Virtual 2D Positioning System by Using Wireless Sensors in Indoor Environment

Koyuncu, Hakan; Yang, Shuang

doi:10.5121/ijwmn.2013.5602

Cited by 8 publications

(11 citation statements)

References 12 publications

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“…In this paragraph, the proposed approach is compared with well-known localization techniques. Koyuncu & Yang [26] present a weighted k-nearest neighbors algorithm (WKNN) for indoor localization. To estimate the position of the sensor, the new received measurement is compared with the elements in the fingerprint database using Euclidean distances.…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

A Hidden Markov Model for Indoor Trajectory Tracking of Elderly People

Alshamaa¹,

Chkeir²,

Mourad-Chehade³

et al. 2019

2019 IEEE Sensors Applications Symposium (SAS)

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Tracking of elderly people is indispensable to assist them as fast as possible. In this paper, we propose a new trajectory tracking technique to localize elderly people in real time in indoor environments. A mobility model is constructed, based on the hidden Markov models, to estimate the trajectory followed by each person. However, mobility models can not be used as standalone tracking techniques due to accumulation of error with time. For that reason, the proposed mobility model is combined with measurements from the network. Here, we use the power of the WiFi signals received from surrounding Access Points installed in the building. The combination between the mobility model and the measurements result in tracking of elderly people. Real experiments are realized to evaluate the performance of the proposed approach.

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Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

A Hidden Markov Model for Indoor Trajectory Tracking of Elderly People

Alshamaa¹,

Chkeir²,

Mourad-Chehade³

et al. 2019

2019 IEEE Sensors Applications Symposium (SAS)

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“…Each sensor acts as a radio beacon to provide positioning services in the indoor environments. Several researches have proposed methods for realizing the indoor positioning system by the WSN [2][3][4][5]. There are other techniques, which provide relative positioning such as, inertial methods, optical, ultrasonic and RF wireless technologies.…”

Section: Introductionmentioning

confidence: 99%

“…The main goal to achieve is experimentally to find locations of a mobile node with the use of different positioning methods.II. POSITIONING ALGORITHMSDifferent algorithms such as triangulation method and the fingerprinting method have been proposed recently to investigate the possibility for indoor positioning[1,2,3,4,5,6]. The triangulation method requires a signal propagation model to convert the RSS value into distances between the nodes.…”

mentioning

confidence: 99%

A 2D Indoor Positioning System Based on a Wireless Sensor Network Technology for Power Adjustable Solutions

Komarov

2014

2014 IEEE 7th International Conference on Service-Oriented Computing and Applications

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In this paper development of a 2D indoor positioning system based on a wireless sensor network (WSN) technology (ZigBeePro protocol) to use for transmission power adjustable solutions is described. There was implemented realtime system with different positioning methods and algorithms: triangulation, fingerprinting, fingerprinting with K weighted nearest neighbor algorithm. There was also implemented median filter to make the raw data smoother. The important point in this study is to compare different methods for different physical areas. Experimental results in real environment showed the difference in accuracy for different methods for different areas. With the software it is possible to configure positioning system by changing weights for different nodes in K weighted nearest neighbor algorithm. The software developed for the coordinates estimation can help determination of the points in the real area with 0 m accuracy where it is possible to set up special control devices and guarantee location of the mobile device and where it is also possible to adjust transmission power after defining the position of the mobile device.

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“…Such an approach allows taking into consideration the stationary characteristics of the environment. Several studies have been made for sensors localization using RSSI-based radio-fingerprinting, such as the weighted K-nearest neighbor (WKNN) algorithm [19]. We have recently proposed in [20] and [21] two localization methods using radio-fingerprinting in WSNs, by taking advantage of kernel methods in machine learning.…”

Section: Introductionmentioning

confidence: 99%

Target Tracking Using Machine Learning and Kalman Filter in Wireless Sensor Networks

et al. 2014

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This paper describes an original method for target tracking in wireless sensor networks. The proposed method combines machine learning with a Kalman filter to estimate instantaneous positions of a moving target. The target's accelerations, along with information from the network, are used to obtain an accurate estimation of its position. To this end, radio-fingerprints of received signal strength indicators (RSSIs) are first collected over the surveillance area. The obtained database is then used with machine learning algorithms to compute a model that estimates the position of the target using only RSSI information. This model leads to a first position estimate of the target under investigation. The kernel-based ridge regression and the vector-output regularized least squares are used in the learning process.The Kalman filter is used afterwards to combine predictions of the target's positions based on acceleration information with the first estimates, leading to more accurate ones. The performance of the method is studied for different scenarios and a thorough comparison to well-known algorithms is also provided.

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Virtual 2D Positioning System by Using Wireless Sensors in Indoor Environment

Abstract: ABSTRACT

Cited by 8 publications

References 12 publications

A Hidden Markov Model for Indoor Trajectory Tracking of Elderly People

A Hidden Markov Model for Indoor Trajectory Tracking of Elderly People

A 2D Indoor Positioning System Based on a Wireless Sensor Network Technology for Power Adjustable Solutions

Target Tracking Using Machine Learning and Kalman Filter in Wireless Sensor Networks

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