CN114007260B - Inter-node cooperative positioning method in wireless sensor network - Google Patents

Inter-node cooperative positioning method in wireless sensor network Download PDF

Info

Publication number
CN114007260B
CN114007260B CN202111224364.4A CN202111224364A CN114007260B CN 114007260 B CN114007260 B CN 114007260B CN 202111224364 A CN202111224364 A CN 202111224364A CN 114007260 B CN114007260 B CN 114007260B
Authority
CN
China
Prior art keywords
nodes
cluster
node
equal
temporary
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN202111224364.4A
Other languages
Chinese (zh)
Other versions
CN114007260A (en
Inventor
周牧
王倩
李耀华
蒲巧林
蓝馨
陈有坤
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Shenzhen Hongyue Information Technology Co ltd
Shenzhen Tonghui Technology Co ltd
Original Assignee
Shenzhen Tonghui Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Shenzhen Tonghui Technology Co ltd filed Critical Shenzhen Tonghui Technology Co ltd
Priority to CN202111224364.4A priority Critical patent/CN114007260B/en
Publication of CN114007260A publication Critical patent/CN114007260A/en
Application granted granted Critical
Publication of CN114007260B publication Critical patent/CN114007260B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
    • 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
    • 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

Landscapes

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

Abstract

The invention provides a cooperative positioning method between nodes in a wireless sensor network (Wireless Sensor Network, WSN). Firstly, deploying and calibrating a plurality of WSN nodes (anchor nodes) with known positions and a plurality of WSN nodes (nodes to be positioned) with unknown positions in a target area; secondly, clustering all WSN nodes according to the residual energy of the nodes to be positioned and the distance from the anchor nodes; thirdly, estimating the distance between each node based on the received signal strength (Received Signal Strength, RSS) in a single cluster, and obtaining the relative coordinates of the WSN nodes by utilizing multidimensional scaling; and finally, deducing a conversion relation based on the global coordinates and the relative coordinates of the anchor nodes, and obtaining the global coordinates of the nodes to be positioned by using the conversion relation. The invention provides a cooperative positioning method between WSN nodes by utilizing the residual energy of nodes in a target area and the geometric relation between the residual energy and the anchor nodes, and ensures the positioning precision while reducing the energy loss and the calculation cost of the system.

