UAV data acquisition trajectory algorithm based on solar-powered agricultural Internet of Things

Abstract

The invention discloses a UAV data acquisition trajectory algorithm based on solar-powered agricultural Internet of Things, which includes first selecting solar nodes as cluster heads for farmland UAV-WSN, and then adopting the FCM clustering algorithm based on the optimal number of clusters to determine clustering Center, and combined with the clustering center to map cluster head nodes to deploy solar energy nodes; propose three different UAV trajectory design schemes: WSN is not clustered, WSN is clustered but cluster head does not fuse data, WSN is clustered and cluster head fuses data. Then, according to the given CRLB, calculate the number of traversal nodes and the number of clusters required for the three schemes; UAV path planning is carried out according to the node distribution uniformity, node remaining energy and solar energy collection value, and three UAV trajectory planning routes are obtained ; Calculate the energy consumption of nodes corresponding to the three routes, and select the optimal solution through energy consumption and UAV flight distance. The invention can design a UAV data collection path with low energy consumption, short path length and wide data collection range.

Description

technical field [0001] The invention relates to the technical field of UAV trajectory design, in particular to a UAV data acquisition trajectory algorithm based on solar-powered agricultural Internet of Things. Background technique [0002] Wireless sensor network (WSN, wireless sensor network), as an important part of the agricultural Internet of Things system, is an important carrier to transform the agricultural production mode and promote the efficient development of agriculture. The traditional WSN is usually composed of a large number of sensor nodes with small size, less energy storage, and low price, and several data fusion centers. The sensor nodes transmit data to the fusion center through clustering, non-clustering, single-hop, and multi-hop methods (FC ), for a static FC, it is easy to cause the early death of sensor nodes and the energy hotspot problem of WSN. In order to optimize this problem, UAV (Unmanned aerial vehicle) is introduced in WSN. The use of UAV ...

AI Technical Summary

Problems solved by technology

The traditional WSN is usually composed of a large number of sensor nodes with small size, less energy storage, and low price, and several data fusion centers. The sensor nodes transmit data to the fusion center through clustering, non-clustering, single-hop, and multi-hop methods (FC ), for a static FC, it is easy to cause the early death of sensor nodes and the energy hotspot problem of WSN

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