VANET cooperative downloading method based on multi-dimensional screening and encrypted reverse auction

Abstract

The invention provides a VANET cooperative downloading method based on multi-dimensional screening and encrypted reverse auction, and belongs to the field of vehicle-mounted ad hoc network excitation mechanisms. According to the method, a network factor screening mechanism based on information entropy is added in a cooperative vehicle selection stage, related knowledge such as information entropy and network delay is creatively fused, high-efficiency cooperative vehicles with good network performance can be screened out, the transaction quality and success rate of the whole vehicle-mounted ad hoc network system are improved, and the cooperative vehicle selection efficiency is improved. The efficiency and the safety fairness of cooperative downloading of the vehicle-mounted ad hoc network are improved; according to the method, a complementary optimized reverse auction mechanism is designed, 'sealed auction 'is fused, the whole transaction process is encrypted, the security and fairness of the transaction are enhanced, a data pricing limiting strategy is added in the data bidding stage of a reverse auction model, data price fluctuation is limited within a certain range, and the data bidding efficiency is improved. And the stability and predictability of the transaction are realized as much as possible.

Description

Technical Field

[0001] The present invention belongs to the field of vehicle-mounted ad hoc network incentive mechanism, and in particular relates to a VANET cooperative downloading method based on multi-dimensional screening and encrypted reverse auction. Background Art

[0002] Vehicular Ad Hoc Network (VANET), as the cornerstone of Intelligent Transportation System (ITS), is an innovative communication technology that cleverly cooperates with fixed infrastructure to realize autonomous networking and information interaction between vehicles. In VANET, each vehicle node with wireless communication equipment can play the dual role of router and host, which can not only execute user-oriented applications, but also run routing protocols, responsible for data forwarding and routing maintenance. This flexible and self-organizing network feature enables vehicles to achieve efficient and stable information transmission even in high-speed movement or limited network coverage. As a core application of VANET, cooperative downloading in VANET aims to solve the downloading problems caused by factors such as changing network conditions and unstable signals during driving. Through direct communication and resource sharing between vehicles, cooperative downloading can significantly improve the efficiency and speed of data downloading. When a vehicle needs to download a large amount of data, it can send a download request to the surrounding vehicles. If the vehicle that receives the request has the required data or is downloading the same data, it can act as a seed node to transmit data blocks to the requesting vehicle. This distributed downloading method not only reduces the download burden of a single vehicle, but also speeds up the entire download process through parallel transmission. However, in the cooperative download of vehicular ad hoc networks, not all vehicles are ideal partners. Since the network conditions, download speeds, driving routes and other factors of the vehicles are different, it is particularly important to screen the cooperative vehicles. The purpose of screening cooperative vehicles is to ensure the efficiency and stability of the download process and avoid download interruptions or speed reductions caused by network differences between vehicles. Through scientific screening methods, such as those based on fuzzy mathematics or repeated game theories, a comprehensive evaluation of multiple dimensions such as the vehicle's network conditions, download capabilities, and driving trajectories can be performed to select the most suitable vehicle for cooperation. The screening of cooperative vehicles is not only related to download efficiency, but also has a profound impact on resource utilization and user experience. First, the selected cooperative vehicles usually have good network conditions and download speeds, which can significantly improve the efficiency of the entire download process and shorten the download time. Secondly, a reasonable screening mechanism can avoid the waste of network resources and ensure that resources are allocated and utilized in the most reasonable way. In addition, efficient download speeds and stable download processes can greatly improve user satisfaction and experience, and enhance users' trust and reliance on vehicle networking technology.

[0003] More importantly, the development of cooperative downloads in vehicle ad hoc networks and their cooperative vehicle screening mechanisms is of great significance for promoting the advancement of vehicle networking technology and the construction of intelligent transportation systems. On the one hand, as one of the important applications of vehicle networking technology, the continuous optimization and improvement of cooperative downloads will promote the overall progress and popularization of vehicle networking technology. On the other hand, intelligent transportation systems require efficient and stable data transmission as support, and cooperative downloads in vehicle ad hoc networks and their screening mechanisms are one of the key technologies to achieve this goal. Therefore, in-depth research and development of cooperative downloads in vehicle ad hoc networks and their screening mechanisms are of great significance for building a more intelligent, efficient and safe transportation system.

[0004] In terms of cooperative vehicle selection, Zhu Wenlong proposed a method for forming vehicle clusters in "Research on Inter-vehicle Data Distribution Based on Network Coding", applying the network coding method to the data distribution process. Vehicles in close proximity on the road form a vehicle cluster, and the vehicles in the cluster cooperate with each other to download data. At the same time, the widely covered mobile 2G / 3G network is used as a control channel. The vehicles in the cluster communicate with the roadside infrastructure (V2I) through roadside access points. The high data download rate improves the average download rate of the vehicle and shortens the download time. Zeng Cheng proposed an efficient incentive method based on infinite repeated games in "Research on the Problem of Cooperative Download in Internet of Vehicles". This scheme screens the cooperative willingness of cooperative vehicles through autonomous judgment, and proves that as long as a certain discount factor is met, its incentive system can meet the interests of most users, and they will have enough motivation to participate in cooperative download, thus achieving the efficiency of the incentive system and the autonomous judgment and screening of the discount factor of cooperative willingness; Lai Chengzhe, Chen Yao, Guo Qili and others proposed a cooperative download incentive scheme based on double-layer games in "Cooperative Download Incentive Scheme Based on Game Theory in Internet of Vehicles", using the reverse auction mechanism to select cooperative download vehicles, and evolutionary games solved the problem of vehicle participation in data forwarding; however, the method of forming vehicle clusters proposed by Zhu Wenlong in "Research on Data Distribution between Vehicles Based on Network Coding" is applied to geographic-based Although the cooperative vehicle selection scheme based on location and driving direction can achieve high efficiency and low network overhead, it lacks the step of screening cooperative vehicles for network factors (such as network delay, packet loss rate, etc.) in the selection of cooperative vehicles; Zeng Cheng proposed an efficient incentive method based on infinite repeated games in "Research on cooperative download problems in the Internet of Vehicles", which also lacks a mechanism to screen out bad vehicles for network factors; Lai Chengzhe, Chen Yao, Guo Qili and others proposed a cooperative download incentive scheme based on double-layer games in the Internet of Vehicles in "Cooperative download incentive scheme based on game theory in the Internet of Vehicles", which only excludes malicious nodes in the screening based on reputation and eliminates the surface risk possibility, without secondary filtering and screening of cooperative vehicles based on network factors, and there may still be some potential problems of poor transaction quality induced by poor network factors. For example, in real life, if the impact of network factors on vehicle transactions is not considered when vehicles screen cooperative vehicles, the communication between vehicles may be affected by poor network performance, resulting in a decline in communication quality, including increased delay, increased packet loss rate and slower data transmission speed. This is reflected in the fact that when the driver's vehicle conducts data transactions with the cooperating vehicle, a series of problems may occur, such as data loss, slow data transmission, and delayed transaction time; secondly, if the cooperative task requires real-time communication or relies on vehicles with higher network performance for collaboration, then choosing a vehicle with poor network performance may reduce the efficiency of collaboration, delay the task, and affect the quality of task completion and real-time benefits.Furthermore, vehicles with poor network performance may face a higher risk of data loss and tampering, especially in scenarios involving the transmission of sensitive information, where this phenomenon is even more grim.

[0005] In terms of data pricing, Wang Xiaodan proposed a routing decision using the OBCDI algorithm in the study of traffic unloading based on intimacy and cooperative downloading in VANET, that is, sorting the cooperative vehicles according to intimacy (vehicle spacing, activity value, relative speed, relative connection time), ensuring that the requesting vehicle has better transmission quality; Zhao Bin proposed a grid resource management model based on reverse auction technology (RAT) in the study of grid resource management model based on reverse auction technology (RAT), which also used the reverse auction mechanism and introduced it into the In the grid resource management, the task cost of grid users is significantly reduced; Lai Chengzhe, Chen Yao, Guo Qili and others proposed a method combining perfect reverse auction (PRA) and random reverse auction mechanism (RRA) used in the cooperative vehicle selection stage in "Cooperative Download Incentive Scheme Based on Game Theory in the Internet of Vehicles", which can enable the decision-making institution RSU to obtain as much and as accurate data as possible. The unique mechanism combining perfect reverse auction and random reverse auction mechanism (perfect reverse auction determines a more accurate transaction price, and random reverse auction introduces random factors) can increase the flexibility, variability and fun of transactions. However, Wang Xiaodan proposed a routing decision based on intimacy and cooperative download in the study of traffic offloading based on intimacy and cooperative download in VANET. The OBCDI algorithm is used in the research of traffic offloading based on intimacy and cooperative download. In the reverse auction of the candidate vehicle election process, only the bidding price is collected, and the bidding of cooperative vehicle data is not subject to a certain data price range limit (such as setting a floor price and a maximum price limit). Zhao Bin proposed a grid resource management model based on reverse auction technology (RAT) in "A Study on a Grid Resource Management Model Based on Reverse Auction Technology (RAT)". In the reverse auction bidding, the data price was also not set in advance within a certain reasonable range. Lai Chengzhe, Chen Yao, Guo Qili and others proposed a method combining perfect reverse auction (PRA) and random reverse auction mechanism (RRA) used in the cooperative vehicle selection stage in "Cooperative Download Incentive Scheme Based on Game Theory in Internet of Vehicles". At the same time, because the transaction price is unlimited and highly random, it may increase the risk of price fluctuations. Especially in the case of large transaction volume, information explosion or unstable market environment, it will lead to drastic fluctuations in transaction prices, affecting the stability and predictability of transactions. At this time, it is particularly important to set a floor price and a maximum and secondary price limit for the transaction data to allow it to fluctuate within a reasonable and predictable range, so that the interests of both parties to the transaction are not "negative" and measures to enhance transaction stability are particularly important.

