ZigBee-based indoor location system by k-nearest neighbor algorithm with weighted RSSI

Huang, Chih Ning; Chan, Chuan-Tsung

doi:10.1016/j.procs.2011.07.010

Cited by 66 publications

(42 citation statements)

References 8 publications

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“…K-Nearest Neighbor (KNN) is a method of classifying an object by using data that has been studied that has the closest distance to the object [3,20]. Distance obtained from this method is often used as a reference for the distance of two samples of existing objects [21].…”

Section: K-nearest Neighbor Algorithmmentioning

confidence: 99%

“…GPS offers an excellent ability when used outdoors, but because of limitations in the use of the transmission line-of-sight (LOS), then the use of GPS is less effective when used indoors, especially in the multi-stored building [3]. One technology that can be utilized to recognize indoor locations is by using wireless technology (for example, using WIFI) contained within a building [4,5].…”

Section: Introductionmentioning

confidence: 99%

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CAPTURE: A Mobile Based Indoor Positioning System using Wireless Indoor Positioning System

Dari

Suyoto

Pranowo

2018

Int. J. Interact. Mob. Technol.

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Abstract-The existence of mobile devices as a location pointing device using Global Positioning System (GPS) is a very common thing nowadays. The use of GPS as a tool to determine the location of course has a shortage when used indoors. Therefore, the used of indoor location-based services in a room that leverages the use of Access Point (AP) is very important. By using the information of the Received Signal Strength (RSS) obtained from AP, then the location of the device can be determined without the need to use GPS. This technique is called the location fingerprint technique using the characteristics of received RSS's fingerprint, then use it to determine the position. To get a more accurate position then authors used the K-Nearest Neighbor (KNN) method. KNN will use some of the data that obtained from some AP to assist in positioning the device. This solution of course would be able to determine the position of the devices in a storied building.

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Section: K-nearest Neighbor Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

CAPTURE: A Mobile Based Indoor Positioning System using Wireless Indoor Positioning System

Dari

Suyoto

Pranowo

2018

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

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“…Since then, other wireless signals have been tested with fingerprinting (e.g. Bluetooth, FM, Cellular) (Chen et al, 2013;Huang & Chan, 2011;Ibrahim & Youssef, 2013;Subhan, Hasbullah, Rozyyev, & Bakhsh, 2011). This article decides to use the WiFi RSS and a smart phone as the main components to perform fingerprinting for two reasons.…”

Section: Location Fingerprintingmentioning

confidence: 99%

A performance guaranteed indoor positioning system using conformal prediction and the WiFi signal strength

Nguyen

2017

Journal of Information and Telecommunication

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Indoor navigation provides the positioning service to the indoor users, where the GPS coverage is not available. The challenges for most signal-based indoor positioning systems are the unpredictable signal propagation caused by the complex building interiors, and the dynamic of the environment caused by the peoples' movements. However, most existing systems made no assumption about the quality of their predictions, which is crucial in such noisy indoor environment. To address this challenge, this article proposes a confidence measure to reflect the uncertainty of the positioning prediction. More importantly, the users may control the size of the prediction set by setting the confidence level tailoring to their personal requirement. The proposed approach in this article has been validated in three real office buildings with challenging indoor environments, which indicated that it performed up to 20% more accurate than traditional Naïve Bayes and Weighted K-nearest neighbours (W-KNN) algorithms. ARTICLE HISTORY

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“…The localization is able to perform location sensing [3,4], target tracking [5], or both features at the same time [6][7][8]. The importance of node localization can be derived from relevant applications ranging from target tracing to safety monitoring.…”

Section: Introductionmentioning

confidence: 99%

A Statistical Approach in Designing an RF-Based Human Crowd Density Estimation System

Fadhlullah

Ismail

2016

International Journal of Distributed Sensor Networks

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The study of human crowd density estimation (H-CDE) using radio frequency is limited due to the nature of wireless medium and the advancement of visual-based systems. There were two statistical methods, namely, One-Way Analysis of Variance and Design of Experiment applied in designing the H-CDE system. One-Way Analysis of Variance is used to investigate the difference in signal attenuation between dynamic and static crowds. The Design of Experiment is utilized to identify significant crowd properties that affect wireless signal propagation. The significant factors were later trained into the H-CDE algorithm for the purpose of estimating the human crowd density in a defined sector. A sector comprising three placements of 2.4 GHz ZigBee wireless nodes continuously reported the received signal strength indicator to the main node. The results showed that the H-CDE system was 75.00% and 70.83% accurate in detecting the low and medium human crowd density, respectively. A signal path loss propagation model was also proposed to assist in predicting the human crowd density. The human crowd properties verified by using the statistical approach may offer a new side of understanding and estimating the human crowd density.

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ZigBee-based indoor location system by k-nearest neighbor algorithm with weighted RSSI

Cited by 66 publications

References 8 publications

CAPTURE: A Mobile Based Indoor Positioning System using Wireless Indoor Positioning System

CAPTURE: A Mobile Based Indoor Positioning System using Wireless Indoor Positioning System

A performance guaranteed indoor positioning system using conformal prediction and the WiFi signal strength

A Statistical Approach in Designing an RF-Based Human Crowd Density Estimation System

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