CN105323775A - A general extraction method for two-dimensional and three-dimensional sensor network line skeletons - Google Patents
A general extraction method for two-dimensional and three-dimensional sensor network line skeletons Download PDFInfo
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- CN105323775A CN105323775A CN201510791840.9A CN201510791840A CN105323775A CN 105323775 A CN105323775 A CN 105323775A CN 201510791840 A CN201510791840 A CN 201510791840A CN 105323775 A CN105323775 A CN 105323775A
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Abstract
The invention provides a general extraction method for two-dimensional and three-dimensional sensor network line skeletons. The method comprises the steps of calculating the feature points of each internal node of a sensor network; forming the feature points of each internal node into a plurality of feature connected components by using a restrictive broadcasting mode; identifying line skeleton nodes; calculating the importance degrees of the line skeleton points; connecting the line skeleton points based on the importance degrees to generate a rough line skeleton; calculating the similarity of the branches of the line skeleton to further optimize the rough line skeleton to obtain the final skeleton. The importance degrees of the line skeleton points proposed by the invention have monotonicity, so that the identified line skeleton points can be connected easily by using the importance degrees; by setting different threshold values for the importance degrees, multi-scale network line skeletons can be obtained to reflect topological details of different scales of networks. The method is applicable to two-dimensional and three-dimensional sensor networks. The control to boundary noise is more flexible and the algorithm is applicable to the line skeleton extraction of the two-dimensional and three-dimensional sensor networks.
Description
Technical field
The invention belongs to wireless sensor network technology field, more specifically, relate to the common extraction procedure of a kind of two dimension and three-dimension sensor network line skeleton.
Background technology
The functional realiey of sensor network and geometry environment residing for it are closely related, and utilize the framework information of sensor network topological, can significantly improve the application performance such as such as location, Route Selection in sensor network.In computer vision research field, the research extracted about topology mainly concentrates on continuous domain, can not be applied directly in discrete sensor network.And the existing document about skeletal extraction, mainly concentrate on the line skeletal extraction of two-dimension sensors network.Wherein that representative is the MAP (MedialAxisbasedroutingProcotol that JehoshuaBruck etc. proposes, MAP) CASE (Connectivity-bAsedSkeletonExtraction of the proposition such as algorithm and HongboJiang, CASE) algorithm, specific practice is: the border first identifying sensor network, recycling maximum inscribed circle method, removes to judge a node whether as axis (also referred to as skeleton) node.In MAP algorithm, if there are two intersection points on the maximum inscribed circle of certain node and border, then namely this node is taken as axis node, and in CASE algorithm, two intersection points on this node and border also should belong to different boundary branch, are taken as line skeleton point just now.Because sensor network nodes is discrete distribution, MAP algorithm is often subject to the impact of boundary perturbation, obtain noise axis node, and CASE algorithm performance may be improper because of the Selecting parameter realizing boundary demarcation, and to make to extract skeleton be the part of true skeleton, so that the axis obtained or skeleton can not the true topological structures of representative sensor network very well.In addition, these algorithms, only for two-dimension sensors network, cannot extract the line skeleton of three-dimension sensor network.
Summary of the invention
For existing methodical deficiency, the present invention proposes the common extraction procedure of a kind of two dimension and three-dimension sensor network line skeleton, the method is applicable to two dimension and three-dimension sensor network simultaneously, and can not be subject to boundary perturbation impact, the network topology structure that can be more similar to.
Pass two dimension and a common extraction procedure for three-dimension sensor network line skeleton, comprise the following steps:
(1) characteristic point of each internal node of calculating sensor network, if the jumping figure of the nearest boundary point of certain internal node is k, then also sees the boundary point of jumping apart from this internal node k+1 as the characteristic point of this internal node; Such characteristic point is claimed to be extension feature point;
(2) adopt restricted broadcast mode, the characteristic point of each internal node formed multiple feature connected component, and carry out on border hop-by-hop expansion connect these feature connected components, make it the feature connected component that formation one is large;
(3) utilize each internal node middle distance characteristic point to be less than the node set of specifying jumping figure, generate a jumping expansion path.If the border of expansion path comprises many closed curves, then respective inner node is line skeleton point; Otherwise it is not line skeleton point;
(4) each line skeleton point calculates the nodes be inflated in the separated each connected component of closed curve in path, and calculates the importance degree of corresponding line skeleton point.
The importance degree of its center line skeleton point refers to: to line skeleton point p, assuming that network decomposition is become l (>=2) individual connected component by (geodetic) shortest path of its characteristic point, is C
1(p), C
2(p) ..., C
l(p), and full C
1(p)≤C
2(p)≤... ≤ C
lp (), then importance degree τ (p) of p is τ (p)=1-C
l(p)/| C|, wherein C is all boundary point set.
