CN112055325A - Combined compression and encryption method for multi-type space-time data in wireless sensor network - Google Patents

Combined compression and encryption method for multi-type space-time data in wireless sensor network Download PDF

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CN112055325A
CN112055325A CN202010969471.9A CN202010969471A CN112055325A CN 112055325 A CN112055325 A CN 112055325A CN 202010969471 A CN202010969471 A CN 202010969471A CN 112055325 A CN112055325 A CN 112055325A
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徐博
韩太林
王啸
鞠明池
刘轩
胡俊
杨絮
王英志
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Changchun University of Science and Technology
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    • H—ELECTRICITY
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Abstract

The invention provides a combined compression and encryption method of multi-type space-time data in a wireless sensor network, which belongs to the field of special signal processing and comprises the following steps: establishing a half tensor compressed sensing measurement matrix of the chaotic sequence; in the signal sensing and sampling of each sensor node, a chaotic semi-tensor compressed sensing measurement matrix is used for carrying out sampling rate reduction encryption sampling; generating a chaos auxiliary matrix, and taking the sum of the chaos auxiliary matrix and the compressed sensing acquisition result as a final acquisition result; generating an encryption coverage matrix L through an asymmetric encryption protocol; randomly selecting any time of a node by adopting a semi-tensor compressed sensing matrix to realize compression of data in a time dimension, and storing the acquired data into a node matrix; dividing the whole sensing network into a plurality of clusters; randomly selecting a plurality of nodes and simultaneously collecting data; the cluster head nodes collect all the data of the collection nodes; and performing signal reconstruction. The method reduces the number and size of the measurement matrixes and improves the operation efficiency of the whole sensing network.

Description

Combined compression and encryption method for multi-type space-time data in wireless sensor network
Technical Field
The invention belongs to the field of special signal processing, and particularly relates to a combined compression and encryption method for multi-type space-time data in a wireless sensor network.
Background
The traditional wireless distributed sensing network needs to maintain high sampling rate, and simultaneously detect and collect various signals at a plurality of test nodes, so that a large amount of redundant data appears in the test process; storage transmission of the whole distributed test system faces a serious challenge, and the open wireless network is vulnerable to data theft in the transmission process. Therefore, how to safely and efficiently collect data and reduce network energy consumption in a wireless sensor network with limited resources becomes an urgent problem to be solved.
Therefore, the application provides a joint compression encryption method for multi-type space-time data in a wireless sensor network.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention provides a combined compression and encryption method for multi-type space-time data in a wireless sensor network.
In order to achieve the above purpose, the invention provides the following technical scheme:
the combined compression and encryption method of the multi-type space-time data in the wireless sensor network comprises the following steps:
step 1, establishing a half tensor compressed sensing measurement matrix of a chaotic sequence;
step 2, the sensor nodes sense and collect signals, and a chaotic semi-tensor compressed sensing measurement matrix is used for carrying out sampling rate reduction encryption sampling in the signal sensing and sampling of each sensor node;
step 3, generating a chaotic auxiliary matrix according to the signal type, wherein each sampling result is the sum of the sampling result in the step 2 and the chaotic auxiliary matrix;
step 4, generating an encryption coverage matrix L through an asymmetric encryption protocol, performing data coverage by performing a half tensor product multiplication method with the sampling result of the step 3 to obtain a covered sampling result, and performing secondary encryption on the data;
step 5, considering the time correlation of data acquired at different times of the same node, randomly selecting any time of the node by adopting a semi-tensor compressed sensing matrix to realize the compression of the data on a time dimension, and storing the acquired data into a node matrix;
step 6, dividing the whole sensing network into a plurality of clusters, wherein different nodes in each cluster acquire data in the same time and have spatial correlation;
step 7, randomly selecting a plurality of nodes to simultaneously acquire data, and reducing redundant information of spatial data;
step 8, collecting all the collected node data by the cluster head nodes;
and 9, signal reconstruction is carried out.