Description

Inter-node cooperative positioning method in wireless sensor network
Technical Field
The invention belongs to the technical field of wireless sensor networks, and particularly relates to a cooperative positioning method.
Background
With the gradual development of the internet of things, the demands of people for location services in life are increasing, such as logistics management, path searching, advertisement delivery, intelligent home and the like. And the combination of the internet of things and the sensor network (Wireless Sensor Network, WSN) is expected to realize the interconnection of everything and the intercommunication of everything. Among the currently popular WSN positioning systems are bluetooth positioning systems, cellular positioning systems, radio frequency identification positioning systems, and wireless local area network (Wireless Location Area Network, WLAN) positioning systems. The existing WSN positioning technology can meet the requirement of users on positioning in partial scenes, but with the increase of the networking scale and networking density of WSN nodes, the WSN positioning has great challenges on the residual energy and computing power of the WSN nodes. Aiming at the problem, the invention utilizes the residual energy of the nodes in the target area and the geometric relation between the nodes and the nodes with known positions to cluster the WSN nodes, and provides a cooperative positioning method among the nodes in the WSN based on multidimensional scaling (Multidimensional Scaling, MDS), which reduces the energy loss and the calculation cost of the system and ensures the positioning precision.
Disclosure of Invention
The invention aims to provide a cooperative positioning method among nodes in a wireless sensor network, which clusters the unknown nodes by calculating the attribution degree of the unknown nodes about the known nodes at each position, and realizes the acquisition of global coordinates of the unknown nodes at the positions based on the distance between the nodes and the known node positions in the clusters.
The invention relates to a cooperative positioning method between nodes in a wireless sensor network, which comprises the following steps:
step one, in the target area (the side length is L 1 Randomly placing WSN nodes (namely anchor nodes) with known positions of M (M is more than or equal to 1, M is an integer) and WSN nodes (namely nodes to be positioned) with unknown positions of N (N is more than or equal to 1, N is an integer) in the square area of the frame;
step two, recording the residual energy E in the node battery to be positioned i (1 is more than or equal to i is more than or equal to N, i is an integer);
step three, defining each anchor node as a temporary cluster head, which is formed byThe temporary cluster head sends out a broadcast signal, and the received signal strength (Received Signal Strength, RSS) from the jth temporary cluster head (j is an integer which is more than or equal to 1 and less than or equal to M) at the ith node to be positioned is recorded as RSS ji ；
Fourth, utilize the logarithmic propagation model formula RSS ji ＝RSS 0 -10βlog 10 (d ji d 0 ) Estimating a distance d between an ith node to be located and a jth temporary cluster head ji I.e. wherein ,RSS0 For reference distance d 0 RSS, β is the path attenuation index (in the present invention, let β=2);
step five, calculating the attribution degree P of the ith node to be positioned belonging to the jth cluster ji I.e. wherein ,/>For the remaining energy mean of all nodes to be located, +.>The average value of the estimated distances between the node i to be positioned and all temporary cluster heads is obtained;
step six, clustering the nodes to be positioned according to the attribution degree, and classifying the nodes to be positioned into clusters with the highest attribution degree;
step seven, setting a threshold d c When the mutual distance between two or more temporary cluster heads is smaller than d c When the temporary cluster heads and the nodes to be positioned in the corresponding clusters are classified into the same cluster, and the temporary cluster head with the minimum intra-cluster mean square distance is selected as the cluster head of the cluster; otherwise, there is no mutual distance between two or more temporary cluster heads smaller than d c And when the cluster structure is not changed, selecting the temporary cluster head at the moment as the cluster head of each cluster. The method specifically comprises the following steps:
step seven (one), nodeThe energy consumed in transmitting a message of data length l is expressed as wherein ,ET Loss energy for transmitting unit message, E R Loss energy, epsilon, for receiving unit messages fs Epsilon is the free space loss coefficient mp Is the multipath loss coefficient, d t Is a switching threshold between multipath fading and free space fading (in the present invention, < +.>) D is the transmission distance;
step seven (two), setting a threshold d c When P (P is more than or equal to 2 and P is an integer) is present, the mutual distance between the temporary cluster heads is smaller than d c When the temporary cluster heads and the corresponding P nodes to be positioned in total Q (Q is more than or equal to 1 and Q is an integer) are classified into the same cluster, and the temporary cluster heads b are calculated 1 (1≤b 1 P) and each node a to be positioned 1 (1≤a 1 Euclidean distance d between +.Q) ba The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, there is no mutual distance between two or more temporary cluster heads smaller than d c When the cluster structure is not changed, the temporary cluster heads at the moment are selected as the cluster heads of all clusters;
