Different Approaches to Indoor Localization Based on Bluetooth Low Energy Beacons and Wi-Fi

Bruha, Radek; Kriz, Pavel

doi:10.1007/978-3-319-67077-5_29

Cited by 2 publications

(1 citation statement)

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“…It uses the existing infrastructure of an indoor environment and compares offline fingerprint RSSI measurement with an online RSSI fingerprint to estimate the location of the user. A combination of Wi-Fi and Bluetooth radio technology-based approach is proposed in [13]. It uses KNN with particle filter and shows that indoor estimation error changes by changing the target area.…”

Section: Related Workmentioning

confidence: 99%

Cuckoo Search-based SVM (CS-SVM) Model for Real-Time Indoor Position Estimation in IoT Networks

Khan

Bangash

et al. 2021

Security and Communication Networks

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Internet of Things (IoT), an emerging technology, is becoming an essential part of today’s world. Machine learning (ML) algorithms play an important role in various applications of IoT. For decades, the location information has been extremely useful for humans to navigate both in outdoor and indoor environments. Wi-Fi access point-based indoor positioning systems get more popularity, as it avoids extra calibration expenses. The fingerprinting technique is preferred in an indoor environment as it does not require a signal’s Line of Sight (LoS). It consists of two phases: offline and online phase. In the offline phase, the Wi-Fi RSSI radio map of the site is stored in a database, and in the online phase, the object is localized using the offline database. To avoid the radio map construction which is expensive in terms of labor, time, and cost, machine learning techniques may be used. In this research work, we proposed a hybrid technique using Cuckoo Search-based Support Vector Machine (CS-SVM) for real-time position estimation. Cuckoo search is a nature-inspired optimization algorithm, which solves the problem of slow convergence rate and local minima of other similar algorithms. Wi-Fi RSSI fingerprint dataset of UCI repository having seven classes is used for simulation purposes. The dataset is preprocessed by min-max normalization to increase accuracy and reduce computational speed. The proposed model is simulated using MATLAB and evaluated in terms of accuracy, precision, and recall with K-nearest neighbor (KNN) and support vector machine (SVM). Moreover, the simulation results show that the proposed model achieves high accuracy of 99.87%.

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Section: Related Workmentioning

confidence: 99%

Cuckoo Search-based SVM (CS-SVM) Model for Real-Time Indoor Position Estimation in IoT Networks

Khan

Bangash

et al. 2021

Security and Communication Networks

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Possibilities of Using Bluetooth Low Energy Beacon Technology to Locate Objects Internally: A Case Study

Ližbetín

Pečman

2023

Technologies

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The developments that are occurring in relation to Industry 4.0 are making it possible to automate a huge number of production activities. Automation includes the possibility of automatically identifying individual elements of a system. One of the options for doing this involves the use of Bluetooth Low Energy technology. The system’s advantages lie in its wide availability, economic simplicity, ability to design individual system elements, and overall system architecture. The system applied in the case study presented in this article consisted of beacons from Accent Systems and identification gateways based on the Raspberry Pi Zero W device. During several hours of testing, the functionality and reliability of all system components was demonstrated. The measurements showed that the system was able to determine the distance from a gate in line of sight with 94% accuracy. With regards to indirect visibility, when a metal crate was used to shield the beacon from the gateway, the system was able to determine the exact distance only 22% of the time. However, the variance between the actual and measured values was found to be small, therefore proving sufficient for most use cases. The major advantage of Bluetooth Low Energy beacons, and Bluetooth technology in general, is its massive ubiquity in the market. Since the Bluetooth module is part of every smartphone, this system can be made available to a wide range of users.

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Different Approaches to Indoor Localization Based on Bluetooth Low Energy Beacons and Wi-Fi

Cited by 2 publications

References 10 publications

Cuckoo Search-based SVM (CS-SVM) Model for Real-Time Indoor Position Estimation in IoT Networks

Cuckoo Search-based SVM (CS-SVM) Model for Real-Time Indoor Position Estimation in IoT Networks

Possibilities of Using Bluetooth Low Energy Beacon Technology to Locate Objects Internally: A Case Study

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