CN108848443A - The bacterium of the unknown sensor node of wireless sensor network is looked for food optimum position method - Google Patents

The bacterium of the unknown sensor node of wireless sensor network is looked for food optimum position method Download PDF

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CN108848443A
CN108848443A CN201810572680.2A CN201810572680A CN108848443A CN 108848443 A CN108848443 A CN 108848443A CN 201810572680 A CN201810572680 A CN 201810572680A CN 108848443 A CN108848443 A CN 108848443A
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coordinate
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beaconing nodes
unknown node
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CN108848443B (en
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乔学工
周文祥
段亚青
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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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • 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

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  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
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Abstract

The present invention relates to wireless sensor network location technology, the bacterium of the specially unknown sensor node of wireless sensor network is looked for food optimum position method, is mainly used for obtaining the accurate location information of the unknown sensor node of wireless sensor network.Solve the problems, such as that the existing location algorithm positioning accuracy based on ranging is low and algorithm is complicated.The method of the invention is converted into euclidean distance between node pair value first with the signal strength indication received between node, finds out two possible coordinates of unknown node using the known location coordinate of 2 beaconing nodes A, B any around unknown node by distance intersection principle、, and it is determined, it is finally optimized using bacterial foraging algorithm (BFO), determines that unknown node coordinate completes positioning.The method of the invention improves the precision of algorithm, reduces the complexity of algorithm, reduces the energy consumption of node, extends the life cycle of node.

