CN110062327A - The wireless sensor network node locating method of microhabitat grey wolf optimization DV-Hop algorithm - Google Patents
The wireless sensor network node locating method of microhabitat grey wolf optimization DV-Hop algorithm Download PDFInfo
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- CN110062327A CN110062327A CN201910338906.7A CN201910338906A CN110062327A CN 110062327 A CN110062327 A CN 110062327A CN 201910338906 A CN201910338906 A CN 201910338906A CN 110062327 A CN110062327 A CN 110062327A
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/06—Selective distribution of broadcast services, e.g. multimedia broadcast multicast service [MBMS]; Services to user groups; One-way selective calling services
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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
- H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
- H04W64/003—Locating users or terminals or network equipment for network management purposes, e.g. mobility management locating network equipment
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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 a kind of wireless sensor network node locating methods based on microhabitat grey wolf optimization DV-Hop algorithm, include: beaconing nodes are to one beacon of Web broadcast, it include that the location information of this beaconing nodes and a hop count information, this beacon are blazed abroad in a manner of flooding in a network in beacon；The distance between each node is after obtaining location information and hop count, the spacing of average each jump between estimation egress, then estimate beaconing nodes；Beaconing nodes make a change its fitness value by comparing the size of distance and microhabitat radius between beaconing nodes using microhabitat grey wolf optimization algorithm refinement Average hop distance；The self-position that unknown node is calculated by beaconing nodes, records its coordinate, and convert the location information for the unknown sensor beaconing nodes being calculated to the location information of known sensor beaconing nodes.Its advantage is that: ability of searching optimum can be improved, position error is reduced, improves positioning accuracy.
Description
Technical field
The present invention relates to a kind of localization methods of wireless sensor network node, and in particular to one kind is based on microhabitat grey wolf
Optimize the wireless sensor network node locating method of DV-Hop algorithm.
Background technique
Wireless sensor network is one of the research hot topic in current information field, it has merged wireless communication, computer, micro-
The multiple technologies such as electronics, sensor can be used for carrying out acquisition, processing, transmission and the monitoring of data in some particular surroundings, than
Such as: environmental monitoring, military prospecting, industrial and agricultural production, traffic monitoring.In wireless sensor network, node locating it is accurate
Property and safety are most important to the normal work of wireless sensor network, however wireless sensor network is by cheap energy
Limited perceptron composition, only least a portion of perceptron node know the position of itself.Therefore, pass through these a small amount of positions
Information goes to position that is accurate and effective, quickly locating all nodes to become a research hotspot.Existing wireless location technology point
For the positioning based on ranging and without the positioning of ranging.Location technology based on ranging passes through by between measurement adjacent node
Absolute distance, and utilize the position of the actual distance calculation unknown node between node.The positioning based on ranging of comparative maturity
Technology has the positioning (TOA) based on signal propagation time, the positioning (TDOA) based on signal propagation time difference, based on receiving signal
Positioning (RSSI) of intensity etc..Positioning accuracy based on ranging is high, but expensive hardware device is needed to support, and positioning expends ratio
It is larger, it is generally not suitable for large-scale occasion.Location technology without ranging is according to information such as network connectivties, using between node
Estimated distance and hop count positioned.The existing location technology without ranging mainly has DV-Hop (based on distance vector) positioning
Algorithmic technique, Amorphous location technology, centroid algorithm location technology etc..Wherein, DV-Hop location technology is because be easier to reality
Now, have the advantages that range capability, etc. low to beaconing nodes proportion requirement without node, become a kind of classical and determine without ranging
Position technology.
Intelligent algorithm directly can not provide new resolving ideas using the optimization problem that mathematical method solves to be many, be
Raising positioning accuracy, researcher apply intelligent algorithm in node locating algorithm.Swarm Intelligence Algorithm is applied to nothing
In the positioning of line sensor network nodes, positioning accuracy can be improved, more famous particle swarm algorithm, genetic algorithm, ant colony are calculated
Method etc., but Swarm Intelligence Algorithm haves the defects that easily to fall into local optimum.
