CN112055303A - Artificial fish swarm optimization positioning method for unknown sensor nodes of wireless sensor network - Google Patents

Artificial fish swarm optimization positioning method for unknown sensor nodes of wireless sensor network Download PDF

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CN112055303A
CN112055303A CN202010886760.2A CN202010886760A CN112055303A CN 112055303 A CN112055303 A CN 112055303A CN 202010886760 A CN202010886760 A CN 202010886760A CN 112055303 A CN112055303 A CN 112055303A
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node
beacon
coordinates
unknown sensor
nodes
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乔学工
李旭
富立琪
李芳�
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Taiyuan University of Technology
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/023—Services making use of location information using mutual or relative location information between multiple location based services [LBS] targets or of distance thresholds
    • 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
    • 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
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Abstract

The invention discloses an artificial fish school optimal positioning method for an intersection point of two circles of an unknown sensor node of a wireless sensor network, relates to a wireless sensor network positioning technology, and is used for acquiring accurate position information of the unknown sensor node of the wireless sensor network. The problems of low positioning accuracy and complex algorithm of the conventional positioning algorithm based on distance measurement are solved. The method of the invention firstly uses the signal intensity value received between the nodes to be converted into the distance value between the nodes, and uses the known position coordinates of any 2 beacon nodes A, B around the unknown sensor node to calculate two possible coordinates P of the unknown sensor node1、P2And judging the node coordinates of the unknown sensor to finally determine the node coordinates of the unknown sensor to finish positioning. The method reduces the complexity of the algorithm and reduces the energy consumption of the nodesAnd the life cycle of the node is prolonged.

Description

Artificial fish swarm optimization positioning method for unknown sensor nodes of wireless sensor network
Technical Field
The invention relates to the technical field of sensor positioning, in particular to an artificial fish school optimal positioning method for unknown sensor nodes of a wireless sensor network.
Background
In recent years, the technology of the internet of things continuously obtains new achievements, and the wireless sensor network serving as one of the bottom important technologies of the internet of things has become a research hotspot when being applied to the fields of national defense and military, environmental monitoring, traffic management, medical treatment and health, manufacturing industry, disaster resistance and emergency rescue and the like. The accurate position information obtained through the positioning algorithm is an important content of the wireless sensor network.
The positioning algorithm is divided into a non-ranging-based positioning algorithm and a ranging-based positioning algorithm. The positioning accuracy of the ranging-based positioning algorithm is higher than that of the non-ranging-based positioning algorithm. Some algorithms related to the positioning algorithm based on the distance measurement include a trilateral positioning algorithm, a trilateral centroid positioning algorithm, a particle swarm positioning algorithm and the like. These existing algorithms have low positioning accuracy, such as centroid positioning algorithm, and the algorithms are too complex because of a large number of iterative operations, such as particle swarm positioning algorithm.
Disclosure of Invention
The invention provides an artificial fish school optimal positioning method for unknown sensor nodes of a wireless sensor network, aiming at solving the problems of low positioning accuracy and complex algorithm of the existing positioning algorithm based on distance measurement.
The technical scheme adopted by the invention for solving the technical problems is as follows: an artificial fish swarm optimization positioning method for unknown sensor nodes of a wireless sensor network is constructed, and comprises the following steps:
the unknown sensor node P receives signals of beacon nodes which can be received around, and converts the received signal strength value into a distance value between the unknown sensor node and the corresponding beacon node;
collecting coordinates of any two beacon nodes A and B, and calculating the distance between the beacon nodes A and B and the distance between an unknown sensor node and the beacon nodes A and B;
judging whether the unknown sensor node P is collinear with the beacon nodes A and B, and if so, calculating the coordinate of the unknown sensor node P according to the coordinates of the beacon nodes A and B;
if not collinear, setting the node P1、P2The unknown sensor node P is the node P if the intersection point of two circles is formed by taking the distance between the beacon node A, B and the unknown sensor node P as the radius and taking the beacon node A, B as the center of the circle1、P2One of the computing nodes P1、P2Coordinates;