[0006] In terms of data pricing, Cai Wansheng, Song Xi, Gao Wenpeng and others proposed the introduction of a multi-user negotiation game framework in "Electric Vehicle Energy Trading Incentive Mechanism Based on Reverse Auction", and then formulated the optimal pricing payment strategy for electric vehicles, which can significantly reduce the cost of energy aggregators and encourage vehicle discharge; Wang Xiaodan proposed the use of the reverse auction model for vehicle cooperative download incentives in "Research on Traffic Offloading Based on Intimacy and Cooperative Download in VANET"; Zhao Bin proposed the establishment of a fair grid resource trading platform in "Research on a Grid Resource Management Model Based on Reverse Auction Technology (RAT)" to allow users to compete fairly for the grid resources they need, which is conducive to the fairness of user incentives. Lai Chengzhe, Chen Yao, Guo Qili and others proposed an incentive mechanism based on game theory in "Cooperative Download Incentive Scheme Based on Game Theory in the Internet of Vehicles", which realized the positive cycle of the overall system enthusiasm of the vehicle. However, in the "Electric Vehicle Energy Trading Incentive Mechanism Based on Reverse Auction", Cai Wansheng, Song Xi, Gao Wenpeng and others proposed an optimal pricing payment strategy method for electric vehicles. In the process of selecting participants in the reverse auction, after the aggregator announced the task to the electric vehicle, the bid price submitted by the electric vehicle was not specially encrypted, lacking data security, and there may be a risk of black-box operation. Wang Xiaodan proposed the use of the reverse auction model in "Research on Traffic Offloading Based on Intimacy and Cooperative Download in VANET", and the data bidding process was also lacking in encryption; Zhao Bin proposed the establishment of a fair grid resource trading platform in "Research on a Grid Resource Management Model Based on Reverse Auction Technology (RAT)", but the grid service provider's bidding in the transaction process also lacked data protection and encryption; Lai Chengzhe, Chen Yao, Guo Qili and others proposed the incentive mechanism based on game theory in "Cooperative Download Incentive Scheme Based on Game Theory in the Internet of Vehicles", and the data quotation of the cooperative vehicle was also not encrypted and hidden in the data auction stage of the cooperative vehicle selection. In summary, the above literature has completed the positive incentive of the system when using the reverse auction mechanism, but has not yet considered the encryption process of data bidding, which will not only cause privacy data leakage and information insecurity, but also lead to unfair and subjective problems in auction competition, which undoubtedly brings huge challenges to fair competition and vehicle incentives. For example, in real life, there may be a dark operation and unfair competition of malicious nodes after the bidding information is leaked, resulting in malicious node vehicles becoming the winners to participate in the transaction, and malicious behaviors in the transaction process will cause the driver to receive malicious information or bad inducement. Moreover, when the auction is about to end, if the competitor corresponding to the cooperative vehicle knows the current bid of the cooperative vehicle, the competitor may adjust its bidding strategy according to the current bid of the cooperative vehicle. This may be another level of "knowing yourself and knowing the enemy", but it will not only damage the interests of other normal bidding vehicles, but also cause the auction to be unfair and unreasonable.After the auction process is over, if the bidding information of the failed vehicle is leaked, its competitors can infer its bidding ability and economic level, which is undoubtedly a huge challenge to the privacy protection of the failed vehicle.

[0007] In summary, the defects of the prior art are mainly reflected in:

[0008] (1) The impact of network factors on vehicle data transactions was not considered during the selection process of cooperative vehicles, and there was no mechanism to screen out vehicles with bad network factors;

[0009] (2) When using the reverse auction mechanism, there is no restriction on the transaction price, which is highly random and may increase the risk of price fluctuations;

[0010] (3) There is a lack of encryption process for data bidding during the transaction process of the reverse auction mechanism, resulting in a lack of data security. Summary of the invention

[0011] In view of the deficiencies in the prior art, the present invention provides a VANET cooperative download method based on multi-dimensional screening and encrypted reverse auction, so as to solve the problem that in the cooperative vehicle selection stage of the prior method, the cooperative vehicle screening mechanism based on the reverse auction mechanism does not take into account the impact of network factors on vehicle data transactions, resulting in decreased communication quality and increased data security risks; solve the problem of information asymmetry and high price fluctuation risks caused by an imperfect reverse auction mechanism in the cooperative vehicle selection stage; and solve the problem of potential backroom operations and information leakage risks caused by failure to encrypt information on the quotations of cooperative vehicles.

[0012] The VANET cooperative download method based on multi-dimensional screening and encrypted reverse auction includes the following steps:

[0013] Step 1: Deploy roadside units on the vehicle route and deploy on-board units on each vehicle;

[0014] Step 2: The roadside unit registers the vehicle and authenticates the vehicle, records the reputation value of the verified vehicle, and then the on-board unit samples the three network performance indicators of the verified vehicle n times within a period of time t and reports them to the roadside unit, that is, each network performance indicator obtains n data values;

[0015] The three network performance indicators include network delay D, packet loss rate L and occupied network throughput T;

[0016] Step 3: Preliminary screening of vehicles based on their reputation and network performance indicators to obtain candidate cooperative vehicles;

[0017] Step 3.1: The roadside unit uses a reputation-based incentive mechanism to evaluate the reputation of vehicles and excludes vehicles with reputation values ​​below a threshold;

[0018] Step 3.2: Further screen the vehicles based on the network performance indicators to obtain candidate cooperative vehicles;

[0019] Step 3.2.1: Determine the event type;

[0020] Specifically, the maximum and minimum values ​​of the three network performance indicators, namely, network delay D, packet loss rate L, and occupied network throughput T, are determined respectively, and the number of divided value intervals is set. Based on the maximum and minimum values ​​of the three network performance indicators and the number of divided value intervals, the three network performance indicators are divided into a number of value intervals, each of which is an event type.

[0021] Step 3.2.2: Define that when the data value of a certain network performance indicator of a vehicle is within the numerical range of a certain event type, it means that an event of the event type occurs to the vehicle;

[0022] Step 3.2.3: Based on the data values ​​of the three network performance indicators of each vehicle obtained within a period of time t, calculate the number of events of each event type that occur in the vehicle;

[0023] Step 3.2.4: Calculate the probability distribution of each event type for each vehicle;

[0024] Step 3.2.5: According to the probability distribution of each event type, determine the probability of occurrence of the event corresponding to the data value of the network performance indicator of each vehicle within a period of time t;

[0025] Step 3.2.6: Calculate the information amount I(l) of each event according to the occurrence probability of the event corresponding to the data value of the network performance indicator of each vehicle;

[0026] Step 3.2.7: According to the information volume of all events of each vehicle, the information entropy of network delay D, packet loss rate L and occupied network throughput T are calculated respectively, and the total information entropy of the vehicle is obtained by summing them up;

[0027] Step 3.2.8: Calculate the average value of network delay D, the average value of packet loss rate L, and the average value of occupied network throughput T for each vehicle;

[0028] Step 3.2.9: Set a set of thresholds, compare and screen the total information entropy, average value of network delay D, average value of packet loss rate L and average value of occupied network throughput T of each vehicle with the set set of thresholds to obtain candidate cooperative vehicles; the set of thresholds includes the total information entropy threshold, network delay threshold, packet loss rate threshold and occupied network throughput threshold;

[0029] The comparison and screening process is specifically as follows: determine whether the total information entropy of the vehicle, the average value of the network delay D, the average value of the packet loss rate L, and the average value of the occupied network throughput T are lower than the corresponding thresholds. If they are lower, the vehicle is qualified and the vehicle number is recorded as a candidate for cooperation. If they are higher, the vehicle is unqualified.

