CN111601278A - Software-defined heterogeneous Internet of vehicles access management and optimization method - Google Patents
Software-defined heterogeneous Internet of vehicles access management and optimization method Download PDFInfo
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Abstract
The invention discloses a software-defined heterogeneous Internet of vehicles access management and optimization method, which comprises the following steps: step 1: designing a vehicle communication network architecture based on an SDN (software defined network), and realizing the global management of a vehicle network; step 2: a local database is established on the data plane, vehicle information is collected, and a management strategy issued by the control plane is stored; and step 3: uploading the collected vehicle data to a control plane, and calculating the performance index of the vehicle network on the control plane; and 4, step 4: and determining the weight and the benefit function of each performance index, finally determining the selection of the access network mode by an evolutionary game method, and issuing the selection to the vehicle of the data plane. The software-defined heterogeneous Internet of vehicles access management and optimization method flexibly selects the vehicle access mode based on different service requirements, optimizes the network service quality of the vehicle heterogeneous network according to the requirements of the required service, greatly improves the network performance and improves the service quality of the vehicle communication network.
Description
Technical Field
The invention belongs to the technical field of vehicle networking, and relates to a heterogeneous vehicle networking access management control and access network optimization method based on an SDN (software defined network).
Background
As a key technology in Intelligent Transportation Systems (ITS), vehicular ad hoc networks (VANETs) may connect vehicles in roads and urban areas and provide wireless communication between vehicles, and between vehicles and roadside infrastructure. The VANETs can be applied to advanced applications by utilizing VANETs communication technology and vehicle sensing capability, for example, the VANETs can support various vehicle services, such as vehicle information exchange (such as speed, acceleration and position), safety services (including cooperative collision warning, intersection collision avoidance and remote vehicle diagnosis), entertainment services (such as online video, online games, online uploading and downloading) and the like. Moreover, VANETs enhance the cooperation between vehicles, pedestrians and traffic infrastructure, which will help eliminate 80% of current road traffic accidents and help promote a more intelligent, safer ground traffic system for the automotive and telecommunications industries.
Due to the wide existence and good infrastructure of cellular base stations, the conventional vehicle communication method is realized by the forwarding of the cellular base stations. Cellular access technologies, such as LTE (Long Term Evolution), can provide reliable and wide internet access for vehicle communication, and the coverage area of a base station is larger than that of other communication methods, so that the communication distance of a vehicle is longer. As technology matures, many autonomous automotive manufacturers (e.g., GM-OnStar and BMW-connected drive) choose cellular access technology as the automotive Internet access solution. However, the cellular access technology also has certain problems, above all overload problems. According to the studies published by Cisco, the total number of mobile links increased from 86 hundred million in 2017 to 123 hundred million by 2022. At the same time, mobile data traffic will proliferate to 77 extents per month. And by 2022 the average traffic per mobile user (all equipment and service plans) is expected to increase from 2.3GB per month in 2017 to 13.3GB per month. At the same time, the average flow per connection increases from 1.3GB to 6.3 GB. The use of cellular networks for vehicular internet access only exacerbates the existing overload problem and further degrades the performance of vehicular and general cellular user equipment, and the dramatic increase in mobile services leads to a severe shortage of cellular spectrum resources.
To solve the overload problem caused by using a cellular network alone, we can perform communication between vehicles by using a heterogeneous network. Heterogeneous networks differ from other communication networks by two points: (1) there are multiple access network technologies in the heterogeneous network, and the transmission frequency band and the transmission protocol of various access networks are different, each having advantages and disadvantages. (2) The user can access the network through different access technologies according to different requirements instead of only communicating in the same way. Therefore, the heterogeneous network has obvious advantages compared with a single communication network, namely, the overload problem caused by the single network is solved, the advantages of each access network can be exerted, and the advantages can be made up for the disadvantages, so that a user can select an optimal access mode to carry out communication according to different service requirements. And because the system comprises a plurality of access networks, the system also comprises a plurality of operator devices, and the shunting can be realized at the base station, so that the pressure of the base station is relieved. Meanwhile, the cellular base station forwards, and the D2D multiplexes the cellular frequency spectrum and the special frequency spectrum to carry out vehicle communication, so that the frequency spectrum utilization rate can be increased, and the network performance can be improved. However, with different access networks, the conventional communication architecture enables the base station to implement not only the data collection and transmission functions but also the control functions, which results in a higher equipment pressure ratio of the base station.