Description

The bacterium of the unknown sensor node of wireless sensor network is looked for food optimum position method
Technical field
The present invention relates to wireless sensor network location technology, the specially unknown sensor node of wireless sensor network Bacterium is looked for food optimum position method, is mainly used for obtaining the accurate location information of the unknown sensor node of wireless sensor network.
Background technique
Technology of Internet of things constantly obtains new achievement in recent years, has applied to defense military, environmental monitoring, traffic pipe Reason, health care, manufacturing industry, the fields such as provide rescue and relief for disasters and emergencies, the wireless sensor network as one of Internet of Things bottom important technology Have become research hotspot.Wherein, obtaining accurate location information by location algorithm is that wireless sensor network is very heavy The content wanted.
Location algorithm is divided into based on non-ranging location algorithm (e.g., DV-HOP algorithm) and based on the location algorithm of ranging. The positioning accuracy of location algorithm based on ranging is higher than based on non-ranging location algorithm.It is related to the location algorithm based on ranging Some algorithms have, three in location algorithm, three centroid localization algorithm, population location algorithm etc..These existing algorithms are wanted Positioning accuracy lower (e.g., centroid localization algorithm) or algorithm need to carry out a large amount of interative computation and excessively it is complicated (e.g., Population location algorithm).
Summary of the invention
The present invention solves the problems, such as that the existing location algorithm positioning accuracy based on ranging is low and algorithm is complicated, provides a kind of nothing The bacterium of the unknown sensor node of line sensor network is looked for food optimum position method.
The present invention adopts the following technical scheme that realization：The bacterium of the unknown sensor node of wireless sensor network is looked for food Optimum position method is realized by following steps：
S1：Unknown node P receives the signal of surrounding beaconing nodes, and converts unknown section for the signal strength indication received The distance between point and beaconing nodes value；
S2：Setting unknown node P can receive the anchor node number of signal as m, m >=2, with wantonly 2 positions Beaconing nodes are one group, share k group, and two beaconing nodes in any group are with A, B representative；
S3：Acquire the coordinate A (x of two beaconing nodes A, B in any groupA,yA), B (xB,yB)；Calculate beaconing nodes A To the distance between beaconing nodes B LAB；It is denoted as according to the distance between step S1 obtained beaconing nodes A to unknown node P LAP, the distance between unknown node P to beaconing nodes B is denoted as LPB；
S4：Judge unknown node P, beaconing nodes A and 3 points of beaconing nodes B it is whether conllinear：Work as LAB=LAP+LPBOr LAB=| LAP-LPB| when, it is judged as three point on a straight line,
LAB=LAP+LPBWhen, between beaconing nodes A, B, the coordinate of unknown node P is unknown node P
LAB=LAP-LPBWhen, unknown node P is located at beaconing nodes A, B extended line, and the coordinate of unknown node P is
LAB=LPB-LAPWhen, unknown node P is located at beaconing nodes B, A extended line, and the coordinate of unknown node P is
Work as LAB≠LAP+LPBOr LAB≠|LAP-LPB| when, judge 3 points it is not conllinear, unknown node P be node PR, node PLTwo One in a, interior joint PRPositioned at the 3 points of anticlockwise positions A, B, P, node PLIt is square clockwise positioned at 3 points of A, B, P To position, set node PR, node PLCoordinate is respectively PR(xPR,yPR)、PL(xPL,yPL)；
S5：Egress P is obtained according to distance intersection principleRCoordinate PR(xPR,yPR)：xPR=xA+L·(xB-xA)+H·(yB- yA)
yPR=yA+L·(yB-yA)+H·(xA-xB)
According to the equation of the connected straight line of beaconing nodes A and beaconing nodes B：Ax+by+c=0, then node PLCoordinate PL (xPL,yPL)：
Wherein,
Meet xB≠xA, yA≠yBWhen,
Meet xB=xA, yA≠yBWhen,
A=1, b=0, c=-xAOr-xB；
Meet xB≠xA, yA=yBWhen,
A=0, b=1, c=-yAOr-yB
The distance intersection principle is at least in title《Surveying》, published by Mapping Press, author is land state Victory, the publication date be in June, 1991 publication on have detailed disclosure.
S6：The selection of unknown node P coordinate value
I-th, 1≤i≤m of de (i, P) expression, distance of the beaconing nodes to unknown node P；de(i,PR) indicate i-th Beaconing nodes are to node PRDistance, de (i, PL) indicate i-th of beaconing nodes to node PLDistance, definition：
DIS(i,PR)=| de (i, P)-de (i, PR) | (i=1,2 ... m)
DIS(i,PL)=| de (i, P)-de (i, PL) | (i=1,2 ... m)
When
PRCoordinate be exactly unknown node P coordinate, otherwise PLCoordinate be exactly unknown node P coordinate；
S7：Coordinate optimizing
Each group in k group beaconing nodes, the coordinate of a unknown node P is obtained using step S3-S6, in this way, there are Coordinate (the x of k unknown node P outP1,yP1),(xP2,yP2)......(xPk,yPk), using based on bacterial foraging algorithm (BFO) The coordinate of k obtained unknown node P is optimized, the fitness function of the bacterial foraging algorithm (BFO) is：
(x in formulas,ys) represent the k coordinate (x of unknown node PP1,yP1),(xP2,yP2)......(xPk,yPk) in appoint One coordinate, coordinate (xi,yi) be any of m beaconing nodes coordinate；In this way, obtaining k fitness function value F (s), in k obtained fitness function value F (s), minimum value is selected, it is corresponding with the fitness function value of minimum value unknown The coordinate of node P is exactly the coordinate of the unknown node P after optimization, that is, the final positioning coordinate of unknown sensor node.
