iBeacon, Eddystone, Bluetooth, IoT sensor beacons, apps, platforms
Minew has a new video showcasing the G1:
The G1 gateway collects advertising data from iBeacon, Eddystone, Bluetooth LE sensor and other Bluetooth LE devices and sends it to your server by HTTP(S) or MQTT/ using WiFi or Ethernet.
More information:
Available Gateways
Beacon Proximity and Sensing for the Internet of Things (IoT)
Beacons in Industry and the 4th Industrial Revolution (4IR)
It’s often the case you need to know if a beacon is working and advertising the correct information. It’s also sometimes necessary to differentiate between beacons, based on their signal strength, so you know you are setting up the correct beacon. Other times, you might want to know a beacon’s MAC address.
The best scanning app is Nordic nRF Connect that’s written by the manufacturer of the System on a Chip (SoC) in most beacons. Nordic nRF Connect detects all beacons and indeed all Bluetooth LE devices, irrespective of the SoC manufacturer because it just looks for standard Bluetooth advertising. nRF Connect is intelligent in that it works out the kind of beacon and displays the appropriate type of information.
It’s important you use the Android version of nRF Connect. Due to over-zealous efforts by Apple to hide identities, it’s not possible for iOS scanning applications to see advertising iBeacon (UUID, major and minor) information nor the Bluetooth MAC address.
Here’s an example scan:
In the above screenshot you can an iBeacon that has been tapped on to show extra information. All devices have the MAC address and a Received Signal Strength Indicator (RSSI). The MAC address uniquely identifies the device.
Devices that scan for beacons will experience a signal strength (RSSI) that varies depending on the distance to the beacon. It’s expressed in dBm and is always negative. A more negative number indicates the beacon is further away. A typical value of -10 to -30 dBm indicates the beacon is close. A typical value of -110 indicates the beacon is near the limit of detection. You can use this to determine which beacons are closest. You usually configure beacons when they are right next to the phone and have a higher, less negative, RSSI.
nRF Connect also shows the advertising period that’s based on how often the app sees the advertising as opposed to what has been set in the beacon. The value is rarely exactly what you have set because Bluetooth requires some randomisation of the advertising period to reduce the possiblity of collisions between devices, in the vicinity, that are set to the same period. Also, being wireless, not all advertising is seen which causes jumps in the shown advertising period. Read more about choosing the advertising period.
There’s also a ‘RSSI at 1m’ which is the beacon’s self-declared value, in the advertising data, of what the RSSI should be at 1m. This can be used by scanning devices, such as apps, as a form of calibration for determining distance. In most cases this value isn’t used and should be ignored. Read more about power and the measured power calibration value.
The demise of Google Nearby prompted some commentators to declare the death of beacons. However, here at BeaconZone we are actually seeing a resurgence of the use of beacons in retail.
Gone are the unsolicited notifications and gone are the ‘get rich quick’ marketers. The scenarios that remain tend to use beacons as an adjunct to something else rather than being the main solution itself. For example, they are used to provide triggering in CloseComm‘s WiFi onboarding app used by Subway, McDonalds, BurgerKing and CircleK and NCR.
Beacons are being rolled out to many food retailers, particularly in the USA. They are also taking new physical forms as witnessed by Mr Beacon:
If you are looking for more innovative uses of beacons in retail, take a look at Alibaba’s Fashion AI concept store as mentioned in the latest Wired (UK):
RFID and Beacons are used to detect items picked up during shopping so that customers can collect what they have looked at, have accessories automatically selected and view what’s in stock. Once they are home, a virtual wardrobe allows customers to buy anything they saw in store.
Beacons can also be used to enable audit compliance. Eric West, Head of Strategy at IMS has a useful free pdf on takeaways from GroceryShop, the retail industry conference. The pdf also mentions the use of beacons in lighting to drive location-based messages and wayfinding. Also:
“Amazon’s 2017 acquisition of WholeFoods was a “tipping point” that ensured all grocery players were speeding up their digital plans.”
Read about Beacons for Marketing
We mentioned Wiliot last March and since then their R&D team has created early engineering samples that prove it’s possible to create a battery-less Bluetooth LE beacon harvesting energy from radio frequencies (RF).
The Wiliot device looks more like a RFID tag than a traditional beacon in that it’s supplied as a very thin PVC inlay sheet containing the chip and wire antenna together. The thin form factor, no battery and the relatively low cost will allow it to be manufactured into or stuck onto clothing and packaging that will provide for many new usecases.
Producing such a device isn’t easy as it can’t use existing System On a Chip (SoC) devices as produced by Nordic, Dialog and Texas Instruments (TI) because they are too large and use too much power. Wiliot has had to create their own SoC from the ground up, including software tools to develop and program the devices. We have been told it will be a year before Wilot has all the components in place for commercial rollout. Meanwhile, selected organisations can join the Early Advantage Program (EAP). There’s a new a product overview (PDF below) that explains the EAP and the main usecases, connected packaging, connected apparel, logistics and asset tracking:
Wiliot already have Early Advantage Program (EAP) agreements in place with over a dozen brands including top fashion brands, a telco, appliance companies, a furniture brand and packaging companies.
