An intelligent reflecting surface assisted vehicle networking security computing offloading method

Abstract

The application discloses a kind of intelligent reflecting surface auxiliary-oriented Internet of Vehicles security computing unloading method, this method includes: obtaining vehicle basic information in Internet of Vehicles, network system is constructed according to vehicle basic information, and optimization problem is constructed under the constraint of network system;According to the initial solution obtained from optimization problem, and the initial solution is defined as initial population, search is carried out to initial population using swarm intelligence optimization algorithm to obtain target population, and the position of global optimal individual in target population is output;According to the position of global optimal individual, safety calculation and efficiency optimization configuration are carried out.The present application supports multi-step unloading strategy, fully considers the selection and angle adjustment of intelligent reflecting surface, can minimize the total energy consumption of all vehicles locally under the condition of meeting transmission rate, vehicle delay, security vulnerability cost and other constraints, optimize the overall performance of Internet of Vehicles system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wireless communication, in particular to a smart reflecting surface assisted vehicle networking security computing offloading method. BACKGROUND

[0002] In the past decade, with the rapid development of 5G networks, intelligent devices and vehicle networking have been widely used. These intelligent devices not only require ultra-high computing performance, but also require extremely low response time, especially in key application scenarios such as autonomous driving, vehicle entertainment, real-time traffic monitoring, and information exchange between vehicles. The widespread use of vehicle networking technology has increased the demand for data exchange and computing processing between vehicles and infrastructure. However, traditional vehicle-mounted computing devices have limitations in processing power and battery life, which directly affects the overall performance and user experience of the vehicle networking system.

[0003] To address this challenge, computing offloading technology has emerged. By offloading part of the computing task from the vehicle-mounted device to the remote cloud or edge computing platform, the computing burden of the vehicle-mounted device can be reduced, thereby improving the overall performance of the system. In particular, in application scenarios such as autonomous driving and real-time navigation, which require extremely high computing power, the advantages of computing offloading are particularly significant. However, in autonomous driving and real-time traffic control, any communication delay or interruption can pose serious safety hazards and operational risks.

[0004] To solve the above wireless communication problems, intelligent reflecting surface (IRS) technology is proposed and applied in the field of wireless communication. IRS is composed of a large number of adjustable reflecting units, which can control and adjust the direction, intensity and phase of reflected signals, thereby optimizing the propagation path of wireless signals. By intelligently controlling these reflecting units, IRS can effectively enhance signal coverage, reduce interference, and improve signal quality, providing more stable communication links in dynamic environments. In vehicle networking, IRS technology is considered a promising solution that can help improve communication quality between vehicles and infrastructure, solving the frequent signal attenuation and interference problems in vehicle networking.

[0005] At the same time, in order to avoid serious interference problems and ensure the safe offloading of tasks, cryptographic algorithms are introduced. Researchers are exploring how to effectively combine IRS with edge servers in the vehicle networking environment, while ensuring quality of service and minimizing energy consumption.

[0006] In addition, although existing sparrow algorithms and whale algorithms have fast convergence speed, they have deficiencies in local search capability, which may limit their effectiveness in practical applications. SUMMARY

[0007] In view of the deficiencies of the prior art, the application provides a smart reflecting surface auxiliary oriented vehicle networking security computing energy consumption optimization method, which aims to solve the problem of low vehicle uplink rate and high power consumption in complex road problems, resulting in high system energy consumption.

[0008] To achieve the above object, the application provides the following technical scheme: a smart reflecting surface auxiliary oriented vehicle networking security computing offloading method, comprising the following steps:

[0009] Step S1: acquiring vehicle basic information of vehicle networking and constructing a network system, the network system comprising a communication model, a computing model and a security model; constructing an optimization problem based on constraints for the network system;

[0010] Step S2: acquiring an initial solution of the optimization problem, and defining the initial solution as an initial population; searching the initial population by using a swarm intelligence optimization algorithm SIOVS to obtain a target population, and acquiring a global optimal solution in the target population; specifically:

[0011] First, initialize the population and determine the historical best and worst individuals; then generate a weight factor and apply it to the process of searching the solution; in the producer update stage, perform exponential decay exploration and update the producer position;

[0012] Secondly, perform Gaussian disturbance to further update the producer position; in the follower update stage, for each follower individual, generate a random disturbance vector; if the index of the follower exceeds half of the population, use Gaussian disturbance and exponential decay to update the position, otherwise, use the update relative to the leader and the random disturbance vector to adjust the follower position; then repeatedly execute the update of the producer, the follower, the historical best and the worst individuals in turn; finally, acquire the global optimal solution;

[0013] Step S3: configuring the network system of the optimized vehicle networking according to the global optimal solution.

[0014] Further, the specific process of step S1 is: acquiring vehicle basic information of vehicle networking, further building a communication model, a computing model and a security model in the network system through the vehicle basic information, and constructing an optimization problem based on the communication model, the computing model and the security model;

[0015] Further, the specific process of acquiring vehicle basic information of vehicle networking in step S1 is:

[0016] Acquiring vehicle information, denoted as: ;

[0017] wherein, indicates the index of any vehicle, indicates the total number of vehicles;

[0018] Obtain the information of the tasks of each vehicle , denoted as: ;

[0019] where, denotes the index of an arbitrary task of each user, denotes the total number of tasks;

[0020] Obtain the information of the cloud CS and the roadside units RSU, denoted as: ;

[0021] where, and denote the index of different roadside units RSU, denotes the total number of roadside units RSU in the network, denotes the set of all V2I infrastructures;

[0022] Obtain the index set of the intelligent reflecting surface IRS, denoted as: ;

[0023] where, denotes the index of an arbitrary intelligent reflecting surface IRS; denotes the total number of intelligent reflecting surfaces IRS;

[0024] The intelligent reflecting surface IRS contains reflective unit coefficients, and the index set of the reflective unit coefficients is denoted as ;

[0025] where, denotes the index of an arbitrary reflective unit coefficient; denotes the total number of reflective unit coefficients;

[0026] The reflective unit coefficient is in the form of a matrix, denoted as:

[0027] ;

[0028] where, denotes the reflective unit coefficient, denotes the reflection coefficient of the th reflective element on the intelligent reflecting surface IRS, denotes the diagonal function;

[0029] Obtain the physical locations of all roadside units RSU, and use the clustering algorithm to divide them into clusters according to the physical locations of all roadside units RSU; set the micro base stations based on the classification results.​

[0030] wherein the communication range of each micro base station in a cluster contains a number of road side units RSUs and a number of vehicles and a number of intelligent reflecting surfaces IRS, each micro base station has sub-channels for the road side units RSUs in the communication range to use, the index set of the sub-channels is denoted as ; wherein denotes the index of any sub-channel, denotes the total number of sub-channels;

[0031] the total bandwidth of the network and the sub-channel bandwidth are obtained the task is transmitted by the vehicle to the intelligent reflecting surface IRS through the sub-channel , the channel gain of the task transmitted by the IRS to the RSU through the sub-channel is denoted as , the channel gain of the task transmitted by the road side unit RSU back to the vehicle is denoted as , and the noise is denoted as .

