CN114125744A - Data acquisition method based on block chain rights and interests certification and terminal system - Google Patents

Data acquisition method based on block chain rights and interests certification and terminal system Download PDF

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CN114125744A
CN114125744A CN202111045849.7A CN202111045849A CN114125744A CN 114125744 A CN114125744 A CN 114125744A CN 202111045849 A CN202111045849 A CN 202111045849A CN 114125744 A CN114125744 A CN 114125744A
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唐晓
钱海涛
赵阳光
梁晨凯
徐健凯
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Northwestern Polytechnical University
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Abstract

The invention belongs to the technical field of wireless communication, and discloses a data acquisition method and a terminal system based on block chain equity certification. And then, constructing a block chain model for data processing and verification between unmanned aerial vehicles based on a rights and interests certification PoS common recognition mechanism in the block chain, designing an optimization model of block chain throughput, and obtaining a transmission power strategy for data transmission of the Internet of things equipment based on the optimization model of the current position and the block chain throughput of the unmanned aerial vehicles. Based on the emission power strategy of the Internet of things equipment, the unmanned aerial vehicle deployment strategy is optimized through a DDPG algorithm, the current position of the unmanned aerial vehicle is updated, the emission power of the Internet of things equipment and the unmanned aerial vehicle deployment are repeatedly and iteratively optimized, and the optimization scheme of the Internet of things data acquisition model of the block chain equity certificate is obtained. Compared with the traditional scheme, the network transmission safety and efficiency are effectively improved.

Description

Data acquisition method based on block chain rights and interests certification and terminal system
Technical Field
The invention belongs to the technical field of wireless communication, and particularly relates to a data acquisition method and a terminal system based on block chain entitlement certification.
Background
The Internet of Things (IoT), namely the Internet with everything connected, combines various information sensor devices with a network to form a huge network, and realizes the intercommunication of people and Things at any time and any place. The internet of things equipment generally has the characteristics of low cost and low power consumption, is deployed on a large scale, and provides services for different application fields such as agriculture, industry and city management. Because a large number of nodes of the internet of things relate to massive data, efficient and safe data collection and processing are very important. Since internet of things devices are often deployed in a wide area, the collection of data is very challenging. On the one hand, the data processing difficulty often exceeds the computing power of the internet of things device itself. On the other hand, because the resources of the internet of things devices are limited, it is difficult to directly feed back data to the core network, and the data also faces various security threats in the transmission process.
With the rapid development of unmanned aerial vehicle technology in recent years, unmanned aerial vehicles play an increasingly important role in the field of wireless communication. Unmanned aerial vehicle can deploy and provide communication service in the thing networking, and does not need traditional network infrastructure, realizes the supplementary thing networking data collection of unmanned aerial vehicle. Therefore, capital and operation expenditure can be saved, energy consumption of the Internet of things can be saved, and the unmanned aerial vehicle can be flexibly deployed near the equipment of the Internet of things to establish a high-quality communication link. The utility model provides a supplementary thing networking data collection scheme of single unmanned aerial vehicle among the prior art, wherein unmanned aerial vehicle passes through different thing networking regions in proper order through the orbit of optimizing to the thing networking data in the collection region.
In addition, the blockchain is widely concerned by people as the integration of technologies such as 'decentralized' cooperation, distributed data storage, point-to-point transmission, consensus mechanism, encryption algorithm and intelligent contract in the field of network trust management, and is applied to the fields of internet of things, smart cities, digital asset transactions and the like. In the prior art, a scheme of ensuring data security by adopting a block chain in the internet of things is provided, and effective connections of various internet of things devices, applications, services and the like are fused by utilizing a block chain 'going-to-center' mechanism, so that mutual cooperation is promoted, and requirements of trust establishment, transaction acceleration, mass connection and the like are met.
However, in the scheme of the unmanned aerial vehicle, the data collection time of the unmanned aerial vehicle is longer in the internet of things with larger area, so that the data value is lost, and meanwhile, the safety problem of data transmission in the internet of things is not considered. In the block chain scheme, the problem that computing resources and energy resources of the internet of things equipment are limited is not considered, and some tasks in the block chain may be difficult to complete by the internet of things equipment.
Disclosure of Invention
The invention aims to provide a data acquisition method and a terminal system based on block chain entitlement certification so as to solve the problems.
