Two-stage bidirectional prediction data acquisition method based on underwater edge equipment

Abstract

The invention discloses a two-stage bidirectional prediction data acquisition method based on underwater edge equipment, and the method comprises the steps: transferring calculation from a centralizedcloud layer to a distributed edge layer, and constructing a bottom layer-edge-cloud three-layer data collection mode; historical information-based analysis and data prediction are carried out by utilizing the mobility and computing power of the underwater edge equipment, so that sound wave communication is effectively reduced on the premise of ensuring accurate data transmission, and the energy consumption of the sensor is reduced. By considering the differentiation capability of heterogeneous equipment, different prediction algorithms are adopted for two layers of nodes of an edge layer anda bottom layer, and an underwater data collection process is converted into a two-stage bidirectional data prediction process. The method can be applied to an underwater sensor network data collectionmechanism.

Description

technical field [0001] The invention relates to the field of data collection in underwater sensor networks, in particular to a two-stage bidirectional predictive data acquisition method based on underwater edge devices. Background technique [0002] In recent years, due to the wide application of underwater wireless sensor networks in marine energy survey, environmental index detection, military surveillance and marine disaster event monitoring, it has received extensive attention at home and abroad. The technologies of underwater surveying and other applications are advancing with the times, and the types of data detected are becoming more and more complicated, such as underwater temperature, salinity, oxygen content, pH, CO 2 , P and other elemental substance concentrations, the collected data values ​​are lengthy and complex. The cost of battery replacement or charging for sensor nodes at the bottom of the ocean is high, and the underwater acoustic wave transmission rate...

AI Technical Summary

Problems solved by technology

First, the general prediction model is generally applied to land wireless sensor networks, and there are few researchers who combine prediction algorithms with underwater sensor networks. Therefore, it is impossible to analyze the suitable model, select the appropriate forecasting algorithm

Second, there is still no good solution to the problems of weak computing power, small storage energy and inability to deliver data to the cloud in time for underwater nodes

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