[0030] Step 4: The requesting vehicle sends a download request to the roadside unit, and the roadside unit broadcasts the download request. After receiving the download request, the candidate cooperative vehicle generates a quotation. The download request includes the download data type and budget.

[0031] Step 5: Encrypt the quote generated by the candidate cooperative vehicle, generate a blind signature and zero-knowledge proof, and submit them to the roadside unit;

[0032] Step 5.1: The candidate cooperation vehicle Vi generates a private key private_key and a public key public_key, where i is the number of the candidate cooperation vehicle;

[0033] Step 5.2: Blind the quote Pi according to the public key public_key to obtain the blinded information;

[0034] Step 5.3: Sign the blinded information according to the private key private_key to generate a blind signature σi;

[0035] Step 5.4: The candidate cooperative vehicle Vi generates a zero-knowledge proof;

[0036] Specifically, vehicle Vi selects a random value r, calculates the challenge value ci and the response value s, and then uses the zero-knowledge proof protocol to generate a zero-knowledge proof Ti = (ci, s);

[0037] Step 5.5: The candidate cooperative vehicle Vi submits the blind signature and zero-knowledge proof to the roadside unit;

[0038] Step 6: The roadside unit unblinds the blind signature, and then verifies the validity of the unblinded signature and the zero-knowledge proof. Vehicles that fail to pass the verification are no longer considered as candidate cooperative vehicles; the unblinded signature includes the quotation;

[0039] Step 6.1: The roadside unit unblinds the blind signature of the candidate cooperative vehicle to obtain an unblinded signature;

[0040] Step 6.2: Verify the unblinded signature of the candidate cooperation vehicle. If the verification is successful, proceed to step 6.3. Otherwise, re-obtain the blind signature for verification. If the verification fails within the set number of times, the vehicle is no longer a candidate cooperation vehicle.

[0041] Step 6.3: Verify the zero-knowledge proof of the selected cooperative vehicle. If the verification is successful, proceed to step 7. Otherwise, re-obtain the blind signature for verification. If the verification fails within the set number of times, it will no longer be considered as a candidate cooperative vehicle.

[0042] Step 7: Set the reserve price, soft ceiling price and ceiling price for the reverse auction;

[0043] Step 7.1: The roadside unit estimates the reserve price of the reverse auction using a linear regression model;

[0044] Step 7.1.1: Collect N historical transaction data of the requested vehicle It includes the number of transactions q, transaction time ti, transaction conditions c, transaction floor price p and cost cost within a period of time;

[0045] Step 7.1.2: Collect historical transaction data of the requested vehicle Calculate the information entropy of transaction quantity, transaction time and transaction conditions respectively;

[0046] Step 7.1.3: Determine the weights of the information entropy of transaction quantity, the information entropy of transaction time, and the information entropy of transaction conditions, and calculate the total information entropy;

[0047] entropy=w q *entropy_q+w ti *entropy_ti+w c *entropy_c

[0048] Among them, w q ,w ti ,w c are the weights of the information entropy of transaction quantity, the information entropy of transaction time and the information entropy of transaction conditions, entropy_q is the information entropy of transaction quantity, entropy_ti is the information entropy of transaction time, and entropy_c is the information entropy of transaction conditions;

[0049] Step 7.1.4: Standardize the transaction quantity, transaction time and transaction conditions in the historical transaction data to obtain the standardized transaction quantity, transaction time and transaction conditions;

[0050] Step 7.1.5: Establish a linear regression model;

[0051] Y=β0+β1X1+β2X2+……+β N X N +ε

[0052] Among them, X is the independent variable, β is the linear regression model parameter, i.e., the regression coefficient, ε is the random error term, and Y is the predicted value of the reserve price;

[0053] Step 7.1.6: Construct a training sample set Each sample in the training sample set includes a standardized transaction quantity, a standardized transaction time, a standardized transaction condition, a total information entropy, a cost, and a transaction floor price p;

[0054] Step 7.1.7: Use the training sample set to fit the constructed linear regression model to obtain a fitted linear regression model;

[0055] Specifically: Use a linear regression model to fit the standardized transaction quantity, transaction time, transaction conditions, total information entropy, cost and transaction floor price p, and determine the estimated value of the best linear regression model parameters by minimizing the residual sum of squares;

[0056] Step 7.1.8: Use the fitted linear regression model to estimate the reserve price and obtain the reserve price of the reverse auction;

[0057] Step 7.2: Set a soft ceiling price and a ceiling price based on the reverse auction reserve price;

[0058] The soft maximum price is:

[0059] price soft_max =price min *(α*w1+b min *w2)

[0060] Among them, price soft_max is the soft maximum price set. min is the reserve price of the reverse auction, α is the expected margin of the requested vehicle, b min is the minimum margin, w1 and w2 are the expected margin α and the minimum margin b of the requested vehicle respectively. min The weight value of

[0061] The maximum price stated is:

[0062] price limit_max =price min *(b max *w2+μ*w3)

[0063] Among them, price limit_max is the maximum price set, b max is the maximum margin, μ is the supply-demand relationship in the market, and w3 is the weight value of the supply-demand relationship in the market;

[0064] Step 8: The roadside unit screens candidate vehicles based on the reserve price, soft maximum price and maximum price of the reverse auction, and then uses a mechanism combining perfect reverse auction and random group reverse auction to screen candidate cooperative vehicles again to obtain the winning vehicle set;

[0065] Step 8.1: Determine whether there are candidate cooperation vehicles whose bids are between the reserve price and the soft maximum price. If yes, select one or more vehicles with the lowest bids as the winning vehicles and execute step 8.3. Otherwise, execute step 8.2.

[0066] Step 8.2: When the bid of the candidate cooperation vehicle is above the soft maximum price, the requesting vehicle will receive a prompt whether to increase the price cap to continue the transaction. If it gives up, the transaction will be terminated. Otherwise, it will continue to determine whether the bids of the candidate cooperation vehicles are below the maximum price. If so, one or more vehicles with the lowest bids will be selected as the winning vehicles and step 8.3 will be executed. If all of them exceed the maximum price, the transaction will be terminated.

[0067] Step 8.3: Use the perfect reverse auction mechanism to select the winning vehicles;

[0068] Step 8.4: Use the random grouping reverse auction mechanism to further screen the winning vehicles and obtain the final winning vehicle set;

[0069] Step 8.4.1: The roadside unit randomly divides the winners into two groups, with each group allocated half of the roadside unit’s budget;

[0070] Step 8.4.2: Apply the perfect reverse auction mechanism to each group, introduce random factors into the auction process, and determine the winning vehicle of each group after the auction;

[0071] Step 8.4.3: Merge the two groups of winning vehicles from the auction to form the final winning vehicle set;

[0072] Step 9: The roadside unit sends a notification to the vehicles in the winner set. After the winning vehicle confirms its intention to cooperate, it is ready to start data downloading;

[0073] Step 10: The winning vehicle downloads the data from the roadside unit and passes the data packet to the requesting vehicle via V2V communication.

[0074] Compared with the prior art, the beneficial effects of the present invention are:

[0075] 1. In the existing Internet of Vehicles, the cooperative download incentive scheme based on game theory usually only considers the exclusion of bad nodes and the maximization of the number of participating vehicles in the cooperative vehicle selection stage, but ignores the screening of network factors and fails to exclude vehicles with poor network status and high transaction risks. The present invention adds a network factor screening mechanism based on information entropy in the cooperative vehicle selection stage, innovatively integrates relevant knowledge such as information entropy and network delay, and forms a network factor screening supplementary optimization process for cooperative vehicle selection, which can screen out high-efficiency cooperative vehicles with good network performance, and eliminate potential problems of poor transaction quality (increased delay, increased packet loss rate, slower data transmission speed, data loss and tampering risk) induced by some poor network factors as much as possible, improve the overall transaction quality and success rate of the vehicle-mounted ad hoc network system, and improve the efficiency, safety and fairness of cooperative downloading in the vehicle-mounted ad hoc network.

