CN110446155A - A kind of underwater wireless sensor network location algorithm based on mobility prediction - Google Patents

A kind of underwater wireless sensor network location algorithm based on mobility prediction Download PDF

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Publication number
CN110446155A
CN110446155A CN201910674730.2A CN201910674730A CN110446155A CN 110446155 A CN110446155 A CN 110446155A CN 201910674730 A CN201910674730 A CN 201910674730A CN 110446155 A CN110446155 A CN 110446155A
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node
anchor node
particle
anchor
unknown
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CN110446155B (en
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万新旺
杨振诚
董帅
王鹤
张海成
李逸伟
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Nanjing Post and Telecommunication University
Nanjing University of Posts and Telecommunications
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Nanjing Post and Telecommunication University
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S5/00—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations
    • G01S5/18—Position-fixing by co-ordinating two or more direction or position line determinations; Position-fixing by co-ordinating two or more distance determinations using ultrasonic, sonic, or infrasonic waves
    • G01S5/26—Position of receiver fixed by co-ordinating a plurality of position lines defined by path-difference measurements
    • 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
    • H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
    • 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
    • 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
    • H04W4/025—Services making use of location information using location based information parameters
    • H04W4/027—Services making use of location information using location based information parameters using movement velocity, acceleration information
    • 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
    • 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
    • H04W64/006—Locating users or terminals or network equipment for network management purposes, e.g. mobility management with additional information processing, e.g. for direction or speed determination

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention discloses a kind of underwater wireless sensor network location algorithms based on mobility prediction to introduce improved backtracking chess game optimization algorithm on the basis of existing mobility predicts localization method.The present invention uses three kinds of sensor nodes: buoy node, anchor node and unknown node, including step (1): the initialization of wireless sensor network；Step (2): anchor node positioning；Step (3): anchor node velocity information is calculated；Step (4): unknown node velocity information calculates；Step (5): unknown node positioning.The present invention adapts to the underwater wireless sensor network in practical application, effectively improves node locating precision and reduces computing cost.

Description

A kind of underwater wireless sensor network location algorithm based on mobility prediction
Technical field
The invention belongs to underwater wireless sensor network applied technical fields, and in particular to it is a kind of based on mobility prediction Underwater wireless sensor network location algorithm.
Background technique
Underwater wireless sensor network (Underwater Wireless Sensor Networks, UWSNs) is that have to lead to The network monitoring system that letter and the sensor node of computing capability are formed by way of self-organizing in water environment.UWSNs by The sensor node of one or more types forms, these nodes are deployed to target by way of itself movement or manual placement Position, ultimately forms the network structure of particular characteristic, and cooperates with the monitoring for completing underwater environment.UWSNs technology is widely used Environmental observation under water, coastal monitoring, sea floor exploration, disaster prevention, Scientific Exploration, business development and military affairs or terrorist incident Detection.These application be required in conjunction with the location information of node to realize its function, so to UWSNs node locating technique into Row further investigation is very necessary.
Node locating algorithm is the reference mode according to known position information, not by the communication estimation between other nodes Know the position of node.But sensor nodes in wireless sensor network constantly moves under the influence of water flow under water, so that It positions very difficult.Fortunately the movement of immersed body is not completely random.Hydrodynamics is studies have shown that immersed body Movement is closely related with many environmental factors, such as water flow and water temperature.For example, due to the influence of tide, the movement of offshore object Sex expression goes out certain half period property.This strong relativity of time domain is taught that can be based on location information in the past accurately Estimate future location information.The kinetic characteristic of immersed body is different in different environment.Although design one is used for institute There is the mobility model of the immersed body of underwater environment to be nearly impossible, but has devised some based on fluid dynamics Specific environment in immersed body model.So it has been proposed that a kind of location algorithm with mobility prediction.
In mobility prediction algorithm the Positioning Precision Control of anchor node the positioning accuracy of whole network, but existing shifting Anchor node positioning is all not high enough in dynamic property prediction algorithm.So proposing a kind of algorithm with high anchor node positioning accuracy.
