CN103973789B - A kind of VANET clustering methods for combining vehicle history credit and current state - Google Patents
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Abstract
The invention discloses a kind of VANET clustering methods for combining vehicle history credit and current state.It is related to mobile communication technology field, according to the period of history, vehicle transmission bandwidth, queue length and the time for serving as cluster head, models the history credit R of vehicle；Include the relative velocity and relative position of node degree number, vehicle and neighbours' vehicle, modeling vehicle's current condition function C according to vehicle's current condition information.According to vehicle history credit and current state function modelling utility function U=α R+ (1 α) C, 0≤α≤1, the maximum candidate cluster head of selection correspondence utility function is as target cluster head, and then selection target cluster head proximal subscribers are cluster member.By the present invention, VANET network topology structures can be achieved and stablize relatively and service transmission performance optimization.
Description
Technical Field
The invention relates to a clustering method of a vehicle self-organizing network, belonging to the technical field of vehicle-mounted communication.
Background
In recent years, an intelligent transportation system based on Vehicular Ad-hoc NETwork VANET (Vehicular Ad-hoc NETwork) has attracted much attention at home and abroad. VANET employs advanced communication and networking technologies capable of providing data communication between vehicles and roadside infrastructure. VANET combines Global Positioning System (GPS) and wireless communication technologies, such as Wireless Local Area Network (WLAN), cellular network, etc., to provide high-speed data access services for vehicles in a high-speed moving state and support information interaction between vehicles, which has become an effective technology for guaranteeing vehicle driving safety and providing high-speed data communication, intelligent traffic management and vehicle-mounted entertainment.
In the VANET, due to factors such as the rapid moving characteristic of the vehicles, the limited coverage range of the access point AP (access point) and the like, part of the vehicles cannot directly communicate with the AP, and data transmission between a Source Vehicle (SV) and the AP can be realized by adopting cooperative forwarding of a Relay Vehicle (RV). According to a specific application scene, such as a densely distributed vehicle area, clusters can be formed by vehicles with a short geographical distance, direct communication of nodes in the clusters is supported by selecting cluster heads in each cluster, and nodes between the clusters are relayed and forwarded through the cluster heads, so that the overhead of routing control information can be effectively reduced, the transmission efficiency of user data is improved, and efficient utilization of network resources is realized. However, characteristics such as dynamic change of link characteristics caused by high-speed movement of nodes, large difference of available resources and service requirements of each node and the like all provide new challenges for the VANET clustering algorithm. How to comprehensively consider the network, node and service characteristics in the VANET and design a reasonable and efficient clustering method is an urgent problem to be solved.
At present, a clustering scheme for designing vehicles based on traffic characteristics of the vehicles is researched, and a method for clustering based on vehicle Mobility is provided in a document [ shear Christine, beam hassanadadi and shahrogh Valaaee, Mobility-based clustering in VANETSuspending affinity mapping, IEEE GLOBECOM, 2009 ]. In the cluster forming stage, the mobility between the cluster head and the cluster members is minimized by using a distributed affinity algorithm, and efficient clustering is realized. A VANET clustering method based on vehicle traffic characteristics is proposed in the document [ treggang et al, VANET clustering method considering vehicle traffic characteristics, publication No. 102307373a, publication date 2012, 1 month 4 ] and the like. The optimal target cluster head is selected by simply weighting the position, the speed, the connectivity and the driving behavior of each vehicle node, so that the optimal clustering of the VANET is realized. A method for performing VANET clustering maintenance based on adjacent vehicle distance is proposed in the document [ Liu congyu and the like, a VANET clustering maintenance method based on adjacent vehicle distance, publication number 102883263A, published 2013, 1 month, 16 days ], and the correlation between vehicles is evaluated through calculation of the safe distance by taking the direct safe distance of the vehicles running on an expressway as a reference basis, so that the vehicles with certain safe correlation are divided into the same cluster, and the corresponding cluster head and cluster members are determined.
The clustering method only considers the characteristics of vehicle speed, position and the like, does not comprehensively consider the factors of candidate cluster head resource availability, service transmission efficiency and the like, possibly causes lower network topology stability and severely limited data transmission performance,
disclosure of Invention
Aiming at the defects and shortcomings of the VANET clustering method in the prior art, the method comprehensively considers the current state characteristics such as relative speed, distance and connectivity of the network and each vehicle, models the historical credit degree of the candidate cluster head based on the factors such as average available bandwidth, queue length and historical acting cluster head time, and determines the optimal clustering scheme by optimizing the current state of the candidate cluster head and the weighted sum of historical credits so as to realize the relative stability of the network state and the optimization of the vehicle service transmission performance.
