scholarly journals An Analysis of Device-Free and Device-Based WiFi-Localization Systems

2014 ◽  
Vol 6 (1) ◽  
pp. 1-19 ◽  
Author(s):  
Heba Aly ◽  
Moustafa Youssef
Keyword(s):  
Indoor Localization ◽  
Indoor Environments ◽  
Factors Affecting ◽  
Localization System ◽  
Finger Print ◽  
Area Of Interest ◽  
Crowd Effect ◽  
Wifi Localization ◽  
Technology Upgrade ◽  
Device Free

WiFi-based localization became one of the main indoor localization techniques due to the ubiquity of WiFi connectivity. However, indoor environments exhibit complex wireless propagation characteristics. Typically, these characteristics are captured by constructing a fingerprint map for the different locations in the area of interest. This finger print requires significant overhead in manual construction, and thus has been one of the major drawbacks of WiFi-based localization. In this paper, the authors present an automated tool for finger print constructions and leverage it to study novel scenarios for device-based and device-free WiFi-based localization that are difficult to evaluate in a real environment. In a particular, the authors examine the effect of changing the access points (AP) mounting location, AP technology upgrade, crowd effect on calibration and operation, among others; on the accuracy of the localization system. The authors present the analysis for the two classes of WiFi-based localization: device-based and device-free. The authors analysis highlights factors affecting the localization system accuracy, how to tune it for better localization, and provides insights for both researchers and practitioners.

Location Fingerprinting Technique for WLAN Device-Free Indoor Localization System

2016 ◽  
Vol 95 (2) ◽  
pp. 445-455 ◽  
Author(s):  
Nasrullah Pirzada ◽  
Mohd Yunus Nayan ◽  
Fazli Subhan ◽  
Adeel Abro ◽  
Mohd Fadzil Hassan ◽  
...  
Keyword(s):  
Indoor Localization ◽  
Localization System ◽  
Fingerprinting Technique ◽  
Location Fingerprinting ◽  
Device Free

scholarly journals A three-dimensional pattern recognition localization system based on a Bayesian graphical model

2020 ◽  
Vol 16 (9) ◽  
pp. 155014771988489 ◽  
Author(s):  
Abdulraqeb Alhammadi ◽  
Fazirulhisyam Hashim ◽  
Mohd. Fadlee A Rasid ◽  
Saddam Alraih
Keyword(s):  
Graphical Model ◽  
Indoor Localization ◽  
Three Dimensional ◽  
Signal Strength ◽  
Local Area ◽  
Received Signal Strength ◽  
Reference Points ◽  
Indoor Environments ◽  
Localization System ◽  
Proposed Model

Access points in wireless local area networks are deployed in many indoor environments. Device-free wireless localization systems based on available received signal strength indicators have gained considerable attention recently because they can localize the people using commercial off-the-shelf equipment. Majority of localization algorithms consider two-dimensional models that cause low positioning accuracy. Although three-dimensional localization models are available, they possess high computational and localization errors, given their use of numerous reference points. In this work, we propose a three-dimensional indoor localization system based on a Bayesian graphical model. The proposed model has been tested through experiments based on fingerprinting technique which collects received signal strength indicators from each access point in an offline training phase and then estimates the user location in an online localization phase. Results indicate that the proposed model achieves a high localization accuracy of more than 25% using reference points fewer than that of benchmarked algorithms.

scholarly journals A Fusion Approach of RSSI and LQI for Indoor Localization System Using Adaptive Smoothers

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Sharly Joana Halder ◽  
Wooju Kim
Keyword(s):  
Indoor Localization ◽  
Distance Estimation ◽  
Link Quality ◽  
Average Error ◽  
Indoor Environments ◽  
Interference Avoidance ◽  
Localization System ◽  
Context Aware Computing ◽  
Link Quality Indicator ◽  
Unknown Node

Due to the ease of development and inexpensiveness, indoor localization systems are getting a significant attention but, with recent advancement in context and location aware technologies, the solutions for indoor tracking and localization had become more critical. Ranging methods play a basic role in the localization system, in which received signal strength indicator- (RSSI-) based ranging technique gets the most attraction. To predict the position of an unknown node, RSSI measurement is an easy and reliable method for distance estimation. In indoor environments, the accuracy of the RSSI-based localization method is affected by strong variation, specially often containing substantial amounts of metal and other such reflective materials that affect the propagation of radio-frequency signals in nontrivial ways, causing multipath effects, dead spots, noise, and interference. This paper proposes an adaptive smoother based location and tracking algorithm for indoor positioning by making fusion of RSSI and link quality indicator (LQI), which is particularly well suited to support context aware computing. The experimental results showed that the proposed mathematical method can reduce the average error around 25%, and it is always better than the other existing interference avoidance algorithms.

