Location – Page 5 – BeaconZone Blog

The Power of the AirTag

Apple announced AirTag this week. Many commentators are asking what’s different or better than Tile and other Bluetooth trackers. Some are even asking why Apple is such an innovative company.

While the accuracy of finding is better for the relatively few Apple iPhones that have the Ultra Wideband (UWB) U1 chip, this isn’t likely to be the main advantage and will in any case be lost on most potential buyers. Similarly, Apple’s claim that it’s private and secure is unlikely to be important or seem significant in most scenarios.

Instead, the power of the AirTag will not come from the technical aspects of the physical AirTag but from being part of the Apple ecosystem. The problem with Tile and other trackers is that the range is only local, typically about 50m. When tags are lost away from the vicinity the system relies on other users to detect your tag. This previously hasn’t worked because there haven’t been enough users. The power of the AirTag will be the reach of the Apple device network that no other tag manufacturer will be able to match.

This isn’t to say AirTags will replace iBeacon and Eddystone beacons. AirTags are only for tracking and are more for finding personal things rather than say assets in a warehouse or factory. AirTags only identify and don’t sense like sensor beacons. While they can be seen by Bluetooth gateways, the privacy and security features will thwart identification and use in real time locating systems. AirTags are only a very small, proprietary and closed part of the tracking and sensing ecosystem.

iBeacon Deployment Performance Evaluation

There’s recent work by researchers at Hong Kong Polytechnic University on Performance Evaluation of iBeacon Deployment for Location-Based Services in Physical Learning Spaces.

The paper examines signal availability, signal stability and position accuracy under different environmental conditions. The aim was to provide recommendations for iBeacon deployment location, density, transmission interval and fingerprint space interval. While the research considered beacons in teaching and learning environments, the conclusions are also applicable to other situations.

The paper describes positioning using the trilateration and fingerprinting methods. Experiments were performed in a 3.44m to 1.80m classroom to determine optimum beacon placement density.

The main conclusion was that greatest signal attenuation and variation was caused by pedestrian traffic blocking the line of sight between iBeacon and receiver. High temperature and strong winds also caused minor discrepancies to the signals. Trees and nearby vehicle traffic didn’t have any negative effects on the signals.

Deployments should consider the line of sight as the first priority. For the above mentioned room size, positional accuracy increased when the number of beacons was increased from three to eight. Using more beacons didn’t improve accuracy. An average spacing of 4.4m is recommended for iBeacon deployment. A settings of 417ms transmission interval is advised as a compromise between battery life and positional accuracy.

Read Determining Location Using Bluetooth Beacons

Bluetooth Beacon Based Student Registration System

The Journal of Physics has new research into a Student Attendance Manager Using Beacons and Deep Learning (pdf).

The system automatically registers attendance without disturbing the class. It uses an iBeacon in each classroom to determine location. It also uses a camera and deep learning analysis to prevent students cheating the system by having someone else attend. The researchers say the system is better than biometric scanning and RFID that requires manual reading one by one.

The solution uses iBeacons but it’s the Bluetooth MAC address that’s used for room identification. The scanner and camera interface uses a Raspberry Pi that sends data to a server.

Read about Beacons in Education

Improving iBeacon Location Accuracy

There are lots of ways of processing Bluetooth signal strength (RSSI) to determine location. Being based on radio, RSSI suffers from fluctuations, over time, even when the sender and receiver don’t move.

The College of Surveying and GeoInformatics, Tongji University, Shanghai , China has new research on iBeacon-based method by integrating a trilateration algorithm with a specific fingerprinting method to resist RSS fluctuations.

Trilateration and fingerprinting are common techniques to improve location accuracy based on RSSI. The paper improves on these by using analysis based on Kalman filtering of segments delimited by turns. This is used to derive locations based on pedestrian dead reckoning.

The researchers achieved a positioning accuracy of 2.75m.

Read about Determining Location Using Bluetooth Beacons

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

Sensor Placement Optimisation Research

There’s interesting new research into Sensor Placement Optimization for Critical-grid Coverage Problem of Indoor Positioning (PDF).