Summary of the invention
The present invention is to solve the above problems, propose a kind of underwater wireless sensor network node location algorithm, in existing shifting It is improved on the basis of dynamic property prediction localization method, this method introduces improved backtracking chess game optimization algorithm, improves anchor section with it Spot placement accuracy, to effectively raise the positioning accuracy of whole network.
The present invention solves its technical problem and is achieved through the following technical solutions:
A kind of underwater wireless sensor network location algorithm based on mobility prediction, using three kinds of sensor nodes: floating Node, anchor node and unknown node are marked, the buoy node passes through GPS positioning self-position；The anchor node is that have in network There is the node of calculated performance, anchor node communicates the location information to position oneself with buoy node；The unknown node with it is adjacent Anchor node communicates the localization to realize its own；
Specifically includes the following steps:
Step (1): the initialization of wireless sensor network: all anchor nodes in network acquire the distance of buoy node The location information of information and buoy node；
Step (2): anchor node positioning: the difference of the range information of the Euclidean distance and acquisition of anchor node to buoy node is made For a fitness function, anchor node location information then is calculated using MBSA algorithm；
Step (3): anchor node velocity information is calculated: using what is estimated in the anchor node location information and step 2 of previous step Anchor node location information calculates anchor node velocity information；
Step (4): unknown node velocity information calculates: the speed of unknown node is calculated according to the group motion characteristic of immersed body Information；
Step (5): unknown node positioning: step calculates unknown node velocity information in (4), we using it is past not Know that node location updates present unknown node location information.
Further, the equal pressure sensor of all the sensors node in the present invention.
Further, step (1) specifically:
In locating periodically T, anchor node of the buoy node into communication radius in network sends location information and transmission Time t1, the time that anchor node receives buoy node information is t2, according to TOA formula calculate anchor node a and buoy node s it Between distance Las:
Las=V* (t2-t1)
Wherein, V is the velocity of sound.
Further, step 2 calculating process includes:
Step (2-1): it when selecting fitness function, is calculated using the Euclidean distance and TOA of anchor node to buoy node The difference of distance out is as fitness function；
Step (2-2): anchor node position is calculated using MBSA algorithm, using its global optimum's particle as the seat of anchor node Mark.
Further, the step (2-1) specifically:
Distance passes through step (1) calculating between each anchor node and buoy node, and between anchor node a and buoy node s Euclidean distance das:
Wherein, (xa,ya) be anchor node a false coordinate information, (xs,ys) it is the buoy node that anchor node a can be communicated The coordinate information of s, so fitness function are as follows:
Wherein, n is the number of buoy node j in anchor node a communication range, and fit (a) is the adaptive value of anchor node a.
Further, the step (2-2) specifically:
(2-2.1) random initializtion population P first and history population oldP is initialized as follows:
Wherein, i=1,2,3 ..., N, j=1,2,3 ..., D, N are the scale of population, and D is particle dimension, Pi.jFor in P The numerical value of the jth dimension of i-th of particle, oldPi.jFor the numerical value of the jth dimension of i-th of particle in oldP；U (*) is random uniform point Cloth function, lowiAnd upiIt is the up-and-down boundary of variable；The initialization procedure of P and oldP is not interfere with each other；
Before (2-2.2) each iteration starts, a new history population oldP is generated first, is shown below, at this time Particle in oldP may get contemporary and value of any one generation before, then carry out again to the particle in the oldP of generation random Sequence,
OldP=permuting (oldp)
Wherein, r1And r2It is to obey (0,1) equally distributed random number, permuting (*) is randomly ordered function；
(2-2.3) variation, variation particle in MBSA is from global optimum's location information and personal best particle information middle school It practises, improves convergence rate, mutation process is:
Mu tan tai=Pi+F*(oldPi-Pi)+rand*(Pibest-Pi)
Mu tan tbi=Pi+rand(Pgbest-Pi)