In summary, the problems now exist: (1) the overload problem of the cellular network is aggravated by singly using the cellular network for communication, and the communication quality requirement of the intelligent traffic system service cannot be met. (2) In the traditional communication architecture, a cellular base station is used as a control and data center, the equipment pressure of the base station is high, and the SDN architecture can realize the separation of data and a control plane, so that the flexible control of network flow is realized. The significance of solving the technical problems is as follows: the overload problem of the cellular network is solved, the access network of the vehicle is flexibly controlled, and the service quality of the whole vehicle network system is improved.
Disclosure of Invention
Aiming at the problems in the prior art, the invention realizes a software-defined heterogeneous Internet of vehicles access management and optimization method.
The invention is realized by establishing a vehicle communication Network management model based on Software definition, and controlling modes of a vehicle access Network, including three modes of base station forwarding, D2D (device-to-device) and DSRC (dedicated short-range communication) by an SDN (Software Defined Network) controller. The SDN-based network access management model comprises a data plane and a control plane, wherein the data plane is responsible for collecting the state of a vehicle and network information, and the control plane calculates the performance indexes of the vehicle communication network, including transmission rate, interference, transmission delay and energy loss, through a reverse power control method according to the collected vehicle information. And then, optimizing the network service quality of the vehicle heterogeneous network according to the obtained performance index on a control plane so as to optimize the network service quality of the vehicle heterogeneous network according to the requirement of the required service. And finally, transmitting the obtained optimized access network mode selection result to each vehicle on the data plane. The specific optimization method comprises the following steps: and establishing a benefit function required by the game, and enabling the benefit function to be maximum by a method for evolving the game so as to manage the access network mode of the vehicle.
The invention discloses a heterogeneous Internet of vehicles access management and optimization method based on software definition, which comprises the following steps:
step 1: designing a vehicle communication network architecture based on an SDN (software defined network), and realizing the global management of a vehicle network;
step 2: a local database is established on the data plane, vehicle information is collected, and a management strategy issued by the control plane is stored;
and step 3: uploading the collected vehicle data to a control plane, and calculating the performance index of the vehicle network on the control plane;
and 4, step 4: and determining the weight and the benefit function of each performance index, finally determining the selection of the access network mode by an evolutionary game method, and issuing the selection to the vehicle of the data plane.
Further, in step 1, the communication network architecture includes a data plane and a control plane, the data plane includes a local database for collecting vehicle data, transmitting the vehicle data to the control plane and receiving a management policy issued by the control plane, the control plane includes a global database and an SDN controller, calculates a vehicle communication performance index according to the collected vehicle information, and determines a network access policy.
Further, the heterogeneous internet of vehicles access technology comprises cellular network and DSRC access technology, wherein the cellular network technology comprises two modes of D2D relaying and multiplexing cellular channels through a base station.
Further, the data plane in step 2 includes a vehicle moving scene, the collected vehicle information includes state information of the vehicle, such as speed, acceleration, position, and the like, and the updated policy information includes an access network control policy, that is, a manner of controlling the vehicle on the data plane to access the network.
Further, the vehicle network performance indexes in the step 3 include throughput, transmission rate, interference and energy loss.
Further, the performance index obtained in step 3 can be obtained by the following steps:
step 3.1: determining a probability density function f of the distance between the vehicles from the distribution of the vehiclesV(x and probability density function f of distance between vehicle and base stationC(x) I.e. a moving model of the vehicle;
step 3.2: energy loss can be expressed by the transmission power of the vehicle, and for vehicles communicating through a cellular network, the transmission power distribution of the vehicle can be determined through a mobile model and a channel reverse power control strategy, wherein the reverse power control strategy refers to reversely deducing the transmission power by setting a receiving power threshold value
I.e. the transmission power
wherein ρ0h represents a received power threshold, h isChannel gain, X represents the communication mode selected by the vehicle,
representing vehicle communication distance, gammaXRepresents a path loss exponent;
step 3.3: for a vehicle communicating via a cellular network, the interference and SINR (signal to interference plus noise ratio) of the vehicle can be found from the transmission power of the vehicle, namely:
for the interference experienced by the ith vehicle:
wherein N represents the set of vehicles, N \ I represents the rest vehicles except the vehicle I, ID,iAnd IC,iRepresenting the interference, P, caused by the vehicle i selecting the D2D mode communication and the base station relay mode communication, respectivelyD,jAnd PC,jThe transmission power, γ, of the vehicle j for which the communication in the D2D mode and the communication in the base station transfer mode are selected, respectivelyDAnd gammaCPath loss exponent, D, representing the vehicle selected for D2D mode communications and base station relay mode communications, respectivelyjiIs the distance from the source of interference to the receiver,
and
is a function of the indication of the function,
indicating that the D2D mode is selected,
indicating that the base station forwarding mode is selected,
the SINR of a vehicle for D2D/uplink transmission may be expressed as
wherein n0Is the noise power;
step 3.4: for vehicles communicating through a cellular network, the SINR interruption probability of the vehicle can be obtained through the sending power, and further, the transmission rate, the throughput and the time delay of the vehicle can be obtained,
the SINR outage probability for a vehicle communicating over a cellular network is:
wherein ,
represents the probability of the occurrence of (-) and,
representative of the SINR break-off threshold value,
and
the laplace transform of the random variable PDF when D2D mode and cellular mode are selected,
the average transmission rate of the vehicle is therefore:
where B denotes the bandwidth of the cellular network,
denotes the expectation of (. beta.), po,i(x) Representing the SINR outage probability as found above,
the time delay is
Step 3.5: a vehicle communicating in DSRC mode can derive its average transmission rate and time delay from the characteristics of an IEEE802.11p network. For vehicles transmitting DSRC, T is usede、Ts and TcTo indicate the duration of idle, the average time the channel is busy detected due to successful transmission, and the average time the vehicle detects that the channel is busy during a collision, where TeI.e. a time gap, and Ts and TcIs composed of
Wherein, E [ P]Is the average size of the data packet. H ═ PHYhdr+MAChdrIs the header length of the packet, SIFS is the short interframe time, DIFS is the distributed interframe time, and ACK is the time from the receiving point to the transmitting point for an acknowledgement.
