CN107426794A - A kind of combined weighted cluster head choosing method suitable for VANET networks - Google Patents
A kind of combined weighted cluster head choosing method suitable for VANET networks Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
- H04W40/32—Connectivity information management, e.g. connectivity discovery or connectivity update for defining a routing cluster membership
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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
Virtual cluster head is determined the invention discloses a kind of combined weighted cluster head choosing method suitable for VANET networks, including according to VANET node location informations；Choose the evaluation indice of cluster algorithm；Each node reports the self information related to sub-clustering index in communication range with broadcast mode；Calculate the comentropy and dynamic entropy weight of each node clustering index；WillNThe dynamic entropy weight vectors of individual node carry out any linear combination, obtain multinode kolmogorov sinai entropy value weight vectors, and are combined with subjective weight to obtain combining weights, and then complete the steps such as rational cluster head selection.Using dynamic entropy combined method, the dynamic characteristic of network is considered in weight computations, the calculating of weight is more nearly the true distribution of network, the selection of cluster head is more reasonable, and what is be consequently formed is cluster structured more stable；Meanwhile with reference to the combined weighted sub-clustering weighing computation method of subjective and objective factor, can both reflect the preference of policymaker, the reasonability of weight distribution is improved again.
Description
Technical field
The invention belongs to communication technical field, is related to a kind of cluster-dividing method of vehicular ad hoc network, refers specifically to one kind and be applied to
The combined weighted cluster head choosing method of vehicular ad hoc network network.
Background technology
Vehicular ad hoc network VANET (Vehicular Ad Hoc Networks) is mobile ad hoc network MANET (Mobile
Ad Hoc Networks) a kind of special shape, it by Che-car, it is car-ground between communication, be embodied as road safety,
The offer support such as traffic administration and comfortableness application.U.S. FCC is by the 75MHz frequency allocations between 5850~5925MHz
Give vehicular ad hoc network application, referred to as DSRC (Dedicated Short Range Communications).
Cluster algorithm has proven to manage the effective ways of vehicular ad hoc network Internet resources.Therefore, one it is stable
The cluster structured performance on whole network has direct influence.Vehicular ad hoc network network structure based on sub-clustering can be more
Network overhead is easily managed, effectively improves the utilization ratio of Internet resources.Existing grinding on vehicular ad hoc network cluster algorithm
Study carefully the cluster algorithm research for being all mainly based upon weights, the calculating of its weight be according to the significance level of different factor indexs come
Determine.Vehicular ad hoc network is a dynamic process, and the relative importance of each factor was dynamically changed in one kind
Cheng Zhong, it is a kind of preferably Objective Weight side although based on the weighing computation method of Information Entropy with stronger theoretical foundation
Method.But Information Entropy determines that the weight of each factor depends only on the difference journey of each factor value between each node of a certain moment
Degree, not in view of the stability of network during each factor weight is calculated, so this methodology can not be fine
Each factor of determination dynamic network weight.
According to common configuration of the positioners such as GPS on vehicle, the cluster-dividing method based on positional information causes research
The close attention of personnel, the GAF (Geographical Adaptive Fidelity algorithm) such as based on grid clustering
Algorithm, node determine position using GPS, and network topology can be primarily determined that by node location information, be advised by grid
The virtual center of mass that certain group node is calculated is drawn as cluster head；But the cluster that this algorithm is generated is shaped as square, same
Reduce the number of cluster in the case of area coverage.LACA (location aware clustering algorithm) algorithms with
Distance R be the border circular areas of radius as model, it is determined that the progress network planning of virtual cluster head, because clustering architecture is regular hexagon
Structure, the cluster algorithm of radio communication ideal overlay model is closest to, but LACA is mainly for wireless sensor network WSN
(Wireless Sensor Network) is designed, and does not consider node mobility problems.
Therefore, existing cluster-dividing methods of the vehicular ad hoc network VANET based on Information Entropy does not consider before the present invention is implemented
The dynamic characteristic of network, it is impossible to the true distribution of reflection network well, it is unreasonable, cluster structured to there is network cluster head selection
Unstable technical problem, directly affect the performance of whole network.
The content of the invention
For above-mentioned technical problem, the invention provides a kind of combined weighted cluster head selection side suitable for VANET networks
Method.
