CN114143709A - DV-Hop-based wireless sensor positioning method - Google Patents

DV-Hop-based wireless sensor positioning method Download PDF

Info

Publication number
CN114143709A
CN114143709A CN202111351965.1A CN202111351965A CN114143709A CN 114143709 A CN114143709 A CN 114143709A CN 202111351965 A CN202111351965 A CN 202111351965A CN 114143709 A CN114143709 A CN 114143709A
Authority
CN
China
Prior art keywords
hop
node
beacon
distance
average
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.)
Pending
Application number
CN202111351965.1A
Other languages
Chinese (zh)
Inventor
时雨农
刘海隆
孙雅琦
牟振汉
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.)
University of Electronic Science and Technology of China
Original Assignee
University of Electronic Science and Technology of China
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 University of Electronic Science and Technology of China filed Critical University of Electronic Science and Technology of China
Priority to CN202111351965.1A priority Critical patent/CN114143709A/en
Publication of CN114143709A publication Critical patent/CN114143709A/en
Pending legal-status Critical Current

Links

Images

Classifications

    • 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
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/11—Complex mathematical operations for solving equations, e.g. nonlinear equations, general mathematical optimization problems
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/10—Complex mathematical operations
    • G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
    • 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
    • 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

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Data Mining & Analysis (AREA)
  • Mathematical Physics (AREA)
  • Pure & Applied Mathematics (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Computational Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Signal Processing (AREA)
  • General Engineering & Computer Science (AREA)
  • Software Systems (AREA)
  • Operations Research (AREA)
  • Databases & Information Systems (AREA)
  • Algebra (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Probability & Statistics with Applications (AREA)
  • Bioinformatics & Computational Biology (AREA)
  • Evolutionary Biology (AREA)
  • Bioinformatics & Cheminformatics (AREA)
  • Position Fixing By Use Of Radio Waves (AREA)

Abstract

The invention discloses a wireless sensor positioning method based on DV-Hop algorithm, which divides the Hop count more finely by adopting a mode of multiple communication radiuses and divides the Hop count summarized by one Hop into a plurality of small Hop counts; then, a mean square error principle is adopted to replace a minimum variance principle, and the average hop distance of the beacon nodes is calculated; then, according to the influence of the hop count on the ranging error, optimizing the average hop distance by adopting a weight method in the hop distance calculation of the unknown node to obtain the distance between the unknown node and the beacon node; and finally, calculating the final position of the unknown node by adopting a weighted least square method. The method can improve the accuracy of the average hop distance of the beacon nodes and the distance from the unknown node to the anchor node, optimizes the calculation process of the final position and has better node positioning precision.