The technical scheme adopted by the invention for solving the technical problems is as follows.
A VANET clustering method combining historical credit of vehicles and current state and historical credit of candidate cluster heads is provided, the specific technical scheme includes that the vehicles send the current state information of the vehicles to a roadside unit RSU, the RSU stores the state information of the vehicles in a cluster table, cluster identification positions CF of the vehicles are set to be 0, cluster identifications m are set to be 1, the RSU calls vehicle cluster utility functions U to be α R + (1- α) C to calculate utility function values of the vehicles according to the historical credit information R and the current state information C of the vehicles, the vehicles with the maximum corresponding utility function values are selected as cluster heads, the CF of the vehicles with the cluster heads is set to be 1, the ID _ CH of the cluster members is set to be (m, 0), and if the number N of neighbor nodes of the cluster heads is NmLess than or equal to the maximum number of members N allowed in each clustermaxIf all the neighbor nodes of the cluster head are cluster members, the CF of the cluster member vehicle is 1, and the cluster member identifiers of the neighbor nodes are sequentially (m, i), i is 1,2m(ii) a If N is presentm>NmaxSelecting N with smaller utility function in neighbor nodesmaxEach node is a cluster member of the node, the CF of a cluster member vehicle is 1, the cluster member identification of each neighbor node is (m, i), and i is 1,2maxThe RSU further checks CF in the cluster table, calculates a clustering utility function of a vehicle with CF of 0, and makes m be m +1, repeats the process of determining the cluster head and the cluster member until the CF bit of all the vehicles is 1, and sends a cluster notification message to each cluster head and cluster member, wherein 0 is not less than α is not more than 1, for the ith vehicle, according to the time of acting as the cluster head in the historical period TDetermining cluster head factorsVehicle-based normalized historical transmission bandwidthNormalizing queue lengthAnd a cluster head factor, wherein the historical credit function of the modeling vehicle i is as follows:wherein, wB、wLWeights corresponding to transmission bandwidth and queue length, α respectivelyR、RRespectively corresponding to the slope and the central value of the historical credit function curve. According to the number N of one-hop neighbor nodes of the vehicle iiDetermining the normalized node degree of the vehicle i1,2, L, N, and modeling the current state function of the candidate vehicle i based on the normalized node degree, the relative speed and the relative position of the vehicle node and the neighbor node:i is 1,2, L, N, wherein,A relative speed confidence value and a relative position confidence value, w, of the vehicle iV,wPThe weights corresponding to the velocity confidence value and the position confidence value, α respectivelyT，TRespectively corresponding to the slope and the central value of the current state function curve. According to the total number M of the vehicle i receiving and sending packets in the T periodiAnd the size S of the hair packBCalling a formulaCalculating the average transmission bandwidth of the vehicle i in the historical period T according to the formulaA normalized historical transmission bandwidth for vehicle i is determined, wherein,for neighbor vehicles within one hop of the range of vehicle i,the maximum bandwidth of vehicle j. When the RSU receives the vehicle information, each data packet queuing model is modeled to be an M/M/1 queuing system, namely, the arrival time interval and the service time of the data packets are both in exponential distribution, namely, the single-window non-rejection system is used for modeling the maximum queue length allowed by the vehicle iAnd average queue lengthCalculate the normalized average queue length asThe relative speed confidence value of the vehicle i is the speed difference value delta v between the vehicle i and all vehicles in a one-hop rangeij，j∈Ωi,j≠i，Δvij＝vi-vjObey Δ vi Normal distribution of (a), Δ vijThe probability density function of (a) is:wherein Andthe average speed of the vehicles i, j,andthe variance of the i, j speed of the vehicle, respectively. Δ vijLess than a speed threshold vthProbability of (2)
Distance d between vehicle i and all vehicles within one hop rangeijObey a probability density ofThe position trust value of the vehicle i is the distance d between the vehicle i and all one-hop neighbor vehiclesij，j∈ΩiJ ≠ i is smaller than distance threshold dthProbability of (2)
According to the method, historical vehicle credit information and the clustering utility function value of the current state modeling vehicle are comprehensively considered, the vehicle with the larger corresponding utility function value is selected as a cluster head vehicle, and then the cluster member vehicles of each cluster are determined. By adopting the method to cluster VANET, the network topology structure can be relatively stable and the vehicle service transmission performance can be optimized.
