CN110662291A - Three-dimensional DV-Hop positioning method based on Hop count weighting and Hop moment optimization - Google Patents

Three-dimensional DV-Hop positioning method based on Hop count weighting and Hop moment optimization Download PDF

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CN110662291A
CN110662291A CN201910870303.1A CN201910870303A CN110662291A CN 110662291 A CN110662291 A CN 110662291A CN 201910870303 A CN201910870303 A CN 201910870303A CN 110662291 A CN110662291 A CN 110662291A
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hop
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张晶
罗施章
王健敏
郭皓
郭立
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Yunnan Wurun Science And Technology Service Co Ltd
Kunming University of Science and Technology
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Yunnan Wurun Science And Technology Service Co Ltd
Kunming University of Science and Technology
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    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
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    • G01S11/02—Systems for determining distance or velocity not using reflection or reradiation using radio waves
    • G01S11/06—Systems for determining distance or velocity not using reflection or reradiation using radio waves using intensity measurements
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
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Abstract

The invention relates to a three-dimensional DV-Hop positioning method based on Hop count weighting and Hop moment optimization, and belongs to the field of wireless sensor network node positioning. The DV-Hop positioning method belongs to an algorithm without ranging, and can greatly reduce the positioning cost in the process of positioning nodes.

Description

Three-dimensional DV-Hop positioning method based on Hop count weighting and Hop moment optimization
Technical Field
The invention relates to a three-dimensional DV-Hop positioning method based on Hop count weighting and Hop moment optimization, and belongs to the field of wireless sensor network node positioning.
Background
In the fields of earthquake monitoring, forest fire prevention and control, livestock habit monitoring and the like, monitoring significance can be realized only by extracting various monitoring data and positioning a target node in real time after analyzing the data, while the traditional DV-Hop positioning method belongs to a node positioning method without distance measurement, has lower positioning cost, but has larger errors in the process of calculating the minimum Hop value among nodes and the average Hop moment of anchor nodes, so that the positioning error of the target node is larger, the earthquake focus, the forest fire position, the livestock activity range and the track can not be accurately positioned in the practical application process, the algorithm is only used for positioning the node on a two-dimensional plane, in the practical application, three-dimensional space terrains such as combination of mountains, hills, plateau basins and the like are common, and the positioning method can not be applied to positioning the target node in the terrains, therefore, the positioning method needs to be improved to reduce the node positioning error, and is expanded to three-dimensional application spaces such as mountain terrains and the like to ensure that the positioning method can adapt to practical application scenes. The invention is derived from technical innovation talent project (2019HB113) and national natural science fund project (61562051) in Yunnan province.
Disclosure of Invention
The invention provides a three-dimensional DV-Hop positioning method based on Hop count weighting and Hop moment optimization, which is used for reducing positioning errors of wireless sensor network nodes and widening the positioning application field.
The technical scheme adopted by the invention is as follows: a three-dimensional DV-Hop positioning method based on Hop count weighting and Hop moment optimization comprises the following specific steps:
step1, randomly launching 1000 wireless sensor network nodes in a 100 x 100 three-dimensional space, wherein the wireless sensor network nodes comprise known anchor nodes and unknown nodes, firstly, each anchor node broadcasts data information comprising self-position information to all nodes adjacent to the anchor node within a sphere range taking the anchor node as a sphere center and taking R as a communication radius of each nodePacket, data packet format { IDi，Idi，xi，yi，zi，disiContains the ID of the anchor nodeiIdentification number Id of neighbor node receiving the Anchor node Beacon messagei(empty at this time), the anchor node's own coordinates (x)i，yi，zi) And distances dis of other unknown nodes from the anchor nodei(the field of the anchor node is 0), the neighbor node continues to use the neighbor node as a sphere center after receiving the data packet, and broadcasts the data packet to the neighbor nodes in the same communication range;
in the broadcasting process, in order to prevent the data from broadcasting endlessly, the algorithm adopts a controllable flooding method, namely when a node receives a data packet with repeated ID number, the newly calculated distance between the node and the anchor node is compared with the distance information stored in the table, if the new distance is less than the original distance, the new distance is used for replacing the distance in the original table, and the new data packet is broadcasted again; otherwise, discarding the new data packet and not forwarding, which can ensure the distance information stored by each unknown node to be the shortest path with the anchor node and can end the algorithm;
step2, each network node participating in the broadcast communication process establishes a route vector table, only the data packet with the minimum hop value away from other nodes is reserved, and the minimum hop value h between the nodes participating in the communication process can be found through each node route vector tableij；
Step3, and the minimum hop count h between the nodes calculated in Step2ijCorrecting, wherein the distance between each anchor node and each anchor node is different in a sphere communication range taking R as a radius, if hop values among the nodes are all calculated by 1 hop, the calculation deviation of an actual hop value can be caused, according to the difference of RSSI values of another anchor node in the communication range of the anchor nodes, the RSSI values among two anchor nodes are corrected, the error is reduced, the RSSI values are corrected by introducing an RSSI positioning model, and the calculation formula of the RSSI values is as follows:
