CN115175133A - Vehicle cooperative communication method based on fuzzy logic and signal game strategy - Google Patents
Vehicle cooperative communication method based on fuzzy logic and signal game strategy Download PDFInfo
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Abstract
A vehicle cooperative communication method based on fuzzy logic and signal game strategies belongs to the field of Internet of things. The method provides a vehicle cooperative communication method based on fuzzy logic and signal game strategies, obtains relatively real vehicle position and speed information through Kalman filtering, and then selects a relay node through the fuzzy logic to assist in requesting information message distribution. And returning the primary data information through the signal game when the vehicle receives the request every time, and if all information requests are not received for the first time, the information request vehicle sends a secondary information request according to the signal strength and the beliefs observed from the previous vehicle. Experiments show that the method can realize the vehicle cooperative communication function, solve the problems of low stability of the VANET link and broadcast storm, and has good supporting effect on the practicability of vehicle cooperation in the VANET.
Description
Technical Field
The invention belongs to the field of Internet of things, and particularly relates to a vehicle cooperative communication method based on a fuzzy logic and signal game strategy.
Background
With the development of 5G communication technology, internet of vehicles (IOV) has attracted attention in recent years as an important component of Internet of things and intelligent traffic systems. As people combine other technologies such as network communication technology and sensing technology, and as automobiles increase on roads and population increases, more and more vehicles are connected into a network space, and vehicle-mounted applications such as an Intelligent Transportation System (ITS) are rapidly emerging, and a large amount of data information needs to be downloaded to the vehicles. It is difficult for a cellular network to support all communications, which requires a vehicle-to-vehicle (V2V) communication cooperation by a vehicle ad-hoc network (VANET).
Due to the characteristics that the topological structure in the VANETs environment changes rapidly, the vehicle movement is limited by roads, the network is broken frequently, and the communication environment is various and complex, the method becomes various problems which must be faced in the design process of the VANETs content distribution protocol. Therefore, it is urgently needed to establish a fast and reliable VANETs content distribution protocol, and how to effectively discover an inter-vehicle content distribution protocol with high link stability, low propagation delay and avoiding Broadcast Storm Problems (BSP) as much as possible. In VANET, when a vehicle requests data information, the requested information cannot always be present within the communication range of the vehicle, and the selection of relay vehicles to transmit the request and return the information is an essential step in VANET.
Disclosure of Invention
The invention provides a vehicle cooperative communication method based on Fuzzy logic and signal game strategies, wherein a relay node is selected by using Fuzzy logic (Fuzzy logic), and due to the fact that a vehicle moves quickly, building shielding signals and vehicle behaviors are difficult to predict, the Fuzzy logic can be used for solving the inaccuracy problem, and compared with methods such as games and the like, the vehicle cooperative communication method has higher robustness and anti-interference performance and better effect under the traffic environment with higher uncertainty and unexpected emergency. Therefore, fuzzy logic is used to select relay vehicles in a more complex environment and analyze the vehicle communication mode. The signal game description is used to own the behavior of the vehicle after receiving the request. I.e. the first time the information transfer is required, the vehicle will describe the behaviour of the sending information vehicle and the requesting information vehicle in a signal game when receiving information requests in order to reduce the frequency of communication.
The invention discloses a vehicle cooperative communication method based on fuzzy logic and signal game strategies, which mainly comprises the following key steps:
1, constructing a system model:
1.1, establishing a network model;
1.2, establishing a communication model;
1.3, establishing a mobile model;
and 2, designing a vehicle cooperative communication method based on fuzzy logic and signal game strategies:
2.1, designing a relay vehicle selection method based on fuzzy logic;
and 2.2, designing an information transmission method based on the signal game.
Further, in step 1.1, a network model is established, and information m which can be requested is sent j Is divided into n parts, and each part of data is equal in size. When the vehicle v i Requires information m j Will broadcast the request information m j When the rest is in v i Vehicle search within communication range for presence of m j If v is i Possession of information m j The power and the information quantity of the transmitted signal are selected in a signaling game mode when v i After the information is received and returned, corresponding income is returned, the information is searched after 0.1s, and whether all content information is acquired is checked. And if not, carrying out secondary information request on the vehicle according to the signal intensity and the belief of the returned information.
If there is no m j Will return to not own m j And vehicle own information, wherein the vehicle information includes a speed s i Acceleration a i Direction of speed
Direction of acceleration
Vehicle buffer size c i Road number id r Lane number id l And current position information x i ,y i . V. when the request message is still not received back transmission at 0.1s after being sent i Will select the relay vehicle from the returned vehicle information, transmit the request m j If a relay vehicle acquires all the information, the data is returned to the upper layer of relay vehicle or the information requesting vehicle by a signaling game methodAnd (4) vehicles.
The method for establishing the communication model in the step 1.2 is as follows, in VANET, all vehicles are provided with vehicle-mounted units (OBUs) capable of V2V communication, adjacent vehicles can exchange information by utilizing a wireless communication technology, all vehicles have caching capacity, and c is used i Indicating vehicle v i Is available.
