CN107222925A - A kind of node positioning method based on cluster optimization - Google Patents
A kind of node positioning method based on cluster optimization Download PDFInfo
- Publication number
- CN107222925A CN107222925A CN201710640245.4A CN201710640245A CN107222925A CN 107222925 A CN107222925 A CN 107222925A CN 201710640245 A CN201710640245 A CN 201710640245A CN 107222925 A CN107222925 A CN 107222925A
- Authority
- CN
- China
- Prior art keywords
- beaconing nodes
- locset
- positioning
- unknown node
- data point
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- 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
Landscapes
- Engineering & Computer Science (AREA)
- Computer Networks & Wireless Communication (AREA)
- Signal Processing (AREA)
- Position Fixing By Use Of Radio Waves (AREA)
Abstract
The invention discloses a kind of node positioning method based on cluster optimization, this method includes：Arbitrarily three beaconing nodes of selection are combined as beaconing nodes in a network, are obtainedIndividual beaconing nodes combination, and judge whether each beaconing nodes combination meets conllinear degree threshold value, the beaconing nodes combination for meeting conllinear degree threshold value is added to container ColSet；To each beaconing nodes combination in container ColSet, judge whether beaconing nodes combination is tested by PIT, it is impossible to which the beaconing nodes tested by PIT are combined and rejected from container ColSet；Three beaconing nodes in being combined using beaconing nodes carry out Primary Location estimation to unknown node, and Primary Location estimated result is added into positioning Candidate Set LocSet；Cluster optimization is carried out to positioning Candidate Set LocSet using clustering algorithm, data point is sorted out and the noise data point in positioning Candidate Set LocSet is removed, finds maximum kernel heart data points cluster, determines the estimated location coordinate of unknown node.This method, which is realized, improves positioning accuracy.
Description
Technical field
The present invention relates to wireless location technology field, more particularly to a kind of node positioning method based on cluster optimization.
Background technology
In recent years, the propulsion developed with Internet of Things, wireless sensor network has obtained application widely, such as exists
Smart home, Industry Control, intelligent transportation, smart city, health care, military and national defense etc..Wireless sensor network is also
Change our life, and be increasingly becoming a part indispensable during we live.In wireless sensor network, Wo Menyou
When need to interact with the monitored object on network, for the ease of being taken appropriate measures to monitored object, it is necessary to monitor pair
As the physical location at place.If the situs ambiguus of monitored object, research work related to this is probably meaningless
's.Therefore, in wireless sensor network system, positioning is a basic critical function.
Wireless sensor network interior joint location technology is one of hot issue for studying at present, and current location algorithm is big
Cause can be divided into two major classes, and location algorithm of the class based on ranging is another kind of based on non-ranging location algorithm.This two classes algorithm
Respectively there are advantage and disadvantage, first kind algorithm is main based on range measurement, passes through trilateration or Maximum Likelihood Estimation Method etc.
Method is positioned to unknown node；Its advantage is that position error is smaller, positional accuracy is higher, but it has the disadvantage to node
Hardware requirement is higher, increases the cost of network.Equations of The Second Kind algorithm due to apart from unrelated, to the hardware requirement phase of sensor node
To relatively low, while the cost of the whole network is also reduced, but positioning precision is not so good as first kind algorithm.Due to right in wireless sensor network
The requirement of positioning precision is generally relevant with application, so taking different location algorithms generally according to different applications.
In massive wireless sensor, it is contemplated that the features such as financial cost, node hardware simplicity, in the network
On use mostly based on non-ranging location algorithm, wherein, DV-Hop node locating algorithms receive the lattice of many scholars
Outer concern.Because there are many weak points, DV-Hop (Distance Vector-Hop) positioning in positioning in DV-Hop algorithms
Algorithm be analogous to a kind of of distance vector routing mechanism in network with apart from unrelated distributed location method.Using distance to
Amount location mechanism realizes minimum hop count between unknown node and beaconing nodes, then by minimum hop count estimate average each jump away from
From then trying to achieve the estimated distance value between unknown node and beaconing nodes by the product of minimum hop count and Average hop distance, most
The coordinate position of unknown node is calculated by Maximum Likelihood Estimation Method or trilateration afterwards.But use DV-Hop algorithms to carry out
Positioning still suffers from positioning noise spot, and locating effect is relatively low, there is that error is on the high side, and such positioning accuracy is not high.
The content of the invention
It is an object of the invention to provide a kind of node positioning method based on cluster optimization, to realize raising accurate positioning
Property.