Preferably, in the step 1, the chaotic system is introduced into the compressed sensing, and the chaotic system generates a measurement matrix Φ required by the chaotic compressed sensing according to the formula (1);
Φ＝T(S(n0,d,C(z0,))) (1)
wherein n is0For sampling the initial position, d for the sampling interval, Z0The method comprises the following steps of (1) mapping a chaotic sequence into a chaotic matrix by taking a chaotic initial value as a chaotic parameter, taking C as a chaotic system, sampling an S representative sequence at a certain initial position and a sampling interval, and taking T as a mapping function;
suppose that the matrix A ∈ Rm×n,B∈Rp×qIf n and p are multiples of each other, then the matrix A, B can be expressed as a half tensor product
Figure BDA0002683572370000022
；
Expressed in a matrix as:
Figure BDA0002683572370000021
because the compressed sensing is realized by multiplying the measurement matrix by the sparse matrix of the compressed signal, the matrix multiplication of the half tensor product is introduced into a half tensor compressed sensing model, and a small measurement matrix is adopted to realize the compressed sensing, namely:
Figure BDA0002683572370000023
wherein the observation matrix A belongs to RM×N,M<N, the collected signal x belongs to RpCompressing the sensing sampling result y;
in conclusion, the chaos sequence and the half tensor product are introduced into the compressed sensing, and the data sub-sampling encryption collection is realized by generating the chaos half tensor compressed sensing and adopting the observation matrix.
Preferably, in step 3, the matrix dimensions of the different types of signals are different, matrix multiplication with different dimensions can be realized by using half tensor matrix multiplication, and a uniform measurement matrix can be adopted for observation in the acquisition process of the different types of signals; assume that the original signal lengths are xm，xn，…，xl，xkMeasurement matrix phi1 ∈mp/nHowever, the dimensionality of the observation result depends on the length of the original signal, and the chaotic system is utilized to generate auxiliary matrixes phi with different dimensionalities according to the signal typesnAdding the obtained signal with the sampling result of the step 2, unifying the matrix dimensions of the signals of different types, and finally obtaining a sampling result y1Comprises the following steps:
Figure BDA0002683572370000031
in the formula, n refers to the number of chaos-assisted observation matrices, and depends on how many types of signals exist in the current system.
Preferably, in step 4, the asymmetric encryption algorithm uses one key value to encrypt the message-public key and another key value to decrypt the message-private key, and the two key values are generated in the same process to form a key pair;
the asymmetric encryption algorithm is realized by using a classical RSA algorithm; selecting a pair of different prime numbers l, k (keeping secret), calculating n ═ l ═ k, f (n)) ═ l-1 (k-1), finding a number e which is coprime to f (n) as a public key index, and 1< e < f (n), calculating a private key index d which meets (d · e) mod ((l-1) × (q-1)) ═ 1, and obtaining a public key KU ═ e, n, and KR private key ═ d, n;
and setting the ciphertext as C, wherein the encryption process comprises the following steps: and C is M ^ e mod n, and the decryption process is as follows: m ═ C ^ d mod n;
generating an encryption matrix L by using the algorithm, covering the sampling result in the step 3 by adopting half tensor product multiplication to obtain a covered final sampling result y2：
Figure BDA0002683572370000032
Preferably, in step 9, a distributed compressed sensing model is established based on the time correlation and the spatial correlation between the data, and signal reconstruction is performed through the distributed compressed sensing model;
the signal reconstruction is divided into two parts: firstly, reconstructing the data information of all cluster head nodes into node signals of a single node through a distributed compressed sensing joint reconstruction algorithm according to the spatial correlation of the collected data in the same time; and secondly, reconstructing an original signal of a single node at a certain moment by a distributed compressed sensing joint reconstruction algorithm according to the time correlation of the acquired data of the same node at different times.