DV-Hop location technology without ranging has range capability because being easier to realize, without node, to beaconing nodes
The advantages such as proportion requirement is low, become a kind of location technology without ranging of classics.But it is existing at present that there is grey wolf optimization to calculate
The DV-Hop algorithm of method, according to fitness value in ecogroup first three grey wolf individual location information variation calculate optimal solution, though
There is its superiority in terms of function optimization, but is also easily trapped into local optimum simultaneously, the disadvantages of solving precision is not high.
Summary of the invention
The purpose of the present invention is to provide a kind of wireless sensor networks based on microhabitat grey wolf optimization DV-Hop algorithm
Microhabitat principle is introduced into basic grey wolf optimization algorithm by node locating algorithm, the algorithm, which utilizes substantially small
Habitat grey wolf optimization algorithm calculates the fitness value of each grey wolf, when the distance between grey wolf is less than microhabitat radius, compares grey wolf
The fitness value of individual, by imposing penalty to the poor grey wolf individual of fitness value, Lai Tigao ability of searching optimum can
Effectively, the position of unknown beaconing nodes in wireless sensor is obtained more accurately.
In order to achieve the above object, the invention is realized by the following technical scheme:
A kind of wireless sensor network node locating method based on microhabitat grey wolf optimization DV-Hop algorithm, comprising following
Step:
S1, distance vector routing protocol, beaconing nodes broadcast data packet, after propagation data packet, in network are based on
Each beaconing nodes can acquire the minimum hop count h of other beaconing nodesijWith location coordinate information X；
S2, the location coordinate information and minimum hop count h of the step S1 beaconing nodes obtained are utilizedij, estimate beaconing nodes
Between network Average hop distance AvgHDi, the result estimated is broadcast in network, and then determine unknown beaconing nodes
To the estimated distance d between beaconing nodesij；
S3, in grey wolf optimization algorithm, for function optimization problem, initialization population size, each grey wolf position letter
Breath, maximum number of iterations and penalty；
S4, the fitness function value f for calculating each beaconing nodes, i.e., the fitness letter of each individual in calculating grey wolf population
The grey wolf individual that fitness value arranges first three is denoted as α, β, δ by numerical value f, and location information is denoted as X respectivelyα, Xβ, Xδ；
S5, the hunting stage in grey wolf, the position of wolf pack individual can be with the change of escaping of prey during chasing, and iteration is more
The location information of new grey wolf α, β, δ and the position for determining prey；
S6, according to microhabitat principle, first calculate the Euclidean distance d between grey wolf individualj, microhabitat radius sigma is provided, d is worked asj
When < σ, compare the fitness value f of grey wolf i and grey wolf ji、fjSize, punished by being imposed to the lesser grey wolf of wherein fitness value
Penalty function eliminates the lesser grey wolf of fitness value；
If S7, current the number of iterations reach the maximum number of iterations of setting, algorithm terminates and exports grey wolf α, β, δ's
Location information Xα, Xβ, Xδ；Otherwise, return step S4；
S8, pass through the location information X of finally determining grey wolf α, β, δα, Xβ, XδThe position of prey is calculated.
Preferably, the step S1 specifically includes:
S11, beaconing nodes include the location information X and one of this beaconing nodes to one beacon of Web broadcast, in beacon
The parameter h for the expression hop count that a initial value is 1, this beacon blazed abroad in a manner of flooding in a network, beacon quilt every time
Hop count h when forwardingijAll increase by 1；
S12, reception beaconing nodes are saved in all beacons about some beaconing nodes that it is received with minimum
The beacon of jumping figure value abandons the beacon with the same beaconing nodes of larger jumping figure value.