The conventionally known bacterial foraging algorithm (BFO) is at least in title《MATLAB optimization algorithm analysis of cases With application》(an advanced piece), is published by publishing house of Tsinghua University, author Yu Shengwei, and the publication date is the publication in June, 2015 There is detailed disclosure on object.
The method of the invention is converted into euclidean distance between node pair value first with the signal strength indication received between node, passes through survey Side intersection principle finds out the two of unknown node using the known location coordinate of 2 beaconing nodes A, B any around unknown node A possible coordinate PR、PL, and it is determined, it is finally optimized using bacterial foraging algorithm (BFO), determines unknown section Point coordinate completes positioning.The method of the invention improves the precision of algorithm, reduces the complexity of algorithm, reduces node Energy consumption extends the life cycle of node.Bacterial foraging algorithm (BFO) is a kind of based on Escherichia coli foraging behavior model A kind of biological cluster algorithm, bacterial foraging algorithm (BFO) has the characteristics that simply to be easily achieved, ability of searching optimum is strong.Cause This, selects bacterial foraging algorithm (BFO) to optimize and has highlighted protrusion substantive distinguishing features of the invention.
Detailed description of the invention
Fig. 1 is the schematic illustration of the method for the invention.
Specific embodiment
The bacterium of the unknown sensor node of wireless sensor network is looked for food optimum position method, is realized by following steps 's：
S1：Unknown node P receives the signal of surrounding beaconing nodes, and converts unknown section for the signal strength indication received The distance between point and beaconing nodes value；
S2：Setting unknown node P can receive the anchor node number of signal as m, m >=2, with wantonly 2 positions Beaconing nodes are one group, share k group, and two beaconing nodes in any group are with A, B representative；
S3：Acquire the coordinate A (x of two beaconing nodes A, B in any groupA,yA), B (xB,yB)；Calculate beaconing nodes A To the distance between beaconing nodes B LAB；It is denoted as according to the distance between step S1 obtained beaconing nodes A to unknown node P LAP, the distance between unknown node P to beaconing nodes B is denoted as LPB；
S4：Judge unknown node P, beaconing nodes A and 3 points of beaconing nodes B it is whether conllinear：Work as LAB=LAP+LPBOr LAB=| LAP-LPB| when, it is judged as three point on a straight line,
LAB=LAP+LPBWhen, between beaconing nodes A, B, the coordinate of unknown node P is unknown node P
LAB=LAP-LPBWhen, unknown node P is located at beaconing nodes A, B extended line, and the coordinate of unknown node P is
LAB=LPB-LAPWhen, unknown node P is located at beaconing nodes B, A extended line, and the coordinate of unknown node P is
Work as LAB≠LAP+LPBOr LAB≠|LAP-LPB| when, judge 3 points it is not conllinear, unknown node P be node PR, node PLTwo One in a, interior joint PRPositioned at the 3 points of anticlockwise positions A, B, P, node PLIt is square clockwise positioned at 3 points of A, B, P To position, set node PR, node PLCoordinate is respectively PR(xPR,yPR)、PL(xPL,yPL)；
S5：Egress P is obtained according to distance intersection principleRCoordinate PR(xPR,yPR)：xPR=xA+L·(xB-xA)+H·(yB- yA)
yPR=yA+L·(yB-yA)+H·(xA-xB)
According to the equation of the connected straight line of beaconing nodes A and beaconing nodes B：Ax+by+c=0, then node PLCoordinate PL (xPL,yPL)：
Wherein,
Meet xB≠xA, yA≠yBWhen,
Meet xB=xA, yA≠yBWhen,
A=1, b=0, c=-xAOr-xB；
Meet xB≠xA, yA=yBWhen,
A=0, b=1, c=-yAOr-yB
The distance intersection principle is at least in title《Surveying》, published by Mapping Press, author is land state Victory, the publication date be in June, 1991 publication on have detailed disclosure.
S6：The selection of unknown node P coordinate value
I-th, 1≤i≤m of de (i, P) expression, distance of the beaconing nodes to unknown node P；de(i,PR) indicate i-th Beaconing nodes are to node PRDistance, de (i, PL) indicate i-th of beaconing nodes to node PLDistance, definition：
DIS(i,PR)=| de (i, P)-de (i, PR) | (i=1,2 ... m)
DIS(i,PL)=| de (i, P)-de (i, PL) | (i=1,2 ... m)
When
PRCoordinate be exactly unknown node P coordinate, otherwise PLCoordinate be exactly unknown node P coordinate；
S7：Coordinate optimizing
Each group in k group beaconing nodes, the coordinate of a unknown node P is obtained using step S3-S6, in this way, there are Coordinate (the x of k unknown node P outP1,yP1),(xP2,yP2)......(xPk,yPk), using based on bacterial foraging algorithm (BFO) The coordinate of k obtained unknown node P is optimized, the fitness function of the bacterial foraging algorithm (BFO) is：
(x in formulas,ys) represent the k coordinate (x of unknown node PP1,yP1),(xP2,yP2)......(xPk,yPk) in appoint One coordinate, coordinate (xi,yi) be any of m beaconing nodes coordinate；In this way, obtaining k fitness function value F (s), in k obtained fitness function value F (s), minimum value is selected, it is corresponding with the fitness function value of minimum value unknown The coordinate of node P is exactly the coordinate of the unknown node P after optimization, that is, the final positioning coordinate of unknown sensor node.
The conventionally known bacterial foraging algorithm (BFO) is at least in title《MATLAB optimization algorithm analysis of cases With application》(an advanced piece), is published by publishing house of Tsinghua University, author Yu Shengwei, and the publication date is the publication in June, 2015 There is detailed disclosure on object.

