CN115865785A - VANET clustering routing method based on k-means clustering - Google Patents

VANET clustering routing method based on k-means clustering Download PDF

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CN115865785A
CN115865785A CN202310166703.0A CN202310166703A CN115865785A CN 115865785 A CN115865785 A CN 115865785A CN 202310166703 A CN202310166703 A CN 202310166703A CN 115865785 A CN115865785 A CN 115865785A
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CN115865785B (en
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郭永安
董理想
佘昊
王胜蓝
王宇翱
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Nanjing University of Posts and Telecommunications
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Abstract

The invention discloses a VANET clustering routing method based on k-means clustering, which comprises the following steps: 1. selecting K clustering center nodes by the vehicle node set through iteration by adopting a K-means clustering algorithm; 2. the cluster center sends a cluster number to the CM, and the CM updates a routing table; 3. selecting a cluster head, and updating a CH routing table after finding a cluster head CH; 4. intra-cluster communication; 5. inter-cluster communication. The k-means algorithm is improved through the clustering index, and the convergence speed of the algorithm is accelerated to obtain the proper number of clusters; through the iterative search of ant colony in the improved artificial bee colony algorithm, the globally optimal k cluster heads in the k clusters are obtained, the cluster head number with similar number can be obtained under the condition of different sizes of vehicle node sets, and the stability of the cluster heads is improved; under the condition that the sizes of the vehicle node sets are different, the end-to-end time delay between the vehicle nodes can be reduced to 5%; the packet arrival rate is increased to 96%.

Description

VANET clustering routing method based on k-means clustering
Technical Field
The invention belongs to the technical field of vehicle networking, and particularly relates to a VANET clustering routing method based on k-means clustering.
Background
A vehicle ad hoc network (VANET) is a branch of an internet of vehicles, and refers to a centerless, mobile ad hoc and multi-hop transmission network formed by roadside units and vehicles, and aims to quickly transmit data to the vehicles moving at high speed and support the vehicles to obtain real-time road conditions and surrounding vehicle information. Due to the strong abruptness of various traffic conditions on roads, better timeliness and reliability are required for information transmission between vehicles. However, the high speed movement of the vehicle may cause frequent interruptions in the wireless communication link, resulting in unstable network connections. In addition, the amount of mutual information between the vehicle and other entities is huge, and information collision and congestion are easily caused. Therefore, it is difficult to guarantee the quality of service requirements of VANET for low latency and high reliability. In order to solve the problem, a clustering algorithm is introduced to reduce network communication delay and link congestion, but the traditional clustering algorithm has the problems of unstable network cluster head number, high end-to-end delay and the like due to excessive clusters, increased calculation complexity and frequent cluster head replacement.
Disclosure of Invention
The purpose of the invention is as follows: aiming at the problems of data routing forwarding delay and reliability in the vehicle ad hoc network, the invention aims to provide a VANET clustering routing method based on k-means clustering, so that the stability of a cluster head and the robustness of a network are improved.
The technical scheme is as follows: the VANET clustering routing method based on k-means clustering executes the following steps according to preset periodicity based on vehicles running in a target road section, ensures the connectivity of links in the process of rapid running of the vehicles, and reduces the loss rate of data packets;
s1: initializing parameters of a vehicle node set, selecting K clustering center nodes through iteration based on a K-means clustering algorithm for the vehicle node set, and simultaneously dividing VANET into K clusters and numbering Num for each cluster;
s2: respectively sending each cluster number Num of each cluster center node to each cluster member CM in the cluster, and storing the number Num into respective routing table by the cluster member CM;
s3: selecting a cluster head node CH in each cluster by using an improved artificial bee colony algorithm; first, a fitness function is utilized
Figure SMS_1
Respectively calculating the fitness value of each vehicle node in the k clusters, further broadcasting a cluster head node ID to each cluster member CM in the cluster, and storing the cluster head node ID in a routing table of the cluster member CM; after receiving the cluster head node ID, the cluster member CM sends the ID of the cluster member CM to the CH, and the CH stores the cluster member ID into a routing table of the CH;
s4, communication in the cluster, wherein a source node sends routing request information to a cluster head node CH, the CH inquires a routing table of the CH, if a destination node ID is in the routing table, the CH selects a shortest path according to a KSP routing selection algorithm and returns the shortest path information to the source node; otherwise, starting the inter-cluster route discovery process; wherein, the source node is a node initiating communication;
s5, communication among clusters is carried out, a cluster head node CH of a cluster where a source node is located does not find a destination node ID in a routing table of the cluster, a routing request is sent to a road side unit RSU, the RSU searches the routing table according to a cluster number Num of the destination node and the destination node ID, if the destination node is found, the shortest path is selected according to a KSP routing algorithm, and the shortest path is returned to the source node; otherwise, the source node is informed that the destination node is lost;
the source node is a description during routing transmission and corresponds to the destination node.