The present invention is achieved through the following technical solutions above-mentioned purpose：
A kind of combined weighted cluster head choosing method suitable for VANET networks, comprise the following steps：
A, position is passed through to determine virtual cluster head as model, the progress network planning using the border circular areas that distance R is radius
Put algorithm and obtain the positional information of other nodes in network, and according to the position of virtual cluster head by neighbouring node division to
In the corresponding cluster of virtual cluster head；
B, the evaluation indice of cluster algorithm is chosenIts
In, Dv is node degree,The distance of the virtual cluster head of nodal point separation is represented, Cstab represents node mobility, and Ev represents node energy consumption；
C, each node in vehicular ad hoc network is made to report oneself related to sub-clustering index in its communication range with broadcast mode
Body information；Then at a time, each network node has recorded m (m >=1) bar message；According to the information obtained, calculate
The comentropy and dynamic entropy weight of each 4 sub-clustering indexs of node, and the dynamic entropy power of node is obtained by dynamic entropy weight
Weight vector；
D, the dynamic entropy weight vectors of N number of node are subjected to any linear combination, obtain multinode dynamic entropy weight
Vector, to improve the science and objectivity of more attribute weight assignment；
E, with reference to subjective weight (S [SA,SB,SC,SD]) and multinode dynamic entropy weight w [wA,wB,wC,wD], obtain group
Close weight；
F, it is leader cluster node to choose the node with minimum combination weighted value W, if the section with minimum combination weighted value W
During point number more than one, then the wherein minimum node of Cstab values is leader cluster node.
As the prioritization scheme of this case, the comentropy of the step c interior joints sub-clustering index and the meter of dynamic entropy weight
Calculation method is specially：
Make EiDThe sub-clustering index D of node i comentropy is represented, then：
EiD=-(ln2)-1(PiD_min lnPiD_min+PiD_max lnPiD_max) (1)
In formula, PiD_minAnd PiD_maxRespectively
In formula, ViD_minAnd ViD_maxThe floor value and upper dividing value (0 of the sub-clustering index D spans of node i are represented respectively
≤ViD_min≤ViD_max), the broadcast message that its value is received by node i can obtain；
Thus the sub-clustering index D of node i dynamic entropy weight can be calculated：
Wherein, Eia、EiB、EiCComentropy respectively corresponding to sub-clustering index A, B, C of node i；Similarly, can distinguish
Obtain the dynamic entropy weight w corresponding to sub-clustering index A, B, C of node iiA、 wiBAnd wiC, they meet following constraints：
The dynamic entropy weight vectors of node i can be obtained by above formula (5)：wi[wiA,wiB,wiC,wiD]。
As the prioritization scheme of this case, in the step d acquirement of multinode dynamic entropy weight vectors include combination system
Several optimization, specifically includes following steps：
The dynamic entropy weight vectors of N number of node are subjected to any linear combination, obtain multinode dynamic entropy weight to
Measure and be：
Wherein, αi(i=1,2 ... N) it is combination coefficient；
In order in multinode weight vectors w*In find optimal weight vectors w, to N linear combination in above formula (6)
Factor alphaiOptimize, the target of optimization is so that w*With each wiDeviation minimization,
Therefore, following game model can be established：
The game model that above formula provides is second order norm, because being related to out radical sign, is not easy to seek factor alpha as partial derivativei, therefore will
It is converted to square of second order norm, i.e.,：
Above formula (8) is to αi(i=1,2 ... N) ask local derviation to obtain：
Above formula (9) is made to be equal to 0, i.e.,：
Then have
Above formula (11) is represented with following system of linear equations：
Combination coefficient (α can be tried to achieve1,α2,...,αN), further, multinode kolmogorov sinai entropy during deviation minimization can be obtained
Value weight vectors are：
As the prioritization scheme of this case, in the step e, with reference to subjective weight (S [SA, SB, SC, SD]) and multinode
Dynamic entropy weight w [wA,wB,wC,wD], can obtain combining weights is：
W(WA,WB,WC,WD)=β1S+β2w (13)
Wherein, SA, SB, SC, SDRespectively index A, B, C, D subjective weight, its value is according to VANET networks in different fields
Application in scape is configured；β1、β1For combining weights coefficient, it meets：
β1+β2=1 0≤β1≤1,0≤β2≤1 (14)
Then the combining weights of each sub-clustering index of node i are represented by：
As the prioritization scheme of this case, in the step a, each node passes through node locating device in vehicular ad hoc network
The positional information of node is obtained, some point positioned at regular hexagon clustering architecture center is preset as virtual cluster head v*, then pass through
Position algorithm obtains the positional information of other nodes in network, and is arrived neighbouring node division according to the position of virtual cluster head
In the cluster corresponding with virtual cluster head.