The combined compression and encryption method for the multi-type space-time data in the wireless sensor network has the following beneficial effects:
(1) the chaos compression sensing is introduced, so that the problem that data compression and encryption cannot be synchronously realized in the traditional wireless sensing network is solved; the method has the advantages that the half tensor product is introduced, the problems of large storage capacity and large calculation amount of a random observation matrix in a wireless sensor network are solved, the same measurement matrix is adopted for various types of data, and the storage calculation pressure of the whole distributed system is reduced; meanwhile, the safety of data is further guaranteed by introducing an asymmetric encryption algorithm; the method provides the joint compressed sensing of the signals based on the time correlation and the space correlation of the data, so that the technology can be popularized to a large-scale wireless sensing network;
(2) the problem that the traditional wireless distributed sensing network is limited in computing resources, storage resources and transmission bandwidth is solved, the computation process of the compressed sensing observation matrix is simplified, and various types of data can be processed simultaneously; the method can be applied to a large-scale wireless sensor network to process multi-type data simultaneously, the operation efficiency of the whole sensor network is improved, and the reliability of the sensor network is enhanced;
(3) the problems of high sampling rate acquisition and unsafe data in the traditional wireless distributed sensor network are solved, and the technology is suitable for processing various types of data in a large-scale wireless sensor network through related optimization of traditional compressed sensing.
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In order to more clearly illustrate the embodiments of the present invention and the design thereof, the drawings required for the embodiments will be briefly described below. The drawings in the following description are only some embodiments of the invention and it will be clear to a person skilled in the art that other drawings can be derived from them without inventive effort.
Fig. 1 is a flowchart of a joint compression encryption method for multi-type spatiotemporal data in a wireless sensor network according to embodiment 1 of the present invention;
FIG. 2 is a schematic diagram of spatiotemporal data correlation;
FIG. 3 is a chaotic compressed sensing matrix generation process;
FIG. 4 is a flow chart of signal encryption and decryption;
fig. 5 is a flow chart of an asymmetric encryption algorithm.
Detailed Description
In order that those skilled in the art will better understand the technical solutions of the present invention and can practice the same, the present invention will be described in detail with reference to the accompanying drawings and specific examples. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present invention is not limited thereby.
Example 1
The invention provides a method for jointly compressing and encrypting multi-type space-time data in a wireless sensor network, which is specifically shown in figures 1 and 5 and comprises the following steps:
step 1, establishing a half tensor compressed sensing measurement matrix of a chaotic sequence; a cryptosystem with small calculated amount and small storage amount is designed, the compression, encryption and acquisition of various types of data are realized simultaneously, and only one observation matrix is needed in the whole sensing network;
the user needs to store an initial value and chaotic parameters, obtains a complete chaotic sequence through the chaotic system, samples the chaotic sequence according to the initial sampling position and the sampling interval, and finally maps the chaotic sequence into a measurement matrix required by chaotic compressed sensing through a mapping function. Specifically, in the step 1, a chaotic system is introduced into compressed sensing, and the chaotic system generates a measurement matrix phi required by the chaotic compressed sensing according to a formula (1);
Φ＝T(S(n0,d,C(z0,))) (1)
wherein n is0For sampling the initial position, d for the sampling interval, Z0The chaotic system is a chaotic initial value, is a chaotic parameter, C is a chaotic system, an S representative sequence is sampled at a certain initial position and sampling interval, T is a mapping function, and the chaotic sequence is mapped into a chaotic matrix. The chaotic compressed sensing matrix generation process is a pseudo-random measurement matrix generated by a deterministic function, as shown in fig. 3, so that a receiving and transmitting user only needs to store the same system parameter value as a secret key and does not need to store the whole measurement matrix. Half tensor product multiplication is a new type of matrix multiplication that is always between the traditional matrix multiplication and tensor product multiplication.