Preferably, the step S2 specifically includes:
The beacon message that S21, each beaconing nodes are broadcasted in acquisition from other beaconing nodes, obtains location information and jump
Number hijLater, the spacing AvgHD of average each jump between each beaconing nodes is estimatedi, it calculates as shown in formula (1):
Wherein: (xi, yi, zi) and (xj, yj, zj) be respectively beaconing nodes i and j coordinate, hijJumping figure value between i and j；
The distance values of calculated average each jump are broadcast to adjacent node by S22, beaconing nodes, and neighbor beacon node connects
After being broadcast the message, the spacing between each beaconing nodes is estimated, wherein unknown beaconing nodes i is to beaconing nodes j's
Estimated distance dijIt calculates as shown in formula (2),
dij=AvgHDi×hij (2)。
Preferably, the step S3 specifically includes:
Initialize N, M, D, t, penatly parameter and grey wolf group X=(X1, X2..., XN) in each grey wolf M dimension
Spatial positional information Xi=(xi1, xi2..., xiM)T, (i=1,2 ..., N),
Wherein N is the total quantity that Population Size is all nodes in wireless sensor, packet of the D between grey wolf and prey
Step-length is enclosed, t is the number of iterations, and penatly is penalty.
Preferably, the step S4 specifically includes:
S41, beaconing nodes refine Average hop distance using microhabitat grey wolf optimization algorithm, use fitness function (3)
Realize the optimization to Average hop distance,
Wherein, djIt is determined by formula (4), is accurate distance of the beaconing nodes i to any beaconing nodes j, dijIt is beaconing nodes i
To the estimated distance of any beaconing nodes j, m is the quantity of beaconing nodes；
S42, the fitness value that each individual in grey wolf population is calculated according to the above-mentioned fitness function (3) provided；
S43, the grey wolf individual that fitness value arranges first three is denoted as α, β, δ, location information is denoted as X respectivelyα, Xβ, Xδ,
Other remaining individuals are denoted as ω.
Preferably, the step S5 specifically includes:
S51, during hunting, wolf pack surrounds prey completely, can be described with following mathematical model:
D=CXP(t)-X(t) (5)
X (t+1)=XP(t)-A·D (6)
Wherein D is to surround step-length, and t is the number of iterations, XPIt (t) is the location information of prey after the t times iteration, X (t) is t
The location information of grey wolf after secondary iteration, A and C are coefficient vector, its calculation formula is:
A=2ar1-a (7)
C=2r2 (8)
Wherein a linearly successively decreases from 2 to 0 with the increase of the number of iterations, r1、r2For the random vector between [0,1]；
S52, after being surrounded to prey, β, δ wolf chases prey under the leading of α wolf, wolf during chasing
The position of group's individual can change with escaping for prey, then can be according to α, and the updated position β, δ redefines prey
Location information, the equation after wolf pack location updating is as follows:
Remaining individual ω and α is calculated according to formula (9), the distance of β, δ according to formula (10), (11) update grey wolf α, β, δ and are hunted
The location information of object, wherein Dα, Dβ, Dδα, β are respectively indicated, the encirclement step-length D between δ wolf and ω wolf；According to formula A=2a
r1- a (7), C=2r2(8), the value of undated parameter a, A, C.
Preferably, the step S6 specifically includes:
It S61, is X for the position of M dimension space grey wolf ii=(xi1, xi2..., xiM)T, the position of grey wolf j is Xj=(xj1,
xj2..., xjM)T, Euclidean distance, that is, accurate distance d between grey wolf i and grey wolf jj:
dj=| | Xi-Xj| | (j=1,2 ..., N) (12)；
S62, specified microhabitat radius parameter σ is provided, if dj< σ compares the fitness value f of grey wolf i Yu grey wolf jiAnd fj
Size the lesser grey wolf of fitness value is eliminated, if d by imposing penalty to the lesser grey wolf of wherein fitness valuej<
σ, then min (fi, fjThe punishment dynamics of)=penalty (13), penatly function are determined by the size of required functional value, wherein punishing
Penalty function is
If dj< σ and fi< fjWhen, fi=0.