In order to solve the above technical problems, the present invention provides a kind of node positioning method based on cluster optimization, this method bag
Include：
Arbitrarily three beaconing nodes of selection are combined as beaconing nodes in a network, obtain Cm 3Individual beaconing nodes combination, and
Judge whether each beaconing nodes combination meets conllinear degree threshold value, the beaconing nodes combination for meeting conllinear degree threshold value is added to appearance
Device ColSet；Wherein, m is the number of beaconing nodes in network；
To each beaconing nodes combination in container ColSet, judge whether beaconing nodes combination is tested by PIT, will not
The beaconing nodes that can be tested by PIT are combined rejects from container ColSet；
To each beaconing nodes combination in container ColSet, three beaconing nodes in being combined using beaconing nodes are not to
Know that node carries out Primary Location estimation, Primary Location estimated result is added to positioning Candidate Set LocSet；
Cluster optimization is carried out to positioning Candidate Set LocSet using clustering algorithm, data point is sorted out and positioning candidate is removed
Collect the noise data point in LocSet, find maximum kernel heart data points cluster, determine the estimated location coordinate of unknown node.
It is preferred that, each beaconing nodes combination in the ColSet to container, three in being combined using beaconing nodes
Beaconing nodes carry out Primary Location estimation to unknown node, and Primary Location estimated result is added into positioning Candidate Set LocSet,
Including：
Three beaconing nodes in being combined for beaconing nodes, are jumped using weighted average and estimate unknown node with appointing away from method
The distance between two beaconing nodes of meaning；
By beacon triangle and the unknown relation of unknown node, unknown node and beacon section are calculated using triangular nature
The distance between remaining beaconing nodes in point combination, carry out rough location to unknown node by trilateration and estimate
Meter；
The Primary Location that three times are carried out to unknown node by each beaconing nodes combination in container ColSet is estimated, incites somebody to action
Primary Location estimated result each time is added in the positioning Candidate Set LocSet of unknown node.
It is preferred that, the beaconing nodes that will not pass through PIT tests are combined after rejecting, also to be wrapped from container ColSet
Include：
More new container ColSet.
It is preferred that, the clustering algorithm includes DBSCAN clustering algorithms.
It is preferred that, the use clustering algorithm carries out cluster optimization to positioning Candidate Set LocSet, and data point is sorted out simultaneously
The noise data point in positioning Candidate Set LocSet is removed, maximum kernel heart data points cluster is found, the estimation position of unknown node is determined
Coordinate is put, including：
DBSCAN clustering algorithms are run on positioning Candidate Set LocSet, the reachable data point of density a class are classified as, together
When remove LocSet in noise spot, obtain remove noise data point after one or more class clusters；
The class cluster of maximum is found from the class cluster of acquisition, the average value of the maximum class cluster is asked for, will be described average
It is worth the final position coordinate as unknown node.
It is preferred that, it is described positioning Candidate Set LocSet on operation DBSCAN clustering algorithms before, in addition to：
Data point density threshold parameter Minpts in DBSCAN algorithms is set；
K-dist figures are set up according to data point density threshold parameter Minpts and positioning Candidate Set LocSet, DBSCAN is determined
Data point field radius parameter Eps in algorithm；
Input value before Minpts, Eps and LocSet are run as DBSCAN algorithms.
It is preferred that, the PIT tests are point test in optimal triangle.
A kind of node positioning method based on cluster optimization provided by the present invention, arbitrarily selects three beacons in a network
Node is combined as beaconing nodes, is obtainedIndividual beaconing nodes combination, and judge whether each beaconing nodes combination meets conllinear
Threshold value is spent, the beaconing nodes combination for meeting conllinear degree threshold value is added to container ColSet；Wherein, m is beaconing nodes in network
Number；To each beaconing nodes combination in container ColSet, judge whether beaconing nodes combination is tested by PIT, will not
The beaconing nodes that can be tested by PIT are combined rejects from container ColSet；To each beaconing nodes group in container ColSet
Close, three beaconing nodes in being combined using beaconing nodes carry out Primary Location estimation to unknown node, and Primary Location is estimated
As a result it is added to positioning Candidate Set LocSet；Cluster optimization is carried out to positioning Candidate Set LocSet using clustering algorithm, by data
Point is sorted out and removes the noise data point in positioning Candidate Set LocSet, finds maximum kernel heart data points cluster, determines unknown node
Estimated location coordinate.It can be seen that, relatively conventional DV-Hop location algorithms, this method is first with conllinear degree diagnostic method and optimal three
The screening of angular interior method of testing participates in the beaconing nodes group of positioning, secondly estimation unknown node and any two beaconing nodes away from
From calculating unknown node and the distance of remaining beaconing nodes, then carry out Primary Location estimation, every group of beacon to unknown node
Node can produce three Primary Locations estimation to unknown node, and each Primary Location estimated result is added into positioning
Candidate Set, is finally optimized by clustering algorithm to positioning Candidate Set, and positioning noise spot is removed, positioning core data is left
Point, obtains the optimal possible position of unknown node, and this method is more excellent than DV-Hop location algorithm locating effects, reduces error,
Improve the accuracy of positioning.
