A high-efficiency dynamic roadside unit deployment method and system capable of detecting witch attacks

Abstract

The application discloses a kind of high-efficiency dynamic roadside unit deployment methods and systems that can detect witch attack, including collection geographic information, assess the service coverage of roadside unit in deployment area, overlapping range and accident information initial diffusion speed, establish the RSU deployment constraint that can detect witch attack;Based on particle swarm optimization and improved genetic algorithm, RSU deployment scheme is constructed;Obtain transportation and reconstruction RSU overhead, obtain minimum RSU adjustment cost based on bipartite graph matching algorithm;Collect each RSU work load, and based on Pareto optimal principle, heuristic RSU minimum cost adjustment scheme is constructed.The application is under the constraint that can detect witch attack, by constructing the efficient deployment and low-cost adjustment scheme of heuristic RSU, improve the service effective coverage range in RSU deployment, accident information initial diffusion speed, minimum costly realize load balancing between RSU and have the ability of detecting witch attack, solve the problem that traditional RSU deployment scheme comprehensive performance is low, cannot detect witch attack.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent transportation technology, and in particular relates to an efficient dynamic roadside unit deployment method and system capable of detecting Sybil attacks. Background Art

[0002] With the rapid development of intelligent vehicles and intelligent transportation systems (ITS), roadside units (RSUs) have become an indispensable component of intelligent traffic management, location privacy protection, and emergency rescue services. Appropriate deployment of RSUs can improve traffic efficiency and reduce the risk of traffic collisions and congestion. However, large-scale deployment of RSUs is costly, impractical, and ineffective. Furthermore, existing RSU deployment solutions still have room for improvement in some key performance indicators. Therefore, to improve network connectivity, it is necessary to comprehensively consider urban characteristics such as traffic, geographical environment, and signal interference to optimally deploy RSUs within limited resource constraints.

[0003] In recent years, the rapid development of ITS has also brought many threats. As a key asset of the Internet of Vehicles (IoV), RSUs are crucial in areas such as multi-agent joint identification of malicious attacks and federated learning task offloading. However, many existing RSU deployment efforts focus solely on efficiency without considering some of the security threats that are prone to occur in the IoV, increasing the operational risks of ITS. Notably, Sybil attacks are a common risk in the IoV, which can severely damage the reliability and integrity of the network. By integrating Sybil attack detection into RSU deployment schemes and considering the necessary cooperative distance constraints for RSU locations in real-world scenarios, RSU deployment schemes can be made secure.

[0004] The number and location of RSUs significantly impact the Quality of Service (QoS) of ITSs. However, given the real-world development of cities and ITSs, long-term fixed RSU locations not only lose the cost-effectiveness of existing deployments but also increase security risks due to unbalanced RSU loads. To ensure QoS in the IoV, RSU deployment locations must be adjusted with minimal cost based on traffic flow changes caused by urban development. Unfortunately, existing RSU adaptation and adjustment work is flawed. Most RSU adjustment work is based on randomly generated RSU sites for optimization. Making RSU deployment and adjustment more consistent and realistic is crucial. Furthermore, existing RSU adjustment work ignores the distance requirements between IoV agents that must be met for secure collaboration in real-world scenarios. Furthermore, the repetitive optimization objectives inherent in RSU deployment schemes make traditional RSU adjustment schemes suboptimal. Therefore, in the planning of smart cities, it is crucial to establish an RSU deployment and adjustment scheme that is consistent, detectable against Sybil attacks, and possesses superior multi-attribute features. Summary of the Invention

[0005] To overcome the shortcomings of the existing technology, the present invention provides an efficient dynamic roadside unit deployment method and system capable of detecting Sybil attacks. The method includes collecting geographic information, evaluating the service coverage, overlap, and initial diffusion rate of accident information in the deployment area, and establishing RSU deployment constraints that can detect Sybil attacks; constructing an RSU deployment plan based on particle swarm optimization and an improved memetic algorithm; obtaining transportation and reconstruction RSU overheads, and obtaining the minimum RSU adjustment cost based on a bipartite graph matching algorithm; and collecting the workload of each RSU and constructing a heuristic RSU minimum cost adjustment plan based on the Pareto optimality principle. Under the constraints of detecting Sybil attacks, the present invention constructs a heuristic RSU efficient deployment and low-cost adjustment plan. This improves the effective service coverage and initial diffusion rate of accident information during RSU deployment, achieves load balancing between RSUs at minimal cost, and provides the ability to detect Sybil attacks. This addresses the problems of traditional RSU deployment plans with low overall performance and the inability to detect Sybil attacks.

[0006] The technical solutions adopted by the present invention to solve the technical problems are as follows:

[0007] S1: Obtain the geographic information of the deployment area, initialize the deployment matrix of the deployment area, collect all possible RSU deployment sites in the deployment area; obtain the RSU transmission and influence range C R 、C e , inter-vehicle communication range C V and the number of available and additionally adjusted available RSUs N r 、N e ;

[0008] S2: Randomly generate RSU deployment plan S based on the geographical information of the deployment area and the information of each entity in the area o , we get solution S o RSU service coverage and overlapping range g cover , and calculate the initial diffusion speed S of accident information in combination with the vehicle flow in the area init ; Based on the signal strength, we get the Sybil attack detection deployment constraint C S ;

[0009] S3: Design the fitness value F to measure the performance of the RSU deployment solution, in C S Under the constraint, the RSU deployment scheme is optimized according to the fitness value F using the particle swarm-two-neighborhood search meme PSO-Meme joint heuristic algorithm to obtain a near-optimal RSU deployment scheme S c ;

[0010] S4: Get RSU reconstruction cost based on market conditions new and transportation adjustment cost adAccording to the deployment area geographic information and RSU adjustment location, the minimum RSU adjustment cost is obtained based on the bipartite graph matching algorithm Hopcroft-Karp total ;

[0011] S5: Evaluate the workload of each RSU based on the traffic volume within the effective range of each RSU in the deployment area Design a fitness value F to measure the performance of the RSU adjustment scheme ad ; in C S Under the constraints, the RSU initial deployment plan is optimized and iterated using the heuristic RSU multi-objective adaptive adjustment algorithm HRMA3 based on the Pareto optimal principle to obtain the RSU minimum cost adjustment plan S ad , while ensuring the overall efficiency of RSU deployment, load balancing between RSUs is achieved.

[0012] Furthermore, the step S2 is specifically as follows:

[0013] S201: The dynamic RSU deployment system extracts the geographical information of the deployment area and the information of each entity in the area and converts it into a deployment matrix M c And randomly generate a deployment plan S in the deployment matrix o ;

[0014] S202: The dynamic RSU deployment system is based on the entity information and deployment plan S in the area. o Get the RSU service coverage and overlapping range g of the solution cover ;

[0015] The effective signal coverage area of ​​an RSU is defined as a polygon P = {P1, P2, ..., P i}, each RSU R n Each has its effective signal coverage area P i Assuming that Nr RSUs are available, the overlapping area of ​​the RSU effective signal is the accumulation of the overlapping areas between multiple polygons, expressed as:

[0016]

[0017] Where O(a,b) represents the function for calculating the overlapping area of ​​two polygons a and b;

[0018] Therefore, g is determined by the coverage area and overlap area of ​​the RSU effective signal. cover Expressed as:

[0019]

[0020] Among them A T The total area in the city that needs to be covered by effective signals.

[0021] S203: The dynamic RSU deployment system is based on the entity information, vehicle flow and deployment plan S in the area. o Get the initial diffusion speed S of the accident information init ;

[0022] There is an accident site A in the RSU deployment area. Assuming that all V2V transmissions are successful, the average ISSAI value is calculated as:

[0023]

[0024] Among them S v is the speed of vehicle v, N is the number of all vehicles involved in the accident information dissemination; T v It represents the sum of the time spent by vehicle v to transmit accident information using carrier unit Ui, expressed as:

[0025]

[0026] S204: The dynamic RSU deployment system establishes a Sybil attack detection deployment constraint C based on the signal strength and the required RSU cooperation location distance in the actual scenario. S .

