An unmanned aerial vehicle charging task scheduling method in a wireless sensor network

Abstract

The invention provides an unmanned aerial vehicle charging task scheduling method in a wireless sensor network. According to the method, classification is carried out according to the power consumption rate of sensor nodes, the nodes are divided into different charging tasks according to a clustering algorithm, a set of charging paths are obtained by solving a path problem, scheduling planning ofthe unmanned aerial vehicle is carried out, and finally a final scheduling scheme is obtained through optimization of an emergency algorithm. The invention has the advantages of wide application range, high charging efficiency, low cost and good market application prospect.

Description

technical field [0001] The invention relates to the technical field of wireless sensor networks, in particular to a method for scheduling a charging task of an unmanned aerial vehicle in a wireless sensor network. Background technique [0002] A wireless sensor network is a communication network composed of sensors connected in a multi-hop manner and deployed in a distributed manner. Each sensor node in the network can independently perceive, collect and process monitored information within a certain range, and is widely used in environmental monitoring, medical care, target tracking and military fields. Although the wireless sensor network has broad application prospects, the sensor consumes a lot of energy, and the battery capacity has not been greatly improved. At the same time, since sensor nodes are often deployed in complex environments and involve a large area, it is expensive to use human power for energy supply. As a result, the current application of wireless sen...

AI Technical Summary

Problems solved by technology

Although the wireless sensor network has broad application prospects, the energy consumption of the sensor is high, and the battery capacity has not been greatly improved.

At the same time, since sensor nodes are often deployed in complex environments and involve a large area, it is expensive to use human power for energy supply

As a result, the current application of wireless sensor networks is limited to short-term scientific research and industrial monitoring

[0003] Traditional energy supply solutions such as reducing energy consumption and energy harvesting technologies cannot effectively solve this problem because they cannot fundamentally solve the problem of battery capacity and are susceptible to environmental and climatic factors.

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