A Data Aggregation Method Based on Compressed Sensing in Wireless Sensor Networks

Abstract

The invention discloses a data aggregation method based on compressed sensing in a wireless sensor network. First, the sensor network is evenly divided into clusters, and the node with the most remaining energy is elected as the cluster head node. The member nodes are clustered with probability p tx Independently choose whether to participate in sampling, and the cluster head node always participates in sampling; then, after obtaining the original signal f, the sampling node transforms it under the sparse transformation basis to obtain its sparse representation x, x is projected under the measurement matrix Φ, and the sparse measurement signal y is obtained. And sent to the cluster head node, the cluster head node uses a vectorization operator on the collected measurement signals, merges them into a signal Y and sends it to the fusion center; finally, the fusion center uses the adaptive weight GPSR algorithm to reconstruct them one by one and recover Its sparse representation X. This invention fully complies with the characteristics of wireless sensor network signals including noise, large data volume, and high real-time requirements; the adaptive weight GPSR algorithm does not need to know the signal sparsity in advance and can accurately reconstruct all high-dimensional signals in a short time. .

Description

technical field [0001] The invention belongs to the technical field of wireless sensor networks, and in particular relates to a data aggregation method based on compressed sensing in a wireless sensor network. Background technique [0002] In recent years, wireless sensor networks have been widely used in many fields due to their advantages of concealment, fault tolerance, and convenient deployment, such as environmental monitoring, security, and smart home. Usually, a wireless sensor network is composed of a large number of nodes with wireless communication capabilities, computing capabilities and sensing capabilities, which can cooperate to complete various tasks of environment perception, information collection and target recognition. In order to complete these tasks, each node needs to collect a large amount of real-time data and send it to the fusion center through multi-hop routing for data processing and analysis. This process requires a large amount of storage space...

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to provide a data aggregation method based on compressed sensing in a wireless sensor network, aiming to solve the problem that the traditional compressed sensing-based wireless sensor network data aggregation method has no joint utilization of information in the time and space domains. The efficiency of the network data acquisition model is low, and the amount of data transmitted between the networks is relatively large

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