Description

DV-Hop-based wireless sensor positioning method
Technical Field
The invention belongs to the technical field of wireless sensor positioning, and particularly relates to a wireless sensor position measuring and calculating method based on DV-Hop.
Background
China is a country with a large number of members, plain plains and basin areas only account for 31% of the total land area, and most of the rest areas are hills, mountains and plateaus with complex topographic relief. The complex terrains contain most natural resources in China, including forest resources, mineral resources, water energy resources, biological resources and the like, and have important significance for social and economic development and ecological protection in China. However, due to climate change, human life influence and the like, these areas are often attacked by natural disasters such as mountain floods, debris flows, forest fires and the like, which causes serious economic property loss and even threatens the life safety of people. Therefore, the method has important significance for carrying out all-around environmental monitoring on the regions. Due to the limitation of factors such as terrain, weather and the like, the traditional environment monitoring method has a lot of difficulties in monitoring the complex terrains, for example, in common remote sensing monitoring, the monitoring effect is greatly influenced in severe weather or when a thick and covered forest is monitored.
With the continuous progress of wireless sensor technology and the great reduction of the price of related hardware, environmental monitoring by using a large-scale wireless sensor network becomes a research trend. The wireless sensor network is a network system which is communicated in a wireless network, forms network connection with itself and supports multi-hop routing. Through a large number of cheap wireless sensor nodes arranged in a target area, the wireless sensor network can acquire, primarily process and transmit information, so that the wireless sensor network is suitable for all-weather long-term monitoring of field environment. Due to the detection requirement, the sensor nodes need to determine the positions of the sensor nodes in the field to ensure that the obtained monitoring data is meaningful, but because of the limitation of volume and cost, all the sensor nodes cannot be provided with positioning devices in reality. At present, a sensor node positioning algorithm for a two-dimensional plane has been researched a lot, but a wireless sensor node positioning technology for a three-dimensional space in a complex environment has certain limitations in theory and technology.
The classical DV-Hop positioning algorithm is a non-ranging positioning algorithm, has low requirements on node configuration, can reduce expenditure, and is simple in algorithm structure and easy to implement. However, the method has the disadvantages that the estimated distance deviates from the actual distance, and the method is greatly influenced by the node density and the beacon node density, the error rate is unstable, and a more accurate positioning result is difficult to obtain. Firstly, in the aspect of calculation of the minimum Hop count, the classical DV-Hop algorithm records the Hop count between nodes within the communication range of each other as one Hop, and the same Hop count is used for estimating the distance when calculating the distance, so that even if nodes at different distances from the same node are fixed, only the same distance can be obtained, and thus, an error in the positioning result can be caused. Secondly, in the aspect of distance estimation, in a classical DV-Hop algorithm, an unknown node selects an average Hop distance of a beacon node with the smallest Hop count as the average Hop distance of the unknown node, several Hop sections with the smallest break points are equivalent to a straight line segment between the unknown node and the beacon node when the distance is calculated, and a numerical value of the average Hop distance multiplied by the Hop count is used as the distance between the two nodes. In the calculation, the unknown node directly uses the average hop distance of the beacon node with the minimum hop number as the average hop distance of the unknown node, and in an actual network, different nodes are not distributed in different areas completely and uniformly, some areas are distributed more densely, and the average hop distance is smaller; if the single hop distance is used as a calculation element of all nodes and the average hop distance values of other beacon nodes are not considered, the overall network distribution condition cannot be correctly reflected, and the error is large; and because the last distance estimation in the DV-Hop algorithm adopts a direct multiplication method, the error is small under the condition that the related nodes are collinear. However, in actual situations, the network topology among different nodes is not a straight-line path, and the probability of collinearity of a plurality of nodes is very low, so that direct calculation has a great influence on the estimation of the distance, and finally influences the final position calculation.