Wherein, Mu tan taiWith Mu tan biIt is variation the particle i, P under Different Variation modeiAnd oldPiIt is respectively The particle i, P of current population and history populationibestIt is local optimum particle, PgbestIt is global optimum's particle, F=3 × randn It is the coefficient of variation, the amplitude of the variation of control particle, randn standardized normal distribution random number can be crossed.Rand is to obey (0,1) The random number of even distribution.I=1,2 ..., N, j=1,2 ..., D；
(2-2.4) intersects, and crossover process in two steps, defines the mapping matrix Map that a size is N × D, initial value first Element value is zero, then randomly updates Map in two ways, is shown below:
Wherein, mixrate is crossing-over rate, value 1, and ceil (*) is positive direction bracket function, and it is zero that a and b, which are mean value, The random number that variance is 1, randi (*) are to generate to be uniformly distributed random integers function, randomly update grain according to mapping matrix Map Certain positions of son, are shown below:
Wherein, newPi,jFor the numerical value that the jth of particle i is tieed up, Mu tan tai,jWith Mu tan tbi,jRespectively Mu tan taiWith Mu tan biJth dimension numerical value, new population particle element may cross the border, if certain positions are crossed the border in new population newP, New position is generated according to initialization；
(2-2.5), using greedy selection mechanism, determines reservation or more new individual according to individual adaptation degree, such as in this step Fruit new population individual newPiThe good individual P of fitnessiFitness, receive newPiAs newest individual, otherwise individual PiNo It is changed, process is shown below:
Wherein, fit (Pi) and fit (newPi) it is particle P respectivelyiUpdate the fitness value of front and back；
(2-2.6) this step decides whether to exit iteration, exits condition there are two iteration, meet one of them:
(a) whether the number of iterations is less than the maximum number of iterations of setting；
(b) fitness value is less than the threshold value of setting；
If meeting one of condition executes (7), otherwise (b) is executed；
(2-2.7) global optimum particle is anchor node coordinate.
Further, the step (3) specifically: step (2) calculates the location information of this locating periodically T anchor node a, often A anchor node can all record over the location information of locating periodically, so calculating the speed of anchor node with following formula:
Wherein, (x1,y1) be last locating periodically coordinate, (x2,y2) be this locating periodically coordinate, Vxa and Vya points It Wei not anchor node x-axis speed and y-axis speed.
Further, the step (4) specifically: by unknown node one jump in anchor node and oriented unknown node It is defined as reference mode, since immersed body has group motion, it is possible to calculate the velocity information of unknown node by following formula:
Wherein, Vxc and Vyc is respectively the x-axis speed and y-axis speed of reference mode c, and Vx and Vy are respectively unknown node X-axis speed and y-axis speed, ηcIt is confidence value parameter, confidence value parameter is determined by following formula:
Wherein, ζcIt is the signal strength for the reference mode that unknown node receives, m is reference mode number.
Further, step (5) calculates unknown node this locating periodically velocity information, according to the position of last locating periodically Set this period location information of information update:
Wherein, (x', y') is the location information of the last locating periodically of unknown node, and (x, y) is this positioning of unknown node The location information in period.
The invention has the benefit that
The present invention predicts location algorithm compared to classical mobility, and the used time that positioning accuracy can be improved, which reduces to calculate, opens Pin.The realization of this algorithm efficiently solves influence bring position error of the underwater wireless sensor node due to water flow Larger problem.
Detailed description of the invention
Fig. 1 is network frame of the invention；
Fig. 2 is flow chart of the method for the present invention；
Fig. 3 is the method for the present invention with classical mobility prediction localization method under different anchor densities compared with position error Figure；
Fig. 4 is the method for the present invention compared with classical mobility prediction localization method is in different anchor density Signal Coverage Percentages Figure；
Fig. 5 is that computing cost of the invention compares figure.
Specific embodiment
Below by specific embodiment, the invention will be further described, and it is not limit that following embodiment, which is descriptive, Qualitatively, this does not limit the scope of protection of the present invention.