[0027] The necessary RSU cooperation distance for Sybil attack detection according to actual scenarios is expressed as:

[0028]

[0029] Among them C e Indicates the impact range of RSU.

[0030] Furthermore, the step S3 is specifically as follows:

[0031] S301: The dynamic RSU deployment system allocates RSU service coverage and overlap range g according to the priority of the deployment target cover and the initial diffusion speed of accident information S init Combined to get the fitness value F;

[0032]

[0033] Among them, θ1 and θ2 represent the normalized weights from 0 to 1, T init Represents the short information dissemination time after the incident;

[0034] S302: In C S Under the constraint, the dynamic RSU deployment system uses the particle swarm optimization algorithm to optimize the r deployment solutions S with the largest response value F according to the response value F. r , and put it into the initial solution set;

[0035] S303: The dynamic RSU deployment system sorts all solutions in the initial solution set according to the fitness value F and selects e elite solutions to form an elite class E;

[0036] E=Sort(F(s1),F(s2),…,F(s e ))e <Num(S r )

[0037] S304: In C S Under the constraints, crossover and mutation operations are performed on the elite solution set E to obtain the offspring solution set P;

[0038] S305: The dynamic RSU deployment system performs a neighborhood search on the offspring solution set obtained after each crossover and mutation operation and selects the solution with the largest fitness value to update the offspring solution set P;

[0039] Define the single solution in the elite offspring population after crossover and mutation operations as S p , its neighborhood is N(S p ), that is, from the current solution S p By exploring the solution set generated by small step size, if make

[0040]

[0041] Then the elite offspring solution S p Update to S′ p ;

[0042] S306: If the two-neighborhood search-meme algorithm has not reached the maximum number of iterations, go to S303, otherwise output a near-optimal RSU deployment solution S c .

[0043] Furthermore, the step S4 is specifically as follows:

[0044] S401: The dynamic RSU deployment system determines the RSU reconstruction cost based on the entities and market information in the region. new ;

[0045] Compared with the deployment plan Sol before adjustment, if the RSU in the adjustment plan Sol′ increases, new RSU equipment will be purchased and deployed. The cost calculation formula for the newly deployed RSU is:

[0046] Cost new =(|Sol′|-|Sol|)·(∈+δ)

[0047] Where ρ is the purchase cost and δ is the construction cost of a new RSU;

[0048] S402: The dynamic RSU deployment system formulates the RSU transportation adjustment cost based on the entities and market information in the region. ad ;

[0049] The shipping adjustment for RSUs includes the costs of disassembly, installation, and transportation. Therefore, the RSU shipping adjustment overhead is calculated as follows:

[0050]

[0051] where |R ad | is the number of RSUs that need to be adjusted, is the transportation cost per kilometer of RSU, D ad is the Euclidean distance between the RSU sites that need to be adjusted;

[0052] S403: Dynamic RSU deployment system rebuilds cost based on RSU new Adjusting the cost of RSU transportation ad Formulate RSU and calculate total cost total ;

[0053] Cost total =Cost new +Cost ad .

[0054] S404: The dynamic RSU deployment system obtains the minimum RSU adjustment total cost based on the bipartite graph matching algorithm Hopcroft-Karp total ;

[0055] Consider the existing RSU positions and the newly adjusted RSU positions as a bipartite graph set V r and V r ′, the weight E of the edge represents the cost of moving from the existing position to the new position, which is expressed as:

[0056] G(V r ,V r ′;e),e∈E(V r ,V r ′)={D(R1,R′1),D(R1,R′2),…,D(R n ,R′ m )},

[0057] Where D(R n ,R′ m ) represents R n ∈V r and R m ′∈V r The Euclidean distance between ′.

[0058] Furthermore, the step S5 is specifically as follows:

[0059] S501: The dynamic RSU deployment system evaluates the workload of the RSU based on the traffic volume within the effective range of each RSU in the deployment area.

[0060] The vehicle selects the nearest RSU to interact with, and n Traffic flow in the coverage area The interaction strength is Define the maximum load threshold of RSU through investigation Each RSU The workload is represented as:

[0061]

[0062] S502: The dynamic RSU deployment system designs a fitness value F to measure the performance of the RSU adjustment scheme based on the RSU service quality and the load balancing index between RSUs. ad ;

[0063] According to the signal coverage and overlapping range of deployed RSU g cover , load balancing between RSUs and the minimum cost of adjusting RSUs, and obtain the fitness value representing the performance of the RSU adjustment scheme:

[0064] F ad =θ1·g cover -θ3·Cost total -θ4·B R ,

[0065] Among them, θ1, θ3 and θ4 represent the normalized weights from 0 to 1, B R To evaluate the standard deviation statistic of the load balance between RSUs, it is expressed as:

[0066]

[0067] in is the average workload of all RSUs in the deployment area;

[0068] S503: The dynamic RSU deployment system randomly generates an RSU adjustment plan and converts the RSU deployment plan S close to the optimal one into an RSU adjustment plan. c Add to the adjusted optimization solution set O;

[0069] S504: The solutions in the solution set O are evaluated based on the fitness value F ad To sort and filter, in C SUnder the constraints, crossover and mutation operations are performed on the selected solutions to obtain an offspring population pop equal to the population size of the optimization solution set O;

[0070] S505: The dynamic RSU deployment system merges the solution set O and the offspring population pop, according to the fitness value F ad Perform fast non-dominated sort;

[0071] According to the fitness value F ad Perform a quick non-dominated sort on the deployment solution population to prepare for the subsequent introduction of deployment efficiency reference points for niche selection:

[0072] Sort non-domin (F ad (s1),F ad (s2),…,F ad (s l ))

[0073] S506: The dynamic RSU deployment system adds the deployment efficiency reference point to the sorted population and performs niche selection, selecting an elite population set e equal to the number of offspring pop populations. ad ;

[0074] S507: Dynamic RSU deployment system performs genetic operations to generate and elitist solution population sets ad Populations of equal size offspring pop′;

[0075] S508: The dynamic RSU deployment system performs a neighborhood search on the offspring solution set obtained after each crossover and mutation operation and selects the solution with the largest fitness value to update the offspring solution set pop′;

[0076] Define the single solution in the offspring population after crossover and mutation operations as S l , whose neighborhood is N(S l ), that is, from the current solution S l By exploring the solution set generated by small step size, if make:

[0077]

[0078] Then the elite offspring solution S l Update to S′ l ;

[0079] S509: If the HRMA3 algorithm has not reached the maximum number of iterations, go to S504; otherwise, output the optimal RSU minimum cost adjustment solution.

[0080] An efficient dynamic roadside unit deployment system capable of detecting Sybil attacks includes a module for acquiring geographic information of the RSU deployment area and information of each entity, a module for multi-performance measurement of the RSU deployment scheme, a PSO-Meme joint heuristic RSU deployment module, and a minimum cost RSU deployment adjustment module.

