CN115514789B - Compression-sensing vehicle network interaction data lightweight security convergence transmission method and system - Google Patents
Compression-sensing vehicle network interaction data lightweight security convergence transmission method and system Download PDFInfo
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Abstract
The invention relates to a compressed sensing vehicle network interaction data lightweight security convergence transmission method and system, which are characterized in that the whole network is randomly divided into non-overlapping clusters to carry out data aggregation, each electric vehicle node is regarded as a data sample, a Bernoulli matrix is utilized to generate a random value at the same time, the random value is used for constructing a global sparse measurement matrix, finally, a cluster head receives a measured value and the Bernoulli random value from a child node, and a convergence node carries out compressed observation. The confidentiality of aggregation is improved by generating the measurement matrix by all nodes, and each sensor node only generates a part of the measurement matrix and only sends the sampling data to the cluster head nodes, so that the communication overhead of aggregation is greatly reduced, and the service life of the network is prolonged.
Description
Technical Field
The invention belongs to the technical field of data interaction of the Internet of things, and particularly relates to a compressed sensing vehicle network interaction data lightweight security convergence transmission method and system.
Background
Under the large background of 'carbon reaching peak' and 'carbon neutralization', the new energy transformation speed of China is gradually increased. The electric automobile is taken as an important main body for new energy consumption, can be matched with the production period of new energy, promotes the continuous development and upgrading of new energy industry, can effectively relieve the load pressure of a power system during peak, and can consume redundant energy during valley so as to achieve the purpose of peak clipping and valley filling. Therefore, the promotion of the bidirectional interaction between the electric automobile and the electric power system is a precondition for realizing the regulation and control of new energy. The wireless sensor network technology is the basis of interaction between the electric automobile and the power grid, the sensor nodes are deployed on the electric automobile and the charging pile, the aggregator aggregates data acquired by the sensors and sends the data to the power grid control center, and the control center further schedules and controls the electric automobile according to the received data so as to realize management of a demand side.
Although the aggregation device can reduce the communication overhead and load of the nodes to a certain extent by aggregating the vehicle network interaction data, the resources of the aggregation nodes cannot meet the rapidly-increased data interaction demands more and more in face of the massive requirements of the electric vehicle for accessing the power grid. On the other hand, in the data aggregation process, a malicious attacker can acquire key privacy information of the electric automobile in a eavesdropping mode and even tamper the key information, so that the purpose of the malicious attacker is achieved. Therefore, the high efficiency and safety of the key privacy data aggregation in the vehicle-network interaction process are hot problems of urgent need to be studied before the large-scale development of the vehicle-network interaction.
The safe and efficient aggregation of data is a hot spot subject, and a plurality of data aggregation models are developed at present. Classical clustering aggregation methods and random fragmentation aggregation methods cannot solve the problems of traffic and privacy protection; the homomorphic encryption-based data aggregation method and elliptic curve-based aggregation method can well realize privacy protection in the aggregation process, but modulus inversion and power operation require a large amount of computing resources, so that aggregation nodes are difficult to bear; the aggregation method based on group intelligent optimization can effectively reduce the energy consumption of the nodes, but has the problem of key sensitive information leakage. Therefore, under the large background of bidirectional interaction of the vehicle network mass interaction data, the realization of light-weight safe convergence transmission of the data is one of the directions needing to be studied in an important way.
Disclosure of Invention
The invention aims to overcome the defects of the prior art, provides a compressed sensing vehicle network interaction data lightweight safety convergence transmission method and system, effectively ensures confidentiality of fusion data by globally constructing an observation matrix, lightens dimensionality of the observation data by compressed sensing, reduces communication overhead of the vehicle network interaction data and finally realizes safety convergence transmission of the vehicle network interaction data.
The invention solves the technical problems by adopting the following technical scheme:
the compressed sensing vehicle network interaction data lightweight security convergence transmission method comprises the following steps:
constructing a network model according to deployment conditions of the electric automobile and the aggregator;
constructing a minimum spanning tree according to the network model;
generating a stitching key for each node of the minimum spanning tree;
performing network interaction data fusion based on compressed sensing;
and transmitting the integrated vehicle network interaction data to a sink node of the minimum spanning tree, and decrypting the integrated vehicle network interaction data at the sink node to obtain the original data.