In order to achieve the purpose, the invention adopts the following technical scheme:
the data acquisition method based on the block chain entitlement certification comprises the following steps:
s101, building a communication model between the internet-of-things equipment and the unmanned aerial vehicles based on a model for collecting ground multi-cluster internet-of-things equipment data by the multiple unmanned aerial vehicles;
s102, constructing a block chain model for data processing and verification between unmanned aerial vehicles based on a rights and interests certification PoS common recognition mechanism in the block chain;
s103, constructing an optimization model of the block chain throughput according to a communication model of the Internet of things equipment and the unmanned aerial vehicle and an unmanned aerial vehicle block chain model;
s104, obtaining a transmitting power strategy of data transmission of the Internet of things equipment based on the current position of the unmanned aerial vehicle and an optimization model of the block chain throughput;
and S105, optimizing the deployment strategy of the unmanned aerial vehicle through a DDPG algorithm based on the optimization model of the block chain throughput in the step S103 and the emission power strategy of the Internet of things equipment in the step S104, updating the current position of the unmanned aerial vehicle, and repeatedly and iteratively optimizing the emission power of the Internet of things equipment and the deployment of the unmanned aerial vehicle until the block chain throughput is converged.
Further, in step S101, a data collection model formed by multiple unmanned aerial vehicles and multiple clusters of internet of things on the ground determines a transmission model of a wireless signal in an uplink according to the data collection model, and then determines a transmission rate of each cluster of internet of things according to the signal transmission model;
an uplink of a wireless signal from the jth cluster of Internet of things equipment to the jth unmanned aerial vehicle forms IjThe transmission model expression of the XK-dimensional virtual multiple-input multiple-output model MIMO is as follows:
yj＝Hjxj+zj.
wherein x isj，yjAnd zjI of the j-th cluster Internet of things equipmentjDimension transmitting signals, dimension K receiving signals and background noise of the jth unmanned aerial vehicle, and the jth cluster of Internet of things equipment consisting of IjEach unmanned aerial vehicle is provided with K antennas;
Figure BDA0003251153210000031
represents a virtual MIMO channel matrix, defined as Hj＝SjLjWherein L isjIs a large scale component, SjFor small scale components, the size of the scale, in particular,
Figure BDA0003251153210000032
djifor the distance between thing networking node and the unmanned aerial vehicle:
Figure BDA0003251153210000033
Figure BDA0003251153210000034
wherein eta isLoSAnd ηNLoSThe average excess loss for line-of-sight and non-line-of-sight links for air-to-ground channels, a and b are environment-dependent variables, f and c are carrier frequency and speed of light,
Figure BDA0003251153210000035
the elevation angle formed for ground IoT node i and drone j.
Further, an expression of the transmission rate of the jth cluster of internet of things equipment is as follows:
Figure BDA0003251153210000036
wherein the content of the first and second substances,
Figure BDA0003251153210000037
is the transmission power, I, of all nodes in the jth cluster of the Internet of thingsKIs a matrix of the units,
Figure BDA0003251153210000038
is the noise power; small scale fading is represented by the desired form;
assuming that only one cluster is in an activated state during each collection, no inter-cluster interference exists in the data collection process; for the internet of things node i in the jth cluster, the constraint conditions of the transmission power are as follows:
Figure BDA0003251153210000039
Figure BDA00032511532100000310
wherein the content of the first and second substances,
Figure BDA00032511532100000311
and
Figure BDA00032511532100000312
respectively for each internet of thingsMaximum power of the device and each internet of things cluster.
Further, in the block chain model based on the PoS consensus mechanism in step S102, the time consumed from the data collection of the internet of things to the whole process of forming the block chain includes the following parts:
the total data volume transmitted by the jth cluster of Internet of things equipment is assumed to be omegaj(bit), the uplink transmission time of the jth cluster of the internet of things is expressed as:
Figure BDA0003251153210000041
after the unmanned aerial vehicle finishes data collection of the Internet of things cluster served by the unmanned aerial vehicle, constructing a candidate block for next verification; the time consumed in this part depends on the computational power of the drone and is expressed as follows:
Figure BDA0003251153210000042
wherein the content of the first and second substances,
Figure BDA0003251153210000043
representing the calculated rate (bit/s) of the unmanned plane j, v being the calculated complexity coefficient of the generation block;
after the current unmanned aerial vehicle generates the block, broadcasting the block to other unmanned aerial vehicles for verification, and adding the block passing the verification to the chain; in this process, the time delay of the broadcast block depends on the verification drone with the lowest receiving rate, so the broadcast time is expressed as:
Figure BDA0003251153210000044
wherein r isjkRepresenting the transmission rate between drone j and drone k,
Figure BDA0003251153210000045
wherein q isjFor the transmit power of drone j, the signal transmission follows a free space transmission model, and K is the array gain of receiving drones equipped with K antennas;
when the verifier receives the block to be verified, the verifier verifies the block by checking the timestamp, the signature, the random number and the like, and replies a confirmation message to determine whether the current block can be added into the block chain, wherein the time consumed by the process is used
Figure BDA0003251153210000046
And (4) showing.
