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.
The simulation community has used metamodels to study the behavior of computer simulations for over twenty-five years. The most popular techniques have been based on parametric polynomial response surface approximations. In this state of the art review, we present recent developments in this area, with a particular focus on new developments in the experimental designs employed.
Berzelius failed to make use of Faraday's electrochemical laws in his laborious determination of equivalent weights.