Since urban road environments are variable, a Free-space propagation Model (Free-space propagation Model) is used as a radio propagation Model of large-scale path loss, a Nakagami-m distribution is used as a description of channel attenuation, and the Free-space path loss (FSPL) is calculated as follows:
the received power under this model is:
P R for received signal power in a free space propagation model, P T To transmit signal power, G T For transmitting the gain of the transmitting antenna, G R λ is the wavelength, and d is the separation distance between the transmitting antenna and the receiving antenna, which is the gain of the receiving antenna. The probability density formula (pdf) of the Nakagami-m distribution is:
Γ (m (d)), the expression:
m (d) is an attenuation parameter, Ω is the transmit power, and m (d) is an independent variable function varying with d, i.e.
The cumulative distribution function (cdf) of the Nakagami-m distribution is:
wherein
Is the lower incomplete gamma function, which is defined as:
the method for building the movement model in step 1.3 is as follows, since the vehicle position information in the real environment is provided by the GPS, the vehicle speed is provided by the sensors, and the sensors have certain errors. The method has certain influence on the subsequent relay vehicle selection and the information return mode selection. Therefore, the Kalman filtering is introduced to correct the position and speed information of the vehicle. The Kalman filtering model assumes that the true state at time t evolves from t-1, as follows.
x t ＝F t x t-1 +B t u t +w t (8)
F t For the state transition model, u t To control the vector, B t For controlling the input model, w t Is process noise. The Kalman filter is a recursive estimator whose a priori state estimates
Sum covariance estimation P t|t-1 Respectively, are as follows,
Q k is the covariance of the process noise, and then the covariance S is updated t Of the formula
R t To observe the covariance of the noise, H t Updating Kalman gain K for the observation model t The formula is
Finally, the state estimation value of the posterior is obtained
And estimate the covariance P t|t 。
p t|t ＝(I-K t H t )P t|t-1 (14)
z t Is the observed value at the time t.
Further, the design method of the fuzzy logic-based relay vehicle selection method in step 2.1 is as follows, when the vehicle v is requested by data i Sending data request information according to the surrounding vehicle v j Returns v without relevant information j V vehicle information of i Can be according to v j Using fuzzy logic to derive vehicle weights. However, since there are a lot of parameters to define the fuzzy rule, the fuzzy rule is difficult to design and inaccuratePart of parameters of the screened relay vehicle are simplified into vehicle communication time T through a process of twice fuzzy logic com Line of sight (LOS) probability T of vehicle type And calculating the coverage rate R ij Will T com ，T type ，c j ，S max And as the input of the last fuzzy process, the vehicle weight is finally obtained through fuzzification, fuzzy reasoning and defuzzification. Finally v i And selecting the relay vehicle according to the vehicle weight and the position information.
1) Vehicle communication time
Firstly, the communication time T of the vehicle is given com Input required in the fuzzy process, v i And v j Relative distance dis ij Relative speed of movement s ij Relative acceleration a ij Relative direction of motion
Difference in lane type L ij . To reduce the number of fuzzy rules, L ij T acting after fuzzy inference com The language descriptions of the other inputs after the fuzzification are { very close, close, medium, far, very far }, { very slow, slow, medium, fast, very fast }, { loW, medium, large }, { proproach, vertical, aloof }, respectively. T is com The language of (1) is described as { very short, short, medium, long, very long }.
Calculating dis ij The formula of (1) is:
wherein x ki ，y ki Is v after Kalman filtering i Position information, x kj ，y kj Is v after Kalman filtering j Location information. V is to be i Direction of travel of
A new coordinate system is established for the positive direction of the x-axis,v i direction of travel in a new coordinate system
The calculation method is as follows
s ij Is calculated by
s kj ，s ki Are each v i ，v j The speed of travel is kalman filtered. a is ij Is calculated by
dis ij Is close, s ij Is very slow, a ij Is a low or medium molecular weight compound,
is an arbitrary value of T com Is very long. L is ij Acting on the value after fuzzy inference, L ij Is calculated as follows
L ij ＝|l i -l j | (20)
l i ，l j V is composed of j ，v j Maximum driving speed of current lane
Thus obtaining the product.
If T is found in a fuzzy rule com Is very long, e.g. L ij =1 then will T com Decrease to long, L ij =2 then T com The membership degree is unchanged when the number is reduced to short. A gravity center method (centroid) is used for selection of the defuzzification method, and the defuzzification is carried out to obtain T com Membership functions of (a).
2) Vehicle line-of-sight transmission probability
Since the vehicle travels in a city, the communication mode may be Line of Sight (LOS) or Non-Line of Sight (NLOS). The non-line-of-sight transmission has a large influence on the communication quality and the maximum communication range, so that a vehicle with a transmission mode of LOS should be selected as much as possible when the relay node is selected.
V is predicted by several variables i And v j Possibility of communication being LOS T type ，v i And v j Relative distance dis ij Relative direction of motion
Road name matching case N ij . The language descriptions after fuzzification are { very close, close, medium, far, very far }, { parallel, oblique, vertical } respectively，{match，not match}。dis ij The calculation method of (a) is the same as the formula 15,
is calculated in the same manner as formula 19, N ij Is calculated in a manner that
dis ij 、N ij A trapezoidal membership function is used.