Disclosure of Invention
In order to solve the problems in the prior art, the invention provides a wireless sensor positioning method based on DV-Hop.
The technical scheme of the invention is as follows: a wireless sensor positioning method based on DV-Hop algorithm specifically comprises the following steps:
s1, thinning the minimum hop count;
s2, using the result obtained in S1, and using the mean square error as a cost function for estimating the sub-errors, wherein the cost function is used for calculating the average hop distance between the beacon nodes;
s3, according to a weight method, bringing the average hop distances of all beacon nodes within a certain hop distance of the unknown node into the calculation of the average hop distance of the unknown node to obtain the final weighted average hop distance;
and S4, adjusting a least square calculation formula according to the relation between the node hop count and the error, adding weight elements, and calculating to obtain the target position.
Further, step S1 specifically includes the following sub-steps:
s11, initializing a network, setting the maximum communication radius of a node as R, subdividing the communication radius into m parts, and setting a radius parameter as i as 1;
s12, the beacon nodes adopt a flooding mode to all nodes in the wireless sensor network according to the communication radius
Figure BDA0003356102650000021
Transmitting including its own position and hop count
Figure BDA0003356102650000022
If i is less than m, go to step S13; if i is m, the process proceeds to step S15;
s13, each node judges whether the grouping information is recorded according to the received data packet: if not, recording the hop count of the beacon node and the corresponding beacon node as
Figure BDA0003356102650000023
If so, comparing the number of received hops with the recordedAnd taking the smaller value to record the hop count.
S14, return to step S12 with i ═ i + 1;
s15, each node judges whether the grouping information is recorded after receiving one data packet: if not, recording the hop count of the beacon node and the corresponding beacon node according to the hop count in the data packet; if yes, comparing the received hop count with the recorded hop count, taking a smaller value for recording, updating the smaller value into the data packet, and then adding 1 to the hop count on the data packet and forwarding the hop count.
Further, step S2 specifically includes the following sub-steps:
s21, by utilizing the position information, any beacon node i in the wireless sensor network can calculate the distance d between the beacon node i and any other beacon node j in the networki，j：
Figure BDA0003356102650000031
Wherein (x)i，yi，zi) Is the three-dimensional coordinate of beacon node i, (x)j，yj，zj) Is the three-dimensional coordinate of beacon node j.
S22, according to an unbiased estimation principle, minimizing the following functions to obtain the average hop distance of the beacon node:
Figure BDA0003356102650000032
wherein N is the number of beacon nodes, hopi，jFor the hop count between any beacon i and any other beacon j in the network obtained in step S1,
Figure BDA0003356102650000033
is the average hop distance of the beacon i.
Further, step S3 specifically includes the following sub-steps:
s31, the beacon node broadcasts the average hop distance calculated by the beacon node, and each unknown node is connected with the beacon node according to the average hop distanceThe hop count of the beacon node only records the average hop distance of the beacon node within a certain hop count t, wherein,
Figure BDA0003356102650000034
l is the maximum distance between beacon nodes in the wireless sensor network;
s32, the unknown node calculates the weight value w of the corresponding beacon node i according to the hop count of the beacon node recorded by the unknown nodei：
Figure BDA0003356102650000035
Wherein N is the number of all beacon nodes meeting the conditions;
s33, obtaining the average hop distance hopsize of the unknown node i according to the weight obtained in the step S32iComprises the following steps:
Figure BDA0003356102650000036
wherein, HopsizejThe average hop distance of the beacon node j with the hop count less than t.
Further, step S4 specifically includes the following sub-steps:
s41, the position of the unknown node U is (x, y, z), the average hop distance is hopsize, and the positions and the hop counts of n beacon nodes capable of directly communicating with the unknown node U are respectively (x, y, z)1，y1，z1)、(x2，y2，z2)、(x3，y3，z3)、…、(xn，yn，zn)，hop1、hop2、hop3、…、hopnObtaining the predicted distance d from the unknown node U to each corresponding beacon node1、d2、d3、…、dnComprises the following steps:
di＝hopsize×hopi，i＝1，2，3，...，n
s42, according to the four-side positioning calculation rule, listing the following equation sets:
Figure BDA0003356102650000041
processing the system of equations to obtain the following formula:
AX＝B
wherein the content of the first and second substances,
Figure BDA0003356102650000042
Figure BDA0003356102650000043
Figure BDA0003356102650000044
s43, corresponding to the equation set in the step S42, setting a weight matrix W, wherein for the unknown node U, the weight matrix definition W is as follows:
Figure BDA0003356102650000045
wherein, wkRepresents the weight value between the unknown node U and the beacon node k, and the value is the hop number hop between two pointskRatio of sum of reciprocal of all hops:
Figure BDA0003356102650000046
s44, processing the equation AX-B in S42 by using a weighted least square method, and converting the result into the following formula:
min[W(B-AX)]T[W(B-AX)]
the partial derivative of X is calculated and made equal to 0, and the result of X is obtained as:
X＝(ATWTWA)-1ATWTWB
namely the final positioning result.
The invention has the beneficial effects that: the method of the invention divides the hop count more finely by adopting a mode of multiple communication radiuses, and subdivides the hop count summarized by one hop into a plurality of small hop counts; calculating the average hop distance of the beacon nodes by adopting a mean square error principle instead of a variance minimum principle; then, according to the influence of the hop count on the ranging error, optimizing the average hop distance by adopting a weight method in the hop distance calculation of the unknown node to obtain the distance between the unknown node and the beacon node; and finally, calculating the final position of the unknown node by adopting a weighted least square method. The method can improve the accuracy of the average hop distance of the beacon nodes and the distance from the unknown node to the anchor node, optimizes the calculation process of the final position and has better node positioning precision.
Drawings
Fig. 1 is a schematic flow chart of a DV-Hop-based wireless sensor positioning method of the present invention.
FIG. 2 is a plot of the mean relative error versus position of the original algorithm in accordance with the present invention.
Fig. 3 is a graph comparing the average relative error of the present invention with the original algorithm for different numbers of beacons.
Fig. 4 is a graph of the average relative error of the present invention versus the original algorithm for different communication radii.
Detailed Description
The invention is described in further detail below with reference to the following figures and specific examples:
the wireless sensor positioning method based on DV-Hop algorithm is shown in figure 1, and specifically comprises the following steps:
s1, thinning the minimum hop count: for the hop count between nodes, the original 1 hop is subdivided into smaller parts according to conditions such as signal strength and the like for calculation, and a more accurate minimum hop count is obtained.
And S2, utilizing the result obtained in S1, and using the mean square error as a cost function for estimating the sub-errors to calculate the average hop distance between the beacon nodes.
S3, according to a weight method, incorporating the average hop distance of all beacon nodes within a certain hop distance of the unknown node into the average hop distance calculation of the unknown node to obtain the final weighted average hop distance
And S4, in the final least square calculation, adjusting a least square calculation formula according to the relation between the node hop count and the error, adding a weight element, and calculating to obtain an estimated position.
In the process of node positioning in the scheme of the invention, firstly, a beacon node which can know the self position in the wireless sensor network can flood a data packet containing the self position and the hop count to the whole network for many times, and each node records and forwards the data packet according to different conditions of the data packet, so that each node can acquire the specific positions of all beacon nodes and the hop count which is away from the beacon nodes after communication is finished.
Based on the obtained data, the mutual distance between the beacon nodes can be calculated. Since the distribution of the wireless sensor network nodes is random, the error of the average hop distance of the beacon nodes presents Gaussian distribution, and therefore the average hop distance of each beacon node is calculated by means of the mean square error minimization of the cost function based on an unbiased estimation criterion.
And after the beacon node obtains the average hop distance, the data is sent out. And after receiving the data packet, each unknown node records the average hop distance, screens out the average hop distance of a part of beacon nodes according to a certain standard to serve as data for calculating the average hop distance of the unknown node, and calculates the average hop distance of the unknown node according to a weight method.
