Morphological Sampling Scheme for Analysis and Optimization of Wireless Indoor Environments

Molina-García, Mariano; Fernandez-Duran, A.; Alonso, J.I.

doi:10.1109/lcomm.2010.07.100438

Cited by 5 publications

(7 citation statements)

References 6 publications

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“…The method is based on intelligent selection of a small number of reference points, which we call key reference points , that are used to scan the RSSI values. This selection is based on a novel scenario‐dependent sampling scheme [13]. We obtain the grid used to build the fingerprint database by using signal propagation modeling techniques.…”

Section: Proposed Methodology For Reducing Calibration Efforts and Bomentioning

confidence: 99%

“…The performance of various sampling schemes is assessed in [13], which also compares a regular grid employed in the fingerprint stage with the one used in this novel sampling method. Several statistical tests were performed in a complex indoor deployment scenario to check the sampling scheme performance in terms of both computational time and accuracy in the characterization of an indoor environment.…”

Section: Proposed Methodology For Reducing Calibration Efforts and Bomentioning

confidence: 99%

“…To decrease this effort, we propose reducing the number of measured reference points via intelligent selection of only a few measured reference points. This selection is based on a novel, scenario‐dependent sampling scheme [13] which will guarantee that the location accuracy is not diminished. Since the fingerprint method requires a dense regular grid of RSSI fingerprints for the highest accuracy, the few measured RSSI samples are further combined with RSSI‐simulated values in different RPs until a regular grid database is built.…”

Section: Introductionmentioning

confidence: 99%

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Enhanced in-building fingerprint positioning using femtocell networks

et al. 2013

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The volume of traffic generated in indoor environments is increasing exponentially, and emerging femtocell networks are being deployed to meet associated challenges. These specific in‐building deployments provide an opportunity for improving the performance of indoor positioning systems. Femtocell networks can be used to support accurate and reliable location services based on received‐ power and fingerprint location techniques. In fingerprint positioning methods, calibration and recalibration procedures are extremely time‐consuming tasks. To address this problem, we developed a novel scenario‐dependent procedure that reduced calibration points. We also analyzed several indoor propagation models, evaluating their suitability to reduce the effort required for calibration as well as their applicability for the recalibration procedures necessary to update previously recorded fingerprints. Our procedure includes both a simulation and a measurement phase, and obtains important improvements in accuracy, precision, and reliability while reducing the time and effort associated with both calibration and recalibration procedures. © 2013 Alcatel‐Lucent.

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Section: Proposed Methodology For Reducing Calibration Efforts and Bomentioning

confidence: 99%

Section: Proposed Methodology For Reducing Calibration Efforts and Bomentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Enhanced in-building fingerprint positioning using femtocell networks

et al. 2013

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“…In that figure, possible locations of HeNB's (red dots) and the test-points to evaluate the objective functions (blue dots) are presented as well. The locations where a HeNB can be located are defined using a regular 2 meters grid, whereas the testpoints are obtained using an irregular morphological sampling scheme [22].…”

Section: Case Of Study: Design Of Henb's Deployment To Provide Mobilementioning

confidence: 99%

Design of in-building wireless networks deployments using evolutionary algorithms

Molina-García

Calle-Sánchez

González-Merino

et al. 2014

ICA

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Abstract. In this article, a novel approach to deal with the design of in-building wireless networks deployments is proposed. This approach known as MOQZEA (Multiobjective Quality Zone Based Evolutionary Algorithm) is a hybrid evolutionary algorithm adapted to use a novel fitness function, based on the definition of quality zones for the different objective functions considered. This approach is conceived to solve wireless network design problems without previous information of the required number of transmitters, considering simultaneously a high number of objective functions and optimizing multiple configuration parameters of the transmitters.

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“…Solutions for optimal sensors deployment have been given in [22][23][24][25] pursuing different aims. Optimisation of wireless indoor environment was discussed in [26] using a scenario-dependent sampling scheme. Optimal accuracy has been achieved in [27] using a variable number of sensors.…”

Section: Introductionmentioning

confidence: 99%

Viewing sector optimisation for indoor localisation using multi‐lateration with unilateral nodes deployment

Gonzalez-Zepeda

Shmaliy

et al. 2019

IET wirel. sens. syst.

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Viewing sector optimisation allows reaching a maximum available localisation accuracy in indoor environments using optimal estimators. In this study, the authors provide such an optimisation for multi-lateration by employing the unbiased finite impulse response (UFIR) filtering technique, which ignores noise and initial values. The authors consider the unilateral deployment of anchor nodes and show, both theoretically and experimentally, that the optimal angle of arrival is zero, optimal viewing angle is π/2, acceptable viewing angle ranges as π/4…3π/4 and nodes density k increases the localisation accuracy as a reciprocal of the root square of k. Experimental verification is provided using the ultra-wide band indoor precise localisation technology, in which the distance is measured via the time of arrival.

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Morphological Sampling Scheme for Analysis and Optimization of Wireless Indoor Environments

Cited by 5 publications

References 6 publications

Enhanced in-building fingerprint positioning using femtocell networks

Enhanced in-building fingerprint positioning using femtocell networks

Design of in-building wireless networks deployments using evolutionary algorithms

Viewing sector optimisation for indoor localisation using multi‐lateration with unilateral nodes deployment

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