Comparative analysis of the Bluetooth Low-Energy indoor positioning systems

2015 ◽  
Author(s):  
Danijel Cabarkapa ◽  
Ivana Grujic ◽  
Petar Pavlovic
Keyword(s):  
Comparative Analysis ◽  
Indoor Positioning ◽  
Low Energy ◽  
Bluetooth Low Energy ◽  
Positioning Systems

scholarly journals A STATISTICAL ANALYSIS ON THE SYSTEM PERFORMANCE OF A BLUETOOTH LOW ENERGY INDOOR POSITIONING SYSTEM IN A 3D ENVIRONMENT

2017 ◽  
Vol IV-2/W4 ◽  
pp. 319-326 ◽  
Author(s):  
G. G. Haagmans ◽  
S. Verhagen ◽  
R. L. Voûte ◽  
E. Verbree
Keyword(s):  
System Performance ◽  
Signal Propagation ◽  
Indoor Positioning ◽  
Low Energy ◽  
Geometric Configuration ◽  
Alternative Methods ◽  
Bluetooth Low Energy ◽  
Positioning Systems ◽  
Definition Of ◽  
A Performance

Since GPS tends to fail for indoor positioning purposes, alternative methods like indoor positioning systems (IPS) based on Bluetooth low energy (BLE) are developing rapidly. Generally, IPS are deployed in environments covered with obstacles such as furniture, walls, people and electronics influencing the signal propagation. The major factor influencing the system performance and to acquire optimal positioning results is the geometry of the beacons. The geometry of the beacons is limited to the available infrastructure that can be deployed (number of beacons, basestations and tags), which leads to the following challenge: Given a limited number of beacons, where should they be placed in a specified indoor environment, such that the geometry contributes to optimal positioning results? This paper aims to propose a statistical model that is able to select the optimal configuration that satisfies the user requirements in terms of precision. The model requires the definition of a chosen 3D space (in our case 7 × 10 × 6 meter), number of beacons, possible user tag locations and a performance threshold (e.g. required precision). For any given set of beacon and receiver locations, the precision, internal- and external reliability can be determined on forehand. As validation, the modeled precision has been compared with observed precision results. The measurements have been performed with an IPS of BlooLoc at a chosen set of user tag locations for a given geometric configuration. Eventually, the model is able to select the optimal geometric configuration out of millions of possible configurations based on a performance threshold (e.g. required precision).

scholarly journals Beacon-Related Param of Bluetooth Low Energy: Development of a Semi-Automatic System to Study Their Impact on Indoor Positioning Systems

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3087 ◽  
Author(s):  
de Blasio ◽  
Rodríguez-Rodríguez ◽  
García ◽  
Quesada-Arencibia
Keyword(s):  
Data Collection ◽  
Indoor Positioning ◽  
Low Energy ◽  
Positioning Accuracy ◽  
Automatic Data ◽  
Bluetooth Low Energy ◽  
Positioning Systems ◽  
Main Challenge ◽  
Automatic Data Collection ◽  
On Chip

Indoor positioning systems (IPS) are used to locate people or objects in environments where the global positioning system (GPS) fails. The commitment to make bluetooth low energy (BLE) technology the leader in IPS and their applications is clear: Since 2009, the Bluetooth Special Interest Group (SIG) has released several improved versions. BLE offers many advantages for IPS, e.g., their emitters or beacons are easily deployable, have low power consumption, give a high positioning accuracy and can provide advanced services to users. Fingerprinting is a popular indoor positioning algorithm that is based on the received signal strength (RSS); however, its main drawbacks are that data collection is a time-consuming and labor-intensive process and its main challenge is that positioning accuracy is affected by various factors. The purpose of this work was to develop a semi-automatic data collection support system in a BLE fingerprinting-based IPS to: (1) Streamline and shorten the data collection process, (2) carry out impact studies by protocol and channel on the static positioning accuracy related to configuration param of the beacons, such as transmission power (Tx) and the advertising interval (A), and their number and geometric distribution. With two types of systems-on-chip (SoCs) integrated in Bluetooth 5 beacons and in two different environments, our results showed that on average in the three BLE advertising channels, the configuration of the highest Tx (+4 dBm) in the beacons produced the best accuracy results. However, the lowest Tx (−20 dBm) did not worsen them excessively (only 11.8%). In addition, in both scenarios, when lowering the density of beacons by around 42.7%–50%, the error increase was only around 8%–9.2%.