The specific implementation method for constructing the network model according to the deployment conditions of the electric automobile and the aggregator comprises the following steps: the deployment condition of the electric automobile and the aggregator is equivalent to a wireless sensing network G (V, E) which is deployed in a rectangular area and provided with N nodes, wherein V represents a set of nodes, E represents a link set among different nodes, data is collected from other nodes in a network model as an aggregation node S, the network model data aggregation is carried out in a periodic form, each node only generates one sample data every period,
is the firstiSamples generated by each node at each cycle, N samples are collected from N nodes at each cycle as
。
Moreover, the communication distance of each node is as follows:
wherein ,
for the length of the largest side of the delimited rectangular region,Nis the number of nodes in the rectangular area.
The specific implementation method for constructing the minimum spanning tree according to the network model comprises the following steps:
the distribution of the electric automobile and the aggregator is equivalent to a graph on the basis of a network model, and all nodes and links in the network model are sequentially added into a priority queue P1;
taking out the minimum link in the priority queue P1, and judging whether two points of the link are communicated;
if the two points of the link are communicated, the two nodes are indicated to have other edges to communicate the two points, and the two points are skipped; otherwise, the two vertices are merged and this link is used;
and judging whether two points of all broken links in the priority queue P1 are communicated or not in sequence until the priority queue P1 is empty, and forming a minimum spanning tree by the used links.
The specific implementation method for generating the stitching key for each node of the minimum spanning tree is as follows:
each node generates a chaotic sequence L by using a Logistic chaotic system:
wherein ,
is a control parameter->
Is a generated chaotic sequence, and is projected through a function according to random mapping generated by Logistic to obtain Bernoulli distribution sequence ∈ ->
：
wherein ,
is a Bernoulli distribution sequence with length N, which is a child of each nodeA key.
The specific implementation method for carrying out data fusion on the stitching key based on compressed sensing comprises the following steps: dividing the network model into M non-overlapping clusters to aggregate vehicle network interaction data, wherein the M clusters are respectively expressed as
Each cluster contains a number of nodes +.>
The measurement data of each node in the cluster is denoted +.>
Will be->
Corresponding to the node->
Multiplying to obtain +.>
And->
Let each leaf node of the network model send his measurement +.>
Giving the cluster head node, and simultaneously adding the measured value received from the leaf node and the measured value of the cluster head to obtain the firstjThe final measurement values of the cluster head nodes are: />
wherein ,
is->
and
For each cluster C, +.>
Measurement data for observation data per period of all nodes +.>
The generation rule of (a) is as follows
The cluster head in the network model transmits the calculated measured value to the sink node in the network model through the minimum spanning tree, and the cluster head encapsulates the current measured value of the cluster head with a relay data packet from the back to the sink node along the minimum spanning tree along the direction from the minimum spanning tree to the sink node; each cluster as a measurement matrix
Each node in the network model is a column of the measurement matrix, M randomly formed cluster heads and the nodes in each cluster correspond to the measurement matrix +.>
And corresponding columns:
wherein ,
for measuring matrix->
Line j, < >>
，
The data packet received by the sink node from the minimum spanning tree contains the elements of the measurement vector +.>
Wherein the element of the measurement vector is contained +.>
Is a linear combination of measured data and node random values:
wherein 
，
，
，
,
。
The specific implementation method for decrypting the integrated vehicle network interaction data at the sink node to obtain the original data is as follows: the sink node receives cluster organization information returned by the cluster head before each cycle of data acquisition and returns a random seed for generating a subkey, and the sink node is provided with the number of each column of a measurement matrixThe value elements, each cluster forms each row of elements of the observation matrix, and the element combinations contained by the sink nodes of different clusters finally obtain the complete measurement matrix
After the vehicle network interaction data aggregation is completed, the vector is measured
Recovery of original signal using OMP orthogonal matching pursuit reconstruction method>
。
A vehicle-network interaction data lightweight security convergence transmission system comprises a network model construction module, a minimum spanning tree construction module, a stitching key generation module, a vehicle-network interaction data fusion module and a decryption module;
the network model construction module is used for equivalently deploying the deployment conditions of the electric automobile and the aggregator into a wireless sensor network which is deployed in a rectangular area and provided with N nodes;
the minimum spanning tree construction module is used for randomly selecting M nodes as aggregation nodes to collect data in each period of the network model, generating paths according to the minimum spanning tree, and finally transmitting the data to the aggregation nodes;
the stitching key generation module is used for generating a key of each node of the minimum spanning tree;
the vehicle network interaction data fusion module is used for compressing the perceptually encrypted data;
the decryption module is used for decrypting the compressed and perceptually encrypted data to obtain the original data.