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
If you work in IT and particularly if you have knowledge of programming, you will know it’s best to be informed of data rather than repeatedly request changes.
Repeatedly requesting changes, called polling, wastes resources when there’s no data returned. It also doesn’t get the data as soon it is available as you have to wait for the next poll.
A feature of our BeaconServer™ and BeaconRTLS™ is that they offer change stream data on all database data. Change stream is a standard web (HTTP(S)) protocol that provides data to systems and apps as and when it becomes available. The client sets up a long running HTTP connection and then receives updates.
The stream looks something like:
First you get an ‘ok’ followed by data as and when it becomes available. The above only shows a generic iBeacon. When used with sensor beacons this also includes all decoded data such as movement, temperature, humidity, air pressure, light and magnetism (hall effect), proximity (short range IR and PIR) and fall detection.
BeaconServer™ and BeaconRTLS™ provide REST based insert, update, query and change stream on all data allowing external systems and apps to fully use the system. This can also be authenticated via HTTP header tokens to prevent unauthorised access.
An example of use of the change stream is BeaconRTLS™ itself. The web UI uses the change stream to asynchronously update the UI with no flicker or redraw. All data, including beacons, locations and alerts are obtained asynchronously from the server (image below not live at it needs login):
We see some companies only after they have gone a long way down a particular road only to discover they made a big mistake early on. It might be, for example, they have heavily committed to the wrong beacon, wrong platform or have assumed something on one of the mobile platforms. They didn’t do their research. Often we can help them get on the right track but sometimes not.
At the other end of the research scale we have other companies who ask us “Will beacons work in an xyz environment?” where xyz has ranged, for example, from underground on the tube for the police to inside cars for a car retailer. Taking this further, we also get many, what we call, “armchair entrepreneurs” who want to work everything out before even looking at a beacon.
While we have a lot of expertise and provide advice through consultancy, it’s often the case that there are some aspects that are unknown until things are tried for real in the actual environment. Wireless solutions can be very fickle.
A lot can be learned about beacons, Bluetooth and the environment by buying one inexpensive beacon and trying things out. In the case of software, try implementing a thin slice through the proposed system touching on the perceived risky or unknown areas. Experiment before committing. Don’t go all in buying thousands of beacons and commissioning full custom software until you are confident things will work.
Consider a Feasibility Study
Our article on the Benefits of Beacons mentions that the data from beacons can enhance productivity. However, what does this mean? ‘Productivity’ seems like a nebulous term that means nothing. Can beacons, and indeed IT in general, increase productivity? Has there been any evidence for this in the past? Will things such as IoT, 4IR and AI machine learning actually improve productivity?
A great place to start quantifying productivity is the France-based Organisation for Economic Co-operation and Development (OECD). They have lots of open data that shows recent productivity gains have been small for most countries. This is a puzzle.
Why hasn’t technology improved productivity significantly? There’s a great post at Focus Economics on 23 economic experts weigh in: Why is productivity growth so low? There’s also speech on Productivity puzzles (pdf) given by Andrew G Haldane, Chief Economist, Bank of England with lots of charts. The UK’s ‘Be the Business’ organisation tasked with driving better productivity also has a useful paper (pdf) on How good is your business really?
The key theme is that not many businesses have adopted earlier productivity improving tools such as cloud computing, customer relationship management (CRM) systems and enterprise resource planning (ERP). There are sectoral patterns of productivity improvement that tend to delineate ‘frontier’ and ‘laggard’ companies. There’s a very long tail of laggard companies that weights the numbers. There’s a fear of technology brought about by inertia and poor management.
Some countries such as Germany have slightly higher productivity but that’s considered to be due to better vocational education rather than technology.
There have been recent improvements in productivity but only for the top 5% frontier companies. These companies have embraced technology as part of improving operational efficiency, future planning, employee engagement, leadership and commercial excellence.
We anticipate IoT, 4IR and AI machine learning will improve productivity but again, only for frontier companies. The difference this time is that the newer technologies will have more far reaching consequences. The frontier companies will further extend their reach over the laggards. This might have existential consequences for many of the laggards.
Most beacons provide a battery level % indication that’s visible in advertising and/or the manufacturer configuration app. It’s also usually visible via a Bluetooth Service Characteristic.
Some observations:
Here’s an example for Energizer Lithium AA:
For a typical CR2032 Lithium coin cell:
A consequence of the above factors is that you can’t estimate battery life by looking at battery percentage over time. You need to measure current use. We have a previous blog post on this topic.
Battery level can only be used as an indication that the battery is low and should be changed.
A problem with navigation in vehicles is that location can be lost in radio-shadows such as in tunnels and in tree covered areas. ChoonSung Nam and Dong-Ryeol Shin of Sungkyunkwan University, Suwon, Korea have a new paper on Vehicle location measurement method for radio-shadow area through iBeacon.
Beacons are placed at the side of the road and instead of advertising unique ids in the form of iBeacon or Eddystone, they advertise absolute Global Positioning System location data. Together with the received signal strength (RSSI) this allows the vehicle to better determine the location.