determining the coordinate value of the unknown sensor node P:
judging whether the other beacons except the beacons A and B are all positioned on the connection line of the beacons A and B:
if all the beacon nodes are not on the connection line of the beacon nodes A and B, any beacon node C' which is positioned outside the connection line of the beacon nodes A and B is selected, and the unknown sensor nodes P and the unknown nodes P are calculated1And node P2The distance from the beacon node C' is,
when the distance between the unknown sensor node P and the beacon node C' and the node P1The absolute value of the distance difference of the distance from the beacon node C 'is smaller than the distance from the unknown sensor node P to the beacon node C' and the node P2Node P's absolute value of distance difference of distance to beacon node C1Is the coordinate of the unknown sensor node P, otherwise the node P2The coordinates of (a) are the coordinates of the unknown sensor node P;
if all the beacon nodes are positioned on the connecting line of the beacon nodes A and B, selecting a beacon node C, calculating a & lt BCP in a triangular CBP (communication based protocol), and calculating a & lt BCP in the triangular CBP1Middle calculation of ≈ BCP1，∠P1CP＝∠BCP-∠BCP1，
In the triangle PCP1And triangular PCP2In the method, unknown sensor nodes P and nodes P are calculated1And node P2When the distance between the node P and the unknown sensor node P is not known1Is less than the unknown sensor node P and the node P2At a distance of, node P1The coordinates of (1) are the coordinates of the unknown sensor node P, otherwise the node P2The coordinates of (2) are the coordinates of the unknown sensor node P;
setting the number of the beacon nodes which can receive signals by the unknown sensor node P as m, wherein m is more than 2, and taking the beacon nodes at any 2 positions asOne group, two beacon nodes in any one group are represented by A and B; optionally, 2 beacons are grouped into a group, and k groups of beacons are obtained, wherein,
Figure BDA0002655801350000031
setting the coordinates of e unknown sensor nodes P as the selection node P according to the calculated coordinates of k unknown sensor nodes P1The coordinates of f unknown sensor nodes P are selected nodes P2If f is k-e, then:
if the selected unknown sensor node P is the node P1Obtaining e fitness function values G(s) by adopting an artificial fish swarm algorithm, selecting a minimum value from the obtained e fitness function values G(s), and selecting an unknown node P corresponding to the fitness function value of the minimum value1The coordinates of the unknown node P are the optimized coordinates of the unknown node P, namely the final positioning coordinates of the unknown sensor node;
if the selected unknown sensor node P is the node P2Obtaining f fitness function values G(s) by adopting an artificial fish swarm algorithm, selecting a minimum value from the obtained f fitness function values G(s), and selecting an unknown node P corresponding to the fitness function value of the minimum value2The coordinates of (2) are the optimized coordinates of the unknown node P, that is, the final positioning coordinates of the unknown sensor node.
Wherein, in the step of judging whether the unknown sensor node P is collinear with the beacon nodes A and B,
coordinates A (x) of the beacon A, B are setA,yA)、B(xB,yB) Calculating the distance L between the beacon node A and the beacon node BAB(ii) a The unknown sensor node P receives signals of surrounding receivable beacons, the received signal strength value is converted into a distance value between the unknown sensor node and the corresponding beacon, and the distance between the unknown sensor node P and the beacon A, B is recorded as LAPAnd LPB；
When L isAB＝LAP+LPBOr LAB＝|LAP-LPBWhen l, judge as threeThe points are collinear with each other and,
LAB＝LAP+LPBwhen the unknown sensor nodes P are located between the beacon nodes A, B, the coordinates of the unknown sensor nodes P are
Figure BDA0002655801350000032
Figure BDA0002655801350000033
LAB＝LAP-LPBWhen the unknown sensor node P is positioned at the extension line of the beacon node A, B, the coordinate of the unknown sensor node P is
Figure BDA0002655801350000041
Figure BDA0002655801350000042
LAB＝LPB-LAPWhen the unknown sensor node P is positioned at the extension line of the beacon node B, A, the coordinate of the unknown sensor node P is
Figure BDA0002655801350000043
Figure BDA0002655801350000044
。
When the unknown sensor node P is not collinear with the beacon node A, B, the node P1Node P2Coordinates are respectively P1(xP1,yP1)、P2(xP2,yP2)；
Then there is
Figure BDA0002655801350000045
Figure BDA0002655801350000046
Solving node P according to formula1Node P2The coordinates of (a).
Wherein, if all the beacon nodes are on the connection line of the beacon nodes A and B, each beacon node is connected to the node P1And node P2Is equal, and one beacon node C, node P is selected1Node P2Symmetrical about the straight line CB, using vector subtraction,
Figure BDA0002655801350000047
the distance from the unknown sensor node P to the beacon node C is recorded as LCPAccording to node P1Node P2Coordinate determination beacon C, B and node P1Is a distance of
Figure BDA0002655801350000048
And
Figure BDA0002655801350000049
c and node P2Is a distance of
Figure BDA00026558013500000410
In the case of a triangular CBP, the,