Further, the optimization model of the blockchain throughput in step S103 is as follows:
the network performance is evaluated by introducing blockchain throughput according to the set transmission and blockchain procedure, defined as:
Figure BDA0003251153210000047
wherein χ is the average transaction scale in the data of the Internet of things;
the throughput of the block chain is maximized by jointly optimizing the transmission power of the internet of things and the deployment of the unmanned aerial vehicle, and the method specifically comprises the following steps:
Figure BDA0003251153210000051
Figure BDA0003251153210000052
Figure BDA0003251153210000053
Figure BDA0003251153210000054
wherein，
Figure BDA0003251153210000055
And the power vectors of all nodes of the internet of things in the jth cluster are obtained.
Further, in step S104, the transmission power policy of the internet of things device is as follows:
the transmission rate of the jth cluster of Internet of things equipment is rewritten as follows according to a large system data analysis technology:
Figure BDA0003251153210000056
wherein the content of the first and second substances,
Figure BDA0003251153210000057
and further obtaining an optimization problem of the j-th cluster of internet of things transmission:
Figure BDA0003251153210000058
Figure BDA0003251153210000059
Figure BDA00032511532100000510
Figure BDA00032511532100000511
by solving the problem, the optimal power distribution of the jth cluster of internet of things equipment is obtained as follows:
Figure BDA00032511532100000512
wherein, mujSatisfy the equation
Figure BDA00032511532100000513
Further, in step S105, based on the optimization model of the block chain throughput in step S103 and the transmission power strategy of the internet of things device in step S104, the deployment strategy of the unmanned aerial vehicle is optimized through the DDPG algorithm, the current position of the unmanned aerial vehicle is updated, and the transmission power of the internet of things device and the deployment of the unmanned aerial vehicle are repeatedly iteratively optimized until the block chain throughput is converged, so as to obtain the internet of things data acquisition scheme for the block chain equity certificate; the DDPG algorithm optimizes unmanned plane deployment strategies as follows:
in DDPG, critics current network is responsible for iteratively updating value network parameter thetaQAnd calculating the current Q value by state s and action a:
yj＝rj+γQ′(sj+1,μ′(sj+1|θμ′)|θQ′)，
wherein γ is a discount factor; the current network of the performer is responsible for updating the network parameter theta of the iteration strategyμSelecting an action a according to the current state s, and further interacting with the environment; the critic target network calculates the Q value through experience playback, updates the parameters of the critic current network through a minimized loss function, and periodically copies the network parameters to the critic target network; the loss function is expressed as follows:
Figure BDA0003251153210000061
in addition, the current network of the performer updates the current network parameters of the performer through strategy gradient based on the target Q value calculated by the critic target network, and periodically copies the network parameters to the target network of the performer; the strategy gradient is represented as follows:
Figure BDA0003251153210000062
the target network adopts soft update:
θ′Q←ρθQ+(1-ρ)θ′Q,ρ＜＜1,
θ′μ←ρθμ+(1-ρ)θ′μ,ρ＜＜1.
adding random noise to the selected action, i.e. action a ═ mu(s)t；θμ)+ne，neIs random noise; the main factors of the DDPG optimizing unmanned plane deployment problem comprise the following contents:
defining the state of the t time slot as the channel state information CIS between the unmanned aerial vehicle and the nodes in the Internet of things cluster served by the unmanned aerial vehicle and the channel state information between the unmanned aerial vehicles in the block chain, namely
Figure BDA0003251153210000063
Defining the action of the t time slot as the variation of the unmanned plane position on the two-dimensional coordinate, and expressing the variation as follows:
Figure BDA0003251153210000064
defining the reward function for the t-slot action as the difference between the blockchain throughput for slot t and the blockchain throughput for slot t-1, in the form:
rt(st,at)＝Ψt-Ψt-1。
further, the data acquisition system based on the block chain equity certification comprises:
the communication model building module of the Internet of things equipment and the unmanned aerial vehicle is used for building communication models of the Internet of things equipment and the unmanned aerial vehicle based on a model for collecting ground multi-cluster Internet of things equipment data by the multiple unmanned aerial vehicles;
the block chain model building module for data processing and verification between unmanned aerial vehicles is used for building a block chain model for data processing and verification between unmanned aerial vehicles based on a rights and interests certification PoS consensus mechanism in a block chain;
the block chain throughput optimization model building module is used for building an optimization model of the block chain throughput according to a communication model of the Internet of things equipment and the unmanned aerial vehicle and an unmanned aerial vehicle block chain model;
the transmitting power strategy construction module for data transmission of the equipment of the Internet of things is used for obtaining a transmitting power strategy for data transmission of the equipment of the Internet of things based on the current position of the unmanned aerial vehicle and an optimization model of block chain throughput;
and the iteration module is used for optimizing the unmanned aerial vehicle deployment strategy through a DDPG algorithm based on the optimization model of the block chain throughput and the transmission power strategy of the Internet of things equipment, updating the current position of the unmanned aerial vehicle, and repeatedly iterating and optimizing the transmission power of the Internet of things equipment and the unmanned aerial vehicle deployment until the block chain throughput is converged.
