Limits...
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.


Comparative of average location error.
© Copyright Policy
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3297145&req=5

f5-sensors-09-03695: Comparative of average location error.

Mentions: In the test phase, all these systems were tested for 40 locations (all these locations were different that the training ones, they were randomly placed and they were not inside the training grid). For each location we gathered a mean of 15 RSS consecutive values. This let us take into account the signal variability in the measurements. Each one of the test samples has been applied to the different location methods. Then, we estimate the error measuring the Euclidean distance between the output of the method and the real location of the sample. Figure 5 shows the results obtained for all the location systems as a function of the number of APs. Their graph follows an exponential tends approximately.


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

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

Comparative of average location error.
© Copyright Policy
Related In: Results  -  Collection

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

f5-sensors-09-03695: Comparative of average location error.
Mentions: In the test phase, all these systems were tested for 40 locations (all these locations were different that the training ones, they were randomly placed and they were not inside the training grid). For each location we gathered a mean of 15 RSS consecutive values. This let us take into account the signal variability in the measurements. Each one of the test samples has been applied to the different location methods. Then, we estimate the error measuring the Euclidean distance between the output of the method and the real location of the sample. Figure 5 shows the results obtained for all the location systems as a function of the number of APs. Their graph follows an exponential tends approximately.

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.