LBP 2＝LCB 2+LCP 2-2·LCB·LCP·cos∠BCP
therefore, it is not only easy to use
Figure BDA0002655801350000051
In the triangle CBP1In (1),
Figure BDA0002655801350000052
therefore, it is not only easy to use
Figure BDA0002655801350000053
Node P1Node P2Symmetrical about a straight line CB, then
∠P2CB＝∠BCP1，∠P1CP＝∠BCP-∠BCP1
In the triangle PCP1In (1),
Figure BDA0002655801350000054
is unknown sensor node P to node P1Is a vector of the beacon node C to the unknown sensor node P
Figure BDA0002655801350000055
With beacon node C to node P1Vector of (2)
Figure BDA0002655801350000056
Modulo of the vector difference of (a);
in the triangle PCP2In (1),
Figure BDA0002655801350000057
is unknown sensor node P to node P2I.e. the vector of the beacon C to the unknown sensor node P
Figure BDA0002655801350000058
With beacon node C to node P2Vector of (2)
Figure BDA0002655801350000059
Modulo of the vector difference of (a); then there is a change in the number of,
Figure BDA00026558013500000510
Figure BDA00026558013500000511
Figure BDA00026558013500000512
Figure BDA00026558013500000513
to obtain
Figure BDA00026558013500000514
And
Figure BDA00026558013500000515
when in use
Figure BDA00026558013500000516
Time, node P1The coordinates of (1) are the coordinates of the unknown sensor node P, otherwise the node P2The coordinates of (2) are the coordinates of the unknown sensor node P;
when the rest beacons are not all located on the connection line of the beacon nodes A and B, any beacon node C' which is not located on the connection line of the beacon nodes A and B is selected, namely
Figure BDA00026558013500000517
Computing
Figure BDA00026558013500000518
And
Figure BDA00026558013500000519
up to
Figure BDA00026558013500000520
When, when
Figure BDA00026558013500000521
Time, node P1The coordinates of (1) are the coordinates of the unknown sensor node P, otherwise the node P2The coordinates of (2) are the coordinates of the unknown sensor node P; wherein, the distance from the unknown sensor node P to the beacon node C' is recorded as LC′PAccording to node P1Node P2Coordinate solving beacon node C' and node P1Is a distance of
Figure BDA0002655801350000061
C' and node P2Is a distance of
Figure BDA0002655801350000062
Wherein, each group of k groups of beacon nodes calculates the coordinates of an unknown sensor node P, and the coordinates of k unknown sensor nodes P are obtained in total, and are expressed as:
Figure BDA0002655801350000063
wherein e is more than or equal to 0 and less than or equal to k, and f is more than or equal to 0 and less than or equal to k-e;
if the unknown sensor node P is determined, the node P is selected1The coordinates of the e unknown sensor nodes P obtained by the artificial fish swarm algorithm are adopted
Figure BDA0002655801350000064
Figure BDA0002655801350000065
And optimizing, wherein the fitness function of the artificial fish swarm algorithm is as follows:
Figure BDA0002655801350000066
in the formula (x)s,ys) E coordinates representing unknown sensor node P
Figure BDA0002655801350000067
Figure BDA0002655801350000068
Of (a), coordinate (x)i,yi) D (i, P) represents the distance from the ith beacon to the unknown sensor node P, which is the coordinate of the ith beacon in the beacons;
e fitness function values G(s) are obtained through calculation, the minimum value in the e fitness function values G(s) is selected, and a node P corresponding to the minimum fitness function value is selected1The coordinates of (2) are the coordinates of the optimized unknown sensor node P;
if the unknown sensor node P is determined, the node P is selected2And coordinates of the f unknown sensor nodes P obtained by adopting an artificial fish swarm algorithm
Figure BDA0002655801350000069
Figure BDA00026558013500000610
And optimizing, wherein the fitness function of the artificial fish swarm algorithm is as follows:
Figure BDA00026558013500000611
in the formula (x)s,ys) F coordinates representing unknown sensor node P
Figure BDA00026558013500000612
Figure BDA00026558013500000613
Of (a), coordinate (x)i,yi) D (i, P) represents the distance from the ith beacon to the unknown sensor node P, which is the coordinate of the ith beacon in the beacons;
f fitness function values G(s) are obtained through calculation, and f fitness function values G(s) are selectedThe node P corresponding to the minimum fitness function value2The coordinates of (2) are the coordinates of the optimized unknown sensor node P.
Different from the prior art, the artificial fish school optimal positioning method for the unknown sensor nodes of the wireless sensor network comprises the steps of firstly converting signal strength values received indirectly by the nodes into distance values between the nodes, and solving two possible coordinates of the unknown sensor node P by using the known position coordinates of any 2 beacon nodes A, B around the unknown sensor node: node P1Node P2And judging the node coordinates of the unknown sensor to finally determine the node coordinates of the unknown sensor to finish positioning. The method of the invention reduces the complexity of the algorithm, reduces the energy consumption of the node and prolongs the life cycle of the node.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
fig. 1 is a schematic diagram illustrating a principle of an artificial fish school optimization positioning method for an unknown sensor node of a wireless sensor network according to the present invention.
