CN110234121B - Self-adaptive step length network node deployment optimization method based on virtual force algorithm - Google Patents

Self-adaptive step length network node deployment optimization method based on virtual force algorithm Download PDF

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CN110234121B
CN110234121B CN201910486965.9A CN201910486965A CN110234121B CN 110234121 B CN110234121 B CN 110234121B CN 201910486965 A CN201910486965 A CN 201910486965A CN 110234121 B CN110234121 B CN 110234121B
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刘忠涛
陈小辉
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China Three Gorges University CTGU
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Abstract

A virtual force algorithm-based adaptive step length network node deployment optimization method comprises the following steps: initializing and distributing n network nodes, setting total iteration times, performing Delaunay triangulation on the network nodes, acquiring an adjacent node set of the network nodes, and calculating a network node siNumber x of adjacent nodesi(ii) a Calculating the self-adaptive coefficient lambda of the moving step length of the network node by adopting the self-adaptive function of the moving step lengthi(ii) a The adaptive coefficient lambda of the moving step length of the convex hull vertex of the Delaunay triangulation is set to be lambdaCAnd correcting the coefficient of the network node at the vertex of the network convex packet. The method reduces the uniformity of the network, improves the balance of network node distribution, reduces coverage holes in the network, can effectively improve the monitoring quality of the network, and prolongs the life cycle of the network.