(5) the line skeleton point that in its neighbours' line skeleton point of each line skeleton point selection, importance degree is maximum as father node, thus sets up the skeletal tree with multiple redundant branch;
(6) calculate the similarity of every bar skeleton branches and other skeleton branches, and utilize recursive procedure to optimize skeletal tree, obtain final grid line skeleton.
Wherein skeleton branches similarity refers to: the skeleton branches B two to same father node p
1, B
2, make L
1, L
2represent its branch length respectively.To branch B
1, B
2upper node of jumping apart from p node i
defined function
if
neighbor node each other, otherwise
then branch B
1and B
2at B
1, B
2between similarity Sim (B
1| B
2) and Sim (B
2| B
1) be defined as follows respectively:
If node p has I child node, and correspondingly produce I skeleton branches, then the similarity of skeleton branches B (p) (comprising the skeleton branches of a node p and I skeleton branches) of node p is
Sim(B(p))＝max
i,j≤I,i≠j{Sim(B
j|B
i)}。
Technique effect of the present invention is embodied in:
The present invention calculates the importance degree of line skeleton point, and utilizes the monotonicity of importance degree to set up skeletal tree; By calculating skeleton branches similarity, rejecting the skeleton branches that similarity is large, being formed and optimizing skeleton, its advantage is to generate according to the monotonicity of line skeleton point importance degree the line skeleton certainly connected.Meanwhile, centralized Global Algorithm is also not suitable for the such distributed network of sensor network, should design the distributed approximation method of a Global Algorithm, while basic reservation Global Algorithm superiority, obtain distributed characteristic.The decentralized algorithm because this algorithm is distributed, it is the distributed implementation of Global Algorithm, and therefore this algorithm is applicable to being applied to the sensor network with distributed nature; No matter this algorithm is time complexity or space complexity, all linear with number of network node, therefore, the packet and the network delay that extract skeleton needs can not affect performance because the nodes of sensor network increases, and are thus with good expansibility; Simultaneously, this algorithm can generate the line skeleton of different scale at any time according to the critical value of line skeleton point importance degree, compared with algorithm in the past, the control of boundary noise is more flexible, and this algorithm can be applied to the line skeletal extraction of two dimension and three-dimension sensor network simultaneously.
Accompanying drawing explanation
Fig. 1 is the common extraction procedure schematic flow sheet of the present invention's two dimension and three-dimension sensor network line skeleton;
Fig. 2 is three-dimension sensor network model example figure in the embodiment of the present invention;
Fig. 3 is the characteristic point schematic diagram of three-dimension sensor network internal node in the embodiment of the present invention;
Fig. 4 represents the geometry geodetic shortest path schematic diagram that the characteristic point in Fig. 3 is formed;
Fig. 5 is the expansion path schematic diagram identified in three-dimension sensor network in the embodiment of the present invention;
Fig. 6 is the expansion path schematic diagram that the characteristic point of non-thread skeleton point in three-dimension sensor network in the embodiment of the present invention is formed;
Fig. 7 is three-dimension sensor network line skeletal tree schematic diagram in the embodiment of the present invention;
Fig. 8 is the optimization skeleton schematic diagram of three-dimension sensor network in the embodiment of the present invention;
Fig. 9 is the skeleton schematic diagram extracted in two-dimension sensors network in the embodiment of the present invention;
Figure 10 is that three-dimension sensor network when importance degree critical value gets 0.1 in the embodiment of the present invention optimizes skeleton schematic diagram;
Figure 11 is that when in the embodiment of the present invention, importance degree critical value gets 0.2, the three-dimension sensor network of example optimizes skeleton schematic diagram.
Embodiment
In order to make object of the present invention, technical scheme and advantage clearly understand, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein only in order to explain the present invention, be not intended to limit the present invention.In addition, if below in described each execution mode of the present invention involved technical characteristic do not form conflict each other and just can mutually combine.
The sensor network that the present invention applies with only the link information between transducer, and we utilize the method in existing document to identify network boundary, therefore can suppose that the boundary information of sensor network is known.
Fig. 1 is the inventive method schematic flow sheet, comprises the following steps:
Step 1. recognition feature point and extension feature point
To each internal node, for identifying its characteristic point, first boundary node carries out an inundation in network internal, inundation information from nearest boundary point forwards by each internal node, finally, its characteristic point is recorded by each internal node, and the Boundary Tree that to be subordinated to one of them characteristic point be root node.Due to the discreteness of sensor network, be in the network of even number jumping in sparse network or network-wide, the feature that some true line skeleton point may identify is counted less, causes the geodetic shortest path between characteristic point can not split border, to such an extent as to can not be correctly validated out.For this reason, if the jumping figure of the nearest boundary point of certain internal node is k, then the boundary point of jumping apart from this internal node k+1 is also seen as characteristic point, claim such characteristic point to be extension feature point.Based on the characteristic point that these identify, tell about emphatically below in three-dimension sensor network, how to identify line skeleton point.