Moreover, the minimum spanning tree construction module includes: a priority queue P1 construction module and a link connection judgment module;
the priority queue P1 construction module is used for equating the distribution of the electric automobile and the aggregator into a graph on the basis of the network model, and sequentially adding all nodes and links in the network model into the priority queue P1;
the link connection judging module is used for taking out the minimum link in the priority queue P1 and judging whether two points of the link are connected;
if the two points of the link are communicated, the two nodes are indicated to have other edges to communicate the two points, and the two points are skipped; otherwise, the two vertices are merged and this link is used;
and judging whether two points of all broken links in the priority queue P1 are communicated or not in sequence until the priority queue P1 is empty, and forming a minimum spanning tree by the used links.
Moreover, the patch key generation module comprises a chaotic sequence L generation module and a Bernoulli distribution sequence
A generating module;
the chaotic sequence L generation module is used for generating a chaotic sequence L by each node through a Logistic chaotic system;
bernoulli distribution sequence
The generation module is used for obtaining the Bernoulli distribution sequence by function projection according to random mapping generated by Logistic>
。
The invention has the advantages and positive effects that:
according to the invention, the whole network is randomly divided into non-overlapping clusters for data aggregation, each electric automobile node is regarded as a data sample, meanwhile, a Bernoulli matrix is utilized to generate a random value for constructing a globally sparse measurement matrix, and finally, a cluster head receives a measured value and the Bernoulli random value from a child node and performs compression observation by a sink node. The confidentiality of aggregation is improved by generating the measurement matrix by all nodes, and each sensor node only generates a part of the measurement matrix and only sends the sampling data to the cluster head nodes, so that the communication overhead of aggregation is greatly reduced, and the service life of the network is prolonged.
Drawings
FIG. 1 is a diagram of a network model architecture of the present invention;
FIG. 2 is a schematic diagram of cluster partitioning and key distribution according to the present invention;
FIG. 3 is a diagram of a compressed sensing-based data aggregation process in accordance with the present invention;
FIG. 4 is a graph comparing the performance of the present invention with a conventional approach in a grid deployment scenario;
FIG. 5 is a graph comparing the performance of the present invention with a conventional method in a random deployment scenario;
FIG. 6 is a graph comparing transmission costs of the present invention with those of the conventional method in a different method;
fig. 7 is a schematic diagram of reconstruction accuracy under different sparse bases according to the present invention and the conventional method.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
The compressed sensing vehicle network interaction data lightweight security convergence transmission method comprises the following steps:
and 1, constructing a network model according to deployment conditions of the electric automobile and the aggregator.
According to the deployment conditions of the electric automobile and the aggregator, the specific implementation method for constructing the network model comprises the following steps: the deployment condition of the electric automobile and the aggregator is equivalent to a wireless sensing network G (V, E) which is deployed in a rectangular area and provided with N nodes, wherein V represents a set of nodes, E represents a link set among different nodes, data are collected from other nodes in a network model to be sink nodes S, the data sampling time of all the nodes is synchronous, the data are all within the coverage range of transmission signals, and each node has the same circular coverage radius. The communication distance of each node is set as follows:
wherein ,
for the length of the largest side of the delimited rectangular region,Nis the number of nodes in the rectangular area. NetThe aggregation of the complex model data is performed in the form of cycles, each cycle generating only one sample of data per node, +.>
Is the firstiSamples generated by each node at each cycle, N samples are collected from N nodes at each cycle, which is +.>
. Meanwhile, the model does not consider the situation of packet loss and delay.
