Category: Sensor Beacons

Machine Learning Accountability

AI machine learning is a great partner for sensor beacon data because it allows you to make sense of data that’s often complex and contains noise. Instead of difficult traditional filtering and algorithmic analysis of the data you train a model using existing data. The model is then used to detect, classify and predict. When training the model, machine learning can pick up on nuances of the data that a human programmer wouldn’t see by analysing the data.

One of the problems with the AI machine learning approach is that you use the resultant model but can’t look inside to see how it works. You can’t say why the model has classified something some way or why it has predicted something. This can make it difficult for us humans to trust the output or understand what the model was ‘thinking’ when the classifications or predictions end up being incorrect. It also makes it impossible to provide rationales in situations such as ‘right to know’ legislation or causation auditing.

A new way to solve this problem is use of what are known as counterfactuals. Every model has inputs, in our case sensor beacon data and perhaps additional contextual data. It’s possible to apply different values to inputs to find tipping points in the model. A simple example from acceleration xyz sensor data might be that a ‘falling’ indicator is based on z going over a certain value. Counterfactuals are generic statements that explain not how the model works but how it behaves. Recently, Google announced their What-If tool that can be used to derive such insights from TensorFlow models.

Read about Machine Learning and Beacons

New Rugged Industrial Sensor Beacons in Stock

We have some early samples of the new INGICS iBS03 range of beacons in stock. They are functionally similar to the iBS01 range except are waterproof to IP67 and have a more robust case with 2m drop protection.

We stock three variants:
iBS03T – Temperature sensor
iBS03G – Motion (starting/moving/stopped) and fall detection
iBS03RG – Accelerometer for raw xyz

New Waterproof S1 Sensor Beacon in Stock

We have the new Minew S1 in stock. It’s a sensor beacon with accelerometer, temperature and humidity as well as iBeacon/Eddystone. Unusually for a temperature/humidity beacon it is waterproof to IP65 making it suitable for use outdoors. Sensor beacons like this usually have the sensor on the PCB and a hole in the case to pass through ambient temperature and humidity. Instead, the sensor is outside the beacon:

This beacon takes 2 AAA batteries and uses a newer, more efficient Nordic nRF52 System on a Chip (SoC) for a long 3 year battery life.

Location-based Ambient Intelligence

ABI Research predicts that there’s going to be an increase in beacon-enabled app shipments mainly due to retail and ambient intelligence:

So what is ambient intelligence? It’s a catch all term for the joining of the Internet of Things (IoT), big data, the connected home, wearables, smartphones, voice/image recognition and artificial intelligence through machine learning.

Sensor beacons enable the gathering of new data. New data not only measures physical things but, more importantly, provides a way of circumventing the problem of silo data in many large organisations. Silo data is data people/departments don’t want to share for fear of losing power or control. Today’s machine learning techniques also require data to be in a specific format and ‘clean’. Creating new data allows it to be more readily formatted and conditioned prior to saving.

This isn’t just about location data. It includes physical quantities such as smaller-scale movement (accelerometer), temperature, humidity, air pressure, light and magnetism (hall effect), proximity, heart rate and fall detection. Our conversations with beacon manufacturers tell us beacons are currently being developed that detect more nuanced quantities such as colour, gas and UV. Some beacons already have general purpose input/output (GPIO) such that custom beacons can can already detect anything for which there’s an electronic sensor.

So why Bluetooth beacons rather than other electronics with the same sensors? Here are the main reasons:

  • Integration without soldering or, in most cases, without custom electronics
  • Communication with iOS and Android apps and computers via existing Bluetooth APIs
  • Remote, low power, data acquisition where there’s no mains power and no connectivity at the place of measurement
  • Significantly lower cost compared to traditional industrial sensing

The Status of Manufacturing and the 4th Industrial Revolution (4IR)

There’s an article in The Manufacturer magazine on “Manufacturing:the numbers” that highlights some numbers from the Hennik Research’s Annual Manufacturing Report.

In practice, we are finding many organisations are struggling to develop skills, business processes and organisational willpower to implement 4IR. There’s a relatively slow pace in many industries, driven down by the uncertainties of Brexit, Europe and International trading tensions.

Nevertheless, we believe that once these political issues start to play out, the more forward-thinking manufacturers will realise they have to revolutionise their processes in order to compete in an market with complex labour availability and tighter margins due to tariffs. Manufacturers that are able to harness 4IR effectively will be the ones that will be able to differentiate themselves, while the laggards will find themselves more and more at a disadvantage.

Read about Sensing for Industry and IoT
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Bluetooth IoT Sensors

There’s a type of beacon that doesn’t send out iBeacon or Eddystone advertising. Instead, it sends out standard Bluetooth 4.0 advertising containing sensor values. This means the data can be picked up via apps, gateways, Raspberry Pis or other devices that can see Bluetooth advertising.

An example of this is the INGICS iBS01 range of beacons.

The round bit in the middle is a button that can be pressed. Here’s an example for the data from the iBS01T temperature/humidity sensor:

Additionally, the ‘event’ data gives the state of the button press.