Fig. 2 is the sensor network schematic diagram of exemplary application, and Fig. 3 shows the characteristic point of an internal node.
Step 2. identifies line skeleton point
Line skeleton point in the present invention, the geometry geodetic path referred between its characteristic point forms a feature ring, network boundary can be divided into multiple connected component.To the leaf node on each Boundary Tree, allow its characteristic point initiate restricted inundation to build one or more connected component be made up of characteristic point, be called feature connected component, and distribute a unique identifier for each connected component.For these feature connected components are coupled together, to judge whether existing characteristics annular, each characteristic point p initiates the broadcast message that comprises its connected component identifier, if any neighbours of certain neighbours' boundary point q of three-dimensional network interior joint p or two-dimensional network interior joint p put q not yet distribution marker, then q point will be assigned with the connected component identifier identical with p; Repeat this process, until two boundary points with different identification symbol meet.This just establishes the geodetic shortest path between feature connected component in a distributed way.Without loss of generality, characteristic point and shortest path will be called characteristic point a little.
Fig. 4 represents the geometry geodetic shortest path that the characteristic point in Fig. 3 is formed.
Identify whether a leaf node is skeleton node, its key is to judge whether existing characteristics annular.This algorithm carrys out recognition feature annular by introducing expansion path.So-called expansion path, refer to that distance feature point distance is less than the node set of specifying jumping figure (as 2 jump), it can be realized by characteristic point inundation in 2 hop neighbors.If the border of expansion path comprises many closed curves, namely distance feature point just 2 jump nodes can not form a connected component, illustrates real features annular existence, thus respective inner node p is line skeleton point; Otherwise p is non-thread skeleton point.
Fig. 5 is the expansion path of this algorithm identified, and corresponding internal node is line skeleton point.
Fig. 6 is the expansion path (right side) that the characteristic point (left side) of non-thread skeleton point is formed.
Step 3. importance degree calculates and builds with skeletal tree
In 3 dimension sensor networks, a series of connected component is resolved in network edge interface by the feature annular of line skeleton point; In 2 dimensions without in empty network, boundary demarcation is also become some branches by the shortest path between the characteristic point of line skeleton point.To line skeleton point p, assuming that network decomposition is become l (>=2) individual connected component by (geodetic) shortest path of its characteristic point, be C
1(p), C
2(p) ... C
l(p), and full C
1(p)≤C
2(p)≤... ≤ C
l(p), then importance degree τ (p) of p is calculated as follows:
τ(p)＝1-C
l(p)/|C|
Wherein C is all boundary point set.
Each line skeleton point, after its importance degree of calculating, utilizes the monotonicity of importance degree, can realize the structure of distributed skeletal tree easily.That is, neighbours' line skeleton point that each line skeleton point selection importance degree is maximum, as father node, finally forms the skeletal tree being root node with the overall core node that importance degree is maximum, i.e. the coarse line skeleton of network.
Fig. 7 is the skeletal tree in example of the present invention three-dimension sensor network region used.
The optimization of step 4. skeleton
Calculate skeleton branches similarity, and based on branch's similarity, simplify skeletal tree by recursion method.Namely first deleting similarity is the branch of 1, if all branches similarity under same father node is all greater than given critical value, so only retains the maximum branch of length.Then, to giving fixed step size δ ∈ (0,1), similarity is greater than the branch of 1-δ by deleted; Delete the branch that similarity is greater than 1-2 δ again, until do not have branch to eliminate, finally remaining skeleton branches just constitutes the most finish line skeleton of network.
Fig. 8 is that the three-dimension sensor network of example of the present invention optimizes line skeleton.
Fig. 9 is the skeleton that this algorithm extracts in two-dimension sensors network.
Figure 10 and Figure 11 be importance degree critical value when getting 0.1 and 0.2 respectively the three-dimension sensor network of example optimize skeleton schematic diagram.Can find out, critical value is larger, and the skeleton obtained is more succinct, and the impact thus by noise at the boundary is less.
Those skilled in the art will readily understand; the foregoing is only preferred embodiment of the present invention; not in order to limit the present invention, all any amendments done within the spirit and principles in the present invention, equivalent replacement and improvement etc., all should be included within protection scope of the present invention.