[0076] 2. The reverse auction model used in the existing game theory-based cooperative download incentive scheme in the Internet of Vehicles is difficult to cope with more complex and unstable traffic environments. The present invention designs a supplementary and optimized reverse auction mechanism, integrates the concept of "sealed auction", encrypts the entire transaction process, enhances the security and fairness of the transaction, and adds a data pricing limitation strategy in the data bidding stage of the reverse auction model, limits data price fluctuations within a certain range, and achieves transaction stability and predictability as much as possible. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] Figure 1 It is a flow chart of a VANET cooperative download method based on multi-dimensional screening and encrypted reverse auction in an embodiment of the present invention;

[0078] Figure 2 It is a box plot of information entropy and network indicators in an embodiment of the present invention;

[0079] Among them, (a) is the box plot of information entropy and network indicators before screening, and (b) is the box plot of information entropy and network indicators after screening;

[0080] Figure 3 Schematic diagram of the average and standard deviation of vehicle information entropy and network indicators after screening in an embodiment of the present invention;

[0081] Figure 4 The mean square error of the three models in the embodiment of the present invention changes with the increase of the number of transactions;

[0082] Figure 5 The graph is a trend chart of the mean absolute errors of the three models in the embodiment of the present invention as the number of transactions increases;

[0083] Figure 6 The error coefficients of the three models in the embodiment of the present invention change trend chart with the increase of transaction number;

[0084] Figure 7 This is a vehicle quotation simulation diagram in an embodiment of the present invention. DETAILED DESCRIPTION

[0085] The present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0086] VANET cooperative download method based on multi-dimensional screening and encrypted reverse auction, such as Figure 1 As shown, the following steps are included:

[0087] Step 1: Deploy roadside units (RSUs) on the vehicle routes and deploy onboard units (OBUs) on each vehicle to ensure that the vehicle can access the Internet of Vehicles system;

[0088] Step 2: The roadside unit (RSU) registers and authenticates the vehicle, records the reputation value of the verified vehicle, and then the on-board unit (OBU) samples the three network performance indicators of the verified vehicle n times within a period of time t and reports them to the roadside unit (RSU), that is, each network performance indicator obtains n data values;

[0089] The three network performance indicators include network delay D, packet loss rate L and occupied network throughput T;

[0090] Step 3: Preliminary screening of vehicles based on their reputation and network performance indicators to obtain candidate cooperative vehicles;

[0091] Step 3.1: The roadside unit (RSU) uses a reputation-based incentive mechanism to evaluate the reputation of vehicles and excludes vehicles with a reputation value below a threshold;

[0092] Step 3.2: Further screen the vehicles based on the network performance indicators to obtain candidate cooperative vehicles;

[0093] Step 3.2.1: Determine the event type;

[0094] Specifically, the maximum and minimum values ​​of the three network performance indicators, namely, network delay D, packet loss rate L, and occupied network throughput T, are determined respectively, and the number of divided value intervals is set; based on the maximum and minimum values ​​of the three network performance indicators and the number of divided value intervals, the three network performance indicators are divided into a number of value intervals, each value interval being an event type, such as low network delay, medium network delay, high network delay, low packet loss rate, medium packet loss rate, high packet loss rate, and low network throughput;

[0095] The boundaries of each numerical interval are:

[0096] edges=linspace(min,max,bins+1)

[0097] Among them, edges is the boundary of the numerical interval, linspace is the average calculation instruction in MATLAB, min is the minimum value of a certain network performance indicator, max is the maximum value of a certain network performance indicator, and bins is the number of set numerical intervals;

[0098] Step 3.2.2: Define that when the data value of a certain network performance indicator of a vehicle is within the numerical range of a certain event type, it means that an event of the event type occurs to the vehicle;

[0099] Step 3.2.3: Based on the data values ​​of the three network performance indicators of each vehicle obtained within a period of time t, calculate the number of events of each event type that occur in the vehicle;

[0100]

[0101] in, is the data value of the network performance indicator, Counts is the number of network performance indicators that fall within a certain numerical interval, that is, the number of events of a certain event type; histcounts is a function in MATLAB used to calculate the number of times a data value falls into a specified interval;

[0102] Step 3.2.4: Calculate the probability distribution of each event type for each vehicle;

[0103] prob = counts / sum(counts)

[0104] Among them, prob is the probability distribution of a certain event type, and sum is the summation function;

[0105] Step 3.2.5: According to the probability distribution of each event type, determine the probability of occurrence of the event corresponding to the data value of the network performance indicator of each vehicle within a period of time t;

[0106] The process is expressed as:

[0107]

[0108] Among them, indies is the event type corresponding to the data value of a certain network performance indicator, and discretize returns the boundary of the data interval to the data value of the network performance indicator, thereby determining the event type to which the data value of the network performance indicator belongs;

[0109] prob*=prob(indies)

[0110] Wherein, prob* is the probability of occurrence of an event of an event type corresponding to the data value of a certain network performance indicator, that is, the probability of occurrence of an event of an event type corresponding to the data value of the network performance indicator is searched by using the event type (indies) corresponding to the data value of the network performance indicator in the probability distribution of the event type (prob);

[0111] P = reshape(prob*, n, 1)

[0112] Wherein, P is a one-dimensional vector, in which each element is the probability of occurrence of an event corresponding to the data value of a certain network performance indicator, and reshape means that the probability of occurrence of an event corresponding to the data value of each network performance indicator of a certain vehicle is arranged in order to ensure that the data value of the network performance indicator, the corresponding event type and the probability of occurrence of the corresponding event are one-to-one corresponding;

[0113] Step 3.2.6: Calculate the information amount I(l) of each event according to the occurrence probability of the event corresponding to the data value of the network performance indicator of each vehicle;

[0114] The information amount of each event is calculated using the information amount formula in information theory. The information amount represents the amount of information provided when an event occurs. Usually, the amount of information is associated with the probability of an event occurring, that is, the lower the probability of an event occurring, the greater the amount of information it provides.

[0115] The information volume formula is:

[0116] I(l)=-log2(P)

[0117] Where l is the number of the event;

[0118] Step 3.2.7: According to the information volume of all events of each vehicle, the information entropy of network delay D, packet loss rate L and occupied network throughput T are calculated respectively, and the total information entropy of the vehicle is obtained by summing them up;

[0119] Information entropy represents the uncertainty or randomness of the entire system or data set. Usually, information entropy is the weighted average of the information content of all events.

[0120] The information entropy formula is expressed as:

[0121]

[0122] Among them, H(n) is the information entropy of a certain network performance indicator; P(l) is the probability of occurrence of a certain event corresponding to the network performance indicator;

[0123] Step 3.2.8: Calculate the average value of network delay D, the average value of packet loss rate L, and the average value of occupied network throughput T for each vehicle;

[0124] ave(D)=mean(D)

[0125] ave(L)=mean(L)

[0126] ave(T)=mean(T)

[0127] Where ave(D) is the average value of the vehicle network delay, ave(L) is the average value of the vehicle packet loss rate, ave(T) is the average value of the vehicle network throughput, and mean is a function in MATLAB used to calculate the average value;

[0128] Step 3.2.9: A set of thresholds is set based on real investigation and analysis, and the total information entropy, average value of network delay D, average value of packet loss rate L and average value of occupied network throughput T of each vehicle are compared with the set set of thresholds to obtain candidate cooperative vehicles; the set of thresholds includes the total information entropy threshold, network delay threshold, packet loss rate threshold and occupied network throughput threshold;

[0129] The comparison and screening process is specifically as follows: because the threshold is the dividing point of whether the vehicle network is stable or not and the quality of the network obtained through actual conditions and investigation and analysis, the basis for judging whether the vehicle's network condition meets the standard is whether the vehicle's total information entropy, the average value of network delay D, the average value of packet loss rate L and the average value of occupied network throughput T are lower than the corresponding threshold threshold. If it is lower than the threshold, the vehicle is qualified, the vehicle number is recorded, and it is used as a candidate cooperative vehicle for subsequent reverse auction. If it is higher than the threshold, it is determined that the vehicle network condition is unstable or the network quality is poor.

[0130] In MATLAB, a simulation experiment of judging the vehicle network status and selecting candidate cooperative vehicles was conducted to verify that the vehicle information entropy can be used to select vehicles with unstable network status, which is one aspect of the poor selection effect of traditional solutions. This simulation selected 10 vehicles numbered 1 to 10, and counted their network delay, packet loss rate, and occupied network throughput within 100 minutes, collecting data once a minute. The information entropy, average network delay, average packet loss rate, and average network throughput of each vehicle were obtained.

[0131] ① In this simulation, the information entropy threshold is set to 1.2, the average network delay threshold is set to 72, the average packet loss rate threshold is set to 0.1, and the average occupied network throughput threshold is set to 80. The information entropy and network performance indicators are standardized. If the data is greater than 1, it exceeds the threshold, which is used to determine whether they are qualified.

[0132] ② Use the standardized data to calculate the average and standard deviation of the information entropy and each network performance index before and after screening.

[0133] ③Through Figure 2 The box plot shown observes the distribution before and after screening.