Further, in step S1, K clustering center nodes are selected through iteration based on a K-means clustering algorithm, which specifically includes the following steps:
s11, the number of the vehicle nodes is N, and one vehicle node is randomly selected from all the vehicle nodes to serve as an initial clustering center
Figure SMS_2
；
S12, calculating the distance from the vehicle node to the initial clustering center
Figure SMS_3
And link connectivity->
Figure SMS_4
And based on the distance of the initial cluster center >>
Figure SMS_5
And link connectivity->
Figure SMS_6
Select the next initial cluster center->
Figure SMS_7
；
S13, repeating the step S12 until K vehicle nodes are selected as initial clustering centers;
s14, calculating clustering indexes from each vehicle node to K initial clustering centers, and distributing the vehicle nodes to the initial clustering centers according to the minimum clustering index values;
s15, according to the contour coefficient
Figure SMS_8
Reselecting the clustering centers in the K clusters;
wherein ,
Figure SMS_9
is the average distance from a certain node i to other nodes in the same cluster; />
Figure SMS_10
The average distance from a certain node i to nodes in other cluster groups is obtained, and the smaller the outline coefficient is, the tighter the contact degree between the members in the cluster and the cluster head is, and the stronger the robustness of the network structure is.
Further, in step S12, the link connectivity
Figure SMS_11
The calculations are defined as follows:
Figure SMS_12
wherein ,/>
Figure SMS_13
Is the communication transmission radius; />
Figure SMS_14
Is the vehicle node i to the initial cluster center->
Figure SMS_15
The sustainable usage time of the link between; />
Figure SMS_16
The probability density function of the vehicle communication time in the traffic flow model;
Figure SMS_17
the definition is as follows:
Figure SMS_18
，/>
Figure SMS_19
the definition is as follows:
Figure SMS_20
wherein ,/>
Figure SMS_21
Is the vehicle node i to the initial cluster center->
Figure SMS_22
The mean of the relative velocities; />
Figure SMS_23
Is the variance of the relative velocity, ved;
according to
Figure SMS_24
Selects the next initial cluster center pick>
Figure SMS_25
。
Further, in step S14, the cluster index cluster _ index is calculated as follows:
Figure SMS_26
,
wherein
Figure SMS_28
For vehicle node i to initial cluster center->
Figure SMS_30
Square of the distance therebetween, or>
Figure SMS_32
For a vehicle node i to an initial clustering center>
Figure SMS_29
The square of the difference in velocity between them, device for selecting or keeping>
Figure SMS_31
For vehicle node i to initial cluster center->
Figure SMS_33
Square of the difference in acceleration between, and->
Figure SMS_34
For vehicle node i to initial cluster center->
Figure SMS_27
Inter-link connectivity.