step seven (three), when two or more temporary cluster heads exist, the mutual distance between the two or more temporary cluster heads is smaller than d c In the time, E is the same as the message length l T and ER Is constant and d is less than or equal to L 1 If d t ＜L 1 Then d > d t Thereby E (E) l ＝l(E T +E R )+l×ε mp ×d 4 Can obtain E l Proportional to d 4 And thereby calculate the intra-cluster mean square distance asIf d t ≥L 1 D < d t Thereby E (E) l ＝l(E T +E R )+l×ε fs ×d 2 Can obtain E l Proportional to d 2 And thus calculate clustersThe inner mean square distance is->Based on the method, a temporary cluster head with the minimum intra-cluster mean square distance is selected as the cluster head of the cluster;
step eight, calculating m by using a logarithmic propagation model formula c (m c ≥1，m c Integer) euclidean distance e between different nodes within a single cluster of nodes ij Obtaining an Euclidean distance matrix E, wherein elements of an ith row and a jth column in the E are E ij ；
Step nine, calculating relative coordinates A of all nodes in a single cluster based on MDS, wherein the relative coordinates of the anchor nodes are P A The relative coordinates of the node to be positioned are G A . The method specifically comprises the following steps:
step nine (one), calculating and />
Step nine (two), constructing an inner product matrix M, wherein the elements of the ith row and the jth column are M ij, wherein ,
step nine (three), decomposing the characteristic value of M to make the maximum two characteristic values be u and v, and their correspondent characteristic vectors are p and q respectively, constructing diagonal matrixEigenvector matrix v= [ p q ]]；
Step nine (four), calculate matrix a = { (vΛ) after dimension reduction 1/2 } T Each column in A represents the relative position coordinates of a node, wherein the relative coordinates of the anchor node are P A The relative coordinates of the node to be positioned are G A ；
Step ten, according to the anchorRelative coordinates P of nodes A And global coordinates P B And calculating conversion relations R and T between the relative coordinates of the anchor nodes and the global coordinates. The method specifically comprises the following steps:
step ten (one), making the relative coordinates and global coordinates of the anchor node be respectivelyAndcalculate the centroid of it> and />Wherein r is more than or equal to 1 and less than or equal to b;
step ten (two), calculating covariance matrix
Performing singular value decomposition on H to obtain H=US N, wherein U and N are feature vector matrixes, and S is a singular value matrix;
step ten (four), calculating a rotation relation matrix r=nu between the relative coordinates and the global coordinates T ；
Step ten (five), calculating a translation relation matrix T= -RC between the relative coordinates and the global coordinates A +C B ；
Step eleven, according to R and T, using G A Calculating global coordinates G of nodes to be positioned B G, i.e B ＝RG A +T。
Advantageous effects
Starting from the residual energy and the position geometric relation of the node to be positioned, firstly, clustering all WSN nodes according to the residual energy of the node to be positioned and the distance from the anchor node; then, estimating the distance between nodes based on RSS in a single cluster, and obtaining the relative coordinates of the nodes by using MDS; and finally, deducing a conversion relation of a coordinate system based on the global coordinates and the relative coordinates of the anchor nodes, and calculating the global coordinates of the nodes to be positioned by using the conversion relation. The invention reduces the complexity of positioning in large-scale WSN networking, and ensures the positioning precision while reducing the energy loss and the calculation cost of the system.
Drawings
FIG. 1 is a flow chart of the present invention;
fig. 2 is a schematic clustering diagram of all WSN nodes in a target area;
FIG. 3 is a graph of the position of real node coordinates versus estimated global coordinates for a 20% range error rate;
fig. 4 is a graph of estimated global coordinate average error versus range error rate.
Detailed description of the preferred embodiments
The invention is further described below with reference to the accompanying drawings:
the method for cooperative positioning between nodes in a wireless sensor network shown in fig. 1 specifically comprises the following steps:
step one, in the target area (the side length is L 1 Randomly placing WSN nodes (namely anchor nodes) with known positions of M (M is more than or equal to 1, M is an integer) and WSN nodes (namely nodes to be positioned) with unknown positions of N (N is more than or equal to 1, N is an integer) in the square area of the frame;
step two, recording the residual energy E in the node battery to be positioned i (1 is more than or equal to i is more than or equal to N, i is an integer);
step three, defining each anchor node as a temporary cluster head, sending a broadcast signal by the temporary cluster head, and recording the RSS from the jth temporary cluster head (j is more than or equal to 1 and less than or equal to M and is an integer) at the ith node to be positioned as the RSS ji ；
Fourth, utilize the logarithmic propagation model formula RSS ji ＝RSS 0 -10βlog 10 (d ji d 0 ) Estimating a distance d between an ith node to be located and a jth temporary cluster head ji I.e. wherein ,RSS0 For reference distance d 0 RSS, β is the path attenuation index (in the present invention, let β=2);