Claims (1)

  1. A kind of optimum position method 1. bacterium of the unknown sensor node of wireless sensor network is looked for food, it is characterised in that be by such as What lower step was realized：
    S1：Unknown node P receive surrounding beaconing nodes signal, and by the signal strength indication received be converted into unknown node and The distance between beaconing nodes value；
    S2：Setting unknown node P can receive the anchor node number of signal as m, m >=2, with the beacon of wantonly 2 positions Node is one group, shares k group, and two beaconing nodes in any group are with A, B representative；
    S3：Acquire the coordinate A (x of two beaconing nodes A, B in any groupA,yA), B (xB,yB)；Beaconing nodes A is calculated to beacon The distance between node B LAB；L is denoted as according to the distance between step S1 obtained beaconing nodes A to unknown node PAP, not Know that the distance between node P to beaconing nodes B is denoted as LPB；
    S4：Judge unknown node P, beaconing nodes A and 3 points of beaconing nodes B it is whether conllinear：Work as LAB=LAP+LPBOr LAB=| LAP- LPB| when, it is judged as three point on a straight line,
    LAB=LAP+LPBWhen, between beaconing nodes A, B, the coordinate of unknown node P is unknown node P
    LAB=LAP-LPBWhen, unknown node P is located at beaconing nodes A, B extended line, and the coordinate of unknown node P is
    LAB=LPB-LAPWhen, unknown node P is located at beaconing nodes B, A extended line, and the coordinate of unknown node P is
    Work as LAB≠LAP+LPBOr LAB≠|LAP-LPB| when, judge 3 points it is not conllinear, unknown node P be node PR, node PLIn two One, interior joint PRPositioned at the 3 points of anticlockwise positions A, B, P, node PLIt is clockwise positioned at 3 points of A, B, P Position sets node PR, node PLCoordinate is respectively PR(xPR,yPR)、PL(xPL,yPL)；
    S5：Egress P is obtained according to distance intersection principleRCoordinate PR(xPR,yPR)：xPR=xA+L·(xB-xA)+H·(yB-yA)
    yPR=yA+L·(yB-yA)+H·(xA-xB)
    According to the equation of the connected straight line of beaconing nodes A and beaconing nodes B：Ax+by+c=0, then node PLCoordinate PL(xPL, yPL)：
    Wherein,
    Meet xB≠xA, yA≠yBWhen,
    B=-1,
    Meet xB=xA, yA≠yBWhen,
    A=1, b=0, c=-xAOr-xB；
    Meet xB≠xA, yA=yBWhen,
    A=0, b=1, c=-yAOr-yB；
    S6：The selection of unknown node P coordinate value
    I-th, 1≤i≤m of de (i, P) expression, distance of the beaconing nodes to unknown node P；de(i,PR) indicate i-th of beacon section Point arrives node PRDistance, de (i, PL) indicate i-th of beaconing nodes to node PLDistance, definition：
    DIS(i,PR)=| de (i, P)-de (i, PR) | (i=1,2 ... m)
    DIS(i,PL)=| de (i, P)-de (i, PL) | (i=1,2 ... m)
    When
    PRCoordinate be exactly unknown node P coordinate, otherwise PLCoordinate be exactly unknown node P coordinate；
    S7：Coordinate optimizing
    Each group in k group beaconing nodes, the coordinate of a unknown node P is obtained using step S3-S6, in this way, there are out k Coordinate (the x of a unknown node PP1,yP1),(xP2,yP2)......(xPk,yPk), using based on bacterial foraging algorithm to obtained k The coordinate of a unknown node P optimizes, and the fitness function of the bacterial foraging algorithm is：
    (x in formulas,ys) represent the k coordinate (x of unknown node PP1,yP1),(xP2,yP2)......(xPk,yPkAny of) Coordinate, coordinate (xi,yi) be any of m beaconing nodes coordinate；In this way, k fitness function value F (s) is obtained, In k obtained fitness function value F (s), minimum value, unknown node P corresponding with the fitness function value of minimum value are selected Coordinate be exactly the unknown node P after optimization coordinate, that is, the final positioning coordinate of unknown sensor node.
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CN112055305A (en) * 2020-08-28 2020-12-08 太原理工大学 Two-circle intersection point positioning method for unknown sensor nodes of wireless sensor network
CN112055304A (en) * 2020-08-28 2020-12-08 太原理工大学 Optimal positioning method for two circle intersection points of unknown sensor nodes of wireless sensor network

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Cited By (3)

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Publication number Priority date Publication date Assignee Title
CN112055303A (en) * 2020-08-28 2020-12-08 太原理工大学 Artificial fish swarm optimization positioning method for unknown sensor nodes of wireless sensor network
CN112055305A (en) * 2020-08-28 2020-12-08 太原理工大学 Two-circle intersection point positioning method for unknown sensor nodes of wireless sensor network
CN112055304A (en) * 2020-08-28 2020-12-08 太原理工大学 Optimal positioning method for two circle intersection points of unknown sensor nodes of wireless sensor network

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