[Retracted] Low‐Power Indoor Positioning Algorithm Based on iBeacon Network

Zhang, Xiaona; Zhang, Shufang; Huai, Shuaiheng

doi:10.1155/2021/8475339

Cited by 7 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Bluetooth low energy (BLE) [11,12], photoelectric sensor [13], smartphone sensor [14], and ultra-wideband location [15]. BLE is easy to deploy, lightweight, low-cost, and lowpowered.…”

Section: Introductionmentioning

confidence: 99%

A K-nearest neighbor indoor fingerprint location method based on coarse positioning circular domain and the highest similarity threshold

Li¹,

Dai²,

He³

2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

There are two problems with traditional indoor fingerprint location methods. First, irrelevant fingerprints in a fingerprint database interfere with the matching phase, which leads to poor positioning accuracy and stability of positioning results, and second, there is a large amount of computational overhead in the matching phase. Therefore, this paper proposes a K-nearest neighbor indoor fingerprint location method based on coarse positioning circular domain and the highest similarity threshold. In this method, a circular domain is formed in a coarse positioning process to narrow the positioning range. It solves the problem of the interference of irrelevant fingerprints. At the same time, a fault-tolerant mechanism is introduced to adjust the circular domain dynamically to ensure that the coarse positioning circular domain contains high similarity reference points and improve the fault tolerance of the coarse positioning. This method consists of offline and online phases. In the offline phase, the values of the received signal strength from Bluetooth low energy are preprocessed using a Gaussian filter to construct a fingerprint database. In the online phase, irrelevant fingerprints are filtered out by using the coarse positioning method. The filtered fingerprints are then matched with a testing point by the K-nearest neighbor algorithm, and the weighted centroids of the nearest reference points are solved. Finally, the coordinate of the testing point is obtained. The experimental results show that this method can effectively improve indoor positioning accuracy when compared with the traditional K-nearest neighbor. The average positioning error of the proposed method is 0.844m.

show abstract

“…Bluetooth low energy (BLE) [11,12], photoelectric sensor [13], smartphone sensor [14], and ultra-wideband location [15]. BLE is easy to deploy, lightweight, low-cost, and lowpowered.…”

Section: Introductionmentioning

confidence: 99%

A K-nearest neighbor indoor fingerprint location method based on coarse positioning circular domain and the highest similarity threshold

Li¹,

Dai²,

He³

2022

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Meanwhile, the second and third phases involve collecting data and then indicating the user's location using a previous database. In addition, some systems that use BLE beacons include [12], [13]. Thus, it can be seen that iBeacon has been used in many studies before.…”

Section: Introductionmentioning

confidence: 99%

iBeacon-based indoor positioning system: from theory to practical deployment

Dinh-Thi

Duong

2023

IJECE

View full text Add to dashboard Cite

<span lang="EN-US">Developing an indoor positioning system became essential when global positioning system signals could not work well in indoor environments. Mobile positioning can be accomplished via many radio frequency technology such as Bluetooth low energy (BLE), wireless fidelity (Wi-Fi), ultra-wideband (UWB), and so on. With the pressing need for indoor positioning systems, we, in this work, present a deployment scheme for smartphone using Bluetooth iBeacons. Three main parts, hardware deployment, software deployment, and positioning accuracy assessment, are discussed carefully to find the optimal solution for a complete indoor positioning system. Our application and experimental results show that proposed solution is feasible and indoor positioning system is completely attainable.</span>

show abstract

“…In recent years, the technology of Internet of Things (IoT), in which sensors are connected to the network to transmit important sensor information, has gradually matured [ 1 , 2 , 3 , 4 , 5 , 6 , 7 ]. Moreover, supporting medical treatment by IoT is even more important [ 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Healthcare System Using Mathematical Model and Simulated Annealing to Hide Patients Data in the Low-Frequency Amplitude of ECG Signals

Hsu

Chen

Liu

et al. 2022

Sensors

View full text Add to dashboard Cite

Healthcare is an important medical topic in recent years. In this study, the novelty we propose is the intelligent healthcare system using an inequality-type optimization mathematical model with signal-to-noise ratio (SNR) and wavelet-domain low-frequency amplitude adjustment techniques to hide patients’ confidential data in their electrocardiogram (ECG) signals. The extraction of the hidden patient information also utilizes the low-frequency amplitude adjustment. The detailed steps of establishing the system are as follows. To integrate confidential patient data into ECG signals, we first propose a nonlinear model to optimize the quality of ECG signals with the embedded patients’ confidential data including patient name, patient birthdate, date of medical treatment, and medical history. Then, we apply Simulated Annealing (SA) to solve the nonlinear model such that the ECG signals with embedded patients’ confidential data have good SNR, good root mean square error (RMSE), and high similarity. In other words, the distortion of the PQRST complexes and the ECG shape caused by the embedded patients’ confidential data is very small, and thus the quality of the embedded ECG signals meets the requirements of physiological diagnostics. In the terminals, one can receive the ECG signals with the embedded patients’ confidential data. In addition, the embedded patients’ confidential data can be received and extracted without the original ECG signals. The experimental results confirm the efficiency that our method maintains a high quality of each ECG signal with the embedded patient confidential data. Moreover, the embedded confidential data shows a good robustness against common attacks.

show abstract

[Retracted] Low‐Power Indoor Positioning Algorithm Based on iBeacon Network

Cited by 7 publications

References 23 publications

A K-nearest neighbor indoor fingerprint location method based on coarse positioning circular domain and the highest similarity threshold

A K-nearest neighbor indoor fingerprint location method based on coarse positioning circular domain and the highest similarity threshold

iBeacon-based indoor positioning system: from theory to practical deployment

Intelligent Healthcare System Using Mathematical Model and Simulated Annealing to Hide Patients Data in the Low-Frequency Amplitude of ECG Signals

Contact Info

Product

Resources

About