Detailed Description
For a more clear understanding of the technical features, objects and effects of the present invention, embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
Referring to fig. 1, the invention provides an artificial fish school optimization positioning method for unknown sensor nodes in a wireless sensor network, which comprises the following steps:
the unknown sensor node P receives signals of beacon nodes which can be received around, and converts the received signal strength value into a distance value between the unknown sensor node and the corresponding beacon node;
collecting coordinates of any two beacon nodes A and B, and calculating the distance between the beacon nodes A and B and the distance between an unknown sensor node and the beacon nodes A and B;
judging whether the unknown sensor node P is collinear with the beacon nodes A and B, and if so, calculating the coordinate of the unknown sensor node P according to the coordinates of the beacon nodes A and B;
if not collinear, setting the node P1、P2The unknown sensor node P is the node P if the intersection point of two circles is formed by taking the distance between the beacon node A, B and the unknown sensor node P as the radius and taking the beacon node A, B as the center of the circle1、P2One of the computing nodes P1、P2Coordinates;
determining the coordinate value of the unknown sensor node P:
judging whether the other beacons except the beacons A and B are all positioned on the connection line of the beacons A and B:
if all the beacon nodes are not on the connection line of the beacon nodes A and B, any beacon node C' which is positioned outside the connection line of the beacon nodes A and B is selected, and the unknown sensor nodes P and the unknown nodes P are calculated1And node P2The distance from the beacon node C' is,
when the distance between the unknown sensor node P and the beacon node C' and the node P1The absolute value of the distance difference of the distance from the beacon node C 'is smaller than the distance from the unknown sensor node P to the beacon node C' and the node P2Node P's absolute value of distance difference of distance to beacon node C1Is the coordinate of the unknown sensor node P, otherwise the node P2The coordinates of (a) are the coordinates of the unknown sensor node P;
if all the beacon nodes are positioned on the connecting line of the beacon nodes A and B, selecting a beacon node C, calculating a & lt BCP in a triangular CBP (communication based protocol), and calculating a & lt BCP in the triangular CBP1Middle calculation of ≈ BCP1，∠P1CP＝∠BCP-∠BCP1，
In the triangle PCP1And triangular PCP2In the method, unknown sensor nodes P and nodes P are calculated1And node P2When the distance between the node P and the unknown sensor node P is not known1Is less than the unknown sensor node P and the node P2At a distance of, node P1The coordinates of (1) are the coordinates of the unknown sensor node P, otherwise the node P2The coordinates of (2) are the coordinates of the unknown sensor node P;
setting the unknown sensor node P to receiveThe number of the beacon nodes of the signal is m, m is more than 2, the beacon nodes at any 2 positions are taken as a group, and two beacon nodes in any group are represented by A and B; optionally, 2 beacons are grouped into a group, and k groups of beacons are obtained, wherein,
Figure BDA0002655801350000081
setting the coordinates of e unknown sensor nodes P as the selection node P according to the calculated coordinates of k unknown sensor nodes P1The coordinates of f unknown sensor nodes P are selected nodes P2If f is k-e, then:
if the selected unknown sensor node P is the node P1Obtaining e fitness function values G(s) by adopting an artificial fish swarm algorithm, selecting a minimum value from the obtained e fitness function values G(s), and selecting an unknown node P corresponding to the fitness function value of the minimum value1The coordinates of the unknown node P are the optimized coordinates of the unknown node P, namely the final positioning coordinates of the unknown sensor node;
if the selected unknown sensor node P is the node P2Obtaining f fitness function values G(s) by adopting an artificial fish swarm algorithm, selecting a minimum value from the obtained f fitness function values G(s), and selecting an unknown node P corresponding to the fitness function value of the minimum value2The coordinates of (2) are the optimized coordinates of the unknown node P, that is, the final positioning coordinates of the unknown sensor node.
The method for positioning the unknown sensor node of the wireless sensor network is realized by the following steps:
s1: the unknown sensor node P receives signals of surrounding beacon nodes and converts the strength value of the received signals into a distance value between the unknown sensor node and the beacon nodes;
s2: setting the number of beacons which can receive signals by the unknown sensor node P to be m, wherein m is greater than 2, the beacons at any 2 positions are taken as a group, k groups are provided in total, and two beacons in any group are represented by A, B;
s3: coordinates A (x) of two beacons A, B in either group are collectedA,yA)，B(xB,yB) (ii) a Calculating the distance L between the beacon node A and the beacon node BAB(ii) a The distance from the beacon node a to the unknown sensor node P obtained in step S1 is denoted as LAPAnd the distance between the unknown sensor node P and the beacon node B is recorded as LPB；
S4: judging whether the three points of the unknown sensor node P, the beacon node A and the beacon node B are collinear: when L isAB＝LAP+LPBOr LAB＝|LAP-LPBIf the two points are collinear, the three points are judged to be collinear,
LAB＝LAP+LPBwhen the unknown sensor nodes P are located between the beacon nodes A, B, the coordinates of the unknown sensor nodes P are
Figure BDA0002655801350000091
Figure BDA0002655801350000092
LAB＝LAP-LPBWhen the unknown sensor node P is positioned at the extension line of the beacon node A, B, the coordinate of the unknown sensor node P is
Figure BDA0002655801350000101
Figure BDA0002655801350000102
LAB＝LPB-LAPWhen the unknown sensor node P is positioned at the extension line of the beacon node B, A, the coordinate of the unknown sensor node P is
Figure BDA0002655801350000103
Figure BDA0002655801350000104
When L isAB≠LAP+LPBOr LAB≠|LAP-LPBWhen the nodes are not collinear, the node P is judged1、P2Is centered around the beacon A, B and is denoted by LAP、LBPThe unknown sensor node P is the node P which is the intersection point of two circles formed by the radius1Node P2One of the two sets a node P1At a position clockwise of the line A to B, node P2At a position counterclockwise of the line A to B, node P1Node P2Coordinates are respectively P1(xP1,yP1)、P2(xP2,yP2)；
S5: solving according to the following system of equations
Figure BDA0002655801350000105
Figure BDA0002655801350000106
Node P can be obtained1Node P2Coordinates;
s6: unknown sensor node P coordinate value selection
(1) When the rest of the beacons are distributed on the connection line of the beacons A and B, the beacon is connected to the node P1Node P2Is equal, and one beacon node C, node P is selected1Node P2Symmetrical about a straight line CB, i.e.
