Compressed data collection method for mobile wireless sensor network

A sensor network, compressing data technology, applied in network topology, wireless communication, transmission system and other directions, can solve the problems of unbalanced energy consumption, affecting the reconstruction accuracy of original sensing data, and the lack of error resistance of sensing data. life, reduce transmission cost, and reduce energy consumption

Active Publication Date: 2019-07-12
HUAIYIN INSTITUTE OF TECHNOLOGY
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Problems solved by technology

The first or second category does not take into account the time and space correlation of sensor node perception data at the same time; in the third category, most of the existing work studies how to design optimal routing protocols, such as designing cluster routing, etc. These routing protocols There are the following common problems: cluster head nodes responsible for forwarding and collecting sensing data are prone to unbalanced energy consumption during the entire network lifetime. In addition, this scheme is not effective for the transmission failure of sensing data and the interference caused by information transmission. Without error resistance, these actual problems will directly affect the reconstruction accuracy of the original perception data at the receiving end

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  • Compressed data collection method for mobile wireless sensor network
  • Compressed data collection method for mobile wireless sensor network
  • Compressed data collection method for mobile wireless sensor network

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Embodiment Construction

[0031] The present invention will be described in further detail below in conjunction with the accompanying drawings.

[0032] Such as figure 1 Shown, the present invention specifically comprises the following steps:

[0033] 1. Design a Treelet-based sparse representation base by using the spatial-temporal correlation of sensor node perception data.

[0034] (1) At the bottom of the tree l=0, it is assumed that the sensory data in the sensor network can be expressed as x 0 =[x 0,1 ,...,x 0,p ] T , and its corresponding Dirac basis is denoted as Ψ 0 =[ψ 0,1 ,ψ 0,2 ,...,ψ 0,p ], where Ψ 0 Is a p×p unit matrix, and then calculate the sample variance ∑ 0 and similarity matrix M 0 .

[0035] (2) According to the similarity matrix M 0 Find the most similar sum variable, assuming Where ζ∈{1,2,...,p}, l∈{1,2,...,L}, L is the maximum number of layers of the tree.

[0036] (3) Calculate the Jacobi matrix of parameters α, β.

[0037]

[0038] where c=cos(θ l ), s=si...

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Abstract

The invention discloses a compressed data collection method for a mobile wireless sensor network, which comprises the following steps of: firstly, designing a Treelet-based sparse representation baseby utilizing the space-time correlation of sensor node perception data; secondly, designing a sparse random measurement matrix; then, designing a Brown tree routing algorithm for data collection; andfinally, carrying out compressed data reconstruction at the Sink node according to the sparse representation base, the sparse random measurement matrix and the Brown tree route. By adopting the method, the data collection efficiency of the sensor network can be improved, the total cost of the nodes of the sensor network is reduced, and the life cycle of the network is prolonged to a certain extent.

Description

technical field [0001] The invention relates to the field of mobile wireless sensor networks, in particular to a compressed data collection method for mobile wireless sensor networks. Background technique [0002] In recent years, the application range of wireless sensor networks has become increasingly extensive, such as intelligent transportation, smart grid, smart medical care, safety production monitoring and other fields. If the wireless sensor network directly transmits the sensing data in practical applications, it will generate a large transmission cost, which will affect the performance of the network and reduce the life cycle of the network. In 2006, the compressed sensing technology proposed by David Donoho, Emmanuel Candes and Tao Zhexuan provided a new solution for the data collection method of wireless sensor networks. Compressed sensing technology can not only greatly reduce the energy consumption of the entire wireless sensor network, but also reconstruct th...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): H04W24/02H04W52/02H04W84/18H04L29/06
CPCH04W24/02H04W52/0203H04W84/18H04L69/04Y02D30/70
Inventor 顾相平常波庄立运王晓燕
Owner HUAIYIN INSTITUTE OF TECHNOLOGY
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