Suppose that the matrix A ∈ Rm×n,B∈Rp×qIf n and p are multiples of each other, then the matrix A, B may be expressed as a half tensorProduct of large quantities
Figure BDA0002683572370000062
；
Expressed in a matrix as:
Figure BDA0002683572370000061
because the compressed sensing is realized by multiplying the measurement matrix by the sparse matrix of the compressed signal, the matrix multiplication of the half tensor product is introduced into a half tensor compressed sensing model, and a small measurement matrix is adopted to realize the compressed sensing, namely:
Figure BDA0002683572370000063
wherein the observation matrix A belongs to RM×N,M<N, the collected signal x belongs to RpCompressing the sensing sampling result y;
in conclusion, the chaotic sequence and the half tensor product are introduced into the compressed sensing, and the data sub-sampling encryption acquisition is realized by generating the chaotic half tensor compressed sensing and adopting a smaller observation matrix;
step 2, the sensor nodes sense and collect signals, and a chaotic semi-tensor compressed sensing measurement matrix is used for carrying out sampling rate reduction encryption sampling in the signal sensing and sampling of each sensor node;
step 3, generating a chaotic auxiliary matrix according to the signal type, wherein each sampling result is the sum of the sampling result in the step 2 and the chaotic auxiliary matrix; the chaos auxiliary matrix is a dimension of a multi-type node data forwarding unified matrix, and each signal only needs one same auxiliary matrix;
specifically, in step 3, the matrix dimensions of different types of signals are different, matrix multiplication of different dimensions can be realized by using semi-tensor matrix multiplication, and a uniform measurement matrix can be adopted for observation in the acquisition process of different types of signals; assume that the original signal lengths are xm,xn,…,xl,xkMeasuring matrixΦ1 ∈mp/nHowever, the dimensionality of the observation result depends on the length of the original signal, and the chaotic system is utilized to generate auxiliary matrixes phi with different dimensionalities according to the signal typesnAdding the sampling result of the step 2, unifying the matrix dimensions of different types of signals, and compressing the sensing sampling result y1Comprises the following steps:
Figure BDA0002683572370000071
in the formula, n refers to the number of chaos-assisted observation matrices, and depends on how many types of signals exist in the current system.
Step 4, generating an encryption coverage matrix L through an asymmetric encryption protocol, performing data coverage by performing a half tensor product multiplication method with the sampling result of the step 3 to obtain a covered sampling result, and performing secondary encryption on the data to improve the data security;
specifically, in step 4, the asymmetric encryption algorithm uses one key value to encrypt the message-public key and uses another key value to decrypt the message-private key, and the two key values are generated in the same process to form a key pair;
the asymmetric encryption algorithm is realized by using a classical RSA algorithm; selecting a pair of different and large enough prime numbers l, k (keeping secret), calculating n ═ l k, f (n) ═ l-1 (k-1), finding a number e which is relatively prime with f (n) as a public key index, and 1< e < f (n), calculating a private key index d which meets (d · e) mod ((l-1) × (q-1)) ═ 1, and obtaining a public key KU ═ e, n, and a private key KR ═ d, n;
and setting the ciphertext as C, wherein the encryption process comprises the following steps: and C is M ^ e mod n, and the decryption process is as follows: m ═ C ^ d mod n;
generating an encryption matrix L by using the algorithm, covering the sampling result in the step 3, improving the data security and obtaining a covered sampling result y2：
Figure BDA0002683572370000072
As shown in fig. 4, the signal encryption and decryption process includes:
a needs to send information to B, firstly A is encrypted by a public key and then sent to B (public key encryption), B is encrypted by a private key of the B and then sent to A (public key encryption + B private key encryption), A is decrypted by the public key and then sent to B (B private key encryption), at this time, the information sent to B by A is encrypted by the private key of B, only B can be decrypted by the private key of the B, and other owners can not decrypt the information.
The encryption and decryption principle of the whole network is explained by taking the encryption and decryption of the single-path data as an example.
In the signal encryption process of the sending end, original signals are subjected to compression encryption sampling through chaotic semi-tensor compression sensing to obtain a primary sampling result, and the primary sampling result and an auxiliary chaotic matrix generated according to signal types are subjected to matrix addition to generate a final sampling result; and generating an asymmetric encryption matrix through a traditional RSA algorithm to cover the sampling result, and finishing the whole encryption process.
