iBeacon, Eddystone, Bluetooth, IoT sensor beacons, apps, platforms
Bluetooth gateways scan for beacons and send detected data your server, BeaconServer™ or BeaconRTLS™. However, what if you don’t have a server yet or want to determine if a gateway is actually sending data?
Ubeac allows you to set up a hook to receive gateway data. What’s more, they have some informative setup videos for our INGICS, Minew, AnkhMaway and Aprilbrother gateways.
Read about Beacon Proximity and Sensing for the Internet of Things (IoT)
View Bluetooth gateways
There’s a research paper by Thomas Tegou, Ilias Kalamaras, Markos Tsipouras, Nikolaos Giannakeas, Kostantinos Votis and Dimitrios Tzovaras of Information Technologies Institute, Greece on A Low-Cost Indoor Activity Monitoring System for Detecting Frailty in Older Adults.
The paper describes a room-level accuracy indoor localization system, based on Bluetooth RSSI, to assess the frailty in older people.
The implementation used smartphones as detectors:
The researchers identified features to classify degrees of movement between rooms:
The system was able to determine rooms to an accuracy above 93%. The results showed subjects with frailty had distinctive movement patterns that could be identified with high accuracy of 98%.
Read about Beacon Proximity and Sensing for the Internet of Things (IoT)
We have a new short 60 second video explaining the use of machine learning with beacon sensor data (best viewed full screen):
Read about AI Machine Learning with Beacons
There’s a new paper by Seyed Mahdi Darroudi, Raül Caldera-Sànchez and Carles Gomez of Department of Network Engineering, Universitat Politècnica de Catalunya/Fundació, Spain on Bluetooth Mesh Energy Consumption: A Model.
They set up some experiments to measure current consumption under various parameters:
They found that a sensor device running on a simple 235 mAh battery, sending a data message every 10 secs, can achieve a lifetime of up to 15.6 months.
This battery is probably a CR2032 battery. Read our post on Beacon Battery Size, Type, Capacity and Life for typical beacons battery sizes and capacities.
Read about Beacons and the Bluetooth Mesh
Untuiface is one of a growing number of products incorporating beacons in their functionality. Untuiface allows you to build interactive multi-touch kiosk type screens without writing any code.
It’s possible to use iBeacons to trigger actions. For a static kiosk, things or people coming close can trigger content. For a moving kiosk, such as a tablet, content can change depending on how close the tablet is to particular areas or things.
The settings provide for actions when beacon advertisements are detected, change or are lost thus providing for different types of interaction. Untuiface have an example to show contextual information as items are picked up from or replaced to their original position.
Read more about using iOS and Android Apps with Beacons
The February 2019 edition of ComputingEdge magazine from IEEE has an article on From Raw Data to Smart Manufacturing (pdf – current and back issues freely available).
The article describes what they call a ‘Semantic Web of Things for Industry 4.0 (SWeTI) platform’. Although it’s very useful, it’s less of a platform in the software sense and more of an ecosystem or model.
The platform describes usecases, tools and techniques for smart applications. Using this model, BeaconZone operates in the Device, Edge and Data Analytic layers. We provide for smart devices and tools, gateways, storage, machine learning (ML) and analytics.
Read about Benefits of Beacons
Read about Beacons in Industry and the 4th Industrial Revolution (4IR)
The Nordic blog has an informative post on How IoT-Based Predictive Maintenance Can Reduce Costs. It explains how connected sensors can save maintenance costs through reduced downtime. The post provides some examples from the power industry and explains how the same techniques can be used in the tools, retail, distribution and physical infrastructure industries.
As the post mentions, the challenge is how to scale this up. We are told IoT is the solution. Here at BeaconZone, we don’t believe IoT is always the solution, especially where there’s a requirement for higher sensor sampling frequencies. There’s too much data, too much data transfer and too much server processing. It really doesn’t scale. Apart from the waste and cost of these resources, the latency of triggering events based on the data is too high. Instead, look to so called ‘edge’ or ‘fog’ computing where more processing is done nearer the sensors and only pertinent data is sent to other systems.
Need more help? Consider a Feasibility Study.
Here’s a new official video of the Minew E8:
The thing sticking out the top is the battery slip. Pull and remove it to activate the beacon.
The E8 is small, thin (5mm), light (8g) and advertises iBeacon, Eddystone and acceleration.
When working with Machine Learning on beacon sensor data or indeed any data, it’s important to realise AI machine learning isn’t magic. It isn’t foolproof and is ultimately only as good as the data passed in. Because it’s called AI and machine learning, people often expect 100% accuracy when this often isn’t possible.
By way of a simple example, take a look at the recent tweet by Max Woolf where he shows a video depicting the results of the Google cloud vision API when asked to identify an ambiguous rotating image that looks like a duck and rabbit:
There are times when it thinks the image is a duck, other times a rabbit and other times when it doesn’t identify either. Had the original learning data included only ducks but no rabbits there would have been different results. Had there been different images of ducks the results would have been different. Machine learning is only a complex form of pattern recognition. The accuracy of what you get out is related to a) The quality of the learning data and b) The quality of the tested data when to try identification.
If your application of machine learning is safety critical and needs 100% accuracy, then machine learning might not be right for you.
Read about AI Machine Learning with Beacons
A common usecase for beacons is time and attendance management. This involves needing to know who has been where and for how long.
Our gateways have been used in education for automatically recording student registration. They have been particularly suitable in ‘open lab’ type scenarios where there’s not always staff around to record attendance. Beacons are given to students that are recorded by gateways. It’s also possible to have the gateways act as beacons so that smartphone apps can unlock things such as electronic teaching materials on a student-by-student basis.
Another usecase is personal tracking of time spent in places or on projects for expensing to clients. Again, this can be done accurately and automatically.
A further usecase we have come across is the use of our beacons on evidence-based policing. Police officers on the beat often have to account for how long they have spent at particular locations. An Android app carried by officers records beacons (location) and sends the data to a central server. This prevents the need for paper based processes to determine who has been where.
There’s ready made software available such as Seats Software and Calamari. However, we find that clients sometimes have more specific, yet simpler needs that don’t necessarily map well to ready-made solutions.
Consider our development services.