CN104796917A - Model for selection and prediction of convergent node of wireless sensor network on basis of Steiner center - Google Patents

Model for selection and prediction of convergent node of wireless sensor network on basis of Steiner center Download PDF

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Publication number
CN104796917A
CN104796917A CN201510134146.XA CN201510134146A CN104796917A CN 104796917 A CN104796917 A CN 104796917A CN 201510134146 A CN201510134146 A CN 201510134146A CN 104796917 A CN104796917 A CN 104796917A
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steiner
center
network
node
mobile
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梁久祯
李军飞
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Jiangnan University
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Jiangnan University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/22—Traffic simulation tools or models
    • 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W84/00—Network topologies
    • H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Mobile Radio Communication Systems (AREA)

Abstract

The invention provides a model for using a convex hull Steiner center of a mobile sensor network as an optimal real-time convergence center position. According to the model, the real-time optimal position of the convergent node of the mobile network is acquired through periodic updating of the edge structure and the position of the Steiner center of a convex hull. The Steiner center of a mobile network group in each period is calculated through a Steiner supporting function; the Steiner center has the continuous feature in the continuous time period, a time axis is added to the obtained Steiner center, and the Steiner center is popularized to the time-space domain; the position of the convergent center in the next moment is predicted through the combination between the steps and a kalman filtering algorithm. Real-time calculation of the position of the convergent node of the mobile sensor network is achieved through the convex hull structure model based on the Steiner center, a movement track of the convergent node can be predicted rapidly and accurately, and the convergent node can have high stability and low eccentricity of a Steiner point.