The unknown node with the average hop distance can calculate the final coordinate of the unknown node by using a weighted least square method according to the existing data, and the weight of the unknown node is obtained by correlating the hop numbers of the beacon nodes and the unknown node.
Firstly, the minimum hop distance is obtained, and the specific implementation steps are as follows:
firstly, initializing a network: nodes capable of determining the self position in the wireless sensor network are called anchor nodes or beacon nodes, and nodes needing to determine the self position are called unknown nodes. In the original DV-Hop method, all nodes in the node communication range are set as one Hop, so that different nodes are set to be processed as the same node distance even if the distances are different, and a point of distance error is inevitably caused, and the final position measurement is influenced. Therefore, in the scheme, the hop count is subdivided, the maximum communication radius is set as R, the communication radius is subdivided into m parts, the radius parameter is set as i ═ 1, and the process is as follows:
the beacon nodes adopt a flooding mode to all nodes in the wireless sensor network according to the communication radius
Figure BDA0003356102650000061
Transmitting including its own position and hop count
Figure BDA0003356102650000062
The data packet of (1). If i is less than m, entering the condition 1; if i is m, then the case 2 is entered.
Case 1: after receiving the data packet, each node judges whether the grouping information is recorded: if not, recording the hop count of the beacon node and the corresponding beacon node as
Figure BDA0003356102650000063
If yes, comparing the received hop count with the recorded hop count, and taking the smaller value to record. Then, the packet is not forwarded, i is made to i +1, and the packet is returned to the beginning.
Case 2: each node judges whether the grouping information is recorded after receiving a data packet: if not, recording the hop count of the beacon node and the corresponding beacon node according to the hop count in the data packet; if yes, comparing the received hop count with the recorded hop count, taking a smaller value for recording, and updating the smaller value into the data packet. Then, add 1 to the number of hops on the packet and forward it out.
In this way, the original 1 hop between the nodes is divided into the following hop numbers, and the error is reduced to a certain extent.
Figure BDA0003356102650000064
After the hop count is subdivided, the ratio between the hop count and the distance is closer to the proportional relation, and further the distance between the node and the neighbor node thereof is more accurately calculated in the subsequent operation.
Through the steps, the nodes in the network can know the minimum hop count and the specific positions of the nodes and all the beacon nodes in the network, and each beacon node can calculate the average hop distance of the node by using the acquired information of other beacon nodes. The method comprises the following specific steps:
firstly, by using the position information, any beacon node i in the wireless sensor network can calculate the distance d between itself and any other beacon node j in the networki，j：
Figure BDA0003356102650000071
Wherein (x)i，yi，zi) Is the three-dimensional coordinate of beacon node i, (x)j，yj，zj) Is the three-dimensional coordinate of beacon node j.
Then, the calculation of the average Hop distance of the beacon node in the conventional DV-Hop algorithm is based on an unbiased estimation criterion, namely, the average Hop distance Hopsize is calculated by setting the following formula to be 0i：
Figure BDA0003356102650000072
Wherein N is the number of the beacon nodes.
The mean value of the estimation errors of the results obtained in this way is 0. However, as the hop distance error follows Gaussian distribution, according to the parameter estimation theory, the use of the mean square error criterion is more reasonable than the use of only the variance as the cost function for estimating the sub-errors. The present embodiment therefore finds the average hop distance by minimizing the following cost function:
Figure BDA0003356102650000073
solving Hopsize for cost function fiAnd the partial derivative of (a) is taken as 0, the following can be obtained:
Figure BDA0003356102650000074
this value is taken as the average hop distance of the beacon i, which is more accurate than the original method.
After the average Hop distance of the beacon nodes is obtained, according to an original DV-Hop method, each beacon node needs to broadcast the average Hop distance to the whole network, and the unknown node only records the average Hop distance of the first beacon node received by the unknown node and sets the average Hop distance as the average Hop distance of the unknown node. However, in simulation, it can be found that the larger the hop count of the beacon node and the hop count of the unknown node, the larger the measured distance error is, and directly taking the data of the beacon node closest to the beacon node can indeed avoid the error influence caused by the nodes with longer hop counts, but the data cannot completely represent all network distribution conditions around the node, so that an error is brought to subsequent calculation. The improved unknown node hop distance determining steps of the embodiment are as follows:
considering the relation between the distance error and the hop count of the beacon node, each unknown node only records the average hop distance of the beacon node within a certain hop count t according to the hop count of the beacon node, and according to the simulation result, t can be set as:
Figure BDA0003356102650000075
and l is the maximum distance between beacon nodes in the wireless sensor network.
The unknown node calculates the weighted value w of the corresponding beacon node i according to the hop count of the beacon node recorded by the unknown nodei：
Figure BDA0003356102650000081
Wherein, hopi，jThe hop count between the unknown node i and the beacon j, and N is the number of all eligible beacons. The distance error of the node is larger due to the longer distanceThe weighting factor can be empirically set as the ratio of the reciprocal of the hop count of each node.
According to the obtained weight, obtaining the average hop distance hopsize of the unknown node iiComprises the following steps:
Figure BDA0003356102650000082
wherein, HopsizejIs the average hop distance of the eligible beacon j.
After all the data are obtained, the final target position can be calculated. Let the location of unknown node U be (x, y, z), the average hop distance be hopsize, and the location and hop count of n beacons capable of directly communicating with it be (x)1，y1，z1)、(x2，y2，z2）、(x3，y3，z3)、…、(xn，yn，zn)，hop1、hop2、hop3、…、hopn. The predicted distance d from the unknown node U to each corresponding beacon node can be obtained1、d2、d3、…、dnComprises the following steps:
di＝hopsize×hopi，i＝1，2，3，...，n
from the four-edge positioning algorithm, the following set of equations can be listed:
Figure BDA0003356102650000083
processing the system of equations yields the following equation:
AX＝B
wherein the content of the first and second substances,
Figure BDA0003356102650000084
Figure BDA0003356102650000085
Figure BDA0003356102650000086
corresponding to the above equation set, setting a weight matrix W, and defining the weight matrix W as follows for the unknown node U:
Figure BDA0003356102650000091
wherein, wkRepresents the weight value between the unknown node U and the beacon node k, and the value is the hop number hop between two pointskRatio of sum of reciprocal of all hops:
Figure BDA0003356102650000092
processing the equation calculation formula by using a weighted least square method, and converting the result into the following formula result:
min[W(B-AX)]T[W(B-AX)]
the partial derivative of X is calculated and made equal to 0, and the result of X is obtained as: x ═ ATWTWA)-1ATWTWB is the final positioning result.
According to the method provided by the embodiment of the invention, 100 sensor nodes are randomly distributed in a three-dimensional space of 100m × 100m × 100m, the total number of the nodes is set to be 100, and the initial communication distance is set to be 40 m. Considering power consumption and realistic needs, the hop count refinement parameter m is set to 2.
Fig. 2 to 4 are all average results of 500 experiments performed under the same experimental conditions to ensure the generality of the results.
Fig. 2 shows the result as the relative error of the method of the present invention in positioning with the original DV-Hop algorithm in the case of 50 unknown nodes and 50 beacon nodes. Wherein the relative error is a ratio of the true error value to the communication distance. Compared with the original positioning algorithm, the method of the invention has the advantages that the positioning accuracy is obviously improved and the positioning effect is more stable under the same condition.
The result shown in fig. 3 is the localization effect of the method of the present invention and the original DV-Hop algorithm, with varying number of beacons, without changing the total number of nodes. It can be seen that the positioning accuracy of both methods is improved with the increase of the number of the beacon nodes, and the result of the method of the present invention is superior to or even equal to that of the original method.
Fig. 4 shows the result of the positioning effect of the method of the present invention and the original DV-Hop algorithm, with only the communication distance changed without changing the total number of nodes. It can be seen that in both methods, the positioning accuracy improves with increasing communication distance in a certain range, but the positioning accuracy decreases to some extent after exceeding a certain value. In the whole process, the positioning accuracy of the method is greatly improved compared with that of the original algorithm.