[0081] The RSU deployment area geographic information and entity information acquisition module is used to obtain the RSU deployment area, including the obstacle location volume, possible RSU deployment sites near intersections and city landmark buildings, RSU entity impact and communication range, vehicle equipment information, signal strength and traffic flow data received by the RSU for each vehicle interaction; the traffic flow information corresponding to each historical timestamp collected by each RSU in the deployment area is cleaned and missing values ​​are filled, and each traffic flow information is normalized by wavelet transform to ensure that the data format is suitable for subsequent RSU load balancing evaluation; the basic performance attributes of each RSU and vehicle in the deployment area are collected, and after removing outliers, a weighted average is used to formulate an entity basic information database without loss of generality; the information of roads, buildings, intersections, etc. in the deployment area is projected onto a plane through the geographic information system and the traffic monitoring system to form an RSU deployment matrix, providing an accurate model basis for the subsequent scientific deployment adjustment plan of the RSU;

[0082] The RSU deployment solution multi-performance measurement module is used to formulate Sybil attack detection constraints and highly representative RSU deployment solution measurement indicators in RSU deployment based on the acquired RSU deployment area geographic information and entity information; through a rule engine, statistical analysis, and mathematical quantification methods, measurement indicators are formulated for various RSU performances, including Sybil attack detection security constraints formulated based on the required RSU cooperation location distance in actual scenarios, RSU signal coverage and overlap range indicators that measure regional RSU service quality and deployment efficiency, accident information initial propagation speed indicators that measure the system's ability to notify future vehicles after an accident occurs in the region, regional RSU standard deviation statistics that measure load balancing between RSUs in the region, RSU minimum adjustment cost indicators that measure the quality of RSU adjustment strategies in the region, fitness values ​​that measure the overall performance of RSU deployment, and fitness values ​​that measure the overall performance of RSU adjustment solutions;

[0083] The PSO-Meme combined heuristic RSU deployment module is used to iteratively optimize the RSU deployment plan. First, the PSO's fast convergence and global search capabilities are used to optimize the initial RSU deployment plan to produce a relatively optimal RSU deployment result. Then, the two-neighborhood search-meme algorithm with a large solution set exploration domain and a high solution set fitness value is used to iteratively optimize the relatively optimal RSU deployment result of the PSO to produce the RSU deployment result closest to the optimal one. Based on the meme algorithm, the two-neighborhood search-meme algorithm performs a neighborhood search after the crossover and mutation steps to improve the fitness value of the overall solution set.

[0084] The minimum cost RSU deployment adjustment module is used to iteratively optimize the minimum cost RSU adjustment scheme based on RSU service quality and load balancing between each RSU. The minimum cost of RSU adjustment is calculated using the Hopcroft-Karp algorithm in a bipartite graph to improve the efficiency of computing resource utilization. The minimum cost RSU adjustment scheme uses fast non-dominated sorting to stratify the population, and then uses the RSU deployment efficiency reference point and niche selection based on the fitness value to perform elite screening on the population in each layer to generate an offspring population. Multiple cycles are repeated until the maximum number of iterations is reached. Finally, the RSU deployment scheme is adjusted to obtain an RSU deployment scheme with the ability to detect Sybil attacks and superior multiple attributes.

[0085] The beneficial effects of the present invention are as follows:

[0086] The present invention provides an efficient dynamic roadside unit (RSU) deployment method and system capable of detecting Sybil attacks. By establishing Sybil attack detection constraints and iteratively optimizing the RSU deployment and adjustment scheme based on multiple heuristic algorithms within these constraints, the method improves the overall performance of the RSU deployment scheme while ensuring Sybil attack detection capabilities. This addresses the issue of low overall performance and the inability to detect Sybil attacks in RSU deployment schemes. The present invention first establishes metrics for measuring RSU deployment performance: RSU service coverage, overlap range, and initial propagation speed of accident information. Under Sybil attack detection constraints, the RSU deployment scheme is iteratively optimized using a PSO and a two-neighborhood search-meme algorithm, enhancing the scheme's overall performance and convergence, resulting in a near-optimal RSU deployment scheme. Secondly, to mitigate the impact of traffic flow fluctuations on load imbalance among RSUs, the present invention utilizes the Hopcroft-Karp bipartite graph algorithm within Sybil attack detection constraints to effectively solve the minimum adjustment cost problem during RSU deployment adjustment. Based on the Pareto principle, a heuristic RSU multi-objective adaptive adjustment algorithm is constructed to adjust the RSU deployment scheme. Compared with the traditional dynamic RSU deployment solution, the present invention has a higher detection capability for Sybil attacks; it has excellent performance in terms of RSU service coverage, overlapping range and initial propagation speed of accident information; it has lower adjustment costs for minimum cost adjustment of RSU deployment solutions, better RSU service coverage and overlapping range indicators, and more balanced workloads between RSUs. BRIEF DESCRIPTION OF THE DRAWINGS

[0087] Figure 1 A simplified flow chart of an embodiment of the present invention;

[0088] Figure 2 This is a diagram illustrating the applicable scenarios of the present invention;

[0089] Figure 3 A specific flow chart for implementing efficient dynamic roadside unit deployment capable of detecting Sybil attacks in the present invention;

[0090] Figure 4 A comparison chart of the comprehensive performance of the RSU deployment solution in the present invention and the traditional RSU deployment solution;

[0091] Figure 5 A comparison chart of the RSU deployment solution in the present invention and the traditional RSU deployment solution in terms of RSU service coverage and overlap indicators;

[0092] Figure 6 This is a comparison chart of the Sybil attack detection capabilities of the RSU deployment solution in the present invention and the traditional RSU deployment solution;

[0093] Figure 7This is a comparison chart of the initial propagation speed of accident information between the RSU deployment solution of the present invention and the traditional RSU deployment solution;

[0094] Figure 8 A comparison chart of the comprehensive performance of the RSU adjustment solution in the present invention and the traditional RSU adjustment solution;

[0095] Figure 9 A comparison chart of the RSU adjustment solution of the present invention and the traditional RSU adjustment solution in terms of RSU service coverage and overlap indicators;

[0096] Figure 10 A comparison chart of the adjustment costs between the RSU adjustment solution of the present invention and the traditional RSU adjustment solution;

[0097] Figure 11 A comparison chart of the RSU adjustment solution of the present invention and the traditional RSU adjustment solution in terms of load balancing between RSUs;

[0098] Figure 12 The figure compares the Sybil attack detection capabilities of the RSU adjustment scheme in the present invention and the traditional RSU adjustment scheme. DETAILED DESCRIPTION

[0099] The present invention will be further described below with reference to the accompanying drawings and examples.

[0100] The purpose of the present invention is to provide an efficient dynamic roadside unit deployment method that can detect Sybil attacks, so as to solve the problem that the RSU deployment scheme has low comprehensive performance and cannot detect Sybil attacks.

[0101] according to Figure 1 As shown, the present invention provides an efficient dynamic roadside unit deployment method capable of detecting Sybil attacks, comprising the following steps:

[0102] Step 1: Collect geographic information to evaluate the roadside unit (RSU) service coverage, overlap, and initial diffusion speed of accident information in the deployment area, and establish RSU deployment constraints that can detect Sybil attacks based on signal strength.

[0103] Obtain the geographic information of the deployment area, initialize the deployment matrix of the deployment area, collect all possible RSU deployment sites in the deployment area, obtain RSU transmission and influence range, vehicle-to-vehicle communication range and the number of available RSUs, randomly generate an RSU deployment plan based on the geographic information of the deployment area and the information of each entity in the area, obtain the RSU service coverage and overlap range of the plan, and calculate the initial diffusion speed of accident information based on the vehicle flow in the area. Based on the signal strength, obtain the deployment constraints for Sybil attack detection.

[0104] Step 2: Build an RSU deployment plan based on particle swarm optimization and improved memetic algorithm;

[0105] Under the constraint of Sybil attack detection, the RSU deployment scheme is optimized by combining the fitness value with the advantages of particle swarm optimization and two-neighborhood search-meme algorithm to obtain a near-optimal RSU deployment scheme.

[0106] Step 3: Obtain the transportation and reconstruction RSU costs, and obtain the minimum RSU adjustment cost based on the bipartite graph matching algorithm;

[0107] The RSU reconstruction cost and transportation adjustment cost are obtained based on the market. The RSU adjustment location is based on the geographic information of the deployment area and the traffic flow within the effective range of each RSU in the deployment area. The minimum RSU adjustment cost is obtained based on the bipartite graph matching algorithm Hopcroft-Karp.