in the above formula, d0D is the distance between the node to be measured and the signal transmitting node, Pr(d) Indicating strength for receiving signals from nodes at distance d, Pr(d0) Receiving a reference distance d for an RSSI receiving node0Signal indication strength at node, d0＝1m，npThe path loss index is a value within a certain range related to the surrounding environment and distance. To reflect the authenticity of the test result, Gaussian white noise X is addedσThe intensity value can be directly read in the calculation, and the specific correction method is as follows:
recording the strength value of the signal indication received by the anchor node from the neighbor node as l, otherwise recording the strength value as l', and taking the hop count weight as the ratio of the two, namely:
Figure BDA0002202609030000022
recording the strength value of the signal indication received by the anchor node from the neighbor node as l, otherwise recording the strength value as l', and taking the hop count weight as the ratio of the two, namely:
Figure BDA0002202609030000023
the corrected minimum inter-node hop count value is recorded as h'ij，h′ij＝hijX h ', and calculating the corrected minimum inter-node hop count value h'ijStoring the route vector table in the node route vector table, and broadcasting the route vector table to the next neighbor node for use in the calculation of the subsequent average hop distance;
step4, minimum inter-node hop count value h 'corrected from each anchor node obtained in Step 3'ijCalculating the known linear distance value according to the coordinates between the anchor node and other anchor nodes, and calculating the average jump moment value c of each anchor node by an arithmetic mean methodiAverage jump moment value c of each anchor node through minimum mean square error and unbiased estimationiOptimizing to obtain final average hop moment c 'of each anchor node'i；
In the traditional three-dimensional DV-Hop positioning method, when the average Hop distance of each anchor node is calculated, the linear distance d between each anchor node and another anchor node is firstly calculatedijAnd using the linear distance and the corrected minimum inter-node hop count value h'ijDividing to obtain the average jump distance c of the anchor nodeiSee the following formula:
Figure BDA0002202609030000031
whereinIn the above formula (x)i，yi，zi)、(xj，yj，zj) Respectively calculating the minimum mean square error of the average jump moment value of the anchor nodes i and j according to the formula (1) for the known parameter values of the anchor nodes i and j on the x axis, the y axis and the z axis:
f∈＝∑(dij-cih′ij)2
according to the unbiased estimation, to minimize the minimum mean square error value, derivative is taken and made 0, then:
2∑(dij-cih′ij)h′ij＝0
and calculating to obtain the average hop moment of the anchor node i as follows:
Figure BDA0002202609030000033
because more than one anchor node is included in the network nodes randomly released to the three-dimensional space, the average hop distance value of the anchor node obtained by the above formula is more, the network structure is not static, is dynamically changed and is irregular, so an averaging process needs to be carried out on the average hop distance values of a plurality of anchor nodes obtained by the above formula to reduce the error caused by the fact that one of the average hop distance values of the anchor nodes is randomly selected and is brought into calculation, namely:
Figure BDA0002202609030000034
m is the number of anchor nodes communicating with the anchor node to be solved, and in order to present a network topological structure near an unknown node and reduce the calculation error, the number m of the anchor nodes should be taken reasonably, the positioning error is comprehensively reduced, and the idea that the unknown node performs coordinate positioning in a three-dimensional space relating to an x axis, a y axis and a z axis is combined, so that the condition that m is 3 is determined to be appropriate, and the final calculation formula of the average hop distance of the anchor node i is as follows:
Figure BDA0002202609030000041
step5, and the corrected minimum inter-node hop count value h 'obtained in Step 3'ijAnd the average hop moment value c 'of each anchor node obtained at Step 4'iThe two can be multiplied to obtain the linear distance D between the unknown node D to be solved and the anchor node i which is close to the unknown node DiNamely:
di＝h′ij×c′i
and then constructing a distance calculation equation set containing coordinate parameters x, y and z of the unknown nodes according to the known distance values between the unknown nodes and the surrounding anchor nodes:
Figure BDA0002202609030000042
in the above formula (x)1，y1，z1)，(x2，y2，z2)，(x3，y3，z3)，…，(xn，yn，zn) (x, y, z) known and unknown parameter values for n anchor nodes and unknown nodes D on the coordinate axes x, y, z, respectively, D1，d2，d3，…，dnThe corrected distance value between the unknown node and the known node is obtained;
the 1 st to n-1 st equations are respectively calculated by difference with the last equation: and (3) converting AX-v into a coordinate value calculation matrix of the unknown node D in the three-dimensional space through a matrix solving method:
X＝(ATA)-1ATb
wherein A is a coordinate difference parameter matrix, b is a coordinate squared difference parameter matrix, ATA transpose matrix which is a coordinate difference parameter matrix;
Figure BDA0002202609030000043
Figure BDA0002202609030000044
step6, and the estimated coordinate values (x, y, z) of the unknown nodes calculated in Step5 and the initial coordinate values (x'i、y′i、z′i) Calculating the average positioning accuracy values accurve of all unknown nodes to prove that the method can greatly reduce the positioning error of the unknown nodes:
Figure BDA0002202609030000051
in formula (II), x'i、y′i、z′iThe initial known coordinate values of the unknown node i on the x axis, the y axis and the z axis are respectively, n is the number of the unknown nodes, and R is the communication radius of the unknown nodes.