Description

A kind of radio sensing network aggregation node based on Steiner center is chosen and the model predicted
[technical field]
The present invention relates to wireless sensor network field, particularly mobile sensor network aggregation node is the model chosen in real time and follow the tracks of.
[background technology]
Choosing of aggregation node position in wireless sensor network is the important content that wireless application is studied, and obtains the extensive concern of academia and industrial quarters for many years, and from theoretical and actual application, expands deep research to the addressing strategy of aggregation node.Obtaining research according to practical application is that occasion is different, and many research work are all unilaterally to launch for the Link State of network, the efficiency of transfer of data, the transmission consumption of network and life-span etc., and the overwhelming majority is for concrete static network.
The model that this invention proposes can all have a wide range of applications in the field such as transducer, wild animal detection, space probation, locomotive networking under water.Reflect global characteristics by the local edge locations of structures information of colony's convex hull, well can react the mobile message of colony's entirety.But at present, these existing aggregation node addressing strategies have following weak point: 1) at present research network all based on the sensor network of static state, lack the real-time accuracy of practical application and dynamic self-awareness.2) within each cycle upgraded, common algorithm is the algorithm based on global network performance, and the node complexity of calculating is O (n).3) center that other the algorithm based on geometric model is found lacks high stable and low eccentricity.
Steiner point is the geometric center of an object.Defined in nineteen sixty by Shephard, hereafter, Steiner point starts to be concerned and solves various problem with it.It is only relevant with the convex closure of object, and can by supporting that function calculates.And the computational complexity of support function is O (n).Steiner point has good geometric properties, such as continuity, rotational invariance, additive property etc.It in Mobile solution, have high stable and low eccentricity is the sharpest edges that mobile sensor network is applied.
Realizing in process of the present invention, inventor utilizes Steiner point to predict and tracked mobile target, mainly solve three problems above-mentioned, that is: 1) for the sensor network of movement, the dynamic change of all nodes, the aggregation node optimum position being difficult to reach acquisition keeps real-time 2) reduce the complexity calculated, utilize priori to predict the position of object, improve the accuracy of prediction.3) feature of aggregation node movement lacks high stable and low eccentricity
[summary of the invention]
The object of the present invention is to provide a kind of model chosen based on mobile network's aggregation node of convex hull Steiner point and predict, achieve the best convergence center position of searching real-time in Mobile solution, the described model based on Steiner point mobile sink node got and predict, real-time convergence center can be calculated fast and accurately, and predict the subsequent time shift position of convergence center.The computation complexity of each calculating convergence center (Steiner point) only needs steiner point has high stable and low eccentricity simultaneously.
In order to reach object of the present invention, according to an aspect of the present invention, the invention provides a kind of calculating of mobile network's aggregation node based on Steiner point and the model of prediction, described model comprises: obtain all node location informations of the whole network, calculates colony's network convex hull and its Steiner point by support function; Add time shaft based on the Steiner point obtained, be generalized on time-space domain; Set up motion prediction model, use Karman formula, by the position of the location coordinate information prediction subsequent time Steiner point of continuous moving aggregation node;
For the mobile network in a certain moment, P is the convex closure of a mobile object of topological structure; Convex closure P has M summit, i.e. P={p 1, p 2... p m; Then by supporting that the Steiner point that function can obtain object P is:
Wherein p jsummit P jcoordinate position, node P jouter angle value, and meet
Further, the Steiner point obtained based on upper step adds time shaft, is generalized on time-space domain, then the mobile Steiner point on space-time spatial domain is:
S ( A : t ) = 1 V ( B n ) ∫ S n - 1 h A ( e , t ) edλ ( e )
Wherein A is the object of movement in video; S n-1it is unit ball; E is the unit vector on unit ball; λ is based on S n-1on legesgue estimate; V (B n) be unit ball B non volume; T is time shaft.
The continuity of Steiner proves: wherein 0 < t 2-t 1< ε, then
| S ( A : t 2 ) - S ( A : t 1 ) | = 1 V ( B n ) | &Integral; S n - 1 h A ( e , t 2 ) ed&lambda; ( e ) - &Integral; S n - 1 h A ( e , t 1 ) ed&lambda; ( e ) | &le; 1 V ( B n ) &Integral; S n - 1 | h A ( e , t 2 ) - h A ( e , t 1 ) | ed&lambda; ( e )
According to the definition of support function, then | h a(e, t 2)-h a(e, t 1) < δ | namely | s (A; t 2)-s (A; t 1) | < k δ, k is constant, makes ξ=k δ, then | s (A; t 2)-s (A; t 1) | < ξ, proves S (A; T) be continuous print on continuous print time point.
The continuity of discrete defined function can be obtained:
Further by mapping function, select suitable coordinate system, can predicting that the virtual borderlines of the two-dimentional Steiner point obtained is in three dimensions above:
S ( p : t ) = P * T = x y 1 t x t y t = x t y t t
Further, set up motion prediction model, by the particular location of the Steiner point of predicted current frame next frame moving target.By the position of Kalman filter formulation prediction subsequent time.
[accompanying drawing explanation]
In conjunction with reference accompanying drawing and ensuing detailed description, the present invention will be easier to understand, wherein:
Fig. 1 is that the radio sensing network aggregation node based on Steiner center in the present invention chooses the flow chart performed with the model method predicted.