Therefore, the average time delay of the vehicle communicating through the DSRC is tDS＝(1-pt)Te+psTs+pcTc，
wherein psIndicates the probability, denoted as p, of any DSRC-mode vehicle within carrier sense range successfully transmitting data within one time slots＝nτ(1-τ)n-1Where n is the number of vehicles selecting the DSRC mode. p is a radical oftIndicating the probability of at least one transmission occurring within a time slot, pt＝1-(1-τ)n. And τ is the average transmission probability of DSRC transmitting vehicles, pcRepresenting the probability of a collision of a vehicle transmission during a time slot, where pc＝pt-ps，
The average rate of vehicle communication over the DSRC is
In the mode, users transmit data in a competitive communication mode, the DSRC mode is mainly based on an IEEE802.11p protocol, a CSMA/CA mechanism is adopted, the channel of a cellular network is not used, and an independent channel of the DSRC mode is used, so that before data is transmitted in the mechanism, the channel state can be detected until the channel is idle, transmission is carried out again, collision is avoided, and the mechanism avoids the generation of interference.
Further, the optimization method in step 4 first needs to determine the weight of each performance index to determine the importance degree of each performance index for a specific service.
Further, the optimization method of step 4 comprises the following steps:
step 4.1: and (4) constructing a benefit function model according to the weight of the vehicle network performance index obtained in the step (3). The benefit function of the vehicle i selecting the jth mode for communication is defined as
wherein ,
is a normalized performance index vector,
is a normalized weight vector.
Step 4.2: carrying out the evolution game on each vehicle according to the benefit function model, wherein the strategy is the selection of each player in the evolution game on the communication mode, and when the kth game is carried out, the average benefit function of all the players is calculated
Set j to 0.
Step 4.3: calculating a benefit function w for each vehicleijAnd its difference from the previous round average merit function
a. If it is not
The benefit function value of the strategy j is superior to the hybrid strategy (average benefit function) of the previous round of game, and the player i in the round of game selects the current strategy;
b. if it is not
It indicates that the current strategy is inferior to the one of the previous round of game intermixing strategies, player i randomly selects an unselected strategy j, and then repeats step (8.3) until
If the ith player finishes selecting all strategies, selecting the strategy with the highest benefit function;
c. this step is repeated for each player.
Step 8.4: and updating the strategies of all the users in each game round until the strategies of all the users are not changed, so that the evolutionary equilibrium is reached.
Firstly, the heterogeneous Internet of vehicles access management and optimization method designed by software provides a solution for vehicle communication network management, and the addition of the heterogeneous network improves the overload problem caused by the use of a single cellular network by vehicles and can also meet the communication quality requirement of the intelligent traffic system service. Meanwhile, the SDN framework is used, so that a data plane is separated from a control plane, and the pressure of a base station is reduced; secondly, the access management optimization method based on the evolutionary game is one of the outstanding contributions of the patent, and establishes a benefit function according to the weight of the performance index to optimize the benefit function, so that the access mode of the vehicle is optimized, and the service quality of the whole vehicle network system is improved; and thirdly, the service with different service requirements can be subjected to differentiated management, and different weights of the performance indexes can be set according to different service requirements, so that targeted optimization can be performed. The optimization management method can flexibly select the access mode of the vehicle based on different service requirements, so that the network service quality of the vehicle heterogeneous network is optimized according to the requirements of the required service. Compared with the traditional single network architecture, the method can flexibly manage the access network, greatly improve the performance of the network and improve the service quality of the vehicle communication network
Drawings
Figure 1 is a diagram of a heterogeneous SDN-based vehicle networking scenario as employed by an embodiment of the present invention;
FIG. 2 is a block diagram of an implementation of a vehicle access network optimization algorithm according to an embodiment of the present invention;
FIG. 3 is a graph of the benefits of three options for different vehicle numbers;
figure 4 average velocity for different vehicle numbers for the three options.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention are described in detail below with reference to the accompanying drawings: the embodiment is implemented on the premise of the technical scheme of the invention, and a detailed implementation mode and a specific operation process are given. It should be understood that the specific examples described herein are merely illustrative of the invention and that the scope of the invention is not limited to the examples described below.