The signal decryption process of the user side is the inverse process of the process, the inverse matrix of the asymmetric encryption matrix is obtained according to the asymmetric encryption matrix, and matrix multiplication is carried out on the inverse matrix and the encryption result to remove the coverage matrix; obtaining a chaotic matrix by chaotic system parameters stored by a user side, and performing matrix subtraction to obtain a first-step sampling result through half tensor compressed sensing; and carrying out compressed sensing reconstruction through a traditional OMP algorithm to obtain an original signal.
Step 5, considering the time correlation of data acquired at different times of the same node, randomly selecting any time of the node by adopting a semi-tensor compressed sensing matrix to realize the compression of the data on a time dimension, and storing the acquired data into a node matrix;
step 6, dividing the whole sensing network into a plurality of clusters, wherein different nodes in each cluster acquire data in the same time and have spatial correlation; dividing clusters to determine whether the nodes sample and transmit data, wherein the process utilizes the spatial correlation of data collected by different nodes in each cluster at the same time;
step 7, randomly selecting a plurality of nodes to simultaneously acquire data, and reducing redundant information of spatial data;
step 8, collecting all the collected node data by the cluster head nodes;
step 9, signal reconstruction is carried out; and establishing a distributed semi-tensor compressed sensing model based on the chaotic sequence for signal reconstruction, and reconstructing the data information of all the fusion nodes to obtain the original node signals through a distributed semi-tensor compressed sensing joint reconstruction algorithm.
Specifically, in step 9, as shown in fig. 2, a distributed compressed sensing model is established based on the temporal correlation and the spatial correlation between the data, and signal reconstruction is performed through the distributed compressed sensing model;
the signal reconstruction is divided into two parts: firstly, reconstructing the data information of all cluster head nodes into node signals of a single node through a distributed compressed sensing joint reconstruction algorithm according to the spatial correlation of data which are not acquired at the same time; and secondly, reconstructing an original signal of a single node at a certain moment by a distributed compressed sensing joint reconstruction algorithm according to the time correlation of the acquired data of the same node at different times.
The combined compression and encryption method for the multi-type space-time data in the wireless sensor network improves the working efficiency and the safety characteristic of signal acquisition and transmission of the wireless sensor network and ensures the safety and confidentiality performance of the signals in the acquisition and transmission process. The compressed sensing is to complete two steps of compressed encryption in the acquisition process, and the transmission is the result of the compressed sensing, so that the acquisition sampling rate is reduced, the transmission bandwidth is saved, and the data security is ensured.
The above-mentioned embodiments are only preferred embodiments of the present invention, and the scope of the present invention is not limited thereto, and any simple modifications or equivalent substitutions of the technical solutions that can be obviously obtained by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention.

Claims (5)

1. A joint compression encryption method for multi-type space-time data in a wireless sensor network is characterized by comprising the following steps:
step 1, establishing a half tensor compressed sensing measurement matrix of a chaotic sequence;
step 2, the sensor nodes sense and collect signals, and a chaotic semi-tensor compressed sensing measurement matrix is used for carrying out sampling rate reduction encryption sampling in the signal sensing and sampling of each sensor node;
step 3, generating a chaotic auxiliary matrix according to the signal type, wherein each sampling result is the sum of the sampling result in the step 2 and the chaotic auxiliary matrix;
step 4, generating an encryption coverage matrix L through an asymmetric encryption protocol, performing data coverage by performing a half tensor product multiplication method with the sampling result of the step 3 to obtain a covered sampling result, and performing secondary encryption on the data;
step 5, considering the time correlation of data acquired at different times of the same node, randomly selecting any time of the node by adopting a semi-tensor compressed sensing matrix to realize the compression of the data on a time dimension, and storing the acquired data into a node matrix;
step 6, dividing the whole sensing network into a plurality of clusters, wherein different nodes in each cluster acquire data in the same time and have spatial correlation;
step 7, randomly selecting a plurality of nodes to simultaneously acquire data, and reducing redundant information of spatial data;
step 8, collecting all the collected node data by the cluster head nodes;
and 9, signal reconstruction is carried out.