Preferably, the step S8 specifically includes:
The position of prey is calculated using formula (11).
Compared with the prior art, the present invention has the following advantages:
(1) the improved wireless sensor network node positioning based on microhabitat grey wolf optimization DV-Hop algorithm of the present invention
Method, by the way that microhabitat principle to be introduced into basic grey wolf optimization algorithm, the algorithm is excellent using basic microhabitat grey wolf
Change the fitness value that algorithm calculates each grey wolf, when the distance between grey wolf is less than microhabitat radius, compares the adaptation of grey wolf individual
Angle value, by imposing penalty to the poor grey wolf individual of fitness value, Lai Tigao ability of searching optimum makes the localization method
It is relatively reliable, effective, it can reduce position error, so that positioning is more accurate, and can solve and be generally basede on grey wolf DV-
Hop algorithm is easily trapped into the not high problem of local optimum and solving precision；
(2) present invention is compared to DV-Hop location technology, APIT algorithm location technology, centroid algorithm location technology etc., tool
Having has the characteristics that easy to operate, adjustment parameter is few, programming is easily realized, it may have positioning accuracy is high, convergence rate is very fast, calculates
Complexity is low, beaconing nodes are than low and small cost of device advantage；
(3) present invention has more superior stabilization when handling function optimization problem compared with traditional grey wolf optimization algorithm
Property and robustness, it may have preferably optimize performance.
Detailed description of the invention
Fig. 1 is flow chart of the method for the present invention；
Fig. 2 is the predation figure of the grey wolf in this method in step S5；
Fig. 3 is microhabitat grey wolf optimization algorithm flow chart.
Specific embodiment
The present invention is further elaborated by the way that a preferable specific embodiment is described in detail below in conjunction with attached drawing.
As shown in Figure 1-3, a kind of wireless sensor network node positioning based on microhabitat grey wolf optimization DV-Hop algorithm
Method comprising the steps of:
S1, using classical based on distance vector routing protocol, beaconing nodes broadcast data packet passes through the method propagation that floods
After data packet, each beaconing nodes in network can acquire the minimum hop count h of other beaconing nodesijWith location coordinate information
X。
The step S1 specifically includes:
S11, beaconing nodes include the location coordinate information X of this beaconing nodes to one beacon of Web broadcast, in beacon
The parameter h for the expression hop count for being 1 with an initial value, this beacon are blazed abroad in a manner of flooding in a network, and beacon is every
Secondary hop count h when being forwardedijAll increase by 1；
S12, reception beaconing nodes are saved in all beacons about some beaconing nodes that it is received with minimum
The beacon of jumping figure value abandons the beacon with the same beaconing nodes of larger jumping figure value.
S2, the location coordinate information and minimum hop count h of the S1 beaconing nodes obtained are utilizedij, estimate between beaconing nodes
Network Average hop distance, the result estimated is broadcast in network, then by using network Average hop distance and
The product of minimum hop count between beaconing nodes indicates unknown beaconing nodes the distance between to beaconing nodes, because of average every jump
Distance estimates, therefore this distance referred to as estimated distance.
The step S2 specifically includes:
The beacon message that S21, each beaconing nodes are broadcasted in acquisition from other beaconing nodes, obtains location information and jump
Number hijLater, the spacing AvgHD of average each jump between each beaconing nodes is estimatedi, it calculates as shown in formula (1):
Wherein: (xi, yi, zi) and (xj, yj, zj) be respectively beaconing nodes i and j coordinate, hijJumping figure value between i and j；
The distance values of calculated average each jump are broadcast to adjacent node by S22, beaconing nodes, and neighbor beacon node connects
After being broadcast the message, the spacing between each beaconing nodes is estimated, wherein the unknown beaconing nodes i of wireless sensor is arrived
The estimated distance d of beaconing nodes jijIt calculates as shown in formula (2),
dij=AvgHDi×hij (2)。
S3, in grey wolf optimization algorithm, for function optimization problem, initialization population size, each grey wolf position letter
Breath, maximum number of iterations T and penalty.