Figure BDA0002655801350000107
At this time, the vector subtraction is adopted,
Figure BDA0002655801350000111
the distance from the unknown sensor node P to the beacon node C is recorded as LCPAccording to node P1Node P2The coordinates can be found out from the beacon C, B and the node P1Is a distance of
Figure BDA0002655801350000112
And
Figure BDA0002655801350000113
c and node P2Is a distance of
Figure BDA0002655801350000114
In the case of a triangular CBP, the,
LBP 2＝LCB 2+LCP 2-2·LCB·LCP·cos∠BCP，
therefore, it is not only easy to use
Figure BDA0002655801350000115
In the triangle CBP1In (1),
Figure BDA0002655801350000116
therefore, it is not only easy to use
Figure BDA0002655801350000117
P1And P2Is symmetrical about the straight line CB, then ≈ P2CB＝∠BCP1，∠P1CP＝∠BCP-∠BCP1
In the triangle PCP1In (1),
Figure BDA0002655801350000118
is unknown sensor node P to node P1I.e. the vector of the beacon C to the unknown sensor node P
Figure BDA0002655801350000119
With beacon node C to node P1Vector of (2)
Figure BDA00026558013500001110
Modulo of vector difference of (2), in triangular PCP2In (1),
Figure BDA00026558013500001111
is unknown sensor node P to node P2I.e. the vector of the beacon C to the unknown sensor node P
Figure BDA00026558013500001112
With beacon node C to node P2Vector of (2)
Figure BDA00026558013500001113
The modulus of the vector difference of (a),
Figure BDA00026558013500001114
Figure BDA00026558013500001115
Figure BDA0002655801350000121
Figure BDA0002655801350000122
to obtain
Figure BDA0002655801350000123
And
Figure BDA0002655801350000124
up to
Figure BDA0002655801350000125
When in use
Figure BDA0002655801350000126
Time, node P1The coordinates of (1) are the coordinates of the unknown sensor node P, otherwise the node P2The coordinates of (2) are the coordinates of the unknown sensor node P;
(2) when the rest beacons are not all located on the connection line of the beacon nodes A and B, any beacon node C' which is not located on the connection line of the beacon nodes A and B is selected, namely
Figure BDA0002655801350000127
Computing
Figure BDA0002655801350000128
And
Figure BDA0002655801350000129
up to
Figure BDA00026558013500001210
When, when
Figure BDA00026558013500001211
Time, node P1The coordinates of (1) are the coordinates of the unknown sensor node P, otherwise the node P2The coordinates of (2) are the coordinates of the unknown sensor node P; wherein, the distance from the unknown sensor node P to the beacon node C' is recorded as LC′PAccording to node P1Node P2The coordinates can be obtained from the beacon node C' and the node P1Is a distance of
Figure BDA00026558013500001212
C' and node P2Is a distance of
Figure BDA00026558013500001213
S7: coordinate optimization
Each of the k sets of beacons, using steps S3-S6, obtains the coordinates of an unknown sensor node P, thus obtaining the coordinates of k unknown sensor nodes P in total
Figure BDA00026558013500001214
Figure BDA00026558013500001215
Wherein e is more than or equal to 0 and less than or equal to k, and f is more than or equal to 0 and less than or equal to k-e;
if the node P is selected1The coordinates of the e unknown sensor nodes P obtained by the Artificial Fish Swarm Algorithm (AFSA) are adopted
Figure BDA00026558013500001216
And optimizing, wherein the fitness function of the Artificial Fish Swarm Algorithm (AFSA) is as follows:
Figure BDA0002655801350000131
in the formula (x)s,ys) E coordinates representing unknown sensor node P
Figure BDA0002655801350000132
Figure BDA0002655801350000133
Of (a), coordinate (x)i,yi) The coordinate of the ith beacon node in the m beacon nodes is calculated, d (i, P) represents the distance from the ith beacon node to the unknown sensor node P, so that e fitness function values G(s) are obtained, the minimum value is selected from the obtained e fitness function values G(s), and the coordinate of the unknown sensor node P corresponding to the fitness function value of the minimum value is the optimized coordinate of the unknown sensor node P, namely the final positioning coordinate of the unknown sensor node;
if the node P is selected2Using an Artificial Fish Swarm Algorithm (AFSA) -based f unknown sensor nodesCoordinates of P
Figure BDA0002655801350000134
And optimizing, wherein the fitness function of the Artificial Fish Swarm Algorithm (AFSA) is as follows:
Figure BDA0002655801350000135
in the formula (x)s,ys) F coordinates representing unknown sensor node P
Figure BDA0002655801350000136
Figure BDA0002655801350000137
Of (a), coordinate (x)i,yi) And d (i, P) represents the distance from the ith beacon node to the unknown sensor node P, so that f fitness function values g(s) are obtained, and the minimum value is selected from the obtained f fitness function values g(s), wherein the coordinate of the unknown sensor node P corresponding to the fitness function value of the minimum value is the optimized coordinate of the unknown sensor node P, namely the final positioning coordinate of the unknown sensor node.