Compared with the prior art, the invention has the following technical effects:
the Internet of things data acquisition scheme based on the block chain equity certificate considers Internet of things clusters, namely each cluster is provided with a corresponding unmanned aerial vehicle for data collection and forwarding. The drone collecting the data then packages the data into blocks and broadcasts the mined blocks to other drones using PoS consensus protocol, which act as verifiers to validate the blocks and link to the ledger. Accordingly, the invention addresses the problem of jointly optimizing internet of things transmission and drone deployment to maximize blockchain throughput. The problem is broken down into two layers, with the inner layer of internet of things transmission being obtained with a closed form of solution, and the outer layer of drone deployment being approximated to the optimum with a learning method based on depth-deterministic policy gradients (DDPG). Finally, the simulation result shows the convergence of the scheme and proves that the performance of the scheme is superior to that of the traditional scheme.
Drawings
Fig. 1 is a flowchart of an internet of things data acquisition scheme based on block chain equity certification according to the present invention.
Fig. 2 is a comparison graph of the convergence of the DDPG algorithm when the critic network sets different learning rates when the learning rate of the performer network is 0.0001 in the internet of things data acquisition scheme based on the block chain equity certification provided by the embodiment of the present invention;
fig. 3 is a comparison graph of the maximum power limit of each internet of things cluster corresponding to the block chain throughput in the internet of things data acquisition scheme based on the block chain equity certification provided by the embodiment of the invention when the unmanned aerial vehicle deploys at different heights;
fig. 4 is a performance comparison graph of the data acquisition scheme of the internet of things based on the block chain equity certificate, the deployment strategy of the single optimized power or unmanned aerial vehicle, and the average power distribution and central deployment strategy provided by the embodiment of the invention.
Fig. 5 is an unmanned aerial vehicle deployment distribution diagram finally obtained by optimization of the internet of things data acquisition scheme based on the blockchain equity certification provided by the embodiment of the invention.
Detailed Description
The present invention will now be described in further detail with reference to the attached drawings, which are illustrative, but not limiting, of the present invention. Referring to fig. 1, a data acquisition scheme of the internet of things based on block chain equity certificate includes the following steps:
s101: the model based on the data of the ground multi-cluster Internet of things equipment collected by the multiple unmanned aerial vehicles is used for constructing a communication model of the Internet of things equipment and the unmanned aerial vehicles.
Specifically, an uplink of a wireless signal from the jth cluster of internet of things equipment to the jth unmanned aerial vehicle forms an IjA xK-dimensional virtual multiple-input multiple-output (MIMO) model whose transmission model expression is as follows:
yj＝Hjxj+zj.
wherein x isj，yjAnd zjI of the j-th cluster Internet of things equipmentjDimension transmitting signals, dimension K receiving signals and background noise of the jth unmanned aerial vehicle, and the jth cluster of Internet of things equipment consisting of IjEach unmanned aerial vehicle is provided with K antennas;
Figure BDA0003251153210000081
represents a virtual MIMO channel matrix, defined as Hj＝SjLjWherein L isjIs a large scale component, SjFor small scale components, the size of the scale, in particular,
Figure BDA0003251153210000082
djifor the distance between thing networking node and the unmanned aerial vehicle:
Figure BDA0003251153210000083
Figure BDA0003251153210000091
wherein eta isLoSAnd ηNLoSThe average excess loss for line-of-sight and non-line-of-sight links for air-to-ground channels, a and b are environment-dependent variables, f and c are carrier frequency and speed of light,
Figure BDA0003251153210000092
the elevation angle formed for ground IoT node i and drone j.
Preferably, the expression of the transmission rate of the jth cluster of internet of things equipment is as follows:
Figure BDA0003251153210000093
wherein the content of the first and second substances,
Figure BDA0003251153210000094
is the transmission power, I, of all nodes in the jth cluster of the Internet of thingsKIs a matrix of the units,
Figure BDA0003251153210000095
is the noise power. In addition, small scale fading is represented by the desired form.
We assume that only one cluster is active per collection, and therefore there is no inter-cluster interference with the data collection process. For the internet of things node i in the jth cluster, the constraint conditions of the transmission power are as follows:
Figure BDA0003251153210000096
Figure BDA0003251153210000097
wherein the content of the first and second substances,
Figure BDA0003251153210000098
and
Figure BDA0003251153210000099
the maximum power of each internet of things device and each internet of things cluster respectively.
S102: and constructing a block chain model for data processing and verification between unmanned aerial vehicles based on a proof of rights (PoS) consensus mechanism in the block chain.
