Using Bluetooth Sensor Beacons for AI Machine Learning

Sensor beacons provide a quick and easy way to obtain data for AI machine learning. They provide a way of measuring physical processes to provide for detection and prediction.

Beacon Temperature Sensor

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.

Such data is often complex and it’s difficult for a human to devise a conventional programming algorithm to extract insights. This is where AI machine learning excels. In simple terms, it reads in recorded data to find patterns in the data. The result of this learning is a model. The model is then used during inference to classify or predict situations based on new incoming data.

The above shows some output from accelerometer data fed into one of our models. The numbers are distinct features found over the time series as opposed to a single x,y,z sample. For example ’54’ might be a peak and ’61’ a trough. More complex features are also detectable such as ‘120’ being the movement of the acceleration sensor in a circle. This is the basis for machine learning classification and detection.

It’s also possible to perform prediction. Performing additional machine learning (yes, machine learning on machine learning!) on the features to produce a new model tells us what usually happens after what. When we feed in new data to this model we can predict what is about to happen.

The problem with sensor data is there can be a lot of it. It’s inefficient and slow to detect events when this processing at the server. We create so called Edge solutions that do this processing closer to the place of detection.

Read more about SensorCognition

What is a Smart Factory?

A new article at IoT World Today asks Is My Smart Factory Smarter Than Yours? It’s Hard to Say.

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”

Such situations are the focus of our new Sensor Cognition™ technology that can provide machine intelligence at the edge.

Edge Computing on the Rise

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.

Read about SensorCognition™, our Edge gateway with machine learning.

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.

Sensor Cognition for Making Sense of Beacon Data

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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