And 2, constructing a minimum spanning tree according to the network model. Based on the network model, the distribution of the electric automobile and the aggregator is equivalent to a graph. In each period, M nodes are randomly selected as aggregation nodes to collect data, then paths of the nodes are generated according to a minimum spanning tree (Minimun Spanning Tree, MST), and finally the data are transmitted to the aggregation nodes. The MST generation process is as follows:
and 2.2, on the basis of a network model, the distribution of the electric automobile and the aggregator is equivalent to a graph, and all nodes and links in the network model are sequentially added into a priority queue P1.
And 2.3, taking out the smallest link in the priority queue P1, and judging whether two points of the link are communicated.
Step 2.4, if two points of the link are communicated, the two nodes are indicated that other edges are communicated with the two points, and the two points are skipped; otherwise, the two vertices are merged and this link is used.
Step 2.5, sequentially judging whether all two points of the broken link in the priority queue P1 are communicated or not until the priority queue P1 is empty, and forming a minimum spanning tree by the used link.
As shown in fig. 1, the network model is defined to be n=100, m=40, the sink node is s=101, represented by a star, the square node represents the cluster head node CHs, the remaining nodes are leaf nodes, and the black bold line represents the Minimum Spanning Tree (MST). Black thin lines represent links of leaf nodes with cluster head nodes.
And 3, generating a stitching key for each node of the minimum spanning tree. The data aggregation method based on compressed sensing provided by the invention disperses the generation of the measurement matrix to each node, and each node is observed independently. And the measurement matrix which is required to be completed in the recovery of the data of the sink node is recovered, so that the sink node cannot realize decryption, and the confidentiality of the vehicle network interaction data is greatly improved. Meanwhile, the observation of the measurement matrix is equivalent to the encryption of data, and the patent breaks and blocks the encryption of the aggregated data, so that frequent generation of the measurement matrix is avoided, and the overhead of node aggregation is greatly reduced. The key seeds of each node are broadcast by the sink node after the sink node obtains the cluster related information, and a cluster division and key distribution schematic diagram is shown in fig. 2.
The concrete implementation method for generating the stitching key for each node of the minimum spanning tree comprises the following steps:
each node generates a chaotic sequence L by using a Logistic chaotic system:
wherein ,
is a control parameter->
Is a generated chaotic sequence, and is projected through a function according to random mapping generated by Logistic to obtain Bernoulli distribution sequence ∈ ->
：
wherein ,
is a bernoulli distribution sequence of length N, which is a subkey for each node.
And 4, carrying out network interaction data fusion based on compressed sensing.
Is a signal of length N containing sampling information of N nodes in the network, M nodes are randomly selected as Cluster heads in order to gather data from all nodes, so that the probability of each node being selected as a Cluster Head (Cluster Head) is +.>
The remaining nodes are connected to the CH nearest to each other by the shortest path. Thus, the entire network model is divided into M non-overlapping clusters to aggregate the vehicle network interaction data.
