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In this paper, we investigate the machine learning based strategies for dynamic channel selection in Cognitive Access Points (CogAPs) of WLANs. We employ Multi-layer Feedforward Neural Network (MFNN) models that utilize historical traffic information from network environment for learning the influence of spatio-temporal-spectral factors on the network and then predicting future traffic loads on each...
One of the early applications of cognitive networking in wireless networks is the problem of dynamic channel selection in multi-channel wireless networks. Dynamic channel selection requires gathering past and current traffic across multiple channels and predict future traffic loads on each of the channels for deciding best channel for the Access Point (AP) to operate on for serving wireless clients...
The growing revolution in wind energy encourages more accurate models for wind speed forecasting as the wind is fluctuate, periodic and volatile. An artificial neural network (ANN) method is used to predict the average hourly wind speed. Different from the multilayer perception network (MLP) which is more conversant, this paper presents a novel technique based on radial basis function (RBF) network...
Abstract. Applications in a wide variety of industries require access to multiple heterogeneous distributed databases. One step in heterogeneous database integration is semantic integration: identifying corresponding attributes in different databases that represent the same real world concept. The rules of semantic integration can not be pre-programmed since the information to be accessed is heterogeneous...
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