The beneficial effects of the invention are as follows：
1st, dynamic entropy combined method is employed, the dynamic characteristic of network is brought into weight computations；Not only examine
Considered each factor value i.e. difference degree of desired value between a certain moment node, and consider same node current time with
The difference degree of each factor value of previous time, the calculating of weight is set to be more nearly the true distribution of network, the selection of cluster head is more
Rationally, what is be consequently formed is cluster structured more stable；
2nd, the purpose of sub-clustering policymaker can only be reflected for the index weights with preference, and the index that Information Entropy determines
Though weight lacks the consideration to policymaker's purpose with stronger mathematical theory foundation；This method combine subjective and objective weight because
The combined weighted sub-clustering weighing computation method of element, can both reflect the preference of policymaker, effectively increase the reasonable of weight distribution again
Property.
Embodiment
The present invention and its effect will be further elaborated below.
A kind of combined weighted cluster head choosing method suitable for VANET networks, first, it is virtual to select node degree, nodal point separation
The distance of cluster head, node mobility, node energy consumption are as sub-clustering Weighted Guidelines, according in vehicular ad hoc network during some node
Message obtained by carving, the upper dividing value and floor value of each index are recorded, the comentropy and single-unit of each indicator deviation is calculated
Point dynamic entropy weight；Then, the linear combination weight set to multiple vehicular ad hoc network nodes is modeled, and turns into second order
Norm game model, then multinode dynamic entropy weight is obtained with the minimum optimization aim that turns to of weight vectors deviation, and combine master
See weight and obtain combining weights, clustering process is chosen to complete cluster head.Specifically include following steps：
A, position is passed through to determine virtual cluster head as model, the progress network planning using the border circular areas that distance R is radius
Put algorithm and obtain the positional information of other nodes in network, and according to the position of virtual cluster head by neighbouring node division to
In the corresponding cluster of virtual cluster head.
Specifically, because clustering architecture is regular hexagon structure, the sub-clustering for being closest to radio communication ideal overlay model is calculated
Method.Under actual scene, because nodes distribution is random, it can not ensure that leader cluster node is exactly in regular hexagon
The heart, but due in vehicular ad hoc network VANET each node can by positioner (such as GPS) obtain node positional information, just
Some point positioned at regular hexagon cluster center can be preset as virtual cluster head v*, then obtained by position algorithm in network
The positional information of other nodes, and according to the position of virtual cluster head by neighbouring node division to corresponding with virtual cluster head
In cluster.
B, the evaluation indice of cluster algorithm is chosenIts
In, Dv is node degree,The distance of the virtual cluster head of nodal point separation is represented, Cstab represents node mobility, and Ev represents node energy consumption.
C, each node in vehicular ad hoc network is made to report oneself related to sub-clustering index in its communication range with broadcast mode
Body information；Then at a time, each network node has recorded m (m >=1) bar message, to show node clustering index
Possible value；According to the information obtained, the comentropy and dynamic entropy weight of each 4 sub-clustering indexs of node are calculated, and by moving
State entropy weight obtains the dynamic entropy weight vectors of node；.