Brief description of the drawings
In order to illustrate more clearly about the embodiment of the present invention or technical scheme of the prior art, below will be to embodiment or existing
There is the accompanying drawing used required in technology description to be briefly described, it should be apparent that, drawings in the following description are only this
The embodiment of invention, for those of ordinary skill in the art, on the premise of not paying creative work, can also basis
The accompanying drawing of offer obtains other accompanying drawings.
Fig. 1 is a kind of flow chart of the node positioning method based on cluster optimization provided by the present invention；
Fig. 2 is the DV-Hop location algorithm flow charts that optimization is clustered based on DBSCAN；
Fig. 3 (a) is influence analogous diagram of the beaconing nodes accounting to average localization error in the inventive method；
Fig. 3 (b) is influence analogous diagram of the inventive method midpoint communication radius to average localization error；
Fig. 3 (c) is influence analogous diagram of the inventive method interior joint sum to average localization error.
Embodiment
The core of the present invention is to provide a kind of node positioning method based on cluster optimization, to realize raising accurate positioning
Property.
In order that those skilled in the art more fully understand the present invention program, below in conjunction with the embodiment of the present invention
Accompanying drawing, the technical scheme in the embodiment of the present invention is clearly and completely described, it is clear that described embodiment is only
A part of embodiment of the present invention, rather than whole embodiments.Based on the embodiment in the present invention, those of ordinary skill in the art
The every other embodiment obtained under the premise of creative work is not made, belongs to the scope of protection of the invention.
Fig. 1 is refer to, Fig. 1 is a kind of flow chart of the node positioning method based on cluster optimization provided by the present invention,
This method includes：
S11：Arbitrarily three beaconing nodes of selection are combined as beaconing nodes in a network, obtain Cm 3Individual beaconing nodes group
Close, and judge whether each beaconing nodes combination meets conllinear degree threshold value, the beaconing nodes combination for meeting conllinear degree threshold value is added
It is added to container ColSet；
Wherein, m is the number of beaconing nodes in network；
S12：To each beaconing nodes combination in container ColSet, judge whether beaconing nodes combination is tested by PIT,
The beaconing nodes that will not pass through PIT tests combine the rejecting from container ColSet；
S13：To each beaconing nodes combination in container ColSet, three beaconing nodes in being combined using beaconing nodes
Primary Location estimation is carried out to unknown node, Primary Location estimated result is added to positioning Candidate Set LocSet；
S14：Cluster optimization is carried out to positioning Candidate Set LocSet using clustering algorithm, data point is sorted out and positioning is removed
Noise data point in Candidate Set LocSet, finds maximum kernel heart data points cluster, determines the estimated location coordinate of unknown node.
It can be seen that, relatively conventional DV-Hop location algorithms, this method is first with conllinear degree diagnostic method and optimal triangle
Point method of testing screening participates in the beaconing nodes group of positioning, secondly estimation unknown node and the distance of any two beaconing nodes, meter
Unknown node and the distance of remaining beaconing nodes are calculated, Primary Location estimation, every group of beaconing nodes then are carried out to unknown node
Three Primary Locations estimation to unknown node can be produced, each Primary Location estimated result is added to positioning candidate
Collection, is finally optimized by clustering algorithm to positioning Candidate Set, and positioning noise spot is removed, and leaves positioning core data point,
The optimal possible position of unknown node is obtained, this method is more excellent than DV-Hop location algorithm locating effects, reduces error, improves
The accuracy of positioning.