The method of the invention firstly uses the signal intensity value received between the nodes to be converted into the distance value between the nodes, and uses the known position coordinates of any 2 beacon nodes A, B around the unknown sensor node to calculate two possible coordinates P of the unknown sensor node1、P2And judging the node coordinates, optimizing by adopting an artificial fish swarm algorithm in order to improve the positioning precision, and finally determining the unknown sensor node coordinates to finish positioning. The method of the invention reduces the complexity of the algorithm, reduces the energy consumption of the node and prolongs the life cycle of the node.
While the present invention has been described with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, which are illustrative and not restrictive, and it will be apparent to those skilled in the art that various changes and modifications can be made therein without departing from the spirit and scope of the invention as defined in the appended claims.

Claims (5)

1. An artificial fish school optimal positioning method for unknown sensor nodes of a wireless sensor network is characterized by comprising the following steps:
the unknown sensor node P receives signals of beacon nodes which can be received around, and converts the received signal strength value into a distance value between the unknown sensor node and the corresponding beacon node;
collecting coordinates of any two beacon nodes A and B, and calculating the distance between the beacon nodes A and B and the distance between an unknown sensor node and the beacon nodes A and B;
judging whether the unknown sensor node P is collinear with the beacon nodes A and B, and if so, calculating the coordinate of the unknown sensor node P according to the coordinates of the beacon nodes A and B;
if not collinear, setting the node P1、P2The unknown sensor node P is the node P if the intersection point of two circles is formed by taking the distance between the beacon node A, B and the unknown sensor node P as the radius and taking the beacon node A, B as the center of the circle1、P2One of the computing nodes P1、P2Coordinates;
determining the coordinate value of the unknown sensor node P:
judging whether the other beacons except the beacons A and B are all positioned on the connection line of the beacons A and B:
if all the beacon nodes are not on the connection line of the beacon nodes A and B, any beacon node C' which is positioned outside the connection line of the beacon nodes A and B is selected, and the unknown sensor nodes P and the unknown nodes P are calculated1And node P2The distance from the beacon node C' is,
when the distance between the unknown sensor node P and the beacon node C' and the node P1The absolute value of the distance difference of the distance from the beacon node C 'is smaller than the distance from the unknown sensor node P to the beacon node C' and the node P2Node P's absolute value of distance difference of distance to beacon node C1Is the coordinate of the unknown sensor node P, otherwise the node P2The coordinates of (a) are the coordinates of the unknown sensor node P;
if all the beacon nodes are positioned on the connecting line of the beacon nodes A and B, selecting a beacon node C, calculating a & lt BCP in a triangular CBP (communication based protocol), and calculating a & lt BCP in the triangular CBP1Middle calculation of ≈ BCP1，∠P1CP＝∠BCP-∠BCP1，
In the triangle PCP1And triangular PCP2In the method, unknown sensor nodes P and nodes P are calculated1And node P2When the distance between the node P and the unknown sensor node P is not known1Is less than the unknown sensor node P and the node P2At a distance of, node P1The coordinates of (1) are the coordinates of the unknown sensor node P, otherwise the node P2The coordinates of (2) are the coordinates of the unknown sensor node P;
setting the number of beacon nodes which can receive signals by the unknown sensor node P as m, wherein m is more than 2, the beacon nodes at any 2 positions are taken as a group, and two beacon nodes in any group are represented by A and B; optionally, 2 beacons are grouped into a group, and k groups of beacons are obtained, wherein,
Figure FDA0002655801340000021
setting the coordinates of e unknown sensor nodes P as the selection node P according to the calculated coordinates of k unknown sensor nodes P1The coordinates of f unknown sensor nodes P are selected nodes P2If f is k-e, then:
if the selected unknown sensor node P is the node P1Obtaining e fitness function values G(s) by adopting an artificial fish swarm algorithm, selecting a minimum value from the obtained e fitness function values G(s), and selecting an unknown node P corresponding to the fitness function value of the minimum value1The coordinates of the unknown node P are the optimized coordinates of the unknown node P, namely the final positioning coordinates of the unknown sensor node;
if the selected unknown sensor node P is the node P2Obtaining f fitness function values G(s) by adopting an artificial fish swarm algorithm, selecting a minimum value from the obtained f fitness function values G(s), and selecting an unknown node P corresponding to the fitness function value of the minimum value2The coordinates of (2) are the optimized coordinates of the unknown node P, that is, the final positioning coordinates of the unknown sensor node.
