Sensor beacons provide a quick and easy way to obtain data for AI machine learning. They provide an alternative to trying to access over-protected siloed company data and a method of measuring physical things that might not already be quantified.
Beacons detect movement (accelerometer), movement (started/stopped moving), button press, temperature, humidity, air pressure, light level, open/closed (magnetic hall effect), proximity (PIR), proximity (cm range), fall detection, smoke and natural gas. The open/closed (magnetic hall effect) is particularly useful as it can be used on a multitude of physical things for scenarios that require digitising counts, presence and physical status.
The data is sent via Bluetooth rather than via cables which means there’s no soldering or physical construction. The Bluetooth data can be read by smartphones, gateways or any devices that have Bluetooth LE. From there it can be stored in files for reading into machine learning.
The latter part of the article explains how, when there’s a problem in a smart factory, it can have large affects. The onus is on technology that can predict problems before they cause downtime. This leads to questions where the data processing should be the observations that:
“In the long-run, pushing everything to the cloud doesn’t work from a cost point of view.”
“Once you aggregate and compress the data, for example, to ‘max,’ ‘min,’ ‘outliers,’ ‘average’ and stuff like that, you lose the ability to run data science”
Strategy Analytics have a new press release on Edge Computing on the Rise in IoT Deployments. Edge computing is where data is processed close to where it originates for better network use, reduced traffic/storage and faster detection/notification times. Strategy Analytics end user research shows that 44% of companies are currently using edge computing, in some form, in their deployments.
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.
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.
As mentioned yesterday, in the Mr Beacon interview with Ajay Malik, beacon positioning and sensor data will be increasingly used as input for AI machine learning (ML). Beacons are a great way of providing the large amounts of data required of ML.
At BeaconZone, we have recently started using beacon data in machine learning. This has been a natural progression of use of our BeaconRTLS to collect large data sets. We have a new subsidiary Sensor Cognition that provides services to extract and use intelligence from sensor data. Unlike most other ML companies, we aren’t interested in computer vision, speech and language recognition, even though the first two could be inferred to be from sensor data. Instead, we specialise in extracting intelligence from industry time series position and sensor data.
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