[0134] Table 1 Mean and standard deviation of information entropy and performance indicators before and after screening by this scheme

[0135] Information Entropy Network latency Packet loss rate Occupied network throughput Average value before screening: 0.9484 0.7174 0.7238 0.8586 Standard Deviation Before Screening: 0.0500 0.1835 0.0740 0.0783 Filtered average: 0.9337 0.6446 0.6967 0.8276 Standard Deviation after Filtering: 0.0444 0.1536 0.0546 0.0412

[0136] As shown in Table 1, the average values ​​of vehicle network delay, packet loss rate, and occupied network throughput after screening by this solution are lower, and the network status is better; the standard deviation is reduced, and the network stability is better. The change in information entropy before and after screening can also explain this result.

[0137] Depend on Figure 2 (a) Box plot before screening and Figure 2 (b) From the comparison of the box plots after screening, it can be seen that after screening by this scheme, the boxes of each network indicator are smaller, which indicates that the data has become more concentrated; there are fewer outliers beyond the range of 1.5 times the IQR, and the data quality is higher.

[0138] Depend on Figure 3 It can be seen that the average values ​​of indicators such as network delay, packet loss rate and occupied network throughput of vehicle 6 are normal, but the information entropy of these indicators of vehicle 6 exceeds the threshold. This reflects that the average value and standard deviation are greatly affected by interference data, and the use of information entropy combined with linear regression model can reduce interference.

[0139] Step 4: The requesting vehicle (vr) sends a download request to the roadside unit (RSU), and the roadside unit (RSU) broadcasts the download request. After receiving the download request, the candidate cooperation vehicle generates a quotation Pi, where i is the number of the candidate cooperation vehicle; the download request includes the download data type and budget;

[0140] Step 5: Encrypt the quote generated by the candidate cooperative vehicle, generate a blind signature and zero-knowledge proof, and submit it to the roadside unit (RSU);

[0141] Step 5.1: The candidate cooperation vehicle Vi generates a private key private_key and a public key public_key;

[0142] The private key is kept confidential and is used to sign bids or other data generated by the vehicle. The public key is used to verify the vehicle's signature and can be securely submitted to a trusted third party, such as an auction system manager or other bidders, who will use the vehicle's public key to verify the signature submitted by the vehicle to ensure that the bid is indeed from the vehicle and has not been tampered with.

[0143] Step 5.2: Blind the quote Pi according to the public key public_key to obtain the blinded information;

[0144] The blinded information Mi is:

[0145] Mi=Pi*(bi**public_key)

[0146] Among them, bi is the blinding factor;

[0147] Step 5.3: Sign the blinded information according to the private key private_key to generate a blind signature σi;

[0148] σi=Blindsign(private_key,Mi)

[0149] Among them, Blindsign is a blind signature algorithm in the cryptography database in Python;

[0150] Step 5.4: The candidate cooperative vehicle Vi generates a zero-knowledge proof;

[0151] Specifically, vehicle Vi selects a random value r, calculates the challenge value ci and the response value s, and then uses the zero-knowledge proof protocol to generate a zero-knowledge proof Ti = (ci, s);

[0152] The challenge value ci is:

[0153] ci=hash(statement+r)

[0154] Among them, hash represents a hash function, and statement refers to a public statement or a statement to be proved.

[0155] The response value s is:

[0156] s=(r+ci*secret)%prime

[0157] Among them, secret refers to the information to be hidden and encrypted, that is, the bid of the cooperating vehicle in the reverse auction, and prime is a prime number;

[0158] Step 5.5: The candidate cooperative vehicle Vi submits the blind signature and zero-knowledge proof to the roadside unit (RSU);

[0159] Step 6: The roadside unit (RSU) unblinds the blind signature and then verifies the validity of the unblinded signature and zero-knowledge proof to ensure the authenticity and security of the quotation. Vehicles that fail to pass the verification are no longer considered as candidate cooperative vehicles; the unblinded signature includes the quotation;

[0160] Step 6.1: The roadside unit (RSU) unblinds the blind signature of the candidate cooperative vehicle to obtain an unblinded signature;

[0161] The signature after unblinding is:

[0162]

[0163] Among them, Signature is the signature after unblinding;

[0164] Step 6.2: Verify the unblinded signature of the candidate cooperation vehicle. If the verification is successful, proceed to step 6.3. Otherwise, re-obtain the blind signature for verification. If the verification fails within the set number of times, the vehicle is no longer a candidate cooperation vehicle.

[0165] In this embodiment, first, the unblinded signature is obtained. The signature is initially encrypted. After unblinding, the plaintext signature value is obtained, which contains a clear data quotation. Next, a predetermined hash algorithm (such as SHA-256) is used to recalculate the hash value. The hash value is usually based on a public statement and the public key (public_key) of the prover, and is calculated by the formula `hash(statement+public_key)`, where the statement is the public information of the participation in the proof, and the public key is the public key of the cooperating vehicle. Finally, the calculated hash value is compared with the unblinded signature. If the unblinded signature is equal to the hash value, that is, `DecryptedSignature=hash(statement+public_key)`, the verification is successful, indicating that the signature is valid and the public key of the prover is consistent with the statement; if they are inconsistent, the verification fails, which means that the signature is invalid or tampered with. If there is a reasonable suspicion that the verification failure is due to a technical problem, the vehicle can be re-requested to submit the signature for secondary verification. If the verification fails multiple times, the vehicle is marked as a malicious node and is deprived of the qualification to participate.

[0166] Step 6.3: Verify the zero-knowledge proof of the selected cooperative vehicle. If the verification is successful, proceed to step 7. Otherwise, re-obtain the blind signature for verification. If the verification fails within the set number of times, it will no longer be considered as a candidate cooperative vehicle.

[0167] Set a challenge value C', then take a prime number from the challenge value C' to get C'_prime:

[0168] C'_prime=hash(statement+(public_key**ci))

[0169] Verify the challenge value ci == C'_prime again? Are they consistent? Same as above. If they are, the zero-knowledge proof verification is successful; if they are inconsistent, the verification fails. In some cases, the verification failure may be caused by a transmission error or technical failure. You can request the prover to submit the proof again and conduct a new verification. If the verification failure occurs multiple times, the prover will be marked as a potential malicious actor or untrusted node, and will be completely denied participation in transactions in future interactions.

[0170] In this implementation, it is assumed that the auction data bid of a cooperative vehicle is 10000. Without the data bid encryption process proposed in this solution, the leaked information is the real information 10000, and data security is at risk. However, after the data bid encryption process combined with blind signature and zero-knowledge proof in this solution, even if the data is leaked, only a string of encrypted information data that is difficult to crack can be obtained. The data output result is as follows:

[0171] The output of the “original code” leak:

[0172] #Price = 10000;

[0173] The output of the code "Combining blind signature and zero-knowledge proof" is as follows:

[0174] Blind signature:b'T\x17\xddN\xb6\xdc\xd05\xc9dVfW\x10\xfd2q\xa6O\x80\xa0\xcai\x80q<W3s\x9c^\x0b?~\xf8Df\x81(\xb0\x06\x90\xc6\x90*\xdd\xf0\x9ab\xfa!\x1e\x87 / \x97 / \xca\x91\x1d\xc8\x95\xf5{\xcf\xc9\x10\x01\xd5gk\xdfRS_!ex\xa4\x06\x0c\xc3O\x88\xbc\xc4\xea\xc9\xe8}V{\xcaF\xc7TV\xfc\x0b\xd5\xcd\xcdq?\xdb|:\x85\x03\xc7\xf8\xe1\xe6f\x1e\x87\xb9L~\x01B\xc7\xed\x00q\xb5*\xec\x89\xac\x04\x01@r\xc3\xd2\xe0\x1bE9ce\xda\xb2\x89\x9c\xed\xe1\xc12Y\xa5|\xa0\xb5\xf3\xfc\\\x9cP#W\xd7d}\x8b\t#\xf4B\x18\x88\x9d\x84\xb3=\x8bWfn\xe3r\xc6\x03\xeb\xda\x0b\xe6\xb8Z\xb5\xeb\xa0-\xf1@?\x12L\xf4\x8e\x00\x94\x04A\x1b\x95\x90g.(\x9d\x91i\xae\x18DICp\xa3G\x9c\xce\xe6\x034\x14\xd0\x1d\x03c\xdcM+\xb8\x91%\x81\xbc\x8bC\xc7\xc7<\xae\x8a~\xfe\xf2q\x9aL\xfbHt\x01A7\xb9\xf9\xd1W\xe8\x97\x05k\xf2n\x8f\xe7\xf3\xb8\xb9\xbc\x91\xd93}\x9aJZt\x9d\xebW\xce+\x1bv\x9c\xc5\x18\xa5\tM\\<\xd50\xb8\x00j\xc14,r\xbc\xcc\x8b\x10\x98\xac,\xb4\xd3\x06\x19\xc5\xd13B\x85\x1f\xa9\x9cPKG\x9b\xb7\x86\x81\x9d\xbc\xc6\x94{\xf5\xee\x1fA\x00\x89\x

[0175] Zero-knowledgeproof:Zero-knowledgeproof

[0176] Blind signature verification successful. (Blind signature verification successful)

[0177] Zero-knowledgeproof verification successful.