Based on the above method, further, step S13 specifically includes following steps：
S21：Three beaconing nodes in being combined for beaconing nodes, are jumped using weighted average and estimate unknown node away from method
The distance between with any two beaconing nodes；
S22：By beacon triangle and the unknown relation of unknown node, unknown node and letter are calculated using triangular nature
The distance between remaining beaconing nodes in combination of nodes are marked, rough location is carried out to unknown node by trilateration
Estimation；
S23：The Primary Location for carrying out three times to unknown node by each beaconing nodes combination in container ColSet is estimated
Meter, Primary Location estimated result each time is added in the positioning Candidate Set LocSet of unknown node.
Further, in step S12, it is impossible to which the beaconing nodes tested by PIT are combined rejects from container ColSet
Afterwards, in addition to：More new container ColSet.
Detailed, the clustering algorithm includes DBSCAN clustering algorithms.
Further, step S14 comprises the following steps：
S31：DBSCAN clustering algorithms are run on positioning Candidate Set LocSet, the reachable data point of density is classified as one
Class, while removing the noise spot in LocSet, obtains the one or more class clusters removed after noise data point；
S32：The class cluster of maximum is found from the class cluster of acquisition, the average value of the maximum class cluster is asked for, will be described
Average value as unknown node final position coordinate.
Wherein, also include before step S31：Data point density threshold parameter Minpts in DBSCAN algorithms is set；According to
K-dist figures are set up according to data point density threshold parameter Minpts and positioning Candidate Set LocSet, the number in DBSCAN algorithms is determined
Strong point field radius parameter Eps；Input value before Minpts, Eps and LocSet are run as DBSCAN algorithms.
Wherein, beaconing nodes are also referred to as anchor node or reference mode, be positional information the characteristics of such node, it is known that institute
It is known to be with positional information because such node has d GPS locating module or manually default mode determines oneself of the node
Body positional information carries out positioning acquisition without being gone again by other approach to this node.
Wherein, it is average to jump away from the entire network, to be jumped between the estimated distance sum and node between any two node
The ratio of number summation, the referred to as average jump of whole network are away from flat between two nodes obtained according to different jumps away from computation rule
Jump away from may be not quite similar.
Wherein, the PIT tests are point test in optimal triangle.Point test is first with network in optimal triangle
Whether the movement of analog node is carried out in the higher region of counterpart node density true in any three beaconing nodes in the hope of unknown node
In fixed triangle, if unknown node is in triangle interior, all unknown nodes that meet are calculated in multigroup three beacons
The lap for the delta-shaped region that node is determined, then tries to achieve barycenter the estimating as unknown node in polygon lap
Count position.Assuming that there is n beaconing nodes in on-premise network, then sharedMiddle different choosing method,Middle different choosing
Unknown node is tested in taking successively whether in each triangle interior, the operation precision or limit needed for meeting positioning is repeated
All possible combination.
Specifically, under given data collection and Minpts, each data point and k-th of nearest data are obtained by calculating
The distance between point, then to this ascending sequence of progress, this process is exactly to set up k-dist figures.Minpts value is
Determined by a kind of heuristic, typically in order to reduce amount of calculation, Minpts is taken as 4 in advance proper.In order to count
It is convenient to calculate, and is 4 by Minpts value value, and k-dist figures are set up to LocSet, so that it is determined that parameter Eps value, by Minpts,
Eps and LocSet is used as the input value before clustering algorithm DBSCAN operations.
The specific establishment step of k-dist figures is as follows：
(1) distance of each data point and other data points is calculated, the distance matrix distn that size is N × N, square is built
Every a line of battle array all represents the distance of a data point and other data points；
(2) the matrix distn every a line of adjusting the distance finds out minimum value, then replaces it an infinity, and apart from square
Other data of battle array keep constant；
(3) go to step (2) to continue executing with, untill finding k-th of minimum distance of each data point, namely perform
The number of times of step 2 be k+1 time because for the first time perform step 2 when, the minimum range found be data point and its own away from
From, be all 0 data；
(4) obtain after all k-th of minimum distances of data point, these data are sorted from small to large, x-axis is data point
Sequence, y-axis is k-th of minimum distance value.