As shown in Figure 1, a kind of underwater wireless sensor network location algorithm based on mobility prediction, is sensed using three kinds Device node: buoy node, anchor node and unknown node.For buoy node by GPS positioning self-position, anchor node is phase in network There is the node of stronger calculated performance, larger communication radius for unknown node, anchor node is communicated with buoy node to position certainly Oneself location information.Unknown node is the node that cannot be communicated with buoy node, and poor relative to anchor node calculated performance, Communication radius is small with finite energy, it is usually not intended to waste energy.Unknown node is communicated with adjacent anchor node to realize it The localization of itself.The equal pressure sensor of all the sensors node in the present invention, such sensor node can be known at the moment The depth of itself, that is, z-axis coordinate can convert two-dimensional localization for three-dimensional localization.
As shown in Fig. 2, the present invention specifically includes the following steps:
Step (1): the initialization of wireless sensor network: all anchor nodes in network acquire the distance of buoy node The location information of information and buoy node.Anchor node hair of the buoy node into communication radius in locating periodically T, in network Send location information and sending time t1, the time that anchor node receives buoy node information is t2, anchor section is calculated according to TOA formula The distance between point a and buoy node s Las:
Las=V* (t2-t1)
Wherein, V is the velocity of sound.
Step (2): anchor node positioning: the difference of the range information of the Euclidean distance and acquisition of anchor node to buoy node is made For a fitness function, anchor node location information then is calculated using MBSA algorithm.
Step (2-1): it when selecting fitness function, is calculated using the Euclidean distance and TOA of anchor node to buoy node The difference of distance out is as fitness function.Distance passes through step (1) and calculates between each anchor node and buoy node, and anchor Euclidean distance d between node a and buoy node sas:
Wherein, (xa,ya) be anchor node a false coordinate information, (xs,ys) it is the buoy node that anchor node a can be communicated The coordinate information of s, so fitness function are as follows:
Wherein, n is the number of buoy node j in anchor node a communication range, and fit (a) is the adaptive value of anchor node a.
Step (2-2): anchor node position is calculated using MBSA algorithm, using its global optimum's particle as the seat of anchor node Mark.
(2-2.1) random initializtion population P first and history population oldP is initialized as follows:
Wherein, i=1,2,3 ..., N, j=1,2,3 ..., D, N are the scale of population, and D is particle dimension, Pi.jFor in P The numerical value of the jth dimension of i-th of particle, oldPi.jFor the numerical value of the jth dimension of i-th of particle in oldP；U (*) is random uniform point Cloth function, lowiAnd upiIt is the up-and-down boundary of variable；The initialization procedure of P and oldP is not interfere with each other；
Before (2-2.2) each iteration starts, a new history population oldP is generated first, is shown below, at this time Particle in oldP may get contemporary and value of any one generation before, then carry out again to the particle in the oldP of generation random Sequence,
OldP=permuting (oldp)
Wherein, r1And r2It is to obey (0,1) equally distributed random number, permuting (*) is randomly ordered function；
(2-2.3) variation, variation particle in MBSA is from global optimum's location information and personal best particle information middle school It practises, improves convergence rate, mutation process is:
Mu tan tai=Pi+F*(oldPi-Pi)+rand*(Pibest-Pi)
Mu tan tbi=Pi+rand(Pgbest-Pi)
Wherein, Mu tan taiWith Mu tan biIt is variation the particle i, P under Different Variation modeiAnd oldPiIt is respectively The particle i, P of current population and history populationibestIt is local optimum particle, PgbestIt is global optimum's particle, F=3 × randn It is the coefficient of variation, the amplitude of the variation of control particle, randn standardized normal distribution random number can be crossed.Rand is to obey (0,1) The random number of even distribution.I=1,2 ..., N, j=1,2 ..., D；
(2-2.4) intersects, and crossover process in two steps, defines the mapping matrix Map that a size is N × D, initial value first Element value is zero, then randomly updates Map in two ways, is shown below:
Wherein, mixrate is crossing-over rate, value 1, and ceil (*) is positive direction bracket function, and it is zero that a and b, which are mean value, The random number that variance is 1, randi (*) are to generate to be uniformly distributed random integers function, randomly update grain according to mapping matrix Map Certain positions of son, are shown below:
Wherein, newPi,jFor the numerical value that the jth of particle i is tieed up, Mu tan tai,jWith Mu tan tbi,jRespectively Mu tan taiWith Mu tan biJth dimension numerical value, new population particle element may cross the border, if certain positions are crossed the border in new population newP, New position is generated according to initialization；
(2-2.5), using greedy selection mechanism, determines reservation or more new individual according to individual adaptation degree, such as in this step Fruit new population individual newPiThe good individual P of fitnessiFitness, receive newPiAs newest individual, otherwise individual PiNo It is changed, process is shown below:
Wherein, fit (Pi) and fit (newPi) it is particle P respectivelyiUpdate the fitness value of front and back；
(2-2.6) this step decides whether to exit iteration, exits condition there are two iteration, meet one of them:
(a) whether the number of iterations is less than the maximum number of iterations of setting；
(b) fitness value is less than the threshold value of setting；
If meeting one of condition executes (7), otherwise (b) is executed；
(2-2.7) global optimum particle is anchor node coordinate.
Step (3): anchor node velocity information is calculated: using what is estimated in the anchor node location information and step 2 of previous step Anchor node location information calculates anchor node velocity information.Step (2) calculates the location information of this locating periodically T anchor node a, each Anchor node can all record over the location information of locating periodically, so calculating the speed of anchor node with following formula:
Wherein, (x1,y1) be last locating periodically coordinate, (x2,y2) be this locating periodically coordinate, Vxa and Vya points It Wei not anchor node x-axis speed and y-axis speed.
Step (4): unknown node velocity information calculates: the speed of unknown node is calculated according to the group motion characteristic of immersed body Information.By unknown node one jump in anchor node and oriented unknown node be defined as reference mode, due to immersed body have There is group motion, it is possible to the velocity information of unknown node is calculated by following formula:
Wherein, Vxc and Vyc is respectively the x-axis speed and y-axis speed of reference mode c, and Vx and Vy are respectively unknown node X-axis speed and y-axis speed, ηcIt is confidence value parameter, confidence value parameter is determined by following formula:
Wherein, ζcIt is the signal strength for the reference mode that unknown node receives, m is reference mode number.
Step (5): unknown node positioning: step calculates unknown node velocity information in (4), we using it is past not Know that node location updates present unknown node location information.
Wherein, (x', y') is the location information of the last locating periodically of unknown node, and (x, y) is this positioning of unknown node The location information in period.
Below to the underwater wireless sensor network location algorithm of the invention based on mobility prediction in different anchor nodes Positioning accuracy, the comparison of Signal Coverage Percentage and computing cost are carried out under ratio respectively, experiment parameter selection includes the following:
It shares 200 nodes to be randomly dispersed in monitoring waters, 20 buoy nodes are randomly dispersed in the water surface.Wherein anchor section Point and buoy node communication radius value are 200 meters, and unknown node communication radius is 100 meters.In order to eliminate as much as random error, All simulation results are averaged after repeating 100 times under the same conditions.
Experiment 1: compare the method for the present invention and missed from the positioning of SLMP, MCL-MP and MP-PSO under different anchor densities Difference.
Fig. 3 predicts localization method under different anchor node ratios compared with position error for the method for the present invention with classical mobility Figure.By Fig. 3 it can be found that the method for the present invention position error is significantly lower than other localization methods.
Experiment 2: compare the method for the present invention and covered from the positioning of SLMP, MCL-MP and MP-PSO under different anchor densities Rate.
Fig. 4 is the method for the present invention and classical mobility prediction localization method Signal Coverage Percentage ratio under different anchor node ratios Compared with figure.By Fig. 4 it can be found that the method for the present invention Signal Coverage Percentage is higher than other localization methods.