A gaussian type membership function is used. Same IF/Then rule is used to obtain T type 。
After the process of defuzzification by the gravity center method, T type The obtainable membership functions of (a).
3) Coverage rate
From free space propagation model and minimum received signal strength P of vehicle Rmin The maximum communication distance r can be obtained max ，P Rmin Meaning that the vehicle will not receive the information when the signal strength of the information received by the vehicle is less than the current value.
Current maximum coverage S ij Is composed of
To facilitate the membership function setting, S ij And the theoretical maximum coverage S max The ratio of the ratio gives the coverage rate R ij 。
R ij The membership function of (2) is a trapezoidal membership function, and the language after fuzzification is described as { low, medium, high }.
4) Available memory rate
Amount of available memory c in vehicle j And maximum available amount c max As the ratio of the available memory rate R c . Its obfuscated language is described as low, medium, high.
5) Vehicle weight
In the formation of R c ，R ij ，T type ，T com Later using IF/Then rule to get vehicle v j Vehicle weight V ij 。
After the process of defuzzification by the gravity center method, V is obtained j Membership functions of (a).
V obtained from defuzzification j And v j Selecting at most four relay nodes according to the position information before the selection starts i The communication range will be divided into 8 regions according to the angle, see fig. 10. The vehicles in each subarea are sorted from large to small according to the vehicle weight, and the vehicle v with the maximum vehicle weight in the communication range is selected firstly k As the first relay node, then according to v k The vehicle of the region is selected and separated by one region to select the next relay node v n And requires v k And v n The interval is not less than 100m, and if no qualified node in the current area sends no relay node request message.
The method of the design of the information transmission method based on the signal game in step 2.2 is as follows, in the game theory, the signaling game is a simple type of dynamic bayesian game, which is characterized by allowing for the first time the solution to be specified for games with incomplete information in the game theory and which requires that the behaviour of the game participants can be observed.
Bayesian extension game with observable actionsIs a tuple<Γ，(Θ i )，(p i )，(μ i )>. Wherein Γ =<N，H，P，>To allow for simultaneous actions in a perfect information dynamic game. The meaning of simultaneous action here is that information is transmitted back to the requesting vehicle at the same time, and no action of another vehicle is obtained during the action. N is the set of participants, N = {1,2}. H is a history set, and the history set is,
wherein C is i (i =0.. N), i number data is selected for the current vehicle. P = H \ Z → N, Z is the final history,
P(A)＝1，P(B)＝1，P(A，C 0 )＝2...P(A，C n )＝2，P(B，C 0 )＝2...P(B，C n )＝2。Θ i is v is i Type space of (1), here the data number owned by the vehicle, p i Is defined as theta i Prior probability distribution of (u) i Is a utility function.
The invention has the advantages and positive effects that:
in the VANET, aiming at the characteristics that the topological structure is changed rapidly, the vehicle movement is limited by roads, the network is broken frequently, the communication environment is various and complex and the like, the invention provides a vehicle-to-vehicle cooperation method based on fuzzy logic and signal game. Firstly, in the communication range of a data request vehicle, the vehicle with the information does not exist, and the two-layer fuzzy logic is used for selecting the relay node to cooperatively distribute the data request. Secondly, when the vehicle having the request data in the request information receives the information, the communication mode is selected according to the distance between the vehicle and the data request vehicle, and then the data sending amount is selected according to the self benefit expectation, risk, benefit and self contribution. The experimental result shows that the method provided by the invention has excellent performance in the aspects of invalid message quantity, success rate and average communication energy consumption.
Drawings
FIG. 1 is a diagram of a constructed network model;
FIG. 2 is a flow chart for determining a weight of a vehicle using fuzzy logic;
FIG. 3 is a graph of the variation of the meaningless message volumes generated by each task in a simulation road network;
FIG. 4 is a graph of the variation of the success rate of tasks in the simulated road network;
FIG. 5 is a graph of variation of average communication energy consumption in a simulated road network;
FIG. 6 is a graph of variation of the number of unexpected communication outages in a simulated road network;
FIG. 7 is a graph of the variation of meaningless message volumes generated by each task in a real road network on average;
FIG. 8 is a graph of the variation of task success rate in real road network;
FIG. 9 is a graph of variation of average communication energy consumption in a real road network;
FIG. 10 is a graph of the variation of the number of unexpected interruptions of real-world network traffic;
fig. 11 is a flow chart of the vehicle cooperative communication method based on the fuzzy logic and signal gaming strategy of the present invention.
Detailed Description
Example 1:
referring to fig. 11, the vehicle cooperative communication method based on the fuzzy logic and signal gaming strategy of the present embodiment mainly includes the following key steps:
1, constructing a system model:
1.1, establishing a network model;
1.2, establishing a communication model;
1.3, establishing a mobile model;
and 2, designing a vehicle cooperative communication method based on fuzzy logic and signal game strategies:
2.1, designing a relay vehicle selection method based on fuzzy logic;
and 2.2, designing an information transmission method based on the signal game.