Description

Self-adaptive step length network node deployment optimization method based on virtual force algorithm
Technical Field
The invention discloses a self-adaptive step length network node deployment optimization method based on a virtual force algorithm, and relates to the technical field of network monitoring.
Background
The mobile wireless sensor network node is applied to scene information detection, and in order to improve the reliability and the continuity of monitoring data of the wireless sensor network, the distribution balance of the network node needs to be ensured. The dynamic sensor nodes are used for deployment detection in the applications of disaster monitoring, emergency scenes and the like. In node deployment, nodes to be deployed need to be rapidly diffused to form balanced distribution, so that high-quality sensing monitoring of a scene is achieved.
Due to different initial distribution densities of the network nodes, the fixed step length method can lead the optimization distribution of the network nodes to be unbalanced, form coverage holes among the network nodes and reduce the quality of network coverage monitoring. The self-adaptive step size network node deployment optimization method based on the virtual force algorithm sets the network node moving step size according to the number of adjacent nodes of the network node, can effectively improve the balance of network node distribution, reduces coverage holes among the network nodes, and accordingly improves the quality of network coverage monitoring.
Disclosure of Invention
In order to solve the problems, the invention provides a virtual force algorithm-based adaptive step size network node deployment optimization method, wherein a coefficient of a moving step size reference value in network node position updating is set through a moving step size adaptive strategy, so that the distribution balance of network nodes can be improved, and coverage holes in a network are reduced.
The technical scheme adopted by the invention is as follows:
a virtual force algorithm-based adaptive step length network node deployment optimization method calculates the moving step length of a network node according to the distribution density of the network node when the position of the network node is updated under the action of virtual force.
And defining the adjacency relation among the network nodes by Delaunay triangulation, and constructing a moving step self-adaptive function according to the number of the adjacent nodes of the network nodes.
And selecting the optimal adjacency relation in the adjacency node set of the node self-deployment network as a new adjacency node set of the node.
The virtual force algorithm calculation model is as follows:
Figure BDA0002085722640000011
wherein the content of the first and second substances,
Figure BDA0002085722640000021
is a node sjTo node siVirtual force of dijIs a node siAnd node sjEuclidean distance between, DthIs a node siAnd node sjVirtual force balance threshold between, CthFor the virtual force cutoff distance, αijIs a node siPointing to a node sjLinear direction of (a) (. omega.)AAs a factor of gravity of virtual force, omegaRIs a virtual force repulsion factor and beta is a virtual force index factor. When node siAnd node sjHas an Euclidean distance d betweenij>CthTime, node siAnd node sjThe virtual force in between is 0.
The moving step length calculation method is used for carrying out local self-adaptive step length setting on a network node and comprises the following steps:
step (1): defining a set of adjacent nodes of the network nodes, network nodes s, by Delaunay triangulation among all the network nodesiThe number of the adjacent nodes is marked as xi；
Step (2): network nodesiAdaptive coefficient lambda of moving step lengthi＝f(xi) Moving step size adaptive function f (x)i) The calculation formula of (A) is as follows: f (x)i)＝|xi-N | + 1; and N is the number of adjacent nodes of the network node.
And (3): the moving step Maxstep of the network node is λ × BStep, where BStep is a reference value of the moving step Maxstep of the network node, and λ is a moving step adaptive coefficient.
And (4): setting the moving step length adaptive coefficient lambda of the convex hull vertex of the Delaunay triangulation in all network nodes as lambdaC，λCIs a constant.
A virtual force algorithm-based adaptive step length network node deployment optimization method comprises the following steps:
step 1: initializing and distributing n network nodes, setting total iteration times, carrying out Delaunay triangulation on the network nodes, and obtaining an adjacent node set S of the network nodesi(i ═ 1,2, …, n), computing network node siNumber x of adjacent nodesi；
Step 2: calculating the self-adaptive coefficient lambda of the moving step length of the network node by adopting the self-adaptive function of the moving step lengthi；
And step 3: the adaptive coefficient lambda of the moving step length of the convex hull vertex of the Delaunay triangulation is set to be lambdaC，λCIs a constant.
The virtual force algorithm calculation model is as follows:
Figure BDA0002085722640000022
wherein the content of the first and second substances,
Figure BDA0002085722640000023
is a node sjTo node siThe virtual force of (a) is,
dijis a node siAnd node sjThe euclidean distance between them,
Dthis a node siAnd node sjThe virtual force balance threshold value between the two,
Cthin order to intercept the distance for the virtual force,
αijis a node siPointing to a node sjIn the direction of the straight line of (a),
ωAin order to be a factor of the virtual force attraction,
ωRin order to be a virtual force repulsion factor,
beta is a virtual force index factor.
When node siAnd node sjHas an Euclidean distance d betweenij>CthTime, node siAnd node sjThe virtual force between is 0;
node si(xi,yi) In the virtual force
Figure BDA0002085722640000031
The calculation formula of the position updating index under action is as follows:
Figure BDA0002085722640000032
wherein, the node siThe coordinates before the position update are (x)i_o,yi_o) The updated coordinate is (x)i_n,yi_n)，
Figure BDA0002085722640000033
Is a node siSubject to a virtual force
Figure BDA0002085722640000034
The component in the x-direction is,
Figure BDA0002085722640000035
is a node siSubject to a virtual force
Figure BDA0002085722640000036
The component in the y-direction is,
maxstep is the moving step size of the network node in one location update.
In said step 2, the network node siAdaptive coefficient lambda of moving step lengthi＝f(xi) Moving step size adaptive function f (x)i) The calculation formula of (A) is as follows: f (x)i)＝|xi-N | + 1; and N is the number of adjacent nodes of the network node.
The self-adaptive step length network node deployment optimization method based on the virtual force algorithm has the advantages that:
1. the method can reduce the coverage holes in the network and effectively improve the monitoring quality of the network;
2. the method can reduce the uniformity of the network, improve the balance of the distribution of the network nodes, effectively prolong the life cycle of the network and improve the quality of the monitoring data of the network nodes.
3. The virtual force algorithm is introduced in the way that a proper virtual potential field is established in the wireless sensor network, the nodes can be subjected to virtual attraction or virtual repulsion in the virtual potential field, the positions of the nodes are adjusted according to the virtual force, and finally the nodes can form balanced distribution. The self-adaptive step length strategy improves the virtual force algorithm to a certain extent, and can effectively improve the balance of network node distribution.
Drawings
Fig. 1 is an initial distribution diagram of network nodes.
FIG. 2 is a diagram of a disk perception model.
Fig. 3 is a flow chart of the virtual force algorithm adaptive step size algorithm.
Fig. 4 is a schematic diagram of a convex hull vertex in the network Delaunay triangulation.
Fig. 5 is a diagram of network node adaptive step size optimization.
Detailed Description
A virtual force algorithm-based adaptive step length network node deployment optimization method is used for calculating the moving step length of a network node according to the distribution density of the network node when the position of the network node is updated under the action of virtual force.
As shown in fig. 1, the network nodes are initially distributed, and the virtual force algorithm is adopted to perform redeployment to optimize the distribution of the network nodes. In the embodiment, a disc sensing model is adopted, in two-dimensional monitoring, the monitoring range of the nodes in the monitoring area can be compared with that of a disc, as shown in fig. 2, the sensing radius of the nodes is R, so that the distances between all points in the circular monitoring area and the nodes are smaller than or equal to R, the nodes can be monitored, and the points outside the circular monitoring area cannot be monitored.
Node Si＝{xi,yiR }, wherein (x)i,yi) The coordinate of the node in the two-dimensional network is shown, R is the sensing radius of the network node, Q is any point in the monitoring area, and the coordinate is (x)q,yq) 0-1 disc sensing model node SiThe probability perception model of Q in the monitoring area is
Figure BDA0002085722640000041
Nodes within the radius R can be monitored.
The self-adaptive step size method is divided into four steps, including:
(1): defining a set of contiguous nodes of nodes, network nodes s, by Delaunay triangulation among all network nodesiThe number of the adjacent nodes is marked as xi；
(2): network node siAdaptive coefficient lambda of moving step lengthi＝f(xi) Moving step size adaptive function f (x)i) Is f (x)i)＝|xi-N|+1；
(3): the moving step length Maxstep of the network node is lambda BStep, wherein BStep is a reference value of the moving step length Maxstep of the network node;
(4): setting the moving step length adaptive coefficient lambda of the convex hull vertex of the Delaunay triangulation in all network nodes as lambdaC，λCIs a constant. Lambda [ alpha ]CThe value range is [0.5,2 ]]。
The virtual force algorithm is calculated by the following model
Figure BDA0002085722640000042
Wherein the content of the first and second substances,
Figure BDA0002085722640000051
is a node sjTo node siVirtual force of dijIs a node siAnd node sjEuclidean distance between, DthIs a node siAnd node sjVirtual force balance threshold between, CthFor the virtual force cutoff distance, αijIs a node siPointing to a node sjLinear direction of (a) (. omega.)AAs a factor of gravity of virtual force, omegaRIs a virtual force repulsion factor and beta is a virtual force index factor. When node siAnd node sjHas an Euclidean distance d betweenij>CthTime, node siAnd node sjThe virtual force in between is 0.
Node si(xi,yi) Resultant force of virtual force
Figure BDA0002085722640000052
The calculation formula of the position updating index under action is as follows:
Figure BDA0002085722640000053
wherein, the node siThe coordinates before the position update are (x)i_o,yi_o) The updated coordinate is (x)i_n,yi_n)，
Figure BDA0002085722640000054
Is a node siResultant force of virtual force
Figure BDA0002085722640000055
The component in the x-direction is,
Figure BDA0002085722640000056
is a node siResultant force of virtual force
Figure BDA0002085722640000057
The component in the y-direction, Maxstep, being a network node in a location updateThe step size is moved.
Fig. 4 is a schematic diagram of convex hull vertices of the network Delaunay triangulation, where network nodes are distributed in fig. 4, and the convex hull vertices of the Delaunay triangulation are marked with small squares, which are numbered 18, 2, 16, 11, 3, 17, 7, 20, 14, 15, and 4. For the convex hull vertexes with numbers of 9, 12 and the like which do not belong to the Delaunay triangulation, the moving step adaptive coefficient lambda is not set in the implementation of the adaptive step method, and the moving step adaptive function calculation value is adopted for setting.
Fig. 5 is a diagram of network node adaptive step size optimization. Fig. 5 is a diagram illustrating the effect of the adaptive step size method on the optimal distribution of the network node deployment shown in fig. 1.
The invention relates to a virtual force algorithm-based adaptive step length network node deployment optimization method, which sets a moving step length in a virtual force algorithm node position updating index calculation formula by constructing a moving step length adaptive function based on the number of adjacent nodes of a network node. According to the method, firstly, the adjacency relation among network nodes is defined through Delaunay triangulation, then the number of the adjacent nodes of the network nodes is counted to serve as the independent variable of the moving step length self-adaptive function, the dependent variable of the moving step length self-adaptive function is the coefficient of the moving step length reference value of the network nodes, and finally the coefficient of the network nodes at the top of the convex packet of the network is corrected.