The invention has the beneficial effects that: the invention improves the calculation method of the distance value between the nodes in the DV-Hop positioning method, performs weighting calculation on the Hop count between the nodes, performs minimum mean square error and unbiased estimation optimization processing on the Hop moment between the nodes, reduces the calculation error of the distance between the nodes, thereby ensuring that the node positioning is more accurate, and simultaneously, the method is applied to a three-dimensional space from a two-dimensional space, so that the algorithm can improve the positioning accuracy and widen the application range while reducing the positioning cost.
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FIG. 1 is a flow chart of the present invention;
FIG. 2 is a graph of the relationship between the anchor node ratio and the average positioning accuracy of unknown nodes;
FIG. 3 is a graph showing the average positioning accuracy of the present invention varying with the total number of nodes.
FIG. 4 is a graph of the mean positioning accuracy of unknown nodes according to the present invention;
fig. 5 is a distribution diagram of nodes of a wireless sensor network in a cubic three-dimensional space according to an embodiment of the present invention.
Detailed Description
The invention is further described with reference to the following figures and specific examples.
Example 1: as shown in fig. 1, a three-dimensional DV-Hop localization method based on Hop count weighting and Hop moment optimization includes the following steps:
step1, randomly launching 1000 wireless sensor network nodes in a 100 x 100 three-dimensional space, wherein the wireless sensor network nodes comprise known anchor nodes and unknown nodes, firstly, each anchor node broadcasts a data packet containing self-position information to all nodes adjacent to the anchor node within a sphere range taking the anchor node as a sphere center and taking R as a communication radius of each node, and the format of the data packet is { IDi，Idi，xi，yi，zi，disiContains the ID of the anchor nodeiIdentification number Id of neighbor node receiving the Anchor node Beacon messagei(empty at this time), the anchor node's own coordinates (x)i，yi，zi) And distances dis of other unknown nodes from the anchor nodei(the field of the anchor node is 0), the neighbor node continues to use the neighbor node as a sphere center after receiving the data packet, and broadcasts the data packet to the neighbor nodes in the same communication range;
in the broadcasting process, in order to prevent the data from broadcasting endlessly, the algorithm adopts a controllable flooding method, namely when a node receives a data packet with repeated ID number, the newly calculated distance between the node and the anchor node is compared with the distance information stored in the table, if the new distance is less than the original distance, the new distance is used for replacing the distance in the original table, and the new data packet is broadcasted again; otherwise, discarding the new data packet and not forwarding, which can ensure the distance information stored by each unknown node to be the shortest path with the anchor node and can end the algorithm;
step2, each network node participating in the broadcast communication process establishes a route vector table, only the data packet with the minimum hop value away from other nodes is reserved, and the minimum hop value h between the nodes participating in the communication process can be found through each node route vector tableij；
Step3, and the minimum hop count h between the nodes calculated in Step2ijCorrecting, wherein the distance between each anchor node and each anchor node is different in a sphere communication range taking R as a radius, if hop values among the nodes are all calculated by 1 hop, the calculation deviation of an actual hop value can be caused, according to the difference of RSSI values of another anchor node in the communication range of the anchor nodes, the RSSI values among two anchor nodes are corrected, the error is reduced, the RSSI values are corrected by introducing an RSSI positioning model, and the calculation formula of the RSSI values is as follows:
in the above formula, d0D is the distance between the node to be measured and the signal transmitting node, Pr(d) Indicating strength for receiving signals from nodes at distance d, Pr(d0) Receiving a reference distance d for an RSSI receiving node0Signal indication strength at node, d0＝1m，npThe path loss index is a value within a certain range related to the surrounding environment and distance. To reflect the authenticity of the test result, Gaussian white noise X is addedσThe intensity value can be directly read in the calculation, and the specific correction method is as follows:
recording the strength value of the signal indication received by the anchor node from the neighbor node as l, otherwise recording the strength value as l', and taking the hop count weight as the ratio of the two, namely:
Figure BDA0002202609030000062
the corrected minimum hop count between nodes is recorded as hij，h′ij＝hijX h ', and calculating the corrected minimum inter-node hop count value h'ijStoring the route vector table in the node route vector table, and broadcasting the route vector table to the next neighbor node for use in the calculation of the subsequent average hop distance;
step4, minimum inter-node hop count value h 'corrected from each anchor node obtained in Step 3'ijCalculating the known linear distance value according to the coordinates between the anchor node and other anchor nodes, and calculating the average jump moment value c of each anchor node by an arithmetic mean methodiAverage jump moment value c of each anchor node through minimum mean square error and unbiased estimationiOptimizing to obtain final average hop moment c 'of each anchor node'i；
In the traditional three-dimensional DV-Hop positioning method, when the average Hop distance of each anchor node is calculated, the linear distance d between each anchor node and another anchor node is firstly calculatedijAnd using the linear distance and the corrected minimum inter-node hop count value h'ijDividing to obtain the average jump distance c of the anchor nodeiSee the following formula:
Figure BDA0002202609030000071
wherein
Figure BDA0002202609030000072
In the above formula (x)i，yi，zi)、(xj，yj，zj) Respectively calculating the minimum mean square error of the average jump moment value of the anchor nodes i and j according to the formula (1) for the known parameter values of the anchor nodes i and j on the x axis, the y axis and the z axis:
f∈=Σ(dij-cih′ij)2
according to the unbiased estimation, to minimize the minimum mean square error value, derivative is taken and made 0, then:
2∑(dij-cih′ij)h′ij＝0
and calculating to obtain the average hop moment of the anchor node i as follows:
because more than one anchor node is included in the network nodes randomly released to the three-dimensional space, the average hop distance value of the anchor node obtained by the above formula is more, the network structure is not static, is dynamically changed and is irregular, so an averaging process needs to be carried out on the average hop distance values of a plurality of anchor nodes obtained by the above formula to reduce the error caused by the fact that one of the average hop distance values of the anchor nodes is randomly selected and is brought into calculation, namely:
Figure BDA0002202609030000074
m is the number of anchor nodes communicating with the anchor node to be solved, and the calculation error is reduced for presenting a network topological structure near an unknown node, so the number m of the anchor nodes should be taken reasonably, the positioning error is comprehensively reduced, and the unknown node is the idea of carrying out coordinate positioning in a three-dimensional space relating to an x axis, a y axis and a z axis, so that the condition that m is 3 is determined to be appropriate, and the final calculation formula of the average jump distance of the anchor node i is as follows:
Figure BDA0002202609030000075
step5, and the corrected minimum inter-node hop count value h 'obtained in Step 3'ijAnd the average hop moment value c 'of each anchor node obtained at Step 4'iThe two can be multiplied to obtain the linear distance D between the unknown node D to be solved and the anchor node i which is close to the unknown node DiNamely:
di＝h′ij×c′i
and then constructing a distance calculation equation set containing coordinate parameters x, y and z of the unknown nodes according to the known distance values between the unknown nodes and the surrounding anchor nodes:
Figure BDA0002202609030000081
in the above formula (x)1，y1，z1)，(x2，y2，z2)，(x3，y3，z3)，…，(xn，yn，zn) (x, y, z) known and unknown parameter values for n anchor nodes and unknown nodes D on the coordinate axes x, y, z, respectively, D1，d2，d3，...，dnThe corrected distance value between the unknown node and the known node is obtained;
the 1 st to n-1 st equations are respectively calculated by difference with the last equation: and (3) converting AX (x) b into a coordinate value calculation matrix of the unknown node D in the three-dimensional space by a matrix solving method:
X＝(ATA)-1ATb
wherein A is a coordinate difference parameter matrix, b is a coordinate squared difference parameter matrix, ATA transpose matrix which is a coordinate difference parameter matrix;
Figure BDA0002202609030000082
Figure BDA0002202609030000083
step6, and the estimated coordinate values (x, y, z) of the unknown nodes calculated in Step5 and the initial coordinate values (x'i、y′i、z′i) Calculating the average positioning accuracy values accurve of all unknown nodes to prove that the method can greatly reduce the positioning error of the unknown nodes:
Figure BDA0002202609030000084
in formula (II), x'i、y′i、z′iThe initial known coordinate values of the unknown node i on the x axis, the y axis and the z axis are respectively, n is the number of the unknown nodes, and R is the communication radius of the unknown nodes.
