CN105960009B - A kind of localization method in software definition wireless sensor network - Google Patents
A kind of localization method in software definition wireless sensor network Download PDFInfo
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Abstract
The present invention provides the localization method in a kind of software definition wireless sensor network.The method comprising the steps of: setting up a contribution matrix for the node in network, a certain element corresponds to the contribution margin of the blind node positioning result on row for the anchor node in the element respective column to the element in matrix；According to the matrix set up, using controller in software defined network to the knowability of global network information, construct an optimization problem, method by one-zero programming is that blind node chooses positioning node from all anchor nodes, the sum of the power for meeting selected link is not more than the general power of the wireless sensor network, and makes the sum of the contribution margin of selected link in network reach maximum under the requirement；Using the known position information of selected positioning node, the position of the blind node is calculated using linear least-squares algorithm.The present invention can choose the node most useful for its positioning in the case where wireless sensor network power limited for blind node, improve its positioning accuracy.
Description
Technical Field
The invention relates to the technical field of wireless positioning, in particular to a positioning method in a software defined wireless sensor network.
Background
In many Wireless communication systems, it has become a practical requirement to be able to know the location information of nodes in the network, especially Wireless Sensor Networks (WSNs), which are widely used at present. In some specific Positioning environments, gps (global Positioning system) satellite signals cannot be accurately received, so that a Positioning method for researching how to accurately calculate the position of a sensor node has a very critical effect on the application of the whole network.
Considering the characteristic of limited power of a wireless sensor network, how to research a high-precision positioning method under the condition of meeting the requirement of total power of the network is very important. Due to the lack of the information of the whole network, the current effective power allocation positioning method researched under the wireless sensor network is mostly distributed, and has a performance disadvantage to some extent compared with the global allocation method.
Disclosure of Invention
The purpose of the invention is as follows: in order to solve the technical problem, the invention provides a positioning method in a software-defined wireless sensor network by utilizing the intelligibility of a software-defined network controller to global network information on the basis of a software-defined network technology. The method can improve the node positioning performance under the condition of meeting the power requirement of the wireless sensor network, and has wide application prospect.
The technical scheme is as follows: in order to realize the technical effects, the technical scheme of the invention is as follows: a positioning method in a software defined wireless sensor network, the software defined wireless sensor network comprising a software defined network controller, NaA blind node and NbAn anchor node; the method comprises the following steps:
(1) calculating the contribution value of any anchor node to any blind node positioning result in the software defined wireless sensor network, and constructing a contribution matrix by using all the calculated contribution valuesElement w in the contribution matrixijRepresenting the contribution value of the anchor node j to the positioning result of the blind node i, i belongs to [1, 2, …, Na]，j∈[1，2，…，Nb]；
(2) Constructing a positioning node selection matrixDefinition fij1 denotes a fixed node which selects an anchor node j as a blind node i, fijA fixed node which represents that the anchor node j does not serve as the blind node i is 0;
(3) the product of the maximum contribution matrix and the positioning node selection matrix transposition is taken as a target problem, namely:
the constraint conditions are set as follows:
①fij1 or fij＝0；
②
③
Wherein epsilonijRepresenting the transmission power on the link formed by anchor node j and blind node i,represents the maximum total power of the network; solving an optimal positioning node selection matrix which simultaneously meets the three constraint conditions;
(4) and the software defined network controller selects a positioning node for each blind node according to the optimal positioning node selection matrix, and calculates the position of the blind node by using a linear least square algorithm according to the position information of the selected positioning node.