In more detail, positioning Candidate Set is optimized using DBSCAN clustering algorithms, data point is sorted out and removed and is determined
Noise data point in the Candidate Set LocSet of position is concretely comprised the following steps：
1st, Minpts value is determined first, is then selected the distance of k-th of neighbour's data point of each data point, is set up k-
Dist schemes, and by the observation to k-dist figures, searches out value of the corresponding distance value as Eps that be recessed in curve map；
2nd, any selection one is not belonging to the starting point that the data point of any cluster is set up as cluster label C from data set, so
The inquiry in Eps fields and statistics are carried out to the data point afterwards, judge that the quantity of statistics is compared with Minpts, if being more than or equal to
Minpts, then it is core data point to illustrate the data point, and all data points in its field are marked to the cluster of same type simultaneously
Core data point in data point Eps fields, is then added to container li by label CsIn t；If less than Minpts, temporarily marking
It is designated as noise data point；
3rd, a data point is taken out from container list, then inquires about and counts the data in the Eps fields of the data point
Point, data point markers C all in the field finally, is added to the core data point in the field in container list；
4th, repeat step 3, thus and thus, constantly expansion cluster C are marked as C until not new data point, now, cluster C
It has been completely set up that, next step continues to select other possible cluster classes.Step 1 is gone to continue executing with；
5th, when can not find the data point that is not belonging to any cluster, while all data points were all to sound out, then it is remaining not exist
Data point in any one cluster, has been marked as noise data point.
It can be seen that, the present invention is adopted to traditional DV-Hop location algorithms first using the improvement strategy optimized based on Density Clustering
Average jump is improved away from the letter of point method of testing screening participation positioning in conllinear degree diagnostic method and optimal triangle with weighting scheme
Node group is marked, secondly estimation unknown node and the distance of any two beaconing nodes, using triangular nature, calculate unknown section
The distance of point and remaining beaconing nodes, then carries out Primary Location estimation, every group of beacon using trilateration to unknown node
Node can produce three Primary Locations estimation to unknown node, and each Primary Location estimated result is added into positioning
Candidate Set, and positioning Candidate Set is optimized by DBSCAN clustering algorithms, positioning noise spot is removed, positioning core is left
Calculation strong point, obtains the optimal possible position of unknown node, and this method is more excellent than DV-Hop location algorithm locating effects, reduces
Error, improves the accuracy of positioning.
This method obtains the positioning candidate to unknown node in on-premise network by follow-on DV-Hop location algorithms
Collect LocSet, cluster optimization is carried out to it using density-based algorithms DBSCAN, find maximum kernel heart data points cluster, enter
And determine the estimated location coordinate of unknown node.
Fig. 2 is the DV-Hop location algorithm flow charts that optimization is clustered based on DBSCAN, detailed, DV-Hop location algorithm mistakes
Journey is divided into three steps：
The purpose of the first step is each node in connected network is record the minimum between each beaconing nodes
The positional information of hop count information and each beaconing nodes.In order to realize the purpose, beaconing nodes each first pass through controllable flooding
Mechanism broadcasts the localizer beacon packet of itself to whole network.Information content in packet includes timestamp, beaconing nodes sequence
Number, current jumping figure value h and beaconing nodes self-position (x, y), current jumping figure value h field initialization values are zero.Then, when it
Neighbor node have received after the packet, is analyzed with the packet that currently preserves, to decide whether to the section
Data in the packet preserved before point are updated preservation, node is preserved at any time in packet optimal under present case
Record, the now current jumping figure value of the record is minimum, and required time is also most short, then relays to the neighbor node of surrounding,
Before forwarding, current hop count information field value is first changed, its field value plus one.After such Consecutive forwarding,
Each node in connected network will obtain above two key message.
Second step need to complete to calculate the estimated distance between unknown node and each beaconing nodes.Estimation beaconing nodes is flat first
Jump away from：
Wherein, (xi,yi)、(xj,yj) be beaconing nodes i and j coordinate, hijIt is the minimum hop count of i, j between beaconing nodes
Value.
Then the average jump of itself is obtained away from after in each beaconing nodes, by controllable flooding mechanism to surrounding
Neighbor node sends it and averagely jumped away from information, and unknown node only preserves the average jump of the beaconing nodes of its nearest neighbours away from information.So
Afterwards, unknown node calculates the estimated distance between beaconing nodes using the information：
di=hi·hopsize
Wherein, hiRepresent unknown node to the minimum hop count value between beaconing nodes i, diRepresent unknown node and beaconing nodes i
Estimated distance, Hopsize values be the beaconing nodes nearest from unknown node average jump away from.
3rd step will realize the positioning of unknown node.Multiple such as 3 of second step acquisition for passing through algorithm when unknown node
Or after more than 3 estimated distance values, the estimation for calculating unknown node with trilateration or Maximum Likelihood Estimation Method is sat
Mark.
Specifically, the present invention is optimized by DBSCAN clustering algorithms to positioning Candidate Set, positioning noise spot is removed,
Positioning core data point is left, DBSCAN clustering algorithms are to be possible to the connected all data points of mutual density to be classified as one
Cluster, whole data set may eventually produce one or more clusters, perhaps also comprising not in the noise data point of any one cluster.