scholarly journals An Efficient Normalized Rank Based SVM for Room Level Indoor WiFi Localization with Diverse Devices

2017 ◽  
Vol 2017 ◽  
pp. 1-19 ◽  
Author(s):  
Yasmine Rezgui ◽  
Ling Pei ◽  
Xin Chen ◽  
Fei Wen ◽  
Chen Han
Keyword(s):  
Indoor Localization ◽  
Signal Strength ◽  
Shopping Mall ◽  
Support Vector ◽  
Indoor Environments ◽  
Localization Algorithm ◽  
Wifi Localization ◽  
Signal Fluctuation ◽  
Strength Difference ◽  
Correct Estimation

This paper proposes an efficient and effective WiFi fingerprinting-based indoor localization algorithm, which uses the Received Signal Strength Indicator (RSSI) of WiFi signals. In practical harsh indoor environments, RSSI variation and hardware variance can significantly degrade the performance of fingerprinting-based localization methods. To address the problem of hardware variance and signal fluctuation in WiFi fingerprinting-based localization, we propose a novel normalized rank based Support Vector Machine classifier (NR-SVM). Moving from RSSI value based analysis to the normalized rank transformation based analysis, the principal features are prioritized and the dimensionalities of signature vectors are taken into account. The proposed method has been tested using sixteen different devices in a shopping mall with 88 shops. The experimental results demonstrate its robustness with no less than 98.75% correct estimation in 93.75% of the tested cases and 100% correct rate in 56.25% of cases. In the experiments, the new method shows better performance over the KNN, Naïve Bayes, Random Forest, and Neural Network algorithms. Furthermore, we have compared the proposed approach with three popular calibration-free transformation based methods, including difference method (DIFF), Signal Strength Difference (SSD), and the Hyperbolic Location Fingerprinting (HLF) based SVM. The results show that the NR-SVM outperforms these popular methods.

scholarly journals Kullback–Leibler Divergence Based Probabilistic Approach for Device-Free Localization Using Channel State Information

Sensors ◽  
2019 ◽  
Vol 19 (21) ◽  
pp. 4783
Author(s):  
Gao ◽  
Zhang ◽  
Xiao ◽  
Li
Keyword(s):  
Channel State Information ◽  
Indoor Localization ◽  
Probabilistic Approach ◽  
Indoor Environments ◽  
Channel State ◽  
State Information ◽  
Localization Accuracy ◽  
Sensing Applications ◽  
Leibler Divergence ◽  
Device Free

Recently, people have become more and more interested in wireless sensing applications, among which indoor localization is one of the most attractive. Generally, indoor localization can be classified as device-based and device-free localization (DFL). The former requires a target to carry certain devices or sensors to assist the localization process, whereas the latter has no such requirement, which merely requires the wireless network to be deployed around the environment to sense the target, rendering it much more challenging. Channel State Information (CSI)—a kind of information collected in the physical layer—is composed of multiple subcarriers, boasting highly fined granularity, which has gradually become a focus of indoor localization applications. In this paper, we propose an approach to performing DFL tasks by exploiting the uncertainty of CSI. We respectively utilize the CSI amplitudes and phases of multiple communication links to construct fingerprints, each of which is a set of multivariate Gaussian distributions that reflect the uncertainty information of CSI. Additionally, we propose a kind of combined fingerprints to simultaneously utilize the CSI amplitudes and phases, hoping to improve localization accuracy. Then, we adopt a Kullback–Leibler divergence (KL-divergence) based kernel function to calculate the probabilities that a testing fingerprint belongs to all the reference locations. Next, to localize the target, we utilize the computed probabilities as weights to average the reference locations. Experimental results show that the proposed approach, whatever type of fingerprints is used, outperforms the existing Pilot and Nuzzer systems in two typical indoor environments. We conduct extensive experiments to explore the effects of different parameters on localization performance, and the results demonstrate the efficiency of the proposed approach.

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