[0108] Step 4: Collect the workload of each RSU and build a heuristic RSU minimum cost adjustment plan based on the Pareto optimal principle.

[0109] The RSU workload is evaluated based on the traffic flow within the effective range of each RSU in the deployment area, and a fitness value is designed to measure the performance of the RSU adjustment scheme. Under the constraint of Sybil attacks, the RSU initial deployment scheme is optimized and iterated using the HRMA3 algorithm based on the Pareto optimality principle to obtain the RSU minimum cost adjustment scheme. While ensuring the overall efficiency of RSU deployment, load balancing between RSUs is achieved at the lowest cost.

[0110] according to Figure 2 As shown in the figure, effective locations such as the middle of the road, intersections and near urban landmark buildings are selected as possible deployment sites of RSU L = {L1, L2, L3, ..., L n} will be marked, each vehicle user Available RSUs R={R1,R2,…,R n} will be placed or adjusted appropriately, and each RSU connects to the IoV control network to provide services for vehicles. In addition, vehicles and RSUs are collectively referred to as carrier units. The influence and communication range of RSUs will affect Sybil attack detection, signal coverage, and overlap metrics, respectively. Accident information can be transmitted through vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications. When an accident occurs in a city block, nearby vehicles will broadcast the accident information, and the IoT control network will receive all IoT activity information in real time. Factors such as RSU load balancing will affect RSU adjustment strategies. Furthermore, malicious vehicles carrying out Sybil attacks can attack RSUs by controlling virtual vehicle users. Therefore, when deploying RSUs, it is important to consider the necessary distance constraints for RSU cooperation in real-world scenarios.

[0111] In steps 2 to 4, according to Figure 3 , an efficient dynamic roadside unit deployment method that can detect Sybil attacks, obtains information and calculates, iterates, and finally gives a near-optimal dynamic RSU deployment result. The process is as follows:

[0112] S1, obtain the geographical information of the deployment area, initialize the deployment matrix of the deployment area, collect all possible RSU deployment sites in the deployment area; obtain the RSU transmission and influence range C R 、C e , inter-vehicle communication range C V and the number of available RSUs N r ;

[0113] S2, randomly generates RSU deployment plan S according to the geographical information of the deployment area and the information of each entity in the area o , get the RSU service coverage and overlapping range g of the scheme cover , and calculate the initial diffusion speed S of accident information in combination with the vehicle flow in the area init ; Based on the signal strength, we get the Sybil attack detection deployment constraint C S ;

[0114] S3, design the fitness value F to measure the performance of the RSU deployment solution, in C S Under the constraints, the RSU deployment scheme is optimized by using the PSO-Meme joint heuristic algorithm according to the fitness value F to obtain a near-optimal RSU deployment scheme S c ;

[0115] S4, obtain RSU reconstruction cost based on market conditions new and transportation adjustment cost ad According to the deployment area geographic information and RSU adjustment location, the minimum RSU adjustment cost is obtained based on the bipartite graph matching algorithm. total ;

[0116] S5: Evaluate the workload of the RSU based on the traffic volume within the effective range of each RSU in the deployment area. Design a fitness value F to measure the performance of the RSU adjustment scheme ad ; in C S Under the constraints, the RSU initial deployment plan is optimized and iterated using the HRMA3 algorithm based on the Pareto optimal principle to obtain the RSU minimum cost adjustment plan S ad , while ensuring the overall efficiency of RSU deployment, load balancing between RSUs is achieved.

[0117] Specifically, in S2, an RSU deployment plan is randomly generated based on the geographic information of the deployment area and the information of each entity in the area. The RSU service coverage and overlap range of the plan are obtained. The initial diffusion speed of the accident information is calculated based on the vehicle flow in the area. The Sybil attack detection deployment constraints are obtained based on the signal strength and the required RSU cooperation location distance in the actual scenario. The specific steps are as follows:

[0118] S201: The dynamic RSU deployment system extracts the geographical information of the deployment area and the information of each entity in the area and converts it into a deployment matrix M. c And randomly generate a deployment plan S in the deployment matrix o ;

[0119] S202, the dynamic RSU deployment system is based on the entity information and deployment plan S in the area. o Get the RSU service coverage and overlapping range g of the solution cover ;

[0120] S203, the dynamic RSU deployment system is based on the entity information, vehicle flow and deployment plan S in the area. o Get the initial diffusion speed S of the accident information init ;

[0121] S204: The dynamic RSU deployment system establishes a Sybil attack detection deployment constraint C based on the signal strength and the required RSU cooperation location distance in the actual scenario. S .

[0122] Specifically, in S3, the fitness value F is designed to measure the performance of the RSU deployment scheme, and based on the fitness value F, in C S Under the constraints, the PSO-Meme joint heuristic algorithm is used to optimize the RSU deployment scheme. The specific steps are as follows:

[0123] S301, the dynamic RSU deployment system allocates RSU service coverage and overlap range g according to the priority of the deployment target cover and the initial diffusion speed of accident information S init Combined to get the fitness value F;

[0124] S302, in C S Under the constraints, the dynamic RSU deployment system uses the particle swarm optimization algorithm to optimize the relatively optimal deployment solution S according to the response value F. r , and put it into the initial solution set;

[0125] S303, the dynamic RSU deployment system sorts all solutions according to the fitness value F and selects the elite solution e with better performance;

[0126] S304, in C s Under the constraints, crossover and mutation operations are performed on the elite solution set to obtain the offspring solution set P;

[0127] S305, the dynamic RSU deployment system performs a neighborhood search on the offspring solution set obtained after each crossover and mutation operation and selects the solution with the largest fitness value to update the offspring solution set P;

[0128] S306: If the two-neighborhood search-meme algorithm has not reached the maximum number of iterations, go to S303, otherwise output the near-optimal RSU deployment solution S c .

[0129] Specifically, in S4, the dynamic RSU deployment system first obtains the RSU reconstruction cost and transportation adjustment cost based on each physical market in the region, and then calculates the total RSU adjustment cost based on the reconstruction and transportation adjustment costs. After that, the dynamic RSU deployment system will use the deployment area geographic information and RSU site adjustment information based on the bipartite graph matching algorithm to obtain the RSU adjustment strategy and the minimum total adjustment cost. The specific steps are as follows:

[0130] S401, the dynamic RSU deployment system formulates the RSU reconstruction cost based on the entities and market information in the region new ;

[0131] S402, the dynamic RSU deployment system formulates the RSU transportation adjustment cost based on the entities and market information in the region ad ;

[0132] S403, the dynamic RSU deployment system rebuilds the cost according to the RSU new Adjusting the cost of RSU transportation ad Formulate RSU and calculate total cost total ;

[0133] S404: The dynamic RSU deployment system obtains the minimum RSU adjustment total cost based on the bipartite graph matching algorithm Hopcroft-Karp total .

[0134] Specifically, in S5, the RSU workload is evaluated based on the traffic volume within the effective range of each RSU in the deployment area. A fitness value is designed to measure the performance of the RSU adjustment scheme. Under the Sybil attack detection deployment constraint, the initial RSU deployment scheme is optimized and iterated using the HRMA3 algorithm to obtain an efficient and secure RSU minimum cost adjustment scheme. The specific steps are as follows:

[0135] S501, the dynamic RSU deployment system evaluates the workload of the RSU based on the traffic flow within the effective range of each RSU in the deployment area.

