CN107454649A - 1 hop and 2 hop clusters in VANETs based on density estimation and the adaptive sub-clustering method deposited - Google Patents
1 hop and 2 hop clusters in VANETs based on density estimation and the adaptive sub-clustering method deposited Download PDFInfo
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- CN107454649A CN107454649A CN201710614332.2A CN201710614332A CN107454649A CN 107454649 A CN107454649 A CN 107454649A CN 201710614332 A CN201710614332 A CN 201710614332A CN 107454649 A CN107454649 A CN 107454649A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
- H04W40/026—Route selection considering the moving speed of individual devices
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
- H04L67/00—Network arrangements or protocols for supporting network services or applications
- H04L67/01—Protocols
- H04L67/12—Protocols specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
- H04W40/20—Communication route or path selection, e.g. power-based or shortest path routing based on geographic position or location
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
- H04W40/22—Communication route or path selection, e.g. power-based or shortest path routing using selective relaying for reaching a BTS [Base Transceiver Station] or an access point
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
Abstract
The invention discloses 1 hop in a kind of VANETs based on density estimation and 2 hop clusters and the adaptive sub-clustering method deposited, including：The 1 hop neighbor node density of future time is estimated in current location and present speed based on nodeBy relatively judging that the node suitably forms 1 hop or 2 hop clusters with threshold value, and to initialize node be leader cluster node；Selection relative mobility minimum is in each clusterMinimum node be cluster head, and other nodes are then reduced to cluster member in cluster, if the minimum node of relative mobility be two or more, the node of selection ID minimums is cluster head；The later maintenance of clustering architecture is carried out based on situation about being likely to occur in network.Cluster algorithm disclosed by the invention has the advantages of low time delay and low packet loss ratio.
Description
Technical field
The present invention relates to clustering route protocol CBRP (the Clustering Based based on minimum ID sub-clusterings in VANETs
Routing Protocol), 1-hop the and 2-hop clusters based on density estimation and deposit adaptive in more particularly to a kind of VANETs
Answer sub-clustering method.
Background technology
With the raising year after year of people's living standard, automobile quantity is increased sharply, and urban transportation density greatly improves, road congestion
Problem is increasingly severe.Therefore along with the fast development of computer technology and the communication technology, inter-vehicular communication V2V and vehicle with
The V2I that communicated between roadside equipment promotes vehicle self-organizing network VANET development and upgrading.Although VANET is Ad Hoc networks
A kind of application, but due to its special network environment, the characteristics of motion and application background so that wireless channel is unstable, network
Finite capacity and mobile topological dynamic change, for these features, the Routing Protocol for the planar structure that compares, clustering route protocol
The whole network can be quickly covered in a short time, and is reached and kept the stable purpose of network structure as far as possible.
In clustering route protocol, several cluster networks are formed according to certain cluster algorithm, a cluster network is generally by one
Individual cluster head and some cluster member compositions.Typical clustering route protocol has the CBRP agreements based on minimum ID algorithms.It is a kind of good
Cluster algorithm should make motion of the algorithm to node have certain stability, i.e., when only some nodes are moved or network is opened up
When flutterring structure slower change occurs, cluster structured that violent change does not occur, whole network is just for the portion to change
Divide and carry out clustering architecture adjustment, the node for being partially disengaged network is added cluster nearby within the most short time, and remainder is then
Keep constant.Especially in having the cluster algorithm of cluster head, the frequent variation of cluster head can cause the unstability of clustering architecture, simultaneously
Influence routing performance and then influence the communication performance of network, increase the control overhead for sub-clustering again, and reduce the profit of channel
With rate.
In view of the communication performance of cluster structured stability and network, it is existing at present it is a variety of based on ID, speed, direction,
The cluster algorithm of node degree, distance and destination etc. is suggested, and these algorithms are all based on the 1-hop neighbor nodes of node mostly
Sub-clustering is carried out, when traffic density is excessive or too small in network, these cluster algorithms easily cause number of clusters amount and cluster is big
It is small unbalance.When traffic is in peak period, the number of vehicles in communication radius drastically increases, due to limiting the scope of sub-clustering,
Easily cause the overlapping situation of cluster, now cluster head quantity is excessive, and routing performance and communication performance all reduce；When traffic density is too low
When, the number of vehicles in communication radius is reduced, and the cluster number of members in cluster is very few, causes cluster structured waste, reduces simultaneously
Communication efficiency.There are Multi-hop cluster algorithms to be suggested in recent years, but these algorithms are all based on single Multi-
Hop clustering architectures.
The content of the invention
It is an object of the invention to overcome the defects of cluster structured single in existing VANETs, there is provided a kind of VANETs
In 1-hop the and 2-hop clusters based on density estimation and the adaptive sub-clustering method deposited, compared with traditional cluster algorithm, this method can
Effectively to improve the communication performance of network.