The working principle of the invention is as follows: firstly, broadcasting a data packet by each network node, establishing a routing vector table, recording a minimum hop count value between nodes in the table, performing weighted correction on the hop count value between adjacent nodes in the communication radius of each node by applying a hop count weight value, calculating an average hop moment value of the nodes by applying the corrected hop count value, optimizing the average hop moment value by applying minimum mean square error and unbiased estimation, finally processing the average hop moment value of the anchor nodes by using mean value operation to obtain a final average hop moment, performing product operation on the average hop moment and the corrected minimum hop count value of the nodes to obtain a linear distance between an unknown node and the anchor nodes, then constructing a distance equation set between the unknown node and each anchor node, and finally calculating an estimated coordinate value of the unknown node in a three-dimensional space by using matrix transformation.
Further, the following example is made for the steps in the present application:
in order to verify that the improved 3D-DVHop positioning method is better than the 3D-DVHop positioning method before improvement, the change trend of the average positioning accuracy value of the two algorithms to the unknown node under different parameter conditions is compared, so that the conclusion is drawn.
1000 wireless sensor network nodes are randomly put in a cubic three-dimensional space with the side length of 100m, as shown in fig. 5.
As shown in fig. 2, when the node communication radius R is set to 60m and the total number of nodes is set to 1000, the algorithm program is set to run circularly 100 times under the conditions of different anchor node ratios, and the trend that the average positioning accuracy of unknown nodes of the conventional 3D-DVHop algorithm and the improved 3D-DVHop algorithm of the present invention changes with the increase of the anchor node ratio can be concluded:
when the proportion of the anchor nodes is 15%, the average positioning accuracy of the unknown nodes of the improved 3D-DVHop algorithm is 0.5326, the average positioning accuracy of the unknown nodes of the traditional 3D-DVHop algorithm is 0.6287, and the accuracy value of the former algorithm is reduced by 0.0961 compared with the latter algorithm; when the proportion of the anchor nodes is 30%, the average positioning accuracy of the unknown nodes of the improved 3D-DVHop algorithm is 0.4434, the average positioning accuracy of the unknown nodes of the traditional 3D-DVHop algorithm is 0.5960, and the accuracy value of the former is 0.1526 lower than that of the latter; when the proportion of the anchor nodes is 45%, the average positioning accuracy of the unknown nodes of the improved 3D-DVHop algorithm is 0.4153, the average positioning accuracy of the unknown nodes of the traditional 3D-DVHop algorithm is 0.5412, and the accuracy of the former is 0.1259 lower than that of the latter. Therefore, in the two algorithms, the unknown node average positioning accuracy value and the anchor node proportion are in inverse proportion relation, and the reduced trend of the improved 3D-DVHop algorithm to the unknown node average positioning accuracy value is more obvious than that of the traditional 3D-DVHop algorithm;
as shown in fig. 3, when R is 60m and the anchor node ratio is 30%, the algorithm program is set to run circularly 100 times under the conditions of different total node numbers, and the situation that the average positioning accuracy of the unknown nodes of the conventional 3D-DVHop algorithm and the improved 3D-DVHop algorithm of the present invention changes with the change of the total node number can be concluded:
when the total number of the nodes is 400, the average positioning accuracy of the unknown nodes of the improved 3D-DVHop algorithm is 0.5089, the average positioning accuracy of the unknown nodes of the traditional 3D-DVHop algorithm is 0.6028, and the accuracy value of the former is 0.0939 lower than that of the latter; when the total number of the nodes is 700, the average positioning accuracy of the unknown nodes of the improved 3D-DVHop algorithm is 0.4834, the average positioning accuracy of the unknown nodes of the traditional 3D-DVHop algorithm is 0.5984, and the accuracy value of the former is 0.1150 lower than that of the latter; when the total number of the nodes is 1000, the average positioning accuracy of the unknown nodes of the improved 3D-DVHop algorithm is 0.4613, the average positioning accuracy of the unknown nodes of the traditional 3D-DVHop algorithm is 0.5896, and the accuracy value of the former is 0.1283 lower than that of the latter. Therefore, the unknown node average positioning accuracy of the traditional 3D-DVHop algorithm basically has no change along with the increase of the total number of the nodes, and the unknown node average positioning accuracy of the improved 3D-DVHop algorithm is reduced along with the increase of the total number of the nodes, but the descending trend is not obvious; in general, the improved 3D-DVHop algorithm of the invention has more accurate positioning to unknown nodes than the traditional 3D-DVHop algorithm;
as shown in fig. 4, when the number of anchor nodes in the total node number ratio is set to 30%, and the total number of nodes is set to 1000, the algorithm program is set to run circularly 100 times under the conditions of different node communication radii, and the situation that the average positioning accuracy of unknown nodes of the conventional 3D-DVHop algorithm and the improved 3D-DVHop algorithm of the present invention changes with the change of the node communication radius R can be concluded:
when the node communication radius R is 40m, the average positioning accuracy of the unknown nodes of the improved 3D-DVHop algorithm is 0.6789, the average positioning accuracy of the unknown nodes of the traditional 3D-DVHop algorithm is 0.8923, and the accuracy value of the former is 0.2134 lower than that of the latter; when the node communication radius R is 70m, the average positioning accuracy of the unknown nodes of the improved 3D-DVHop algorithm is 0.4834, the average positioning accuracy of the unknown nodes of the traditional 3D-DVHop algorithm is 0.6213, and the accuracy value of the former is 0.1379 lower than that of the latter; when the node communication radius R is 100m, the average positioning accuracy of the unknown nodes of the improved 3D-DVHop algorithm is 0.3213, the average positioning accuracy of the unknown nodes of the traditional 3D-DVHop algorithm is 0.4857, and the former has a precision value lower than that of the latter by 0.1644. From the above experimental data, it can be seen that: in the two algorithms, the average positioning accuracy value of the unknown node and the communication radius R are in inverse proportion, and the improved 3D-DVHop algorithm of the invention has more accurate positioning on the unknown node than the traditional 3D-DVHop algorithm.
Example 2: embodiment 1 is an overall algorithm of the present invention, which can be applied to various practical situations to solve practical technical problems, and this embodiment takes forest fire prevention and control as an example to specifically describe the practical application of the algorithm of the present invention.
Step1, constructing a three-dimensional coordinate system in an original forest three-dimensional space in a fixed range, and randomly putting 1000 wireless sensor network nodes into the three-dimensional coordinate system, wherein the wireless sensor network nodes comprise known anchor nodes and unknown nodes, the known anchor nodes in the forest are used for positioning the unknown nodes, firstly, if the fire position is located at the anchor nodes, position information can be directly obtained, secondly, if the fire is located at the unknown nodes, the positioning method is used for positioning the fire to obtain the position information, thirdly, if the fire position is not located at the deployed nodes, the anchor nodes or the unknown nodes closest to the fire position are selected for positioning, and the position information is obtained in time; if the situation of the third step occurs, the positioning method is needed to position the forest fire in time, firstly, the positioning method is used for positioning the forest fire in timeEach anchor node in the forest is taken as a sphere center, R is taken as the communication radius of each node, all nodes in the forest adjacent to the anchor node broadcast a data information packet containing position information of the anchor node in the sphere range, and the format of the data packet is { IDi，Idi，xi，yi，zi，disiContains the ID of the anchor nodeiIdentification number Id of neighbor node receiving the Anchor node Beacon messagei(empty at this time), the anchor node's own coordinates (x)i，yi，zi) And distances dis of other unknown nodes from the anchor nodei(the field of the anchor node is 0), the neighbor node continues to use the neighbor node as a sphere center after receiving the data packet, and broadcasts the data packet to the neighbor nodes in the same communication range;
in the broadcasting process, in order to prevent the data from broadcasting endlessly, the algorithm adopts a controllable flooding method, namely when a node receives a data packet with repeated ID number, the newly calculated distance between the node and the anchor node is compared with the distance information stored in the table, if the new distance is less than the original distance, the new distance is used for replacing the distance in the original table, and the new data packet is broadcasted again; otherwise, discarding the new data packet and not forwarding, which can ensure the distance information stored by each unknown node to be the shortest path with the anchor node and can end the algorithm;
step2, each network node in the forest participating in the broadcast communication process establishes a route vector table, only a data packet with the minimum hop value from other nodes is reserved, and the minimum hop value h between the nodes participating in the communication process can be found through each node route vector tableij；
Step3, calculating the minimum jump value h between each node in the forest obtained in Step2ijCorrecting, because the distance between each anchor node and each anchor node is different when the node is in a sphere communication range taking R as a radius, if hop values between the nodes are all counted by 1 hop, calculation deviation of actual hop values can be caused, constructing a hop count weight h' according to difference of RSSI values of another anchor node in the communication range of the anchor node, wherein the RSSI values of the other anchor node are sent and received by the anchor node, correcting the hop count values between the two anchor nodes, reducing errors, and introducing RS (Reed-Solomon) into the sphere communication rangeThe SI positioning model corrects the hop count, and the RSSI value is calculated according to the following formula:
Figure BDA0002202609030000111
in the above formula, d0D is the distance between the node to be measured and the signal transmitting node, Pr(d) Indicating strength for receiving signals from nodes at distance d, Pr(d0) Receiving a reference distance d for an RSSI receiving node0Signal indication strength at node, d0＝1m，npThe path loss index is a value within a certain range related to the surrounding environment and distance. Considering objective natural factors existing in forest space, and adding Gaussian white noise X to reflect authenticity of positioning resultσIn order to simulate a real natural environment, the intensity value can be directly read in the calculation, and the specific correction method is as follows:
