Prediction of wireless network connectivity using a Taylor Kriging approach

Authors:
Heping Liu;Soroor K. Al-Khafaji;Alice E. Smith
Affiliations:
Department of Industrial and Systems Engineering, Auburn University, Auburn, AL, 36849, USA.;Department of Industrial and Systems Engineering, Auburn University, Auburn, AL, 36849, USA.;Department of Industrial and Systems Engineering, Auburn University, Auburn, AL, 36849, USA
Venue:
International Journal of Advanced Intelligence Paradigms
Year:
2011

Citing 4
Cited 1

Metamodeling: a state of the art review

WSC '94 Proceedings of the 26th conference on Winter simulation
Interpolation for wireless sensor network coverage

EmNets '05 Proceedings of the 2nd IEEE workshop on Embedded Networked Sensors
A neural network-based approach for predicting connectivity in wireless networks

International Journal of Mobile Network Design and Innovation
A kriging approach to predicting coverage in wireless networks

International Journal of Mobile Network Design and Innovation

Simulation optimization based on Taylor Kriging and evolutionary algorithm

Applied Soft Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

The research aim of this paper is to investigate the effectiveness of a new Kriging model which uses Taylor expansion to predict wireless network connectivity. Wireless network connectivity is measured by the strength of emitted signal power from the tower to the point in question. The prediction results are compared with those from the literature where an Ordinary Kriging model and a neural network are used to conduct the same prediction. Root mean squared error (RMSE) and maximum absolute relative error (MARE) show that the prediction results of the new Kriging model are much better than those obtained before with average differences from 51.56% to 85%. This study shows the promise of the new Kriging model to accurately estimate wireless signal strength.