To reach above-mentioned purpose, the present invention is achieved by the following technical solutions：
1-hop the and 2-hop clusters based on density estimation and the adaptive sub-clustering method deposited, comprise the following steps in VANETs：
1) the adaptive cluster-dividing method based on 1-hop neighbor node density estimations：By Euclidean distance node and logical
Letter radius TR, which compares, obtains 1-hop neighbor node density, and future tense is estimated in current location, speed and acceleration based on node
The 1-hop neighbor node density at quarter, it is stored in the list item of node after normalization and weight, by relatively judging with threshold value
The node suitably forms 1-hop or 2-hop clusters, and it is leader cluster node to initialize node；
2) the cluster head system of selection based on minimum relative mobility：It is that all node divisions are big to fix in step 1)
Other in each node and cluster in each cluster, are saved by small 1-hop or 2-hop clusters and to initialize each node be cluster head
The velocity magnitude of point is compared, and the node that the velocity variance of selection and 1-hop neighbor nodes is minimum is cluster head, and other in cluster save
Point is then reduced to cluster member, if the node of relative mobility minimum is two or more, it is cluster to select the minimum nodes of ID
Head；
3) maintenance of clustering architecture：Occur two kinds of situations in a network, one kind is that a stateless vehicle node has just enter into
Neighbours' cluster is added to system request, and another kind is that the cluster head of a 1-hop cluster sends the request 1-hop cluster adjacent with another and entered
Row merges.
Further improve of the invention is, in step 1), number of clusters amount and cluster size in VANETs are influenceed between vehicle
Communication performance, number of clusters amount and cluster size are related to traffic density, by making to the 1-hop neighbor node density estimations of vehicle node
The adaptively formed 1-hop or 2-hop clusters of vehicle node are obtained to balance the number of clusters amount and cluster size in VANETs；Traffic density is low
When, suitably form 1-hop clusters, cluster head and all cluster members are 1-hop distances in cluster, between node can direct communication, vehicle
When density is relatively high, 2-hop clusters are suitably formed, cluster head and part cluster member are 1-hop distances, direct communication in cluster, and another
Part cluster member is 2-hop distances, indirect communication.
Further improve of the invention is, in step 1), is less than in t arbitrary node j to node i Euclidean distance
TR, i.e.,Then the node be node i 1-hop neighbor nodes, (xi,t,yi,t) and (xj,t,yj,t) it is respectively node i
With j t two-dimensional coordinate,It is calculated as follows：
In formula,For node i t 1-hop neighbor node numbers,By the maximum allowed in the range of TR
1-hop neighbor node numbers, DF of the node i in ti,t(Density Factor) is defined as：
In formula,It is defined as the DF after weighti,t, ξ is weight factor, TsFor Fixed Time Interval；
Node i is in (t+Ts) momentAnd it is stored in the list item of each node：
In formula,It is node i in (t+Ts) when the 1-hop neighbor node numbers inscribed,It is the position based on node
Put, the 1-hop neighbor node estimates of speed and acceleration,And vi,tRespectively node i is in (t-Ts) moment and t
Velocity magnitude, ai,tFor the acceleration of t：
Node i is in (t+Ts) two-dimensional coordinate at moment is：
Node i and j are in (t+Ts) moment Euclidean distanceFor：
IfThen estimation node j is node i in (t+Ts) moment 1-hop neighbor nodes.
Further improve of the invention is, in step 1),For the 1-hop neighbor node collection of node i, forIn arbitrary node j,The 1-hop neighbor nodes j of node i 1-hop neighbor node density cases are represented, i.e.,The 2-hop neighbor node density cases of node i are characterized, record meetsQuantity, if should
Quantity is more than the 1-hop neighbor node numbers of node i, then it represents that the 2-hop neighbor node density of node i is excessive, is adapted to shape
Into 2-hop clusters, 1-hop clusters are otherwise suitably formed, and the state for initializing node is cluster head.