[0032] Further, the process of constructing the communication model in step S1 is specifically:

[0033] the total bandwidth of the network system is divided into and for the road side units RSUs and the cloud to use respectively under the condition that any task of the vehicle does not exist intra-cluster interference; wherein is a frequency band division factor, and ; the number of sub-channels used by each micro base station is denoted as ; wherein, denotes the floor function, denotes the number of sub-channels;

[0034] when the vehicle is associated with the road side unit RSU, the to-be-unloaded part of the vehicle task is simultaneously sent to multiple road side units RSUs by using the non-orthogonal multiple access NOMA technology;

[0035] the uplink transmission rate is calculated, that is, the uplink NOMA transmission rate of the vehicle sending the task to the road side unit RSU on the sub-channel is calculated; it is denoted as:

[0036] ;

[0037] ;

[0038] ;

[0039] ;

[0040] ;

[0041] ;

[0042] ;

[0043] wherein, denotes the bandwidth for the roadside unit, denotes the vehicle associated intelligent reflecting surface IRS ; denotes the vehicle associated intelligent reflecting surface IRS ; denotes the vehicle own transmit power; denotes the vehicle own transmit power; denotes the interference caused by other vehicles and other intelligent reflecting surfaces IRS to the vehicle and the intelligent reflecting surface IRS in the process of uplink NOMA transmission; denotes the channel gain of the task of any vehicle in the range of the micro base station through the sub-channel to the intelligent reflecting surface IRS by uplink NOMA transmission; denotes the channel gain interference caused by other vehicles sending tasks to the intelligent reflecting surface IRS in the process of uplink NOMA transmission; and denote the path loss factor and the Rice fading factor, respectively, denotes the distance from the vehicle in the range of the micro base station to the intelligent reflecting surface IRS , , and denote the task of any vehicle Three-dimensional Cartesian coordinates, intelligent reflective surface IRS 3D Cartesian coordinates and roadside unit RSU The three-dimensional Cartesian coordinates of Indicates vehicle to the IRS Line-of-sight link component; is the frequency; The elements in represent the intelligent reflective surface IRS The channel coefficient vector of each reflective element in ; represents the phase change caused by signal propagation, and Respectively represent vehicles to the IRS The direction angle of Indicates vehicle to the IRS Non-line-of-sight link component; represents the first noise;

[0044] When the vehicle is connected to the cloud, any vehicle Uplink NOMA transmission rate to the cloud Expressed as: ;

[0045] in, Indicates the bandwidth used by the cloud; Indicates vehicle IRS The decision index coefficient of represents any vehicle in the cluster The task goes through the sub-channel Transmitted to IRS via uplink NOMA The channel gain of Indicates the task from IRS Through the sub-channel Channel gain to the cloud; Indicates cloud to vehicle The channel gain of Represents the second noise.

[0046] Furthermore, the process of building the security model in step S1 is specifically as follows:

[0047] When a vehicle is associated with a roadside unit (RSU), part of the vehicle's task can be offloaded to the RSU after encryption. The RSU decrypts the encrypted task and then encrypts it again before transmitting it to the cloud.

[0048] When the vehicle is connected to the cloud CS, part of the vehicle's mission can be offloaded to the cloud CS after encryption, and the cloud CS will perform the mission after decryption.

[0049] During the encryption or decryption process, different security levels are set. Expressed as: middle;

[0050] in, Representative Algorithms The security level index, Indicates the total number of encryption algorithms or decryption algorithms; algorithm Indicates encryption algorithm or decryption algorithm;

[0051] Encryption and decryption algorithms The computing power is and ; Set the encryption energy consumption and decryption energy consumption to be the same, the algorithm The energy consumption of encryption or decryption is expressed as ;

[0052] When the vehicle Mission Using encryption algorithm When part of its tasks is safely offloaded, the failure probability is ;

[0053] vehicle Mission Cost of security breaches Expressed as:

[0054] ;

[0055] in, For the task financial losses in the event of failure; A binary variable indicating whether vehicle v passes through the roadside units RSUs; For vehicles Mission Security decision indicators, set the selection algorithm To handle vehicles Mission ,but , otherwise 0;

[0056] Based on this, the vehicle Total security breach cost Expressed as:

[0057] .

[0058] Further, the processing flow of the calculation model constructed in step S1 is as follows:

[0059] Local calculation:

[0060] When the vehicle is associated with a roadside unit RSU or a cloud CS, the vehicle performs the task in a local processing data volume of , wherein represents the total data volume of the vehicle performing the task ; is the data volume of the task offloaded from the vehicle to the roadside unit RSU or the cloud CS;

[0061] The local execution time used by the vehicle to perform the task associated with the roadside unit RSU or the cloud CS in the local calculation process is represented as:

[0062] ;

[0063] In the formula, is the computing power of the vehicle ; is the calculation time; is the encryption time, represents the cost of the vehicle performing the task ;

[0064] Offloaded to the roadside unit:

[0065] When the vehicle is associated with a roadside unit RSU, the following steps are performed on the task ;

[0066] Part of the is encrypted and offloaded from the vehicle to the roadside unit RSU; The roadside unit RSU decrypts

[0067] and then performs ; wherein represents the decryption data volume of the vehicle performing the task process during the decryption process; Part of the

[0068] is encrypted and offloaded from the vehicle to the roadside unit RSU; ​part of which is encrypted offloaded from the road side unit RSU to a nearby cloud CS;

[0069] the cloud CS performs after decryption

[0070] offloaded to the road side unit to obtain road side unit RSU processing of the vehicle 's tasks remote in time denoted as

[0071]

[0072] where denotes the vehicle 's association decision on the subchannel ; the vehicle 's association decision on the subchannel ; is the finite backhaul rate between the road side unit RSU and the cloud CS; is the computational power of the road side unit RSU allocated to the vehicle 's tasks ; is the computational power of the cloud CS allocated to the vehicle 's tasks ; denotes the time to upload from the vehicle to the road side unit RSU; denotes the time to compute on the road side unit RSU; denotes the time to upload from the road side unit RSU to the nearby cloud CS; denotes the time to compute on the cloud CS; denotes the time to decrypt on the road side unit RSU;

[0073] offloaded to the cloud:

[0074] when the vehicle is associated with the cloud CS, a part of is encrypted offloaded from the vehicle to the cloud CS; second, the cloud CS decrypts and performs; offloaded to the cloud for processing of the vehicle 's tasks associated with the cloud CS​ The remote time is expressed as:

[0075] ;

[0076] in, Indicates that the vehicle Upload Time to cloud CS; Indicates CS computing in the cloud time, Indicates decryption at the cloud CS time, 0 represents cloud CS;

[0077] vehicle The total time required for the task , expressed as:

[0078] ;

[0079] The local energy consumption of all vehicles is:

[0080] ;

[0081] Where, is the chip architecture energy coefficient, represents the calculated total energy consumption of the vehicle; represents the total energy consumption of encryption; Indicates the total energy consumption of uploading; Indicates vehicle consumption rate; Indicates the upload power.