Read more about Using Bluetooth Wireless Sensors

Sensor Beacons

Bluetooth Sensor Tags

Bluetooth sensor tags and sensor beacons are essentially the same thing. The terminology of tags vs beacons stems from how they are used. If the devices are fixed, they tend to be called beacons and if they are placed on assets or people they tend to be called tags because they are tagging things and people. However, the terminology is interchangeable, irrespective of the use.

The use of the term tags also comes from the use in RFID, barcode and UWB devices that can also be used to uniquely identify devices.

Bluetooth sensors can be used in two ways, either via connection-less advertising or having another  Bluetooth device connect and examine values. This is explained further in our article on Using Bluetooth Wireless Sensors.

Tagging implies locating. However unlike other technologies, devices can do a lot more than just locating and can detect movement (accelerometer), temperature, humidity, air pressure, light and magnetism (hall effect), proximity, heart rate and fall detection.

Read more about:
Using Beacons, iBeacons for Real-time Locating Systems (RTLS)

Beacon Proximity and Sensing for the Internet of Things (IoT)

What New Things Could Machine Learning Enable?

Benedict Evans of Andreessen Horowitz, a venture capital firm in Silicon Valley, has a thought-provoking blog post on Ways to Think About Machine Learning.

Benedict asks what new things machine learning (ML) could enable. What important problems might it actually be able to solve? There are (too) many examples of machine learning being used to analyse images, audio and text, usually using the same example data. However, the main question for organisations is how can they use ML? What should they look for in data? What can be done?

Much of the emphasis is currently on making use of existing captured data. However, such data is often trapped in siloed company departments and usually needs copious amounts of pre-processing to make it suitable for machine learning.

We believe some easier-to-exploit and more profound opportunities exist if you use new data from sensors attached to physical things to create new data. Data from physical things can provide deeper insights than existing company administrative data. The data can also be captured in more suitable formats and can be shared rather than stored by protectionist company departments.

For example, let’s take movement xyz that’s just one aspect of movement that can be detected by beacons. Machine learning allows use of accelerometer xyz motor vibration to predict the motor is about to fail. Human posture, recorded as xyz allows detection that patients are overly-wobbly and might be due for a fall. The same human posture information can be used to classify sports moves and fine tune player movement. xyz from a vehicle can be used to classify how well a person is driving and hence allow insurers provide behavioural based insurance. xyz from human movement might even allow that movement to uniquely identify a person and be used as a form of identification. The possibilities and opportunities are extensive.

As previously mentioned, the above examples are just one aspect of movement. If you also consider movement between zones, movement from stationary and fall detection itself, more usecases become evident. Sensor beacons also allow measuring of temperature, humidity, air pressure, light and magnetism (hall effect), proximity and heart rate. There are so many possibilities it can seem difficult to know where to start.

One solution is to look at your business rather than technical solutions or even machine learning. Don’t expect or look for a ready-made solution or product as the most appropriate machine learning solutions will usually need be custom and proprietary to your company. Start by looking for aspects of your business that are currently very costly or very risky. How might more ‘intelligence’ be used to cut these costs or reduce these risks?

Practical examples are How might we use less fuel? How might we use less people? How might we concentrate on the types of work that are least risky? How might be preempt costly or risky situations? How might we predict stoppages or over-runs?

Next, use your organisation domain experts to assess what data might be needed to measure data associated with these situations. Humans often have insight that patterns in particular data types will help classify and predict situations. They just can work out the patterns. That’s where machine learning excels.

Read About AI Machine Learning with Beacons

Detecting Temperature With Beacons

Some sensor beacons can be used to monitor temperature. The first thing to consider when comparing temperature beacons is whether they have a dedicated hardware temperature sensor. Some beacons have a temperature sensor inside the main chip (System on a Chip – SoC) that’s less accurate and has less precision. The sensor is mainly there to give an indication of the chip temperature, not the ambient (outside the beacon) temperature. Most beacons only transmit for the order of 1ms every 10 to 5 seconds and enter a very low power state the remainder of the time. This means they not only use low power but don’t significantly heat the SoC. This means the SoC roughly tracks the outside temperature.

In our sensor beacon listings, when we say a beacon has a temperature sensing it has a separate hardware sensor, usually the Sensirion SHT20, providing more accuracy and precision than the sensor in a SoC. Some of our beacons, such as the Minew i3 and i7 have an internal SoC temperature sensor that’s readable but we don’t classify that as a sensor beacon.

The next thing to consider is the casing. In order to quickly track ambient temperature, the casing needs to be open somewhere and usually have a hole. Beacons that say they are waterproof and have temperature sensing won’t track ambient temperature well.

We have had customers use temperature sensing beacons in scientific situations and where they need to periodically calibrate sensing equipment. How do you calibrate temperature sensor beacons? The SHT20 is has a long term drift of only <0.04 deg C/year (the humidity reading vaies difts by <0.5%RH/year) so it doesn’t need calibration for most situations. However, if you need better than this, or check calibration, you will need to periodically calibrate in the software of the device (usually an app) that receives the beacon sensor data.