Intelligent Traffic Management and Load Balance Based on Spike ISDN-IoT

An intelligent software defined network (ISDN) based on an controller can manage and control the in a remarkable way. In this article, methodology is proposed to estimate packet flow at sensing plane network-Internet of Things partial recurrent spike neural (PRSNN) congestion controller, predict next step ahead thus, reduce that may occur. That is, model (spike ISDN-IoT) enhanced with controller. This works as proactive model. addition, we propose another clustering artificial network, which operates reactive area ISDN-IoT. Hence, queuing introduced table buffer capacity ISDN-IoT such quality service (QoS) whole improved. A modified training algorithm train PRSNN adjust its weight threshold. The simulation results demonstrate QoS improved by (14.36%) when using compared convolutional network.