[0082] Furthermore, in step S1, an optimization problem is constructed for the network system based on constraints, specifically:

[0083] ;

[0084] in, They represent the parameters of the constraint-based optimization problem, which are specifically expressed as:

[0085] ;

[0086] also, Represent different constraints respectively; Indicates vehicle The execution time of a task cannot exceed its deadline ; Indicates vehicle The total security breach cost cannot exceed its maximum acceptable cost ; and Indicates that a vehicle can only be associated with one facility, including the cloud CS side unit RSU; Indicates vehicle Mission Only one encryption algorithm can be selected; and Indicates that a vehicle can only select one sub-channel when passing through the IRS; Indicates the vehicle The lower and upper bounds of the transmit power ; Indicates the uninstall part and Are greater than or equal to , but less than or equal to the vehicle Mission Data size ,at the same time, Must be less than or equal to ; and Indicates vehicle Only one IRS can be selected for association; Indicates the angular range of the elements in the IRS reflective surface, Indicates the preset minimum parameter.

[0087] Furthermore, step S2 is specifically as follows:

[0088] Step S21: Initialize the maximum number of iterations using the swarm intelligence optimization algorithm SIOVS , and the current number of iterations Set to 1;

[0089] Step S22: Define population , individual use To represent the population set ;

[0090] The constraint-based optimization problem parameters Encoded , Represents population Medium vehicle An index set of status indicators for establishing a connection with a base station; Encoded , Represents population Medium vehicle The index set with the selected cryptographic algorithm; Encoded , Represents population Medium vehicle The index set of the selected subchannels; encoded into , denotes a set of indices of reflectors of the IRS associated with the vehicle ; encoded into , denotes a set of indices of reflectors of the IRS associated with the vehicle ; encoded into , denotes a set of indices of reflectors of the IRS associated with the vehicle ; encoded into , denotes a set of indices of reflectors of the IRS associated with the vehicle ; encoded into , denotes a set of indices of reflectors of the IRS associated with the vehicle ; encoded into ,

[0091] Step S23: evaluate the fitness of the individual, based on the constraints and are nonlinear mixed integer forms, and and are introduced into the fitness function as penalty terms, which are used to prevent the individual from falling into infeasible regions;

[0092] In order to minimize the energy consumed by all users under the constraints and , the fitness function of the individual is defined as:

[0093] ;

[0094] wherein, denotes the fitness function value of the individual ; is a delay constraint penalty factor of the vehicle , is a total cost of security vulnerability constraint penalty factor of the vehicle , represents the total time of the computing task, represents the maximum allowed delay of task execution, is the total cost of security vulnerability of the vehicle , is the maximum allowed total cost of security vulnerability of the vehicle ;

[0095] Step S24: population initialization; to meet the constraints , the initial population can be generated using the following rules; in particular, any individual can be initialized as:

[0096] ;

[0097] wherein, represents a random output of an element from a set, represents generating a random number between and ; represents a preset parameter,

[0098] Step S25: calculate the fitness values of all individuals in the population using the fitness function, and take the individual with the highest fitness value as the optimal individual, while finding the worst individual;

[0099] Step S25: determine whether the current iteration index is less than or equal to the maximum number of iterations ; if so, calculate the current weight factor , and update it with the following formula: ; if not, output the global optimal solution in the target population;

[0100] wherein, is the initial weight value, used to start the initial global search; is the decay weight coefficient, taking a value in the range of , used to gradually reduce the weight value; is the current number of iterations;

[0101] In each iteration, in order to better control the update probability of individuals, normalization processing is also needed; the calculation formula of the normalized weight value is:

[0102]

[0103] wherein: is the set minimum weight value, which is the target weight at the end of algorithm iteration; takes a value in the range of ; it is an important parameter for dynamically adjusting the probability in the algorithm; if is less than 0 in calculation, it is forcibly set to 0;

[0104] Step S26: generate is a random number in the range of , and is the number of producers, whose calculation formula is , and , is a random number in the range of if , the position of the producer is updated using exponential decay; otherwise, the position of the producer is updated using Gaussian disturbance;

[0105] Further, exponential decay means that the moving amplitude of the producer gradually decreases with the increase of the number of iterations, so as to carry out fine search in the solution space. The specific update formula is as follows:

[0106]

[0107] wherein, , , , , , , are respectively replaced by for updating; , , , , , , correspond to respectively replacing for updating; denotes the position of the individual with the optimal fitness; is a random number in the range of for introducing randomness; is the index of the producer; denotes the maximum number of iterations; if the producer does not select exponential decay exploration, the way of developing by random disturbance is adopted, that is, small amplitude random disturbance is carried out near the position of the leader, so as to develop the local area of the solution space. The specific update formula is as follows:

[0108] ;

[0109] wherein: , , , , , , are respectively replaced by for updating; , , , , , , correspond to respectively replacing for updating; Gaussian-distributed random number; introduce small perturbation in the local region of the solution space, make the producer develop around the leader;

[0110] Step S27: For the followers in the upper half of the population behind the producer, the update strategy is to move towards the individual with the worst fitness in the current population; the specific update formula is as follows:

[0111] ;

[0112] where the follower , , , , , , , is replaced by to update; , , , , , , is replaced by to update; denotes the position of the individual with the worst fitness in the current population;

[0113] For the followers in the lower half of the population, the update strategy is to move towards the individual with the best fitness, thereby performing local search and development; the specific update formula is as follows:

[0114] ;

[0115] where , , , , , , is replaced by to update; , , , , , , is replaced by to update; is a random perturbation vector, where the elements are randomly taken as -1 or 1.

[0116] Further, the specific process of step S3 is to restore the position of the global optimal individual into the format of the parameters of the constraint-based optimization problem according to the index set of the optimal position corresponding to the global optimal solution obtained by the swarm intelligence optimization algorithm in step S2; and perform vehicle task offloading, algorithm selection, vehicle computing resource allocation, infrastructure resource allocation and vehicle power control according to the obtained global optimal solution.

[0117] Compared with the prior art, the present application has the following beneficial effects:

[0118] (1) The algorithm of the present application uses a swarm intelligence optimization method for searching; by introducing an adaptive adjustment mechanism, the algorithm can dynamically decay the weight factor according to the number of iterations, realizing smooth transition from global search to local optimization; when updating the position of the producer, the algorithm performs fine search on the solution space through exponential decay and Gaussian disturbance, thereby ensuring the balance between global convergence and local adjustment; when updating the position of the follower, the algorithm uses different update strategies according to the position of the individual in the population. For the followers in the upper half of the population, the update strategy is to move towards the individual with the worst current fitness to explore the potential unexplored areas in the solution space, thereby avoiding falling into a local optimal solution. The followers in the lower half move towards the individual with the best current fitness for local search to further optimize the quality of the solution.

[0119] (2) The present application constructs an optimization problem according to the vehicle basic information of the Internet of Vehicles, and constructs the optimization problem under the constraints of the network system; obtains an initial solution according to the optimization problem, searches the initial population using a swarm intelligence optimization algorithm to obtain a target population, and outputs the position of the global optimal individual in the target population; and performs safety-type computing efficiency optimization configuration according to the position of the global optimal individual. The present application considers the intelligent reflecting surface factor to optimize vehicle association, channel selection, IRS angle, IRS selection, safety decision and power allocation to minimize the total energy consumption of the user locally.

