Category: Sensor Beacons

How is IoT Going?

Vodafone have an informative new report, the Internet of Things (IoT) Barometer. It’s a survey of 1,430 companies worldwide into their use of IoT.

IoT adoption is increasing now that companies are buying more cost-effective, off the-shelf solutions rather than building their own from scratch:

74% of adopters believe that within five years, companies that haven’t adopted IoT will have fallen behind their competition.

Adoption is across all sectors:

“95% of adopters are already seeing benefits. Over half
(52%) say that the returns have been significant and
79% say IoT is enabling positive outcomes that would be
impossible without it.”

The main gains have been:

  • reduced operating costs (53%)
  • improved collection of data (48%)
  • increased revenue from existing streams (42%)

There’s also an accompanying video:

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

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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Smart Cycling Helmet

Nordic have news of a new cycling helmet with embedded nRF52 device, also used inside many beacons, that detects acceleration and in conjunction with an app, can send location and crash alerts.

While it’s an interesting and innovative product, most of the work is done by the app. There’s no reason why a generic acceleration sensor beacon couldn’t have been used within the helmet (or elsewhere). However, we guess including anything extra inside a helmet, in a safe manner, poses some challenges.

An insight from this is that there are probably many untapped opportunities for vertical sensor beacon type applications that predominantly make use of apps to provide for much of the functionality.

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
Read about Machine Learning

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