[0136] S502, the dynamic RSU deployment system designs a fitness value F to measure the performance of the RSU adjustment scheme based on the RSU service quality and the load balancing index between RSUs. ad ;

[0137] S503, the dynamic RSU deployment system randomly generates an RSU adjustment plan and converts the RSU deployment plan S close to the optimal one into an RSU adjustment plan. c Add to the adjusted optimization solution set O;

[0138] S504: The solutions in the solution set O are evaluated based on the fitness value F. ad To sort and filter, in C S Under the constraints, crossover and mutation operations are performed on the selected solutions to obtain an offspring population pop equal to the population size of the optimization solution set O;

[0139] S505, the dynamic RSU deployment system merges the solution set O and the offspring population pop, according to the fitness value F ad Perform fast non-dominated sort;

[0140] S506, the dynamic RSU deployment system adds the deployment efficiency reference point to the sorted population and performs niche selection, screening out the elite population set e equal to the number of offspring pop populations ad ;

[0141] S507, dynamic RSU deployment system performs genetic operation to generate and elite solution population set e ad Populations of equal size offspring pop′;

[0142] S508, the dynamic RSU deployment system performs a neighborhood search on the offspring solution set obtained after each crossover and mutation operation and selects the solution with the largest fitness value to update the offspring solution set pop′;

[0143] S509: If the HRMA3 algorithm has not reached the maximum number of iterations, go to S504; otherwise, output the optimal RSU minimum cost adjustment solution.

[0144] In particular, in S202, due to the influence of obstacles such as buildings on the RSU signal, the signal strength will be interfered to a certain extent, affecting the communication quality. The effective signal coverage area of ​​an RSU is in square meters and is defined as a polygon P = {P1, P2, ..., P i}, each RSU R n Each has its effective signal coverage area P i Assuming that Nr RSUs are available, the overlapping area of ​​the RSU effective signal is the accumulation of the overlapping areas between multiple polygons, which can be expressed as:

[0145]

[0146] Where O(a,b) represents the function for calculating the overlapping area of ​​two polygons a and b. Therefore, g is determined by the coverage area and overlapping area of ​​the RSU effective signal. cover It can be expressed as:

[0147]

[0148] Among them A T The total area in the city that needs to be covered by effective signals.

[0149] In particular, in S203, there is an accident site A in the RSU deployment area. Assuming that all V2V transmissions are successful, the calculation of the average ISSAI value can be expressed as:

[0150]

[0151] Among them S v is the speed of vehicle v, and N is the number of all vehicles involved in the accident information dissemination. v The sum of the time it takes for vehicle v to transmit accident information using carrier unit Ui can be expressed as:

[0152]

[0153] In particular, in S204, the necessary RSU cooperation location distance for Sybil attack detection according to the actual scenario can be expressed as:

[0154]

[0155] Among them C e Indicates the impact range of RSU.

[0156] Specifically, in S301, the RSU service coverage and overlap range g are allocated according to the priority of the deployment target. cover and the initial diffusion speed of accident information S init Combined to get the fitness value F:

[0157]

[0158] Among them, θ1 and θ2 represent the normalized weights from 0 to 1, T init Represents the short information dissemination time after the incident.

[0159] In particular, in S303, all solutions are sorted according to the fitness value F and the elite solutions with better performance are selected:

[0160] Sort(F(s1),F(s2),…,F(s e ))e <Num(S r )

[0161] In particular, in S305, the single solution in the elite offspring population after crossover and mutation operations is defined as S p , its neighborhood is N(S p ), that is, from the current solution S p By exploring the solution set generated by small step size, if make:

[0162]

[0163] Then the elite offspring solution S p Update to S′ p .

[0164] In particular, in S401, if the number of RSUs in the adjusted solution Sol′ increases compared to the deployment solution Sol before adjustment, new RSU equipment will be purchased and deployed. The cost calculation formula for the newly deployed RSUs is:

[0165] Cost new =(|Sol′|-|Sol|)·(∈+δ)

[0166] Where ∈ is the purchase cost and δ is the construction cost of a new RSU.

[0167] In particular, in S402, the RSU transportation adjustment includes the disassembly, installation, and transportation costs, so the RSU transportation adjustment cost is calculated as follows:

[0168]

[0169] where |R ad | is the number of RSUs that need to be adjusted, is the transportation cost per kilometer of RSU, D ad is the Euclidean distance between RSU sites that needs to be adjusted.

[0170] In particular, in S403, based on the RSU transport adjustment cost and reconstruction cost obtained above, the total cost of RSU adjustment can be expressed as:

[0171] Cost total =Cost new +Cost ad

[0172] Specifically, in S404, the existing RSU positions and the newly adjusted RSU positions are considered as a bipartite graph set V r and V r ′, the weight E of the edge represents the cost of moving from the current position to the new position, so the bipartite graph can be expressed as:

[0173] G(V r ,V r ′;e),e∈E(V r ,V r ′)={D(R1,R′1),D(R1,R′2),…,D(R n ,R′ m )}

[0174] Where D(R n ,R′ m ) represents R n ∈V r and R m ′∈V r The Euclidean distance between ′.

[0175] Specifically, in S501, the vehicle selects the nearest RSU to interact with, and according to the RSU R n Traffic flow in the coverage area The interaction strength is Define the maximum load threshold of RSU through investigation Each RSU The workload can be expressed as:

[0176]

[0177] In particular, in S502, according to the signal coverage and overlapping range g of the deployed RSU cover , load balancing between RSUs and the minimum cost of adjusting RSUs, and obtain the fitness value representing the performance of the RSU adjustment scheme:

[0178] F ad =θ1·g cover -θ3·Cost total -θ4·B R

[0179] Among them, θ1, θ3 and θ4 represent the normalized weights from 0 to 1, B R To evaluate the standard deviation statistic of the load balance between RSUs, it is expressed as:

[0180]

[0181] in is the average workload of all RSUs in the deployment area.

[0182] In particular, in S505, according to the fitness value F ad Perform a quick non-dominated sort on the deployment solution population to prepare for the subsequent introduction of deployment efficiency reference points for niche selection:

[0183] Sort non-domin (F ad (s1),F ad (s2),…,F ad (s l ))

[0184] In particular, in S508, the single solution in the offspring population after crossover and mutation operations is defined as S l , whose neighborhood is N(S l ), that is, from the current solution S l By exploring the solution set generated by small step size, if make:

[0185]

[0186] Then the elite offspring solution S l Update to S′ l .

[0187] according to Figure 4 As shown, Figure 4 A comparison chart of the comprehensive performance of the RSU deployment scheme PSO-Meme proposed in this invention and the traditional RSU deployment scheme is given. Figure 4 In the , higher fitness values ​​indicate better overall RSU deployment performance. The PSO-Meme scheme demonstrated excellent performance, with an average fitness value of 0.9431. Although the optimal condition (using enumeration, ignoring obstacles, and achieving a 100% information transmission success rate) achieved the highest fitness value, its impractical space and time complexity make it unsuitable for RSU deployment.

[0188] according to Figure 5 As shown, Figure 5A comparison chart shows the RSU service coverage and overlap metrics of the proposed PSO-Meme RSU deployment scheme and traditional RSU deployment schemes. Clearly, the PSO-Meme scheme achieves the highest RSU service coverage and overlap values ​​across different RSU deployment numbers, indicating that the PSO-Meme scheme maximizes the efficient use of RSU resources.

[0189] according to Figure 6 As shown, Figure 6 A comparison chart shows the Sybil attack detection capabilities of the proposed RSU deployment scheme, PSO-Meme, and traditional RSU deployment schemes. As the number of RSUs increases, the probability of detecting a Sybil attack also increases. Note that the detection rate here does not include the false positive rate. Because PSO-Meme incorporates Sybil-safe constraints, the detection rate reaches 88.92% when the number of RSUs reaches 6. This demonstrates that PSO-Meme provides enhanced security for Sybil detection.

[0190] according to Figure 7 As shown, Figure 7 A comparison chart shows the initial propagation speed of accident information between the proposed RSU deployment scheme, PSO-Meme, and traditional RSU deployment schemes. A higher initial propagation speed indicates faster initial propagation, which in turn means that accident information reaches the RSU more quickly. As can be seen, the PSO-Meme scheme consistently achieves the highest initial propagation speed in environments with varying vehicle counts, enabling accident vehicles to transmit accident information to RSUs in the shortest possible time, regardless of the number of vehicles involved.

