Bluetooth Mesh for Industrial IoT (IIoT)

There’s an informative video presentation on the Bluetooth SIG web site on Simplifying Multi-Vendor Mesh and Sensor Networks. It provides an introduction to Bluetooth mesh and explains the ways in which it can provide for Industrial IoT (IIoT).

To add to this, Bluetooth Mesh is suitable for use on the factory floor where the environment can be electrically noisy. Standard Bluetooth Mesh uses advertising on several channels rather than (GATT) connections so as to provide for more reliable communication in environments with wireless interference.

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Why Bluetooth is Perfect for the Industrial IoT (IIoT)

U-Blox has a useful article on Seven reasons why Bluetooth is perfect for the Industrial IoT, echoing some of our observations on Bluetooth LE on the Factory Floor. The article explains why Industrial Internet of Things (IIoT) systems and networks should consider Bluetooth as the communications infrastructure.

As U-Blox mentions, there’s predicted to be 5.2 billion Bluetooth device shipments by 2022. This data is from the latest Bluetooth SIG/ABI research:

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How to Start Industry 4.0 and Digital Transformation

There’s lots said about the advantages of Industry 4.0 or Digital Transformation and the associated new technologies but it’s a lot harder to apply this to the context of a business that has legacy equipment and no real way of knowing where to start.

Our previous article on productivity explained how, historically, digital transformation has been only been implemented in the top 5% ‘frontier’ companies. These have tended to be very large companies with large R&D budgets that have enabled customised digital solutions. More recently, the availability of less expensive sensors and software components have extended opportunities to the SME companies. These companies are already realising gains in profitability, customer experience and operational efficiency. Unlike previous technologies, such as CRM, the newer technologies such as IoT and AI are more transformative. Companies that don’t update their processes risk being outranked by their competition with a greater possibility of going out of business. But where do you start?

The place to start is not technology but instead something you and your colleagues fortunately have lots of experience of : Your company. Take an honest look at your processes and work out the key problems that, if solved, would achieve the greatest gains. You might have ignored problems or inefficiencies for years or decades because they were thought to be insolvable. Technology might now be able to solve some of these problems. So what kind of problems? Think in terms of bottlenecks, costly workrounds, human effort-limited tasks, stoppages, downtimes, process delays, under-used equipment and even under-used people. Can you measure these things and react? Can you predict they are about to happen? This is where sensing comes in.

The next stage is connectivity. You will almost certainly need to upgrade or expand your WiFi and/or Ethernet network. It can be impractical to put sensors on everything and everyone and connect everything by WiFi/Ethernet. Instead, consider Bluetooth LE and sensor beacons to provide a low cost, low power solution for the last 50 to 100m. Bluetooth mesh can provide site-wide connectivity.

Initially implement a few key improvements that offer good payback for the effort (ROI). The improvements in efficiency, productivity, reduced costs and even customer experience should be enough to convince stakeholders to expand and better plan the digital transformation. This involves replacement of inefficient equipment and inefficient processes using, for example, robotics and 3D printing. It also involves analysing higher order information combined from multiple sources and using more advanced techniques such as AI machine learning to recognise and detect patterns to detect, classify and predict. This solves problem complexity beyond that able to be solved by the human mind or algorithmic program created by a programmer.

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SensorCognition™ – Machine Learning Sensor Data at the Edge

The traditional IoT strategy of sending all data up to the cloud for analysis doesn’t work well for some sensing scenarios. The combination of lots of sensors and/or frequent updates leads to lots of data being sent to the server, sometimes needlessly. The server and onward systems usually only need to now about abnormal situations. The data burden manifests itself as lots of traffic, lots of stored data, lots of complex processing and significant, unnecessary costs.

The processing of data and creating of ongoing alerts by a server can also imply longer delays that can be too long or unreliable for some time-critical scenarios. The opposite, doing all or the majority of processing near the sensing is called ‘Edge’ computing. Some people think that edge computing might one day become more normal as it’s realised that the cloud paradigm doesn’t scale technically or financially. We have been working with edge devices for a while now and can now formally announce a new edge device with some unique features.

Another problem with IoT is every scenario is different, with different inputs and outputs. Most organisations start by looking for a packaged, ready-made solution to their IoT problem that usually doesn’t exist. They tend to end up creating a custom coded solution. Instead, with SensorCognition™ we use pre-created modules that we ‘wire’ together, using data, to create your solution. We configure rather than code. This speeds up solution creation, providing greater adaptability to requirements changes and ultimately allows us to spend more time on your solution and less time solving programming problems.