2. The method for optimal positioning of an artificial fish school of unknown sensor nodes in a wireless sensor network according to claim 1, wherein in the step of determining whether the unknown sensor node P is collinear with the beacon nodes A and B,
coordinates A (x) of the beacon A, B are setA,yA)、B(xB,yB) Calculating the distance L between the beacon node A and the beacon node BAB(ii) a The unknown sensor node P receives signals of surrounding receivable beacons, the received signal strength value is converted into a distance value between the unknown sensor node and the corresponding beacon, and the distance between the unknown sensor node P and the beacon A, B is recorded as LAPAnd LPB；
When L isAB＝LAP+LPBOr LAB＝|LAP-LPBIf the two points are collinear, the three points are judged to be collinear,
LAB＝LAP+LPBwhen the unknown sensor nodes P are located between the beacon nodes A, B, the coordinates of the unknown sensor nodes P are
Figure FDA0002655801340000031
Figure FDA0002655801340000032
LAB＝LAP-LPBWhen the unknown sensor node P is positioned at the extension line of the beacon node A, B, the coordinate of the unknown sensor node P is
Figure FDA0002655801340000033
Figure FDA0002655801340000034
LAB＝LPB-LAPWhen the unknown sensor node P is positioned at the extension line of the beacon node B, A, the coordinate of the unknown sensor node P is
Figure FDA0002655801340000035
Figure FDA0002655801340000036
3. The method for optimal positioning of artificial fish school of unknown sensor node in wireless sensor network according to claim 1, wherein when unknown sensor node P is not collinear with beacon node A, B, node P is1Node P2Coordinates are respectively P1(xP1,yP1)、P2(xP2,yP2)；
Then there is
Figure FDA0002655801340000037
Figure FDA0002655801340000038
Solving node P according to formula1Node P2The coordinates of (a).
4. According to the claimsSolving 1 the method for optimizing and positioning the artificial fish school of the unknown sensor nodes of the wireless sensor network is characterized in that if all the beacon nodes are positioned on the connecting line of the beacon nodes A and B, each beacon node is positioned to the node P1And node P2Is equal, and one beacon node C, node P is selected1Node P2Symmetrical about the straight line CB, using vector subtraction,
Figure FDA0002655801340000041
the distance from the unknown sensor node P to the beacon node C is recorded as LCPAccording to node P1Node P2Coordinate determination beacon C, B and node P1Is a distance of
Figure FDA0002655801340000042
And
Figure FDA00026558013400000414
c and node P2Is a distance of
Figure FDA0002655801340000043
In the case of a triangular CBP, the,
LBP 2＝LCB 2+LCP 2-2·LCB·LCP·cos∠BCP
therefore, it is not only easy to use
Figure FDA0002655801340000044
In the triangle CBP1In (1),
Figure FDA0002655801340000045
therefore, it is not only easy to use
Figure FDA0002655801340000046
Node P1Node P2Symmetrical about a straight line CB, then
∠P2CB＝∠BCP1，∠P1CP＝∠BCP-∠BCP1
In the triangle PCP1In (1),
Figure FDA0002655801340000047
is unknown sensor node P to node P1Is a vector of the beacon node C to the unknown sensor node P
Figure FDA0002655801340000048
With beacon node C to node P1Vector of (2)
Figure FDA0002655801340000049
Modulo of the vector difference of (a);
in the triangle PCP2In (1),
Figure FDA00026558013400000410
is unknown sensor node P to node P2I.e. the vector of the beacon C to the unknown sensor node P
Figure FDA00026558013400000411
With beacon node C to node P2Vector of (2)
Figure FDA00026558013400000412
Modulo of the vector difference of (a); then there is a change in the number of,
Figure FDA00026558013400000413
Figure FDA0002655801340000051
Figure FDA0002655801340000052
Figure FDA0002655801340000053
to obtain
Figure FDA0002655801340000054
And
Figure FDA0002655801340000055
when in use
Figure FDA0002655801340000056
Time, node P1The coordinates of (1) are the coordinates of the unknown sensor node P, otherwise the node P2The coordinates of (2) are the coordinates of the unknown sensor node P;
when the rest beacons are not all located on the connection line of the beacon nodes A and B, any beacon node C' which is not located on the connection line of the beacon nodes A and B is selected, namely
Figure FDA0002655801340000057
Computing
Figure FDA0002655801340000058
And
Figure FDA0002655801340000059
up to
Figure FDA00026558013400000510
When, when
Figure FDA00026558013400000511
Time, node P1The coordinates of (1) are the coordinates of the unknown sensor node P, otherwise the node P2The coordinates of (2) are the coordinates of the unknown sensor node P; wherein, the distance from the unknown sensor node P to the beacon node C' is recorded as LC′PAccording to node P1Node P2Coordinate solving beacon node C' and node P1Is a distance of
Figure FDA00026558013400000512
C' and node P2Is a distance of
Figure FDA00026558013400000513
5. The method for optimizing and positioning the artificial fish school of the unknown sensor nodes in the wireless sensor network according to claim 1, wherein the coordinates of one unknown sensor node P are calculated for each of k groups of beacon nodes, and the coordinates of k unknown sensor nodes P are obtained in total, and are expressed as:
Figure FDA00026558013400000514