The specific implementation method for carrying out data fusion on the stitching key based on compressed sensing comprises the following steps: dividing the network model into M non-overlapping clusters to aggregate vehicle network interaction data, wherein the M clusters are respectively expressed as
Each cluster contains a number of nodes +.>
The measurement data of each node in the cluster is denoted +.>
Will be->
Corresponding to the node->
Multiplying to obtain +.>
And->
Causing each leaf node of the network model to send his measurementsValue->
Giving the cluster head node, and simultaneously adding the measured value received from the leaf node and the measured value of the cluster head to obtain the firstjThe final measurement values of the cluster head nodes are:
wherein ,
is->
and
For each cluster C, +.>
Measurement data for observation data per period of all nodes +.>
The generation rule of (a) is as follows
The cluster head in the network model transmits the calculated measured value to the sink node in the network model through the minimum spanning tree, and the cluster head encapsulates the current measured value of the cluster head with a relay data packet from the back to the sink node along the minimum spanning tree along the direction from the minimum spanning tree to the sink node; each cluster as a measurement matrix
Each node in the network model being a row of the measurement matrixOne column, M randomly formed cluster heads and nodes in each cluster correspond to a measurement matrix +.>
And corresponding columns:
wherein ,
for measuring matrix->
Line j, < >>
，
The data packet received by the sink node from the minimum spanning tree contains the elements of the measurement vector +.>
Wherein the element of the measurement vector is contained +.>
Is a linear combination of measured data and node random values:
wherein 
，
，
，
,
。
Assume a mesh deployment network has 100 nodes, with an aggregation node (s=101) placed in the center of the network. Taking node 98 of fig. 1 as an example, it has two sub-nodes 88, 97, one row in the measurement matrix is made up of the cluster, where the cluster has only three non-zero values, corresponding to the 88, 97, 98 nodes respectively, the corresponding compressed sensing aggregation process is shown in fig. 3,
represents sparse signals, represents information to be collected in each cluster after node clustering, and is sparse because the information of the cluster is only contained>
Information representing the cluster to be collected, +.>
Is the total signal length, +.>
Representing a sparse observation matrix with the size of MxN formed after node clustering, gradually converging through a spanning tree, and obtaining corresponding aggregation elements from each cluster to finally obtain aggregated data y.
And 5, transmitting the integrated vehicle network interaction data to a sink node of the minimum spanning tree, and decrypting the integrated vehicle network interaction data at the sink node to obtain original data. After the vehicle network interaction data aggregation is completed, namely the data after compressed sensing encryption, if the organization mode of the clusters and the subkeys of each node are not known, the original data cannot be obtained. The integrated vehicle network interaction dataThe method for decrypting the integrated vehicle network interaction data at the sink node to obtain the original data comprises the following steps of: the sink node receives cluster organization information returned by the cluster head before each cycle of data acquisition and returns a random seed for generating a subkey, the sink node is provided with numerical elements of each column of the measurement matrix, each cluster forms each row of elements of the observation matrix, and the sink nodes of different clusters contain element combinations to finally obtain a complete measurement matrix
After the completion of the aggregation of the vehicle network interaction data, the measurement vector is +.>
The original signal is restored by using the reconstruction method>
. In this way it is avoided that every node will +.>
The information of (2) is transmitted to the receiver together with the measurement data, the risk of key leakage is greatly reduced. Since the equation is underdetermined, there will be an infinite number of solutions when X is recovered from Y, and the sink node can pass through +.>
The reconstruction method can reconstruct and obtain +.>
。
According to the compressed sensing vehicle network interaction data lightweight security convergence transmission method, reconstruction error comparison analysis is carried out with the existing CS-based data aggregation method. To demonstrate the efficiency of the method of the present invention, it is first compared to the shortest path route measurement method (SPRM) in a grid deployment scenario. Then, the method is compared with a cluster-based weighted compressed data aggregation method (CWCDA) and a Hybrid compressed sensing method (Hybrid CS) in a random deployment scene. Fig. 4 compares the average reconstruction error of the proposed data aggregation method with the SPRM method at different compression rates CR. Here, the average error is an error average of 100 experiments at each CR. As can be seen from fig. 4, the proposed method has lower recovery errors compared to SPRM for all considered compression ratios. Fig. 5 shows a data recovery performance comparison of the proposed method with a sensor node random deployment method. As can be seen from fig. 5, the proposed method has better recovery performance in all considered compression ratios compared to ccda. Compared with the mixed CS, the method has more excellent reconstruction performance at a higher compression ratio (CR > 50).
