Gamma-modulated Wavelet model for Internet of Things traffic

Promoted by sensor, big data and mobile computing technologies, the number of Internet of Things (IoT) applications and services is increasing rapidly. The massive amounts of heterogeneous data produced by a large variety of IoT devices require us to re-think its influence on the network. In this paper, we study the characteristics of IoT data traffic in the context of smart city. We generate data traffic according to the characteristics of different IoT applications. We propose a Gamma modulated wavelet method for statistical characterization of both IoT data and the aggregated traffic, aiming at analyzing the influence of IoT data traffic on the access and core network. By using Gamma function to modulate the coefficients of the wavelet, both the long range and short range dependency of the IoT data traffic can be described through fewer parameters. The Gamma modulation also reduces the independency of the coefficients and improves the accuracy of the Wavelet model.