The present invention proposes to obtain the positioning to unknown node by follow-on DV-Hop location algorithms in on-premise network
Candidate Set, cluster optimization is carried out to it using density-based algorithms DBSCAN, finds maximum kernel heart data points cluster, and then
Determine the estimated location coordinate of unknown node.
In this method, after traditional DV-Hop is averagely jumped to unknown node away from weighting processing, unknown node is recycled
With the topological relation between beacon triangle, original position fixing process is adjusted, based on two estimation sides to unknown node
Location estimation is carried out, follow-on DV-Hop location algorithms (An Improved DV-Hop Localization are formed
Algorithm, abbreviation IDV-Hop).Due to that arbitrarily two beaconing nodes can be selected to use modified in three beaconing nodes
DV-Hop location algorithms unknown node is positioned, so every three beaconing nodes may constitute one group on network, and
Every group of beaconing nodes therein can carry out three Primary Locations to unknown node and estimate, then the Primary Location of maximum possible
Estimate that number of times isWherein m is the sum of beaconing nodes in network, and clustering algorithm is used on the estimation of these Primary Locations
This is analyzed, the position that unknown node most possibly occurs is obtained, this process is formed being based on clustering strategy
Modified DV-Hop algorithms (An Improved DV-Hop Algorithm Based on Clustering Analysis
Of DBSCAN, abbreviation IDV-HopCAD).
Simulation result such as Fig. 3 (a), Fig. 3 (b) and Fig. 3 (c) are shown, and Fig. 3 (a) is beaconing nodes accounting in the inventive method
Influence analogous diagram to average localization error, Fig. 3 (b) is influence of the inventive method midpoint communication radius to average localization error
Analogous diagram, Fig. 3 (c) is influence analogous diagram of the inventive method interior joint sum to average localization error, these figures be
MATLAB sets up simulation model, to DV-Hop algorithms and set forth herein the result figure that is analyzed of innovatory algorithm, specifically
It is average localization error under the factors such as beaconing nodes ratio, node communication radius and node total number respectively to three kinds of algorithms
Influence tested, find out from Fig. 3 (a), Fig. 3 (b) and Fig. 3 (c), it is of the invention based on cluster optimization node locating algorithm and
Remaining two kinds of algorithm is smaller compared to position error, and locating effect is more preferable.
Because DV-Hop algorithms have many weak points in positioning, the present invention proposes a kind of based on Density Clustering optimization
Improvement strategy, average jump away from conllinear degree diagnostic method is improved using weighting scheme to traditional DV-Hop location algorithms first
With the beaconing nodes group that method of testing screening participation positioning is put in optimal triangle, secondly, estimation unknown node and any two letter
The distance of node is marked, using triangular nature, unknown node and the distance of remaining beaconing nodes is calculated, is then surveyed using three sides
Amount method carries out Primary Location estimation to unknown node, repeats aforesaid operations, and every group of beaconing nodes can be produced to unknown node
The estimation of three Primary Locations, each Primary Location estimated result is added to positioning Candidate Set, it is finally poly- by DBSCAN
Class algorithm is optimized to positioning Candidate Set, and positioning noise spot is removed, and is left positioning core data point, is obtained unknown node most
Good possible position.Simulation model is set up by MATLAB, to DV-Hop algorithms and innovatory algorithm proposed by the present invention progress pair
Than analysis, simulation result shows, the location algorithm optimized based on Density Clustering is more excellent than DV-Hop location algorithm locating effects, drop
Low error, improves the accuracy of positioning.
A kind of node positioning method based on cluster optimization provided by the present invention is described in detail above.Herein
In apply specific case the principle and embodiment of the present invention be set forth, the explanation of above example is only intended to side
The method and its core concept of the assistant solution present invention.It should be pointed out that for those skilled in the art, not
On the premise of departing from the principle of the invention, some improvement and modification can also be carried out to the present invention, these are improved and modification is also fallen into
In the protection domain of the claims in the present invention.