To compare transmission costs, consider 625 nodes to be deployed in a 256m×256m area for testing. Fig. 6 shows a comparison of transmission costs for data aggregation at different compression rates using the proposed method, SPRM, hybrid CS, CWCDA and non-CS methods. The conventional shortest path method for data collection is herein considered as a non-CS method, wherein each node in the network sends its data to the receiver over the shortest path. As can be seen from fig. 6, the different compression rates at the node random deployment of the methods herein all require very low data aggregate transmission costs compared to non-CS, hybrid CS and CWCDA. In the case of grid deployment, the data aggregation performance of the methods herein is superior to the SPRM method at compression rates less than 80%. As can be seen from the analysis of the method herein, an increase in compression ratio reduces the number of clusters required for data aggregation, which increases the transmission cost required for data aggregation, since leaf nodes need to send their measurements from a greater distance to the cluster head node. Furthermore, as CR increases, the transmission cost required to collect measurement data from cluster head nodes using the minimum spanning tree also increases. This results in an increase in total transmission costs at higher compression rates (CR > 80%). In summary, the methods herein can transmit data to a receiver at a lower transmission cost while improving network lifetime compared to other data aggregation methods.
Meanwhile, a theory for verification is established by setting up a simulation system based on a public charging station. The simulation system consists of a number of DC charging pile nodes, each node consists of a ZigBee module and a 2.4GHz IEEE 802.15.4 wireless transceiver and an 8MHz 8-bit processor, wherein the processor is provided with 128KB flash memory and 8KB RAM. Within the area, 50 dc charging pile nodes are deployed, and the receiver node is connected to a PC that collects measurement data from all nodes in the network. Each node represents an electric automobile and a sink node respectively, and the proposed method is realized at each node in a programming mode. In order to evaluate the effectiveness of the method, the reconstruction accuracy is used as an index, the compression rate is respectively 0.1-0.9 for testing, the system is divided into 5-45 clusters at the moment, the data aggregation is respectively carried out in a distributed mode under each condition, and then the reconstruction accuracy of the signals is calculated on a PC. In order to verify the adaptation degree of the proposed scheme to different sparse bases, the same experiment was performed under the different sparse bases, and the result is shown in fig. 7. Proved by verification, the method has good effect in reconstruction, and the method has good effect in vehicle-network interaction real-time aggregation. Meanwhile, the calculation resources of the experimental module are very limited, which proves that the method is light in weight, does not need any extra calculation cost, and is suitable for scenes with limited resources but huge data aggregation requirements under the interaction scene of the vehicle network.
Based on the vehicle network interaction data lightweight security convergence transmission method based on compressed sensing, the invention also provides a convergence transmission system of the vehicle network interaction data lightweight security convergence transmission method based on compressed sensing, comprising the following steps:
the system comprises a network model construction module, a minimum spanning tree construction module, a stitching key generation module, a vehicle network interaction data fusion module and a decryption module;
the network model construction module is used for equivalently deploying the deployment conditions of the electric automobile and the aggregator into a wireless sensor network which is deployed in a rectangular area and provided with N nodes;
the minimum spanning tree construction module is used for randomly selecting M nodes as aggregation nodes to collect data in each period of the network model, generating paths according to the minimum spanning tree, and finally transmitting the data to the aggregation nodes;
the stitching key generation module is used for generating a key of each node of the minimum spanning tree;
the vehicle network interaction data fusion module is used for compressing the perceptually encrypted data;
the decryption module is used for decrypting the compressed and perceptually encrypted data to obtain the original data.
Wherein, minimum spanning tree construction module includes: a priority queue P1 construction module and a link connection judgment module;
the priority queue P1 construction module is used for equating the distribution of the electric automobile and the aggregator into a graph on the basis of the network model, and sequentially adding all nodes and links in the network model into the priority queue P1;
the link connection judging module is used for taking out the minimum link in the priority queue P1 and judging whether two points of the link are connected;
if the two points of the link are communicated, the two nodes are indicated to have other edges to communicate the two points, and the two points are skipped; otherwise, the two vertices are merged and this link is used;
and judging whether two points of all broken links in the priority queue P1 are communicated or not in sequence until the priority queue P1 is empty, and forming a minimum spanning tree by the used links.