[0120] (3) The present application can well realize the minimization of the total energy consumption of the user locally under the constraints of computing resources and time delay, safety cost, task offloading ratio, user transmission power ratio, minimum rate and maximum energy consumption. BRIEF DESCRIPTION OF DRAWINGS

[0121] Figure 1 is the flow chart of the method of the present application.

[0122] Figure 2 is a schematic diagram of the present application showing the influence of the number of vehicles on the total energy consumption of the user locally.

[0123] Figure 3 is a schematic diagram of the present application showing the influence of the maximum computing capacity of the vehicle on the total energy consumption of the user locally.

[0124] Figure 4 The present application discloses the convergence graph of the iterative process of the SIOVS fitness function value of the method of the present application. DETAILED DESCRIPTION

[0125] As shown in the figure, a smart reflecting surface assisted V2X security computing offloading method includes the following steps: Figure 1

[0126] Step S1: Obtain the vehicle basic information of V2X and build a network system, the network system including a communication model, a computing model and a security model; build an optimization problem based on constraints for the network system;

[0127] Step S2: Obtain the initial solution of the optimization problem, and define the initial solution as an initial population; search the initial population by using a swarm intelligence optimization algorithm SIOVS to obtain a target population and acquire the global optimal solution in the target population; specifically:

[0128] First, initialize the population and determine the historical best and worst individuals; then generate a weight factor and apply it to the process of searching solutions; in the producer update stage, perform exponential decay exploration and update the producer position;

[0129] Secondly, perform Gaussian disturbance to further update the producer position; in the follower update stage, for each follower individual, generate a random disturbance vector; if the index of the follower exceeds half of the population, use Gaussian disturbance and exponential decay to update the position, otherwise, use the update relative to the leader and the random disturbance vector to adjust the follower position; then repeatedly execute the update of the producer, follower, historical best and worst individuals in turn; finally, obtain the global optimal solution;

[0130] Step S3: Configure the network system of the optimized V2X according to the global optimal solution.

[0131] Further, the specific process of step S1 is: obtaining the vehicle basic information of V2X, further building the communication model, computing model and security model in the network system through the vehicle basic information, and building the optimization problem based on the communication model, computing model and security model;

[0132] Further, the specific process of obtaining the vehicle basic information of V2X in step S1 is:

[0133] Obtain the information of the vehicle , which is represented as: ;

[0134] Wherein, represents the index of any vehicle, represents the total number of vehicles;

[0135] Obtain the information of the task of each vehicle​ is denoted as: ;

[0136] wherein, denotes the index of an arbitrary task for each user, denotes the total number of tasks;

[0137] The cloud CS information and the information of the road side unit RSU are obtained, and the road side unit RSU is denoted as: ;

[0138] wherein, and denote the index of different road side units RSU, denotes the total number of road side units RSU in the network, denotes the set of all vehicle networking infrastructures;

[0139] The index set of the intelligent reflecting surface IRS is obtained , which is denoted as: ;

[0140] wherein, denotes the index of an arbitrary intelligent reflecting surface IRS; denotes the total number of intelligent reflecting surfaces IRS;

[0141] The intelligent reflecting surface IRS contains reflection unit coefficients, and the index set of the reflection unit coefficient is , which is denoted as ;

[0142] wherein, denotes the index of an arbitrary reflection unit coefficient; denotes the total number of reflection unit coefficients;

[0143] The reflection unit coefficient is in a matrix format, which is denoted as:

[0144] ;

[0145] wherein, denotes the reflection unit coefficient, denotes the reflection coefficient of the reflection element on the intelligent reflecting surface IRS, denotes a diagonal function;

[0146] The physical positions of all road side units RSU are obtained, and the clustering algorithm is used according to the physical positions of all road side units RSU to divide into clusters; the micro base station is set based on the classification result position;

[0147] The communication range of each micro base station in the cluster includes several roadside units RSU, several vehicles and several intelligent reflective surfaces IRS. sub-channels are used by the roadside units (RSUs) within the communication range, and the sub-channel index set is recorded as ;in, represents the index of any subchannel, Indicates the total number of subchannels;

[0148] Get the total bandwidth of the network and subchannel bandwidth , the mission is through the vehicle Channel Transmit to intelligent reflective surface IRS The channel gain is recorded as , the task passes through the IRS via the sub-channel Transfer to RSU The channel gain is recorded as , the task is transmitted back to the vehicle through the roadside unit RSU The channel gain is recorded as and the noise is denoted as .

[0149] Furthermore, the process of constructing the communication model in step S1 is specifically as follows:

[0150] Based on the condition that there is no intra-cluster interference in any vehicle task, the total bandwidth of the network system is divided into and They are used by the roadside unit RSU and the cloud respectively; is the frequency band division factor, and ; The number of sub-channels used by each micro base station is expressed as ;in, represents the floor function, Indicates the number of subchannels;

[0151] When a vehicle is associated with a roadside unit (RSU), the vehicle is connected to the roadside unit (RSU) by using the non-orthogonal multiple access (NOMA) technology. The unloaded part of the task is sent to multiple roadside units (RSUs) at the same time;

[0152] Calculate the uplink transmission rate, that is, calculate the sub-channel On the vehicle Send the task to the roadside unit RSU Uplink NOMA transmission rate ; expressed as:

[0153] ;

[0154] ;

[0155] ;

[0156] ;

[0157] ;

[0158] ;

[0159] ;

[0160] in, Indicates the bandwidth used by the roadside unit, Indicates vehicle Associated Intelligent Reflecting Surface IRS The decision index coefficient of Indicates vehicle Associated Intelligent Reflecting Surface IRS The decision index coefficient of Indicates vehicle Its own transmission power; Indicates vehicle Its own transmission power; It means that in the process of uplink NOMA transmission, except for vehicles, and intelligent reflective surface IRS Other vehicles and other intelligent reflective surfaces other than IRS For vehicles and intelligent reflective surface IRS the interference caused; Represents any vehicle within the range of the micro base station The task goes through the sub-channel Transmitted via uplink NOMA to the intelligent reflector IRS The channel gain of Indicates that other vehicles are in the process of uplink NOMA transmission Send the task to the intelligent reflective surface IRS The resulting channel gain interference; and denote the path loss factor and the Rice fading factor, respectively, Indicates the vehicles within the range of the micro base station To the intelligent reflective surface IRS distance, 、 and Represents any vehicle Three-dimensional Cartesian coordinates, intelligent reflective surface IRS 3D Cartesian coordinates and roadside unit RSU The three-dimensional Cartesian coordinates of Indicates vehicle to the IRS Line-of-sight link component; is the frequency; The elements in represent the intelligent reflective surface IRS The channel coefficient vector of each reflective element in ; represents the phase change caused by signal propagation, and Respectively represent vehicles to the IRS The direction angle of Indicates vehicle to the IRS Non-line-of-sight link component; represents the first noise;

[0161] When the vehicle is connected to the cloud, any vehicle Uplink NOMA transmission rate to the cloud Expressed as: ;

[0162] in, Indicates the bandwidth used by the cloud; Indicates vehicle IRS The decision index coefficient of represents any vehicle in the cluster The task goes through the sub-channel Transmitted to IRS via uplink NOMA The channel gain of Indicates the task from IRS Through the sub-channel Channel gain to the cloud; Indicates cloud to vehicle The channel gain of Represents the second noise.