Fig. 2 be this model practical application continuous time top structure and Steiner center with the consecutive variations figure of reality.
Fig. 3 is in the model of fast moving, and absolute position is converted to relative position.
Fig. 4 is in the dynamic change based on the convex hull of limit structure, and internal node appears at convex hull outside and waits situation in violation of rules and regulations, and the method being upgraded convex hull structure by request recovers limit structure
[embodiment]
For enabling above-mentioned purpose of the present invention, feature and advantage become apparent more, and below in conjunction with the drawings and specific embodiments, the present invention is further detailed explanation.
Embodiment of the present invention proposes a kind of mobile sensor network aggregation node based on Steiner center and chooses and the model predicted, the described model as aggregation node position based on Steiner point, by periodically upgrading convex hull structure, the Steiner center of high stable and low bias can be found fast and accurately, each renewal only needs to obtain mid-side node partial network information, and predicts the next moment position of convergence center by continuity.Wherein Fig. 2 is the effect plays figure dynamically updated continuously of this model convex hull and Steiner convergence center on continuous time.
Please refer to Fig. 1, the model method flow diagram in one embodiment that it illustrates the prediction of the mobile network's aggregation node based on convex hull Steiner point in the present invention and follow the tracks of.Described model performs flow and method 100 and comprises:
Step 102, center convergence nodes broadcast solicitations, obtains whole network convex hull node location information.
Based on the characteristic of wireless sensor network, be separate individuality between network node, the information interaction between node, by the access mode of wireless data packet, must can obtain the information of the other side.So center convergence node is when each update cycle arrives, send a broadcast data packet to the whole network, the node on request convex hull summit uploads current self-position.
This model only needs the node location information on convex hull, and the positional information of convex hull internal node does not need to upload, and whether its self-position wanting each update cycle only need judge is inner at convex hull.Computing node complexity based on convex hull summit, local is and be O (n) based on the node complexity of the overall situation.
Step 104, mobile network absolute position is converted to relative position.
As Fig. 3, in the transducer practical application of movement rapidly, very large change may be there is in the actual absolute location coordinates of node, and there is small relative movement in intra-node, loaded down with trivial details in order to reduce calculating, the position in each cycle is transformed in the relative coordinate system of a relatively little position.This step can according to practical application scene, not essential.
Step 106, to the topological structure in each cycle, utilizes support function to obtain the Steiner point of convex closure;
In step 102 above, arrived the latest position of the convex hull of the whole network after center convergence node by acquisition request, recalculated new convex hull structural information according to these positions, and broadcast convex hull vertex information to other nodes of network.And according to current convex hull vertex position information, utilize support function to obtain the Steiner point of convex closure.
Wherein p jsummit P jcoordinate position, p jouter angle value, and meet
Step 108, is generalized to time-space domain two-dimentional Steiner point and gets on.
Steiner point is tried to achieve to 106 steps, by Fig. 2 and proof above, Steiner point is also have continuity on the continuous print time, add time shaft, be generalized to time-space domain to get on, a mobile network only has a Steiner point, and network is mobile change, so add that time domain can reflect the trail change of the Steiner point of movement, two continuous Steiner point time intervals are update cycle T.
Further by mapping function, can predicting that the virtual borderlines of the two-dimentional Steiner point obtained is in three dimensions above:
S ( p : t ) = P * T = x y 1 t x t y t = x t y t t
Step 110, the subsequent time position of the continuous Steiner point of Kalman Prediction.
Through step 108, obtain the position of the Steiner of mobile network and the relation of time, due to the time interval of update cycle, purely to occur at new convergence center (Steiner), and aggregation node is also in the position of the calculating in a upper moment, so our position that may be occurred by Kalman's look-ahead subsequent time, arrive Steiner center in advance.The result utilizing laststate to predict, optimum result and actual measured value are to estimate the positional value of subsequent time.
X(k|k-1)＝A X(k-1|k-1)+B U(k-1)+W(k-1)
Point out further, the present invention to mobile sensor network aggregation node addressing very useful, there are extraordinary real-time and universality, the colony of movement is only needed to have group property, this model also has extraordinary self-awareness adaptive ability, as Fig. 4, when internal node moves to convex hull outside, find autoclasia convex hull structure, active request can add oneself in convex hull.Be well suited for real-time scene application.
A kind of model chosen based on mobile network's aggregation node of convex hull Steiner point and predict that the present invention proposes, maintenance aggregation node that can be real-time in the Steiner center of Exist Network Structure, and can predict the positional information of subsequent time.Experimental comparison is based in the minimum encirclement garden of geometry, barycenter, geometry.The Steiner point that the present invention uses has obvious advantage in stability and eccentricity.
Above-mentioned explanation fully discloses the specific embodiment of the present invention.It is pointed out that the scope be familiar with person skilled in art and any change that the specific embodiment of the present invention is done all do not departed to claims of the present invention.Correspondingly, the scope of claim of the present invention is also not limited only to described embodiment.