Further, in step S3, a cluster head node CH in each cluster is selected based on an artificial bee colony algorithm, which specifically includes the following steps:
s31, initializing basic parameters of an algorithm, namely the number of the population, namely the number of vehicle nodes, and setting a maximum iteration number Max, a maximum iteration number limit of a honey source and an algorithm dimension d; according to
Figure SMS_37
Initializing a honey source; wherein the initial honey source L = { [ MEAL ] } { [ means of storing honey in a storage tank>
Figure SMS_39
(ii) a i isThe number of initial sources, i.e. the number of initial cluster centers,
Figure SMS_41
，/>
Figure SMS_35
is a d-dimensional vector, is combined with a vector in a combination of five dimensions>
Figure SMS_38
、/>
Figure SMS_40
Are respectively>
Figure SMS_42
Maximum and minimum values of (a); />
Figure SMS_36
Representing random numbers in the range of 0 to 1 and obeying Cauchy distribution; the honey sources correspond to the leading bees one by one;
s32, enabling each leading bee to search a new honey source near the initial honey source
Figure SMS_43
The update formula is as follows:
Figure SMS_44
，i≠k
s33, leading bees, calculating the fitness value of the honey source in the S32 according to the fitness function
Figure SMS_45
；
Fitness value of honey source
Figure SMS_46
Sorting to form honey source set, and utilizing
Figure SMS_47
Calculating to obtain a reverse honey source set; if->
Figure SMS_48
>/>
Figure SMS_49
Replacing the honey source set with a reverse honey source set; otherwise, the honey source set is unchanged;
s34, in the stage of following bees, the leading bees share the honey source set information in the S33, the following bees further search the honey sources in the honey source set, and all the honey sources are subjected to probability
Figure SMS_50
Selecting the following honey source>
Figure SMS_51
I.e. the currently tradable optimal honey source, <' > in>
Figure SMS_52
Greater corresponds to a greater fitness value, greater>
Figure SMS_53
The greater the probability of being mined according to
Figure SMS_54
Updating the position of the honey source, wherein n is the current iteration number; recording an optimal honey source by using a parameter trim, wherein the trim is 0 when the honey source is reserved; when the honey source is updated, adding 1 to the deal;
s35, scouting bee stage, honey source parameter real>If the fitness value of the honey source is still unchanged during limit, the honey source falls into a local optimal solution, and the honey source is recorded as the current optimal solution
Figure SMS_55
Abandoning the honey source, the lead bees corresponding to the honey source will abandon the honey source and turn it into scout bees
Figure SMS_56
Randomly generating a new honey source, searching the new honey source by the scout bees and converting the new honey source into the leading bees again;
s36, completing one iteration, and recording the optimal honey source in the iteration; judging that the algorithm reaches the maximum iteration times Max, and if the algorithm reaches the maximum iteration times Max, outputting the optimal honey source as a cluster head after the operation is finished; if not, go to S33 for the next iteration.
Has the advantages that: compared with the prior art, the invention has the following remarkable advantages: the vehicle nodes of the VANET have dynamic characteristics, and the communication link is unstable due to frequent change of a network topological structure; improving the k-means algorithm through the clustering index, and accelerating the convergence speed of the algorithm to obtain a proper cluster number; further, the globally optimal k cluster heads in the k clusters are obtained through iterative search of ant colonies in an improved artificial bee colony algorithm, the cluster head quantity with similar quantity can be obtained under the condition of different sizes of vehicle node sets, and the stability of the cluster heads is improved; under the condition that the sizes of the vehicle node sets are different, the end-to-end time delay between the vehicle nodes can be reduced to 5 percent; the packet arrival rate is increased to 96%.
Drawings
Fig. 1, a flowchart of a VANET clustering routing method;
FIG. 2 is an explanatory view of step S1;
fig. 3 and step S3 are explanatory diagrams.
Detailed Description
The technical scheme of the invention is further explained by combining the attached drawings.
Example 1
S0, initializing parameters, and setting the size of a vehicle node set to be 100, 150 and 250; the length of the road section to be measured is 5km; the vehicle speed range is 60km to 120km; the communication transmission radius is 200m;
s1, selecting 4 clustering center nodes by a vehicle node set through iteration by adopting an improved k-means clustering algorithm, and further dividing the VANET into 4 clusters;
s2, cluster center nodes number the cluster to which the cluster center nodes belong
Figure SMS_57
Broadcast to each cluster member CM in the cluster, which will number->
Figure SMS_58
Storing the routing table into the routing table of the user;
s3, selecting cluster head nodes CH in each cluster by using an improved artificial bee colony algorithm, and firstly, using a fitness function
Figure SMS_59
Respectively calculating the fitness value of each vehicle node in the k clusters, and taking the cluster center in the S1 as an initial honey source; further, the cluster head node ID is broadcast and sent to each cluster member CM in the cluster, and the cluster member CM stores the cluster head node ID into a routing table of the cluster member CM; after receiving the cluster head node ID, the cluster member CM sends the ID of the cluster member CM to the CH, and the CH stores the cluster member ID into a routing table of the CH;
s4, communication in the cluster is carried out, a source node cluster head node CH sends routing request information, the CH inquires a routing table of the CH, if a destination node ID is in the routing table, the CH selects a shortest path according to a KSP routing selection algorithm, and the shortest path information is returned to the source node; otherwise, starting the inter-cluster route discovery process;
s5, inter-cluster communication is carried out, if the cluster head node CH of the cluster where the source node is located does not find the destination node ID in the routing table of the cluster, the routing request is sent to a Road Side Unit (RSU), and the RSU sends the routing request according to the cluster number of the destination node
Figure SMS_60
Searching a routing table by using the ID of the destination node, if the destination node is found, selecting the shortest path according to a KSP routing algorithm, and returning the shortest path to the source node; otherwise, the source node is informed that the destination node is lost;
when the size of the vehicle node set is 100%, the end-to-end delay and the data packet arrival rate between vehicle nodes obtained by the method are respectively 5% and 96%; when the size of the vehicle node set is 150%, the end-to-end delay and the data packet arrival rate obtained by using the method are respectively 5% and 95%; when the size of the vehicle node set is 250%, the end-to-end delay and the data packet arrival rate obtained by using the method are respectively 7% and 94%. Further, when the vehicle node set size is 100, 150, 250, the number of cluster heads is stable, and the number of cluster heads is 4.