As shown in fig. 1, our system is implemented with an SDN architecture in order to achieve global management of the vehicle network. The cellular base station controls a forwarding plane of a network switch or a router through an OpenFlow protocol, so that a network path of a network packet is changed, and centralized control is performed through an SDN controller. Our architecture is largely divided into a data layer and a control layer. The data layer mainly collects information through the base station, and the control layer is mainly responsible for global strategies related to the access network. Including access control, traffic management, mobility control, etc.
In our framework, each base station is equipped with a local database for storing collected vehicle information including density, position, velocity, acceleration, etc. When a new vehicle enters or leaves the coverage area of the base station, the database can update the information of all parts of the vehicle. The vehicle information collected by the plurality of base stations will be sent to the SDN controller. The global database of the control layer stores global vehicle status information and is periodically updated by each base station. The data layer comprises a vehicle mobile communication network consisting of vehicles and base stations and a local database.
The control layer controls the global vehicle mainly through the SDN controller. The SDN controller realizes quality coordination and information sharing of the vehicle communication network by separating data from a control plane. The control layer mainly comprises a global information database and decision modules, including access control, flow control, channel allocation and the like. The decision module calculates the vehicle information collected in the database and runs the programmable application on the controller in a global view of the entire service area to achieve global functionality. And then sending the decision result to a base station of a data layer to realize the decision.
For communication networks, typically two vehicles can relay information to each other through a base station. However, if the distance between the two vehicles is short, they can transmit data in a single-hop manner through D2D or DSRC without the need for base station relaying. For the cellular mode and the D2D mode, they share the cellular spectrum. In consideration of cellular spectrum, i.e., frequency division duplex LTE networks, orthogonal channel sets are used for uplink and downlink transmissions. There are multiple channels in the cellular network and, similar to calculating the interference for all channels, our analysis may be limited to one channel for simplicity. I.e., D2D transmissions and cellular uplink transmissions, multiplex a channel within the coverage of the base station.
In order to simplify the analysis of the vehicle motion model, a uniform distribution model of the vehicles on the road is established, and each base station communication range covers a section with the length L, wherein the section is a part of the whole section. We take mainly three base station coverage areas as an example, the steady state distribution of the vehicle positions r is nearly uniform:
since the steady state distribution of vehicle locations is nearly uniform, meaning that the probability of a vehicle being present anywhere is equal, V2V transmits a distance dV2VFollow a triangular distribution, thus dV2VThe Probability Density Function (PDF) of (a) can be expressed as:
thus, within the coverage area of a base station, the distance of the vehicle to the midpoint of the route
Thus, the PDFf of the distance from the vehicle to the base stationC(x) Comprises the following steps:
wherein ,l0Distance of base station to the middle point of the road section, andmaxthe furthest distance of the vehicle from the base station
For cellular networks, the large scale fading effects of path loss and shadowing can be present when vehicles in cellular mode and D2D mode are communicating. According to this model, the attenuation ratio is d-γWhere d is the distance between two vehicles transmitting data to each other and γ is the path loss exponent. In particular, gammaC and γDPath loss exponents for the cellular uplink and the D2D link, respectively. The communication channel follows a rayleigh fading model in which the channel gain h between any two locations follows an independent unity exponential distribution, i.e., h exp (1).
The method adopts channel reverse power control to adjust the transmitting power of a vehicle, so that the average received signal power (including base station uplink transmission and D2D transmission) of a receiving vehicle reaches a certain received power threshold value rho after the power of a transmission signal passes through path loss0h。ρ0h is recorded as instantaneous received power.