Further improve of the invention is, in step 2), relative mobility is the relative speed difference definition based on node
, it is assumed that the acceleration of node is constant, uses ai,tTo (t+Ts) speed at moment estimated：
vi,t-Ts=vi,t+ai,t·Ts
In formula,For the 2-hop neighbor node numbers of node i,For 1-hop the and 2-hop neighbor nodes of node i
Collection,WithAverage speed for node i and 1-hop, 2-hop neighbor node is poor：
VF of the node i in ti,t(Velocity Factor) is defined as：
Node i is in (t+Ts) momentIt is defined as：
Method of estimation andThe estimation technique is similar, i.e. the 1-hop neighbours to the 1-hop neighbor nodes of node i
The estimation that node carries out Euclidean distance calculates,After weightε is weight factor：
WithAll it is stored in the list item of node, the cluster head selection of 1-hop clusters：
The cluster head selection of 2-hop clusters：
Further improve of the invention is, by all dividing all vehicle nodes in VANETs in step 1) and step 2)
Get well cluster and specify that the state of node, in step 3), in order to ensure the stability of clustering architecture, the cluster divided has been safeguarded,
In view of two kinds of situations：
301) when the stateless for having just enter into a system vehicle node request adds adjacent cluster, selection and adjacent cluster cephalomere
The minimum cluster of the Euclidean distance of point adds, in order to avoid the frequent updating of cluster, by the moving direction, speed and the position that obtain node
Put to estimate (t+Ts) moment Euclidean distanceAnd the state for initializing node is cluster member, by the node and cluster head
Relative mobility is compared, if being more than or equal to, node state is constant, and otherwise the state of the node is set to cluster head, originally
Cluster head is reduced to cluster member；
302) system allows when traffic density is excessive, and 1-hop clusters and adjacent 1-hop clusters can be merged into 2-hop clusters, but
It is that system does not allow 1-hop clusters and 2-hop clusters to be merged into bigger cluster；(t+T between the cluster head of two adjacent 1-hop clusterss)
The Euclidean distance at momentDuring less than TR, it is allowed to which two clusters merge, due to allowing a cluster head be present in cluster, therefore to two
(the t+T of individual cluster heads) relative mobility at moment is compared, the small cluster head as 2-hop clusters of relative mobility is selected, separately
One is then reduced to cluster member.
The present invention has following beneficial effect：
The present invention is when the vehicle node in vehicular ad hoc network carries out sub-clustering, to the 1-hop neighbours of future time
Node density is estimated, enables formation 1-hop clusters and 2-hop clusters that the node in network is adaptive, and initialize node
State be 1-hop cluster heads or 2-hop cluster heads, then unique cluster head section in each cluster is selected by minimum speed variance method
Point, other nodes are then reduced to cluster member.It is cluster to select the node that ID is minimum in node 1-hop neighbor nodes compared to minimum ID methods
Head, sub-clustering method proposed by the present invention considered node local density i.e. 1-hop neighbor nodes density and node it is relative
Mobility, overcomes single cluster structured in network, ensure that the reasonability of sub-clustering, improves the steady of clustering architecture in network
Qualitative and inter-node communication efficiency.The invention also provides the maintenance strategy of two for being likely to occur in network kind situation, two
Kind strategy is all based on the estimation of future time, avoids the frequent updating of cluster head.
Further, the present invention proposes a kind of current location based on node, speed and acceleration to estimate node in the future
The position at moment and speed, so as to estimate the 1-hop neighbor node density of Euclidean distance and node between future time node, root
It is divided into 1-hop clusters and 2-hop clusters according to what density size made node self-adapting, while it is cluster head to initialize node state, to network
In each 1-hop clusters or 2-hop clusters unique leader cluster node is selected according to minimum speed variance method.Adaptive sub-clustering method
The estimation technique of future time is all based on minimum speed variance method, cluster structured robustness and stability is improved, overcomes
It is single cluster structured in network, number of clusters amount and cluster size in network are balanced, so as to improve the communication performance of network.
Further, two kinds of situations of the invention to being likely to occur in network：New node adds adjacent cluster and two 1-hop clusters
Merging, add based on future time estimation clustering architecture maintenance mechanism, reduce the renewal frequency of cluster head, ensure that network
Stability.
Brief description of the drawings
Fig. 1 is the FB(flow block) of the cluster algorithm of the present invention；
Fig. 2 is the illustraton of model with the simulating scenes of VanetMobiSim modelings；
Fig. 3 is the average delay performance map that the present invention is emulated with NS-2.35；
Fig. 4 is the packet loss performance map that the present invention is emulated with NS-2.35.
Embodiment
The present invention is described in further detail with reference to the accompanying drawings and examples：
As shown in figure 1,1-hop the and 2-hop clusters based on density estimation and deposited adaptive in VANETs provided by the invention
Sub-clustering method is answered, is comprised the following steps：
By the current location (x for obtaining nodei,t,yi,t), speed vi,tWith acceleration ai,tEstimate node in (t+Ts) when
The position at quarterAnd speed
By judging (t+Ts) Euclidean distance between moment nodeComparison with node communication radius TR obtains node
In (t+Ts) moment 1-hop neighbor node numbers estimateAnd then each node is calculated in (t+Ts) moment DF, 1-
HopVF and 2-hopVF is simultaneously stored in the list item of each node：
In formula：
For the 1-hop neighbor node collection of node i, forIn arbitrary node j,Represent node i
1-hop neighbor nodes j 1-hop neighbor node density cases, i.e.,Characterize the 2-hop neighbor node density of node i
Situation, record meetQuantity NDFIf NDFMore than the 1-hop neighbor node numbers of node i's
, then it represents that the 2-hop neighbor node density of node i is excessive, suitably forms 2-hop clusters, otherwise suitably forms 1-hop clusters, and just
The state of each node of beginningization is cluster head.