Claims (5)

1. A VANET clustering routing method based on k-means clustering is characterized in that the following steps are executed according to preset periodicity based on vehicles running in a target road section, so that the connectivity of links in the process of rapid running of the vehicles is ensured, and the loss rate of data packets is reduced;
s1: initializing parameters of a vehicle node set, selecting K clustering center nodes through iteration based on a K-means clustering algorithm aiming at the vehicle node set, and simultaneously dividing the VANET into K clusters and numbering Num for each cluster;
s2: respectively sending each cluster number Num to which each cluster central node belongs to each cluster member CM in the cluster, and storing the number Num into each routing table by each cluster member CM;
s3: selecting a cluster head node CH in each cluster based on an artificial bee colony algorithm, wherein the cluster head node CH stores the ID of a cluster member CM in a self routing table;
s4: a source node sends routing request information to a cluster head node CH, the cluster head node CH inquires a routing table of the cluster head node CH, if the ID of a target node exists in the routing table, intra-cluster communication is carried out, and the step S5 is carried out, otherwise inter-cluster communication is carried out, and the step S6 is carried out;
s5: the cluster head node CH selects the shortest path based on the KSP routing algorithm and returns the shortest path information to the source node;
s6: the cluster head node CH sends a routing request to the road side unit RSU, the road side unit RSU searches a routing table according to the cluster number Num of the target node and the ID of the target node, if the target node is found, the shortest path is selected according to the KSP routing algorithm, and the shortest path is returned to the source node; otherwise, the source node is informed that the destination node is lost.
2. The VANET clustering routing method based on K-means clustering according to claim 1, wherein in step S1, K clustering center nodes are selected by iteration based on a K-means clustering algorithm, and the method specifically comprises the following steps:
s11, the number of vehicle nodes is N, allRandomly selecting one vehicle node from the vehicle nodes as an initial clustering center
Figure QLYQS_1
；
S12, calculating the distance from the vehicle node to the initial clustering center
Figure QLYQS_2
And link connectivity->
Figure QLYQS_3
And based on the distance of the initial cluster center >>
Figure QLYQS_4
And link connectivity->
Figure QLYQS_5
Select the next initial cluster center->
Figure QLYQS_6
；
S13, repeating the step S12 until K vehicle nodes are selected as initial clustering centers;
s14, calculating clustering indexes from each vehicle node to K initial clustering centers, and distributing the vehicle nodes to the initial clustering centers according to the minimum clustering index values;
s15, according to the contour coefficient
Figure QLYQS_7
Reselecting the clustering centers in the K clusters;
wherein ,
Figure QLYQS_8
is the average distance from a node i to other nodes in the same cluster; />
Figure QLYQS_9
Is the average distance from a certain node i to nodes in other cluster groups, and the smaller the contour coefficient is, the members in the cluster and the cluster are shownThe tighter the degree of connection between the heads, the stronger the robustness of the network structure.