[0163] Furthermore, the process of building the security model in step S1 is specifically as follows:

[0164] When a vehicle is associated with a roadside unit (RSU), part of the vehicle's task can be offloaded to the RSU after encryption. The RSU decrypts the encrypted task and then encrypts it again before transmitting it to the cloud.

[0165] When the vehicle is associated with the cloud CS; part of the task of the vehicle can be offloaded to the cloud CS after encryption, and the cloud CS decrypts and executes the task;

[0166] In the process of encryption or decryption, different security levels are set, and the security level is represented as: In the process of encryption or decryption, different security levels are set, and the security level

[0167] Among them, represents the security level index of the algorithm ; represents the total number of encryption algorithms or decryption algorithms; the algorithm represents the encryption algorithm or decryption algorithm;

[0168] The computing power of the encryption and decryption algorithm is and respectively; the encryption energy consumption and the decryption energy consumption are set to be the same, and the algorithm The energy consumption of encryption or decryption in the algorithm is represented as ;

[0169] When the task of the vehicle adopts an encryption algorithm to safely offload part of its task, the failure probability is ;

[0170] The security vulnerability cost of the task of the vehicle is represented as:

[0171] ;

[0172] Among them, is the financial loss when the task fails; is a binary variable index indicating whether the vehicle v passes through the roadside unit RSU; is a security decision index of the task of the vehicle , and the algorithm is set to process the task of the vehicle ; if , then , otherwise 0; Based on this, the overall security vulnerability cost of the vehicle is represented as:

[0173] .

[0174] .

[0175] ​​​​​Further, the processing flow of the calculation model constructed in step S1 is as follows:

[0176] Local calculation:

[0177] When the vehicle is associated with the road side unit RSU or the cloud CS, the vehicle performs the task , and the local processing data volume is , wherein, represents the total data volume of the vehicle performing the task ; is the data volume of the task unloaded from the vehicle to the road side unit RSU or the cloud CS;

[0178] The local execution time used by the vehicle for the task associated with the road side unit RSU or the cloud CS in the local calculation process is represented as:

[0179] ;

[0180] In the formula, is the computing power of the vehicle ; is the calculation time; is the encryption time, represents the cost of the vehicle performing the task ;

[0181] Unloading to the road side unit:

[0182] When the vehicle is associated with the road side unit RSU, the following steps need to be performed for the task ;

[0183] Part of the is encrypted and then unloaded from the vehicle to the road side unit RSU; The road side unit RSU decrypts

[0184] and then performs ; wherein, represents the decryption data volume of the vehicle performing the task process during the decryption process; Part of the

[0185] is decrypted and then unloaded from the vehicle to the road side unit RSU; ​part of which is encrypted offloaded from the road side unit RSU to a nearby cloud CS;

[0186] the cloud CS performs after decryption

[0187] offloaded to the road side unit to obtain road side unit RSU processing of the vehicle 's tasks remote in time denoted as

[0188]

[0189] where denotes the vehicle 's association decision on the subchannel ; the vehicle 's association decision on the subchannel ; is the finite backhaul rate between the road side unit RSU and the cloud CS; is the computational power of the road side unit RSU assigned to the vehicle 's tasks ; is the computational power of the cloud CS assigned to the vehicle 's tasks ; denotes the time to upload from the vehicle to the road side unit RSU; denotes the time to compute on the road side unit RSU; denotes the time to upload from the road side unit RSU to the nearby cloud CS; denotes the time to compute on the cloud CS; denotes the time to decrypt on the road side unit RSU; denotes the time to encrypt on the road side unit RSU; denotes the time to decrypt on the nearby cloud CS;

[0190] offloaded to the cloud:

[0191] when the vehicle is associated with the cloud CS, a part of is encrypted offloaded from the vehicle to the cloud CS; second, the cloud CS decrypts and performs; offloaded to the cloud for processing of the vehicle 's tasks associated with the cloud CS​ The remote time is expressed as:

[0192] ;

[0193] in, Indicates that the vehicle Upload Time to cloud CS; Indicates CS computing in the cloud time, Indicates decryption at the cloud CS time, 0 represents cloud CS;

[0194] vehicle The total time required for the task , expressed as:

[0195] ;

[0196] The local energy consumption of all vehicles is:

[0197] ;

[0198] Where, is the chip architecture energy coefficient, represents the calculated total energy consumption of the vehicle; represents the total energy consumption of encryption; Indicates the total energy consumption of uploading; Indicates vehicle consumption rate; Indicates the upload power.

[0199] Furthermore, in step S1, an optimization problem is constructed for the network system based on constraints, specifically:

[0200] ;

[0201] in, They represent the parameters of the constraint-based optimization problem, which are specifically expressed as:

[0202] ;

[0203] also, Represent different constraints respectively; Indicates vehicle The execution time of a task cannot exceed its deadline ; Indicates vehicle The total security breach cost cannot exceed its maximum acceptable cost ; and It indicates that a vehicle can only be associated with one facility, and the facility includes a cloud CS side unit RSU; It indicates the vehicle The task Only one encryption algorithm can be selected; And It indicates that the vehicle can only select one subchannel through IRS; It indicates the lower and upper bounds of the transmit power of the vehicle ; ; It indicates the offloading part And Both are greater than or equal to , but less than or equal to the data size of the task of the vehicle ; At the same time, Must be less than or equal to ; And It indicates that the vehicle Can only select one IRS for association; It indicates the angle range of the elements in the intelligent reflecting surface IRS reflecting surface, It indicates the preset minimum value parameter.

[0204] Further, step S2 is specifically:

[0205] Step S21: initialize the maximum number of iterations of the swarm intelligence optimization algorithm SIOVS , and set the current iteration number To 1;

[0206] Step S22: define a population , an individual is represented by , and the population set ;

[0207] Encode the constrained optimization problem parameters Into , Indicate the index set of the state indicators of the vehicle in the population Connect with the base station; Encode into , Indicate the index set of the vehicle in the population Select a cryptographic algorithm; Encode into , Indicate the index set of the vehicle in the population Select a subchannel; encoded into , denotes a population of vehicles ; encoded into , denotes a population of vehicles selecting a set of indices of IRSs; encoded into , denotes a set of indices of reflection coefficients of reflection elements of IRSs associated with vehicles encoded into , denotes a population of vehicles unloading data bit sizes to roadside units; encoded into , denotes a set of indices of data bit sizes of tasks unloaded by roadside units to the cloud;

[0208] Step S23: evaluating the fitness of the individual, based on constraints and are nonlinear mixed integer forms, and and are introduced into the fitness function as penalty terms, which are used to prevent the individual from falling into infeasible regions;

[0209] In order to minimize the energy consumed by all users under constraints and , the fitness function of the individual is defined as:

[0210] ;

[0211] wherein, denotes the fitness function value of the individual ; is a delay constraint penalty factor of the vehicle , is a total cost of security vulnerabilities constraint penalty factor of the vehicle , represents the total time of computing tasks, represents the maximum allowed delay of task execution, is the total cost of security vulnerabilities of the vehicle , is the maximum allowed total cost of security vulnerabilities of the vehicle ;