[0178] The above is a simulation of running a vehicle on PyCharm using Python language to encrypt and hide the quote of the cooperative vehicle using a combination of blind signature and zero-knowledge proof and verify the subsequent process. The output results output the encrypted content of the blind signature and the corresponding zero-knowledge proof and show that both the blind signature and the zero-knowledge proof are successful. The above results show that the implementation of this mechanism is feasible and can provide a new idea for the encryption and verification of the quotes of cooperative vehicles in the vehicle-mounted ad hoc network.

[0179] Combining blind signature technology with zero-knowledge proof technology can not only provide encryption functions for vehicle quotation information and data pricing, but also give full play to the unique properties and characteristics of each, with advanced privacy protection, anonymity, verifiability, and the characteristics of increasing the participation of cooperative vehicles, achieving the effect of "1+1>2".

[0180] 1. Advanced privacy protection: Blind signature technology can protect the privacy of the signing vehicle, making it impossible for the signing vehicle to know the content of the message during the signing process. Combined with zero-knowledge proof technology, it can further ensure that the verification agency cannot obtain the identity of the signing vehicle or the content of the message after receiving the signature, thus providing a higher level of privacy protection.

[0181] 2. Anonymity: The combination of blind signature technology and zero-knowledge proof technology can realize anonymous signature, that is, the signing vehicle can sign the vehicle quotation and data pricing without revealing its identity, and prove the validity of the signature to the verification agency.

[0182] 3. Verifiability: Combining blind signature and zero-knowledge proof technology can ensure the validity of the signature, that is, the verification agency can verify the validity of the signature without knowing the identity of the signing vehicle and the quotation information. This prevents the possibility that malicious vehicles may collude with auction agencies to conduct dark operations and unfair competition, greatly enhancing the credibility and security of the signature.

[0183] 4. Increase the participation of cooperating vehicles: Since vehicle quotes and data pricing are encrypted and privacy-protected, vehicles’ trust in the system increases, and they may be more willing to participate in the auction process, thereby increasing the number of cooperating vehicles and significantly improving the efficiency and performance of the system.

[0184] Step 7: Set the reserve price, soft ceiling price and ceiling price for the reverse auction;

[0185] Step 7.1: The roadside unit (RSU) uses a linear regression model to estimate the reserve price of the reverse auction;

[0186] Step 7.1.1: Collect N historical transaction data of the requested vehicle It includes the number of transactions q, transaction time ti, transaction conditions c, transaction floor price p and cost cost within a period of time;

[0187] Step 7.1.2: Collect historical transaction data of the requested vehicle Calculate the information entropy of transaction quantity, transaction time and transaction conditions respectively;

[0188] Similar to the previous method, determine the event type based on the vehicle's historical transaction data Find the probability distribution of event types corresponding to the transaction quantity, transaction time and transaction conditions, calculate the probability of each corresponding event, and then obtain the amount of information. Based on the amount of information, calculate the information entropy of the transaction quantity, the information entropy of the transaction time and the information entropy of the transaction conditions respectively.

[0189] Step 7.1.3: Based on the application research, determine the weights of the information entropy of transaction quantity, the information entropy of transaction time, and the information entropy of transaction conditions, and calculate the total information entropy;

[0190] entropy=w q *entropy_q+w ti *entropy_ti+w c *entropy_c

[0191] Among them, w q ,w ti ,w c are the weights of the information entropy of transaction quantity, the information entropy of transaction time and the information entropy of transaction conditions, entropy_q is the information entropy of transaction quantity, entropy_ti is the information entropy of transaction time, and entropy_c is the information entropy of transaction conditions;

[0192] Step 7.1.4: Standardize the transaction quantity, transaction time and transaction conditions in the historical transaction data to obtain the standardized transaction quantity, transaction time and transaction conditions;

[0193] Step 7.1.5: Establish a linear regression model;

[0194] Y=β0+β1X1+β2X2+……+β N X N +ε

[0195] Among them, X is the independent variable, β is the linear regression model parameter, i.e., the regression coefficient, ε is the random error term, and Y is the predicted value of the reserve price;

[0196] Step 7.1.6: Construct a training sample set Each sample in the training sample set includes a standardized transaction quantity, a standardized transaction time, a standardized transaction condition, a total information entropy, a cost, and a transaction floor price p;

[0197] Step 7.1.7: Use the training sample set to fit the constructed linear regression model to obtain a fitted linear regression model;

[0198] Specifically: Use a linear regression model to fit the standardized transaction quantity, transaction time, transaction conditions, total information entropy, cost and transaction floor price p, and determine the best parameter estimate by minimizing the residual sum of squares

[0199] The process is expressed as:

[0200]

[0201] Among them, LinearRegression.fit is the training of the linear regression model, and p[:N] is the observed value of the reserve price;

[0202]

[0203] Among them, p i To minimize the predicted value, To minimize the observed value;

[0204] Step 7.1.8: Use the fitted linear regression model to estimate the reserve price and obtain the reserve price of the reverse auction;

[0205]

[0206] Among them, price.Ir is the predicted reserve price, and LinearRegression.predict is the reserve price predicted by the trained linear regression model;

[0207] In this embodiment, 200 historical transaction records of different vehicles were selected for simulation, and the transaction quantity, transaction time, transaction conditions (set as transaction range in this case), and cost of successful transaction at the time of vehicle transaction closing were counted. The actual reserve price at that time was determined through human analysis and market research to judge the accuracy of the estimated reserve price.

[0208] The original scheme directly used the information entropy model to estimate the reserve price, while this scheme uses a linear regression model combined with the information entropy model to estimate the reserve price.

[0209] The ridge regression model expression is as follows:

[0210] in, is the parameter estimate of the ridge regression model, Y is the dependent variable, X is the independent variable, β is the model parameter, and α is the regularization parameter, which is used to control the influence of the regularization term.

[0211] In order to prevent the overfitting problem of the linear regression model, the ridge regression model is used for further comparison in the experiment. If the reserve price predicted by the linear regression model is very different from the reserve price estimated by the ridge regression model, it means that the linear regression model may be overfitting. If the curves after their simulation are not much different, this may indicate that the linear regression model can capture the main trends and relationships in the data when fitting the training data, without overfitting noise or outliers.

[0212] As the amount of training data gradually increases, the performance indicators of the three models in estimating the reserve price are recorded, including the mean square error (MSE), mean square absolute error (MAE), and determination coefficient (R 2 ) situation.

[0213] Mean Squared Error (MSE): Measures the difference between the predicted value and the actual value by squared error and is more sensitive to large errors.

[0214] Mean Absolute Error (MAE): measures the difference between the predicted value and the actual value by absolute error, weighting all errors equally.

[0215] Depend on Figure 4 , Figure 5 From the two pictures, we can see that:

[0216] ① The MSE and MAE values ​​of this scheme are smaller than those of the original scheme, which shows that the combination of linear regression model and information entropy model can better predict the reserve price.

[0217] ② This scheme uses a linear regression model combined with an information entropy model, which has smaller MSE and MAE when the training data is small, and increases with the increase of training data; while the ridge regression model combined with the information entropy model has larger MSE and MAE, and decreases with the increase of training data.

[0218] This is because the linear regression model is overfitting, but it tends to be stable as the number of training data increases (this simulation is limited by the training data set and fails to capture the characteristics of variables and predicted values ​​well; if it is real data, the MSE and MAE of the linear regression model will begin to decrease after the training set increases to a certain value). The ridge regression model combined with the information entropy model has a greater impact on the predicted value due to the influence of the regularization term, but it decreases as the number of training sets increases. Finally, after the amount of training set data exceeds 150, the MSE and MAE of the linear regression model and the ridge regression model tend to be consistent, indicating that the linear regression model is not overfitting and can accurately predict the reserve price.

[0219] Coefficient of determination (R 2 ): Measures how well the model explains the data. 2 The closer it is to 1, the stronger the explanatory power of the model. It is suitable for evaluating the overall fitting results of the model. Figure 6 It can be seen that:

[0220] ③ When there are enough training sets (higher than 150), the determination coefficient of this scheme is close to 0.8, which is higher than the original scheme, indicating that the linear regression model combined with the information entropy model can better explain the original data and has a better overall fitting effect.

