Wireless sensor data collection method based on ElGamal algorithm

Abstract

The invention discloses a wireless sensor data collection method based on an ElGamal algorithm, and the method comprises the steps: S1, enabling a sensor to calculate a sparse random matrix, and calculating the data transmitted in the collection process of each round of measurement value according to the sparse random matrix; S2, sending the calculated data to a base station through an El Gammal encryption and compressed sensing technology; and S3, after the base station collects enough measurement data, recovering the ciphertext into plaintext data, and calculating the original data of each sensor by using a compressed sensing technology. According to the method, a sparse sub-Gaussian random matrix is used for replacing a traditional Gaussian random matrix to serve as a sensing matrix, the characteristics of a planar network are comprehensively considered, and in the data collection process, a pipeline technology is used for collecting measurement values, so that the energy consumption of the network is reduced, and the data collection time is shortened.

Description

technical field [0001] The invention relates to the technical field of wireless communication, in particular to a method for collecting wireless sensor data based on an ElGamal algorithm. Background technique [0002] Compressed sensing, also known as compressed sampling, is an emerging information processing theory. According to the compressed sensing theory, if the original signal itself is sparse or sparse in a certain transformation domain, the original signal can be accurately restored by a small number of random linear measurements. Compressed sensing theory breaks through the limitations of the Nyquist sampling theorem from the signal sampling frequency, and lays the foundation for a new signal processing method. Compressed sensing has the characteristics of simple encoding and complex decoding, which is very suitable for wireless sensor networks with weak performance of ordinary nodes and strong performance of base station nodes. computing power on the base station...

AI Technical Summary

Problems solved by technology

[0008] However, the existing wireless sensor data collection mainly has the following two problems: first, the existing data collection basically assumes that the sensing matrix is ​​a dense matrix (each element in the matrix is ​​a non-zero element), and the data collection The efficiency is low; second, the previous work is basically to build a complete spanning tree with the base station as the root, and then collect the data of each node according to the spanning tree. During the collection of measurement values, each node in the network is need to send data

High energy consumption and long data collection latency

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