and recording the signal indication strength value received by the anchor node from the neighbor node in the forest as l, otherwise recording as l', wherein the hop count weight is the ratio of the two values, namely:
the corrected minimum hop count between nodes is recorded as hij，h′ij＝hijX h ', and calculating the minimum hop value h ' between nodes in the forest after correction 'ijStoring the route vector table in the node route vector table, and broadcasting the route vector table to the next neighbor node for use in the calculation of the subsequent average hop distance;
step4, obtaining the minimum jump value h 'between each node in the forest after correction according to Step 3'ijCalculating the known linear distance value according to the coordinates between the anchor node and other anchor nodes, and calculating the average jump moment value c of each anchor node by an arithmetic mean methodiAverage jump moment value c of each anchor node through minimum mean square error and unbiased estimationiOptimizing to obtain the final average jump moment c' i of each anchor node;
tradition IIIThe dimensional DV-Hop positioning method comprises the steps of firstly calculating the linear distance d between each anchor node and another anchor node when calculating the average Hop distance of each anchor nodeijAnd using the linear distance and the corrected minimum inter-node hop count value h'ijDividing to obtain the average jump distance c of the anchor nodeiSee the following formula:
wherein
Figure BDA0002202609030000123
In the above formula (x)i，yi，zi)、(xj，yj，zj) Respectively calculating the minimum mean square error of the average jump moment value of the anchor nodes i and j according to the formula (1) for the known parameter values of the anchor nodes i and j on the x axis, the y axis and the z axis:
f∈＝∑(dij-cih′ij)2
according to the unbiased estimation, to minimize the minimum mean square error value, derivative is taken and made 0, then:
2∑(dij-cih′ij)h′ij=0
and calculating to obtain the average hop moment of the anchor node i as follows:
Figure BDA0002202609030000124
because the network nodes randomly released to the fixed forest space contain more than one anchor node, the average jump distance value of the anchor nodes obtained by the formula is more, the network structure in the space is not static, is dynamically changed and irregular, and an averaging process needs to be carried out on the average jump distance values of the anchor nodes obtained by the formula so as to reduce the error caused by the fact that one of the average jump distance values of the anchor nodes is randomly selected and is brought into calculation, namely:
Figure BDA0002202609030000131
m is the number of anchor nodes communicating with the anchor node to be solved, and the calculation error is reduced for presenting a network topological structure near an unknown node, so the number m of the anchor nodes should be taken reasonably, the positioning error is comprehensively reduced, and the unknown node is the idea of carrying out coordinate positioning in a three-dimensional space relating to an x axis, a y axis and a z axis, so that the condition that m is 3 is determined to be appropriate, and the final calculation formula of the average jump distance of the anchor node i is as follows:
Figure BDA0002202609030000132
step5, obtaining the minimum jump value h 'between each node in the forest after correction according to Step 3'ijAnd the average hop moment value c 'of each anchor node obtained at Step 4'iThe two can be multiplied to obtain the linear distance D between the unknown node D to be solved and the anchor node i which is close to the unknown node DiNamely:
di＝h′ij×c′i
and then constructing a distance calculation equation set containing coordinate parameters x, y and z of the unknown nodes according to the known distance values between the unknown nodes and the surrounding anchor nodes:
Figure BDA0002202609030000133
in the above formula (x)1，y1，z1)，(x2，y2，z2)，(x3，y3，z3)，…，(xn，yn，zn) (x, y, z) are respectively known and unknown parameter values of the n anchor nodes and the unknown node D on coordinate axes x, y, z formed by the three-dimensional forest space, D1，d2，d3，…，dnThe corrected distance value between the unknown node and the known node is obtained;
the 1 st to n-1 st equations are respectively calculated by difference with the last equation: and (3) converting AX (x) b into a coordinate value calculation matrix of the unknown node D in the three-dimensional forest space by a matrix solving method:
X＝(ATA)-1ATb
wherein A is a coordinate difference parameter matrix, b is a coordinate squared difference parameter matrix, ATA transpose matrix which is a coordinate difference parameter matrix;
Figure BDA0002202609030000141
step6, and the estimated coordinate values (x, y, z) of the unknown nodes in the three-dimensional forest space calculated in Step5 and the initial coordinate values (x 'of all the unknown nodes in the three-dimensional forest space'i、y′i、z′i) Calculating the average positioning accuracy values accurve of all unknown nodes to prove that the method can greatly reduce the positioning error of the unknown nodes:
in formula (II), x'i、y′i、z′iThe method comprises the steps of respectively obtaining initial known coordinate values of unknown nodes i on an x axis, a y axis and a z axis of a three-dimensional forest space, wherein n is the number of the unknown nodes, and R is the communication radius of the unknown nodes.