However, the main reason for creating SensorCognition™ has been to provide for easier machine learning of sensor data. Machine learning is a two stage process. First data is collected, cleaned and fed into the ‘learning’ stage to create models. Crudely speaking, these models represent patterns that have been detected in the data to DETECT, CLASSIFY, PREDICT. During the production or ‘inference’ stage, new data is fed through the models to gain real-time insights. It’s important to clean the new data in exactly the same way as was done with the learning stage otherwise the models don’t work. The traditional method of data scientists manually cleaning data prior to creating models isn’t easily transferable to using those same models in production. SensorCognition™ provides a way of collecting sensor data for learning and inference with a common way of cleaning it, all without using a cloud server.

Sensor data and machine learning isn’t much use unless your solution can communicate with the outside world. SensorCognition™ modules allow us to combine inputs such as MQTT, HTTP, WebSocket, TCP, UDP, Twitter, email, files and RSS. SensorCognition™ can also have a web user interface, accessible on the same local network, with buttons, charts, colour pickers, date pickers, dropdowns, forms, gauges, notifications, sliders, switches, labels (text), play audio or text to speech and use arbitrary HTML/Javascript to view data from other places. SensorCognition™ processes the above inputs and provides output to files, MQTT, HTTP(S), Websocket, TCP, UDP, Email, Twitter, FTP, Slack, Kafka. It can also run external processes and Javascript if needed.

With SensorCognition™ we have created a general purpose device that can process sensor data using machine learning to provide for business-changing Internet of Things (IoT) and ‘Industry 4.0’ machine learning applications. This technology is available as a component of BeaconZone Solutions.

Industry 4.0 Platform

The February 2019 edition of ComputingEdge magazine from IEEE has an article on From Raw Data to Smart Manufacturing (pdf – current and back issues freely available).

The article describes what they call a ‘Semantic Web of Things for Industry 4.0 (SWeTI) platform’. Although it’s very useful, it’s less of a platform in the software sense and more of an ecosystem or model.

The platform describes usecases, tools and techniques for smart applications. Using this model, BeaconZone operates in the Device, Edge and Data Analytic layers. We provide for smart devices and tools, gateways, storage, machine learning (ML) and analytics.

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Reducing Costs with Predictive Maintenance

The Nordic blog has an informative post on How IoT-Based Predictive Maintenance Can Reduce Costs. It explains how connected sensors can save maintenance costs through reduced downtime. The post provides some examples from the power industry and explains how the same techniques can be used in the tools, retail, distribution and physical infrastructure industries.

As the post mentions, the challenge is how to scale this up. We are told IoT is the solution. Here at BeaconZone, we don’t believe IoT is always the solution, especially where there’s a requirement for higher sensor sampling frequencies. There’s too much data, too much data transfer and too much server processing. It really doesn’t scale. Apart from the waste and cost of these resources, the latency of triggering events based on the data is too high. Instead, look to so called ‘edge’ or ‘fog’ computing where more processing is done nearer the sensors and only pertinent data is sent to other systems.

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IoT Sensors

Bluetooth LE provides a compelling way of implementing IoT sensing because:

  • The sensors are usually already cased and certified rather than experimentor, bare printed circuit boards.
  • Being wireless, they can be placed in remote areas that have no power.
  • Being Bluetooth LE, they can last on battery power for years.
  • Again, being Bluetooth LE, they are suitable for use in noisy electrical areas.
  • They are commodity rather than proprietary items and hence very low cost compared to legacy industrial sensors.
  • No soldering or wiring up is required.
  • They are easy to interface, for example, to Bluetooth gateways and smartphones.
  • They can participate in Bluetooth Mesh to communicate over large areas.
  • They detect a variety of quantities such as movement (accelerometer), temperature, humidity, air pressure, light and magnetism (hall effect), proximity, heart rate, fall detection, smoke, gas and water leak.
  • They are proven. For example, some of our temperature sensors are used to monitor airline cargo.
  • Software exists, such as BeaconServer™ such that you don’t need to write any software.
INGICS Movement Sensor

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Learn about the 4th Industrial Revolution (4IR), Industry 4.0

The 4th Industrial Revolution (4IR), also known as Industry 4.0, is the use of technology to improve operational efficiency, increase throughput, minimise downtime, improve quality and lower costs. We have an article that explains how beacons are part of 4IR.

There’s a lot more to 4IR than tracking items and analysing data. It also includes areas such as automation, robotics, cyber security and 3D printing. There’s a free online Industry 4.0 Magazine that can help you get up to speed.

It’s also possible to view recent back issues.

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