Using Bluetooth and WiFi RSSI for Locating

There’s a recent paper by Hongji Cao,Yunjia Wang,Jingxue Bi and Hongxia Qi of China University of Mining and Technology on An Adaptive Bluetooth/Wi-Fi Fingerprint Positioning Method based on Gaussian Process Regression and Relative Distance.

The paper looks into how to combine both Bluetooth fingerprint positioning (BFP) and Wi-Fi fingerprint positioning (WFP) to provide for an adaptive Bluetooth/Wi-Fi fingerprint positioning system based on Gaussian process regression (GPR).

The adapative feature is particularly useful because fingerprint acquisition requires a great deal of effort and requires subsequent update and maintenance.This new method provides a better positioning than Bluetooth and Wi-Fi positioning alone but at the cost of extra computation.

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

Using Beacons for Race Timing

There’s novel recent research on City Marathon Active Timing System Using Bluetooth Low Energy Technology by Chun-I Sun, Jung-Tang Huang, Shih-Chi Weng and Meng-Fan Chien of Taiwan.

The authors discuss the use of beacons vs RFID and create a system using Received Signal Strength Indicator RSSI and gateways connected to detector mats:

Beacons are carried by athletes. The gateways sync their times via NTP and send data up to a MongoDB database:

An accuracy of ±156 ms was achieved which compares well to the nearest second used to generally record times and resolution accuracy of 0.1s for commercial transponder timing systems.

The Affect of Transmission Power, Advertising Interval and Beacon Placement Density on Location Accuracy

There’s recent research by Gabriele Salvatore de Blasi, José Carlos Rodríguez-Rodríguez, Carmelo R. García and Alexis Quesada-Arencibia of University of Las Palmas de Gran Canaria, Spain on Beacon-Related Parameters of Bluetooth Low Energy: Development of a Semi-Automatic System to Study Their Impact on Indoor Positioning Systems.

The paper starts by giving an overview of fingerprinting. It explains how fingerprinting is time-consuming and labour-intensive. Fingerprinting is affected by:

“Reflection, refraction, path loss, large fluctuations, multipath fading, non-line-of-sight (NLOS) conditions”

A problem is that some environment-related factors change over time, such as changes in hardware/furniture, the presence of people and ambient humidity conditions meaning that fingerprinting isn’t a one-off activity.

The researchers conclude that the highest transmission power (+4 dBm) produces the best location accuracy. However, this uses a lot of battery power. Use of the lowest power (−20 dBm) only worsened the accuracy by 11.8%. Similarly, lowering the density of the beacons by around 50%, the error increase was only about 9.2%. Increasing the advertising interval didn’t have a significant impact on the accuracy.

The affect of beacon orientation was assessed and vertical orientation was found to be best. Read our previous article on orientation. As expected, introducing a slight electrical noise during the positioning phase did not significantly affect accuracy.

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Using iBeacons with Intelligent Displaying and Alerting Systems

There’s recent research into using iBeacons with intelligent displaying and alerting systems (SICIAD) typically found in public buildings and offices. The paper An Intelligent Low-Power Displaying System with Integrated Emergency Alerting Capability by Marius Vochin, Alexandru Vulpe, Laurentiu Boicescu, Serban Georgica Obreja and George Suciu of the University of Bucharest shows how beacons can be used to determine indoor position of mobile terminals or signalling points of interest.

An Android app uses the beacons to detect location and sends it to the SICIAD system. The researchers concluded that:

“By using an appropriate number of beacons and optimal positions, a relatively precise indoor localization can be obtained with iBeacon technology”

Beacon Based Navigation for the Visually Impaired

There’s a useful recent research paper by Basem AL-Madani, Farid Orujov, Rytis Maskeliūnas, Robertas Damaševičius,and Algimantas Venčkauskas on Fuzzy Logic Type-2 Based Wireless Indoor Localization System for Navigation of Visually Impaired People in Buildings.

The paper explores indoor location algorithms and implements a fingerprinting system using RSSI that achieves an average error of 0.43m.

The authors’ ‘fuzzy logic type-2’ system allows for complex environments such as buildings with glass/metal corridors. They comment that fingerprinting requires pre-configuration which is one of the main disadvantages of this method.