In general, if the channel reverse power control mode and the required transmit power are greater than the maximum transmit power PmaxThe signal will be truncated. Thus, if the required work of transmission is requiredRate less than PmaxThen both vehicles can transmit data using single hop communication without using base station relay. This requires that the distance between the two vehicles must be at DmaxThis is related to the single hop distance threshold. Single hop distance threshold DmaxBy launching P of vehiclesmaxAnd a received power threshold ρ0And (4) determining. Namely, it is
The vehicles may choose to communicate in a single hop, i.e., the distance between the vehicles is less than DmaxThe probability of (c) is:
probability p that the vehicle is not a potential single-hop communication vehicle and can only communicate in a base station relay modec0＝1-pv0。
The distance between the two vehicles must be at DmaxThis is referred to as a potential single hop communication vehicle pair. A potential single hop communication vehicle pair does not necessarily have to select the D2D/DSRC mode for transmitting data. They can also select the mode of base station relay.
The probability density function of a potential single hop communicating vehicle is
For DSRC mode, due to CSMA/CA (Carrier Sense Multiple Access with collision avoidance) mechanism, DSRC vehicles first Sense the channel before transmitting data. If the channel is detected to be busy, there is a random backoff, the length of which is controlled by a backoff counter. The backoff counter randomly selects a value between 0 and CW for initialization, where CW is denoted as the initial contention window size. If the channel is detected to be busy, the channel freezes and the vehicle can transmit data until the backoff counter is decremented to zero. Further, if the channel busy is detected, the backoff counter is decremented to 1. If the transmission fails, the contention window will double if it does not reach the maximum contention window size, and the DSRC vehicle will retransmit the packet and repeat the above process.
The vehicles can communicate with each other through three modes of base station relay, D2D or DSRC mode. In cellular mode, the vehicle transmits data in two hops for transmission via a transmitter-base station-receiver mode. In D2D mode, by effectively managing interference, two vehicles can communicate directly by multiplexing cellular uplinks. In the DSRC mode, a vehicle transmits data by one hop by adopting a CSMA/CA mechanism of an IEEE802.11p protocol, and the selection of the access network mode has great influence on the network performance. The communication mode of each vehicle is reasonably selected, so that the frequency spectrum resources can be effectively multiplexed without generating excessive interference, and the performance of the whole network is improved.
We set the proportion of relayed communications through the base station in a potential single-hop communication vehicle as a, the proportion of D2D communications as b, and the proportion of DSRC mode communications as c. The proportion of communication through the base station relay is p in the entire networkC＝pc0+apv0The proportion of D2D communication is pD＝bpv0The ratio of DSRC mode communication is cpv0。
The transmit power in the cellular mode and the D2D mode can be derived from the channel reverse power control model and the distance probability distribution described by the above mobility model.
Probability density function f of distance when passing through vehicle D2D communication modeD(x) And the derivation is carried out, according to the channel reverse power control model,
the probability density function of the vehicle in the D2D communication mode
Comprises the following steps:
wherein ,
similarly, the transmit power for a vehicle communicating via cellular relay mode can be found from the channel reverse power control model:
probability density function of vehicle in cellular relay communication mode
Comprises the following steps:
in our network, since cellular and D2D mode vehicles reuse the same channel, interference comes not only from cellular uplink in cellular mode but also from D2D links that reuse the same channel. Therefore, herein we consider the interference of other cellular uplinks and the D2D link covered by the same base station. The interference caused by other vehicles not covered by the base station is considered as noise.
For the interference experienced by the ith vehicle:
wherein ,PDIndicating the transmission power, P, of D2D transmission of the transmitting vehicle iCWhich represents the transmit power of the cellular uplink transmission. djiIs the distance from the interfering transmitter to the transmitting receiver. For D2D communication mode, DjiRefers to the distance from the interfering sending vehicle j to the receiving vehicle i; for base station relay mode, djiRefers to the distance d from the interfering transmitting vehicle j to the base stationC。
And
is a function of the indication of the function,
indicating that the D2D mode is selected,
indicating that the base station forwarding mode is selected, if vehicle j has a transmission request and mode X is selected, then
Otherwise
wherein 
According to the given total interference IiThen one can deduce that the SINR of the vehicle for D2D/uplink transmission in our heterogeneous internet of vehicles system can be expressed as
wherein n0Is the noise power.
For vehicles communicating through a cellular network, the SINR interruption probability of the vehicles can be obtained through the transmission power, and further the transmission rate and the time delay of the vehicles can be obtained.
The SINR outage probability for a vehicle communicating over a cellular network is:
wherein ,
representative of the SINR break-off threshold value,
and
the laplace transform of the random variable PDF when D2D mode and cellular mode are selected.