Unique leader cluster node in each cluster is selected by minimum speed variance method, other leader cluster nodes be then reduced to cluster into
Member, the cluster head back-and-forth method of 1-hop clusters are：
The cluster head back-and-forth method of 2-hop clusters is：
In order to avoid cluster head frequent updating and ensure network in clustering architecture stability, to be likely to occur in network two
Kind situation proposes maintenance strategy：
1) when the stateless for having just enter into a system vehicle node request adds adjacent cluster, selection and adjacent leader cluster node
(t+Ts) the minimum cluster of moment Euclidean distance adds, and the state for initializing node is cluster member, passes through minimum speed variance
Method choosing judges that the node is to maintain the state of cluster member and still turns into leader cluster node.
2) system allows when traffic density is excessive, and as the (t+T between the cluster head of two adjacent 1-hop clusterss) moment
Euclidean distanceWhen (estimate) is less than TR, it is allowed to two clusters merge, due to only allowing to have a cluster head in cluster, it is necessary to
To (the t+T of two cluster headss) relative mobility (estimate) at moment is compared, select relative velocity variance it is small be used as 2-
The cluster head of hop clusters, another is then reduced to cluster member.
Fig. 2 is modelings of the vehicle movable simulation device VanetMobiSim to vehicle mobility model in vehicular ad hoc network.
Simulating area is 2000 × 1000m2, simulation time 1000s, number of track-lines 4, the maximum travelling speed of vehicle is 30km/h,
Vehicle mobility model is IDM-IM (intelligent Driver Model for carrying crossroad management function).
Fig. 3 and Fig. 4 is average delay and packet loss simulation performance figure of the ID algorithms of the invention and minimum in NS-2.35.
Design parameter is arranged to：Simulation time is 100s, and propagation model is two-path model, mac-layer protocol IEEE802.11p, data
Packet type is CBR, node communication radius TR=250m, ζ=0.5, ε=0.5,DFThresh=5.71.
Fig. 3 curve shows that, with the increase of vehicle node number in network, network load increase, the present invention carries algorithm
Due to having considered local density and the relative mobility of node, adaptively formed 1-hop clusters and 2-hop clusters, so as to balance
Number of clusters amount and cluster size in network.In low-density (vehicle node number is less than 60), average delay of the invention is slow from 3ms
Rise to 10ms；In high density, rate of rise slightly improves, and average delay rises to 0.12s from 4ms.It can be seen that contrast
The average delay of algorithm carries algorithm apparently higher than the present invention, and cluster algorithm of the invention is more suitable for tight to propagation delay time requirement
The application of the vehicular ad hoc network of lattice.
Fig. 4 curve shows, with the increase of vehicle node number in network, number of clusters amount can also accordingly increase so that section
The average length increase of point route, so as to cause packet loss to be consequently increased, it can be seen that the present invention carries the packet loss of algorithm
Less than contrast algorithm.The cluster algorithm of the present invention ensure that low packet loss ratio while low transmission time delay is realized, and have more preferable
Robustness and stability.
Claims (6)
1-hop and 2-hop clusters based on density estimation and the adaptive sub-clustering method deposited in 1.VANETs, it is characterised in that including
Following steps：
1) the adaptive cluster-dividing method based on 1-hop neighbor node density estimations：By Euclidean distance node and communication half
Footpath TR, which compares, obtains 1-hop neighbor node density, and future time is estimated in current location, speed and acceleration based on node
1-hop neighbor node density, it is stored in the list item of node after normalization and weight, by relatively judging the section with threshold value
Point suitably forms 1-hop or 2-hop clusters, and it is leader cluster node to initialize node；
2) the cluster head system of selection based on minimum relative mobility：By all node divisions for fixed size in step 1)
1-hop or 2-hop clusters and to initialize each node be cluster head, in each cluster, to other nodes in each node and cluster
Velocity magnitude is compared, and the minimum node of the velocity variance of selection and 1-hop neighbor nodes is cluster head, and other nodes are then in cluster
Cluster member is reduced to, if the node of relative mobility minimum is two or more, it is cluster head to select the minimum nodes of ID；
3) maintenance of clustering architecture：Occur two kinds of situations in a network, one kind is that a stateless vehicle node has just enter into and is
System request adds neighbours' cluster, and another kind is that the cluster head of a 1-hop cluster sends the request 1-hop cluster adjacent with another and closed
And.