[0191] according to Figure 8 As shown, Figure 8 The comparison chart of the comprehensive performance between the RSU adjustment scheme HRMA3 proposed by the present invention and the traditional RSU adjustment scheme is given. Figure 4 same, Figure 8 Among them, the HRMA3 scheme has the average fitness value closest to the optimal condition (using the enumeration method, ignoring obstacles, and the information transmission success rate is 100%). This result shows that the comprehensive performance of the HRMA3 scheme is better than other traditional schemes.

[0192] according to Figure 9 As shown, Figure 9 A comparison chart comparing the RSU service coverage and overlap metrics between the proposed RSU adjustment scheme and traditional RSU adjustment schemes is provided. It can be seen that HRMA3 achieves the highest RSU service coverage and overlap under different additional adjustment available RSU quantities, demonstrating that it maintains the best RSU service quality even after RSU adjustment.

[0193] according to Figure 10 As shown, Figure 10 A comparison chart comparing the adjustment costs of the proposed RSU adjustment scheme, HRMA3, and traditional RSU adjustment schemes is presented. HRMA3 requires minimal changes to existing RSU deployment schemes, resulting in the lowest RSU adjustment costs. This demonstrates HRMA3's superior adjustment capabilities in different scenarios requiring additional adjustment of the available RSU count.

[0194] according to Figure 11 As shown, Figure 11 A comparison chart shows the load balancing between RSUs using the proposed RSU adjustment scheme, HRMA3, and traditional RSU adjustment schemes. It can be seen that compared to other traditional schemes, HRMA3 achieves the lowest load balancing standard deviation for various additional RSU numbers, demonstrating that HRMA3 is the most capable of achieving workload balancing between each RSU.

[0195] according to Figure 12 As shown, Figure 12 A comparison chart shows the Sybil attack detection capabilities of the proposed RSU adjustment scheme, HRMA3, and traditional RSU adjustment schemes. Although the Sybil attack detection rate of HRMA3 decreases slightly compared to the original RSU deployment when the number of additional available RSUs is zero due to optimized target priorities, the decrease is minimal, approximately 2.3%. Compared to other traditional RSU adjustment schemes, HRMA3 achieves the highest Sybil attack detection rate for all available RSUs.

[0196] Example:

[0197] To ensure the reliability of the results without loss of generality, this example evaluates a simulation system for an efficient dynamic roadside unit deployment scheme that can detect Sybil attacks. Each set of scenarios in the experiment was simulated 20 times, and the average value was used as the final result. The experiment randomly selected a city block in California, USA, with a size of 994×1011m 2 , AT is 66.41% of the total block area. The number of available RSUs N rThe number of vehicles, V, is between 3 and 6, and between 20 and 100. Assuming a channel bandwidth of 20 MHz, the signal strength within the effective signal range of the RSU should be greater than -65 dBm, the RSU influence radius is 250 m, the RSU transmission radius is 200 m, and the V2V communication radius is 25 m. Physical CPUs range from 700 MHz to 2000 MHz, the accident information size is 10 Mb, and 30 CPU instructions are required for each Mb of information. The population size in the PSO algorithm is 16, and the number of iterations is 10 rounds. In the two-neighbor search-meme algorithm, the population size is 8, the number of offspring is 10, the number of elites is 3, and the maximum number of iterations is 20. In addition, in the HRMA3 algorithm, the maximum number of relocated RSUs is 3, the number of additional available RSUs is between 0 and 2, the maximum number of iterations is 60, and the number of offspring populations is 42.

[0198] In summary, the present invention provides an efficient dynamic roadside unit (RSU) deployment method capable of detecting Sybil attacks. By establishing Sybil attack detection constraints and iteratively optimizing RSU deployment and adjustment schemes based on multiple heuristic algorithms within these constraints, the method improves the overall performance of the RSU deployment scheme while ensuring Sybil attack detection capabilities. This addresses the issues of low overall performance and the inability to detect Sybil attacks in RSU deployment schemes. The present invention first establishes metrics for measuring RSU deployment performance: RSU service coverage, overlap range, and initial accident information propagation speed. Under Sybil attack detection constraints, the RSU deployment scheme is iteratively optimized using a PSO and two-neighborhood search-meme algorithm, enhancing the scheme's overall performance and convergence, resulting in a near-optimal RSU deployment scheme. Secondly, to address the issue of load imbalance among RSUs caused by changes in traffic flow, the present invention utilizes the Hopcroft-Karp bipartite graph algorithm within Sybil attack detection constraints to effectively solve the minimum adjustment cost problem during RSU deployment adjustment. Furthermore, based on the Pareto principle, a heuristic RSU multi-objective adaptive adjustment algorithm is constructed to adjust the RSU deployment scheme. Compared with the traditional dynamic RSU deployment solution, the present invention has a higher detection capability for Sybil attacks; it has excellent performance in terms of RSU service coverage, overlapping range and initial propagation speed of accident information; it has lower adjustment costs for minimum cost adjustment of RSU deployment solutions, better RSU service coverage and overlapping range indicators, and more balanced workloads between RSUs.

[0199] In one embodiment, the present invention provides an efficient dynamic roadside unit deployment system capable of detecting Sybil attacks, which can be used to implement the above-mentioned efficient dynamic roadside unit deployment method capable of detecting Sybil attacks. Specifically, the system includes:

[0200] The module for acquiring geographic information and entity information of the RSU deployment area is used to obtain data within the RSU deployment area, including obstacle locations and volumes, possible RSU deployment sites near intersections and city landmarks, RSU entity impact and communication range, vehicle equipment information, signal strength and traffic volume received by the RSU from each vehicle interaction, etc. The traffic volume information corresponding to each historical timestamp collected by each RSU in the deployment area is cleaned and missing values ​​are filled. Each piece of traffic volume information is normalized through wavelet transform to ensure that the data format is suitable for subsequent RSU load balancing evaluation; the basic performance attributes of each RSU, vehicle, etc. in the deployment area are collected, and after removing outliers, a weighted average is used to formulate a basic entity information database without loss of generality; the information of roads, buildings, intersections, etc. in the deployment area is projected onto a plane through the geographic information system and traffic monitoring system to form an RSU deployment matrix, providing an accurate model basis for the subsequent scientific deployment adjustment plan of the RSU.

[0201] The RSU deployment solution multi-performance measurement module is used to formulate Sybil attack detection constraints and highly representative RSU deployment solution metrics based on the acquired geographic information of the RSU deployment area and information about each entity. Through methods such as rule engines, statistical analysis, and mathematical quantification, various RSU performance metrics are formulated. These include Sybil attack detection security constraints based on the required RSU cooperative location distance in actual scenarios; RSU signal coverage and overlap range metrics to measure regional RSU service quality and deployment efficiency; initial accident information propagation speed metrics to measure the system's ability to notify future vehicles after an accident in the region; regional RSU standard deviation statistics to measure load balancing between RSUs in the region; RSU minimum adjustment cost metrics to measure the quality of RSU adjustment strategies in the region; fitness values ​​to measure the overall performance of RSU deployments; and fitness values ​​to measure the overall performance of RSU adjustment solutions.

[0202] The PSO-Meme joint heuristic RSU deployment module is used to iteratively optimize the RSU deployment plan. First, the PSO's fast convergence and global search capabilities are used to optimize the initial RSU deployment plan to produce a relatively optimal RSU deployment result. Then, the two-neighborhood search-meme algorithm with a large solution set exploration domain and a high solution set fitness value is used to iteratively optimize the relatively optimal RSU deployment result of PSO to produce the RSU deployment result closest to the optimal one. Based on the traditional meme algorithm, the two-neighborhood search-meme algorithm performs a neighborhood search after the crossover and mutation steps to improve the fitness value of the overall solution set.