Wherein the patch key generation module comprises a chaos sequence L generation module and a Bernoulli distribution sequence
A generating module;
the chaotic sequence L generation module is used for generating a chaotic sequence L by each node through a Logistic chaotic system;
bernoulli distribution sequence
The generation module is used for obtaining the Bernoulli distribution sequence by function projection according to random mapping generated by Logistic>
。
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein. The solutions in the embodiments of the present application may be implemented in various computer languages, for example, object-oriented programming language Java, and an transliterated scripting language JavaScript, etc.
The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiments and all such alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various modifications and variations can be made in the present application without departing from the spirit or scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims and the equivalents thereof, the present application is intended to cover such modifications and variations.
Claims (7)
1. The compressed sensing vehicle network interaction data lightweight security convergence transmission method is characterized by comprising the following steps of: the method comprises the following steps:
constructing a network model according to deployment conditions of the electric automobile and the aggregator;
the deployment condition of the electric automobile and the aggregator is equivalent to a wireless sensing network G (V, E) which is deployed in a rectangular area and provided with N nodes, wherein V represents a set of nodes, E represents a link set among different nodes, data is collected from other nodes in a network model as an aggregation node S, the network model data aggregation is carried out in a periodic form, each node only generates one sample data every period,
is the firstiSamples generated by each node at each cycle, N samples are collected from N nodes at each cycle as
；
Constructing a minimum spanning tree according to the network model;
generating a stitching key for each node of the minimum spanning tree;
performing network interaction data fusion based on compressed sensing;
dividing the network model into M non-overlapping clusters to aggregate vehicle network interaction data, wherein the M clusters are respectively expressed as
Each cluster contains a number of nodes +.>
The measurement data of each node in the cluster is denoted +.>
Will be->
Corresponding to the node->
Multiplying to obtain +.>
And->
Causing each leaf node of the network model to transmit his measurements
Giving the cluster head node, and simultaneously adding the measured value received from the leaf node and the measured value of the cluster head to obtain the firstjThe final measurement values of the cluster head nodes are:
wherein ,
is->
and
For each cluster C, +.>
Measurement data for observation data per period of all nodes +.>
The generation rule of (a) is as follows
The cluster head in the network model transmits the calculated measured value to the sink node in the network model through the minimum spanning tree, and the cluster head encapsulates the current measured value of the cluster head with a relay data packet from the back to the sink node along the minimum spanning tree along the direction from the minimum spanning tree to the sink node; each cluster as a measurement matrix
Each node in the network model is a column of the measurement matrix, M randomly formed cluster heads and the nodes in each cluster correspond to the measurement matrix +.>
And corresponding columns:
wherein ,
for measuring matrix->
Line j, < >>
，
The data packet received by the sink node from the minimum spanning tree contains the elements of the measurement vector +.>
In which the elements of the measurement vector are contained
Is a linear combination of measured data and node random values:
wherein 
，
，
，
,
；
And transmitting the integrated vehicle network interaction data to a sink node of the minimum spanning tree, and decrypting the integrated vehicle network interaction data at the sink node to obtain the original data.
2. The compressed sensing vehicle network interaction data lightweight security convergence transmission method according to claim 1, wherein the method comprises the following steps: the communication distance of each node is as follows:
wherein ,
for the length of the largest side of the delimited rectangular region,Nis the number of nodes in the rectangular area.
3. The compressed sensing vehicle network interaction data lightweight security convergence transmission method according to claim 1, wherein the method comprises the following steps: the specific implementation method for constructing the minimum spanning tree according to the network model comprises the following steps:
the distribution of the electric automobile and the aggregator is equivalent to a graph on the basis of a network model, and all nodes and links in the network model are sequentially added into a priority queue P1;
taking out the minimum link in the priority queue P1, and judging whether two points of the link are communicated;
if the two points of the link are communicated, the two nodes are indicated to have other edges to communicate the two points, and the two points are skipped; otherwise, the two vertices are merged and this link is used;
and judging whether two points of all broken links in the priority queue P1 are communicated or not in sequence until the priority queue P1 is empty, and forming a minimum spanning tree by the used links.