This research looked into optimising the location of sensors as opposed to the more usual methods of filtering signals to improve accuracy. The aim was to reduce deployment costs by deploying more sensors in critical areas that were identified as needing greater positioning accuracy.

The critical-grid coverage scheme and NSGA-II algorithm were used to optimise the placement of iBeacon nodes in underground parking lots.

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

Occupancy Detection Using BLE Beacons

The Covid pandemic has resulted in many organisations looking to quantify occupancy. This is especially so in education where government guidelines tend to be based on occupancy as well as social distancing.

Occupancy isn’t just relevant to pandemics. It’s also a factor in, for example, building emergency management when determining the optimal plan of action, for example, when allocating emergency personnel. Similar situations exist in police and military settings where, additionally, it’s advantageous to know the real time location of assets, people and casualties.

Past research on Occupancy Detection for Building Emergency Management Using BLE Beacons investigated use of a system made up of Bluetooth beacons installed in rooms and an app installed on occupants’ smartphones.

The research system used Raspberry Pis as iBeacons and Android phones as Bluetooth detectors. Fingerprinting was used to to produce data that fed into a multi-class SVM classification with classes being different room areas. The system was able to provide high occupancy accuracy and identify occupant movement patterns.

There are many problems with using such a system in real life. The Raspberry Pi beacons are fragile and have long term reliability problems due to the use of Micro SD storage. Systems based on fingerprinting rarely work long term because wireless signals change when there are changes in the physical environment such as more people or change in furniture. Using smartphones as detectors also isn’t always reliable because people fiddle with apps, change permissions and real time use implies a larger battery drain.

Instead, it’s necessary to turn the system around and have beacons on people and use dedicated devices, gateways, as detectors. In the simplest case, the gateways send detections to a server to be processed. More sophisticated systems such as our BeaconRTLS™ provide intelligent processing, mapping, alerts and reporting such as occupancy per zone.

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

iBeacon RSSI Anomaly Detection for Indoor Positioning

There’s new research on iBeacon Indoor Positioning Method Combined with Real-Time Anomaly Rate to Determine Weight Matrix that uses a weighted Levenberg-Marquadt (LM) algorithm to determine the location of ibeacons.

The solution processes the received signal strength (RSSI) to determine anomaly rates of beacons and hence filter out abnormal signals. This helps to overcome the problems of unreliable signal strength in indoor locations due to reflections and obstacles.

The system achieves an average positioning error of 1.5m.

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

Bluetooth Asset Tracking

Bluetooth tags/beacons detect the position of people and assets. Software maps jobs, valuable tools, parts, sub-assemblies and people onto your floor plans or maps.

The main uses are:

Searching. Knowing the location of something such as a piece of equipment, parts, stock, pallets, a job or person without ringing round. Locating expensive, shared, equipment so fewer spare assets are required to cover an area.
Security. Alerting when people or assets enter or leave an area.
Protection. Detecting quantities such as temperature and humidity for sensitive items that can spoil.
Process Control. Knowing where things have been. Knowing what happened at a particular location. Knowing when measured values exceeded their expected range.

Bluetooth LE is particularly suitable because it is:

Real Time. Better than barcode scans and NFC tags where the data is only as up to date as the last successful manual scan.
Compatible. Bluetooth LE works with existing devices such as smartphones, tablets, laptops and desktops.
Reliable. Works in electrically noisy situations such as the factory.
Inexpensive. Commodity hardware is more affordable than non-standard technologies such as ultra wideband (UWB).

The end result is reduced downtime, less time re-ordering or re-making things that have been lost, optimum productivity and better use of skilled staff doing their job rather than searching for assets and people.

Read about Beacons in Industry and the 4th Industrial Revolution (4IR)

Learn about Asset and Pallet Tracking for Manufacturers

Discover BeaconRTLS™

Read about BluetoothLocationEngine™

Why Real Time Locating is Becoming More Popular

The recent Nordic Semiconductor wireless quarter magazine contained an article on positioning and real time locating systems (RTLS). RTLS is experiencing growth:

RTLS detects the position of people and assets in real time. Tags are attached to people or assets and the radio signals from the tag allow the location to be determined. The real time aspect is important because it provides the current position automatically, unlike barcode scans and and NRF tags that are only as up to date as the last successful scan. With older, manual, systems, people are lazy and forget to scan.