Grey wolf algorithm is a kind of colony intelligence optimization algorithm simulating the grey wolf hunting behavior in nature and proposing.Grey wolf belongs to
Social animal, population are divided into four grades, and it is the leader in population that the highest wolf of grade, which is α, are secondly β, are mainly responsible for association
The work of α is helped, three grades is δ, obeys the leader of α and β, and the wolf grade of most layer is ω, is responsible for completing high-rise wolf explanation
Task.
The step S3 specifically includes: initialization N, M, D, the parameters such as t, penatly and grey wolf group X=(X1,
X2..., XN) in each grey wolf M dimension space location information Xi=(xi1, xi2..., xiM)T, (i=1,2 ..., N), wherein
N is the total quantity that Population Size is unknown node and known node in wireless sensor, encirclement of the D between grey wolf and prey
Step-length, t are the number of iterations, and penatly is penalty.
S4, the fitness function value f for calculating each beaconing nodes, i.e., the fitness letter of each individual in calculating grey wolf population
The grey wolf individual that fitness value arranges first three is denoted as α, β, δ by numerical value f, and location information is denoted as X respectivelya, Xβ, Xδ。
The step S4 specifically includes:
S41, beaconing nodes refine Average hop distance using microhabitat grey wolf optimization algorithm (NGWO), that is, use fitness
Function (3) realizes the optimization to Average hop distance.
Wherein, djIt is determined by formula (4), is accurate distance of the beaconing nodes i to any beaconing nodes j, dijIt is beaconing nodes i
To the estimated distance of any beaconing nodes j, m is the quantity of beaconing nodes；
S42, the fitness value that each individual in grey wolf population is calculated according to the above-mentioned fitness function (3) provided；
S43, the grey wolf individual that fitness value arranges first three is denoted as α, β, δ, location information is denoted as X respectivelyα, Xβ, Xδ,
Other remaining individuals are denoted as ω.
S5, the hunting stage in grey wolf, the position of wolf pack individual can change with escaping for prey during chasing, iteration
The location information of grey wolf is updated to redefine the prey i.e. position of optimal solution.
The step S5 specifically includes:
S51, during hunting, wolf pack surrounds prey completely, can be described with following mathematical model:
D=CXP(t)-X (t), (5)
X (t+1)=XP(t)-AD, (6)
Wherein D is to surround step-length, and t is the number of iterations, XPIt (t) is the location information of prey after the t times iteration, i.e. optimal solution
Position, X (t) is the location information of grey wolf after t iteration, and A and C are coefficient vector, its calculation formula is:
A=2ar1-a (7)
C=2r2 (8)
Wherein a linearly successively decreases from 2 to 0 with the increase of the number of iterations, r1、r2For the random vector between [0,1]；
S52, after being surrounded to prey, β, δ wolf chases prey under the leading of α wolf, wolf during chasing
The position of group's individual can change with escaping for prey, then can be according to α, and the updated position β, δ redefines prey
That is the position of optimal solution, the equation after wolf pack location updating are as follows:
The encirclement step distance that remaining individual ω and α, β, δ are calculated according to formula (9) updates grey wolf according to formula (10), (11)
The position of α, β, δ and prey, wherein Dα, Dβ, DδRespectively indicate α, β, the encirclement step-length D between δ wolf and ω wolf i.e. other individuals；
The value of undated parameter a, A, C, specifically: according to formula A=2ar1- a (7), C=2r2(8), undated parameter a,
The value of A, C；
Attack is the final stage of prey process, and wolf pack attacks prey and bag the game to arrive optimal solution.It should
Process passes through formula A=2ar1A value in-a is successively decreased to realize, when the value of a is from 2 linear decreases to 0, corresponding A value
Also change at section [- a, a].In existing grey wolf algorithm, as | A | when≤1, i.e. the value range of A is at [- 1,1], then table
Next position of bright wolf pack can be more nearly the position of prey；As 1 < | A | when≤2, wolf pack will be away from the side of prey
To scattering, GWO algorithm is caused to lose optimal solution position, to fall into the process of a local optimum.