Claims (7)
1. a common extraction procedure for two dimension and three-dimension sensor network line skeleton, is characterized in that, comprise the following steps:
(1) characteristic point of each internal node of calculating sensor network, if the jumping figure of the nearest boundary point of certain internal node is k, then also sees the boundary point of jumping apart from this internal node k+1 as the characteristic point of this internal node;
(2) adopt restricted broadcast mode, the characteristic point of each internal node formed multiple feature connected component, and carry out on border hop-by-hop expansion connect these feature connected components, make it the feature connected component that formation one is large;
(3) utilize each internal node middle distance characteristic point to be less than the node set of specifying jumping figure, generate a jumping expansion path; If the border of expansion path comprises many closed curves, then respective inner node is line skeleton point; Otherwise it is not line skeleton point;
(4) each line skeleton point calculates the nodes be inflated in the separated each connected component of closed curve in path, and calculates the importance degree of corresponding line skeleton point;
(5) the line skeleton point that in its neighbours' line skeleton point of each line skeleton point selection, importance degree is maximum as father node, thus sets up the line skeletal tree with multiple redundant branch;
(6) calculate the similarity of every bar skeleton branches and other skeleton branches, and utilize recursive procedure to optimize skeletal tree, obtain final grid line skeleton.
2. the common extraction procedure of two dimension according to claim 1 and three-dimension sensor network line skeleton, it is characterized in that, the importance degree of described step (4) center line skeleton point refers to: to line skeleton point p, assuming that network decomposition is become l (>=2) individual connected component by the shortest path of its characteristic point, be C
1(p), C
2(p) ..., C
l(p), and meet C
1(p)≤C
2(p)≤... ≤ C
lp (), then importance degree τ (p) of p is τ (p)=1-C
l(p)/| C|, wherein C is all boundary point set.
3. the common extraction procedure of two dimension according to claim 1 and 2 and three-dimension sensor network line skeleton, is characterized in that, in described step (6), skeleton branches similarity refers to: the skeleton branches B two to same father node p
1, B
2, make L
1, L
2represent its branch length respectively; To branch B
1, B
2upper node of jumping apart from p node i
if
neighbor node each other, defined function
otherwise
then branch B
1and B
2at B
1, B
2between similarity Sim (B
1| B
2) and Sim (B
2| B
1) be defined as follows respectively:
If node p has I child node, and correspondingly produce I skeleton branches, then the similarity of skeleton branches B (p) of node p is
Sim(B(p))＝max
i,j≤I,i≠j{Sim(B
j|B
i)}。
Wherein B (p) refers to the skeleton branches comprising a node p and I skeleton branches.
4. the common extraction procedure of two dimension according to claim 1 and 2 and three-dimension sensor network line skeleton, is characterized in that, described step (1) is specially:
To each internal node, boundary node carries out an inundation in network internal, inundation information from nearest boundary point forwards by each internal node, and its characteristic point is recorded by each internal node, and the Boundary Tree that to be subordinated to one of them characteristic point be root node;
Further, if the jumping figure of the nearest boundary point of certain internal node is k, then the boundary point of jumping apart from this internal node k+1 is also seen as characteristic point, claim such characteristic point to be extension feature point.
5. the common extraction procedure of two dimension according to claim 1 and 2 and three-dimension sensor network line skeleton, is characterized in that, described step (2) is specially:
To the leaf node on each Boundary Tree, allow its characteristic point initiate restricted inundation to build one or more connected component be made up of characteristic point, be called feature connected component, and distribute a unique identifier for each connected component; For these feature connected components are coupled together, to judge whether existing characteristics annular, each characteristic point p initiates the broadcast message that comprises its connected component identifier, if any neighbours of certain neighbours' boundary point q of three-dimensional network interior joint p or two-dimensional network interior joint p put q not yet distribution marker, then q point will be assigned with the connected component identifier identical with p; Repeat this process, until two boundary points with different identification symbol meet; This just establishes the geodetic shortest path between feature connected component in a distributed way.
6. the common extraction procedure of two dimension according to claim 1 and 2 and three-dimension sensor network line skeleton, is characterized in that, described step (3) is specially:
Whether existing characteristics is annular to identify leaf node by expansion path, if the border of expansion path comprises many closed curves, namely distance feature point is just for specifying the node of jumping figure can not form a connected component, illustrate that real features annular exists, thus respective inner node p is line skeleton point; Otherwise p is non-thread skeleton point; Wherein, expansion path refers to that distance feature point distance is less than the node set of specifying jumping figure, and it is realized by characteristic point inundation in appointment jumping figure neighbours.
7. the common extraction procedure of two dimension according to claim 1 and 2 and three-dimension sensor network line skeleton, is characterized in that, in described step (6), utilizes recursive procedure to be specially to optimize skeletal tree:
First deleting similarity is the branch of 1, if all branches similarity under same father node is all greater than given critical value, so only retains the maximum branch of length; Then, to giving fixed step size δ ∈ (0,1), similarity is greater than the branch of 1-δ by deleted; Delete the branch that similarity is greater than 1-2 δ again, until do not have branch to eliminate, finally remaining skeleton branches just constitutes the most finish line skeleton of network.
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Application publication date: 20160210 |