[0221] Step 7.2: Set a soft ceiling price and a ceiling price based on the reverse auction reserve price;

[0222] Two-level maximum price strategy: the soft maximum price is considered to be a reasonable price cap, and the maximum price is considered to be the final price that relaxes the price cap in consideration of the urgent needs of the requester and does not affect the balance of market transaction prices.

[0223] The maximum price limit refers to the highest price limit set during the transaction process. If the price exceeds this limit, the transaction cannot be carried out. Its role is to prevent the price of vehicle transactions from being too high, protect the interests of the requesting vehicle, control market fluctuations, standardize the transaction process, and protect the rational use of resources.

[0224] The soft maximum price is set on the basis of the reserve price to leave a certain profit for the transaction vehicle, and takes into account the expected value of the requesting vehicle in this reverse auction. If the requesting vehicle urgently needs help from other vehicles, but the offer of the transaction vehicle is above the soft maximum price, the requesting vehicle may be willing to accept a price above the soft maximum price, so the strategy of setting a two-level maximum price is adopted.

[0225] During the implementation of the two-level maximum price strategy, in order to reduce the risk of price limit setting, macro-monitoring of market research was introduced. Based on market analysis, two margins were artificially set: the minimum margin and the maximum margin, which were used to set the two price limits respectively.

[0226] Since the soft ceiling price is a guideline price, it should be above the reserve price of the reverse auction. min Based on the accepted vehicle request margin α and market research macro monitoring - minimum margin b min The combined impact of

[0227] price soft_max =price min *(α*w1+b min *w2)

[0228] Among them, price soft_max is the set soft maximum price, w1 and w2 are the expected margin α and minimum margin b of the requested vehicle, respectively. min The weight value of

[0229] The maximum price is needed to limit the transaction price to prevent price gouging. It should be above the reserve price of the reverse auction. min Based on the macro monitoring of market research - maximum margin b max and the impact of the supply-demand relationship μ;

[0230] price limit_max =price min *(b max *w2+μ*w3)

[0231] Among them, price limit_max is the maximum price set, b max is the maximum margin, μ is the supply-demand relationship in the market, and w3 is the weight value of the supply-demand relationship in the market;

[0232] Case 1: When the number of vehicles participating in the bidding is higher than the standard number of vehicles: μ is 1; the standard number of vehicles refers to the number of vehicles participating in the bidding, and the market supply and demand relationship of the reverse auction reaches normal;

[0233] Case 2: When the number of vehicles participating in the bidding is less than the minimum number of participating vehicles: μ is The maximum value of μ max ; The μ max It is the maximum value set for the market supply and demand relationship. The minimum number of participating vehicles is the number of standard vehicles divided by μ max to determine;

[0234] Case 3: When the number of vehicles participating in the bidding is between the minimum number of participating vehicles and the standard number of vehicles: μ is calculated based on To determine, the value range of μ is [1, μ max ];

[0235] In this implementation, the predicted reserve price is:

[0236] Predictedprice for the new transaction with entropy assumption:34.25447852798585

[0237] If there are 10 vehicles participating in the bidding, assuming that the expected margin of the requested vehicle is 0.6, the lowest margin is 0.4 after market research, and the highest margin is 1.0, the weight values ​​are w1=0.4, w2=0.6, and w3=0.4.

[0238] Set the soft ceiling price and the ceiling price to:

[0239] max_price_values:[50.69662822]

[0240] max_limit_price_values:[75.35985276]

[0241] Vehicle price list Figure 7 shown. Figure 7 The horizontal lines from bottom to top represent the set floor price, soft ceiling price and ceiling price.

[0242] Step 8: The roadside unit (RSU) screens candidate vehicles based on the reserve price, soft maximum price and maximum limit price of the reverse auction, and then uses a mechanism combining perfect reverse auction (PRA) and random group reverse auction (RRA) to screen candidate cooperative vehicles again to obtain the winning vehicle set;

[0243] Step 8.1: Determine whether there are candidate cooperation vehicles whose bids are between the reserve price and the soft maximum price. If yes, select one or more vehicles with the lowest bids as the winning vehicles and execute step 8.3. Otherwise, execute step 8.2.

[0244] Step 8.2: When the bid of the candidate cooperation vehicle is above the soft maximum price, the requesting vehicle will receive a prompt whether to increase the price cap to continue the transaction. If it gives up, the transaction will be terminated. Otherwise, it will continue to determine whether the bids of the candidate cooperation vehicles are below the maximum price. If so, one or more vehicles with the lowest bids will be selected as the winning vehicles and step 8.3 will be executed. If all of them exceed the maximum price, the transaction will be terminated.

[0245] The purpose of setting up two-level maximum price limits is to adjust to the needs of requesting vehicles. It is hoped that vehicle transactions will be completed within the first-level price limit to protect the stability of vehicle transaction prices and control market fluctuations. Therefore, even if a transaction is successfully completed within the second-level price limit outside the first-level price limit, both parties will be punished (for example, the credibility of the requesting vehicle will be reduced; if the winning vehicle participates in auctions outside the first-level price limit too much within a certain period of time, it will affect its reward and punishment factor and reduce its remuneration);

[0246] Step 8.3: Screen the winning vehicles using the perfect reverse auction (PRA) mechanism;

[0247] Step 8.4: Use the random group reverse auction (RRA) mechanism to further screen the winning vehicles and obtain the final winning vehicle set;

[0248] Step 8.4.1: The roadside unit (RSU) randomly divides the winners into two groups (e.g., group T and group W), and each group is allocated half of the budget of the roadside unit (RSU);

[0249] Step 8.4.2: Apply the perfect reverse auction (PRA) mechanism to each group, but introduce a random factor in the auction process, and determine the winning vehicle of each group after the auction;

[0250] The random factors include randomly adjusting the bid range, randomly selecting some vehicles to participate, etc., to increase the flexibility and fairness of the auction;

[0251] Step 8.4.3: Merge the two groups of winning vehicles from the auction to form the final winning vehicle set;

[0252] Taking into account the influence of random factors, the set may include some or all of the preliminary winning vehicles, and may also include new vehicles added due to random factors.

[0253] Step 9: The roadside unit (RSU) sends a notification to the vehicles in the winner set. After the winning vehicle confirms its intention to cooperate, it is ready to start data downloading.

[0254] Step 10: The winning vehicle downloads the data from the roadside unit (RSU) and passes the data packet to the requesting vehicle (VR) via V2V communication.

Claims

1. A VANET cooperative download method based on multi-dimensional screening and encrypted reverse auction, characterized in that: The steps include: Step 1: Deploy roadside units on the vehicle route and deploy on-board units on each vehicle; Step 2: The roadside unit registers the vehicle and authenticates the vehicle, records the reputation value of the verified vehicle, and then the on-board unit samples the three network performance indicators of the verified vehicle n times within a period of time and reports them to the roadside unit, that is, each network performance indicator obtains n data values; Step 3: Preliminary screening of vehicles based on their reputation and network performance indicators to obtain candidate cooperative vehicles; Step 4: The requesting vehicle sends a download request to the roadside unit, the roadside unit broadcasts the download request, and the candidate cooperative vehicle generates a quotation after receiving the download request; Step 5: Encrypt the quote generated by the candidate cooperative vehicle, generate a blind signature and zero-knowledge proof, and submit them to the roadside unit; Step 6: The roadside unit unblinds the blind signature, and then verifies the validity of the unblinded signature and the zero-knowledge proof. Vehicles that fail to pass the verification are no longer considered as candidate cooperative vehicles; the unblinded signature includes the quotation; Step 7: Set the reserve price, soft ceiling price and ceiling price for the reverse auction; Step 8: The roadside unit screens candidate vehicles based on the reserve price, soft maximum price and maximum price of the reverse auction, and then uses a mechanism combining perfect reverse auction and random group reverse auction to screen candidate cooperative vehicles again to obtain the winning vehicle set; Step 9: The roadside unit sends a notification to the vehicles in the winner set. After the winning vehicle confirms its intention to cooperate, it is ready to start data downloading; Step 10: The winning vehicle downloads the data from the roadside unit and passes the data packet to the requesting vehicle via V2V communication.

2. The VANET cooperative downloading method based on multi-dimensional screening and encrypted reverse auction according to claim 1 is characterized in that: The three network performance indicators in step 2 include network delay D, packet loss rate L and occupied network throughput T.