The invention step 1.1 in the establishment of the network model, figure 1 is a diagram of the network model constructed, as shown in the figure, will request possible information m j Is divided into n parts, and each part has equal size. When the vehicle v i Requires information m j Will broadcast the request information m j When the rest is in v i Vehicle search within communication range for presence of m j If v is i Possession of information m j The power of the transmitted signal and the information quantity are selected in a signaling game mode when v is i After the information is received and returned, corresponding income is returned, the information is searched after 0.1s, and whether all content information is acquired is checked. And if not, carrying out secondary information request on the vehicle according to the signal intensity and the belief of the returned information.
If there is no m j Will return that it does not own m j And vehicle own information, wherein the vehicle information includes a speed s i Acceleration a i Direction of speed
Direction of acceleration
Vehicle buffer size c i Road number id r Lane number id l And current position information x i ，y i . V. when the request message is still not received back transmission at 0.1s after being sent i Will select the relay vehicle from the returned vehicle information, transmit the request m j If a relay vehicle acquires all the information, the data is returned to the upper layer of relay vehicles or the vehicles requesting the information by a signaling game method.
The method for establishing the communication model in the step 1.2 is as follows, in VANET, all vehicles are provided with vehicle-mounted units (OBUs) capable of V2V communication, adjacent vehicles can exchange information by utilizing a wireless communication technology, all vehicles have caching capacity, and c is used i Indicating a vehicle v i The available buffer size of.
Since urban road environments are variable, a Free-space propagation Model (Free-space propagation Model) is used as a radio propagation Model of large-scale path loss, a Nakagami-m distribution is used as a description of channel attenuation, and the Free-space path loss (FSPL) is calculated as follows:
the received power under this model is:
P R for received signal power in a free space propagation model, P T To transmit signal power, G T For transmitting the gain of the transmitting antenna, G R λ is the wavelength, and d is the separation distance between the transmitting antenna and the receiving antenna, which is the gain of the receiving antenna. The probability density formula (pdf) of the Nakagami-m distribution is:
Γ (m (d)), the expression:
m (d) is an attenuation parameter, Ω is the transmit power, and m (d) is an independent variable function varying with d, i.e.
The cumulative distribution function (cdf) of the Nakagami-m distribution is:
wherein
Is the lower incomplete gamma function, which is defined as:
the method for building the movement model in step 1.3 is as follows, since the vehicle position information in the real environment is provided by the GPS, the vehicle speed is provided by the sensors, and the sensors have certain errors. The method has certain influence on the subsequent relay vehicle selection and the information return mode selection. Therefore, the Kalman filtering is introduced to correct the position and speed information of the vehicle. The Kalman filtering model assumes that the true state at time t evolves from t-1, as follows.
x t ＝F t x t-1 +B t u t +w t (8)
F t For the state transition model, u t To control the vector, B t For controlling the input model, w t Is process noise. The Kalman filter is a recursive estimator whose a priori state estimates
Sum covariance estimation P t|t-1 Respectively, are as follows,
Q k is the covariance of the process noise, and then the covariance S is updated t Of the formula
R t To observe the covariance of the noise, H t Updating Kalman gain K for the observation model t The formula is
Finally, the state estimation value of the posterior is obtained
And estimating the covariance P t|t 。
P t|t ＝(I-K t H t )P t|t-1 (14)
z t Is the observed value at time t.
Further, the design method of the fuzzy logic based relay vehicle selection method in step 2.1 is as follows, and fig. 2 is a flow chart for determining the weight of the vehicle using the fuzzy logic, as shown in the figure, when the vehicle v is requested by data i Sending data request information according to the surrounding vehicle v j Returns v without relevant information j V vehicle information of i Can be according to v j The vehicle weight is derived using fuzzy logic. However, when the fuzzy rule is defined by having a large number of parameters, the fuzzy rule willThe design is difficult and inaccurate, so that part of parameters for screening the relay vehicles are simplified into the vehicle communication time T through the process of twice fuzzy logic com Line of sight (LOS) probability T type And calculating the coverage rate R ij Will T com ，T type ，c j ，S max And as the input of the last fuzzy process, the vehicle weight is finally obtained through fuzzification, fuzzy reasoning and defuzzification. Finally v i And selecting the relay vehicle according to the vehicle weight and the position information.
1) Vehicle communication time
Firstly, the communication time T of the vehicle is given com Input required in the fuzzy process, v i And v j Relative distance dis ij Relative speed of movement s ij Relative acceleration a ij Relative direction of motion
Difference in lane type L ij . To reduce the number of fuzzy rules, L ij T acting after fuzzy inference com The language description after the other inputs are fuzzified is { very close, close, medium, far, very far }, { very slow, slow, medium, fast, very fast }, { low, medium, large }, { aproach, vertical, aloof }, respectively. T is com The language of (1) is described as { very short, short, medium, long, very long }.
Calculating dis ij The formula of (1) is:
wherein x ki ，y ki Is v after Kalman filtering i Position information, x kj ，y kj Is v after Kalman filtering j Location information. V is to be i Direction of travel of
Establishing a new coordinate system, v, for the positive direction of the x-axis i Direction of travel in a new coordinate system
The calculation method is as follows
s ij Is calculated by
s kj ，s ki Are each v i ，v j The speed of travel is kalman filtered. a is a ij Is calculated by
dis ij Is close, s ij Is very slow, a ij Is a low or medium molecular weight compound of,
is an arbitrary value of T com Is very long. L is ij Acting on the value after fuzzy inference, L ij Is calculated as follows
L ij ＝|l i -l j | (20)
l i ，l j By v j ，v j Maximum driving speed of current lane
Thus obtaining the product.
