2011 IEEE Workshop on Merging Fields of Computational Intelligence and Sensor Technology 2011
DOI: 10.1109/mfcist.2011.5949514
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Locating sensors with fuzzy logic algorithms

Abstract: In a system formed by hundreds of sensors deployed in a huge area it is important to know the position where every sensor is.This information can be obtained using several methods. However, if the number of sensors is high and the deployment is based on ad-hoc manner, some auto-locating techniques must be implemented.In this paper we describe a novel algorithm based on fuzzy logic with the objective of estimating the location of sensors according to the knowledge of the position of some reference nodes.This al… Show more

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Cited by 6 publications
(3 citation statements)
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“…The optimal estimation for x* is the mean of this distribution x*true¯=K*K1x [20,21], and the uncertainty in the estimation is the variance var(x*)=K**K*K1K*T. The aforementioned description indicates that the GPR is suitable for predicting unknown data with limited known data.…”
Section: Preliminariesmentioning
confidence: 99%
“…The optimal estimation for x* is the mean of this distribution x*true¯=K*K1x [20,21], and the uncertainty in the estimation is the variance var(x*)=K**K*K1K*T. The aforementioned description indicates that the GPR is suitable for predicting unknown data with limited known data.…”
Section: Preliminariesmentioning
confidence: 99%
“…Infrastructure bound wired solutions do have an advantage in terms of security over wireless ones, due the inevitable physical connection to the target network [1][2][3][4][5]. The possibility to determine the physical location of any connected client would result in a significant gain of the achievable security level.…”
Section: Introductionmentioning
confidence: 99%
“…Depends on the used localization methods [3]: based on Time of Arrival (ToA), Angle of Arrival (AoA), Radio Signal Strenght (RSS) or Time Difference of Arrival (TDoA), localization techniques merged from classical and probabilistic approaches [7] to Evolutionary computation (EC) using Neural networks (NN) [8], Support Vector Machines (SVM) [9], Fuzzy logic (FL) [10,11,12] and hybrid approaches.…”
Section: Introductionmentioning
confidence: 99%