Claims (5)

1. A wireless sensor positioning method based on DV-Hop algorithm specifically comprises the following steps:
s1, thinning the minimum hop count;
s2, using the result obtained in S1, and using the mean square error as a cost function for estimating the sub-errors, wherein the cost function is used for calculating the average hop distance between the beacon nodes;
s3, according to a weight method, bringing the average hop distances of all beacon nodes within a certain hop distance of the unknown node into the calculation of the average hop distance of the unknown node to obtain the final weighted average hop distance;
and S4, adjusting a least square calculation formula according to the relation between the node hop count and the error, adding weight elements, and calculating to obtain the target position.
2. The DV-Hop algorithm-based wireless sensor positioning method according to claim 1, wherein the step S1 specifically comprises the following sub-steps:
s11, initializing a network, setting the maximum communication radius of a node as R, subdividing the communication radius into m parts, and setting a radius parameter as i as 1;
s12, the beacon nodes adopt a flooding mode to all nodes in the wireless sensor network according to the communication radius
Figure FDA0003356102640000011
Transmitting including its own position and hop count
Figure FDA0003356102640000012
If i is less than m, go to step S13; if i is m, the process proceeds to step S15;
s13, each node judges whether the grouping information is recorded according to the received data packet: if not, recording the hop count of the beacon node and the corresponding beacon node as
Figure FDA0003356102640000013
If yes, comparing the received hop count with the recorded hop count, and taking the smaller value to record.
S14, return to step S12 with i ═ i + 1;
s15, each node judges whether the grouping information is recorded after receiving one data packet: if not, recording the hop count of the beacon node and the corresponding beacon node according to the hop count in the data packet; if yes, comparing the received hop count with the recorded hop count, taking a smaller value for recording, updating the smaller value into the data packet, and then adding 1 to the hop count on the data packet and forwarding the hop count.
3. The DV-Hop algorithm-based wireless sensor positioning method according to claim 2, wherein the step S2 specifically comprises the following sub-steps:
s21, by utilizing the position information, any beacon node i in the wireless sensor network can calculate the distance d between the beacon node i and any other beacon node j in the networki，j：
Figure FDA0003356102640000014
Wherein (x)i，yi，zi) Is the three-dimensional coordinate of beacon node i, (x)j，yj，zj) Is the three-dimensional coordinate of beacon node j.
S22, according to an unbiased estimation principle, minimizing the following functions to obtain the average hop distance of the beacon node:
Figure FDA0003356102640000021
wherein N is the number of beacon nodes, hopi，jFor the hop count between any beacon i and any other beacon j in the network obtained in step S1,
Figure FDA0003356102640000022
is the average hop distance of the beacon i.
4. The DV-Hop algorithm-based wireless sensor positioning method according to claim 3, wherein the step S3 specifically comprises the following sub-steps:
s31, the beacon node broadcasts the average hop distance calculated by the beacon node, and each unknown node only records the average hop distance of the beacon node within a certain hop number t according to the hop number of the unknown node and the beacon node, wherein,
Figure FDA0003356102640000023
l is the maximum distance between beacon nodes in the wireless sensor network;
s32, the unknown node calculates the weight value w of the corresponding beacon node i according to the hop count of the beacon node recorded by the unknown nodei：
Figure FDA0003356102640000024
Wherein N is the number of all beacon nodes meeting the conditions;
s33, obtaining the weight according to the weight obtained in the step S32Average hop length hopsize of unknown node iiComprises the following steps:
Figure FDA0003356102640000025
wherein, HopsizejThe average hop distance of the beacon node j with the hop count less than t.
5. The DV-Hop algorithm-based wireless sensor positioning method according to claim 4, wherein the step S4 specifically comprises the following sub-steps:
s41, the position of the unknown node U is (x, y, z), the average hop distance is hopsize, and the positions and the hop counts of n beacon nodes capable of directly communicating with the unknown node U are respectively (x, y, z)1，y1，z1)、(x2，y2，z2)、(x3，y3，z3)、…、(xn，yn，zn)，hop1、hop2、hop3、…、hopnObtaining the predicted distance d from the unknown node U to each corresponding beacon node1、d2、d3、…、dnComprises the following steps:
di＝hopsize×hopi，i＝1，2，3，...，n
s42, according to the four-side positioning calculation rule, listing the following equation sets:
Figure FDA0003356102640000026
processing the system of equations to obtain the following formula:
AX＝B
wherein the content of the first and second substances,
Figure FDA0003356102640000031
Figure FDA0003356102640000032
Figure FDA0003356102640000033
s43, corresponding to the equation set in the step S42, setting a weight matrix W, wherein for the unknown node U, the weight matrix definition W is as follows:
Figure FDA0003356102640000034
wherein, wkRepresents the weight value between the unknown node U and the beacon node k, and the value is the hop number hop between two pointskRatio of sum of reciprocal of all hops:
Figure FDA0003356102640000035
s44, processing the equation AX-B in S42 by using a weighted least square method, and converting the result into the following formula:
min[W(B-AX)]T[W(B-AX)]
the partial derivative of X is calculated and made equal to 0, and the result of X is obtained as:
X＝(ATWTWA)-1ATWTWB
namely the final positioning result.
CN202111351965.1A 2021-11-16 2021-11-16 DV-Hop-based wireless sensor positioning method Pending CN114143709A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202111351965.1A CN114143709A (en) 2021-11-16 2021-11-16 DV-Hop-based wireless sensor positioning method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202111351965.1A CN114143709A (en) 2021-11-16 2021-11-16 DV-Hop-based wireless sensor positioning method