If T is found in a fuzzy rule com Is very long, such as L ij =1 then will T com Decrease to long, L ij =2 then T com The membership degree is unchanged when the number is reduced to short. A gravity center method (centroid) is used for selection of the defuzzification method, and the defuzzification is carried out to obtain T com Membership functions of (a).
2) Vehicle line-of-sight transmission probability
Since the vehicle travels in a city, the communication mode may be Line of Sight (LOS) or Non-Line of Sight (NLOS). The non-line-of-sight transmission has a large influence on the communication quality and the maximum communication range, so that a vehicle with a transmission mode of LOS should be selected as much as possible when the relay node is selected.
V is predicted by several variables i And v j Possibility of communication being LOS T type ，v i And v j Relative distance dis ij Relative direction of motion
Road name matching case N ij . The language descriptions after fuzzification are respectively { very close, close, medium, far, very far },{parallel，oblique，vertical}，{match，not match}。dis ij the calculation method of (a) is the same as the formula 15,
is calculated in the same manner as formula 19, N ij Is calculated in a manner that
dis ij 、N ij A trapezoidal membership function is used.
A gaussian type membership function is used. The same IF/Then rule is used to obtain T type 。
After the process of defuzzification by the gravity center method, T type The obtainable membership functions of (a).
3) Coverage rate
From free space propagation model and minimum received signal strength P of vehicle Rmin The maximum communication distance r can be obtained max ，P Rmin Meaning that the vehicle will not receive the information when the signal strength of the information received by the vehicle is less than the current value.
Current maximum coverage S ij Is composed of
To facilitate the membership function setting, S ij And the theoretical maximum coverage S max The ratio of the ratio gives the coverage rate R ij 。
R ij The membership function of (2) is a trapezoidal membership function, and the language after fuzzification is described as { low, medium, high }.
4) Available memory rate
Amount of available memory c in vehicle j And maximum available amount c max As the ratio of the available memory rate R c . Its obfuscated language is described as low, medium, high.
5) Vehicle weight
In the formation of R c ，R ij ，T type ，T com Later using IF/Then rule to get vehicle v j Vehicle weight V ij 。
After the process of defuzzification by the gravity center method, V is obtained j Membership functions of (a).
V obtained from defuzzification j And v j The position information of (a) selects a maximum of four relay nodes before the selection starts v i The communication range will be divided into 8 regions according to the angle, see fig. 10. The vehicles in each subarea are sorted according to the vehicle weight from large to small, and the vehicle v with the maximum vehicle weight in the communication range is selected firstly k As the first relay node, then according to v k The vehicle of the region is selected to select the next relay node v n And requires v k And v n The interval is not less than 100m, and if no qualified node in the current area sends no relay node request message.
The method of the design of the information transmission method based on the signal game in step 2.2 is as follows, in the game theory, the signaling game is a simple type of dynamic bayesian game, which is characterized by allowing for the first time the solution to be specified for games with incomplete information in the game theory and which requires that the behaviour of the game participants can be observed.
Has the advantages of good effectThe Bayes spreading game of the action is a tuple<Γ，(Θ i )，(p i )，(μ i )>. Wherein Γ =<N，H，P，>To allow for simultaneous actions in a perfect information dynamic game. The meaning of the simultaneous action is that information is transmitted back to the requesting vehicle at the same time, and no action is obtained from other vehicles during the action. N is the set of participants, N = {1,2}. H is a history set, and the history set is,
wherein C i (i =0.. N), i number data is selected for the current vehicle. P = H \ Z → N, Z is the final history,
P(A)＝1，P(B)＝1，P(A，C 0 )＝2...P(A，C n )＝2，P(B，C 0 )＝2...P(B，C n )＝2。Θ i is v is i Type space of (1), here the data number owned by the vehicle, p i Is defined as theta i Prior probability distribution of (u) i Is a utility function.
In this experiment, a road network of manhattan grids was used, and vehicles could use LOS communication without buildings in the grids. The number of vehicles is N, and the possibility that the data required for the task is present in the vehicle is P.
TABLE 1 corresponding parameters
| N | -130dBm |
| P Rmin | --85dBm |
| P T | [1020]mW |
| N | [100200] |
| P | [15％40％] |
The simulation will consider four performance indicators, which are:
1. number of invalid messages. An invalid packet means that the packet does not affect data forwarding or auxiliary data forwarding. Fig. 3 is a graph showing the variation of meaningless message volumes generated by each task in a simulation road network, as shown in the figure, the number of invalid messages generated by CFT is large, and is mainly caused by the behavior of cluster members. And the generated invalid messages will increase as the vehicle density increases. In the proposed method FLSGCM, the number of invalid messages is mainly in the message retransmission, and the data messages that may be sent by the nearby vehicle are already owned by the requesting vehicle or the relay vehicle. Also, the number of invalid messages in the CA is mainly in the message return. Before the simulation starts, no vehicle exists in the road network, the vehicle will continuously enter the road network after the simulation starts, and then leave the current road network after the vehicle runs through the preset path, so the vehicle density can change along with the time. When N is increased, it can be seen that the invalid packet volume of the CFT is significantly increased, and there is no significant increase in FLSGCM and AC, mainly because the member in the CFT sends a request for joining a cluster to an adjacent node, and the cluster head hardly receives the request under the condition of sending at normal power, if the distance between two vehicles is calculated and then sent according to the distance, a certain invalid packet volume will be reduced, but its energy consumption will be increased.
