An extension of Bayesian algorithm into gaussian processes for predicting sensor network

Abstract

This paper makes a long-term prediction of the sensor nodes network over some information seeking research in some randomly localized environment with the application of the Bayesian and Gaussian processes models. These properties may include life span of the sensors and their distance measurements from a decision centre. The data generated in probabilities representations are fitted with the Exponential, Gaussian and Fourier functions to enable decision taking. Of the three functions, Fourier series was estimated to be the best function for the curve fitting both graphically and numerically as shown through the experiments. It has a far and wider predictive horizon beyond the data locations than the other distributive functions.

Keywords and phrases:

• Sensor network
• Bayesian network
• Gaussian processes
• Curve fittings and long-term prediction