability and social impact. Everything matters in this approach: from the layouts of the streets, buildings, common areas, etc. The foundational point is sustainability, everything is built around sustainability and sustainable development.

· To do so, we need to create new KPIs and approaches that take these sustainable goals as the centre. It is about processes and flows that are scalable and realistic.

· Building a green city doesn’t follow a linear plan but everything within development in the city has to be taken into consideration. It created a bit of confusion in the beginning as it made the city planning more complex but innovation and research is helping with the follow-up once the plans have been laid.

· There has been a shift from eco-cities to smart cities. 5 years ago eco-cities were the big buzz, now it is all about smart cities. Using sustainability as the main point, it makes city planning and follow-up development very difficult, almost impossible so that is why they have been shifting to smart cities. Smart cities also take sustainability and ecology as one of the foundations, but not the only one. They are still needed but smart cities are much broader than only creating sustainable cities. One reason is because focusing on technology, sustainability is easier achieved.

· As of now, there are ISO standards right now for smart cities. That standard is relatively new and there are many countries involved in creating that standard, including China, Canada or the UK. One of the main problems they face is extracting relevant data for what smart cities really mean. That is why we need benchmarks, projects as citiesabc to get that valuable data to create standards that can be applied everywhere in the world.

· Challenges: every city is different so their needs are also different. I found that smart cities are being built using different approaches across the world. In China, for example, the government is really ambitious. They don’t only want to build smart cities, but also building smart schools, hospitals, and almost in every important part of the city. It is more ambitious than in other countries. It is a holistic approach to building solutions.

· Other challenges: scalability. It is not only about building a system that works now but it can be upgraded and escalated in the future.

· Some of the pillars of smart cities are:

(1) AI & big data analytics for potato disease monitoring and prevention

(2) Digitisation of culture heritage & museum collections

(3) Integrated health & social care digital collaboration and service design

(4) 5G rural area investment and impact evaluation of precision farming and Agritech

(5) Footfall analytics and automated surveillance in in-door venues.

· One4City Project. We want to overcome these challenges, so we have created a vertical model that integrates all the solutions that have been deployed in different parts of the world through data, researches, technologies deployed, etc. We have taken those solutions and categorized them so they can be easily understood and provide valuable insights. What we ultimately want is to help and foster smart city business models in different places. In my opinion, what we want to provide is the benefits of doing so by giving evidence, facts.

· About China. The speed of the development in the Chinese market is faster than anywhere in the world. The chinese government is investing heavily in business solutions, technology adoptions, companies, etc. Anywhere else, fragmentation is one of the main differences between China and the rest of the world. Out of China, for example in the UK, most investment comes from the private sector so it is really hard to get investment for smart city solutions in rural areas because the return on investment always looks really low.

· Privacy and surveillance. Footfall analytics and automated surveillance in in-door venues. Surveillance is actually important for cities as it improves security throughout the cities and collects data to create patterns and data that can be used to protect the people in different ways.

· Digital privacy, differences between China and the world. Digital privacy is a huge issue. Europe’s GDP was a huge step forward towards that. There are red lines where technology can go and I believe that also technology can help also with that. In China, the approach to digital privacy is different given our history and traditional background. Today, technology is involved greatly in people’s lives. For example, there are restaurants that only take orders through apps. So people give away their privacy for convenience and the value that provides the digital transformation. Overall, people in China aren’t that concerned about digital privacy and one of the reasons is lack of literacy. China is still a developing country and smartphone widespread adoption only took place some years ago.

· COVID-19 has already changed everybody’s lives. Things that have brought the coronavirus pandemic is social distancing, remote working so it has transformed a bit our culture and businesses by breaking down walls. From my perspective and through our company, what we want is to create links between the UK, China and the rest of the world and using our platform to help cities and citizens.

· Healthy agent solutions and sector – solutions based on User experience.

· Final takeaway: start with small – design programs and ways to create a huge impact.

Dr William Wu biography

Dr William Wu is an innovative thinker and technology evangelist with 10+ year experience of leading, executing and managing innovation programs, engaging with public, private and academic stakeholders to accelerate new business opportunities. Founder of One4City and the Innovation Insight Lead at Cisco Innovation EMEAR. Dr William Wu has experience in delivering more than 20 multi-million digital & innovation projects across transport, energy, environment, healthcare & social care, agriculture, and pharmaceutical sectors. Experience of creating an incubation/ innovation centre from scratch. Focus on business model innovation, incubation, and early stage investment.

William Wu has an in-depth knowledge of smart city planning, autonomous vehicle test beds & HMI, precision farming, integrated health and social care, and urban resilience. Connected to wide networks across UK local governments, incumbents, SMEs and universities. He is the founder of One4City – a comprehensive and evolving digital portal for a one-stop shop for UK Smart City solutions, demonstrating realistic, significant potential especially for capitalising the expanding Chinese market.


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Video camera cellular connections to reach 20.4mn in Europe and N America by 2024

By Anasia D’mello

IoT market research provider, Berg Insight, has released a new market report covering the connected video camera market. The report focuses on the following five application areas: city surveillance; commercial buildings and industrial site surveillance; smart home security cameras; body-worn cameras; and video telematics for commercial vehicles. The installed base of video cameras in Europe

The post Video camera cellular connections to reach 20.4mn in Europe and N America by 2024 appeared first on IoT Now – How to run an IoT enabled business.

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FaceMe Facial Recognition for Retail, Hospitality and Security AIoT Applications

By IoT – Internet of Things

CyberLink Corp. recently announced a partnership integrating its high-accuracy facial recognition software development kit (SDK), FaceMe®, into Advantech’s new FaceView industrial app. The integration empowers the app to perform real-time facial recognition and analysis of visitors’ gender, age and emotions for IoT applications in scenarios such as retail, hospitality, transportation and commercial building management, among […]

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