Claims (7)
1. a kind of node positioning method based on cluster optimization, it is characterised in that including：
Arbitrarily three beaconing nodes of selection are combined as beaconing nodes in a network, are obtainedIndividual beaconing nodes combination, and judge
Whether each beaconing nodes combination meets conllinear degree threshold value, and the beaconing nodes combination for meeting conllinear degree threshold value is added into container
ColSet；Wherein, m is the number of beaconing nodes in network；
To each beaconing nodes combination in container ColSet, judge whether beaconing nodes combination is tested by PIT, it is impossible to logical
The beaconing nodes for crossing PIT tests combine the rejecting from container ColSet；
To each beaconing nodes combination in container ColSet, three beaconing nodes in being combined using beaconing nodes are to unknown section
Point carries out Primary Location estimation, and Primary Location estimated result is added into positioning Candidate Set LocSet；
Cluster optimization is carried out to positioning Candidate Set LocSet using clustering algorithm, data point is sorted out and positioning Candidate Set is removed
Noise data point in LocSet, finds maximum kernel heart data points cluster, determines the estimated location coordinate of unknown node.
2. the method as described in claim 1, it is characterised in that each beaconing nodes combination in the ColSet to container,
Three beaconing nodes in being combined using beaconing nodes carry out Primary Location estimation to unknown node, by Primary Location estimated result
It is added to positioning Candidate Set LocSet, including：
Three beaconing nodes in being combined for beaconing nodes, are jumped using weighted average and estimate unknown node and any two away from method
The distance between individual beaconing nodes；
By beacon triangle and the unknown relation of unknown node, unknown node and beaconing nodes group are calculated using triangular nature
The distance between remaining beaconing nodes in conjunction, rough location estimation is carried out by trilateration to unknown node；
The Primary Location that three times are carried out to unknown node by the combination of each beaconing nodes in container ColSet estimates, will be each
Secondary Primary Location estimated result is added in the positioning Candidate Set LocSet of unknown node.
3. the method as described in claim 1, it is characterised in that the beaconing nodes combination that will not pass through PIT tests from
After being rejected in container ColSet, in addition to：
More new container ColSet.
4. the method as described in claim 1, it is characterised in that the clustering algorithm includes DBSCAN clustering algorithms.
5. method as claimed in claim 4, it is characterised in that the use clustering algorithm is carried out to positioning Candidate Set LocSet
Cluster optimization, data point is sorted out and the noise data point in positioning Candidate Set LocSet is removed, maximum kernel calculation strong point is found
Cluster, determines the estimated location coordinate of unknown node, including：
DBSCAN clustering algorithms are run on positioning Candidate Set LocSet, the reachable data point of density is classified as a class, gone simultaneously
Except the noise spot in LocSet, the one or more class clusters removed after noise data point are obtained；
The class cluster of maximum is found from the class cluster of acquisition, the average value of the maximum class cluster is asked for, the average value is made
For the final position coordinate of unknown node.
6. method as claimed in claim 5, it is characterised in that the operation DBSCAN clusters on positioning Candidate Set LocSet
Before algorithm, in addition to：
Data point density threshold parameter Minpts in DBSCAN algorithms is set；
K-dist figures are set up according to data point density threshold parameter Minpts and positioning Candidate Set LocSet, DBSCAN algorithms are determined
In data point field radius parameter Eps；
Input value before Minpts, Eps and LocSet are run as DBSCAN algorithms.
7. the method as described in any in claim 1 to 6, it is characterised in that the PIT tests are surveyed for point in optimal triangle
Examination.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710640245.4A CN107222925A (en) | 2017-07-31 | 2017-07-31 | A kind of node positioning method based on cluster optimization |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710640245.4A CN107222925A (en) | 2017-07-31 | 2017-07-31 | A kind of node positioning method based on cluster optimization |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN107222925A true CN107222925A (en) | 2017-09-29 |
Family
ID=59954277
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710640245.4A Pending CN107222925A (en) | 2017-07-31 | 2017-07-31 | A kind of node positioning method based on cluster optimization |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107222925A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108882174A (en) * | 2018-07-03 | 2018-11-23 | 北京三快在线科技有限公司 | Mobile terminal locating method, device, electronic equipment and storage medium |