2. And (5) the success rate of the task. Assuming that the probability of other vehicles having requested data is P, fig. 4 is a variation graph of the task success rate in the simulation road network, as shown in the figure, since only a few vehicles within the vehicle communication range can communicate in the early stage, the probability of having requested data is low, the task success rate is low, and then even if some vehicles leave the road network, since a part of the requested data is reserved in the previous communication, the success rate in the later stage of the simulation is high and the simulation starts. Since the AC assists in establishing clusters for RSUs, its information coverage is better than CFT. In the FLSGCM, since the algorithm selects the relay node to forward the request dynamically, it has a good effect even when there are few vehicles. Considering that the CFT neighbor node acts to improve a part of the task success rate when communication with the cluster head node is certainly completed.
3. Average communication energy consumption. In the average energy consumption, only the process of completing data acquisition and forwarding is considered, and the incomplete process does not count the calculation range. FIG. 5 is a graph showing the variation of average communication energy consumption in a simulated road network, wherein RSUs are arranged in an AC for assisting in building a cluster, the communication energy consumption is large when a vehicle is far away from the RSUs, and the energy consumption of the RSUs for transmitting data to the vehicle is not considered in the average power. The initial energy consumption is higher in the AC, mainly due to the fact that the vehicle is to communicate with the RSU, and the amount of duplicate data is smaller in the content distribution than in the CFT. The main energy consumption in the CFT is the energy consumption of the vehicle for retransmitting data, and since the energy consumption is the information of adding a data position in a request, the return information of a plurality of vehicles is always the same, so that the possibility of repeated distribution is improved, especially the energy consumption is greatly increased when the vehicle requests are more, but the reason that the energy consumption is lower when the vehicles in the previous period are less is that tasks completed when the vehicles are less, and most of the tasks can finish data transmission before leaving a cluster. In the FLSGCM, a large number of relay nodes are selected for searching information when there are few vehicles, which results in high energy consumption, and certain energy consumption is reduced after the vehicles are dense.
4. Number of unexpected interruptions of communication. Fig. 6 is a graph showing the variation of the number of unexpected communication interruptions in a simulated road network. Since there is no obstruction in the manhattan network, all vehicles use SOL communication, the communication range is wide, the communication interruption is only possible when the vehicle is driven out of the communication range, and when the vehicle and data existence possibility ratio is compared, it can be seen that CFT is cluster communication, the more the vehicles are, the more the CFT is disconnected, and when the vehicles need less data information, the unexpected interruption of communication often means that the data required by the task may not be received before the task is cut off. Unexpected communication interruptions in FZSGCM occur mainly when the broadcast seeks data, but the task is not much affected by the unexpected communication interruptions since secondary communication is possible.
In this experiment, 5km of a real map was selected 2 In the region, the number of vehicles is N, the normal mode transmission power is 10mw, the reinforced mode transmission power is 20mw, the background noise is-130 dBm, the minimum received signal strength recognizable by the vehicles is-85 dBm, the maximum speed of the vehicles is given by the maximum speed of the road where the vehicles are located, and the lane change rule used in SUMO is LC2013.
The results of the simulation experiments for this example are as follows:
1. number of invalid messages
FIG. 7 is a graph of the average meaningless message volume per task in a real road network. Because of the obstruction of buildings or other objects in the real road network, in the CFT, since the signal strength is weakened when the vehicle enters other regions with shelters, the information return and cluster joining requests are often reduced to invalid information, and vehicle energy is wasted. The cluster building in the AC is mainly assisted by the RSU, and the invalid message is mostly information sent after the vehicle is out of the communication range.
2. Task success rate
Fig. 8 is a graph showing the variation of the task success rate in the real road network, as shown in the figure, since only a few vehicles can communicate in the communicable range of the vehicle in the early stage, the probability of having the requested data is low, the task success rate is low, and then even if some vehicles leave the road network, since a part of the requested data is reserved in the previous communication, the success rate in the later stage of the simulation is high and the simulation starts. Since the AC assists in establishing clusters for RSUs, its information coverage is better than CFT. In the FLSGCM, since the algorithm will select the relay node to forward the request dynamically, it will also work well with fewer vehicles and lower likelihood of data being present.