step five, calculating the attribution degree P of the ith node to be positioned belonging to the jth cluster ji I.e. wherein ,/>For the remaining energy mean of all nodes to be located, +.>The average value of the estimated distances between the node i to be positioned and all temporary cluster heads is obtained;
step six, clustering the nodes to be positioned according to the attribution degree, classifying the nodes to be positioned into clusters with the highest attribution degree, and fig. 2 is a clustering schematic diagram of a target area;
step seven, setting a threshold d c When the mutual distance between two or more temporary cluster heads is smaller than d c When the temporary cluster heads and the nodes to be positioned in the corresponding clusters are classified into the same cluster, and the temporary cluster head with the minimum intra-cluster mean square distance is selected as the cluster head of the cluster; otherwise, there is no mutual distance between two or more temporary cluster heads smaller than d c And when the cluster structure is not changed, selecting the temporary cluster head at the moment as the cluster head of each cluster. The method specifically comprises the following steps:
step seven (one), the energy consumed when the node transmits the message with the data length of l is expressed as wherein ,ET Loss energy for transmitting unit message, E R Loss energy, epsilon, for receiving unit messages fs Epsilon is the free space loss coefficient mp Is the multipath loss coefficient, d t Is a switching threshold between multipath fading and free space fading (in the present invention, < +.>) D is the transmission distance;
step seven (two), setting a threshold d c When P (P is more than or equal to 2 and P is an integer) is present, the mutual distance between the temporary cluster heads is smaller than d c When the temporary cluster heads and the corresponding P nodes to be positioned in total Q (Q is more than or equal to 1 and Q is an integer) are classified into the same cluster, and the temporary cluster heads b are calculated 1 (1≤b 1 P) and each node a to be positioned 1 (1≤a 1 Euclidean distance d between +.Q) ba The method comprises the steps of carrying out a first treatment on the surface of the Otherwise, there is no mutual distance between two or more temporary cluster heads smaller than d c When the cluster structure is not changed, the temporary cluster heads at the moment are selected as the cluster heads of all clusters;
step seven (three), when two or more temporary cluster heads exist, the mutual distance between the two or more temporary cluster heads is smaller than d c In the time, E is the same as the message length l T and ER Is constant and d is less than or equal to L 1 If d t ＜L 1 Then d > d t Thereby E (E) l ＝l(E T +E R )+l×ε mp ×d 4 Can obtain E l Proportional to d 4 And thereby calculate the intra-cluster mean square distance asIf d t ≥L 1 D < d t Thereby E (E) l ＝l(E T +E R )+l×ε fs ×d 2 Can obtain E l Proportional to d 2 And thereby calculates the intra-cluster mean square distance as +.>
Step seven (four), when two or more temporary cluster heads exist, the mutual distance between the two or more temporary cluster heads is smaller than d c When the cluster head is in the same cluster, the temporary cluster head with the minimum intra-cluster mean square distance is selected as the cluster head of the cluster; otherwise, selecting the temporary cluster head at the moment as the cluster head of each cluster;
step eight, utilizing a logarithmic propagation model formula,calculating the content m c (m c ≥1，m c Integer) euclidean distance e between different nodes within a single cluster of nodes ij Obtaining an Euclidean distance matrix E, wherein elements of an ith row and a jth column in the E are E ij ；
Step nine, calculating relative coordinates A of all nodes in a single cluster based on MDS, wherein the relative coordinates of the anchor nodes are P A The relative coordinates of the node to be positioned are G A . The method specifically comprises the following steps:
step nine (one), calculating and />
Step nine (two), constructing an inner product matrix M, wherein the elements of the ith row and the jth column are M ij, wherein ,
step nine (three), decomposing the characteristic value of M to make the maximum two characteristic values be u and v, and their correspondent characteristic vectors are p and q respectively, constructing diagonal matrixEigenvector matrix v= [ pq ]]；
Step nine (four), calculate matrix a = { (vΛ) after dimension reduction 1/2 } T Each column in A represents the relative position coordinates of a node, wherein the relative coordinates of the anchor node are P A The relative coordinates of the node to be positioned are G A ；
Tenth, in a single cluster, global position coordinates P based on anchor nodes A And relative position coordinates P B Deriving relative coordinatesTo global coordinates->Coordinate conversion relation of (a). The method specifically comprises the following steps:
step ten (one), making the relative coordinates and global coordinates of the anchor node be respectivelyAndcalculate its correspondence +.> and />Wherein r is more than or equal to 1 and less than or equal to b;
step ten (two), calculating covariance matrix
Performing singular value decomposition on H to obtain H=US N, wherein U and N are feature vector matrixes, and S is a singular value matrix;
step ten (four), calculating a rotation relation matrix r=nu between the relative coordinates and the global coordinates T ；
Step ten (five), calculating a translation relation matrix T= -RC between the relative coordinates and the global coordinates A +C B The simulation results are shown in fig. 3 and 4;
step eleven, according to R and T, using G A Calculating global coordinates G of nodes to be positioned B G, i.e B ＝RG A +T。