[0212] Step S24: population initialization; in order to meet the constraints , the initial population can be generated using the following rules; specifically, any individual can be initialized as:

[0213] ;

[0214] wherein, represents randomly outputting an element from a set, represents generating a random number between and ; represents a preset parameter,

[0215] Step S25: calculating the fitness values of all individuals in the population using the fitness function, and taking the individual with the highest fitness value as the optimal individual, while finding the worst individual;

[0216] Step S25: judging whether the current iteration index is less than or equal to the maximum iteration number ; if yes, calculating the current weight factor , and updating it with the following formula: ; if no, outputting the global optimal solution in the target population;

[0217] wherein, is an initial weight value, used to start the initial global search; is a decay weight coefficient, taking a value in , used to gradually reduce the weight value; is the current iteration number;

[0218] In each iteration, in order to better control the update probability of individuals, normalization processing is also needed; the calculation formula of the normalized weight value is:

[0219]

[0220] wherein: is a set minimum weight value, which is the target weight at the end of algorithm iteration; takes a value in ; it is an important parameter for dynamically adjusting the probability in the algorithm; if is less than 0 in calculation, it is forcibly set to 0;

[0221] Step S26: generating is a random number in the range of , and is the number of producers, whose calculation formula is , generating , is a random number in the range of if , the position of the producer is updated using exponential decay; otherwise, the position of the producer is updated using Gaussian disturbance;

[0222] Further, exponential decay means that the moving amplitude of the producer gradually decreases with the increase of the number of iterations, so as to carry out fine search in the solution space. The specific update formula is as follows:

[0223]

[0224] wherein, , , , , , , are respectively replaced by to update; , , , , , , correspond to respectively replacing to update; represents the position of the individual with the optimal fitness; is a random number in the range of for introducing randomness; is the index of the producer; represents the maximum number of iterations; if the producer does not choose exponential decay exploration, the way of developing by random disturbance is adopted, that is, small amplitude random disturbance is carried out near the position of the leader to develop the local area of the solution space; the specific update formula is as follows:

[0225] ;

[0226] wherein: , , , , , , replace to update; , , , , , , correspond to respectively replacing to update; Random number representing Gaussian distribution; introduce small perturbation in local region of solution space, make the producer develop around the leader;

[0227] Step S27: For the followers in the upper half of the population behind the producer, the update strategy is to move towards the individual with the worst fitness in the current population; the specific update formula is as follows:

[0228] ;

[0229] Where, the follower , , , , , , , Replace Update; , , , , , , Replace Update; Indicates the position of the individual with the worst fitness in the current population;

[0230] For the followers in the lower half of the population, the update strategy is to move towards the individual with the best fitness, thereby performing local search and development; the specific update formula is as follows:

[0231] ;

[0232] Where, , , , , , , Replace Update; , , , , , , Replace Update; Is a random perturbation vector, where the elements are randomly taken as -1 or 1.

[0233] Further, the specific process of step S3 is to restore the position of the global optimal individual into the format of the parameters of the constraint-based optimization problem according to the index set of the optimal position corresponding to the global optimal solution obtained by the swarm intelligence optimization algorithm in step S2, and perform vehicle task offloading, algorithm selection, vehicle computing resource allocation, infrastructure resource allocation and vehicle power control according to the obtained global optimal solution.

[0234] The effect of the embodiment of the present application can be further illustrated by simulation.

[0235] The simulation conditions are set as follows: 31 infrastructures (30 road side units and 1 cloud) are considered; 20 vehicles, each user has 5 sub-tasks; the system bandwidth is 50MHz; two intelligent reflecting surfaces, the number of reflecting small blocks in one IRS unit is 60; 6 encryption algorithms, which encrypt 1 bit of data respectively need [100 200 250 300 350 1050] CPU cycles, and decrypt 1 bit of data respectively need [90 280 350 300 400 1700] CPU cycles, and the energy consumption of encrypting and decrypting 1 bit of data is [2.5296 5.0425 6.837 7.8528 8.7073 26.3643]*1e-7 joule; the maximum allowed time delay of the computing task is 5-10s; the user energy coefficient is 10 -24 ; the base station energy coefficient is 10 -26 ; the task security factor generates a cost due to the failure of protection, and the task security coefficient is {5, 6}.

[0236] Figure 2 The figure shows the influence of the maximum computing capacity of the vehicle on the total energy consumption of the vehicle. When the maximum computing capacity of the vehicle increases from 0.5GHz to 2GHz, the total energy consumption of the vehicle of the WOA, SSA and SIOVS algorithms all increases with the increase of the maximum computing capacity of the vehicle, but the total energy consumption of the user of the SIOVS algorithm proposed by the present application is always better than that of the other two algorithms.

[0237] Figure 3 The figure shows the influence of the number of vehicles on the total energy consumption of the vehicle. When the number of vehicles in the network increases from 10 to 19, the total energy consumption value rises. But the total energy consumption of the SIOVS algorithm proposed by the present application is always better than the energy consumption values of the other two algorithms.

[0238] As shown in Figure 4 , the SIOVS algorithm of the present application converges to a fixed value after a certain number of iterations of the fitness function, so that the solution of the optimization problem is obtained.

[0239] While embodiments of the application have been shown and described, it is to be understood that the embodiments described are merely exemplary of the principles and application of the present application. Numerous modifications and adaptions can be effected without departing from the spirit and scope of the present application, which is not limited to the exact construction and arrangement described. It is intended, therefore, to cover all modifications and adaptions that fall within the scope of the claims and their equivalents.

Claims

1. A method for intelligent reflecting surface assisted V2X security computation offloading, comprising: The method comprises the following steps: Step S1: acquiring vehicle basic information of the Internet of Vehicles and constructing a network system, the network system comprising a communication model, a calculation model and a security model; constructing an optimization problem based on constraints for the network system; Step S2: acquiring an initial solution of the optimization problem, defining the initial solution as an initial population, searching the initial population by using a swarm intelligence optimization algorithm SIOVS to obtain a target population, and acquiring a global optimal solution in the target population; specifically: First, initialize the population and determine the historical best and worst individuals; then generate a weight factor and apply it to the process of searching for a solution; in the producer update stage, perform exponential decay exploration and update the producer position; Secondly, Gaussian perturbation is performed to further update the producer position; in the follower update stage, for each follower individual, a random perturbation vector is generated; if the index of the follower exceeds half of the population, the position is updated using Gaussian perturbation and exponential decay, otherwise, the position is adjusted using the update relative to the leader and the random perturbation vector; then the update of the producer, the follower and the historical best and worst individuals is repeatedly performed in turn; finally, the global optimal solution is obtained; Step S3: configuring the network system of the optimized Internet of Vehicles according to the global optimal solution; In step S1, the optimization problem is constructed based on constraints for the network system, specifically: ； wherein, respectively, represent parameters of the constraint-based optimization problem, and are specified as: ； also, Represent different constraints respectively; Indicates vehicle The execution time of a task cannot exceed its deadline ; Indicates vehicle The total security breach cost cannot exceed its maximum acceptable cost ; and Indicates that a vehicle can only be associated with one facility, including the cloud CS side unit RSU; Indicates vehicle Mission Only one encryption algorithm can be selected; and Indicates that a vehicle can only select one sub-channel when passing through the IRS; Indicates the vehicle The lower and upper bounds of the transmit power ; Indicates the uninstall part and Are greater than or equal to , but less than or equal to the vehicle Mission Data size ,at the same time, Must be less than or equal to ; and Indicates vehicle Only one IRS can be selected for association; Indicates the angular range of the elements in the IRS reflective surface, Indicates the preset minimum parameter.