3. Average communication energy consumption
Fig. 9 is a graph showing the variation of the average communication energy consumption in the real road network. With the RSU assisting in building the cluster in the AC, the vehicle is far away from the RSU and there is a strong possibility of hindering the energy consumption of the NLOS communication only, the energy consumption of the RSU transmitting data to the vehicle is not taken into account in the average power. The initial energy consumption is higher in the AC, mainly due to the fact that the vehicle is to communicate with the RSU, and the amount of duplicate data is smaller in the content distribution than in the CFT. The main energy consumption in the CFT is the energy consumption of the vehicle for retransmitting data, and since the energy consumption is the information of adding a data position in a request, the return information of a plurality of vehicles is always the same, so that the possibility of repeated distribution is improved, especially the energy consumption is greatly increased when the vehicle requests are more, but the energy consumption is lower when the vehicles in the previous period are less because most tasks completed when the vehicles are less can finish the data transmission before leaving the cluster. In the FLSGCM, a large number of relay nodes are selected for searching information when there are few vehicles, which results in high energy consumption, and certain energy consumption is reduced after the vehicles are dense.
4. Number of unexpected communication interruptions
Fig. 10 is a graph showing a variation of the number of unexpected interruptions of real network communication. In the case of obstructions, both CFT and AC have a high probability that communication with the cluster head or the desired vehicle cannot be completed due to insufficient signal strength after a change in vehicle position occurs at the time of task-required data transfer. Even if a part of data cannot be transmitted in the FLSGCM, the data can be requested again, and the influence on the task completion time is small.
Claims (6)
1. A vehicle cooperative communication method based on fuzzy logic and signal game strategy is characterized in that the method mainly comprises the following steps:
1, constructing a system model:
1.1, establishing a network model;
1.2, establishing a communication model;
1.3, establishing a mobile model;
and 2, designing a vehicle cooperative communication method based on fuzzy logic and signal game strategies:
2.1, designing a relay vehicle selection method based on fuzzy logic;
and 2.2, designing an information transmission method based on the signal game.
2. The method for cooperative vehicle communication based on fuzzy logic and signal gaming strategy as claimed in claim 1 wherein step 1.1 is establishing a network model, information m that will be possibly requested j Dividing into n parts, and each part has equal size when the vehicle v is i Requires information m j Will broadcast the request information m j When the rest are in v i Vehicle search within communication range for presence of m j If v is i Possession of information m j The power and the information quantity of the transmitted signal are selected in a signaling game mode when v i After the information is received and returned, returning corresponding income, searching the information after 0.1s, checking whether all content information is acquired, and if not, performing secondary information request on the vehicle according to the signal strength and the belief of the returned information;
if there is no m j Will return to not own m j And vehicle own information, wherein the vehicle information includes a speed s i Acceleration a i Direction of speed
Direction of acceleration
Vehicle buffer size c i Road number id r Lane number id l And current position information x i ,y i V, when the request message is still not received at 0.1s after being sent, v i The relay vehicle will be selected from the returned vehicle informationPassing request m j If a relay vehicle acquires all the information, the data is returned to the upper layer of relay vehicles or the information requesting vehicles by a signaling game method.
3. The vehicle cooperative communication method based on the fuzzy logic and signal game strategy as claimed in claim 1, wherein the method for establishing the communication model in step 1.2 is characterized in that in VANET, vehicles are all provided with vehicle-mounted units capable of V2V communication, adjacent vehicles are subjected to information interaction by utilizing wireless communication technology, and all vehicles have buffer capacity and use c i Indicating vehicle v i Available cache size;
the Free space propagation Model is used as a radio propagation Model of large-scale path loss, nakagami-m distribution is used as description of channel attenuation, and the Free space path loss calculation method is as follows:
the received power under this model is:
P R for received signal power in a free space propagation model, P T To transmit signal power, G T For transmitting the gain of the transmitting antenna, G R In order to obtain the gain of the receiving antenna, λ is the wavelength, d is the spacing distance between the transmitting antenna and the receiving antenna, and the probability density formula of the Nakagami-m distribution is as follows:
Γ (m (d)), the expression:
m (d) is an attenuation parameter, Ω is the transmit power, and m (d) is an independent variable function varying with d, i.e.
The cumulative distribution function for the Nakagami-m distribution is:
wherein
Is the lower incomplete gamma function, which is defined as:
4. the method for vehicle cooperative communication based on fuzzy logic and signal gaming strategy of claim 1 wherein the method for building the motion model in step 1.3 is characterized by introducing kalman filter to modify the position and velocity information of the vehicle, the kalman filter model assuming that the true state of time t is derived from t-1 as follows
x t ＝F t x t-1 +B t u t +w t (8)
F t For the state transition model, u t To control the vector, B t For controlling the input model, w t For process noise, the Kalman filter is a recursive estimator with a priori state estimation
Sum covariance estimation P t|t-1 Respectively, are as follows,
Q k is the covariance of the process noise, and then the covariance S is updated t Of the formula
R t To observe the covariance of the noise, H t Updating Kalman gain K for the observation model t The formula is
Finally, the state estimation value of the posterior is obtained
And estimate the covariance P t|t ，
P t|t ＝(I-K t H t )P t|t-1 (14)
z t Is the observed value at time t.
