A hybrid stochastic approach for self-location of wireless sensors in indoor environments.
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.
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PubMed Central - PubMed
Affiliation: Departamento de Comunicaciones, Universidad Politécnica 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. |
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Mentions: In the training phase, we have estimated p(oij /li,Bj). In this stage several methods such as histogram or kernel can be used. Then, using equation (8), we can write:(9)p(oj∣l,Bj,Tij)=p(oj−10nlog(djdij)+LwjLwij∣l,Bj)where n = 2 in free space. In order to obtain the optimal location for equation (2), the proposed algorithm is written in pseudocode in Figure 1. Its explanation is as follows: given the input signal strength the location probability for each point is evaluate using 0.5 meter greed. For each point the k nearest samples are taken. The probability of this location is calculated using equation (5), but, using only these k samples instead the all training set. Farther samples will distort the results. For each sample (of k nearest samples), first we use equation (9) to combine the deductive approach, to take into account the shift from the actual location to the sample location, and, second the inductive approach, to obtain the signal probability in a well known place. |
View Article: PubMed Central - PubMed
Affiliation: Departamento de Comunicaciones, Universidad Politécnica de Valencia. Camino Vera s/n, 46022, Valencia, Spain; E-Mails: jtomas@dcom.upv.es ; migarpi@posgrado.upv.es ; alcasol@epsg.upv.es.
No MeSH data available.