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A hybrid stochastic approach for self-location of wireless sensors in indoor environments.

Lloret J, Tomas J, Garcia M, Canovas A - Sensors (Basel) (2009)

Bottom Line: Our goal is to reduce the training phase in an indoor environment, but, without an loss of precision.Finally, we compare the measurements taken using our proposed method in a real environment with the measurements taken by other developed systems.Comparisons between the proposed system and other hybrid methods are also provided.

View Article: PubMed Central - PubMed

Affiliation: Departamento de Comunicaciones, Universidad Polit├ęcnica de Valencia. Camino Vera s/n, 46022, Valencia, Spain; E-Mails: jtomas@dcom.upv.es ; migarpi@posgrado.upv.es ; alcasol@epsg.upv.es.

ABSTRACT
Indoor location systems, especially those using wireless sensor networks, are used in many application areas. While the need for these systems is widely proven, there is a clear lack of accuracy. Many of the implemented applications have high errors in their location estimation because of the issues arising in the indoor environment. Two different approaches had been proposed using WLAN location systems: on the one hand, the so-called deductive methods take into account the physical properties of signal propagation. These systems require a propagation model, an environment map, and the position of the radio-stations. On the other hand, the so-called inductive methods require a previous training phase where the system learns the received signal strength (RSS) in each location. This phase can be very time consuming. This paper proposes a new stochastic approach which is based on a combination of deductive and inductive methods whereby wireless sensors could determine their positions using WLAN technology inside a floor of a building. Our goal is to reduce the training phase in an indoor environment, but, without an loss of precision. Finally, we compare the measurements taken using our proposed method in a real environment with the measurements taken by other developed systems. Comparisons between the proposed system and other hybrid methods are also provided.

No MeSH data available.


Average location estimation error as a function of the number of APs.
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f4-sensors-09-03695: Average location estimation error as a function of the number of APs.

Mentions: In order to test the influence of the number of APs in our proposal, we measured the error of the approach adding access point one by one in each location (in the same place of the 56 samples previously taken). In Figure 4 we can observe that the localization error tends to decrease exponentially (blue line with squares). Therefore, with higher number of APs we obtain lower error values in the sensor location estimation. This tendency is given because one of the methods used in our hybrid system is based on the triangulation method. This method uses the distance from the sensor to various access points based on RSS. Once the sensor obtains the value of at least three distances, between the sensor and the APs, the sensor estimates its position. Therefore, the more distances the sensor to different APs has, the higher the accuracy of the localization sensor will be, in other words, the error of location will be lower.


A hybrid stochastic approach for self-location of wireless sensors in indoor environments.

Lloret J, Tomas J, Garcia M, Canovas A - Sensors (Basel) (2009)

Average location estimation error as a function of the number of APs.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3297145&req=5

f4-sensors-09-03695: Average location estimation error as a function of the number of APs.
Mentions: In order to test the influence of the number of APs in our proposal, we measured the error of the approach adding access point one by one in each location (in the same place of the 56 samples previously taken). In Figure 4 we can observe that the localization error tends to decrease exponentially (blue line with squares). Therefore, with higher number of APs we obtain lower error values in the sensor location estimation. This tendency is given because one of the methods used in our hybrid system is based on the triangulation method. This method uses the distance from the sensor to various access points based on RSS. Once the sensor obtains the value of at least three distances, between the sensor and the APs, the sensor estimates its position. Therefore, the more distances the sensor to different APs has, the higher the accuracy of the localization sensor will be, in other words, the error of location will be lower.

Bottom Line: Our goal is to reduce the training phase in an indoor environment, but, without an loss of precision.Finally, we compare the measurements taken using our proposed method in a real environment with the measurements taken by other developed systems.Comparisons between the proposed system and other hybrid methods are also provided.

View Article: PubMed Central - PubMed

Affiliation: Departamento de Comunicaciones, Universidad Polit├ęcnica de Valencia. Camino Vera s/n, 46022, Valencia, Spain; E-Mails: jtomas@dcom.upv.es ; migarpi@posgrado.upv.es ; alcasol@epsg.upv.es.

ABSTRACT
Indoor location systems, especially those using wireless sensor networks, are used in many application areas. While the need for these systems is widely proven, there is a clear lack of accuracy. Many of the implemented applications have high errors in their location estimation because of the issues arising in the indoor environment. Two different approaches had been proposed using WLAN location systems: on the one hand, the so-called deductive methods take into account the physical properties of signal propagation. These systems require a propagation model, an environment map, and the position of the radio-stations. On the other hand, the so-called inductive methods require a previous training phase where the system learns the received signal strength (RSS) in each location. This phase can be very time consuming. This paper proposes a new stochastic approach which is based on a combination of deductive and inductive methods whereby wireless sensors could determine their positions using WLAN technology inside a floor of a building. Our goal is to reduce the training phase in an indoor environment, but, without an loss of precision. Finally, we compare the measurements taken using our proposed method in a real environment with the measurements taken by other developed systems. Comparisons between the proposed system and other hybrid methods are also provided.

No MeSH data available.