5. The vehicle based on the fuzzy logic and signal gaming strategy of claim 1The cooperative communication method is characterized in that the relay vehicle selection method based on fuzzy logic in the step 2.1 is designed as follows, and when a vehicle v is requested by data i Sending data request information according to the surrounding vehicle v j Returns v without relevant information j V vehicle information of i According to v j The vehicle information uses fuzzy logic to obtain the vehicle weight, but when a large number of parameters are used for defining the fuzzy rule, the fuzzy rule is difficult to design and inaccurate, so that part of parameters for screening the relay vehicle are simplified into the vehicle communication time T through two fuzzy logic processes com Vehicle line-of-sight transmission probability T type And calculating the coverage rate R ij Will T com ,T type ,c j ,S max As the input of the last fuzzy process, the vehicle weight is finally obtained through fuzzification, fuzzy reasoning and defuzzification, and finally v i Selecting a relay vehicle according to the vehicle weight and the position information;
1) Vehicle communication time
Firstly, the communication time T of the vehicle is given com Input required in the fuzzy process, v i And v j Relative distance dis ij Relative speed of movement s ij Relative acceleration a ij Relative direction of motion
Difference of lane type L ij To reduce the number of fuzzy rules, L is added ij T acting after fuzzy inference com The language description after the other inputs are fuzzified is { very close, close, medium, far, very far }, { very slow, slow, medium, fast, very fast }, { low, medium, large }, { aproach, vertical, aloof }, T, and T com The language of (1) is described as { very short, short, medium, long, very long },
calculating dis ij The formula of (1) is:
wherein x is ki ,y ki Is v after Kalman filtering i Position information, x kj ,y kj Is v after Kalman filtering j Position information, v i Direction of travel of
Establishing a new coordinate system v for the positive direction of the x-axis i Direction of travel in a new coordinate system
The calculation method is as follows
s ij Is calculated by
s kj ,s ki Are each v i ，v j Speed of travel after Kalman filtering, a ij Is calculated by
L ij ＝|l i -l j | (20)
l i ,l j V is composed of i ,v j Maximum driving speed of current lane
So as to obtain the compound with the characteristics of,
if T is found in a fuzzy rule com Is very long, such as L ij =1 then will T com Decrease to long, L ij =2 then will T com Reducing the value to short, keeping the membership degree unchanged, and performing defuzzification on the short by using a centroid method in the selection of the defuzzification method to obtain T com A membership function of;
2) Vehicle line-of-sight transmission probability
Since the vehicles are running in the city, the communication mode may be line-of-sight transmission or non-line-of-sight transmission, and the non-line-of-sight transmission has a great influence on the communication quality and the maximum communication range, the vehicles with the transmission mode of LOS should be selected as much as possible when the relay node is selected,
predicting v by several variables i And v j Possibility of communication being LOS T type ，v i And v j Relative distance dis ij Relative direction of motion
Road name matching case N ij The language description after the fuzzification is { very close, close, medium, far, very far }, { parallel, object, vertical }, { match, not match }, dis ij The calculation method of (2) is the same as that of the formula (15),
the calculation method of (2) is the same as formula (19), N ij Is calculated in a manner that
dis ij 、N ij Adopting a trapezoidal membership function,
obtaining T by using Gaussian type membership function and IF/Then rule type ，
After the process of defuzzification by the gravity center method, T type The obtainable membership functions of;
3) Coverage rate
From free space propagation model and minimum received signal strength P of vehicle Rmin Obtaining the maximum communication distance r max ，P Rmin Means that the vehicle will not receive the information when the signal strength of the information received by the vehicle is less than the current value,
current maximum coverage S ij Is composed of
To facilitate the setting of membership functions, S is ij And the theoretical maximum coverage S max The ratio of the ratio gives the coverage rate R ij ，
R ij The membership function of (2) is a trapezoidal membership function, and the language after fuzzification is described as { low, medium, high };
4) Available memory rate
Amount of available memory c in vehicle j And maximum available amount c max As the ratio of the available memory rate R c The obfuscated language is described as low, medium, high,
5) Vehicle weight
In the formation of R c 、R ij 、T type 、T com Later using IF/Then rule to get vehicle v j Vehicle weight V ij ，
After the process of defuzzification by the gravity center method, V is obtained j The membership function of (a) is selected,
v obtained from defuzzification j And v j The position information of (a) selects a maximum of four relay nodes before the selection starts v i Dividing the communication range into 8 areas according to angles, sorting the vehicles in each subarea from large to small according to the vehicle weights, and firstly selecting the vehicle v with the maximum vehicle weight in the communication range k As the first relay node, then according to v k The vehicle of the region is selected and separated by one region to select the next relay node v n And require v k And v n The interval is not less than 100m, and if no qualified node in the current area sends no relay node request message.
6. The method for vehicle cooperative communication based on fuzzy logic and signal gaming strategies as claimed in claim 1, characterized in that the method for the design of the information transmission method based on signal gaming in step 2.2 is as follows, in the gaming theory, the signaling gaming is a simple type of dynamic bayesian gaming, which is characterized by allowing for the first time the solution to be specified for the gaming with incomplete information in the gaming theory and which requires the behaviour of the gaming participants to be observable;
the Bayesian spread game with observable actions is a tuple<Γ,(Θ i ),(p i ),(μ i )>Wherein Γ =<N,H,P,>In order to allow for a game of simultaneous actions in the Perfect information dynamic game, where simultaneous actions are in the sense that information is transmitted back to the requesting vehicle at the same time and no other vehicle's actions are obtained while acting, N is the set of participants, N = {1,2}, H is the history set,
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