| CN112929879A (en) * | 2021-01-15 | 2021-06-08 | 电子科技大学 | NBDP clustering-based wireless sensor network malicious node detection method |
| CN113596989A (en) * | 2021-08-04 | 2021-11-02 | 电子科技大学 | Indoor positioning method and system for intelligent workshop |
| CN113820663A (en) * | 2021-08-02 | 2021-12-21 | 中南大学 | Robust microseismic/acoustic emission source positioning method and system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104135749A (en) * | 2014-08-20 | 2014-11-05 | 河海大学常州校区 | Mobile beacon path planning method based on network density clustering of wireless sensor network |
| CN105044662A (en) * | 2015-05-27 | 2015-11-11 | 南京邮电大学 | Fingerprint clustering multi-point joint indoor positioning method based on WIFI signal intensity |
-
2017
- 2017-07-31 CN CN201710640245.4A patent/CN107222925A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104135749A (en) * | 2014-08-20 | 2014-11-05 | 河海大学常州校区 | Mobile beacon path planning method based on network density clustering of wireless sensor network |
| CN105044662A (en) * | 2015-05-27 | 2015-11-11 | 南京邮电大学 | Fingerprint clustering multi-point joint indoor positioning method based on WIFI signal intensity |
Non-Patent Citations (2)
| Title |
|---|
| 陆娴,彭勇: "一种改进的APIT定位算法", 《计算机工程与应用》 * |
| 陈继峰,刘广聪,彭成平: "一种改进的无线传感器网络DV-Hop定位算法", 《广东工业大学学报》 * |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108882174A (en) * | 2018-07-03 | 2018-11-23 | 北京三快在线科技有限公司 | Mobile terminal locating method, device, electronic equipment and storage medium |
| CN108882174B (en) * | 2018-07-03 | 2020-12-04 | 北京三快在线科技有限公司 | Mobile terminal positioning method and device, electronic equipment and storage medium |
| CN112929879A (en) * | 2021-01-15 | 2021-06-08 | 电子科技大学 | NBDP clustering-based wireless sensor network malicious node detection method |
| CN112929879B (en) * | 2021-01-15 | 2022-05-03 | 电子科技大学 | NBDP clustering-based wireless sensor network malicious node detection method |
| CN113820663A (en) * | 2021-08-02 | 2021-12-21 | 中南大学 | Robust microseismic/acoustic emission source positioning method and system |
| CN113820663B (en) * | 2021-08-02 | 2024-04-16 | 中南大学 | Robust microseism/acoustic emission source positioning method and system |
| CN113596989A (en) * | 2021-08-04 | 2021-11-02 | 电子科技大学 | Indoor positioning method and system for intelligent workshop |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107222925A (en) | A kind of node positioning method based on cluster optimization | |
| CN102665277B (en) | A kind of method that wireless sensor network interior joint is positioned | |
| CN107113764B (en) | Method and device for improving positioning performance of artificial neural network | |
| CN103747419B (en) | A kind of indoor orientation method based on signal strength difference and dynamic linear interpolation | |
| CN105828287B (en) | A kind of wireless sensor network cooperative tracking method based on intensified learning | |
| CN106792540B (en) | A kind of improvement DV-Hop localization method based on route matching | |
| CN101820676B (en) | Sensor node positioning method | |
| WO2014062118A1 (en) | System and method in connection with occurrence of platoons | |
| CN105372628A (en) | Wi-Fi-based indoor positioning navigation method | |
| CN103338511A (en) | WSN (wireless sensor network) rapid self-positioning method based on triangle form estimation | |
| CN103415072B (en) | Based on the localization method estimating distance in a kind of radio sensing network | |
| CN105407529B (en) | Localization Algorithm for Wireless Sensor Networks based on fuzzy C-means clustering | |
| CN104144499A (en) | Wireless sensor network positioning method based on RSSI vector similarity degree and generalized inverse | |
| CN106125037B (en) | Localization method is recalled based on the indoor wireless hot spot of WiFi signal intensity and Micro-Model | |
| CN104039008B (en) | A kind of hybrid locating method | |
| CN106597370A (en) | Indoor wireless positioning method based on RFID | |
| CN107786449A (en) | Routing resource, device, server and storage medium based on FSR agreements | |
| CN104301996B (en) | A kind of wireless sensor network locating method | |
| CN104080169B (en) | A kind of underwater wireless sensor network dynamic self-adapting localization method | |
| CN104093182A (en) | Method for acquiring a plurality of reliable communication paths based on field intensity in multi-layer wireless network | |
| Wang et al. | Optimal target tracking based on dynamic fingerprint in indoor wireless network | |
| CN113329490B (en) | Wireless sensor network node positioning method based on quantum tiger shark mechanism | |
| Zobel et al. | Topology-aware path planning for in-transit coverage of aerial post-disaster communication assistance systems | |
| CN102752850A (en) | Range-free based device and method for screening network anchor nodes | |
| Qin et al. | Node localization with a mobile beacon based on ant colony algorithm in wireless sensor networks |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20170929 |
|
| RJ01 | Rejection of invention patent application after publication |