[0203] The minimum cost RSU deployment adjustment module is used to iteratively optimize the minimum cost RSU adjustment scheme based on RSU service quality and load balancing between RSUs. The minimum cost of RSU adjustment is calculated using the Hopcroft-Karp algorithm in a bipartite graph to improve the efficiency of computing resources. The minimum cost RSU adjustment scheme uses fast non-dominated sorting to stratify the population. Then, based on fitness values, the RSU deployment efficiency reference point and niche selection are used to perform elite screening on the population in each layer to produce the offspring population. This cycle is repeated until the maximum number of iterations is reached. Finally, the RSU deployment scheme is adjusted to obtain an RSU deployment scheme that has the ability to detect Sybil attacks and has superior multiple attributes.

[0204] The module division in the embodiments of the present invention is illustrative and represents only one logical functional division. In actual implementation, other division methods may be used. Furthermore, the functional modules in various embodiments of the present invention may be integrated into a single processor, exist physically as separate modules, or two or more modules may be integrated into a single module. The integrated modules may be implemented in either hardware or software functional modules.

[0205] In one embodiment of the present invention, a computer device is provided, comprising a processor and a memory, wherein the memory is used to store a computer program, the computer program including program instructions, and the processor is used to execute the program instructions stored in the computer storage medium. The processor may be a central processing unit (CPU), or may be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing core and control core of the terminal, and is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions in the computer storage medium to implement the corresponding method flow or corresponding function; the processor described in the embodiment of the present invention can be used to operate an efficient dynamic roadside unit deployment method that can detect Sybil attacks.

[0206] In one embodiment of the present invention, the present invention also provides a storage medium, specifically a computer-readable storage medium (Memory), which is a memory device in a computer device for storing programs and data. It is understandable that the computer-readable storage medium here can include both built-in storage media in the computer device and, of course, extended storage media supported by the computer device. The computer-readable storage medium provides a storage space, which stores the operating system of the terminal. In addition, one or more instructions suitable for being loaded and executed by the processor are also stored in the storage space. These instructions can be one or more computer programs (including program codes). It should be noted that the computer-readable storage medium here can be a high-speed RAM memory or a non-volatile memory, such as at least one disk memory. The processor can load and execute one or more instructions stored in the computer-readable storage medium to implement the corresponding steps of the efficient dynamic roadside unit deployment method that can detect witch attacks in the above embodiment.

[0207] It will be understood by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Thus, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0208] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowcharts and / or block diagrams, as well as combinations of processes and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowcharts and / or block diagrams. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0209] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0210] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0211] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered by the scope of protection of the claims of the present invention.

Claims

1. An efficient dynamic roadside unit deployment method capable of detecting Sybil attacks, characterized in that: The steps include: S1: Obtain the geographic information of the deployment area, initialize the deployment matrix of the deployment area, collect all possible RSU deployment sites in the deployment area; obtain the RSU transmission and influence range C R 、C e , inter-vehicle communication range C V and the number of available and additionally adjusted available RSUs N r 、N e ; S2: Randomly generate RSU deployment plan S based on the geographical information of the deployment area and the information of each entity in the area o , we get solution S o RSU service coverage and overlapping range g cover , and calculate the initial diffusion speed S of accident information in combination with the vehicle flow in the area init ; Based on the signal strength, we get the Sybil attack detection deployment constraint C S ; S3: Design the fitness value F to measure the performance of the RSU deployment solution, in C S Under the constraint, the RSU deployment scheme is optimized according to the fitness value F using the particle swarm-two-neighborhood search meme PSO-Meme joint heuristic algorithm to obtain a near-optimal RSU deployment scheme S c ; S4: Get RSU reconstruction cost based on market conditions new and transportation adjustment cost ad According to the deployment area geographic information and RSU adjustment location, the minimum RSU adjustment cost is obtained based on the bipartite graph matching algorithm Hopcroft-Karp total ; S5: Evaluate the workload of each RSU based on the traffic volume within the effective range of each RSU in the deployment area Design a fitness value F to measure the performance of the RSU adjustment scheme ad ; in C S Under the constraints, the RSU initial deployment plan is optimized and iterated using the heuristic RSU multi-objective adaptive adjustment algorithm HRMA3 based on the Pareto optimal principle to obtain the RSU minimum cost adjustment plan S ad , while ensuring the overall efficiency of RSU deployment, load balancing between RSUs is achieved.

2. The method for efficient dynamic roadside unit deployment capable of detecting Sybil attacks according to claim 1, characterized in that: The step S2 is specifically as follows: S201: The dynamic RSU deployment system extracts the geographical information of the deployment area and the information of each entity in the area and converts it into a deployment matrix M c And randomly generate a deployment plan S in the deployment matrix o ; S202: The dynamic RSU deployment system is based on the entity information and deployment plan S in the area. o Get the RSU service coverage and overlapping range g of the solution cover ; The effective signal coverage area of ​​an RSU is defined as a polygon P = {P1, P2, ..., P i }, each RSUR n Each has its effective signal coverage area P i Assuming that Nr RSUs are available, the overlapping area of ​​the RSU effective signal is the accumulation of the overlapping areas between multiple polygons, expressed as: Where O(a,b) represents the function for calculating the overlapping area of ​​two polygons a and b; Therefore, g is determined by the coverage area and overlap area of ​​the RSU effective signal. cover Expressed as: Among them A T is the total area in the city that needs to be covered by effective signals; S203: The dynamic RSU deployment system is based on the entity information, vehicle flow and deployment plan S in the area. o Get the initial diffusion speed S of the accident information init ; There is an accident site A in the RSU deployment area. Assuming that all V2V transmissions are successful, the average ISSAI value is calculated as: Among them S v is the speed of vehicle v, N is the number of all vehicles involved in the accident information dissemination; T v It represents the sum of the time spent by vehicle v to transmit accident information using carrier unit Ui, expressed as: S204: The dynamic RSU deployment system establishes a Sybil attack detection deployment constraint C based on the signal strength and the required RSU cooperation location distance in the actual scenario. S ; The necessary RSU cooperation distance for Sybil attack detection according to actual scenarios is expressed as: Among them C e Indicates the impact range of RSU.

3. The method for efficient dynamic roadside unit deployment capable of detecting Sybil attacks according to claim 2, characterized in that: The step S3 is specifically as follows: S301: The dynamic RSU deployment system allocates RSU service coverage and overlap range g according to the priority of the deployment target cover and the initial diffusion speed of accident information S init Combined to get the fitness value F; Among them, θ1 and θ2 represent the normalized weights from 0 to 1, T init Represents the short information dissemination time after the incident; S302: In C S Under the constraint, the dynamic RSU deployment system uses the particle swarm optimization algorithm to optimize the r deployment solutions S with the largest response value F according to the response value F. r , and put it into the initial solution set; S303: The dynamic RSU deployment system sorts all solutions in the initial solution set according to the fitness value F and selects e elite solutions to form an elite class E; E=Sort(F(s1),F(s2),…,F(s e ))e<Num(S r ) S304: In C S Under the constraints, crossover and mutation operations are performed on the elite solution set E to obtain the offspring solution set P; S305: The dynamic RSU deployment system performs a neighborhood search on the offspring solution set obtained after each crossover and mutation operation and selects the solution with the largest fitness value to update the offspring solution set P; Define the single solution in the elite offspring population after crossover and mutation operations as S p , whose neighborhood is M(S p ), that is, from the current solution S p By exploring the solution set generated by small step size, if make Then the elite offspring solution S p Update to S′ p ; S306: If the two-neighborhood search-meme algorithm has not reached the maximum number of iterations, go to S303, otherwise output a near-optimal RSU deployment solution S c .