Further, the step (1) is to construct a contribution matrixThe method comprises the following steps:
(1-1) acquiring the signal strength P of the blind node i receiving the anchor node jijComprises the following steps:
Pij＝P0-10αlog10dij+v
wherein, P0In dBm for the received signal strength at distance anchor node j1m, α is a path attenuation factor in the software defined wireless sensor network, v is a random variable that follows a gaussian distribution,and according to the signal strength PijCalculating the distance d between the blind node i and the anchor node jij；
(1-2) d obtained according to the step (2)ijCalculating the contribution value of the anchor node j to the positioning result of the blind node i as follows:
wherein,dijand dikRespectively representing the distances between the blind node i and anchor nodes j and anchor nodes k, wherein the anchor nodes k are any anchor nodes different from the anchor nodes j; djkRepresents the distance between anchor node j and anchor node k; di⊥jkRepresenting the shortest distance from the blind node i to a connecting line of the anchor node j and the anchor node k;
(1-3) all of w calculated based on the step (1-2)ijConstructing a contribution matrix Q:
further, the above technical solution further comprises the steps of: initializing a positioning node selection matrix before performing the step (3)The method for initializing the positioning node selection matrix comprises the following steps:
obtaining any blind node i and any anchor node j according to the step (1-1)A distance d betweenijJudging whether the anchor node j is in the communication range of the blind node i; if the determination result is yes, let fijIf not, let fij＝0。
Further, the method for calculating the position of the blind node in step (4) includes:
(4-1) setting the number of the positioning nodes selected by the network controller for the blind node i according to the optimal positioning node selection matrix as N, and defining the signal strength value of the blind node i receiving the positioning node l as { P }il}，l∈[1，2，…，N]；
(4-2) calculating a distance estimation vector of the blind node i according to the step (1-1) as follows:
whereinRepresents the estimated distance value of the positioning node l from the blind node i,
(4-3) selecting the node r with the smallest estimated distance value from the N positioning nodes as a reference node, namely
Using the distance estimation vectors, the following equation is constructed:
wherein x { (x)i，yi)∈R2Denotes the position estimate vector for the blind node i;
solving equations according to LLS algorithmThe position estimation vector of the blind node i is obtained as follows:
has the advantages that: compared with the prior art, the invention has the following advantages:
the method utilizes software to define the intelligibility of a network controller to global network information, and converts the problem of selecting positioning nodes for blind nodes in the network into a 0-1 planning problem by constructing a global optimization problem, thereby selecting the positioning node which is most beneficial to the positioning result for each blind node under the condition that the sum of the powers of all selected links in a wireless sensor network is not more than the total power which can be borne by the network, ensuring that the sum of the contribution values of the selected links to the positioning is maximum, and improving the positioning precision.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention;
fig. 2 is a positioning scenario diagram of a software defined wireless sensor network.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some embodiments of the present invention, but not all embodiments.
Example (b): in a software defined wireless sensor network, assume that there is N in the networkaA blind node whose coordinate position is unknown, and NbAn anchor node with a known coordinate location. As shown in fig. 2, in the software-defined wireless sensor network, the controller grasps information of the global network and allocates the best positioning node selected from all anchor nodes to the blind node.
Fig. 1 illustrates a process of estimating a blind node position in the network, in the wireless sensor network, a Received Signal Strength Indicator (RSSI) of a blind node to an anchor node is modeled as follows:
Pd＝P0-10α log10d+v (1)
in the formula (1), P0Is the received signal strength at 1m from the anchor node in dBm, PdIs RSSI of a blind node at a distance from the anchor node d, <tt translation = α ">tα <t/t >tis a path attenuation factor set according to a radio propagation environment, v is a random variable subject to gaussian distribution,
suppose a blind node i in the network receives the message of an anchor node jNumber intensity Pij(dBm) obeys the following gaussian distribution:
calculating the following expression of the blind node i and any two anchor nodes m and n in the network:
in the formula (3), the reaction mixture is,dim、dinand dmnRespectively representing the distance of the blind node i from the anchor nodes m and n and the distance between the anchor nodes m and n, di⊥mnIs the shortest distance of the connecting line from the blind node i to the anchor nodes m and n.
For a given blind node i, any anchor node m is associated with other N nodes in the networkb-1 anchor node combination can yield Nb1 expression values of the formula (3), Nb-1 value is summed to obtain
The inverse of the sum is defined as the contribution of the anchor node m to the blind node i positioning result, i.e. the
The following contribution matrix is constructed:
in the formula (6), the element wim(i＝1，2，…，Na，m＝Na+1，Na+2，…Na+Nb) And representing the contribution value of the anchor node m to the positioning result of the blind node i.
Constructing a positioning node selection matrix as shown in the following:
element f in the matrixij(i＝1，2，…，Na，m＝Na+1，Na+2，…Na+Nb) Has a value of 0 or 1, fijIf f is 1, the anchor node j is selected as a positioning node for positioning the blind node iijIf 0, anchor node j does not serve as the positioning node for blind node i.
Before positioning begins, initializing a positioning node selection matrix, and if an anchor node j is in the communication range of a blind node i, fij1, otherwise fij＝0。
Considering the problems of limited power of a wireless sensor network and the like, the following global optimization problems are constructed:
constraint conditions are as follows:
fij1 or fij＝0 (9)
In the optimization problem, an objective function represents solving an optimal positioning node selection matrix so that the sum of the positioning contribution values of the whole network is maximized. The constraint condition (9) represents that the element value in the positioning node selection matrix is 0 or 1; (10) middle epsilonijRepresenting the transmission power on the link formed by anchor node j and blind node i,representing the maximum total power of the network, the sum of the powers of all the links involved in the positioning must not exceed this value; the constraint (11) indicates that in two-dimensional positioning, according to the triangular characteristic, at least 3 positioning nodes are required for position estimation of each blind node.