2. 1-hop the and 2-hop clusters based on density estimation and the adaptive sub-clustering deposited in VANETs according to claim 1
Method, it is characterised in that in step 1), number of clusters amount in VANETs and cluster size influence the communication performance between vehicle, number of clusters amount and
Cluster size is related to traffic density, adaptive by the 1-hop neighbor node density estimation vehicle nodes to vehicle node
1-hop or 2-hop clusters are formed to balance the number of clusters amount and cluster size in VANETs；When traffic density is low, 1-hop is suitably formed
Cluster, cluster head and all cluster members are 1-hop distances in cluster, between node can direct communication, when traffic density is relatively high, be adapted to
2-hop clusters are formed, cluster head and part cluster member are 1-hop distances, direct communication in cluster, and another part cluster member is 2-hop
Distance, indirect communication.
3. 1-hop the and 2-hop clusters based on density estimation and the adaptive sub-clustering deposited in VANETs according to claim 1
Method, it is characterised in that in step 1), be less than TR in t arbitrary node j to node i Euclidean distance, i.e.,Then
The node be node i 1-hop neighbor nodes, (xi,t,yi,t) and (xj,t,yj,t) it is respectively the two dimension of node i and j in t
Coordinate,It is calculated as follows：
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<mo>,</mo>
<mi>t</mi>
<mo>-</mo>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>v</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
</mrow>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
</mfrac>
</mrow>
Node i is in (t+Ts) two-dimensional coordinate at moment is：
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
<mo>+</mo>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
</mrow>
</msub>
<mo>=</mo>
<msub>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>v</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>&CenterDot;</mo>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
<mo>+</mo>
<msub>
<mi>a</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>&CenterDot;</mo>
<msup>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
<mn>2</mn>
</msup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>y</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
<mo>+</mo>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
</mrow>
</msub>
<mo>=</mo>
<msub>
<mi>y</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>v</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>&CenterDot;</mo>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
<mo>+</mo>
<msub>
<mi>a</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>&CenterDot;</mo>
<msup>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
<mn>2</mn>
</msup>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
Node i and j are in (t+Ts) moment Euclidean distanceFor：
<mrow>
<msubsup>
<mi>&Delta;D</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>j</mi>
</mrow>
<mrow>
<mi>t</mi>
<mo>+</mo>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
</mrow>
</msubsup>
<mo>=</mo>
<msqrt>
<mrow>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>x</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
<mo>+</mo>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>x</mi>
<mrow>
<mi>j</mi>
<mo>,</mo>
<mi>t</mi>
<mo>+</mo>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
<mo>+</mo>
<msup>
<mrow>
<mo>(</mo>
<msub>
<mi>y</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
<mo>+</mo>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
</mrow>
</msub>
<mo>-</mo>
<msub>
<mi>y</mi>
<mrow>
<mi>j</mi>
<mo>,</mo>
<mi>t</mi>
<mo>+</mo>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
</mrow>
</msub>
<mo>)</mo>
</mrow>
<mn>2</mn>
</msup>
</mrow>
</msqrt>
</mrow>
IfThen estimation node j is node i in (t+Ts) moment 1-hop neighbor nodes.
4. 1-hop the and 2-hop clusters based on density estimation and the adaptive sub-clustering deposited in VANETs according to claim 3
Method, it is characterised in that in step 1),For the 1-hop neighbor node collection of node i, forIn arbitrary node j,The 1-hop neighbor nodes j of node i 1-hop neighbor node density cases are represented, i.e.,Characterize the 2- of node i
Hop neighbor node density cases, record meetQuantity, if the 1-hop that the quantity is more than node i is adjacent
Occupy nodesThen represent that the 2-hop neighbor node density of node i is excessive, suitably forms 2-hop clusters, otherwise suitably forms
1-hop clusters, and the state for initializing node is cluster head.