Specifically, the time consumed from the data collection of the internet of things to the whole process of forming the block chain comprises the following parts:
the total data volume transmitted by the jth cluster of Internet of things equipment is assumed to be omegaj(bit), the uplink transmission time of the jth cluster of the internet of things is expressed as:
Figure BDA00032511532100000910
and after the unmanned aerial vehicle finishes data collection of the Internet of things cluster served by the unmanned aerial vehicle, constructing a candidate block for next verification. The time consumed in this part depends on the computational power of the drone and is expressed as follows:
Figure BDA0003251153210000101
wherein the content of the first and second substances,
Figure BDA0003251153210000102
represents the calculated rate (bit/s) of drone j, v is the calculated complexity coefficient of the generation block.
After the current unmanned aerial vehicle generates the block, the block is broadcasted to other unmanned aerial vehicles for verification, and only the block passing the verification can be added to the chain. In this process, the time delay of the broadcast block depends on the verification drone with the lowest receiving rate, so the broadcast time is expressed as:
Figure BDA0003251153210000103
wherein r isjkRepresenting the transmission rate between drone j and drone k,
Figure BDA0003251153210000104
wherein q isjFor the transmit power of drone j, the signal transmission follows a free space transmission model, and K is the array gain of the receiving drone equipped with K antennas.
When the verifier receives the block to be verified, the verifier verifies the block by checking the timestamp, the signature, the random number and the like, and replies a confirmation message to determine whether the current block can be added into the block chain, wherein the time consumed by the process is used
Figure BDA0003251153210000105
It is shown that a small constant is assumed, since this time is small compared to the time of the previous process.
S103: and designing an optimization model of the block chain throughput according to a communication model of the Internet of things equipment and the unmanned aerial vehicle and an unmanned aerial vehicle block chain model.
Specifically, according to the described transmission and block chain process, the block chain throughput is introduced to evaluate the network performance, which is defined as:
Figure BDA0003251153210000106
and x is the average transaction scale in the data of the Internet of things.
Correspondingly, the throughput of the blockchain is maximized by jointly optimizing the transmission power of the internet of things and the deployment of the unmanned aerial vehicle, which is as follows:
Figure BDA0003251153210000111
Figure BDA0003251153210000112
Figure BDA0003251153210000113
Figure BDA0003251153210000114
wherein the content of the first and second substances,
Figure BDA0003251153210000115
and the power vectors of all nodes of the internet of things in the jth cluster are obtained.
S104: and obtaining a transmitting power strategy of the data transmission of the equipment of the Internet of things based on the current position of the unmanned aerial vehicle and the optimization model of the block chain throughput.
Specifically, the transmission rate of the jth cluster of internet of things equipment is rewritten as follows according to a large system data analysis technology:
Figure BDA0003251153210000116
wherein the content of the first and second substances,
Figure BDA0003251153210000117
and further obtaining an optimization problem of the j-th cluster of internet of things transmission:
Figure BDA0003251153210000118
Figure BDA0003251153210000119
Figure BDA00032511532100001110
Figure BDA00032511532100001111
by solving the problem, the optimal power distribution of the j-th cluster of internet of things equipment can be obtained as follows:
Figure BDA00032511532100001112
wherein, mujSatisfy the equation
Figure BDA00032511532100001113
S105: based on the optimization model of the block chain throughput in the step S103 and the transmission power strategy of the Internet of things equipment in the step S104, the unmanned aerial vehicle deployment strategy is optimized through a DDPG algorithm, the current position of the unmanned aerial vehicle is updated, the transmission power of the Internet of things equipment and the unmanned aerial vehicle deployment are repeatedly optimized in an iterative mode until the block chain throughput is converged, and the Internet of things data acquisition scheme of the block chain equity proof is obtained.
Specifically, in the DDPG, the critics current network is responsible for iteratively updating the value network parameter thetaQAnd calculating the current Q value by state s and action a:
yj＝rj+γQ′(sj+1,μ′(sj+1|θμ′)|θQ′)，
where γ is the discount factor. The current network of the performer is responsible for updating the network parameter theta of the iteration strategyμAnd selects action a according to the current state s, thereby interacting with the environment. The critic target network calculates the Q value through experience playbackAnd (4) minimizing a loss function to update parameters of the critic current network and periodically copying the network parameters to the critic target network. The loss function is expressed as follows:
Figure BDA0003251153210000121
in addition, the performer current network updates performer current network parameters through a policy gradient based on a target Q value calculated by the critic target network, and periodically copies the network parameters to the performer target network. The strategy gradient is represented as follows:
Figure BDA0003251153210000122
the target network adopts soft update:
θ′Q←ρθQ+(1-ρ)θ′Q,ρ＜＜1,
θ′μ←ρθμ+(1-ρ)θ′μ,ρ＜＜1.
in addition, in order to increase the randomness during the learning process and improve the convergence of the learning, random noise is added to the selected action, i.e., action a is μ(s)t；θμ)+ne，neIs random noise. In addition, the main factors of the DDPG optimization drone deployment problem include the following:
defining the state of the t time slot as the channel state information (CIS) between the unmanned aerial vehicle and the nodes in the Internet of things cluster served by the unmanned aerial vehicle and the channel state information between the unmanned aerial vehicles in the block chain, namely
Figure BDA0003251153210000123
Defining the action of the t time slot as the variation of the unmanned plane position on the two-dimensional coordinate, and expressing the variation as follows:
Figure BDA0003251153210000124
defining the reward function for the t-slot action as the difference between the blockchain throughput for slot t and the blockchain throughput for slot t-1, in the form:
rt(st,at)＝Ψt-Ψt-1
the technical effects of the present invention will be described in detail with reference to simulations.