Drawings
Fig. 1 is a clustering scenario of the VANET of the present invention;
FIG. 2 is a clustering flow chart of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be further described in detail with reference to the accompanying drawings.
Fig. 1 shows a VANET clustering scenario based on historical credit and current status according to the present invention, where each road segment is deployed with a roadside unit (RSU) responsible for managing all vehicles in the road segment.
Fig. 2 is a flowchart of the VANET clustering method based on historical credit and current status, specifically including the following steps:
201: each vehicle sends its own state information to the RSU, which stores vehicle information in its cluster table, calculates utility function values of each vehicle, sets cluster identifiers (CF) of each vehicle to be 0, and records an initial cluster identifier m as 1.
The information stored in the cluster table (see table one) mainly includes the following information:
vehicle ID: vehicle identification, each vehicle having a unique ID.
Cluster Flag (CF): whether the node is clustered or not is characterized, wherein the CF is 1 to indicate that the node is clustered, and the CF is 0 to indicate that the node is not clustered.
Current state information: the node vehicle speed and position information mainly comprises the current speed, position and neighbor list of the node vehicle.
History information: mainly comprises the historical transmission bandwidth, the available bandwidth, the queue length and the time of acting as a cluster head of the vehicle in the past period time T.
Table one: information stored in cluster table
202: the RSU models a historical credit function of a candidate cluster head vehicle i according to the historical transmission bandwidth, the queue length and the time of acting as a cluster head of the vehicle as follows:wherein,normalized historical bandwidth for vehicle i, i.e.: for neighboring vehicles within one hop of the vehicle i,for the average transmission bandwidth, M, of vehicle i over the historical period TiThe total number of the transceiving data packets, S, of the vehicle i in the T periodBIs the size of the data packet and,to satisfyAll of j ≠ iIs measured.
For normalized historical queue length of vehicle i, i.e. Andthe average queue length of vehicle i and the maximum allowed queue length, respectively. According to different service characteristics of vehicle nodes, a data packet queuing model is modeled to be an M/M/1 queuing system, and the average arrival rate of data packets of a vehicle i is assumed to be lambdaiAverage service rate of muiAverage captain of the team
Is a cluster head factor in which, among others,time for vehicle i to act as cluster head in historical period T, wB，wL，αR，RAre all constants, wherein wB，wLWeights corresponding to historical transmission bandwidth and queue length, α respectivelyR，RRespectively corresponding to the slope and the central value of the historical credit function curve.
203: the RSU models the current state function of the candidate cluster head vehicle i according to the degree of the vehicle node, the relative speed and the relative position of the vehicle node and the neighbor nodeWhereinNormalized node degree for vehicle i, i.e.:wherein N isiThe number of neighbor nodes of one hop for vehicle i.
Is the relative speed confidence value of vehicle i. Modeling each vehicle speed as a normal distribution random variable independent of each other, and obeying the speed of the ith vehiclei 1,2, L, N, the relative velocity Δ v of the vehicle i, j is availableij＝vi-vjCompliance withNormal distribution of (a), wherein ΔvijThe probability density function of (a) is:modeling the speed confidence value of vehicle i as its speed difference Δ v from all vehicles in a one-hop rangeij，j∈ΩiJ ≠ i all below a given speed threshold vthIs a probability of
Modeling the distance d between the vehicle i and all vehicles in one-hop range of the vehicle i for the relative position trust value of the vehicle iijObey a probability density ofThe position trust value of the vehicle i is the distance d between the vehicle i and all one-hop neighbor vehiclesij，j∈ΩiJ ≠ i is smaller than distance threshold dthIs a probability of
204: modeling the ith vehicle clustering utility function as U based on the historical credit function and the current state functioni＝αRi+(1-α)Ci。
205: the RSU selects a vehicle with the CF of 0 and the maximum effect as a cluster head, sets the CF of the vehicle to be 1, marks the cluster head/member identifier as (m, 0), and marks the number of adjacent vehicles of the cluster head as Nm。
206, judging the number of the neighbor nodes of the cluster head vehicle and the maximum allowable member number in the cluster, namely Nm≤Nmax。
207: if yes, all the adjacent vehicles of the cluster head are cluster members of the cluster head, and the cluster head/member identification which sequentially identifies each vehicle is (m, i), i is 1,2iEach cluster member CF is set to 1.
208: if not, selecting the less effective N in all the adjacent vehiclesmaxA vehicle, as a cluster member thereof, a cluster head/member identifier (m, i) sequentially identifying each vehicle, i being 1,2maxEach cluster member CF is set to 1.