5. 1-hop the and 2-hop clusters based on density estimation and the adaptive sub-clustering deposited in VANETs according to claim 4
Method, it is characterised in that in step 2), relative mobility is that the relative speed difference based on node defines, it is assumed that the acceleration of node
Spend constant, use ai,tTo (t+Ts) speed at moment estimated：
<mrow>
<msub>
<mi>v</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
<mo>-</mo>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
</mrow>
</msub>
<mo>=</mo>
<msub>
<mi>v</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>+</mo>
<msub>
<mi>a</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>&CenterDot;</mo>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
</mrow>
In formula,For the 2-hop neighbor node numbers of node i,For 1-hop the and 2-hop neighbor node collection of node i,WithAverage speed for node i and 1-hop, 2-hop neighbor node is poor：
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mover>
<mi>v</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mn>1</mn>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<msubsup>
<mi>N</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
<mrow>
<mn>1</mn>
<mo>-</mo>
<mi>h</mi>
<mi>o</mi>
<mi>p</mi>
</mrow>
</msubsup>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mi>j</mi>
<msubsup>
<mi>N</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
<mrow>
<mn>1</mn>
<mo>-</mo>
<mi>h</mi>
<mi>o</mi>
<mi>p</mi>
</mrow>
</msubsup>
</munderover>
<mo>|</mo>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<msub>
<mi>v</mi>
<mi>j</mi>
</msub>
<mo>|</mo>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>j</mi>
<mo>&Element;</mo>
<msubsup>
<mi>S</mi>
<mi>i</mi>
<mrow>
<mn>1</mn>
<mo>-</mo>
<mi>h</mi>
<mi>o</mi>
<mi>p</mi>
</mrow>
</msubsup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mover>
<mi>v</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mn>2</mn>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<msubsup>
<mi>N</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
<mrow>
<mn>2</mn>
<mo>-</mo>
<mi>h</mi>
<mi>o</mi>
<mi>p</mi>
</mrow>
</msubsup>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mi>j</mi>
<msubsup>
<mi>N</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
<mrow>
<mn>2</mn>
<mo>-</mo>
<mi>h</mi>
<mi>o</mi>
<mi>p</mi>
</mrow>
</msubsup>
</munderover>
<mo>|</mo>
<msub>
<mi>v</mi>
<mi>i</mi>
</msub>
<mo>-</mo>
<msub>
<mi>v</mi>
<mi>j</mi>
</msub>
<mo>|</mo>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>j</mi>
<mo>&Element;</mo>
<msubsup>
<mi>S</mi>
<mi>i</mi>
<mrow>
<mn>2</mn>
<mo>-</mo>
<mi>h</mi>
<mi>o</mi>
<mi>p</mi>
</mrow>
</msubsup>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
VF of the node i in ti,t(Velocity Factor) is defined as：
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>VF</mi>
<mrow>
<msub>
<mi>i</mi>
<mn>1</mn>
</msub>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<msubsup>
<mi>N</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
<mrow>
<mn>1</mn>
<mo>-</mo>
<mi>h</mi>
<mi>o</mi>
<mi>p</mi>
</mrow>
</msubsup>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mi>j</mi>
<msubsup>
<mi>N</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
<mrow>
<mn>1</mn>
<mo>-</mo>
<mi>h</mi>
<mi>o</mi>
<mi>p</mi>
</mrow>
</msubsup>
</munderover>
<mo>|</mo>
<msub>
<mi>v</mi>
<mi>j</mi>
</msub>
<mo>-</mo>
<msub>
<mover>
<mi>v</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mn>1</mn>
</mrow>
</msub>
<msup>
<mo>|</mo>
<mn>2</mn>
</msup>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>j</mi>
<mo>&Element;</mo>
<msubsup>
<mi>S</mi>
<mi>i</mi>
<mrow>
<mn>1</mn>
<mo>-</mo>
<mi>h</mi>
<mi>o</mi>
<mi>p</mi>
</mrow>
</msubsup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>VF</mi>
<mrow>
<msub>
<mi>i</mi>
<mn>2</mn>
</msub>
<mo>,</mo>
<mi>t</mi>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<msubsup>
<mi>N</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
<mrow>
<mn>2</mn>
<mo>-</mo>
<mi>h</mi>
<mi>o</mi>
<mi>p</mi>
</mrow>
</msubsup>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mi>j</mi>
<msubsup>
<mi>N</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
</mrow>
<mrow>
<mn>2</mn>
<mo>-</mo>
<mi>h</mi>
<mi>o</mi>
<mi>p</mi>
</mrow>
</msubsup>
</munderover>
<mo>|</mo>
<msub>
<mi>v</mi>
<mi>j</mi>
</msub>
<mo>-</mo>
<msub>
<mover>