The Affect of Power Levels on Wireless Indoor Localisation Accuracy

There’s new research by Umair Mujtaba Qureshi, Zuneera Umair and Gerhard Petrus Hancke of the Department of Computer Science, City University of Hong Kong on Evaluating the Implications of Varying Bluetooth Low Energy (BLE) Transmission Power Levels on Wireless Indoor Localization Accuracy and Precision. The paper takes a deep look into the relationship between transmitted power and signal stability. It also looks at ways of filtering received signal strength (RSSI) data to improve the location accuracy.

The main insight is that along with the expected difference in the RSSI attenuation there is a considerable difference in the BLE signal variation at all transmission power levels with respect to distance. The variation increases and the localisation accuracy decreases from high to low transmission power levels:

Another observation is that outliers in the data tend to affect the localisation accuracy. Applying filters to the data, they achieved a location accuracy of 2.2 meters with a precision of 95%.

One comment we have is that the researchers didn’t try different beacons. As we mentioned in 2016, the RSSI stability also varies across different beacon models.

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Bluetooth Positioning Using Separate Bluetooth Channels

While we wait for commercial Bluetooth 5.1 direction finding solutions to become available, people are trying to refine traditional locating methods to gain more accuracy. Baichuan Huang, Jingbin Liu, Wei Sun and Fan Yang have a research paper on A Robust Indoor Positioning Method based on Bluetooth Low Energy with Separate Channel Information.

They have observed that the stability of the received Bluetooth signal strength RSSI depends on which Channel 37, 38 or 39 the signal is being received on. This is because the channels slightly overlap the WiFi channels and there can be other Bluetooth devices also using the same channels.

The method analyses the channels over time and chooses those it thinks has least interference and most stable RSSI. This reduces the positioning error by 0.2m, to 2.2m, at a distance of 3.6m.

Read about Determining Location Using Bluetooth Beacons

Using AI Machine Learning on Bluetooth RSSI to Obtain Location

In our previous post on iBeacon Microlocation Accuracy we explained how distance can be inferred from the received signal strength indicator (RSSI). We also explained how techniques such as trilateration, calibration and angle of arrival (AoA) can be used to improve location accuracy.

There’s new research presented at The 17th Annual International Conference on Mobile Systems, Applications, and Services (MobiSys ’19) by researchers from Nagoya University, Japan that looks into the use of AI machine learning to process Bluetooth RSSI to obtain location.

Their study was based on a large-scale exhibition where they placed scanning devices:

They implemented a LSTM neural network and experimented with the number of layers:

They obtained best results with the simplest machine learning model with only 1 LSTM:

As is often the case with machine learning, more complex models over-learn on the training data such that they don’t work with new, subsequent data. Simple models are more generic and work not just with the training data but with new scenarios.

The researchers managed to achieve an accuracy of 2.44m at 75 percentile – whatever that means – we guess in 75% of the cases. 2.44m is ok and compares well to accuracies of about 1.5m within a shorter range confined space and 5m at the longer distances achieved using conventional methods. As with all machine learning, further parameter tuning usually improves the accuracy further but can take along time and effort. It’s our experience that using other types of RNN in conjunction with LSTM can also improve accuracy.

If you want to view the research paper you need to download all the papers from the conference (zip) and extract p558-uranoA.pdf. Some of the other papers also make interesting, if not directly relevant, reading.

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Using iBeacon to Assess Elderly Frailty

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)

Bluetooth LE on the Factory Floor

Connected factory implementations require a large number of connected assets for condition-based monitoring, asset tracking, inventory (stock) management or for building automation. Bluetooth is a secure, low cost, low power and reliable solution suitable for use in connected factories. In this post, we examine the reasoning behind some out-of-date thinking on industrial wireless, uncover the real problems in factories and provide some explanations how Bluetooth overcomes these challenges.

Operations teams are usually very sceptical about industrial wireless. They have usually tried proprietary industry solutions using wireless with mixed results. They might have experienced how connections, such as WiFi, can become unreliable in the more electrically noisy areas of factories. The usual approach is to use cable. However, this can become expensive and time consuming. Using cable isn’t possible when assets are moving and becomes impractical when the number of connected items becomes large as in the case of connected factories. As we shall explain, Bluetooth is intrinsically more reliable than WiFi even through they share the same 2.4GHz frequency band.