Experiment 3: compare computing cost of the method for the present invention from MP-PSO under different anchor densities.
Fig. 5 is the comparison of the method for the present invention and MP-PSO method computing cost under different anchor node ratios.It can by Fig. 5 To see that the method for the present invention calculating starts the only one third of MP-PSO.
The above is only a preferred embodiment of the present invention, it is noted that for the ordinary skill people of the art For member, various improvements and modifications may be made without departing from the principle of the present invention, these improvements and modifications are also answered It is considered as protection scope of the present invention.

Claims (9)

1. a kind of underwater wireless sensor network location algorithm based on mobility prediction, it is characterised in that: using three kinds of sensings Device node: buoy node, anchor node and unknown node, the buoy node pass through GPS positioning self-position；The anchor node is There is the node of calculated performance, anchor node communicates the location information to position oneself with buoy node in network；The unknown section Point communicates the localization to realize its own with adjacent anchor node；
Specifically includes the following steps:
Step (1): the initialization of wireless sensor network: all anchor nodes in network acquire the range information of buoy node With the location information of buoy node；
Step (2): anchor node positioning: the difference of the range information of the Euclidean distance and acquisition of anchor node to buoy node is as one Then a fitness function calculates anchor node location information using MBSA algorithm；
Step (3): anchor node velocity information is calculated: using the anchor section estimated in the anchor node location information and step 2 of previous step Dot position information calculates anchor node velocity information；
Step (4): unknown node velocity information calculates: being believed according to the speed that the group motion characteristic of immersed body calculates unknown node Breath；
Step (5): unknown node positioning: step calculates unknown node velocity information in (4), we use past unknown section The present unknown node location information of point location updating.
2. a kind of underwater wireless sensor network location algorithm based on mobility prediction as described in claim 1, feature It is: the equal pressure sensor of all the sensors node in the present invention.
3. a kind of underwater wireless sensor network location algorithm based on mobility prediction as claimed in claim 2, feature It is: step (1) specifically:
In locating periodically T, anchor node of the buoy node into communication radius in network sends location information and sending time t1, the time that anchor node receives buoy node information is t2, calculated between anchor node a and buoy node s according to TOA formula Distance Las:
Las=V* (t2-t1)
Wherein, V is the velocity of sound.
4. a kind of underwater wireless sensor network location algorithm based on mobility prediction as claimed in claim 3, feature Be: step 2 calculating process includes:
Step (2-1): calculated using the Euclidean distance and TOA of anchor node to buoy node when selecting fitness function The difference of distance is as fitness function；
Step (2-2): anchor node position is calculated using MBSA algorithm, using its global optimum's particle as the coordinate of anchor node.
5. a kind of underwater wireless sensor network location algorithm based on mobility prediction as claimed in claim 4, feature It is: the step (2-1) specifically:
Distance passes through step (1) calculating between each anchor node and buoy node, and European between anchor node a and buoy node s Distance das:
Wherein, (xa,ya) be anchor node a false coordinate information, (xs,ys) it is the seat of buoy node s that anchor node a can be communicated Information is marked, so fitness function are as follows:
Wherein, n is the number of buoy node j in anchor node a communication range, and fit (a) is the adaptive value of anchor node a.