Based on above procedure, microhabitat principle is introduced into grey wolf optimization algorithm, using the distance between individual to grey wolf
The similarity of group's life habit is differentiated, and by imposing penalty to the poor grey wolf individual of fitness value, Lai Shixian is sought
Excellent process.
In the present invention, S6, according to microhabitat principle, first calculate the Euclidean distance d between grey wolf individualj, provide your pupil
Border radius sigma, works as djWhen < σ, compare the fitness value f of grey wolf i and grey wolf ji、fjSize, and it is lesser to wherein fitness value
Grey wolf imposes penalty.
The step S6 specifically includes:
Euclidean distance between S61, individual can reflect the evacuation degree between individual, and the position for M dimension space grey wolf i is
Xi=(xi1, xi2..., xiM)T, the position of grey wolf j is Xj=(xj1, xj2..., xjM)T, European between grey wolf i and grey wolf j
Distance djAre as follows:
dj=| | Xi-Xj| | (j=1,2 ..., N) (12),
I.e.
S62, specified microhabitat radius parameter σ is provided, if dj< σ, then the individual is added in microhabitat group, is compared
The fitness value f of grey wolf i and grey wolf jiAnd fjSize, and penalty is imposed to the lesser grey wolf of fitness value therein, i.e.,
If djWhen < σ, then min (fi, fjThe punishment dynamics of)=penalty (13), penatly function are determined by the size of required functional value
Fixed, wherein penalty is
Even dj< σ and fi< fjWhen, fi=0.
When calculating the fitness value of individual, a penalty is sentenced, to reduce the fitness value of the individual, that is, is eliminated
The small grey wolf individual of fitness value.After imposing penalty, the lesser grey wolf of fitness value, fitness value resets to 0, i.e.,
Eliminate the lesser grey wolf individual of fitness value, the i.e. survival of the fittest in nature.Imposing effect brought by penalty is exactly
By comparing the fitness value of grey wolf and penalty is imposed to the poor grey wolf individual of fitness value, it is possible to reduce blind search
Probability, to realize the process of optimizing.
If S7, t at this time reach the maximum number of iterations T of setting, (T is maximum number of iterations set by user or to be
The number of iterations needed for reaching certain computational accuracy), then algorithm terminates and exports grey wolf α, the location information X of β, δα, Xβ, Xδ；It is no
Then, return step S4.
S8, the position of prey is obtained by the positional information calculation of finally determining grey wolf α, β, δ.The step S8 tool
Body includes: the unknown beaconing nodes i.e. coordinate value of prey solved using centroid algorithm, i.e., calculates prey position with formula (11),
That is calculating target function optimal solution.
It as shown in table 1 below, is the comparison result of the present invention and other various methods.From following table it is found that it is of the invention based on
The wireless sensor network node locating method of the DV-Hop algorithm of microhabitat grey wolf optimization has beacon compared to other algorithms
Node is than the advantage that low, positioning accuracy is high, convergence rate is very fast and computation complexity is low, other required cost of device of the invention
Very little is conducive to further a wide range of promote the use of.
Table 1
In conclusion a kind of wireless sensor network based on microhabitat grey wolf optimization DV-Hop algorithm proposed by the present invention
Node positioning method can reduce position error, improve positioning accuracy, and is able to solve the DV-Hop based on grey wolf optimization and calculates
Method falls into the problem of local optimum.