There’s usually lots of electrical noise in an industrial environment that tends to be one of two types:

  • Electromagnetic radiation emitted by equipment. This typically includes engines, charging devices, frequency converters, power converters and welding. It also includes transmissions from other radio equipment such as DECT phones and mobile telephones.
  • Multipath propagation which is reflection of radio signals off, usually metallic, surfaces and received again slightly later.

It’s important to understand how Bluetooth and other competing technologies react to these types of interference. There’s a useful study (pdf) by Linköping University, Swedish Defence Research Agency (FOI) and the University of Gävle on noise industrial environments.

Noise in industrial environments tends to follow the following spectral pattern:

Electrical noise spectrum

There’s usually lots of electrical noise up to about 500MHz. This means wireless communication using lower frequencies, such as two way radio, exhibits a lot of noise. Pertinently, several wireless solutions for industrial applications use frequencies in the 30–80 MHz and 400–450 MHz bands. Bluetooth’s and WiFi’s 2.4GHz frequency is well above 500MHz so exhibits better reliability than some industrial wireless solutions. Incidentally, in the above charts, the peaks around 900 MHz and 1800 MHz mobile phone signals and 1880–1890 MHz come from DECT phones.

The magnitude of multipath propoagation depends on the environment. It’s greatest in buildings having highly reflective, usually metallic, floors, walls and roofs. If you imagine a radio signal wave being received and then received again nanoseconds later, you can imagine how both the amplitude (peaks) and the phase of the received signal becomes distorted over time. Bluetooth uses Adaptive Frequency Hopping (AFH) which means that packets transferred consecutively in time do not use the same frequency. Thus each packet acts like a single narrowband transmission and there’s less affect of one packet on the next one. However, what is more affected is amplitude which manifests itself as the received overall signal strength (RSSI). RSSI is often used by Bluetooth applications to infer distance from sender to receiver. We will mention mitigations for varying RSSI later.

It’s important to consider what happens when there is electrical noise. It turns out that technologies invented to ensure reliable transmission behave much less well in noisy situations. One such technique is carrier sense multiple access (CSMA), used by WLAN (WiFi), that listens to the channel before transmitting and waits until a free channel is observed. CSMA and automatic auto repeat (ARQ) also have re-transmission mechanisms. The retrying can also incur significant extra traffic that can overwhelm the communication in noisy environment. Bluetooth doesn’t use such schemes.

The previously mentioned research classifies different wireless technologies according to the delay when used in noisy environments:

Bluetooth (and WISA) is a good choice for noisier environments. It’s particularly suited for applications with lower data rates and sending at periodic intervals.

A final consideration is interference between Bluetooth and other technologies, such as WiFi, that use similar 2.4GHz frequencies. As mentioned in a previous post, there’s negligible overlap between Bluetooth and WiFi channel frequencies.

In summary, Bluetooth is more suited to electrically noisy environments and also offers low cost, low power and secure wireless communication.

These conclusions correlate well with our own empirical observations. We have found that Bluetooth advertising is still received in environments we have measured, using a RF spectrum analyser, to be electrically noisy around 2.4GHz . We believe this is because Bluetooth advertising hops across three frequencies such that there’s less likelihood of noise on all three. Advertising is also very short, typically taking 1 or 2 ms, making the coincidence of the noise and the advertising less likely than would be the case of a longer transmission.

It has been our experience that solutions using Bluetooth advertising are more reliable than those using Bluetooth (GATT) connections, especially in noisy environments when it’s difficult to maintain the chatty protocol of a connection over a long time period. In noisy situations, advertising is usually seen on a future transmit/scan if the first advertising is lost. By coincidence or design, Bluetooth Mesh is built on communication via advertising rather than connection and for this reason is also reliable on the factory floor.

However, using Bluetooth isn’t a silver bullet. There are situations where factories, or more usually parts of factories, have reflective surfaces or unusual radio frequency (RF) characteristics stretching into unforeseen frequencies. Poorer performing WiFi also needs to be considered in context of choosing between Ethernet and WiFi gateways and Bluetooth mesh.

It’s important to do a site survey including RF spectral analysis. This will uncover nuances of particular critical locations or coverage that can drive subsequent hardware planning. It can also feed into requirements for software processing, for example particular settings for processing within a real time locating system (RTLS) to cater for more varying RSSI.

Consider a Feasibility Study if you need expert help.

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