Road sign map coloring unmanned aerial vehicle energy-saving endurance data collection method based on metric learning

Abstract

The invention discloses a road sign coloring unmanned aerial vehicle energy-saving endurance data collection method based on metric learning. The problems of throughput and energy consumption in an unmanned aerial vehicle auxiliary sensor network are mainly solved. The method comprises the steps that model establishment is carried out on the unmanned aerial vehicle auxiliary sensor network, an isomorphic network model is provided, and nodes in the network have mobility. And continuing to optimize the trajectory of the unmanned aerial vehicle by using a metric learning method, training a metric matrix offline, and constructing the metric matrix by using an LMNN algorithm in an offline training stage. The network can be represented by a graph, each point in the graph represents a node in the network, and an edge connecting two points represents packet sending communication of the two points, so that the channel allocation problem of the multi-hop network can be converted into a graph coloring problem. In an unmanned aerial vehicle auxiliary network supporting quick movement, the flight speed of an unmanned aerial vehicle is relatively high, and a ground node can be a mobile robot with mobility, so that a channel access method in which priority is divided by contact time is adopted.

Description

technical field [0001] The invention belongs to the field of UAV-assisted sensor networks, and in particular relates to a method for collecting UAV energy-saving battery life data based on metric learning-based coloring of roadmap graphs. Background technique [0002] With the rapid development of electronics, sensors and communication counting, drones are widely used in various military and civilian fields. Applying drones to wireless sensor networks can effectively collect sensor data, prolong network life, and reduce network energy consumption. In UAV-assisted sensor networks (UWSNs), the channel access algorithm is very important, which affects not only the system performance but also the energy efficiency of sensor nodes. Therefore, designing an efficient channel access algorithm for UWSNs is a problem worthy of research. [0003] In the UAV-assisted sensor network, since the UAV usually has other tasks, the UAV flies according to a fixed flight trajectory, and comple...

AI Technical Summary

Problems solved by technology

[0006] In order to solve the problem of low throughput performance of fast-moving UAV-assisted network and insufficient energy consumption of UAV flight, the example of the present invention adopts the metric learning method for path planning to optimize the throughput. The method obtains the metric matrix through offline training and uses the metric online The matrix predicts the optimal information of throughput and energy consumption performance. In order to achieve the above-mentioned purpose, the example of the present invention provides a roadmap map coloring UAV energy-saving endurance data collection method for metric learning

Images