It is discussed in detail although the contents of the present invention have passed through above preferred embodiment, but it should be appreciated that above-mentioned
Description is not considered as limitation of the present invention.After those skilled in the art have read above content, for of the invention
A variety of modifications and substitutions all will be apparent.Therefore, protection scope of the present invention should be limited to the appended claims.
Claims (8)
1. a kind of wireless sensor network node locating method based on microhabitat grey wolf optimization DV-Hop algorithm, feature exist
In comprising the steps of:
S1, it is based on distance vector routing protocol, beaconing nodes broadcast data packet is each in network after propagation data packet
Beaconing nodes can acquire the minimum hop count h of other beaconing nodesijWith location coordinate information X；
S2, the location coordinate information and minimum hop count h of the step S1 beaconing nodes obtained are utilizedij, estimate between beaconing nodes
Network Average hop distance AvgHDi, the result estimated is broadcast in network, and then determine unknown beaconing nodes to letter
Mark the estimated distance d between nodeij；
S3, in grey wolf optimization algorithm, for function optimization problem, initialization population size, the location information of each grey wolf, most
Big the number of iterations and penalty；
S4, the fitness function value f for calculating each beaconing nodes, i.e., the fitness function value of each individual in calculating grey wolf population
The grey wolf individual that fitness value arranges first three is denoted as α, β, δ by f, and location information is denoted as X respectivelyα, Xβ, Xδ；
S5, the hunting stage in grey wolf, the position of wolf pack individual can update ash with the change of escaping of prey, iteration during chasing
The location information of wolf α, β, δ and the position for determining prey；
S6, according to microhabitat principle, first calculate the Euclidean distance d between grey wolf individualj, microhabitat radius sigma is provided, d is worked asj< σ
When, compare the fitness value f of grey wolf i and grey wolf ji、fjSize, by imposing punishment to the lesser grey wolf of wherein fitness value
Function eliminates the lesser grey wolf of fitness value；
If S7, current the number of iterations reach the maximum number of iterations of setting, algorithm terminates and exports grey wolf α, the position of β, δ
Information Xα, Xβ, Xδ；Otherwise, return step S4；
S8, pass through the location information X of finally determining grey wolf α, β, δα, Xβ, XδThe position of prey is calculated.
2. the wireless sensor network node positioning side as described in claim 1 based on microhabitat grey wolf optimization DV-Hop algorithm
Method, which is characterized in that the step S1 specifically includes:
S11, beaconing nodes to one beacon of Web broadcast, in beacon include this beaconing nodes location information X and one at the beginning of
The parameter h for the expression hop count that initial value is 1, this beacon are blazed abroad in a manner of flooding in a network, and beacon is forwarded every time
When hop count hijAll increase by 1；
S12, reception beaconing nodes are saved in all beacons about some beaconing nodes that it is received with minimum hop count
The beacon of value abandons the beacon with the same beaconing nodes of larger jumping figure value.
3. the wireless sensor network node positioning side as described in claim 1 based on microhabitat grey wolf optimization DV-Hop algorithm
Method, which is characterized in that the step S2 specifically includes:
The beacon message that S21, each beaconing nodes are broadcasted in acquisition from other beaconing nodes, obtains location information and hop count hij
Later, the spacing AvgHD of average each jump between each beaconing nodes is estimatedi, it calculates as shown in formula (1):
Wherein: (xi, yi, zi) and (xj, yj, zj) be respectively beaconing nodes i and j coordinate, hijJumping figure value between i and j；
The distance values of calculated average each jump are broadcast to adjacent node by S22, beaconing nodes, and neighbor beacon node receives
After broadcast message, the spacing between each beaconing nodes is estimated, wherein the estimation of unknown beaconing nodes i to beaconing nodes j
Distance dijIt calculates as shown in formula (2),
dij=AvgHDi×hij (2)。
4. the wireless sensor network node positioning side as described in claim 1 based on microhabitat grey wolf optimization DV-Hop algorithm
Method, which is characterized in that the step S3 specifically includes:
Initialize N, M, D, t, penatly parameter and grey wolf group X=(X1, X2..., XN) in each grey wolf M dimension space
Location information Xi=(xi1, xi2..., xiM)T, (i=1,2 ..., N),
Wherein N is the total quantity that Population Size is all nodes in wireless sensor, encirclement step of the D between grey wolf and prey
Long, t is the number of iterations, and penatly is penalty.
