Accurate Gridless Indoor Localization Based on Multiple Bluetooth Beacons and Machine Learning

Kotrotsios, Konstantinos; Orphanoudakis, Theofanis

doi:10.1109/icara51699.2021.9376476

Cited by 14 publications

(11 citation statements)

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“…The improved method is shown to reduce the positioning error, while being independent of a preconfigured grid design of the interior space. Specifically, the results in [18] show that the system has an average accuracy of 69.58 cm and can predict the location with less than a meter accuracy in 80.55% of all cases and with an accuracy of less than 1.5 m in 93.92% of the cases.…”

Section: Related Work and Requirement Analysismentioning

confidence: 99%

“…An average positioning accuracy of around 70 cm and an inference with an error less than 1 m in 72% of the cases were achieved. In [18], an improved method was proposed to exploit the signals of all beacons within range. The enhanced method improves accuracy by developing independent ML models for each of the received RSSI signals to infer the distance of the device from the beacon and concurrently implementing a geometric approximation of the final position from these distance measurements.…”

Section: Related Work and Requirement Analysismentioning

confidence: 99%

“…As mentioned in the previous section, in this work, we extended the design proposed initially in [18] in two ways; first, we improved accuracy through an enhanced ML model design, investigating the relation between the model complexity, accuracy and cost; then, we applied a second-level optimization to achieve the porting of our implementation in an embedded system in the small form. In the following section, we described in more detail the proposed method and the process of the evaluation of different NN architectures.…”

Section: Related Work and Requirement Analysismentioning

confidence: 99%

“…Based on the above remarks, we initially investigated the application of ML techniques to infer the position of a BLE device moving within a fixed grid of an interior space covered by Bluetooth signals of beacons installed in fixed locations in [17]. In [18], an improved method was proposed to exploit the signals of different beacons in order to improve accuracy by developing independent ML models for each of the received RSSI signals to infer the distance of the device from the beacon and concurrently implementing a geometric approximation of the final position from these distance measurements. The improved method was shown to reduce the positioning error, while being independent of a preconfigured grid design of the interior space.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Design Space Exploration of a Multi-Model AI-Based Indoor Localization System

Kotrotsios

Fanariotis

Leligou

et al. 2022

Sensors

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In this paper, we present the results of a performance evaluation and optimization process of an indoor positioning system (IPS) designed to operate on portable as well as miniaturized embedded systems. The proposed method uses the Received Signal Strength Indicator (RSSI) values from multiple Bluetooth Low-Energy (BLE) beacons scattered around interior spaces. The beacon signals were received from the user devices and processed through an RSSI filter and a group of machine learning (ML) models, in an arrangement of one model per detected node. Finally, a multilateration problem was solved using as an input the inferred distances from the advertising nodes and returning the final position approximation. In this work, we first presented the evaluation of different ML models for inferring the distance between the devices and the installed beacons by applying different optimization algorithms. Then, we presented model reduction methods to implement the optimized algorithm on the embedded system by appropriately adapting it to its constraint resources and compared the results, demonstrating the efficiency of the proposed method.

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Section: Related Work and Requirement Analysismentioning

confidence: 99%

Section: Related Work and Requirement Analysismentioning

confidence: 99%

Section: Related Work and Requirement Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Design Space Exploration of a Multi-Model AI-Based Indoor Localization System

Kotrotsios

Fanariotis

Leligou

et al. 2022

Sensors

Self Cite

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show abstract

“…This research thus focuses on applying the BLE technology to develop indoor localization methods. Many BLE indoor localization methods [ 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ] have been proposed in the literature. Among them, BLE fingerprint-based methods have comparably good localization accuracy.…”

Section: Introductionmentioning

confidence: 99%

Fingerprint Feature Extraction for Indoor Localization

Jiang

Subakti

Liang

2021

Sensors

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This paper proposes a fingerprint-based indoor localization method, named FPFE (fingerprint feature extraction), to locate a target device (TD) whose location is unknown. Bluetooth low energy (BLE) beacon nodes (BNs) are deployed in the localization area to emit beacon packets periodically. The received signal strength indication (RSSI) values of beacon packets sent by various BNs are measured at different reference points (RPs) and saved as RPs’ fingerprints in a database. For the purpose of localization, the TD also obtains its fingerprint by measuring the beacon packet RSSI values for various BNs. FPFE then applies either the autoencoder (AE) or principal component analysis (PCA) to extract fingerprint features. It then measures the similarity between the features of PRs and the TD with the Minkowski distance. Afterwards, k RPs associated with the k smallest Minkowski distances are selected to estimate the TD’s location. Experiments are conducted to evaluate the localization error of FPFE. The experimental results show that FPFE achieves an average error of 0.68 m, which is better than those of other related BLE fingerprint-based indoor localization methods.

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