Specifically, the computational methods of the comentropy of node clustering index and dynamic entropy weight are：
Make EiDThe sub-clustering index D of node i comentropy is represented, then：
EiD=-(ln2)-1(PiD_min lnPiD_min+PiD_max lnPiD_max) (1)
In formula, PiD_minAnd PiD_maxRespectively
In formula, ViD_minAnd ViD_maxThe floor value and upper dividing value (0 of the sub-clustering index D spans of node i are represented respectively
≤ViD_min≤ViD_max),
Consider there is the situation of N number of evaluation node in VANET networks.For node i (i=1,2 ... N), its index D
Span floor value and upper dividing value can be designated as [ViD_min,ViD_max](0≤ViD_min≤ViD_max) (index A/B/C's takes
Value is similar with D), the broadcast message that its value is received by node i can obtain.For some sub-clustering index of node i, its index
Deviation between value is bigger, then the evaluation index is bigger to its effect in cluster head selected and sorted, the power of the evaluation index
Weight also should be bigger, conversely, the weight of the factor also should be smaller；The deviation size of some sub-clustering desired value of node i is mainly by it
Upper dividing value and floor value determine；
Node clustering index D dynamic entropy weight can be calculated by node i sub-clustering index D comentropy：
Wherein, Eia、EiB、EiCComentropy respectively corresponding to sub-clustering index A, B, C of node i；Similarly, can distinguish
Obtain the dynamic entropy weight w corresponding to sub-clustering index A, B, C of node iiA、 wiBAnd wiC, they meet following constraints：
The dynamic entropy weight vectors of node i can be obtained by above formula (5)：wi[wiA,wiB,wiC,wiD]。
D, the dynamic entropy weight vectors of N number of node are subjected to any linear combination, obtain multinode dynamic entropy weight
Vector, to improve the science and objectivity of more attribute weight assignment；
Specifically, multinode dynamic entropy weight vectors comprise the following steps：
The dynamic entropy weight vectors of N number of node are subjected to any linear combination, obtain multinode dynamic entropy weight to
Measure and be：
Wherein, αi(i=1,2 ... N) it is combination coefficient；
In order in multinode weight vectors w*In find optimal weight vectors w, to N linear combination in above formula (6)
Factor alphaiOptimize, the target of optimization is so that w*With each wiDeviation minimization,
Therefore, following game model can be established：
The game model that above formula provides is second order norm, because being related to out radical sign, is not easy to seek factor alpha as partial derivativei, therefore will
It is converted to square of second order norm, i.e.,：
Above formula (8) is to αi(i=1,2 ... N) ask local derviation to obtain：
Above formula (9) is made to be equal to 0, i.e.,：
Then have
Above formula (11) is represented with following system of linear equations：
Combination coefficient (α can be tried to achieve1,α2,...,αN), further, multinode kolmogorov sinai entropy during deviation minimization can be obtained
Value weight vectors are：
E, with reference to subjective weight (S [SA,SB,SC,SD]) and multinode dynamic entropy weight w [wA,wB,wC,wD], it can obtain
Combining weights are：
W(WA,WB,WC,WD)=β1S+β2w (13)
Wherein, SA, SB, SC, SDRespectively index A, B, C, D subjective weight, its value is according to VANET networks in different fields
Application in scape is configured；β1、β1For combining weights coefficient, it meets：
β1+β2=1 0≤β1≤1,0≤β2≤1 (14)
Then the combining weights of each sub-clustering index of node i are represented by：
F, it is leader cluster node to choose the node with minimum combination weighted value W, if the section with minimum combination weighted value W
During point number more than one, then the wherein minimum node of Cstab values is leader cluster node.
Above example is only exemplary, can't limit to the present invention, it should be pointed out that for those skilled in the art
For, the other equivalent modifications made under technical inspiration provided by the present invention and improvement, it is regarded as the present invention's
Protection domain.
Claims (5)
1. a kind of combined weighted cluster head choosing method suitable for VANET networks, it is characterised in that comprise the following steps：
A, position algorithm is passed through to determine virtual cluster head as model, the progress network planning using the border circular areas that distance R is radius
Obtain the positional information of other nodes in network, and according to the position of virtual cluster head by neighbouring node division to virtual cluster head
In corresponding cluster；
B, the evaluation indice of cluster algorithm is chosenWherein, Dv
For node degree,The distance of the virtual cluster head of nodal point separation is represented, Cstab represents node mobility, and Ev represents node energy consumption；
C, each node in vehicular ad hoc network is made to report itself letter related to sub-clustering index with broadcast mode in its communication range
Breath；Then at a time, each network node has recorded m (m >=1) bar message；According to the information obtained, each node is calculated
The comentropy and dynamic entropy weight of 4 sub-clustering indexs, and the dynamic entropy weight vectors of node are obtained by dynamic entropy weight；
D, the dynamic entropy weight vectors of N number of node are subjected to any linear combination, obtain multinode dynamic entropy weight vectors,
To improve the science and objectivity of more attribute weight assignment；
E, with reference to subjective weight (S [SA, SB, SC, SD]) and multinode dynamic entropy weight w [wA, wB, wC, wD], obtain combined weights
Weight；
F, it is leader cluster node to choose the node with minimum combination weighted value W, if the node number with minimum combination weighted value W
During more than one, then the wherein minimum node of Cstab values is leader cluster node.
