Intra-cluster Data Sparse Basis Generation Method for Sequence Prediction in Farmland Wireless Networks

Abstract

The invention discloses a method for generating sequence prediction on the basis of the farmland wireless network intra-cluster data sparsity. The method includes the steps that a cluster head node obtains the spatial association between environment data initial fusion results according to the position relations between ordinary nodes after receiving the environment data initial fusion results uploaded by the ordinary nodes; the cluster head conducts secondary fusion on the environment data initial fusion results according to the spatial association between the environment data initial fusion results so as to obtain the fusion results of data in a farmland wireless sensor network cluster. According to the method, a hierarchical type clustering network is constructed, the monitoring data prediction is conducted according to the time relevance, the intra-cluster data fusion is conducted according to the spatial association, an intra-cluster sparse mapping matrix is rapidly generated according to the periodical change rule of monitoring data and the sparsity of spatial data, network data communication loads are reduced, and therefore the overall energy consumption of the network is reduced, and the aims of prolonging the life cycle of the network and improving the network transmission efficiency are achieved.

Description

technical field [0001] The invention relates to the field of agricultural technology, in particular to a sequence prediction method for generating data sparse bases in farmland wireless network clusters. Background technique [0002] Wireless sensor network (WSN) is a wireless network without infrastructure, which can monitor, perceive and collect information of various environments or monitoring objects in the network distribution area in real time. key technologies. Traditional wireless sensor networks are generally powered by batteries, but for the complex environment of large-scale farmland, batteries with limited energy cannot support them for a long enough time. There are a large number of sensor nodes for large-scale farmland monitoring, and the manual battery replacement cycle is long and the workload is heavy. Once the energy of the node battery is exhausted, the network performance and coverage will be greatly affected. [0003] Commonly used low-energy design ma...

AI Technical Summary

Problems solved by technology

[0005] In the perception fusion of wireless sensor networks, it is necessary to reduce the communication between nodes as much as possible. The existing methods generate a large amount of algorithm communication overhead when constructing data sparse representation, so that the effect of data fusion cannot be guaranteed.

For hierarchical networks, starting fusion from the cluster head does not reduce intra-cluster data traffic, thus limiting the performance of network-aware fusion

The data fusion algorithm at the pure data level is generally more complicated, and the compression ratio is not high, and it is not suitable for wireless sensor network application scenarios with limited resources; data fusion at the network layer requires network topology information, which is not suitable for distributed algorithms; the decision-making layer Data fusion filters a large amount of raw data, and finally only obtains decision-making result information, which is not practical in agricultural production where the current level of informatization is not very high

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