4. The method for efficient dynamic roadside unit deployment capable of detecting Sybil attacks according to claim 3, characterized in that: The step S4 is specifically as follows: S401: The dynamic RSU deployment system determines the RSU reconstruction cost based on the entities and market information in the region. new ; Compared with the deployment plan Sol before adjustment, if the RSU in the adjustment plan Sol′ increases, new RSU equipment will be purchased and deployed. The cost calculation formula for the newly deployed RSU is: Cost new =(|Sol′|-|Sol|)·(∈+δ) Where ∈ is the purchase cost and δ is the construction cost of a new RSU; S402: The dynamic RSU deployment system formulates the RSU transportation adjustment cost based on the entities and market information in the region. ad ; The shipping adjustment for RSUs includes the costs of disassembly, installation, and transportation. Therefore, the RSU shipping adjustment overhead is calculated as follows: where |R ad | is the number of RSUs that need to be adjusted, is the transportation cost per kilometer of RSU, D ad is the Euclidean distance between the RSU sites that need to be adjusted; S403: Dynamic RSU deployment system rebuilds overhead CDost based on RSU new Adjusting the cost of RSU transportation ad Formulate RSU and calculate total cost total ; Cost total =Cost new +Cost ad S404: The dynamic RSU deployment system obtains the minimum RSU adjustment total cost based on the bipartite graph matching algorithm Hopcroft-Karp total ; Consider the existing RSU positions and the newly adjusted RSU positions as a bipartite graph set V r and V r ′, the weight E of the edge represents the cost of moving from the existing position to the new position, which is expressed as: G(V r ,V r ′;e),e∈E(V r ,V r ′)={D(R1,R′1),D(R1,R′2),…,D(R n ,R′ m )}, where D(R n ,R′ m ) represents R n ∈V r and R m ′∈V r The Euclidean distance between ′.

5. The method for efficient dynamic roadside unit deployment capable of detecting Sybil attacks according to claim 4, characterized in that: The step S5 is specifically as follows: S501: The dynamic RSU deployment system evaluates the workload of the RSU based on the traffic volume within the effective range of each RSU in the deployment area. The vehicle selects the nearest RSU to interact with, and n Traffic flow in the coverage area The interaction strength is Define the maximum load threshold of RSU through investigation Each RSU The workload is represented as: S502: The dynamic RSU deployment system designs a fitness value F to measure the performance of the RSU adjustment scheme based on the RSU service quality and the load balancing index between RSUs. ad ; According to the signal coverage and overlapping range of deployed RSU g cover , load balancing between RSUs and the minimum cost of adjusting RSUs, and obtain the fitness value representing the performance of the RSU adjustment scheme: F ad =θ1·g cover -θ3·Cost total -θ4·B R , Among them, θ1, θ3 and θ4 represent the normalized weights from 0 to 1, B R To evaluate the standard deviation statistic of the load balance between RSUs, it is expressed as: in is the average workload of all RSUs in the deployment area; S503: The dynamic RSU deployment system randomly generates an RSU adjustment plan and converts the RSU deployment plan S close to the optimal one into an RSU adjustment plan. c Add to the adjusted optimization solution set O; S504: The solutions in the solution set O are evaluated based on the fitness value F ad To sort and filter, in C S Under the constraints, crossover and mutation operations are performed on the selected solutions to obtain an offspring population pop equal to the population size of the optimization solution set O; S505: The dynamic RSU deployment system merges the solution set O and the offspring population pop, according to the fitness value F ad Perform fast non-dominated sort; According to the fitness value F ad Perform a quick non-dominated sort on the deployment solution population to prepare for the subsequent introduction of deployment efficiency reference points for niche selection: Sort non-domin (F ad (s1),F ad (s2),…,F ad (s l )) S506: The dynamic RSU deployment system adds the deployment efficiency reference point to the sorted population and performs niche selection, selecting an elite population set e equal to the number of offspring pop populations. ad ; S507: Dynamic RSU deployment system performs genetic operations to generate and elitist solution population sets ad Populations of equal size offspring pop′; S508: The dynamic RSU deployment system performs a neighborhood search on the offspring solution set obtained after each crossover and mutation operation and selects the solution with the largest fitness value to update the offspring solution set pop′; Define the single solution in the offspring population after crossover and mutation operations as S l , whose neighborhood is N(S l ), that is, from the current solution S l By exploring the solution set generated by small step size, if make: Then the elite offspring solution S l Update to S′ l ; S509: If the HRMA3 algorithm has not reached the maximum number of iterations, go to S504; otherwise, output the optimal RSU minimum cost adjustment solution.

6. A roadside unit deployment system using the efficient dynamic roadside unit deployment method according to claim 1, comprising a module for acquiring geographic information of an RSU deployment area and information of each entity, a module for multi-performance measurement of an RSU deployment solution, a PSO-Meme joint heuristic RSU deployment module, and a minimum cost RSU deployment adjustment module; The RSU deployment area geographic information and entity information acquisition module is used to obtain the RSU deployment area, including the obstacle location volume, possible RSU deployment sites near intersections and city landmark buildings, RSU entity impact and communication range, vehicle equipment information, RSU received signal strength and traffic flow data of each vehicle interaction; the traffic flow information corresponding to each historical time stamp collected by each RSU in the deployment area is cleaned and missing values ​​are filled, and each traffic flow information is normalized through wavelet transform to ensure that the data format is suitable for subsequent RSU load balancing evaluation; The basic performance attributes of each RSU and vehicle in the deployment area are collected, and after removing outliers, a weighted average is used to develop a basic entity information database without loss of generality. Through the geographic information system and traffic monitoring system, information such as roads, buildings, and intersections in the deployment area is projected onto a plane to form an RSU deployment matrix, providing an accurate model foundation for subsequent scientific deployment and adjustment plans of RSUs. The RSU deployment solution multi-performance measurement module is used to formulate Sybil attack detection constraints and highly representative RSU deployment solution measurement indicators in RSU deployment based on the acquired RSU deployment area geographic information and entity information; through a rule engine, statistical analysis, and mathematical quantification methods, measurement indicators are formulated for various RSU performances, including Sybil attack detection security constraints formulated based on the required RSU cooperation location distance in actual scenarios, RSU signal coverage and overlap range indicators that measure regional RSU service quality and deployment efficiency, accident information initial propagation speed indicators that measure the system's ability to notify future vehicles after an accident occurs in the region, regional RSU standard deviation statistics that measure load balancing between RSUs in the region, RSU minimum adjustment cost indicators that measure the quality of RSU adjustment strategies in the region, fitness values ​​that measure the overall performance of RSU deployment, and fitness values ​​that measure the overall performance of RSU adjustment solutions; The PSO-Meme combined heuristic RSU deployment module is used to iteratively optimize the RSU deployment plan. First, the PSO's fast convergence and global search capabilities are used to optimize the initial RSU deployment plan to produce a relatively optimal RSU deployment result. Then, the two-neighborhood search-meme algorithm with a large solution set exploration domain and a high solution set fitness value is used to iteratively optimize the relatively optimal RSU deployment result of the PSO to produce the RSU deployment result closest to the optimal one. Based on the meme algorithm, the two-neighborhood search-meme algorithm performs a neighborhood search after the crossover and mutation steps to improve the fitness value of the overall solution set. The minimum cost RSU deployment adjustment module is used to iteratively optimize the minimum cost RSU adjustment scheme based on RSU service quality and load balancing between each RSU. The minimum cost of RSU adjustment is calculated using the Hopcroft-Karp algorithm in a bipartite graph to improve the efficiency of computing resource utilization. The minimum cost RSU adjustment scheme uses fast non-dominated sorting to stratify the population, and then uses the RSU deployment efficiency reference point and niche selection based on the fitness value to perform elite screening on the population in each layer to generate an offspring population. Multiple cycles are repeated until the maximum number of iterations is reached. Finally, the RSU deployment scheme is adjusted to obtain an RSU deployment scheme with the ability to detect Sybil attacks and superior multiple attributes.

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