The method simulates the data acquisition scheme of the Internet of things based on the block chain equity certificate, and verifies the superiority of the scheme. The method comprises the following specific steps: setting basic parameters that 20 internet of things nodes are randomly distributed in an internet of things network in a square area of 1000m multiplied by 1000m to form 4 clusters, wherein 4 unmanned aerial vehicles respectively serve the 4 clusters, and each cluster is provided with 5 nodes; the communication bandwidth is 1MHz, the noise power is-174 dBm, the carrier frequency is 2GHz, the total data volume of each cluster of nodes is 5M, the average service size is 200kB, the block mining difficulty is 1, the block verification time is 0.5s, and the unmanned aerial vehicle computing power is 2.7 multiplied by 106bit/s; the discount factor is 0.95 and the memory pool is 50000. The related performance simulation results of the present invention are shown in fig. 2-5.
In an exemplary embodiment, a computer-readable storage medium is also provided, which stores a computer program that, when executed by a processor, implements the steps of the reconfigurable smart surface and active interference based secure transmission method. The computer storage medium may be any available medium or data storage device that can be accessed by a computer, including but not limited to magnetic memory (e.g., floppy disk, hard disk, magnetic tape, magneto-optical disk (MO), etc.), optical memory (e.g., CD, DVD, BD, HVD, etc.), and semiconductor memory (e.g., ROM, EPROM, EEPROM, nonvolatile memory (NANDFLASH), Solid State Disk (SSD)), etc.
In an exemplary embodiment, there is also provided a terminal system, including a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the reconfigurable smart surface and active interference based secure transmission method when executing the computer program. The Processor may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic, discrete hardware components, etc.
The invention belongs to the technical field of wireless communication, and discloses an Internet of things data acquisition scheme and a terminal system based on block chain equity certification. And then, constructing a block chain model for data processing and verification between unmanned aerial vehicles based on a rights and interests (PoS) consensus mechanism in the block chain, designing an optimization model of block chain throughput, and obtaining a transmission power strategy for data transmission of the equipment of the Internet of things based on the optimization model of the current position and the block chain throughput of the unmanned aerial vehicles. And finally, optimizing an unmanned aerial vehicle deployment strategy through a DDPG algorithm based on the emission power strategy of the Internet of things equipment, updating the current position of the unmanned aerial vehicle, and repeatedly iterating to optimize the emission power of the Internet of things equipment and the unmanned aerial vehicle deployment to obtain an optimization scheme of the Internet of things data acquisition model for the block chain equity certificate. Compared with the traditional data collection scheme, the invention more effectively improves the network transmission safety and efficiency.
The above-mentioned contents are only for illustrating the technical idea of the present invention, and the protection scope of the present invention is not limited thereby, and any modification made on the basis of the technical idea of the present invention falls within the protection scope of the claims of the present invention.

Claims (10)

1. A data acquisition method based on block chain entitlement certification is characterized by comprising the following steps:
s101, building a communication model between the internet-of-things equipment and the unmanned aerial vehicles based on a model for collecting ground multi-cluster internet-of-things equipment data by the multiple unmanned aerial vehicles;
s102, constructing a block chain model for data processing and verification between unmanned aerial vehicles based on a rights and interests certification PoS common recognition mechanism in the block chain;
s103, constructing an optimization model of the block chain throughput according to a communication model of the Internet of things equipment and the unmanned aerial vehicle and an unmanned aerial vehicle block chain model;
s104, obtaining a transmitting power strategy of data transmission of the Internet of things equipment based on the current position of the unmanned aerial vehicle and an optimization model of the block chain throughput;
and S105, optimizing the deployment strategy of the unmanned aerial vehicle through a DDPG algorithm based on the optimization model of the block chain throughput in the step S103 and the emission power strategy of the Internet of things equipment in the step S104, updating the current position of the unmanned aerial vehicle, and repeatedly and iteratively optimizing the emission power of the Internet of things equipment and the deployment of the unmanned aerial vehicle until the block chain throughput is converged.