And according to the optimal positioning node selection matrix obtained by solving the optimization problem, the software defined network controller selects positioning nodes for positioning the blind nodes. Suppose that N anchor nodes are selected as positioning nodes for the blind node i, and the received signal strength value is marked as { P }ilAnd l is 1, 2, …, N }, and the estimated distance value between each positioning node and the blind node i is calculated according to the formula (1) to be
The N distance estimation values form a distance estimation vectorThe position of the blind node i is calculated using a linear least squares algorithm. Selecting a node r with the minimum distance estimation value from the N positioning nodes as a reference node of a linear least square algorithm:
using the distance estimation vectors, the following equation is constructed:
in formula (14), x { (x)i，yi)∈R2Denotes a blind node position estimation vector and is expressed by the following expression:
according to LLS algorithm, the position of the blind node is obtained by solving
The above description is only of the preferred embodiments of the present invention, and it should be noted that: it will be apparent to those skilled in the art that various modifications and adaptations can be made without departing from the principles of the invention and these are intended to be within the scope of the invention.
Claims (4)
1. A positioning method in a software defined wireless sensor network is characterized in that the software defined wireless sensor network comprises a software defined network controller and NaA blind node and NbAn anchor node;
the method comprises the following steps:
(1) calculating the contribution value of any anchor node to any blind node positioning result in the software defined wireless sensor network, and constructing a contribution matrix by using all the calculated contribution valuesElement w in the contribution matrixijRepresenting the contribution value of the anchor node j to the positioning result of the blind node i, i belongs to [1, 2, …, Na]，j∈[1，2，…，Nb]；
(2) Constructing a positioning node selection matrixDefinition fij1 represents a positioning node for selecting an anchor node j as a blind node i, and fij0 means that the anchor node j does not act as a positioning node for the blind node i;
(3) the product of the maximum contribution matrix and the positioning node selection matrix transposition is taken as a target problem, namely:
the constraint conditions are set as follows:
①fij1 or fij＝0；
②
③
Wherein epsilonijRepresenting the transmission power on the link formed by anchor node j and blind node i,represents the maximum total power of the network; solving an optimal positioning node selection matrix which simultaneously meets the three constraint conditions;
(4) and the software defined network controller selects a positioning node for each blind node according to the optimal positioning node selection matrix, and calculates the position of the blind node by using a linear least square algorithm according to the position information of the selected positioning node.
2. The method according to claim 1, wherein the step (1) of constructing the contribution matrix comprises constructing the contribution matrixThe method comprises the following steps:
(1-1) acquiring the signal strength P of the blind node i receiving the anchor node jijComprises the following steps:
wherein, Pij(dBm) obeying a Gaussian distributionP0In dBm for received signal strength at anchor node j1m, α is a path attenuation factor in the software defined wireless sensor network, v is a random variable that obeys a gaussian distribution, is the variance of the gaussian distribution; and according to the signal strength PijCalculating the distance d between the blind node i and the anchor node jij；
(1-2) d obtained according to the step (1-1)ijCalculating the contribution value of the anchor node j to the positioning result of the blind node i as follows:
wherein,dijand dikRespectively representing the distances between the blind node i and anchor nodes j and anchor nodes k, wherein the anchor nodes k are any anchor nodes different from the anchor nodes j; djkRepresents the distance between anchor node j and anchor node k; di⊥jkRepresenting the shortest distance from the blind node i to a connecting line of the anchor node j and the anchor node k;
(1-3) all of w calculated based on the step (1-2)ijConstructing a contribution matrix Q:
3. the method of claim 2, further comprising the steps of: initializing a positioning node selection matrix before performing the step (3)The method for initializing the positioning node selection matrix comprises the following steps:
obtaining the distance d between any blind node i and any anchor node j according to the step (1-1)ijJudging whether the anchor node j is in the communication range of the blind node i; if the determination result is yes, let fijIf not, let fij＝0。
4. The method according to claim 2, wherein the method for calculating the position of the blind node in the step (4) comprises:
(4-1) setting the number of the positioning nodes selected by the network controller for the blind node i according to the optimal positioning node selection matrix as N, and defining the signal strength value of the blind node i receiving the positioning node l as { P }il}，l∈[1，2，…，N]；
(4-2) calculating a distance estimation vector of the blind node i according to the step (1-1) as follows:
whereinRepresents the estimated distance value of the positioning node l from the blind node i,
(4-3) selecting the node r with the smallest estimated distance value from the N positioning nodes as a reference node, namely
Using the distance estimation vectors, the following equation is constructed:
wherein x { (x)i，yi)∈R2Denotes the position estimate vector for the blind node i;
according toLLS algorithm, solving equationThe position estimation vector of the blind node i is obtained as follows:
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