2. the combined weighted cluster head choosing method according to claim 1 suitable for VANET networks, it is characterised in that：Institute
The computational methods of the comentropy and dynamic entropy weight of stating step c interior joint sub-clustering indexs are specially：
Make EiDThe sub-clustering index D of node i comentropy is represented, then：
EiD=-(ln2)-1(PiD_minln PiD_min+PiD_maxln PiD_max) (1)
In formula, PiD_minAnd PiD_maxRespectively
In formula, VID_min andViD_maxThe floor value of the sub-clustering index D spans of node i and upper dividing value (0≤V are represented respectivelyiD_min
≤ViD_max), the broadcast message that its value is received by node i can obtain；
Thus the sub-clustering index D of node i dynamic entropy weight can be calculated：
Wherein, Eia、EiB、EiCComentropy respectively corresponding to sub-clustering index A, B, C of node i；Similarly, can obtain respectively
Dynamic entropy weight w corresponding to sub-clustering index A, B, C of node iiA、wiBAnd wiC, they meet following constraints：
The dynamic entropy weight vectors of node i can be obtained by above formula (5)：wi[wiA, wiB, wiC, wiD]。
3. the combined weighted cluster head choosing method according to claim 1 suitable for VANET networks, it is characterised in that：Institute
Stating the acquirement of multinode dynamic entropy weight vectors in step d includes the optimization of combination coefficient, specifically includes following steps：
The dynamic entropy weight vectors of N number of node are subjected to any linear combination, obtaining multinode dynamic entropy weight vectors is：
Wherein, αi(i=1,2 ... N) are combination coefficient；
In order in multinode weight vectors w*In find optimal weight vectors w, to N number of linear combination coefficient α in above formula (6)i
Optimize, the target of optimization is so that w*With each wiDeviation minimization,
Therefore, following game model can be established：
The game model that above formula provides is second order norm, because being related to out radical sign, is not easy to seek factor alpha as partial derivativei, therefore be converted
For square of second order norm, i.e.,：
Above formula (8) is to αi(i=1,2 ... N) ask local derviation to obtain：
Above formula (9) is made to be equal to 0, i.e.,：
Then have
Above formula (11) is represented with following system of linear equations：
Combination coefficient (a can be tried to achieve1, a2..., aN), further, multinode dynamic entropy weight during deviation minimization can be obtained
Vector is：
4. the combined weighted cluster head choosing method according to claim 1 suitable for VANET networks, it is characterised in that：Institute
State in step e, with reference to subjective weight (S [SA, SB, SC, SD]) and multinode dynamic entropy weight w [wA, wB, wC, wD], it can obtain
Combining weights are：
W(WA, WB, WC, WD)=β1S+β2w (13)
Wherein, SA, SB, SC, SDRespectively index A, B, C, D subjective weight, its value is according to VANET networks in different scenes
Using being configured；β1、β1For combining weights coefficient, it meets：
β1+β2=1 0≤β1≤ 1,0≤β2≤1 (14)
Then the combining weights of each sub-clustering index of node i are represented by：
5. the combined weighted cluster head choosing method according to claim 1 suitable for VANET networks, it is characterised in that：Institute
State in step a, each node obtains the positional information of node by node locating device in vehicular ad hoc network, will be located at positive six side
Some point at shape clustering architecture center is preset as virtual cluster head v*, the position of other nodes in network is then obtained by position algorithm
Information, and according to the position of virtual cluster head by neighbouring node division into the cluster corresponding with virtual cluster head.
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