scholarly journals Indoor positioning utilizing bluetooth low energy (BLE) RSSI on LoRa system

2021 ◽  
Vol 23 (2) ◽  
pp. 927
Author(s):  
Kavetha Suseenthiran ◽  
Abd Shukur Ja'afar ◽  
Ku Wei Heng ◽  
Mohamad Zoinol Abidin Abd Aziz ◽  
Azmi Awang Md Isa ◽  
...  
Keyword(s):  
Particle Filter ◽  
Multipath Fading ◽  
Indoor Positioning ◽  
Low Energy ◽  
Cumulative Distribution ◽  
Receive Signal Strength Indicator ◽  
Positioning System ◽  
Coverage Problem ◽  
Bluetooth Low Energy ◽  
Positioning Systems

Indoor positioning systems has become popular in this era where it is a network of devices used to locate people or object especially in indoor environment instead of satellite-based positioning. The satellite-based positioning global positioning system (GPS) signal is affected and loss incurred by the wall of the building causes the GPS lack of precision which leads to large positioning error. As a solution to the indoor area coverage problem, an indoor positioning based on bluetooth low energy (BLE) and long range (LoRa) system utilising the receive signal strength indicator (RSSI) is proposed, designed and tested. In this project, the prototype of indoor positioning system is built using node MCU ESP 32, LoRa nodes and BLE beacons. The node MCU ESP 32 will collect RSSI data from each BLE beacons that deployed at decided position around the area. Then, linear regression algorithm will be used in distance estimation. Next, particle filteris implemented to overcome the multipath fading effect and the trilateration technique is applied to estimate the user’s location. The estimated location is compared to the actual position to analyze the root mean square error (RMSE) and cumulative distribution function (CDF). Based on the experiment result, implementing the particle filter reduces the error of location accuracy. The particle filter achieves accuracy with 90% of the time the location error is lower than 2.6 meters.

scholarly journals A Low Cost Indoor Positioning System Using Bluetooth Low Energy

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 136858-136871
Author(s):  
Lu Bai ◽  
Fabio Ciravegna ◽  
Raymond Bond ◽  
Maurice Mulvenna
Keyword(s):  
Low Cost ◽  
Indoor Positioning ◽  
Low Energy ◽  
Positioning System ◽  
Bluetooth Low Energy ◽  
Indoor Positioning System

scholarly journals Integrity Monitoring for Bluetooth Low Energy Beacons RSSI Based Indoor Positioning

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 215173-215191
Author(s):  
Haiyun Yao ◽  
Hong Shu ◽  
Xinlian Liang ◽  
Hongji Yan ◽  
Hongxing Sun
Keyword(s):  
Indoor Positioning ◽  
Low Energy ◽  
Integrity Monitoring ◽  
Bluetooth Low Energy

scholarly journals An Ensemble Filter for Indoor Positioning in a Retail Store Using Bluetooth Low Energy Beacons

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4550 ◽  
Author(s):  
Vasilis Stavrou ◽  
Cleopatra Bardaki ◽  
Dimitris Papakyriakopoulos ◽  
Katerina Pramatari
Keyword(s):  
Random Forest ◽  
Indoor Localization ◽  
Indoor Positioning ◽  
Retail Store ◽  
Low Energy ◽  
Location Based Services ◽  
Shopping Behavior ◽  
Multiple Sources ◽  
Bluetooth Low Energy ◽  
Product Categories

This paper has developed and deployed a Bluetooth Low Energy (BLE) beacon-based indoor positioning system in a two-floor retail store. The ultimate purpose of this study was to compare the different indoor positioning techniques towards achieving efficient position determination of moving customers in the retail store. The innovation of this research lies in its context (the retail store) and the fact that this is not a laboratory, controlled experiment. Retail stores are challenging environments with multiple sources of noise (e.g., shoppers’ moving) that impede indoor localization. To the best of the authors’ knowledge, this is the first work concerning indoor localization of consumers in a real retail store. This study proposes an ensemble filter with lower absolute mean and root mean squared errors than the random forest. Moreover, the localization error is approximately 2 m, while for the random forest, it is 2.5 m. In retail environments, even a 0.5 m deviation is significant because consumers may be positioned in front of different store shelves and, thus, different product categories. The more accurate the consumer localization, the more accurate and rich insights on the customers’ shopping behavior. Consequently, retailers can offer more effective customer location-based services (e.g., personalized offers) and, overall, better consumer localization can improve decision making in retailing.

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