Unmanned aerial vehicle network dynamic load balancing QoS guarantee implementation method and system

Abstract

The invention discloses an unmanned aerial vehicle network dynamic load balancing QoS guarantee realization method and system, a QoS scheme is subdivided into four modules, a network module and a control module are constructed by using an SDN technology, and network information management and control, especially traffic information, are realized by using the characteristic that a data plane and a control plane are separated. The queue module uses a differential service idea, configures queues by means of a TC tool to realize differentiated services, distinguishes link traffic through a DSCP field, allocates bandwidths according to different service types, and provides sufficient link bandwidths for high-speed services. The routing module considers the influence of the link bandwidth utilization rate on the QoS of the unmanned aerial vehicle, combines the bandwidth utilization rate with a shortest path algorithm, provides real-time flow information through the control module, realizes dynamic load balancing routing calculation, and reduces the occurrence of high-bandwidth occupied links. Finally, an SDN unmanned aerial vehicle network is built for simulation implementation. The result shows that QoS indexes such as the link bandwidth utilization rate, the time delay, the packet loss rate and the jitter are obviously improved.

Description

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine

Login to view more

AI Technical Summary

Benefits of technology

This patented describes an improved way of managing wireless networks' resources efficiently based upon their usage levels or performance metrics. It divides up these schemes into seven components: Network Module (NSM), Transportation Modules (TMs) and Controllers (CC). These systems use software defined networking techniques like SDN technologies to manage them separately from each other. They also have separate queueing functions called Queue Management Functions (QMIesFEs) to help allocate space between certain connections while ensuring efficient communication over multiple paths. Additionally, they consider how resource allocation affects signal quality during transmission across both directions at once. Overall, this new approach helps optimize wireless networks more accurately reflect its own needs better efficiency and reliability.

Problems solved by technology

This patented technical describes two different ways to enhance the performance capabilities of droid robots: one involves adding software or hardware components on top of existing ones; another focuses on improving communication between machines over wireless channels like radio waves.

Images