<mi>v</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mn>2</mn>
</mrow>
</msub>
<msup>
<mo>|</mo>
<mn>2</mn>
</msup>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>j</mi>
<mo>&Element;</mo>
<msubsup>
<mi>S</mi>
<mi>i</mi>
<mrow>
<mn>2</mn>
<mo>-</mo>
<mi>h</mi>
<mi>o</mi>
<mi>p</mi>
</mrow>
</msubsup>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
2
Node i is in (t+Ts) momentIt is defined as：
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msub>
<mi>VF</mi>
<mrow>
<msub>
<mi>i</mi>
<mn>1</mn>
</msub>
<mo>,</mo>
<mi>t</mi>
<mo>+</mo>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<msubsup>
<mi>N</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
<mo>+</mo>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
</mrow>
<mrow>
<mn>1</mn>
<mo>-</mo>
<mi>h</mi>
<mi>o</mi>
<mi>p</mi>
</mrow>
</msubsup>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mi>j</mi>
<msubsup>
<mi>N</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
<mo>+</mo>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
</mrow>
<mrow>
<mn>1</mn>
<mo>-</mo>
<mi>h</mi>
<mi>o</mi>
<mi>p</mi>
</mrow>
</msubsup>
</munderover>
<mo>|</mo>
<msub>
<mi>v</mi>
<mi>j</mi>
</msub>
<mo>-</mo>
<msub>
<mover>
<mi>v</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mn>1</mn>
</mrow>
</msub>
<msup>
<mo>|</mo>
<mn>2</mn>
</msup>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>j</mi>
<mo>&Element;</mo>
<msubsup>
<mi>S</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
<mo>+</mo>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
</mrow>
<mrow>
<mn>1</mn>
<mo>-</mo>
<mi>h</mi>
<mi>o</mi>
<mi>p</mi>
</mrow>
</msubsup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msub>
<mi>VF</mi>
<mrow>
<msub>
<mi>i</mi>
<mn>2</mn>
</msub>
<mo>,</mo>
<mi>t</mi>
<mo>+</mo>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
</mrow>
</msub>
<mo>=</mo>
<mfrac>
<mn>1</mn>
<msubsup>
<mi>N</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
<mo>+</mo>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
</mrow>
<mrow>
<mn>2</mn>
<mo>-</mo>
<mi>h</mi>
<mi>o</mi>
<mi>p</mi>
</mrow>
</msubsup>
</mfrac>
<munderover>
<mo>&Sigma;</mo>
<mi>j</mi>
<msubsup>
<mi>N</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
<mo>+</mo>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
</mrow>
<mrow>
<mn>2</mn>
<mo>-</mo>
<mi>h</mi>
<mi>o</mi>
<mi>p</mi>
</mrow>
</msubsup>
</munderover>
<mo>|</mo>
<msub>
<mi>v</mi>
<mi>j</mi>
</msub>
<mo>-</mo>
<msub>
<mover>
<mi>v</mi>
<mo>&OverBar;</mo>
</mover>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mn>2</mn>
</mrow>
</msub>
<msup>
<mo>|</mo>
<mn>2</mn>
</msup>
<mo>,</mo>
</mrow>
</mtd>
<mtd>
<mrow>
<mi>j</mi>
<mo>&Element;</mo>
<msubsup>
<mi>S</mi>
<mrow>
<mi>i</mi>
<mo>,</mo>
<mi>t</mi>
<mo>+</mo>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
</mrow>
<mrow>
<mn>2</mn>
<mo>-</mo>
<mi>h</mi>
<mi>o</mi>
<mi>p</mi>
</mrow>
</msubsup>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
Method of estimation andThe estimation technique is similar, i.e., the 1-hop neighbor nodes of the 1-hop neighbor nodes of node i is entered
The estimation of row Euclidean distance calculates,After weightε is weight factor：
<mfenced open = "{" close = "">
<mtable>
<mtr>
<mtd>
<mrow>
<msubsup>
<mi>VF</mi>
<mrow>
<msub>
<mi>i</mi>
<mn>1</mn>
</msub>
<mo>,</mo>
<mi>t</mi>
<mo>+</mo>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
</mrow>
<mi>w</mi>
</msubsup>
<mo>=</mo>
<mi>&epsiv;</mi>
<mo>&CenterDot;</mo>
<msub>
<mi>VF</mi>
<mrow>
<msub>
<mi>i</mi>
<mn>1</mn>
</msub>
<mo>,</mo>
<mi>t</mi>
<mo>+</mo>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
</mrow>
</msub>
<mo>+</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<mi>&epsiv;</mi>
<mo>)</mo>
</mrow>
<mo>&CenterDot;</mo>
<msubsup>
<mi>VF</mi>
<mrow>
<msub>
<mi>i</mi>
<mn>1</mn>
</msub>
<mo>,</mo>
<mi>t</mi>
</mrow>
<mi>w</mi>
</msubsup>
</mrow>
</mtd>
</mtr>
<mtr>
<mtd>
<mrow>
<msubsup>
<mi>VF</mi>
<mrow>
<msub>
<mi>i</mi>
<mn>2</mn>
</msub>
<mo>,</mo>
<mi>t</mi>
<mo>+</mo>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
</mrow>
<mi>w</mi>
</msubsup>
<mo>=</mo>
<mi>&epsiv;</mi>
<mo>&CenterDot;</mo>
<msub>
<mi>VF</mi>
<mrow>
<msub>
<mi>i</mi>
<mn>2</mn>
</msub>
<mo>,</mo>
<mi>t</mi>
<mo>+</mo>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
</mrow>
</msub>
<mo>+</mo>
<mrow>
<mo>(</mo>
<mn>1</mn>