Claims (1)

1. The method for cooperatively positioning the nodes in the wireless sensor network is characterized by comprising the following steps of:
step one, at the side length L 1 Is to randomly place M wireless sensor networks (Wireless Sensor Netwo) with known positions in a square target areark, WSN) nodes and N WSN nodes with unknown positions, wherein the nodes with known positions are anchor nodes, the nodes with unknown positions are nodes to be positioned, M is more than or equal to 1, N is more than or equal to 1, and M, N is an integer;
step two, recording the residual energy E in the node battery to be positioned i Wherein i is more than or equal to 1 and less than or equal to N, i is an integer;
step three, defining each anchor node as a temporary cluster head, sending a broadcast signal by the temporary cluster head, and recording the received signal strength (Received Signal Strength, RSS) from the j temporary cluster head at the i-th node to be positioned as RSS ji Wherein j is more than or equal to 1 and less than or equal to M, j is an integer;
fourth, utilize the logarithmic propagation model formula RSS ji ＝RSS 0 -10βlog 10 (d ji /d 0 ) Estimating a distance d between an ith node to be located and a jth temporary cluster head ji I.e. wherein ,RSS0 For reference distance d 0 RSS, β is the path attenuation index, let β=2;
step five, calculating the attribution degree P of the ith node to be positioned belonging to the jth cluster ji I.e. wherein ,for the remaining energy mean of all nodes to be located, +.>The average value of the estimated distances between the node i to be positioned and all temporary cluster heads is obtained;
step six, clustering the nodes to be positioned according to the attribution degree, and classifying the nodes to be positioned into clusters with the highest attribution degree;
step seven, setting a threshold d c When two or more temporary cluster heads are in phaseThe mutual distance is smaller than d c When the temporary cluster heads and the nodes to be positioned in the corresponding clusters are classified into the same cluster, and the temporary cluster head with the minimum intra-cluster mean square distance is selected as the cluster head of the cluster; otherwise, there is no mutual distance between two or more temporary cluster heads smaller than d c When the cluster structure is not changed, the temporary cluster heads at the moment are selected as the cluster heads of all clusters;
step eight, calculating m by using a logarithmic propagation model formula c Euclidean distance e between different nodes within a single cluster of individual nodes ij Obtaining an Euclidean distance matrix E, wherein m is c ≥1，m c The elements in the ith row and the jth column in E are integers and E ij ；
Step nine, calculating relative coordinates A of all nodes in a single cluster based on multi-dimensional scale transformation, wherein the relative coordinates of anchor nodes are P A The relative coordinates of the node to be positioned are G A ；
Step ten, according to the relative coordinates P of the anchor node A And global coordinates P B Calculating conversion relations R and T between the relative coordinates of the anchor nodes and the global coordinates;
step eleven, according to R and T, using G A Calculating global coordinates G of nodes to be positioned B G, i.e B ＝RG A +T；
The seventh step comprises the following steps:
step seven (one), the energy consumed when the node transmits the message with the data length of l is expressed as
wherein ,ET Loss energy for transmitting unit message, E R Loss energy, epsilon, for receiving unit messages fs Epsilon is the free space loss coefficient mp Is the multipath loss coefficient, d t For the switching threshold between multipath fading and free space fading, let +.>d is the transmission distance;
step seven (two), setting a threshold d c When the mutual distance between the P temporary cluster heads is smaller than d c When the temporary cluster heads and Q nodes to be positioned in the P corresponding clusters are classified into the same cluster, and the temporary cluster heads b are calculated 1 With each node a to be positioned 1 Euclidean distance d between ba Wherein P is more than or equal to 2, Q is more than or equal to 1, P and Q are integers, and 1 is less than or equal to a 1 、b 1 P is less than or equal to; otherwise, there is no mutual distance between two or more temporary cluster heads smaller than d c When the cluster structure is not changed, the temporary cluster heads at the moment are selected as the cluster heads of all clusters;
step seven (three), when two or more temporary cluster heads exist, the mutual distance between the two or more temporary cluster heads is smaller than d c In the time, E is the same as the message length l T and ER Is constant and d is less than or equal to L 1 If d t ＜L 1 Then d > d t Thereby E (E) l ＝l(E T +E R )+l×ε mp ×d 4 Can obtain E l Proportional to d 4 Thereby calculating the intra-cluster mean square distance asIf d t ≥L 1 D < d t Thereby E (E) l ＝l(E T +E R )+l×ε fs ×d 2 Can obtain E l Proportional to d 2 Thereby calculating the intra-cluster mean square distance as +.>Based on the method, a temporary cluster head with the minimum intra-cluster mean square distance is selected as the cluster head of the cluster;
the step nine comprises the following steps:
step nine (one), calculating and />
Step nine (two), constructing an inner product matrix M, wherein the elements of the ith row and the jth column are M ij, wherein ,
step nine (three), decomposing the characteristic value of M to make the maximum two characteristic values be u and v, and their correspondent characteristic vectors are p and q respectively, constructing diagonal matrixEigenvector matrix v= [ p q ]]；
Step nine (four), calculate matrix a = { (vΛ) after dimension reduction 1/2 } T Each column in A represents the relative position coordinates of a node, wherein the relative coordinates of the anchor node are P A The relative coordinates of the node to be positioned are G A ；
The step ten comprises the following steps:
step ten (one), making the relative coordinates and global coordinates of the anchor node be respectivelyAndcalculate its corresponding centroid +.> and />Wherein r is more than or equal to 1 and less than or equal to b;
step ten (two), calculating covariance matrix
Performing singular value decomposition on H to obtain H=US N, wherein U and N are feature vector matrixes, and S is a singular value matrix;
step ten (four), calculating a rotation relation matrix r=nu between the relative coordinates and the global coordinates T ；
Step ten (five), calculating a translation relation matrix T= -RC between the relative coordinates and the global coordinates A +C B 。
CN202111224364.4A 2021-10-21 2021-10-21 Inter-node cooperative positioning method in wireless sensor network Active CN114007260B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111224364.4A CN114007260B (en) 2021-10-21 2021-10-21 Inter-node cooperative positioning method in wireless sensor network

