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MagicFinger: 3D Magnetic Fingerprints for Indoor Location.

Carrillo D, Moreno V, Úbeda B, Skarmeta AF - Sensors (Basel) (2015)

Bottom Line: The resulting system does not rely on any infrastructure devices and therefore is easy to manage and deploy.Experimental evaluations carried out in two different buildings confirm the satisfactory performance of indoor location based on magnetic field vectors.These evaluations provided an error of (11.34 m, 4.78 m) in the (x; y) components of the estimated positions in the first building where the experiments were carried out, with a standard deviation of (3.41 m, 4.68 m); and in the second building, an error of (4 m, 2.98 m) with a deviation of (2.64 m, 2.33 m).

View Article: PubMed Central - PubMed

Affiliation: Department of Information and Communications Engineering, University of Murcia, 30100 Murcia, Spain. daniel.carrillo2@um.es.

ABSTRACT
Given the indispensable role of mobile phones in everyday life, phone-centric sensing systems are ideal candidates for ubiquitous observation purposes. This paper presents a novel approach for mobile phone-centric observation applied to indoor location. The approach involves a location fingerprinting methodology that takes advantage of the presence of magnetic field anomalies inside buildings. Unlike existing work on the subject, which uses the intensity of magnetic field for fingerprinting, our approach uses all three components of the measured magnetic field vectors to improve accuracy. By using adequate soft computing techniques, it is possible to adequately balance the constraints of common solutions. The resulting system does not rely on any infrastructure devices and therefore is easy to manage and deploy. The proposed system consists of two phases: the offline phase and the online phase. In the offline phase, magnetic field measurements are taken throughout the building, and 3D maps are generated. Then, during the online phase, the user's location is estimated through the best estimator for each zone of the building. Experimental evaluations carried out in two different buildings confirm the satisfactory performance of indoor location based on magnetic field vectors. These evaluations provided an error of (11.34 m, 4.78 m) in the (x; y) components of the estimated positions in the first building where the experiments were carried out, with a standard deviation of (3.41 m, 4.68 m); and in the second building, an error of (4 m, 2.98 m) with a deviation of (2.64 m, 2.33 m).

No MeSH data available.


Related in: MedlinePlus

Distribution of features extracted from the data collected in Building A after being pre-processed.
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f5-sensors-15-17168: Distribution of features extracted from the data collected in Building A after being pre-processed.

Mentions: As an initial part of the experimental study, we analyzed the distribution of each variable (feature) in order to observe its behavior (see Figure 5). As a result, it was realized that the distribution of values of some variables had a particular shape, so it seemed reasonable to apply algorithms that work on distributed data, rather than algorithms that rely on distances. This is why we choose the expectation maximization (EM) algorithm [30] to perform the data partition. Nevertheless, we also try another distance-based approach to test its feasibility, in this case, the k-means technique. In particular, the algorithm chosen for k-means was that proposed by Witten et al., [31], which selects the best subset of features based on a lasso-type penalty criterion. The core idea is that clusters may differ in a few variables, so using all of the features would probably produce worse clusters. A priori, we do not know which features could lead to optimum partitioning, so it is convenient to use this algorithm, which has been demonstrated to be a better approach than standard k-means and other clustering algorithms, as Witten et al. show.


MagicFinger: 3D Magnetic Fingerprints for Indoor Location.

Carrillo D, Moreno V, Úbeda B, Skarmeta AF - Sensors (Basel) (2015)

Distribution of features extracted from the data collected in Building A after being pre-processed.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4541928&req=5

f5-sensors-15-17168: Distribution of features extracted from the data collected in Building A after being pre-processed.
Mentions: As an initial part of the experimental study, we analyzed the distribution of each variable (feature) in order to observe its behavior (see Figure 5). As a result, it was realized that the distribution of values of some variables had a particular shape, so it seemed reasonable to apply algorithms that work on distributed data, rather than algorithms that rely on distances. This is why we choose the expectation maximization (EM) algorithm [30] to perform the data partition. Nevertheless, we also try another distance-based approach to test its feasibility, in this case, the k-means technique. In particular, the algorithm chosen for k-means was that proposed by Witten et al., [31], which selects the best subset of features based on a lasso-type penalty criterion. The core idea is that clusters may differ in a few variables, so using all of the features would probably produce worse clusters. A priori, we do not know which features could lead to optimum partitioning, so it is convenient to use this algorithm, which has been demonstrated to be a better approach than standard k-means and other clustering algorithms, as Witten et al. show.

Bottom Line: The resulting system does not rely on any infrastructure devices and therefore is easy to manage and deploy.Experimental evaluations carried out in two different buildings confirm the satisfactory performance of indoor location based on magnetic field vectors.These evaluations provided an error of (11.34 m, 4.78 m) in the (x; y) components of the estimated positions in the first building where the experiments were carried out, with a standard deviation of (3.41 m, 4.68 m); and in the second building, an error of (4 m, 2.98 m) with a deviation of (2.64 m, 2.33 m).

View Article: PubMed Central - PubMed

Affiliation: Department of Information and Communications Engineering, University of Murcia, 30100 Murcia, Spain. daniel.carrillo2@um.es.

ABSTRACT
Given the indispensable role of mobile phones in everyday life, phone-centric sensing systems are ideal candidates for ubiquitous observation purposes. This paper presents a novel approach for mobile phone-centric observation applied to indoor location. The approach involves a location fingerprinting methodology that takes advantage of the presence of magnetic field anomalies inside buildings. Unlike existing work on the subject, which uses the intensity of magnetic field for fingerprinting, our approach uses all three components of the measured magnetic field vectors to improve accuracy. By using adequate soft computing techniques, it is possible to adequately balance the constraints of common solutions. The resulting system does not rely on any infrastructure devices and therefore is easy to manage and deploy. The proposed system consists of two phases: the offline phase and the online phase. In the offline phase, magnetic field measurements are taken throughout the building, and 3D maps are generated. Then, during the online phase, the user's location is estimated through the best estimator for each zone of the building. Experimental evaluations carried out in two different buildings confirm the satisfactory performance of indoor location based on magnetic field vectors. These evaluations provided an error of (11.34 m, 4.78 m) in the (x; y) components of the estimated positions in the first building where the experiments were carried out, with a standard deviation of (3.41 m, 4.68 m); and in the second building, an error of (4 m, 2.98 m) with a deviation of (2.64 m, 2.33 m).

No MeSH data available.


Related in: MedlinePlus