Laplace transform of interference to D2D transmitting vehicles for other D2D vehicles:
wherein 
Laplace transform of interference to D2D transmitting vehicles for vehicles performing base station relay communication:
wherein ,
the same can be said for the laplace transform of the interference of the D2D transmitting vehicle to the vehicle performing the base station relay communication:
laplace transform of interference of other vehicles performing base station relay communication with respect to the vehicle performing base station relay communication:
the average transmission rate of the vehicle is therefore:
the time delay is
Under the CSMA/CA mechanism of the IEEE802.11p protocol, considering the performance analysis of the DSRC mode, N vehicles are considered to be in the carrier sensing range, and considering that N is the number of vehicles for selecting the DSRC communication mode, N can be less than or equal to ncpv0Is the largest integer of (a). We mainly studied the transmission problem between vehicle i and vehicle j, which mutually transmit data through the CSMA/CA mechanism. We consider that the average transmission probability of each DSRC transmitting vehicle is denoted by τ, which is calculated as 2/(CW + 1). p is a radical ofsIndicates the probability, denoted as p, of any DSRC-mode vehicle within carrier sense range successfully transmitting data within one time slots＝nτ(1-τ)n-1。ptIndicating the probability of at least one transmission occurring within a time slot, pt＝1-(1-τ)n. We use T separatelye、Ts and TcTo indicate the channel idle duration, the average time the channel is busy detected due to successful transmission, and the average time the VUE detects that the channel is busy during a collision. In the IEEE802.11p standard, TeI.e. a time gap. Successful frame transmission TsAnd the time of flight T of the collisioncCan be expressed as:
wherein, E [ P]Is the average size of the data packet. H ═ PHYhdr+MAChdrIs the header length of the packet, SIFS is the short interframe time, DIFS is the distributed interframe time, and ACK is the time from the receiving point to the transmitting point for an acknowledgement.
Therefore, the average time delay of the vehicle communicating through the DSRC is tDS＝(1-pt)Te+psTs+pcTc. wherein psIndicates the probability, denoted as p, of any DSRC-mode vehicle within carrier sense range successfully transmitting data within one time slots＝nτ(1-τ)n-1。ptIndicating the probability of at least one transmission occurring within a time slot, pt＝1-(1-τ)n. And τ is the average transmission probability of the DSRC transmitting vehicles.
The average rate of vehicle communication over the DSRC is
Note that there is no interference in DSRC mode.
The performance analysis of the system is carried out through the vehicle information collected by the data plane, and the performance index of the vehicle can be obtained: transmission rate V, interference I, delay T and energy loss P. Where V is a positive variable, I, T and P are negative variables, and P can be represented by vehicle transmit power. Since the units of these performance indicators are different, we need to normalize them.
Is provided with
For the actual value of the ith performance indicator,
and
the maximum expected value and the minimum expected value of the ith performance index are respectively.
With respect to the forward direction variable, it is,
normalized variable of
For a negative-going variable,
normalized variable of
Then XiAfter normalization, the attribute is
Since the service of the vehicle service is different, the required service quality requirement is also different, so the priority for different performance indexes is also different. For example, if the communication of the emergency information such as the vehicle state is an instant service and the requirement for the time delay is high, the priority of the time delay is increased. The weights of the performance indexes can be set to represent the priorities of different performance indexes, and in order to enable the weight setting to be more reasonable, the weights of the performance indexes can be determined through an analytic hierarchy process, and the weights can also be determined through other methods.
First, a decision matrix is established, and we need to set a scale value p firstijThe relative importance of the two attributes is represented by 1-9, and the larger the number, the more important i is relative to j, so that the decision matrix P is (P)ij)n×n. In the decision matrix, it is determined that,
j ∈ {1,2, 3, …, n }, having:
so that the decision matrix
After obtaining the decision matrix, we need to check consistency to determine whether the scale value we give is reasonable. It can be judged by the consistency ratio CR.
The maximum characteristic value of P is calculated to be lambda, and the consistency ratio CI is
Wherein, CI is the consistency ratio,
n is the order of the matrix, how many performance indexes, we define 4 here, i.e. n is 4. The decision matrix needs to be checked for plausibility by the consistency ratio CR only if CR is present<0.1 is that the consistency of the decision matrix is acceptable. RI is the average consistency index.
| Order of the |
1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 |
| RI | 0.00 | 0.00 | 0.58 | 0.90 | 1.12 | 1.24 | 1.32 | 1.41 | 1.46 |
According to the decision matrix P and the maximum eigenvalue lambda thereof, the normalized eigenvector of lambda corresponding to P can be obtained by calculation
Each vehicle in the coverage range of the three base stations is set as a game player, any communication mode can be selected, all vehicles in the coverage range of the three base stations are a group, and the selection of the communication mode by each game player in the evolutionary game is a strategy. And the benefit function of each player is determined by the performance indicators.
Defining the benefit function of the evolutionary game as a benefit function, and defining the benefit function of the vehicle i for selecting the jth mode to communicate as a benefit function
wherein ,
is a normalized performance index vector
Respectively normalized transmission rate, interference, transmission delay and energy loss P,
is a normalized weight vector.
