Towards an indoor navigation system using Bluetooth Low Energy Beacons

Campana, Fernando; Pinargote, Adriano; Domínguez, Federico; Peláez, Enrique

doi:10.1109/etcm.2017.8247464

Cited by 17 publications

(25 citation statements)

References 8 publications

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“…The ML algorithms have been employed in several studies with the aim to improve the performance of localization techniques [ 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 ]. In the following text, we will make an overview of the state of the art to summarize the most popular techniques and their expected performance.…”

Section: Related Workmentioning

confidence: 99%

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Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning

Polák

Rozum

Slanina

et al. 2021

Sensors

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The fingerprinting technique is a popular approach to reveal location of persons, instruments or devices in an indoor environment. Typically based on signal strength measurement, a power level map is created first in the learning phase to align with measured values in the inference. Second, the location is determined by taking the point for which the recorded received power level is closest to the power level actually measured. The biggest limit of this technique is the reliability of power measurements, which may lack accuracy in many wireless systems. To this end, this work extends the power level measurement by using multiple anchors and multiple radio channels and, consequently, considers different approaches to aligning the actual measurements with the recorded values. The dataset is available online. This article focuses on the very popular radio technology Bluetooth Low Energy to explore the possible improvement of the system accuracy through different machine learning approaches. It shows how the accuracy–complexity trade-off influences the possible candidate algorithms on an example of three-channel Bluetooth received signal strength based fingerprinting in a one dimensional environment with four static anchors and in a two dimensional environment with the same set of anchors. We provide a literature survey to identify the machine learning algorithms applied in the literature to show that the studies available can not be compared directly. Then, we implement and analyze the performance of four most popular supervised learning techniques, namely k Nearest Neighbors, Support Vector Machines, Random Forest, and Artificial Neural Network. In our scenario, the most promising machine learning technique being the Random Forest with classification accuracy over 99%.

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Section: Related Workmentioning

confidence: 99%

“…Another study focused on an improved BLE fingerprinting based localization has been published in [ 24 ]. Here, the authors compare the performance of Random Forest and Naive Bayes in a relatively large area (whole building floor—rooms with different dimensions and a hall) with a dense deployment of 30 beacons.…”

Section: Related Workmentioning

confidence: 99%

Received Signal Strength Fingerprinting-Based Indoor Location Estimation Employing Machine Learning

Polák

Rozum

Slanina

et al. 2021

Sensors

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“…There are many authors who have explored traffic problems by capturing Bluetooth signals, such as Reiff [ 6 ], who placed a Bluetooth sensor on a section of road to track the travel path of a vehicle and obtained the OD distribution of the road network at a low cost. Campana [ 7 ] applied Bluetooth to indoor navigation. Abedi [ 8 , 9 ] reported that a Wi-Fi probe could detect a larger volume of sample data than Bluetooth and proposed that the Wi-Fi probe data be used to measure the travel time and motion characteristics of pedestrians and bicycles.…”

Section: Introductionmentioning

confidence: 99%

Estimating Bus Cross-Sectional Flow Based on Machine Learning Algorithm Combined with Wi-Fi Probe Technology

Chen

Lai

2021

Sensors

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With expansion of city scale, the issue of public transport systems will become prominent. For single-swipe buses, the traditional method of obtaining section passenger flow is to rely on surveillance video identification or manual investigation. This paper adopts a new method: collecting wireless signals from mobile terminals inside and outside the bus by installing six Wi-Fi probes in the bus, and use machine learning algorithms to estimate passenger flow of the bus. Five features of signals were selected, and then the three machine learning algorithms of Random Forest, K-Nearest Neighbor, and Support Vector Machines were used to learn the data laws of signal features. Because the signal strength was affected by the complexity of the environment, a strain function was proposed, which varied with the degree of congestion in the bus. Finally, the error between the average of estimation result and the manual survey was 0.1338. Therefore, the method proposed is suitable for the passenger flow identification of single-swiping buses in small and medium-sized cities, which improves the operational efficiency of buses and reduces the waiting pressure of passengers during the morning and evening rush hours in the future.

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“…On the field of LBS, especially when numeric datasets are gathered, they can be utilized with machine learning algorithms since the field is recent and new information can be unveiled through this process [20]. Furthermore, most of classification and clustering algorithms seem to fit and bring additional value to RSSI measurements that can be translated into distance metrics and coordinates by the end of this process [21].…”

Section: Introductionmentioning

confidence: 99%