<mo>-</mo>
<mi>&epsiv;</mi>
<mo>)</mo>
</mrow>
<mo>&CenterDot;</mo>
<msubsup>
<mi>VF</mi>
<mrow>
<msub>
<mi>i</mi>
<mn>2</mn>
</msub>
<mo>,</mo>
<mi>t</mi>
</mrow>
<mi>w</mi>
</msubsup>
</mrow>
</mtd>
</mtr>
</mtable>
</mfenced>
WithAll it is stored in the list item of node, the cluster head selection of 1-hop clusters：
<mrow>
<mi>C</mi>
<mi>H</mi>
<mo>=</mo>
<munder>
<mrow>
<mi>arg</mi>
<mi> </mi>
<mi>min</mi>
</mrow>
<mrow>
<mi>j</mi>
<mo>&Element;</mo>
<mrow>
<mo>(</mo>
<msubsup>
<mi>S</mi>
<mi>i</mi>
<mrow>
<mn>1</mn>
<mo>-</mo>
<mi>h</mi>
<mi>o</mi>
<mi>p</mi>
</mrow>
</msubsup>
<mo>+</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
</mrow>
</munder>
<msubsup>
<mi>VF</mi>
<mrow>
<msub>
<mi>j</mi>
<mn>1</mn>
</msub>
<mo>,</mo>
<mi>t</mi>
<mo>+</mo>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
</mrow>
<mi>w</mi>
</msubsup>
</mrow>
The cluster head selection of 2-hop clusters：
<mrow>
<mi>C</mi>
<mi>H</mi>
<mo>=</mo>
<munder>
<mrow>
<mi>arg</mi>
<mi> </mi>
<mi>min</mi>
</mrow>
<mrow>
<mi>j</mi>
<mo>&Element;</mo>
<mrow>
<mo>(</mo>
<msubsup>
<mi>S</mi>
<mi>i</mi>
<mrow>
<mn>2</mn>
<mo>-</mo>
<mi>h</mi>
<mi>o</mi>
<mi>p</mi>
</mrow>
</msubsup>
<mo>+</mo>
<mi>i</mi>
<mo>)</mo>
</mrow>
</mrow>
</munder>
<msubsup>
<mi>VF</mi>
<mrow>
<msub>
<mi>j</mi>
<mn>2</mn>
</msub>
<mo>,</mo>
<mi>t</mi>
<mo>+</mo>
<msub>
<mi>T</mi>
<mi>s</mi>
</msub>
</mrow>
<mi>w</mi>
</msubsup>
</mrow>
6. 1-hop the and 2-hop clusters based on density estimation and the adaptive sub-clustering deposited in VANETs according to claim 5
Method, it is characterised in that by all having divided cluster to all vehicle nodes in VANETs in step 1) and step 2) and having specify that section
The state of point, in step 3), in order to ensure the stability of clustering architecture, the cluster divided is safeguarded, it is contemplated that two kinds of situations：
301) when the stateless for having just enter into a system vehicle node request adds adjacent cluster, selection and adjacent leader cluster node
The minimum cluster of Euclidean distance adds, in order to avoid the frequent updating of cluster, by obtain moving direction, speed and the position of node come
Estimate (t+Ts) moment Euclidean distanceAnd the state for initializing node is cluster member, by the relative of the node and cluster head
Mobility is compared, if being more than or equal to, node state is constant, and otherwise the state of the node is set to cluster head, cluster head originally
It is reduced to cluster member；
302) system allows when traffic density is excessive, and 1-hop clusters and adjacent 1-hop clusters can be merged into 2-hop clusters, but be
System does not allow 1-hop clusters and 2-hop clusters to be merged into bigger cluster；(t+T between the cluster head of two adjacent 1-hop clusterss) moment
Euclidean distanceDuring less than TR, it is allowed to which two clusters merge, due to allowing a cluster head be present in cluster, therefore to two clusters
(the t+T of heads) relative mobility at moment is compared, the small cluster head as 2-hop clusters of relative mobility is selected, another
Then it is reduced to cluster member.
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| CN108834195A (en) * | 2018-06-19 | 2018-11-16 | 中国联合网络通信集团有限公司 | Cluster merging method and its module and microcontroller in clustered network structure |
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| CN111354193A (en) * | 2020-02-26 | 2020-06-30 | 江苏大学 | Highway vehicle abnormal behavior early warning system based on 5G communication |
| CN111354193B (en) * | 2020-02-26 | 2021-09-10 | 江苏大学 | Highway vehicle abnormal behavior early warning system based on 5G communication |
| CN112004208A (en) * | 2020-06-07 | 2020-11-27 | 西南民族大学 | Artificial fish school clustering method, storage medium and device in Internet of vehicles |
| CN112004208B (en) * | 2020-06-07 | 2021-07-20 | 西南民族大学 | Artificial fish school clustering method, storage medium and device in Internet of vehicles |
| CN113329368A (en) * | 2021-06-07 | 2021-08-31 | 清华大学深圳国际研究生院 | Route clustering method and device based on affinity propagation algorithm |
| CN113329368B (en) * | 2021-06-07 | 2022-03-25 | 清华大学深圳国际研究生院 | Route clustering method and device based on affinity propagation algorithm |
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