6. a kind of underwater wireless sensor network location algorithm based on mobility prediction as claimed in claim 5, feature It is: the step (2-2) specifically:
(2-2.1) random initializtion population P first and history population oldP is initialized as follows:
Wherein, i=1,2,3 ..., N, j=1,2,3 ..., D, N are the scale of population, and D is particle dimension, Pi.jIt is in P i-th The numerical value of the jth dimension of a particle, oldPi.jFor the numerical value of the jth dimension of i-th of particle in oldP；U (*) is to be uniformly distributed letter at random Number, lowiAnd upiIt is the up-and-down boundary of variable；The initialization procedure of P and oldP is not interfere with each other；
Before (2-2.2) each iteration starts, a new history population oldP is generated first, is shown below, oldP at this time In particle may get contemporary and value of any one generation before, then the particle in the oldP of generation is arranged at random again Sequence,
OldP=permuting (oldp)
Wherein, r1And r2It is to obey (0,1) equally distributed random number, permuting (*) is randomly ordered function；
(2-2.3) makes a variation, and the variation particle in MBSA learns from global optimum's location information and personal best particle information, mentions High convergence rate, mutation process are:
Mu tan tai=Pi+F*(oldPi-Pi)+rand*(Pibest-Pi)
Mu tan tbi=Pi+rand(Pgbest-Pi)
Wherein, Mu tan taiWith Mu tan biIt is variation the particle i, P under Different Variation modeiAnd oldPiIt is current kind respectively The particle i, P of group and history populationibestIt is local optimum particle, PgbestIt is global optimum's particle, F=3 × randn is variation Coefficient can cross the amplitude of the variation of control particle, randn standardized normal distribution random number.Rand is to obey (0,1) to be uniformly distributed Random number.I=1,2 ..., N, j=1,2 ..., D；
(2-2.4) intersects, and crossover process in two steps, defines the mapping matrix Map that a size is N × D, initial value element first Value is zero, then randomly updates Map in two ways, is shown below:
Wherein, mixrate is crossing-over rate, value 1, and ceil (*) is positive direction bracket function, and it is zero variance that a and b, which are mean value, For 1 random number, randi (*) is to generate to be uniformly distributed random integers function, randomly updates particle according to mapping matrix Map Certain positions, are shown below:
Wherein, newPi,jFor the numerical value that the jth of particle i is tieed up, Mu tan tai,jWith Mu tan tbi,jRespectively Mu tan taiWith Mu tan biJth dimension numerical value, new population particle element may cross the border, if certain positions are crossed the border in new population newP, according to Initialization generates new position；
(2-2.5), using greedy selection mechanism, determines reservation or more new individual according to individual adaptation degree, if newly in this step Population at individual newPiThe good individual P of fitnessiFitness, receive newPiAs newest individual, otherwise individual PiDo not changed Become, process is shown below:
Wherein, fit (Pi) and fit (newPi) it is particle P respectivelyiUpdate the fitness value of front and back；
(2-2.6) this step decides whether to exit iteration, exits condition there are two iteration, meet one of them:
(a) whether the number of iterations is less than the maximum number of iterations of setting；
(b) fitness value is less than the threshold value of setting；
If meeting one of condition executes (7), otherwise (b) is executed；
(2-2.7) global optimum particle is anchor node coordinate.
7. a kind of underwater wireless sensor network location algorithm based on mobility prediction as claimed in claim 6, feature It is: the step (3) specifically: step (2) calculates the location information of this locating periodically T anchor node a, and each anchor node can The location information of record past locating periodically, so calculating the speed of anchor node with following formula:
Wherein, (x1,y1) be last locating periodically coordinate, (x2,y2) be this locating periodically coordinate, Vxa and Vya are respectively anchor Node x-axis speed and y-axis speed.
8. a kind of underwater wireless sensor network location algorithm based on mobility prediction as claimed in claim 7, feature Be: the step (4) specifically: by unknown node one jump in anchor node and oriented unknown node be defined as reference node Point, since immersed body has group motion, it is possible to calculate the velocity information of unknown node by following formula:
Wherein, Vxc and Vyc is respectively the x-axis speed and y-axis speed of reference mode c, and Vx and Vy are respectively the x-axis of unknown node Speed and y-axis speed, ηcIt is confidence value parameter, confidence value parameter is determined by following formula:
Wherein, ζcIt is the signal strength for the reference mode that unknown node receives, m is reference mode number.
9. a kind of underwater wireless sensor network location algorithm based on mobility prediction as claimed in claim 8, feature Be: step (5) calculates unknown node this locating periodically velocity information, according to the updating location information sheet of last locating periodically Period location information:
Wherein, (x', y') is the location information of the last locating periodically of unknown node, and (x, y) is this locating periodically of unknown node Location information.
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