Publications (1)

Publication Number Publication Date
CN114143709A true CN114143709A (en) 2022-03-04

Family

ID=80394303

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202111351965.1A Pending CN114143709A (en) 2021-11-16 2021-11-16 DV-Hop-based wireless sensor positioning method

Country Status (1)

Country Link
CN (1) CN114143709A (en)

Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070159986A1 (en) * 2006-01-09 2007-07-12 Samsung Electronics Co., Ltd. Method of refining node position information in wireless sensor network
CN105682026A (en) * 2016-01-08 2016-06-15 南昌大学 Improved DV-Hop localization method based on hop count threshold optimal average hop distance
CN106341785A (en) * 2015-07-12 2017-01-18 上海明想电子科技有限公司 Improved wireless sensor DV-Hop algorithm
CN110267279A (en) * 2019-05-31 2019-09-20 暨南大学 Three-dimensional ECDV-Hop localization method based on differential evolution
CN113438732A (en) * 2021-06-16 2021-09-24 江南大学 DV-Hop positioning method based on Hop distance weighting and gold sinusoidal particle swarm

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20070159986A1 (en) * 2006-01-09 2007-07-12 Samsung Electronics Co., Ltd. Method of refining node position information in wireless sensor network
CN106341785A (en) * 2015-07-12 2017-01-18 上海明想电子科技有限公司 Improved wireless sensor DV-Hop algorithm
CN105682026A (en) * 2016-01-08 2016-06-15 南昌大学 Improved DV-Hop localization method based on hop count threshold optimal average hop distance
CN110267279A (en) * 2019-05-31 2019-09-20 暨南大学 Three-dimensional ECDV-Hop localization method based on differential evolution
CN113438732A (en) * 2021-06-16 2021-09-24 江南大学 DV-Hop positioning method based on Hop distance weighting and gold sinusoidal particle swarm

Non-Patent Citations (4)

* Cited by examiner, † Cited by third party
Title
YUJIAN WANG: "An improved DV-HOP positioning algorithm based on Hop distance correction", 《2014 11TH INTERNATIONAL COMPUTER CONFERENCE ON WAVELET ACTIEV MEDIA TECHNOLOGY AND INFORMATION PROCESSING(ICCWAMTIP)》 *
董军等: "无线传感器网络DV-hop定位算法的改进", 《计算机与现代化》 *
邴晓瑛等: "基于多通信半径的加权DV-Hop改进算法", 《计算机应用与软件》 *
黄春华等: "基于补偿系数的DV-Hop定位的改进算法", 《计算机技术与发展》 *

Similar Documents

Publication Publication Date Title
CN108848449B (en) Wireless sensor network node positioning algorithm based on DV-Hop improvement
Chen et al. An improved DV-Hop localization algorithm with reduced node location error for wireless sensor networks
Shakshuki et al. Comparative study on range free localization algorithms
Chen et al. Smart safety early warning system of coal mine production based on WSNs
CN112469117B (en) Improved DV-hop positioning method for irregular wireless sensor network
Chen et al. Energy-efficient clustering and localization technique using genetic algorithm in wireless sensor networks
Zhang et al. A node localization approach using particle swarm optimization in wireless sensor networks
CN103415072A (en) Positioning method based on distance estimation in wireless sensor network
CN111277968B (en) Wireless sensor network non-ranging positioning method based on stack self-encoder
Hadir et al. An improved DV-Hop localization algorithm for wireless sensor networks
Silmi et al. A self-localization range free protocol for wireless sensor networks
CN112488151B (en) Semi-supervised manifold learning-based position fingerprint database construction method
Jia et al. A high accuracy localization algorithm with DV-Hop and fruit fly optimization in anisotropic wireless networks
CN114143709A (en) DV-Hop-based wireless sensor positioning method
Kumar et al. TASLT: Triangular Area Segmentation based Localization Technique for Wireless Sensor Networks using AoA and RSSI Measures–A New Approach
Zhu et al. An Improved Localization Scheme Based on DV-Hop for Large-Scale Wireless Sensor Networks.
CN102325372A (en) Hybrid positioning method for wireless sensor network
CN113453335B (en) DV-hop-based improved convex optimization WSNs node positioning method
CN104540095A (en) Positioning method based on propagation least error principle for use under condition of limited anchor node quantity
Chen et al. Wireless Sensor Network Location Based On Improved Distance Vector Algorithm
Xiang Application of Ranging Difference Location Algorithm in Wireless Sensor Network Location.
Hu et al. An improved dv distance location algorithm for wireless sensor networks
CN107144810B (en) Wireless multi-hop non-ranging positioning method based on structure risk minimization
Cheng et al. Performance of improved fuzzy indoor zone positioning systems in wireless sensor networks
Ho et al. RSSI-based localization for wireless sensor networks with grid topologies

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
RJ01 Rejection of invention patent application after publication
RJ01 Rejection of invention patent application after publication

Application publication date: 20220304