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111224364.4A CN114007260B (en) 2021-10-21 2021-10-21 Inter-node cooperative positioning method in wireless sensor network

Publications (2)

Publication Number Publication Date
CN114007260A CN114007260A (en) 2022-02-01
CN114007260B true CN114007260B (en) 2023-09-01

Family

ID=79923530

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111224364.4A Active CN114007260B (en) 2021-10-21 2021-10-21 Inter-node cooperative positioning method in wireless sensor network

Country Status (1)

Country Link
CN (1) CN114007260B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117528413B (en) * 2023-10-13 2024-07-30 大连浩如科技有限公司 UWB base station self-calibration method based on multidimensional scale transformation

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009044465A (en) * 2007-08-09 2009-02-26 Nippon Telegr & Teleph Corp <Ntt> Inter-node distance estimating method for communication network, and device therefor
CN104780507A (en) * 2015-05-07 2015-07-15 广州大学 High-precision AOA clustering and locating method
EP2945457A1 (en) * 2014-05-14 2015-11-18 Alcatel Lucent Sensor clustering and data aggregation in wireless sensor networks
CN107820207A (en) * 2017-11-16 2018-03-20 西京学院 The node fuzzy message three-dimensional localization model of the adaptive sub-clustering of dimensionality reduction
CN110519818A (en) * 2019-07-31 2019-11-29 西北工业大学 Water sound sensor network opportunistic routing protocol implementation method based on cluster type topology
CN112469115A (en) * 2020-10-21 2021-03-09 南京邮电大学 FC-MDS (fiber channel-minimum-signal-density-measurement-system) improved wireless sensor network positioning algorithm