2. The intelligent reflecting surface assisted Internet of Vehicles security calculation offloading method according to claim 1, characterized in that, The specific process of step S1 is: acquiring vehicle basic information of the Internet of Vehicles, further building a communication model, a calculation model and a security model in the network system through the vehicle basic information, and constructing an optimization problem based on the communication model, the calculation model and the security model; Further, the specific process of acquiring vehicle basic information of the Internet of Vehicles in step S1 is: Get a vehicle The information is expressed as: ; wherein, denotes an index of an arbitrary vehicle, denotes the total number of vehicles; Obtaining information of a task of each vehicle , denoted as: ; wherein, represents an index of an arbitrary task of each user, represents the total number of tasks; Obtaining cloud CS information and information of a road side unit RSU, the road side unit RSU is expressed as: ; wherein, and denote different indices of road side units RSUs, respectively, denotes the total number of road side units RSUs in the network, denotes the set of all connected vehicle infrastructures; Obtaining an index set of an intelligent reflecting surface IRS , denoted as: ; wherein, denotes an index of an arbitrary intelligent reflecting surface, IRS; denotes the total number of intelligent reflecting surfaces, IRSs. The intelligent reflecting surface IRS contains a reflection unit coefficient, and an index set of the reflection unit coefficient is represented as ;​​ wherein denotes an index of an arbitrary reflection element coefficient; denotes the total number of reflection element coefficients; The reflection unit coefficient is in matrix format, represented as: ； wherein, denotes a reflection unit coefficient, denotes a reflection coefficient of the nth reflection element on the intelligent reflecting surface IRS, denotes a diagonal function; acquiring physical locations of all road side units RSUs and using the physical locations of all road side units RSUs to determine a location of the vehicle The clustering algorithm will be divided into clusters; setting a micro base station based on the location of the classification result; wherein the communication range of each micro base station within a cluster contains a number of road side units RSUs and a number of vehicles and a number of intelligent reflecting surfaces IRS, each micro base station has sub-channels for the road side units RSUs within the communication range to use, the index set of the sub-channels is denoted as ; wherein denotes the index of an arbitrary sub-channel, denotes the total number of sub-channels; Get the total bandwidth of the network and subchannel bandwidth , the mission is through the vehicle Channel Transmit to intelligent reflective surface IRS The channel gain is recorded as , the task passes through the IRS via the sub-channel Transfer to RSU The channel gain is recorded as , the task is transmitted back to the vehicle through the roadside unit RSU The channel gain is recorded as and the noise is denoted as .

3. The smart intelligent reflecting surface assisted V2X security computation offloading method of claim 2, wherein, The process of constructing the communication model in step S1 is specifically: The network system total bandwidth is divided into and for roadside unit RSU and cloud respectively; wherein is the frequency band division factor, and ; the number of sub-channels used by each micro base station is denoted as ; wherein, denotes the floor function, denotes the number of sub-channels; When the vehicle is associated with a road side unit, RSU, the vehicle The to-be-offloaded part of the task is sent to multiple road side units, RSUs, simultaneously; computing an uplink transmission rate, i.e. computing an uplink NOMA transmission rate up, vehicle sending the task to a road side unit, RSU uplink NOMA transmission rate is expressed as: ； ； ； ； ； ； ； in, Indicates the bandwidth used by the roadside unit, Indicates vehicle Associated Intelligent Reflecting Surface IRS The decision index coefficient of Indicates vehicle Associated Intelligent Reflecting Surface IRS The decision index coefficient of Indicates vehicle Its own transmission power; Indicates vehicle Its own transmission power; It means that in the process of uplink NOMA transmission, except for vehicles, and intelligent reflective surface IRS Other vehicles and other intelligent reflective surfaces other than IRS For vehicles and intelligent reflective surface IRS the interference caused; Represents any vehicle within the range of the micro base station The task goes through the sub-channel Transmitted via uplink NOMA to the intelligent reflector IRS The channel gain of Indicates that other vehicles are in the process of uplink NOMA transmission Send the task to the intelligent reflective surface IRS The resulting channel gain interference; and denote the path loss factor and the Rice fading factor, respectively. Indicates the vehicles within the range of the micro base station To the intelligent reflective surface IRS distance, 、 and Represents any vehicle Three-dimensional Cartesian coordinates, intelligent reflective surface IRS 3D Cartesian coordinates and roadside unit RSU The three-dimensional Cartesian coordinates of Indicates vehicle to the IRS Line-of-sight link component; is the frequency; The elements in represent the intelligent reflective surface IRS The channel coefficient vector of each reflective element in ; represents the phase change caused by signal propagation, Indicates vehicle to the IRS Non-line-of-sight link component; represents the first noise; Indicates vehicle To the intelligent reflective surface IRS The direction angle of Indicates IRS To the roadside unit RSU The direction angle of When the vehicle is associated with the cloud, any vehicle Uplink NOMA transmission rate to the cloud is represented as: ； wherein, denotes the bandwidth used by the cloud; denotes the vehicle associated IRS decision index coefficient; denotes any vehicle within the cluster task through sub-channel to the IRS channel gain; denotes the task from the IRS through sub-channel to the cloud channel gain; denotes the cloud to vehicle channel gain; denotes the second noise.