2. The data acquisition method based on the blockchain equity certification according to claim 1, wherein in step S101, a data collection model formed by multiple drones and multiple clusters of ground internet of things devices determines a transmission model of a wireless signal in an uplink according to the data collection model, and then determines a transmission rate of each cluster of IoT devices according to a signal transmission model;
an uplink of a wireless signal from the jth cluster of Internet of things equipment to the jth unmanned aerial vehicle forms IjThe transmission model expression of the XK-dimensional virtual multiple-input multiple-output model MIMO is as follows:
yj＝Hjxj+zj.
wherein x isj，yjAnd zjI of the j-th cluster Internet of things equipmentjDimension transmitting signals, dimension K receiving signals and background noise of the jth unmanned aerial vehicle, and the jth cluster of Internet of things equipment consisting of IjEach unmanned aerial vehicle is provided with K antennas;
Figure FDA0003251153200000011
representing virtual MIMO channel momentsArray, defined as Hj＝SjLj，LjIs a large scale component, SjIs a small scale component.
3. The method of claim 2, wherein the large scale component L is a large scale componentjThe method specifically comprises the following steps:
Figure FDA0003251153200000012
Figure FDA0003251153200000021
Figure FDA0003251153200000022
wherein d isjiIs the distance, eta, between the Internet of things node and the unmanned aerial vehicleLoSAnd ηNLoSThe average excess loss for line-of-sight and non-line-of-sight links for air-to-ground channels, a and b are environment-dependent variables, f and c are carrier frequency and speed of light,
Figure FDA0003251153200000023
the elevation angle formed for ground IoT node i and drone j.
4. The data collection method based on the blockchain equity certification as claimed in claim 2, wherein the expression of the transmission rate of the jth cluster of internet of things devices is as follows:
Figure FDA0003251153200000024
wherein the content of the first and second substances,
Figure FDA0003251153200000025
is the transmission power, I, of all nodes in the jth cluster of the Internet of thingsKIs a matrix of the units,
Figure FDA0003251153200000026
is the noise power; small scale fading is represented by the desired form;
assuming that only one cluster is in an activated state during each collection, no inter-cluster interference exists in the data collection process; for the internet of things node i in the jth cluster, the constraint conditions of the transmission power are as follows:
Figure FDA0003251153200000027
Figure FDA0003251153200000028
wherein the content of the first and second substances,
Figure FDA0003251153200000029
and
Figure FDA00032511532000000210
the maximum power of each internet of things device and the maximum power of each internet of things cluster are respectively.
5. The method for acquiring data based on blockchain equity certification according to claim 1, wherein the step S102 of using the blockchain model based on the PoS consensus mechanism consumes the following time from the internet of things data collection to the whole process of forming the blockchain:
the total data volume transmitted by the jth cluster of Internet of things equipment is assumed to be omegaj(bit), the uplink transmission time of the jth cluster of the internet of things is expressed as:
Figure RE-FDA00034712577700000211
after the unmanned aerial vehicle finishes data collection of the Internet of things cluster served by the unmanned aerial vehicle, constructing a candidate block for next verification; the time consumed in this part depends on the computational power of the drone and is expressed as follows:
Figure RE-FDA0003471257770000031
wherein the content of the first and second substances,
Figure RE-FDA0003471257770000032
representing the calculated rate (bit/s) of the unmanned plane j, v being the calculated complexity coefficient of the generation block;
after the current unmanned aerial vehicle generates the block, broadcasting the block to other unmanned aerial vehicles for verification, and adding the block passing the verification to the chain; in this process, the time delay of the broadcast block depends on the verification drone with the lowest receiving rate, so the broadcast time is expressed as:
Figure RE-FDA0003471257770000033
wherein r isjkRepresenting the transmission rate between drone j and drone k,
Figure RE-FDA0003471257770000034
wherein q isjFor the transmit power of drone j, the signal transmission follows a free space transmission model, and K is the array gain of receiving drones equipped with K antennas;
when the verifier receives the block to be verified, the verifier verifies the block by checking the timestamp, the signature, the random number and the like, and replies a confirmation message to determine whether the current block can be added into the block chain, wherein the time consumed by the process is used
Figure RE-FDA0003471257770000035
And (4) showing.
6. The method of claim 1, wherein the optimization model of blockchain throughput in step S103 is as follows:
the network performance is evaluated by introducing blockchain throughput according to the set transmission and blockchain procedure, defined as:
Figure FDA0003251153200000036
wherein χ is the average transaction scale in the data of the Internet of things;
the throughput of the block chain is maximized by jointly optimizing the transmission power of the internet of things and the deployment of the unmanned aerial vehicle, and the method specifically comprises the following steps:
Figure FDA0003251153200000041
s.t.
Figure FDA0003251153200000042
Figure FDA0003251153200000043
Figure FDA0003251153200000044
wherein the content of the first and second substances,
Figure FDA0003251153200000045
and the power vectors of all nodes of the internet of things in the jth cluster are obtained.