wherein e is more than or equal to 0 and less than or equal to k, and f is more than or equal to 0 and less than or equal to k-e;
if the unknown sensor node P is determined, the node P is selected1The coordinates of the e unknown sensor nodes P obtained by the artificial fish swarm algorithm are adopted
Figure FDA00026558013400000515
Figure FDA00026558013400000516
And optimizing, wherein the fitness function of the artificial fish swarm algorithm is as follows:
Figure FDA00026558013400000517
in the formula (x)s,ys) E coordinates representing unknown sensor node P
Figure FDA00026558013400000518
Figure FDA00026558013400000519
Of (a), coordinate (x)i,yi) D (i, P) represents the distance from the ith beacon to the unknown sensor node P, which is the coordinate of the ith beacon in the beacons;
e fitness function values G(s) are obtained through calculation, the minimum value in the e fitness function values G(s) is selected, and a node P corresponding to the minimum fitness function value is selected1The coordinates of (2) are the coordinates of the optimized unknown sensor node P;
if the unknown sensor node P is determined, the node P is selected2And coordinates of the f unknown sensor nodes P obtained by adopting an artificial fish swarm algorithm
Figure FDA0002655801340000061
Figure FDA0002655801340000062
And optimizing, wherein the fitness function of the artificial fish swarm algorithm is as follows:
Figure FDA0002655801340000063
in the formula (x)s,ys) F coordinates representing unknown sensor node P
Figure FDA0002655801340000064
Figure FDA0002655801340000065
Any one of the coordinates of the other one of the coordinates,coordinate (x)i,yi) D (i, P) represents the distance from the ith beacon to the unknown sensor node P, which is the coordinate of the ith beacon in the beacons;
f fitness function values G(s) are obtained through calculation, the minimum value in the f fitness function values G(s) is selected, and a node P corresponding to the minimum fitness function value is selected2The coordinates of (2) are the coordinates of the optimized unknown sensor node P.
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Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101191832A (en) * 2007-12-11 2008-06-04 宁波中科集成电路设计中心有限公司 Wireless sensor network node position finding process based on range measurement
US20080231511A1 (en) * 2007-03-22 2008-09-25 Nortel Networks Limited Beacon-Assisted Precision Location of Untethered Client in Packet Networks
CN104301996A (en) * 2014-04-15 2015-01-21 河南科技大学 Wireless sensor network positioning method
CN104965193A (en) * 2015-06-19 2015-10-07 中南大学 Grid weighing-based wireless mobile terminal RSSI (Received Signal Strength Indication) positioning method
CN105828435A (en) * 2016-05-30 2016-08-03 天津大学 Distance correction weighted centroid localization method based on reception signal intensity optimization
US20160359963A1 (en) * 2007-08-29 2016-12-08 Oracle International Corporation Method and system for selecting a storage node based on a distance from a requesting device
CN108848443A (en) * 2018-06-05 2018-11-20 太原理工大学 The bacterium of the unknown sensor node of wireless sensor network is looked for food optimum position method
CN108848559A (en) * 2018-06-05 2018-11-20 太原理工大学 The localization method of the unknown sensor node of wireless sensor network
CN108848474A (en) * 2018-06-05 2018-11-20 太原理工大学 The localization method of the not conllinear unknown sensor node of wireless sensor network
CN109041210A (en) * 2018-08-14 2018-12-18 长春理工大学 A kind of wireless sensor network locating method

Patent Citations (10)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20080231511A1 (en) * 2007-03-22 2008-09-25 Nortel Networks Limited Beacon-Assisted Precision Location of Untethered Client in Packet Networks
US20160359963A1 (en) * 2007-08-29 2016-12-08 Oracle International Corporation Method and system for selecting a storage node based on a distance from a requesting device
CN101191832A (en) * 2007-12-11 2008-06-04 宁波中科集成电路设计中心有限公司 Wireless sensor network node position finding process based on range measurement
CN104301996A (en) * 2014-04-15 2015-01-21 河南科技大学 Wireless sensor network positioning method
CN104965193A (en) * 2015-06-19 2015-10-07 中南大学 Grid weighing-based wireless mobile terminal RSSI (Received Signal Strength Indication) positioning method
CN105828435A (en) * 2016-05-30 2016-08-03 天津大学 Distance correction weighted centroid localization method based on reception signal intensity optimization
CN108848443A (en) * 2018-06-05 2018-11-20 太原理工大学 The bacterium of the unknown sensor node of wireless sensor network is looked for food optimum position method
CN108848559A (en) * 2018-06-05 2018-11-20 太原理工大学 The localization method of the unknown sensor node of wireless sensor network
CN108848474A (en) * 2018-06-05 2018-11-20 太原理工大学 The localization method of the not conllinear unknown sensor node of wireless sensor network
CN109041210A (en) * 2018-08-14 2018-12-18 长春理工大学 A kind of wireless sensor network locating method

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
CHUANJIN ZHANG: "Cluster analysis based and threshold based selection localization algorithm for WSN", 《IEEEXPLORE》 *
何杰等: "第三十九讲 基于WIA-PA的工厂人员定位系统", 《仪器仪表标准化与计量》 *
何艳丽: "无线传感器网络质心定位算法研究", 《计算机仿真》 *
许红艳等: "无线传感器网络节点定位算法的改进", 《微计算机信息》 *

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