4. The smart reflecting surface assisted V2X security computation offloading method of claim 3, wherein, The process of constructing the security model in step S1 is specifically: When the vehicle is associated with the road side unit RSU, part of the task of the vehicle can be offloaded to the road side unit RSU after encryption; the road side unit RSU decrypts the encrypted task and then transmits it to the cloud after re-encryption; When the vehicle is associated with the cloud CS; part of the task of the vehicle can be offloaded to the cloud CS after encryption, and the cloud CS decrypts and executes the task; In the process of encryption or decryption, different security levels are set, and the security level is represented as: in the middle; wherein represents the security level of an algorithm with index , the algorithm being an encryption algorithm or a decryption algorithm, denotes the total number of encryption or decryption algorithms; Encryption and decryption algorithms The computing power of the encryption and decryption algorithms respectively is and ; the encryption energy consumption and the decryption energy consumption are set to be the same, and the energy consumption of encryption or decryption in the algorithm is expressed as ; When the vehicle is in the task of adopting the encryption algorithm to safely offload its partial task, the failure probability is ; Vehicle of the task of the security breach cost is expressed as: ； wherein, financial loss in case of a task failure; a binary variable indicator of whether a vehicle v passed a roadside unit RSU or not; a vehicle task safety decision indicator, setting a selection algorithm to handle a vehicle task then , otherwise 0; Based on this, the overall safety vulnerability cost of a vehicle is represented as: ​ 。 5. The smart reflecting surface assisted V2X security computation offloading method of claim 4, wherein The processing flow of the calculation model constructed in step S1 is: Local calculation: When the vehicle is associated with a road side unit RSU or a cloud CS, the task has a local processing data volume of , wherein denotes the total data volume of the vehicle performing the task ; is the data volume of the task offloaded from the vehicle to the road side unit RSU or the cloud CS. Handling vehicles in local computing processes Tasks associated with a road side unit, RSU, or a cloud, CS Local execution time used is represented as: ； wherein the computing power of the vehicle ; the computation time; the encryption time, the cost of performing a task by the vehicle ; the cost of performing a task Offload to roadside unit: When the vehicle When associated with a road side unit, RSU, there is a need to perform a task The following steps are performed; The encrypted parts are offloaded from the vehicle to a roadside unit RSU ; Roadside unit, rsu, decryption Then performing ; wherein, Indicates the amount of decryption data in the process of the vehicle Performing tasks Decryption data in the process The encrypted parts of the are offloaded from the road side unit RSU to a nearby cloud CS.​ Cloud CS executes after decryption ; Offloading into a roadside unit to obtain roadside unit, RSU, processing of vehicles the tasks of remote time is represented as: ； wherein denotes a vehicle associated decision on a sub-channel ; vehicle associated decision on a sub-channel ; is the limited backhaul rate between the road side unit RSU and the cloud CS; is the computing power of the road side unit RSU assigned to the tasks of the vehicle ; is the computing power of the cloud CS assigned to the tasks of the vehicle ; denotes the time to upload from the vehicle to the road side unit RSU; denotes the time to compute on the road side unit RSU; denotes the time to upload from the road side unit RSU to the nearby cloud CS; denotes the time to compute on the cloud CS; denotes the time to decrypt on the road side unit RSU; denotes the time to encrypt on the road side unit RSU; denotes the time to decrypt on the nearby cloud CS; Offload to cloud: When the vehicle is associated with the cloud CS, the part of the is offloaded from the vehicle to the cloud CS after being partially encrypted; secondly, the cloud CS decrypts and executes; offloaded to the cloud for processing the tasks of the vehicle associated with the cloud CS remote time is represented as: ； wherein, represents the time of uploading from the vehicle to the cloud CS; represents the time of computing at the cloud CS, represents the time of decrypting at the cloud CS, 0 represents the cloud CS;​ Vehicle total time of the required tasks is expressed as: ； The local energy consumption of all vehicles is: ； Where, is the chip architecture energy coefficient, represents the calculated total energy consumption of the vehicle; represents the total energy consumption of encryption; Indicates the total energy consumption of uploading; Indicates vehicle consumption rate; Indicates the upload power.

6. The intelligent reflecting surface assisted Internet of Vehicles security calculation offloading method according to claim 5, characterized in that, Step S2 is specifically: Step S21: initialize the maximum iteration number of the swarm intelligence optimization algorithm SIOVS and set the current iteration number to 1; Step S22: Define a population , where individuals are denoted by , and a collection of populations is denoted by ; Parameterizing constraint-based optimization problems Encoding into , An index set representing the population of vehicles In a vehicle State indicators of the connection established with the base station; Encoding into , An index set representing the population of vehicles In a vehicle With the selected cryptographic algorithm; Encoding into , An index set representing the population of vehicles In a vehicle Of the selected subchannel; Encoding into , An index set representing the population of vehicles In a vehicle Of the transmission power; Encoding into , An index set representing the population of vehicles In a vehicle Of the selected IRS; Encoding into , An index set representing the population of vehicles Reflectance coefficients of the reflective elements of the associated IRS; , An index set representing the population of vehicles Data bit size of the tasks offloaded from the vehicle To the roadside unit; Encoding into , An index set representing the data bit size of the tasks offloaded by the roadside unit to the cloud; Step S23: evaluating the fitness of the individual, based on the constraints and are mixed integer nonlinear forms, introducing and as penalty terms into the fitness function, using the penalty terms to prevent the individual from falling into infeasible regions; To minimize the constraint and the fitness function of an individual is defined as: ； wherein, represents a fitness function value of the individual ; is a latency constraint penalty factor of the vehicle , is a total cost of safety vulnerability constraint penalty factor of the vehicle , represents a total time of the computational task, represents a maximum allowed latency of the task execution, is a total cost of safety vulnerability of the vehicle , is a maximum allowed total cost of safety vulnerability of the vehicle ; Step S24: Population initialization; to satisfy constraints , the initial population can be generated using the following rules; in particular, any individual can be initialized as: ； wherein, represents randomly outputting one element from a set, represents generating a random number between and ; represents a preset parameter, The fitness function is used to calculate the fitness values of all individuals in the population, and the individual with the highest fitness value is taken as the optimal individual, and the worst individual is found; Step S25: judging whether the current iteration index is less than or equal to the maximum iteration number ; if yes, calculating the current weight factor , and updating it with the following formula: ; if no, outputting the global optimal solution in the target population; wherein, is an initial weight value for starting the initial global search; is a decay weight coefficient, taking a value in , for gradually reducing the weight value; is the current iteration number; In each iteration, in order to better control the update probability of the individual, normalization processing is also needed; the normalization weight value The calculation formula is: ； wherein: is the set minimum weight value, which is the target weight at the end of the algorithm iteration; is in the range of ; it is an important parameter in the algorithm for dynamically adjusting the probability; if is less than 0, it is forced to be 0; Step S26: generating is a random number within the range of is the number of producers, whose calculation formula is , generating , is a random number within the range of , if , the position of the producer is updated using exponential decay; otherwise, the position of the producer is updated using Gaussian disturbance; Further, the exponential decay indicates that the moving range of the producer gradually decreases with the increase of the number of iterations, so as to perform fine search in the solution space, and the specific update formula is as follows: ； wherein, , , , , , , are replaced by respectively; , , , , , , are replaced by respectively; denotes the position of the individual with the best fitness; is a random number in the range of , which is used to introduce randomness; is the index of the producer; denotes the maximum number of iterations; if the producer does not select the exponential decay exploration, a random perturbation development is adopted, that is, a small random perturbation is made near the leader position to develop the local area of the solution space; the specific update formula is as follows: ； wherein: , , , , , , substitute update; , , , , , , respectively substitute update; represent a random number of Gaussian distribution; introduce a small amplitude disturbance in the local region of the solution space, so that the producer develops around the leader; Step S27: for the followers in the upper half of the population after the producer, the update strategy is to move towards the worst individual in the current population; the specific update formula is as follows: ； wherein the follower , , , , , , , replaced updated; , , , , , , replaced updated; denotes the position of the individual with the worst fitness in the current population; For the followers in the lower half of the population, the update strategy is to move towards the best individual, thereby performing local search and development; the specific update formula is as follows: ； wherein , , , , , , replaced updated; , , , , , , replaced updated; is a random perturbation vector with elements randomly taking -1 or 1.

7. The method of claim 6, wherein the method is a smart reflector surface assisted V2X security computing offloading method. The specific process of step S3 is to restore the position of the global optimal individual to the format of the parameters of the constraint-based optimization problem according to the index set of the optimal position corresponding to the global optimal solution obtained by the swarm intelligence optimization algorithm in step S2. And according to the global optimal solution obtained, vehicle task offloading, algorithm selection, vehicle computing resource allocation, infrastructure resource allocation and vehicle power control are performed.

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