Traffic flow scheduling optimization of IoT network based on data analysis and knowledge mining

There are terminal devices with different processing rates and different types of communication networks in the Internet of Things. Most of the transmitted data have certain requirements for delay and bandwidth. How to ensure effective data transmission between communication devices in the Internet of Things, make the data flow on each link meet the requirements and prevent data overflow is of great significance to control the flow in the Internet of Things. In this paper, the traffic scheduling with multiple QoS constraints is converted as a multiple objective optimization. The mathematical model of multiple objective optimization is solved by a multiple objective particle swarm optimization algorithm to obtain Pareto optimal solution. The experimental results on Mininet show that the proposed traffic scheduling is superior to previous ones.