3. The VANET clustering routing method based on k-means clustering as claimed in claim 2, wherein in step S12, the link connectivity is
Figure QLYQS_10
The calculations are defined as follows:
Figure QLYQS_11
wherein ,/>
Figure QLYQS_12
Is the communication transmission radius;
Figure QLYQS_13
is the vehicle node i to the initial cluster center->
Figure QLYQS_14
The sustainable use time of the link between; />
Figure QLYQS_15
The probability density function of the vehicle communication time in the traffic flow model; />
Figure QLYQS_16
The definition is as follows:
Figure QLYQS_17
，/>
Figure QLYQS_18
the definition is as follows:
Figure QLYQS_19
wherein ,/>
Figure QLYQS_20
Is the vehicle node i to the initial cluster center->
Figure QLYQS_21
The mean of the relative velocities; />
Figure QLYQS_22
Is the variance of the relative velocity ved;
according to
Figure QLYQS_23
Selects the next initial cluster center pick>
Figure QLYQS_24
。
4. The VANET clustering routing method based on k-means clustering according to claim 2, wherein in step S14, the clustering index cluster _ index is calculated as follows:
Figure QLYQS_25
,
wherein
Figure QLYQS_27
For vehicle node i to initial cluster center->
Figure QLYQS_29
Square of the distance between, is greater than or equal to>
Figure QLYQS_31
For a vehicle node i to an initial clustering center>
Figure QLYQS_28
Square of the difference in speed between, and->
Figure QLYQS_30
For vehicle node i to initial cluster center->
Figure QLYQS_32
Square of the difference in acceleration between, and->
Figure QLYQS_33
For vehicle node i to initial cluster center->
Figure QLYQS_26
Inter-link connectivity.
5. The VANET clustering routing method based on k-means clustering according to claim 1, wherein in step S3, the cluster head node CH in each cluster is selected based on an artificial bee colony algorithm, and the method specifically comprises the following steps:
s31, initializing basic parameters of an algorithm, namely the number of the population, namely the number of vehicle nodes, and setting a maximum iteration number Max, a maximum iteration number limit of a honey source and an algorithm dimension d; according to
Figure QLYQS_35
Initializing a honey source; wherein the initial honey source L = { [ MEAL ] } { [ means of storing honey in a storage tank>
Figure QLYQS_38
(ii) a i is the number of initial sources, i.e. the number of initial cluster centers,
Figure QLYQS_40
，/>
Figure QLYQS_36
is a d-dimensional vector, is greater than or equal to>
Figure QLYQS_37
、/>
Figure QLYQS_39
Are respectively>
Figure QLYQS_41
Maximum and minimum values of; />
Figure QLYQS_34
Representing random numbers in the range of 0 to 1 and obeying Cauchy distribution; the honey sources correspond to the leading bees one by one;
s32, enabling each leading bee to search a new honey source near the initial honey source
Figure QLYQS_42
The update formula is as follows:
Figure QLYQS_43
，i≠k
s33, in the bee leading stage, the fitness value of the honey source in the S32 is calculated according to the fitness function
Figure QLYQS_44
；
Fitness value of honey source
Figure QLYQS_45
Sorting to form honey source set, and utilizing
Figure QLYQS_46
Calculating to obtain a reverse honey source set; if->
Figure QLYQS_47
>/>
Figure QLYQS_48
Replacing the honey source set with the reverse honey source set; otherwise, the honey source set is unchanged; />
S34, in the stage of following bees, the leading bees share the honey source set information in the S33, the following bees further search honey sources in the honey source set, and all the honey sources are subjected to probability
Figure QLYQS_49
Selecting the following honey source>
Figure QLYQS_50
I.e. the currently tradable optimum honey source->
Figure QLYQS_51
Greater corresponds to a greater fitness value, greater>
Figure QLYQS_52
The greater the probability of being mined according to
Figure QLYQS_53
Updating the position of the honey source, wherein n is the current iteration number; recording an optimal honey source by using a parameter trim, wherein the trim is 0 when the honey source is reserved; when the honey source is updated, the real is added with 1;
s35, scouting bee stage, honey source parameter deal>If the fitness value of the honey source is still unchanged during limit, the honey source falls into a local optimal solution, and the honey source is recorded as the current optimal solution
Figure QLYQS_54
Abandoning the honey source, the lead bees corresponding to the honey source will abandon the honey source and turn it into scout bees
Figure QLYQS_55
Randomly generating a new honey source, and searching the new honey source by the scout bees and converting the new honey source into the leading bees again;
s36, completing one iteration, and recording the optimal honey source in the iteration; judging that the algorithm reaches the maximum iteration number Max, if so, outputting the optimal honey source as a cluster head after the operation is finished; if not, go to S33 for the next iteration.
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