Technological Viability Assessment of Bluetooth Low Energy Technology for Indoor Localization

2018 ◽  
Vol 32 (5) ◽  
pp. 04018034 ◽  
Author(s):  
Fatih Topak ◽  
Mehmet Koray Pekeriçli ◽  
Ali Murat Tanyer
Keyword(s):  
Indoor Localization ◽  
Low Energy ◽  
Energy Technology ◽  
Viability Assessment ◽  
Bluetooth Low Energy

A Method of Indoor Localization Based on BLE4.0 Protocol of Using Beacons

2015 ◽  
Vol 740 ◽  
pp. 765-768
Author(s):  
He Shan Bian ◽  
Zhao Hui Li ◽  
Fang Zhao
Keyword(s):  
Kalman Filter ◽  
Power Consumption ◽  
Indoor Localization ◽  
Signal Strength ◽  
Mobile Terminal ◽  
High Accuracy ◽  
Low Energy ◽  
Energy Technology ◽  
Bluetooth Low Energy ◽  
Distance Model

In this paper we discuss our attempt to solve the problem of HAIP(High Accuracy Indoor Position) by using BLE4.0(Bluetooth Low Energy). According to previous research, Wi-Fi Positioning has mainly faced some big challenges. Accuracy is deteriorated by directional handset antennas, which affect the relative AP signal strength; Practical maximum reachable accuracy is 3-10 meters depending on environment; Wi-Fi activities is a big consumption of battery on Mobile Terminal; Now, The Bluetooth Low Energy technology is getting mature. In this paper, we use Bluetooth low energy on iOS device to solve the problem of high accuracy indoor position. In the data-preprocessing step, we use Kalman filter to process the RSSI. In the transition step of RSSI to Distance, we propose a novelty method to adjust the parameters of Log-Distance model dynamically and adaptively according to diagonal beacons’ measurement. We implement our technique and algorithm on iOS device with iOS7.0 SDK. The result shows that error reduced to 0.5m-1.2m range depending on the distance, achieved smaller power consumption.

scholarly journals Improving Indoor Localization Using Bluetooth Low Energy Beacons

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Pavel Kriz ◽  
Filip Maly ◽  
Tomas Kozel
Keyword(s):  
Mobile Devices ◽  
Distributed System ◽  
Indoor Localization ◽  
Low Energy ◽  
Energy Technology ◽  
Brand Name ◽  
Basic Principles ◽  
Bluetooth Low Energy ◽  
Stationary Objects ◽  
Android Operating System

The paper describes basic principles of a radio-based indoor localization and focuses on the improvement of its results with the aid of a new Bluetooth Low Energy technology. The advantage of this technology lies in its support by contemporary mobile devices, especially by smartphones and tablets. We have implemented a distributed system for collecting radio fingerprints by mobile devices with the Android operating system. This system enables volunteers to create radio-maps and update them continuously. New Bluetooth Low Energy transmitters (Apple uses its “iBeacon” brand name for these devices) have been installed on the floor of the building in addition to existing WiFi access points. The localization of stationary objects based on WiFi, Bluetooth Low Energy, and their combination has been evaluated using the data measured during the experiment in the building. Several configurations of the transmitters’ arrangement, several ways of combination of the data from both technologies, and other parameters influencing the accuracy of the stationary localization have been tested.

The methodology of using bluetooth technology to solve the problems of identifying and determining the position of objects.

2021 ◽  
Vol 5 ◽  
Author(s):  
P.V. Stepanov ◽  
Keyword(s):  
Information System ◽  
Low Energy ◽  
Comparison Of Methods ◽  
Energy Technology ◽  
Adaptive Logic ◽  
Selective Activation ◽  
Bluetooth Low Energy ◽  
Intelligent Processing ◽  
Positioning Algorithm

The article analyzes the possibility of using Bluetooth Low Energy technology to solve the problem of identifying and positioning objects. The analysis and comparison of methods for solving the problem of navigation in the room and the problems of identification and positioning of objects is carried out. The features in the methodology, the positioning algorithm and the architecture of the information system are revealed. An adaptive logic for the operation of labels is proposed. The methods of intelligent processing of signals from labels are considered. The method of selective activation of labels and methods of limiting the activation and signal reception zones are described.

scholarly journals Indoor Bluetooth Low Energy Dataset for Localization, Tracking, Occupancy, and Social Interaction

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4462 ◽  
Author(s):  
Paolo Baronti ◽  
Paolo Barsocchi ◽  
Stefano Chessa ◽  
Fabio Mavilia ◽  
Filippo Palumbo
Keyword(s):  
Social Interaction ◽  
Indoor Localization ◽  
Meaningful Use ◽  
Research Area ◽  
Low Energy ◽  
Bluetooth Low Energy ◽  
Novel Technologies ◽  
Wide Availability ◽  
Low Costs ◽  
Open Datasets

Indoor localization has become a mature research area, but further scientific developments are limited due to the lack of open datasets and corresponding frameworks suitable to compare and evaluate specialized localization solutions. Although several competitions provide datasets and environments for comparing different solutions, they hardly consider novel technologies such as Bluetooth Low Energy (BLE), which is gaining more and more importance in indoor localization due to its wide availability in personal and environmental devices and to its low costs and flexibility. This paper contributes to cover this gap by: (i) presenting a new indoor BLE dataset; (ii) reviewing several, meaningful use cases in different application scenarios; and (iii) discussing alternative uses of the dataset in the evaluation of different positioning and navigation applications, namely localization, tracking, occupancy and social interaction.

Bluetooth Low-Energy-Based Applications

2018 ◽  
pp. 95-112
Author(s):  
Smita Sanjay Ambarkar ◽  
Rakhi Dattatraya Akhare
Keyword(s):  
Indoor Localization ◽  
Low Energy ◽  
Smart Devices ◽  
The Internet ◽  
Bluetooth Low Energy ◽  
Iot Applications ◽  
Proximity Detection ◽  
Battery Power ◽  
Commercial Market ◽  
The Internet Of Things

This chapter focuses on the comprehensive contents of various applications and principles related to Bluetooth low energy (BLE). The internet of things (IoT) applications like indoor localization, proximity detection problem by using Bluetooth low energy, and enhancing the sales in the commercial market by using BLE have the same database requirement and common implementation idea. The real-world applications are complex and require intensive computation. These computations should take less time, cost, and battery power. The chapter mainly focuses on the usage of BLE beacons for indoor localization. The motive behind the study of BLE devices is that it is supported by mobile smart devices that augment its application exponentially.

STUPEFY: Set-Valued Box Particle Filtering for Bluetooth Low Energy-Based Indoor Localization

2019 ◽  
Vol 26 (12) ◽  
pp. 1773-1777 ◽  
Author(s):  
Parvin Malekzadeh ◽  
Arash Mohammadi ◽  
Mihai Barbulescu ◽  
Konstantinos N. Plataniotis
Keyword(s):  
Particle Filtering ◽  
Indoor Localization ◽  
Low Energy ◽  
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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