209: and judging whether all the vehicles have clustered vehicles, namely whether the CF of all the vehicles is 1, if so, all the vehicles are clustered, and ending the algorithm.
210: if not, then there are non-clustered vehicles, and the cluster identifier m is m +1, go to 205 until all vehicles have been clustered.
Claims (2)
1. A VANET clustering method combining vehicle historical credit and current state is characterized in that a vehicle sends current state information of the vehicle to a roadside unit RSU, the RSU stores the state information of each vehicle in a cluster table, cluster identification positions CF of all vehicles are juxtaposed to be 0, cluster identification m is recorded to be 1, the RSU calls a vehicle clustering utility function U to be α R + (1- α) C to calculate utility function values of the vehicle according to the historical credit information R and the current state information C of the vehicle, the vehicle with the largest corresponding utility function value is selected as a cluster head, the CF of the vehicle with the cluster head is 1, and cluster member identification ID _ CH is (m, 0), and if the cluster head is a neighbor vehicle, the vehicle is adjacent to the cluster head, the cluster head is a neighbor vehicle, and the cluster head is a neighbor vehicleNumber of nodes NmLess than or equal to the maximum number of members N allowed in each clustermaxIf all the neighbor nodes of the cluster head are cluster members, the CF of the cluster member vehicle is 1, and the cluster member identifiers of the neighbor nodes are sequentially (m, i), i is 1,2m(ii) a If N is presentm>NmaxSelecting N with smaller utility function in neighbor nodesmaxEach node is a cluster member of the node, the CF of a cluster member vehicle is 1, the cluster member identification of each neighbor node is (m, i), and i is 1,2maxThe RSU further checks CF in the cluster table, calculates a clustering utility function of a vehicle with CF of 0, and enables m to be m +1, the process of determining the cluster heads and the cluster members is repeated until CF bits of all vehicles are 1, the RSU sends a cluster notification message to each cluster head and each cluster member, wherein 0 is more than or equal to α and is less than or equal to 1, and for the ith vehicle, the time T of acting as the cluster head in a historical period T is used as the time T of the ith vehiclei CHDetermining cluster head factorsVehicle-based normalized historical transmission bandwidthNormalizing queue lengthAnd a cluster head factor, wherein the historical credit function of the modeling vehicle i is as follows:wherein, the total number M of the vehicle i receiving and sending packets in the T period is determinediAnd the size S of the hair packBCalling a formulaCalculating the average transmission bandwidth of the vehicle i in the historical period T according to the formulaDetermining a normalized calendar for vehicle iHistory transmission bandwidth, wB、wLWeights corresponding to transmission bandwidth and queue length, α respectivelyR、RRespectively corresponding to the slope and the central value of the historical credit function curve,for neighbor vehicles within one hop of the range of vehicle i,is the maximum bandwidth of vehicle j; when the RSU receives the vehicle information, each data packet queuing model is modeled to be an M/M/1 queuing system, namely, the arrival time interval and the service time of the data packets are both in exponential distribution, namely, the single-window non-rejection system is used for modeling the maximum queue length allowed by the vehicle iAnd average queue lengthCalculate the normalized average queue length asAccording to the number N of one-hop neighbor nodes of the vehicle iiDetermining the normalized node degree of the vehicle iModeling a current state function of a candidate vehicle i based on the normalized node degrees, the relative speed and the relative position of the vehicle node and the neighbor node:whereinA relative speed confidence value and a relative position confidence value, w, of the vehicle iV,wPRespectively corresponding speed trustWeight of value and location confidence value, αT，TRespectively corresponding to the slope and the central value of the current state function curve.
2. The VANET clustering method according to claim 1, wherein the relative speed trust value of the vehicle i is △ v difference value between the relative speed trust value and all vehicles in a one-hop rangeij，j∈Ωi,j≠i，△vij＝vi-vjCompliance withNormal distribution of (2), △ vijThe probability density function of (a) is:wherein Andthe average speed of the vehicles i, j,andvariance of i, j speed of vehicle, △ v respectivelyijLess than a speed threshold vthProbability of (2)
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| CN108882329B (en) * | 2018-07-27 | 2020-06-19 | 中国联合网络通信集团有限公司 | Clustering method and device |
| CN109769224B (en) * | 2019-03-05 | 2021-11-09 | 维沃移动通信有限公司 | Internet of vehicles information transmission method, network equipment, medium and terminal |
| CN109922511A (en) * | 2019-04-29 | 2019-06-21 | 中国联合网络通信集团有限公司 | Cluster-head node selection method, node clustering method and cluster-head node selection device |
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