7. The method for acquiring data based on block chain equity certification as claimed in claim 1, wherein the transmit power strategy of the internet of things device in step S104 is as follows:
the transmission rate of the jth cluster of Internet of things equipment is rewritten as follows according to a large system data analysis technology:
Figure FDA0003251153200000046
wherein the content of the first and second substances,
Figure FDA0003251153200000047
and further obtaining an optimization problem of the j-th cluster of internet of things transmission:
Figure FDA0003251153200000048
s.t.
Figure FDA0003251153200000049
Figure FDA00032511532000000410
Figure FDA00032511532000000411
by solving the problem, the optimal power distribution of the jth cluster of internet of things equipment is obtained as follows:
Figure FDA00032511532000000412
wherein, mujSatisfy the equation
Figure FDA00032511532000000413
8. The data acquisition method based on the blockchain equity certificate as claimed in claim 1, wherein in step S105, based on the optimization model of the blockchain throughput in step S103 and the transmission power strategy of the internet of things device in step S104, the unmanned aerial vehicle deployment strategy is optimized through a DDPG algorithm, the current position of the unmanned aerial vehicle is updated, the internet of things device transmission power and the unmanned aerial vehicle deployment are iteratively optimized repeatedly until the blockchain throughput is converged, and then the internet of things data acquisition scheme based on the blockchain equity certificate is obtained.
9. The method of claim 8, wherein the DDPG algorithm optimizes unmanned aerial vehicle deployment strategies as follows:
in DDPG, critics current network is responsible for iteratively updating value network parameter thetaQAnd calculating the current Q value by state s and action a:
yj＝rj+γQ′(sj+1,μ′(sj+1|θμ′)|θQ′)，
wherein γ is a discount factor; the current network of the performer is responsible for updating the network parameter theta of the iteration strategyμSelecting an action a according to the current state s, and further interacting with the environment; the critic target network calculates the Q value through experience playback, updates the parameters of the critic current network through a minimized loss function, and periodically copies the network parameters to the critic target network; the loss function is expressed as follows:
Figure FDA0003251153200000051
in addition, the current network of the performer updates the current network parameters of the performer through strategy gradient based on the target Q value calculated by the critic target network, and periodically copies the network parameters to the target network of the performer; the strategy gradient is represented as follows:
Figure FDA0003251153200000052
the target network adopts soft update:
θ′Q←ρθQ+(1-ρ)θ′Q,ρ＜＜1,
θ′μ←ρθμ+(1-ρ)θ′μ,ρ＜＜1.
adding random noise to the selected action, i.e. action a ═ mu(s)t；θμ)+ne，neIs random noise; the main factors of the DDPG optimizing unmanned plane deployment problem comprise the following contents:
defining the state of the t time slot as the channel state information CIS between the unmanned aerial vehicle and the nodes in the Internet of things cluster served by the unmanned aerial vehicle and the channel state information between the unmanned aerial vehicles in the block chain, namely
Figure FDA0003251153200000053
Defining the action of the t time slot as the variation of the unmanned plane position on the two-dimensional coordinate, and expressing the variation as follows:
Figure FDA0003251153200000061
defining the reward function for the t-slot action as the difference between the blockchain throughput for slot t and the blockchain throughput for slot t-1, in the form:
rt(st,at)＝Ψt-Ψt-1。
10. a data collection system based on blockchain equity certification, characterized in that, the data collection method based on blockchain equity certification according to any one of claims 1 to 7 comprises:
the communication model building module of the Internet of things equipment and the unmanned aerial vehicle is used for building communication models of the Internet of things equipment and the unmanned aerial vehicle based on a model for collecting ground multi-cluster Internet of things equipment data by the multiple unmanned aerial vehicles;
the block chain model building module for data processing and verification between unmanned aerial vehicles is used for building a block chain model for data processing and verification between unmanned aerial vehicles based on a rights and interests certification PoS consensus mechanism in a block chain;
the block chain throughput optimization model building module is used for building an optimization model of the block chain throughput according to a communication model of the Internet of things equipment and the unmanned aerial vehicle and an unmanned aerial vehicle block chain model;
the transmitting power strategy construction module for data transmission of the equipment of the Internet of things is used for obtaining a transmitting power strategy for data transmission of the equipment of the Internet of things based on the current position of the unmanned aerial vehicle and an optimization model of block chain throughput;
and the iteration module is used for optimizing the unmanned aerial vehicle deployment strategy through a DDPG algorithm based on the optimization model of the block chain throughput and the transmission power strategy of the Internet of things equipment, updating the current position of the unmanned aerial vehicle, and repeatedly iterating and optimizing the transmission power of the Internet of things equipment and the unmanned aerial vehicle deployment until the block chain throughput is converged.
CN202111045849.7A 2021-09-07 2021-09-07 Data acquisition method based on block chain rights and interests certification and terminal system Pending CN114125744A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114630322A (en) * 2022-03-30 2022-06-14 南京航空航天大学 Task-oriented unmanned aerial vehicle network mutual authentication method based on stateless block chain

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