An Empirical Evaluation of a Probabilistic RF Signature for WLAN Location Fingerprinting

Alsindi, Nayef; Chaloupka, Zdenek; AlKhanbashi, Nuha; Aweya, James

doi:10.1109/twc.2014.041714.131113

Cited by 37 publications

(16 citation statements)

References 37 publications

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“…The analysis in this paper considers an indoor scenario with a high Signal-to-Noise Ratio (SNR) [27], [28]. The CTF, H(f ), is considered as an RF feature that would be distinctively unique for every spatial position within the indoor environment.…”

Section: A Primary Rf Featuresmentioning

confidence: 99%

Indoor Localization for IoT Using Adaptive Feature Selection: A Cascaded Machine Learning Approach

AlHajri¹,

Ali²,

Shubair³

2019

Antennas Wirel. Propag. Lett.

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Evolving Internet-of-Things (IoT) applications often require the use of sensor-based indoor tracking and positioning, for which the performance is significantly improved by identifying the type of the surrounding indoor environment. This identification is of high importance since it leads to higher localization accuracy. This paper presents a novel method based on a cascaded two-stage machine learning approach for highly-accurate and robust localization in indoor environments using adaptive selection and combination of RF features. In the proposed method, machine learning is first used to identify the type of the surrounding indoor environment. Then, in the second stage, machine learning is employed to identify the most appropriate selection and combination of RF features that yield the highest localization accuracy. Analysis is based on k-Nearest Neighbor (k-NN) machine learning algorithm applied on a real dataset generated from practical measurements of the RF signal in realistic indoor environments.Received Signal Strength, Channel Transfer Function, and Frequency Coherence Function are the primary RF features being explored and combined. Numerical investigations demonstrate that prediction based on the concatenation of primary RF features enhanced significantly as the localization accuracy improved by at least 50% to more than 70%.

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Section: A Primary Rf Featuresmentioning

confidence: 99%

Indoor Localization for IoT Using Adaptive Feature Selection: A Cascaded Machine Learning Approach

AlHajri¹,

Ali²,

Shubair³

2019

Antennas Wirel. Propag. Lett.

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“…where ( ) parameter represents the path loss at a reference distance 0 , typically one meter. is the constant propagation obtain the optimal nearest neighbor points set Θ = ( ( ) , , ( ) , , ( ) , ); (10) else (11) continue; (12) end if (13) value, is the distance between the transmitter and the receiver devices, and is a Gaussian random variable with mean 0 and standard deviation . From the signal propagation model, when the mobile devices are equidistant from the AP in line of sight, the mobile devices receive the same signal strength.…”

Section: Precise Localizationmentioning

confidence: 99%

“…Although GPS works extremely well in outdoor localization, unfortunately, it does not perform well in indoors, urban canyons, construction and basement, and places close to the wall as the signal from the GPS satellites is too weak to come across most construction, thus making GPS hard for indoor localization. Attempting to find the accurate indoor localization, many indoor localization technologies are proposed, such as infrared [3], ultrawideband (UWB) [4], ultrasonic [5], Bluetooth [6], Radio Frequency Identification (RFID) [7], Zigbee [8], frequency modulation (FM) broadcast [9,10], geomagnetism [11], and Wireless Fidelity (WiFi) [12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

5 G WiFi Signal-Based Indoor Localization System Using Cluster k-Nearest Neighbor Algorithm

Jiang

Liang

et al. 2014

International Journal of Distributed Sensor Networks

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Indoor localization based on existent WiFi signal strength is becoming more and more prevalent and ubiquitous. Unfortunately, the WiFi received signal strength (RSS) is susceptible by multipath, signal attenuation, and environmental changes, which is the major challenge for accurate indoor localization. To overcome these limitations, we propose the cluster -nearest neighbor (KNN) algorithm with 5 G WiFi signal to reduce the environmental interference and improve the localization performance without additional equipment. In this paper, we propose three approaches to improve the performance of localization algorithm. For one thing, we reduce the computation effort based on the coarse localization algorithm. For another, according to the detailed analysis of the 2.4 G and 5 G signal fluctuation, we expand the real-time measurement RSS before matching the fingerprint map. More importantly, we select the optimal nearest neighbor points based on the proposed cluster KNN algorithm. We have implemented the proposed algorithm and evaluated the performance with existent popular algorithms. Experimental results demonstrate that the proposed algorithm can effectively improve localization accuracy and exhibit superior performance in terms of localization stabilization and computation effort.

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“…The major advantages of RSS-based methods are its low complexity and speed of calculation compared to the other methods [14][15][16]18]. Thus, RSS-based methods are the most widely used [19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

Distributed and adaptive location identification system for mobile devices

Awad

Al-Sadi

Al-Quran

et al. 2018

EURASIP J. Adv. Signal Process.

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Indoor location identification and navigation need to be as simple, seamless, and ubiquitous as its outdoor GPS-based counterpart is. It would be of great convenience to the mobile user to be able to continue navigating seamlessly as he or she moves from a GPS-clear outdoor environment into an indoor environment or a GPS-obstructed outdoor environment such as a tunnel or forest. Existing infrastructure-based indoor localization systems lack such capability, on top of potentially facing several critical technical challenges such as increased cost of installation, centralization, lack of reliability, poor localization accuracy, poor adaptation to the dynamics of the surrounding environment, latency, system-level and computational complexities, repetitive labor-intensive parameter tuning, and user privacy. To this end, this paper presents a novel mechanism with the potential to overcome most (if not all) of the abovementioned challenges. The proposed mechanism is simple, distributed, adaptive, collaborative, and cost-effective. Based on the proposed algorithm, a mobile blind device can potentially utilize, as GPS-like reference nodes, either in-range location-aware compatible mobile devices or preinstalled low-cost infrastructure-less location-aware beacon nodes. The proposed approach is model-based and calibration-free that uses the received signal strength to periodically and collaboratively measure and update the radio frequency characteristics of the operating environment to estimate the distances to the reference nodes. Trilateration is then used by the blind device to identify its own location, similar to that used in the GPS-based system. Simulation and empirical testing ascertained that the proposed approach can potentially be the core of future indoor and GPS-obstructed environments.

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An Empirical Evaluation of a Probabilistic RF Signature for WLAN Location Fingerprinting

Cited by 37 publications

References 37 publications

Indoor Localization for IoT Using Adaptive Feature Selection: A Cascaded Machine Learning Approach

Indoor Localization for IoT Using Adaptive Feature Selection: A Cascaded Machine Learning Approach

5 G WiFi Signal-Based Indoor Localization System Using Cluster k-Nearest Neighbor Algorithm

Distributed and adaptive location identification system for mobile devices

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