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2009044465A (en) * 2007-08-09 2009-02-26 Nippon Telegr & Teleph Corp <Ntt> Inter-node distance estimating method for communication network, and device therefor
EP2945457A1 (en) * 2014-05-14 2015-11-18 Alcatel Lucent Sensor clustering and data aggregation in wireless sensor networks
CN104780507A (en) * 2015-05-07 2015-07-15 广州大学 High-precision AOA clustering and locating method
CN107820207A (en) * 2017-11-16 2018-03-20 西京学院 The node fuzzy message three-dimensional localization model of the adaptive sub-clustering of dimensionality reduction
CN110519818A (en) * 2019-07-31 2019-11-29 西北工业大学 Water sound sensor network opportunistic routing protocol implementation method based on cluster type topology
CN112469115A (en) * 2020-10-21 2021-03-09 南京邮电大学 FC-MDS (fiber channel-minimum-signal-density-measurement-system) improved wireless sensor network positioning algorithm

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
一种适用于大规模无线传感器网络的定位算法;王健;赵亚凤;刘劲风;;东北林业大学学报(第08期);全文 *

Also Published As

Publication number Publication date
CN114007260A (en) 2022-02-01

Similar Documents

Publication Publication Date Title
Su et al. An optimal clustering mechanism based on Fuzzy-C means for wireless sensor networks
Fang et al. Principal component localization in indoor WLAN environments
Yin et al. Cluster head selection using analytical hierarchy process for wireless sensor networks
CN108696932A (en) It is a kind of using CSI multipaths and the outdoor fingerprint positioning method of machine learning
CN108848449B (en) Wireless sensor network node positioning algorithm based on DV-Hop improvement
CN114007260B (en) Inter-node cooperative positioning method in wireless sensor network
CN110113713A (en) Based on the improved wireless sensor network DV-Hop node positioning method of DE
Chen et al. Algorithm of data compression based on multiple principal component analysis over the WSN
Liu et al. Improved DV-hop localization algorithm based on bat algorithm in wireless sensor networks
CN114158010B (en) Unmanned aerial vehicle communication system and resource allocation strategy prediction method based on neural network
Yang et al. Distributed censoring with energy constraint in wireless sensor networks
CN102098082A (en) Channel cluster tracking method and device
Bensaid et al. A combined cluster-chain based routing protocol for lifetime improvement in WSN
CN111523571B (en) Semi-supervised learning positioning method for distributed large-scale multi-antenna system
CN106341785A (en) Improved wireless sensor DV-Hop algorithm
CN106507425B (en) Improved distributed energy efficient clustering method suitable for heterogeneous wireless sensor network
CN112488151A (en) Semi-supervised manifold learning-based position fingerprint database construction method
Zhang et al. An event-triggered localization algorithm for mobile wireless sensor networks
Zhang et al. An energy efficient localization algorithm for wireless sensor networks using a mobile anchor node
CN113923141B (en) Wireless local area network throughput estimation method and system for high-density AP distribution
Pan Node localization method for massive sensor networks based on clustering particle swarm optimization in cloud computing environment
CN110536373A (en) A kind of optical-wireless sensor network clustering method based on copula theory
CN114205905B (en) Complex environment indoor fingerprint positioning method based on integrated federal learning
Wang et al. An adaptive location estimator based on Kalman filtering for wireless sensor networks
CN115334552B (en) Distributed data rapid transmission method and transmission platform

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
TA01 Transfer of patent application right

Effective date of registration: 20230807

Address after: Room 1104, Building A, Zhiyun Industrial Park, No. 13 Huaxing Road, Henglang Community, Dalang Street, Longhua District, Shenzhen City, Guangdong Province, 518100

Applicant after: Shenzhen Hongyue Information Technology Co.,Ltd.

Address before: 400065 No. 2, Chongwen Road, Nan'an District, Chongqing

Applicant before: CHONGQING University OF POSTS AND TELECOMMUNICATIONS

Effective date of registration: 20230807

Address after: Room 1501-1502, 15th Floor, Building B, Gongcun New City Commercial Center (Huihai Plaza), No. 19, Sanlian Chuangye Road, Sanlian Community, Longhua District, Shenzhen City, Guangdong Province, 518112

Applicant after: Shenzhen Tonghui Technology Co.,Ltd.

Address before: Room 1104, Building A, Zhiyun Industrial Park, No. 13 Huaxing Road, Henglang Community, Dalang Street, Longhua District, Shenzhen City, Guangdong Province, 518100

Applicant before: Shenzhen Hongyue Information Technology Co.,Ltd.

TA01 Transfer of patent application right
GR01 Patent grant
GR01 Patent grant