The vehicle i selects the j modeTo be marked as xij,
j-0 stands for vehicle communication in DSRC mode, j-1 stands for vehicle communication in cellular relay mode, and j-2 stands for vehicle communication in D2D mode where cellular channels are multiplexed. The benefit function of our communication pattern j is
So that the average benefit function value of all users in the network is
Defining the difference value of the benefit function value of any player i in the k game period and the average benefit function value of all users in the network as
The specific process of the evolutionary game is shown in fig. 2, and the benefit function w of each vehicle is first calculatedijAnd its difference from the previous round average merit function
a. If it is not
The benefit function value of the strategy j is superior to the hybrid strategy (average benefit function) of the previous game, and the player i selects the current strategy;
b. if it is not
It indicates that the current strategy is inferior to the previous round of game mixing strategies, player i randomly selects an unselected strategy j, and repeats the previous steps until the current strategy is inferior to the previous round of game mixing strategies
If the ith player finishes selecting all strategies, selecting the strategy with the maximum benefit function;
c. this step is repeated for each player.
And updating the strategies of all the users in each game round until the strategies of all the users are not changed, so that the evolutionary equilibrium is reached.
In order to make this embodiment more intuitively exhibit the performance of the software-defined heterogeneous internet of vehicles access management and optimization method, we select a decision matrix that makes the transmission rate priority highest to determine the weight of each network performance index, that is, the decision matrix is
A benefit function is determined based on this decision matrix. Fig. 3 and 4 show the benefit of different numbers of vehicles and the magnitude of average speed of each vehicle under three selection schemes of evolutionary game, random selection and single selection base station forwarding mode. It can be seen that the software-defined heterogeneous internet of vehicles access management and optimization method can effectively manage vehicles, and the benefit and the vehicle speed are far greater than those of the other two schemes.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (7)
1. A software-defined heterogeneous Internet of vehicles access management and optimization method is characterized by comprising the following steps: the method comprises the following steps:
step 1: designing a vehicle communication network architecture based on an SDN (software defined network), and realizing the global management of a vehicle network;
step 2: a local database is established on the data plane, vehicle information is collected, and a management strategy issued by the control plane is stored;
and step 3: uploading the collected vehicle data to a control plane, and calculating the performance index of the vehicle network on the control plane;
and 4, step 4: and determining the weight and the benefit function of each performance index, finally determining the selection of the access network mode by an evolutionary game method, and issuing the selection to the vehicle of the data plane.
2. The software-defined heterogeneous internet of vehicles access management and optimization method of claim 1, wherein: in step 1, the communication network architecture comprises a data plane and a control plane, wherein the data plane comprises a local database and is used for collecting vehicle data, transmitting the vehicle data to the control plane and receiving a management strategy issued by the control plane, the control plane comprises a global database and an SDN controller, vehicle communication performance indexes are calculated through collected vehicle information, and a network access strategy is determined.
3. The software-defined heterogeneous internet of vehicles access management and optimization method of claim 1, wherein: the heterogeneous Internet of vehicles access technology comprises cellular network and DSRC access technology, wherein the cellular network technology comprises two modes of D2D for relaying and multiplexing cellular channels through a base station.
4. The software-defined heterogeneous internet of vehicles access management and optimization method of claim 1, wherein: the data plane in the step 2 includes a vehicle moving scene, the collected vehicle information includes state information of the vehicle, such as speed, acceleration, position, and the like, and the updated policy information includes an access network control policy, that is, a manner of controlling the vehicle on the data plane to access the network.
5. The software-defined heterogeneous internet of vehicles access management and optimization method of claim 1, wherein: and the vehicle network performance indexes in the step 3 comprise throughput, transmission rate, interference and energy loss.
