scholarly journals iBeacon indoor localization using trusted-ranges model

2019 ◽  
Vol 15 (1) ◽  
pp. 155014771882430 ◽  
Author(s):  
Tuan D Vy ◽  
Yoan Shin
Keyword(s):  
Nearest Neighbor ◽  
Indoor Localization ◽  
Radio Signal ◽  
Signal Strength ◽  
Signal Propagation ◽  
Received Signal Strength ◽  
Training Phase ◽  
Partition Scheme ◽  
Accurate Localization ◽  
Localization Performance

In this article, we propose an efficient approach to address mobile indoor localization using received signal strength from iBeacon combined with trusted-ranges model. In order to overcome the inconsistency of radio signal propagation, the trusted-ranges model supplies reliable ranges of received signal strength values from a certain number of nearest neighbor iBeacon nodes by classifying received signal strength values into various levels of range. By observing the signal propagation, the trusted-ranges model is built to provide important information for the training phase. Based on this, a partition scheme is applied to effectively determine the position of mobile devices. The experimental results show fast, robust, and accurate localization performance in the proposed method.

scholarly journals Received Signal Strength-Based Indoor Localization Using Hierarchical Classification

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1067 ◽  
Author(s):  
Chenbin Zhang ◽  
Ningning Qin ◽  
Yanbo Xue ◽  
Le Yang
Keyword(s):  
Clustering Algorithm ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Hierarchical Classification ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Position Information ◽  
Area Of Interest

Commercial interests in indoor localization have been increasing in the past decade. The success of many applications relies at least partially on indoor localization that is expected to provide reliable indoor position information. Wi-Fi received signal strength (RSS)-based indoor localization techniques have attracted extensive attentions because Wi-Fi access points (APs) are widely deployed and we can obtain the Wi-Fi RSS measurements without extra hardware cost. In this paper, we propose a hierarchical classification-based method as a new solution to the indoor localization problem. Within the developed approach, we first adopt an improved K-Means clustering algorithm to divide the area of interest into several zones and they are allowed to overlap with one another to improve the generalization capability of the following indoor positioning process. To find the localization result, the K-Nearest Neighbor (KNN) algorithm and support vector machine (SVM) with the one-versus-one strategy are employed. The proposed method is implemented on a tablet, and its performance is evaluated in real-world environments. Experiment results reveal that the proposed method offers an improvement of 1.4% to 3.2% in terms of position classification accuracy and a reduction of 10% to 22% in terms of average positioning error compared with several benchmark methods.

scholarly journals An Improved Weighted K-Nearest Neighbor Algorithm for Indoor Localization

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2117
Author(s):  
Xuesheng Peng ◽  
Ruizhi Chen ◽  
Kegen Yu ◽  
Feng Ye ◽  
Weixing Xue
Keyword(s):  
Euclidean Distance ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Physical Distance ◽  
Spatial Distance ◽  
K Nearest Neighbor ◽  
Nearest Neighbor Algorithm ◽  
K Nearest Neighbor Algorithm

The weighted K-nearest neighbor (WKNN) algorithm is the most commonly used algorithm for indoor localization. Traditional WKNN algorithms adopt received signal strength (RSS) spatial distance (usually Euclidean distance and Manhattan distance) to select reference points (RPs) for position determination. It may lead to inaccurate position estimation because the relationship of received signal strength and distance is exponential. To improve the position accuracy, this paper proposes an improved weighted K-nearest neighbor algorithm. The spatial distance and physical distance of RSS are used for RP selection, and a fusion weighted algorithm based on these two distances is used for position calculation. The experimental results demonstrate that the proposed algorithm outperforms traditional algorithms, such as K-nearest neighbor (KNN), Euclidean distance-based WKNN (E-WKNN), and physical distance-based WKNN (P-WKNN). Compared with the KNN, E-WKNN, and P-WKNN algorithms, the positioning accuracy of the proposed method is improved by about 29.4%, 23.5%, and 20.7%, respectively. Compared with some recently improved WKNN algorithms, our proposed algorithm can also obtain a better positioning performance.

scholarly journals Received-Signal-Strength-Based Approach for Detection and 2D Indoor Localization of Evil Twin Rogue Access Point in 802.11

2021 ◽  
Vol 11 (1) ◽  
pp. 13-20
Author(s):  
Roman Y. Korolkov ◽  
Serhii V. Kutsak
Keyword(s):  
Indoor Localization ◽  
Signal Strength ◽  
Access Point ◽  
Signal Propagation ◽  
Received Signal Strength ◽  
False Alarms ◽  
Access Points ◽  
Network Analyzers ◽  
Indoor Conditions ◽  
Rogue Access Point

The “Evil twin” rogue access point is one of the most serious security threats to wireless LANs. To solve this problem, a practical approach has been proposed for detecting rogue access points using the received signal strength indicator (RSSI). First, a distributed architecture is presented, which consists of three network analyzers. Then, a cluster analysis of the RSSI vectors is performed to determine the attack. The coordinates of the centroids of clusters obtained were converted into the distance by using an empirical model of signal propagation under indoor conditions. The obtained distances are used to determine the localization of a rogue access point (RAP) using the trilateration method. Finally, we are conducting experiments to evaluate the performance of practical RAP detection. The results show that the proposed approach to detecting rogue access points can significantly reduce the frequency of false alarms, while providing an average localization error of 1.5m, which is quite acceptable for RAP localization in real indoor conditions.