A complete RTLS system comprises of readers, tag/sensors, application software and communications/network infrastructure.

Asset tracking is being used in industry verticals such as healthcare, defence, education and manufacturing. It commonly tracks tools, equipment, pallets, sub-assemblies, jobs and completed goods.

People tracking has tended to be used more in education and health where the security of individuals is more important than privacy concerns related to tracking people.

RTLS growth is being driven by the benefit of real time tracking allowing processes to be much more efficient. Effort and time is saved when things and people can be found quickly. Alerts notify abnormal conditions to provide for proactive actions. Reports track long term trends to allow identification of patterns that can be used to change processes to improve efficiency.

Bluetooth is popular for use with RTLS because tags and readers are inexpensive compared to other technologies. Bluetooth also works indoors where GPS fails. Unlike other technologies, Bluetooth LE tags have a long battery life of up to several years. There are also tags that perform sensing and Bluetooth LE is suitable for use in electrically noisy environments. Bluetooth also integrates with Bluetooth LE devices such as smartphones, tablets, laptops and desktops.

At Beaconzone we are seeing two new trends in use of RTLS. The first is using RTLS for multiple purposes. Customers often come to us wanting to solve a particular problem but later find the RTLS has a multitude of uses and benefits. This is where a closed solution offering, for example, a lone worker solution, won’t be so flexible.

Established real-time location system market players are shifting from closed solution offerings to including best-in-breed components in application layer
Allied Market Research

The second trend, brought on by Covid, is the tracking of office workers. What might have used to be seen as an invasion of privacy is now being seen as an essential way to monitor room occupancy and determine who has been in the same room as someone else when a person tests positive for Covid.

Read about BeaconRTLS

Read about BluetoothLocationEngine™

iBeacon Deployment Parameters for Locating

Researchers from the The Hong Kong Polytechnic University have a new paper on Performance Evaluation of iBeacon Deployment for Location-Based Services in Physical Learning Spaces (pdf) that tests environmental and deployment factors, indoors and outdoors, related to using ibeacons for locating. It provides recommendations for iBeacon deployment in terms of location, density, transmission interval, fingerprint space interval and collection time.

The paper provides a great introduction to positioning using beacon received signal strength (RSSI). It describes trilateration and fingerprinting methods for determining location.

Key insights are:

High temperature, strong wind and blocking by pedestrians degraded the signal strength.
Pedestrians traffic blocking the line of sight caused the most signal attenuation and variation.
High air temperature caused significant increase of packet loss that affected the RSSI.
Strong wind reduced the signal strength but didn’t affect the stability of signals.
Trees and nearby vehicle traffic didn’t have any negative effects on signals.
Lower error rates were observed when beacons were deployed on the ceiling as opposed to on the wall.
Positioning accuracy improved with ceiling placement due to the reduction of obstructions.
If ceilings are too high or ceiling deployment is impracticable wall mounted iBeacons should be placed as high as possible.
For fingerprinting, sample at 2m grid intervals for 6s to 10s at each point. Avoid having too many beacons as this won’t improve the positioning accuracy. A transmission interval of 100ms is detrimental to the positioning accuracy. 417ms is better.
For fingerprinting, positioning accuracy varies greatly according to the what is in the room.

The paper mentions that beacon UUID, major and minor are used to uniquely identify beacons. While this is true in the context of detecting using apps, most locating systems use gateways. Gateways use the Bluetooth MAC address to uniquely identify beacons and the advertising type, iBeacon, Eddystone or other, is irrelevant. Using gateways as receivers is also a solution to the problem of variability in receiving capability across smartphones.

The study only considered one beacon type and two receiving smartphones. At Beaconzone, we recommend experimenting with the actual hardware in the actual environment as, being wireless radio, optimum settings and can vary considerably.

Read about location accuracy

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