5. the wireless sensor network node positioning side as claimed in claim 4 based on microhabitat grey wolf optimization DV-Hop algorithm
Method, which is characterized in that the step S4 specifically includes:
S41, beaconing nodes refine Average hop distance using microhabitat grey wolf optimization algorithm, are realized using fitness function (3)
Optimization to Average hop distance,
Wherein, djIt is determined by formula (4), is accurate distance of the beaconing nodes i to any beaconing nodes j, dijIt is that beaconing nodes i takes office
The estimated distance of meaning beaconing nodes j, m is the quantity of beaconing nodes；
S42, the fitness value that each individual in grey wolf population is calculated according to the above-mentioned fitness function (3) provided；
S43, the grey wolf individual that fitness value arranges first three is denoted as α, β, δ, location information is denoted as X respectivelyα, Xβ, Xδ, remaining
Other individuals are denoted as ω.
6. a kind of wireless sensor network node based on microhabitat grey wolf optimization DV-Hop algorithm as claimed in claim 5 is fixed
Position method, the step S5 specifically includes:
S51, during hunting, wolf pack surrounds prey completely, can be described with following mathematical model:
D=CXP(t)-X(t) (5)
X (t+1)=XP(t)-A·D (6)
Wherein D is to surround step-length, and t is the number of iterations, XPIt (t) is the location information of prey after the t times iteration, X (t) is t iteration
The location information of grey wolf afterwards, A and C are coefficient vector, its calculation formula is:
A=2ar1-a (7)
C=2r2 (8)
Wherein a linearly successively decreases from 2 to 0 with the increase of the number of iterations, r1、r2For the random vector between [0,1]；
S52, after being surrounded to prey, β, δ wolf chases prey under the leading of α wolf, the wolf pack during chasing
The position of body can change with escaping for prey, then can be according to α, and the updated position β, δ redefines the position of prey
Confidence ceases, and the equation after wolf pack location updating is as follows:
Remaining individual ω and α, the distance of β, δ, according to formula (10), (11) update grey wolf α, β, δ and prey are calculated according to formula (9)
Location information, wherein Dα, Dβ, Dδα, β are respectively indicated, the encirclement step-length D between δ wolf and ω wolf；
According to formula A=2ar1- a (7), C=2r2(8), the value of undated parameter a, A, C.
7. a kind of wireless sensor network node based on microhabitat grey wolf optimization DV-Hop algorithm as claimed in claim 6 is fixed
Position method, the step S6 specifically includes:
It S61, is X for the position of M dimension space grey wolf ii=(xi1, xi2..., xiM)T, the position of grey wolf j is Xj=(xj1,
xj2..., xjM)T, Euclidean distance, that is, accurate distance d between grey wolf i and grey wolf jj:
dj=| | Xi-Xj| | (j=1,2 ..., N) (12)；
S62, specified microhabitat radius parameter σ is provided, if dj< σ compares the fitness value f of grey wolf i Yu grey wolf jiAnd fjIt is big
It is small, by imposing penalty to the lesser grey wolf of wherein fitness value, the lesser grey wolf of fitness value is eliminated, if dj< σ, then
min(fi, fjThe punishment dynamics of)=penalty (13), penatly function are determined by the size of required functional value, wherein punishing letter
Number is
If dj< σ and fi< fjWhen, fi=0.
8. a kind of wireless sensor network node based on microhabitat grey wolf optimization DV-Hop algorithm as claimed in claim 7 is fixed
Position method, the step S8 specifically includes:
The position of prey is calculated using formula (11).
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