6. The software-defined heterogeneous internet of vehicles access management and optimization method of claim 5, wherein: the performance index obtained in the step 3 can be obtained by the following steps:
step 3.1: determining a probability density function f of the distance between the vehicles from the distribution of the vehiclesV(x) And the probability density function f of the distance between the vehicle and the base stationC(x) I.e. a moving model of the vehicle;
step 3.2: energy loss can be expressed by the transmission power of the vehicle, and for vehicles communicating through a cellular network, the transmission power distribution of the vehicle can be determined through a mobile model and a channel reverse power control strategy, wherein the reverse power control strategy refers to reversely deducing the transmission power by setting a receiving power threshold value
I.e. the transmission power
wherein ρ0h represents a received power threshold, h is a channel gain, X represents a communication mode selected by the vehicle,
representing vehicle communication distance, gammaXRepresents a path loss exponent;
step 3.3: for a vehicle communicating via a cellular network, the interference and SINR (signal to interference plus noise ratio) of the vehicle can be found from the transmission power of the vehicle, namely:
for the interference experienced by the ith vehicle:
wherein N represents the set of vehicles, N \ I represents the rest vehicles except the vehicle I, ID，iAnd IC，iRepresenting the interference, P, caused by the vehicle i selecting the D2D mode communication and the base station relay mode communication, respectivelyD，jAnd PC，jRepresenting vehicles j selecting D2D mode communication and base station relay mode communication respectivelyTransmission power, gammaDAnd gammaCPath loss exponent, D, representing the vehicle selected for D2D mode communications and base station relay mode communications, respectivelyjiIs the distance from the source of interference to the receiver,
and
is a function of the indication of the function,
indicating that the D2D mode is selected,
indicating that the base station forwarding mode is selected,
the SINR of a vehicle for D2D/uplink transmission may be expressed as
wherein n0Is the noise power;
step 3.4: for vehicles communicating through a cellular network, the SINR interruption probability of the vehicle can be obtained through the sending power, and further, the transmission rate, the throughput and the time delay of the vehicle can be obtained,
the SINR outage probability for a vehicle communicating over a cellular network is:
wherein ,
is shown (.)The probability of the occurrence of the event is,
representative of the SINR break-off threshold value,
and
the laplace transform of the random variable PDF when D2D mode and cellular mode are selected,
the average transmission rate of the vehicle is therefore:
where B denotes the bandwidth of the cellular network,
denotes the expectation of (. beta.), po，i(x) Representing the SINR outage probability as found above,
the time delay is
Step 3.5: a vehicle communicating in DSRC mode can derive its average transmission rate and time delay from the characteristics of an IEEE802.11p network. For vehicles transmitting DSRC, T is usede、Ts and TcTo indicate the duration of idle, the average time the channel is busy detected due to successful transmission, and the average time the vehicle detects that the channel is busy during a collision, where TeI.e. a time gap, and Ts and TcIs composed of
Wherein, E [ P]Is the average size of the data packet。H＝PHYhdr+MAChdrIs the header length of the packet, SIFS is the short interframe time, DIFS is the distributed interframe time, and ACK is the time from the receiving point to the transmitting point for an acknowledgement.
Therefore, the average time delay of the vehicle communicating through the DSRC is
tDS＝(1-pt)Te+psTs+pcTc，
wherein psIndicates the probability, denoted as p, of any DSRC-mode vehicle within carrier sense range successfully transmitting data within one time slots＝nτ(1-τ)n-1Where n is the number of vehicles selecting the DSRC mode. p is a radical oftIndicating the probability of at least one transmission occurring within a time slot, pt＝1-(1-τ)n. And τ is the average transmission probability of DSRC transmitting vehicles, pcRepresenting the probability of a collision of a vehicle transmission during a time slot, where pc＝pt-ps，
The average rate of vehicle communication over the DSRC is
In the mode, users transmit data in a competitive communication mode, the DSRC mode is mainly based on an IEEE802.11p protocol, a CSMA/CA mechanism is adopted, the channel of a cellular network is not used, and an independent channel of the DSRC mode is used, so that before data is transmitted in the mechanism, the channel state can be detected until the channel is idle, transmission is carried out again, collision is avoided, and the mechanism avoids the generation of interference.
7. The software-defined heterogeneous internet of vehicles access management and optimization method of claim 1, wherein: the optimization method of the step 4 comprises the following steps:
step 4.1: and (4) constructing a benefit function model according to the weight of the vehicle network performance index obtained in the step (3). The benefit function of the vehicle i selecting the jth mode for communication is defined as
wherein ,
is a normalized performance index vector,
is a normalized weight vector.
Step 4.2: carrying out the evolution game on each vehicle according to the benefit function model, wherein the strategy is the selection of each player in the evolution game on the communication mode, and when the kth game is carried out, the average benefit function of all the players is calculated
Set j to 0.
Step 4.3: calculating a benefit function w for each vehicleijAnd its difference from the previous round average merit function
a. If it is not
The benefit function value of the strategy j is superior to the hybrid strategy (average benefit function) of the previous round of game, and the player i in the round of game selects the current strategy;
b. if it is not
It indicates that the current strategy is inferior to the one of the previous round of game intermixing strategies, player i randomly selects an unselected strategy j, and then repeats step (8.3) until
If the j gameAfter the user has selected all the strategies, selecting the strategy with the highest benefit function;
c. this step is repeated for each player.
Step 8.4: and updating the strategies of all the users in each game round until the strategies of all the users are not changed, so that the evolutionary equilibrium is reached.
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