Claims (5)

1., based on the model choosing and predict mobile network's aggregation node of convex hull center (Steiner point), it is characterized in that, described method comprises:
In the sensor network of movement, aggregation node, by the position coordinates of each node in the acquisition network in cycle, according to the geometric properties of current network topology structure, constructs the convex polygon of network.
Center convergence node to the convex polygon process of network, by the Steiner point of support function computing network convex hull target;
The Steiner point upgraded in the cycle adds, adds time shaft, is generalized on time-space domain;
Set up motion prediction model, by the position when Steiner center, by Kalman Prediction algorithm, predict the Position Approximate of the Steiner point of aggregation node of lower moment.
2. this model according to claim 1 is the scheme that an aggregation node optimum position solving mobile wireless sensor network is chosen, and Steiner center is a center of a geometry convex hull, the convex hull structure of network must be obtained before calculating Steiner:
In some moment, sent the broadcast packet of the information of an all nodes of locations of request to whole network by center convergence node, the node receiving request bag calculates self positional information current by the locating module (GPS) of self, and is uploaded to aggregation node.The aggregation node receiving all node location informations just can construct the male structure of current network.Following calculating Steiner center, and move to Steiner place, keep the best aggregation node position of network.
3. this model according to claim 1 is a kind of motion model based on Steiner point, it is characterized in that, describedly processes mobile network's target, is calculated the Steiner point of mobile network's convex hull by support function:
For a certain moment network topology structure, P is the convex closure of a mobile object in an image; Convex closure P has M summit and P={p 1, p 2... p m; Then by support function can the Steiner point of object P be (discrete type):
Wherein p jnode P jcoordinate position, p jouter angle value, and meet
4. according to claim 1ly a kind ofly to it is characterized in that based on the prediction of Steiner point and the method for tracked mobile target, add time shaft based on the Steiner point obtained, be generalized on time-space domain, then the mobile Steiner point on time-space domain is
Wherein p jtnode P jt coordinate position, p jthe outer angle value of t, it is continuous print that the position due to node each in mobile sensor network is moved, i.e. p jtcontinuously, also can draw simultaneously also be continuous print.
Then by mapping function, select suitable coordinate system, can predicting that the virtual borderlines of the two-dimentional Steiner point obtained is in three dimensions above:
Wherein (x y) is the coordinate in two-dimensional space, (x ty tt) be coordinate points in three dimensions.
5. according to claim 1ly a kind ofly it is characterized in that based on the prediction of Steiner point and the method for tracked mobile target, set up motion prediction model, the particular location by the Steiner point of predicted current frame next frame moving target:
Kalman Prediction: X (k)=A X (k-1)+B U (k-1)+W (k)
Add the measured value of system: Z (k)=H X (k)+V (k)
In upper formula, X (k) is the system mode in k moment, and U (k) is the controlled quentity controlled variable of k moment to system.A and B is system parameters, and Z (k) is the measured value in k moment, and H is the parameter of measuring system.W (k) and V (k) represents the noise of process and measurement respectively.
Subsequent time predicted value: X (k|k-1)=A X (k-1|k-1)+B U (k-1)+W (k-1)
X (k|k-1) is the result utilizing laststate to predict, X (k-1|k-1) is the result of laststate optimum, the controlled quentity controlled variable that U (k) is present status.
CN201510134146.XA 2015-03-24 2015-03-24 Model for selection and prediction of convergent node of wireless sensor network on basis of Steiner center Pending CN104796917A (en)

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CN106358256A (en) * 2016-10-26 2017-01-25 上海电机学院 Multi-robot control coordinator generating method

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Publication number Priority date Publication date Assignee Title
CN106358256A (en) * 2016-10-26 2017-01-25 上海电机学院 Multi-robot control coordinator generating method
CN106358256B (en) * 2016-10-26 2019-09-17 上海电机学院 A kind of multirobot control coordinator's production method

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