4. The compressed sensing vehicle network interaction data lightweight security convergence transmission method according to claim 1, wherein the method comprises the following steps: the concrete implementation method for generating the stitching key for each node of the minimum spanning tree comprises the following steps:
each node generates a chaotic sequence L by using a Logistic chaotic system:
wherein ,
is a control parameter->
Is a generated chaotic sequence, and is projected through a function according to random mapping generated by Logistic to obtain Bernoulli distribution sequence ∈ ->
：
wherein ,
is a bernoulli distribution sequence of length N, which is a subkey for each node.
5. The compressed sensing vehicle network interaction data lightweight security convergence transmission method according to claim 1, wherein the method comprises the following steps: the specific implementation method for decrypting the integrated vehicle network interaction data at the sink node to obtain the original data comprises the following steps of: the sink node receives cluster organization information returned by the cluster head before each cycle of data acquisition and returns a random seed for generating a subkey, the sink node is provided with numerical elements of each column of the measurement matrix, each cluster forms each row of elements of the observation matrix, and the sink nodes of different clusters contain element combinations to finally obtain a complete measurement matrix
After the completion of the aggregation of the vehicle network interaction data, the measurement vector is +.>
In using OMP orthogonal matching pursuit reconstruction algorithmsRestoration of original signal->
。
6. The vehicle-network interaction data lightweight security convergence transmission system is characterized by comprising a network model construction module, a minimum spanning tree construction module, a stitching key generation module, a vehicle-network interaction data fusion module and a decryption module;
the network model construction module is used for equivalently deploying the deployment conditions of the electric automobile and the aggregator into a wireless sensor network which is deployed in a rectangular area and provided with N nodes;
the minimum spanning tree construction module is used for randomly selecting M nodes as aggregation nodes to collect data in each period of the network model, generating paths according to the minimum spanning tree, and finally transmitting the data to the aggregation nodes;
the minimum spanning tree construction module comprises: a priority queue P1 construction module and a link connection judgment module;
the priority queue P1 construction module is used for equating the distribution of the electric automobile and the aggregator into a graph on the basis of the network model, and sequentially adding all nodes and links in the network model into the priority queue P1;
the link connection judging module is used for taking out the minimum link in the priority queue P1 and judging whether two points of the link are connected;
if the two points of the link are communicated, the two nodes are indicated to have other edges to communicate the two points, and the two points are skipped; otherwise, the two vertices are merged and this link is used;
sequentially judging whether two points of all broken links in the priority queue P1 are communicated or not until the priority queue P1 is empty, wherein the used links form a minimum spanning tree;
the stitching key generation module is used for generating a key of each node of the minimum spanning tree;
the vehicle network interaction data fusion module is used for compressing the perceptually encrypted data;
the decryption module is used for decrypting the compressed and perceptually encrypted data to obtain the original data.
7. The vehicle network interactive data lightweight security convergence transmission system as claimed in claim 6, wherein: the patch key generation module comprises a chaos sequence L generation module and a Bernoulli distribution sequence
A generating module;
the chaotic sequence L generation module is used for generating a chaotic sequence L by each node through a Logistic chaotic system;
bernoulli distribution sequence
The generation module is used for obtaining the Bernoulli distribution sequence by function projection according to random mapping generated by Logistic>
。/>
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| CN112055325A (en) * | 2020-09-15 | 2020-12-08 | 长春理工大学 | Combined compression and encryption method for multi-type space-time data in wireless sensor network |
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| CN112055325A (en) * | 2020-09-15 | 2020-12-08 | 长春理工大学 | Combined compression and encryption method for multi-type space-time data in wireless sensor network |
| CN113660614A (en) * | 2021-08-23 | 2021-11-16 | 重庆第二师范学院 | Distributed compressed sensing method of wireless sensor network based on Internet of things |
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