2. The method for jointly compressing and encrypting the multi-type spatiotemporal data in the wireless sensor network according to claim 1, wherein in the step 1, a chaotic system is introduced into compressed sensing, and the chaotic system generates a measurement matrix phi required by the chaotic compressed sensing according to a formula (1);
Φ＝T(S(n0,d,C(z0,))) (1)
wherein n is0For sampling the initial position, d for the sampling interval, Z0The method comprises the following steps of (1) mapping a chaotic sequence into a chaotic matrix by taking a chaotic initial value as a chaotic parameter, taking C as a chaotic system, sampling an S representative sequence at a certain initial position and a sampling interval, and taking T as a mapping function;
suppose that the matrix A ∈ Rm×n,B∈Rp×qIf n and p are multiples, then the matrix A, B can be multiplied and expressed as a half tensor product
Figure FDA0002683572360000022
Expressed in a matrix as:
Figure FDA0002683572360000021
matrix multiplication of the half tensor product is introduced into a half tensor compressed sensing model, and compressed sensing is realized by adopting a measurement matrix, namely:
Figure FDA0002683572360000023
wherein the observation matrix A belongs to RM×N,M<N, the collected signal x belongs to RpCompressing the sensing sampling result y;
and introducing the chaotic sequence and the half tensor product into compressed sensing, generating the chaotic half tensor compressed sensing, and realizing data sub-sampling encrypted acquisition by adopting an observation matrix.
3. The method for jointly compressing and encrypting the multi-type space-time data in the wireless sensor network according to claim 2, wherein in the step 3, the matrix dimensions of the different types of signals are different, the matrix multiplication with different dimensions is realized by using half tensor matrix multiplication, and a unified measurement matrix can be adopted for observation in the acquisition process of the different types of signals; assume that the original signal lengths are xm,xn,…,xl,xkMeasurement matrix phi1 ∈mp/nHowever, the dimensionality of the observation result depends on the length of the original signal, and the chaotic system is utilized to generate auxiliary matrixes phi with different dimensionalities according to the signal typesnAdding the obtained signal with the sampling result of step 2 to unify the matrix dimensions of different types of signals, and finallyFinal sampling result y1Comprises the following steps:
Figure FDA0002683572360000024
in the formula, n refers to the number of chaos-assisted observation matrices, and depends on how many types of signals exist in the current system.
4. The method for joint compression encryption of multi-type spatio-temporal data in a wireless sensor network according to claim 3, wherein in the step 4, the asymmetric encryption algorithm uses one key value for encrypting the message-public key and another key value for decrypting the message-private key, and the two key values are generated in the same process to become a key pair;
the asymmetric encryption algorithm is realized by using a classical RSA algorithm; selecting a pair of different prime numbers l, k, calculating n ═ l ═ k, f (n) ═ l-1 (k-1), finding a number e which is coprime to f (n) as a public key index, and 1< e < f (n), calculating a private key index d which meets (d · e) mod ((l-1) × (q-1)) ═ 1, and obtaining a public key KU ═ (e, n), and a private key KR ═ (d, n);
and setting the ciphertext as C, wherein the encryption process comprises the following steps: and C is M ^ e mod n, and the decryption process is as follows: m ═ C ^ d mod n;
generating an encryption matrix L by using the algorithm, covering the sampling result in the step 3 by adopting half tensor product multiplication to obtain a covered final sampling result y2：
Figure FDA0002683572360000031
5. The method for jointly compressing and encrypting the multi-type spatio-temporal data in the wireless sensor network according to claim 4, wherein in the step 9, a distributed compressed sensing model is established based on the time correlation and the space correlation among the data, and the signal is reconstructed through the distributed compressed sensing model;
the signal reconstruction is divided into two parts: firstly, reconstructing the data information of all cluster head nodes into node signals of a single node through a distributed compressed sensing joint reconstruction algorithm according to the spatial correlation of the collected data in the same time; and secondly, reconstructing an original signal of a single node at a certain moment by a distributed compressed sensing joint reconstruction algorithm according to the time correlation of the acquired data of the same node at different times.
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