Claims (1)

1. A self-adaptive step length network node deployment optimization method based on a virtual force algorithm is characterized by comprising the following steps:
step 1: initializing and distributing n network nodes, setting total iteration times, carrying out Delaunay triangulation on the network nodes, and obtaining an adjacent node set S of the network nodesi(i ═ 1,2, …, n), computing network node siNumber x of adjacent nodesi；
Step 2: network node siAdaptive coefficient lambda of moving step lengthi＝f(xi) Moving step size adaptive function f (x)i) The calculation formula of (A) is as follows: f (x)i)＝|xi-N | + 1; n is the number of adjacent nodes of the network node;
and step 3: the moving step length Maxstep of the network node is lambda BStep, wherein BStep is a reference value of the moving step length Maxstep of the network node, and lambda is a moving step length self-adaptive coefficient;
and 4, step 4: the adaptive coefficient lambda of the moving step length of the convex hull vertex of the Delaunay triangulation is set to be lambdaC，λCIs a constant;
the virtual force algorithm calculation model is as follows:
Figure FDA0003517396840000011
wherein the content of the first and second substances,
Figure FDA0003517396840000012
is a node sjTo node siThe virtual force of (a) is,
dijis a node siAnd node sjThe euclidean distance between them,
Dthis a node siAnd node sjThe virtual force balance threshold value between the two,
Cthin order to intercept the distance for the virtual force,
αijis a node siPointing to a node sjIn the direction of the straight line of (a),
ωAin order to be a factor of the virtual force attraction,
ωRin order to be a virtual force repulsion factor,
beta is a virtual force index factor and is a virtual force index factor,
when node siAnd node sjHas an Euclidean distance d betweenij>CthTime, node siAnd node sjThe virtual force between is 0;
node si(xi,yi) In the virtual force
Figure FDA0003517396840000013
The calculation formula of the position updating index under action is as follows:
Figure FDA0003517396840000021
wherein, the node siThe coordinates before the position update are (x)i_o,yi_o) The updated coordinate is (x)i_n,yi_n)，
Figure FDA0003517396840000022
Is a node siSubject to a virtual force
Figure FDA0003517396840000023
The component in the x-direction is,
Figure FDA0003517396840000024
is a node siSubject to a virtual force
Figure FDA0003517396840000025
The component in the y-direction is,
maxstep is the moving step size of the network node in one location update.
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