3. The VANET cooperative download method based on multi-dimensional screening and encrypted reverse auction according to claim 2 is characterized in that: Step 3 specifically includes: Step 3.1: The roadside unit uses a reputation-based incentive mechanism to evaluate the reputation of vehicles and excludes vehicles with reputation values ​​below a threshold; Step 3.2: Further screen the vehicles based on the network performance indicators to obtain candidate cooperative vehicles.

4. The VANET cooperative downloading method based on multi-dimensional screening and encrypted reverse auction according to claim 3 is characterized in that: Step 3.2 specifically includes: Step 3.2.1: Determine the event type; Specifically, the maximum and minimum values ​​of the three network performance indicators, namely, network delay D, packet loss rate L, and occupied network throughput T, are determined respectively, and the number of divided value intervals is set. Based on the maximum and minimum values ​​of the three network performance indicators and the number of divided value intervals, the three network performance indicators are divided into a number of value intervals, each of which is an event type. Step 3.2.2: Define that when the data value of a certain network performance indicator of a vehicle is within the numerical range of a certain event type, it means that an event of the event type occurs to the vehicle; Step 3.2.3: Based on the data values ​​of the three network performance indicators of each vehicle obtained within a period of time t, calculate the number of events of each event type that occur in the vehicle; Step 3.2.4: Calculate the probability distribution of each event type for each vehicle; Step 3.2.5: According to the probability distribution of each event type, determine the probability of occurrence of the event corresponding to the data value of the network performance indicator of each vehicle within a period of time t; Step 3.2.6: Calculate the information amount I(l) of each event according to the occurrence probability of the event corresponding to the data value of the network performance indicator of each vehicle; Step 3.2.7: According to the information volume of all events of each vehicle, the information entropy of network delay D, packet loss rate L and occupied network throughput T are calculated respectively, and the total information entropy of the vehicle is obtained by summing them up; Step 3.2.8: Calculate the average value of network delay D, the average value of packet loss rate L, and the average value of occupied network throughput T for each vehicle; Step 3.2.9: Set a set of thresholds, compare and screen the total information entropy, average value of network delay D, average value of packet loss rate L and average value of occupied network throughput T of each vehicle with the set set of thresholds to obtain candidate cooperative vehicles; the set of thresholds includes the total information entropy threshold, network delay threshold, packet loss rate threshold and occupied network throughput threshold; The comparison and screening process is specifically as follows: determine whether the total information entropy of the vehicle, the average value of the network delay D, the average value of the packet loss rate L and the average value of the occupied network throughput T are lower than the corresponding thresholds. If so, the vehicle is qualified and the vehicle number is recorded as a candidate cooperative vehicle. If so, the vehicle is unqualified.

5. The VANET cooperative downloading method based on multi-dimensional screening and encrypted reverse auction according to claim 1 is characterized in that: Step 5 specifically includes: Step 5.1: The candidate cooperation vehicle Vi generates a private key private_key and a public key public_key, where i is the number of the candidate cooperation vehicle; Step 5.2: Blind the quote Pi according to the public key public_key to obtain the blinded information; Step 5.3: Sign the blinded information according to the private key private_key to generate a blind signature σi; Step 5.4: The candidate cooperative vehicle Vi generates a zero-knowledge proof; Specifically, vehicle Vi selects a random value r, calculates the challenge value ci and the response value s, and then uses the zero-knowledge proof protocol to generate a zero-knowledge proof Ti = (ci, s); Step 5.5: The candidate cooperative vehicle Vi submits the blind signature and zero-knowledge proof to the roadside unit.

6. The VANET cooperative download method based on multi-dimensional screening and encrypted reverse auction according to claim 1 is characterized in that: Step 6 specifically includes: Step 6.1: The roadside unit unblinds the blind signature of the candidate cooperative vehicle to obtain an unblinded signature; Step 6.2: Verify the unblinded signature of the candidate cooperation vehicle. If the verification is successful, proceed to step 6.

3. Otherwise, re-obtain the blind signature for verification. If the verification fails within the set number of times, the vehicle is no longer a candidate cooperation vehicle. Step 6.3: Verify the zero-knowledge proof of the selected cooperative vehicle. If the verification is successful, execute step 7. Otherwise, re-obtain the blind signature for verification. If it fails to pass the verification within the set number of times, it will no longer be considered as a candidate cooperative vehicle.

7. The VANET cooperative downloading method based on multi-dimensional screening and encrypted reverse auction according to claim 1 is characterized in that: Step 7 specifically includes: Step 7.1: The roadside unit estimates the reserve price of the reverse auction using a linear regression model; Step 7.2: Set a soft ceiling price and a ceiling price based on the reverse auction reserve price; The soft maximum price is: price soft_max =price min *(α*w1+b min *w2) Among them, price soft_max is the soft maximum price set. min is the reserve price of the reverse auction, α is the expected margin of the requested vehicle, b min is the minimum margin, w1 and w2 are the expected margin α and the minimum margin b of the requested vehicle respectively. min The weight value of The maximum price stated is: price limit_max =price min *(b max *w2+μ*w3) Among them, price limit_max is the maximum price set, b max is the maximum margin, μ is the supply and demand relationship in the market, and w3 is the weight value of the supply and demand relationship in the market.

8. The VANET cooperative downloading method based on multi-dimensional screening and encrypted reverse auction according to claim 7 is characterized in that: Step 7.1 specifically includes: Step 7.1.1: Collect N historical transaction data of the requested vehicle It includes the number of transactions q, transaction time ti, transaction conditions c, transaction floor price p and cost cost within a period of time; Step 7.1.2: Collect historical transaction data of the requested vehicle Calculate the information entropy of transaction quantity, transaction time and transaction conditions respectively; Step 7.1.3: Determine the weights of the information entropy of transaction quantity, the information entropy of transaction time, and the information entropy of transaction conditions, and calculate the total information entropy; entropy=w q *entropy_q+w ti *entropy_ti+w c *entropy_c Among them, w q ,w ti ,w c are the weights of the information entropy of transaction quantity, the information entropy of transaction time and the information entropy of transaction conditions, respectively. entropy_q is the information entropy of transaction quantity, entropy_ti is the information entropy of transaction time, and entropy_c is the information entropy of transaction conditions. Step 7.1.4: Standardize the transaction quantity, transaction time and transaction conditions in the historical transaction data to obtain the standardized transaction quantity, transaction time and transaction conditions; Step 7.1.5: Establish a linear regression model; Y=β0+β1X1+β2X2+……+β N X N +e Among them, X is the independent variable, β is the linear regression model parameter, i.e., the regression coefficient, ε is the random error term, and Y is the predicted value of the reserve price; Step 7.1.6: Construct a training sample set Each sample in the training sample set includes a standardized transaction quantity, a standardized transaction time, a standardized transaction condition, a total information entropy, a cost, and a transaction floor price p; Step 7.1.7: Use the training sample set to fit the constructed linear regression model to obtain a fitted linear regression model; Specifically: Use a linear regression model to fit the standardized transaction quantity, transaction time, transaction conditions, total information entropy, cost and transaction floor price p, and determine the estimated value of the best linear regression model parameters by minimizing the residual sum of squares; Step 7.1.8: Use the fitted linear regression model to estimate the reserve price and obtain the reserve price of the reverse auction.

9. The VANET cooperative download method based on multi-dimensional screening and encrypted reverse auction according to claim 1 is characterized in that: Step 8 specifically includes: Step 8.1: Determine whether there are candidate cooperation vehicles whose bids are between the reserve price and the soft maximum price. If yes, select one or more vehicles with the lowest bids as the winning vehicles and execute step 8.

3. Otherwise, execute step 8.

2. Step 8.2: When the bid of the candidate cooperation vehicle is above the soft maximum price, the requesting vehicle will receive a prompt whether to increase the price cap to continue the transaction. If it gives up, the transaction will be terminated. Otherwise, it will continue to determine whether the bids of the candidate cooperation vehicles are below the maximum price. If so, one or more vehicles with the lowest bids will be selected as the winning vehicles and step 8.3 will be executed. If all of them exceed the maximum price, the transaction will be terminated. Step 8.3: Use the perfect reverse auction mechanism to select the winning vehicles; Step 8.4: Use the random grouping reverse auction mechanism to further screen the winning vehicles and obtain the final set of winning vehicles.

10. The VANET cooperative download method based on multi-dimensional screening and encrypted reverse auction according to claim 9 is characterized in that: Step 8.4 specifically includes: Step 8.4.1: The roadside unit randomly divides the winners into two groups, with each group allocated half of the roadside unit’s budget; Step 8.4.2: Apply the perfect reverse auction mechanism to each group, introduce random factors into the auction process, and determine the winning vehicle of each group after the auction; Step 8.4.3: Combine the two sets of auction winning vehicles to form the final set of winning vehicles.

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