scholarly journals A Generative Method for Indoor Localization Using Wi-Fi Fingerprinting

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2392
Author(s):  
Óscar Belmonte-Fernández ◽  
Emilio Sansano-Sansano ◽  
Antonio Caballer-Miedes ◽  
Raúl Montoliu ◽  
Rubén García-Vidal ◽  
...  
Keyword(s):  
Markov Model ◽  
Hidden Markov Model ◽  
Indoor Localization ◽  
Hidden Markov ◽  
Signal Strength ◽  
Access Point ◽  
Received Signal Strength ◽  
Machine Learning Algorithms ◽  
Forward Algorithm ◽  
Generative Method

Indoor localization is an enabling technology for pervasive and mobile computing applications. Although different technologies have been proposed for indoor localization, Wi-Fi fingerprinting is one of the most used techniques due to the pervasiveness of Wi-Fi technology. Most Wi-Fi fingerprinting localization methods presented in the literature are discriminative methods. We present a generative method for indoor localization based on Wi-Fi fingerprinting. The Received Signal Strength Indicator received from a Wireless Access Point is modeled by a hidden Markov model. Unlike other algorithms, the use of a hidden Markov model allows ours to take advantage of the temporal autocorrelation present in the Wi-Fi signal. The algorithm estimates the user’s location based on the hidden Markov model, which models the signal and the forward algorithm to determine the likelihood of a given time series of Received Signal Strength Indicators. The proposed method was compared with four other well-known Machine Learning algorithms through extensive experimentation with data collected in real scenarios. The proposed method obtained competitive results in most scenarios tested and was the best method in 17 of 60 experiments performed.

scholarly journals Indoor Localization Based on Wi-Fi Received Signal Strength Indicators: Feature Extraction, Mobile Fingerprinting, and Trajectory Learning

2019 ◽  
Vol 9 (18) ◽  
pp. 3930 ◽  
Author(s):  
Jaehyun Yoo ◽  
Jongho Park
Keyword(s):  
Feature Extraction ◽  
Indoor Localization ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Position Estimation ◽  
Training Data ◽  
Data Sets ◽  
Feature Extraction Method ◽  
Fingerprinting Method ◽  
Trajectory Learning

This paper studies the indoor localization based on Wi-Fi received signal strength indicator (RSSI). In addition to position estimation, this study examines the expansion of applications using Wi-Fi RSSI data sets in three areas: (i) feature extraction, (ii) mobile fingerprinting, and (iii) mapless localization. First, the features of Wi-Fi RSSI observations are extracted with respect to different floor levels and designated landmarks. Second, the mobile fingerprinting method is proposed to allow a trainer to collect training data efficiently, which is faster and more efficient than the conventional static fingerprinting method. Third, in the case of the unknown-map situation, the trajectory learning method is suggested to learn map information using crowdsourced data. All of these parts are interconnected from the feature extraction and mobile fingerprinting to the map learning and the estimation. Based on the experimental results, we observed (i) clearly classified data points by the feature extraction method as regards the floors and landmarks, (ii) efficient mobile fingerprinting compared to conventional static fingerprinting, and (iii) improvement of the positioning accuracy owing to the trajectory learning.

scholarly journals SDR Based Indoor Beacon Localization Using 3D Probabilistic Multipath Exploitation and Deep Learning

Electronics ◽  
2019 ◽  
Vol 8 (11) ◽  
pp. 1323 ◽  
Author(s):  
Donald L. Hall ◽  
Ram M. Narayanan ◽  
David M. Jenkins
Keyword(s):  
Deep Learning ◽  
Software Defined Radio ◽  
High Performance ◽  
Nearest Neighbor ◽  
Indoor Localization ◽  
Signal Propagation ◽  
Multipath Channel ◽  
K Nearest Neighbor ◽  
Positioning Systems ◽  
Channel Characteristics

Wireless indoor positioning systems (IPS) are ever-growing as traditional global positioning systems (GPS) are ineffective due to non-line-of-sight (NLoS) signal propagation. In this paper, we present a novel approach to learning three-dimensional (3D) multipath channel characteristics in a probabilistic manner for providing high performance indoor localization of wireless beacons. The proposed system employs a single triad dipole vector sensor (TDVS) for polarization diversity, a deep learning model deemed the denoising autoencoder to extract unique fingerprints from 3D multipath channel information, and a probabilistic k-nearest-neighbor (PkNN) to exploit the 3D multipath characteristics. The proposed system is the first to exploit 3D multipath channel characteristics for indoor wireless beacon localization via vector sensing methodologies, a software defined radio (SDR) platform, and multipath channel estimation.

Sign in / Sign up

Export Citation Format

Share Document