Network Traffic Prediction Based on Deep Belief Network and Spatiotemporal Compressive Sensing in Wireless Mesh Backbone Networks

Wireless mesh network is prevalent for providing a decentralized access for users and other intelligent devices. Meanwhile, it can be employed as the infrastructure of the last few miles connectivity for various network applications, for example, Internet of Things (IoT) and mobile networks. For a wireless mesh backbone network, it has obtained extensive attention because of its large capacity and low cost. Network traffic prediction is important for network planning and routing configurations that are implemented to improve the quality of service for users. This paper proposes a network traffic prediction method based on a deep learning architecture and the Spatiotemporal Compressive Sensing method. The proposed method first adopts discrete wavelet transform to extract the low-pass component of network traffic that describes the long-range dependence of itself. Then, a prediction model is built by learning a deep architecture based on the deep belief network from the extracted low-pass component. Otherwise, for the remaining high-pass component that expresses the gusty and irregular fluctuations of network traffic, the Spatiotemporal Compressive Sensing method is adopted to predict it. Based on the predictors of two components, we can obtain a predictor of network traffic. From the simulation, the proposed prediction method outperforms three existing methods

Internet traffic volumes characterization and forecasting

Internet usage increases every year and the need to estimate the growth of the generated traffic has become a major topic. Forecasting actual figures in advance is essential for bandwidth allocation, networking design and investment planning. In this thesis novel mathematical equations are presented to model and to predict long-term Internet traffic in terms of total aggregating volume, globally and more locally. Historical traffic data from consecutive years have revealed hidden numerical patterns as the values progress year over year and this trend can be well represented with appropriate mathematical relations. The proposed formulae have excellent fitting properties over long-history measurements and can indicate forthcoming traffic for the next years with an exceptionally low prediction error. In cases where pending traffic data have already become available, the suggested equations provide more successful results than the respective projections that come from worldwide leading research. The studies also imply that future traffic strongly depends on the past activity and on the growth of Internet users, provided that a big and representative sample of pertinent data exists from large geographical areas. To the best of my knowledge this work is the first to introduce effective prediction methods that exclusively rely on the static attributes and the progression properties of historical values

Modelo de propagación para un entorno urbano que identifica las oportunidades espectrales para redes móviles de radio cognitiva

El pronóstico de ocupación del espectro radioeléctrico es útil en el diseño de sistemas inalámbricos que aprovechan las oportunidades en el espectro como la radio cognitiva. En este documento se propone el desarrollo de un modelo de propagación, que a través del pronóstico de la potencia recibida, identifica las oportunidades espectrales en canales de una red móvil celular para un entorno urbano. El modelo propuesto integra un modelo de propagación a gran escala con un modelo neuronal wavelet, que combina las pérdidas promedio con las pérdidas instantáneas. Los resultados del modelo, obtenidos a través de simulaciones, son consistentes con el comportamiento observado en experimentos de este tipo de sistemas inalámbricos.Abstract. The forecast of the radioelectric spectrum occupancy is useful for wireless systems designs that take advantage of spectrum opportunities, such as cognitive radio. In this document the development of a propagation model is proposed, that through the forecasting of received power, identifies the spectral opportunities in channels of a cellular mobile network for an urban environment. The proposed model integrates a large-scale propagation model with a wavelet neural model, which combines the average losses with the instantaneous losses. The results of this model, which are obtained through simulations, are consistent with the behavior observed experimentally of this class of wireless systems.Doctorad