A study of indoor positioning systems using iBeacons with different transmission power levels

Lu, Ta-Te; Yeh, Sheng‐Cheng; Chen, Chia‐Yen

doi:10.1080/02533839.2017.1356692

Cited by 7 publications

(8 citation statements)

References 9 publications

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“…In this section, we shall compare our results with results of the two relevant works i.e., [24,28], present in the literature. The best result reported in [24] is a localization accuracy of 2 m with matching precision (localization precision) of approximately 86.74% by using 5 BLE anchors with Mode-KNN based signal pattern matching method.…”

Section: Experiments and Resultsmentioning

confidence: 87%

“…The best result reported in [24] is a localization accuracy of 2 m with matching precision (localization precision) of approximately 86.74% by using 5 BLE anchors with Mode-KNN based signal pattern matching method. Whereas, the best results reported in [28] is a localization accuracy of 2.8 m only, achieved by using 3 BLE anchors with minimum signal replacement based signal patter matching method.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…Recently, multiple transmission power level is used with filter-based approach. The authors in [28] explored different transmission power levels (i.e., 4 dBm, 0 dBm, −4 dBm and −8 dBm) and studied their effect on RSS with distance. The authors used a K-Nearest Neighbour (KNN) based fingerprinting technique and four different filters to smooth the RSS.…”

Section: Related Workmentioning

confidence: 99%

“…The works presented in [24,28] motivated us to conduct a comprehensive evaluation of a BLE based WILS by exploring the multiple power transmission feature for target localization in an indoor environment. In [56], we analyzed a BLE based WILS with multiple transmission power levels and used a trilateration algorithm for target localization.…”

Section: Related Workmentioning

confidence: 99%

“…There are other factors such as the effects of the non-linear amplifiers of the BLE devices, the antenna gain variation of the signal transmitters and different kinds of antennae used by various vendors [27]. But their effect is of constant proportion in comparison to the effects of fast fading noise [28], the change in transmission power levels [29] and interference level of multi-paths [23,24]. Regardless of the gain, antennae and device heterogeneity, the random variation persists in every BLE signal mainly due to its low energy characteristic.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Evaluating the Implications of Varying Bluetooth Low Energy (BLE) Transmission Power Levels on Wireless Indoor Localization Accuracy and Precision

Qureshi

Umair

Hancke

2019

Sensors

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Bluetooth Low Energy (BLE) based Wireless Indoor Localization System (WILS) with high localization accuracy and high localization precision is a key requirement in enabling the Internet of Things (IoT) in today’s applications. In this paper, we investigated the effect of BLE signal variations on indoor localization caused by the change in BLE transmission power levels. This issue is not often discussed as most of the works on localization algorithms use the highest power levels but has important practical implications for energy efficiency, e.g., if a designer would like to trade-off localization performance and node lifetime. To analyze the impact, we used the established trilateration based localization model with two methods i.e., Centroid Approximation (CA) and Minimum Mean Square Error (MMSE). We observed that trilateration based localization with MMSE method outperforms the CA method. We further investigated the use of two filters i.e., Low Pass Filter (LPF) and Kalman Filter (KF) and evaluated their effects in terms of mitigating the random variations from BLE signal. In comparison to non-filter based approach, we observed a great improvement in localization accuracy and localization precision with a filter-based approach. Furthermore, in comparison to LPF based trilateration localization with CA, the performance of a KF based trilateration localization with MMSE is far better. An average of 1 m improvement in localization accuracy and approximately 50% improvement in localization precision is observed by using KF in trilateration based localization model with the MMSE method. In conclusion, with KF in trilateration based localization model with MMSE method effectively eliminates random variations in BLE RSS with multiple transmission power levels and thus results in a BLE based WILS with high accuracy and high precision.

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Section: Experiments and Resultsmentioning

confidence: 87%

Section: Experiments and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evaluating the Implications of Varying Bluetooth Low Energy (BLE) Transmission Power Levels on Wireless Indoor Localization Accuracy and Precision

Qureshi

Umair

Hancke

2019

Sensors

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A Protocol-Channel-Based Indoor Positioning Performance Study for Bluetooth Low Energy

et al. 2018

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Comparative Study of Supervised Learning and Metaheuristic Algorithms for the Development of Bluetooth-Based Indoor Localization Mechanisms

et al. 2019

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The development of the Internet of Things (IoT) benefits from 1) the connections between devices equipped with multiple sensors; 2) wireless networks and; 3) processing and analysis of the gathered data. The growing interest in the use of IoT technologies has led to the development of numerous diverse applications, many of which are based on the knowledge of the end user's location and profile. This paper investigates the characterization of Bluetooth signals behavior using 12 different supervised learning algorithms as a first step toward the development of fingerprint-based localization mechanisms. We then explore the use of metaheuristics to determine the best radio power transmission setting evaluated in terms of accuracy and mean error of the localization mechanism. We further tune-up the supervised algorithm hyperparameters. A comparative evaluation of the 12 supervised learning and two metaheuristics algorithms under two different system parameter settings provide valuable insights into the use and capabilities of the various algorithms on the development of indoor localization mechanisms.

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A study of indoor positioning systems using iBeacons with different transmission power levels

Cited by 7 publications

References 9 publications

Evaluating the Implications of Varying Bluetooth Low Energy (BLE) Transmission Power Levels on Wireless Indoor Localization Accuracy and Precision

Evaluating the Implications of Varying Bluetooth Low Energy (BLE) Transmission Power Levels on Wireless Indoor Localization Accuracy and Precision

A Protocol-Channel-Based Indoor Positioning Performance Study for Bluetooth Low Energy

Comparative Study of Supervised Learning and Metaheuristic Algorithms for the Development of Bluetooth-Based Indoor Localization Mechanisms

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