And (4) conclusion: the algorithm is applied to the prevention and control of the forest fire, firstly, the algorithm does not need to calculate the linear distance between nodes, the positioning cost is greatly reduced, secondly, the algorithm corrects and optimizes the hop number and the hop moment between the nodes, reduces the positioning error of the fire position, ensures the timeliness and the accuracy of rescue, improves the prevention and control quality of the forest fire, and finally, the application dimension of the actual application space of the algorithm is improved from two dimensions to three dimensions, so that the algorithm is not limited to a two-dimensional space plane, and the application scene of the algorithm is perfectly matched with the complex three-dimensional forest terrain.
While the present invention has been described in detail with reference to the embodiments shown in the drawings, the present invention is not limited to the embodiments, and various changes can be made without departing from the spirit of the present invention within the knowledge of those skilled in the art.

Claims (1)

1. A three-dimensional DV-Hop positioning method based on Hop count weighting and Hop moment optimization is characterized in that: the method comprises the following specific steps:
step1, randomly launching 1000 wireless sensor network nodes in a 100 x 100 three-dimensional space, wherein the wireless sensor network nodes comprise known anchor nodes and unknown nodes, firstly, each anchor node broadcasts a data packet containing self-position information to all nodes adjacent to the anchor node within a sphere range taking the anchor node as a sphere center and taking R as a communication radius of each node, and the format of the data packet is { IDi，Idi，xi，yi，zi，disiContains the ID of the anchor nodeiIdentification number Id of neighbor node receiving the Anchor node Beacon messageiAt this time IdiNull, anchor node self coordinates (x)i，yi，zi) And distances dis of other unknown nodes from the anchor nodeiThe field of the anchor node is 0, and the neighbor node continues to broadcast the data packet to the neighbor nodes in the same communication range by taking the neighbor node as the sphere center after receiving the data packet;
in the broadcasting process, the algorithm adopts a controllable flooding method, namely when a node receives a data packet with repeated ID number, the newly calculated distance between the anchor node and the distance information stored in the table is compared, if the new distance is less than the original distance, the new distance is used for replacing the distance in the original table, and the new data packet is broadcasted again; otherwise, discarding the new data packet and not forwarding;
step2, each network node participating in the broadcast communication process establishes a route vector table, only the data packet with the minimum hop value away from other nodes is reserved, and the minimum hop value h between the nodes participating in the communication process can be found through each node route vector tableij；
Step3, and the minimum hop count h between the nodes calculated in Step2ijCorrecting, wherein the distance between each anchor node and each anchor node is different in a sphere communication range taking R as a radius, if hop values among the nodes are all calculated by 1 hop, the calculation deviation of an actual hop value can be caused, according to the difference of RSSI values of another anchor node in the communication range of the anchor nodes, the RSSI values among two anchor nodes are corrected, the error is reduced, the RSSI values are corrected by introducing an RSSI positioning model, and the calculation formula of the RSSI values is as follows:
Figure FDA0002202609020000011
in the above formula, d0D is the distance between the node to be measured and the signal transmitting node, Pr(d) Indicating strength for receiving signals from nodes at distance d, Pr(d0) Receiving a reference distance d for an RSSI receiving node0Signal indication strength at node, d0＝1m，npAdding Gaussian white noise X to the path loss index related to the surrounding environment and distanceσThe intensity value can be directly read in the calculation, and the specific correction method is as follows:
recording the strength value of the signal indication received by the anchor node from the neighbor node as l, otherwise recording the strength value as l', and taking the hop count weight as the ratio of the two, namely:
Figure FDA0002202609020000021
the corrected minimum inter-node hop count value is recorded as h'ij，h′ij＝hijX h ', and calculating the corrected minimum inter-node hop count value h'ijStoring the route vector table in the node route vector table, and broadcasting the route vector table to the next neighbor node for use in the calculation of the subsequent average hop distance;
step4, minimum hop count value h 'corrected by each anchor node obtained in Step 3'ijCombining the anchor nodes with other anchor nodes according to a coordinate systemCalculating the known linear distance value, and calculating the average jump moment value c of each anchor node by an arithmetic mean methodiAverage jump moment value c of each anchor node through minimum mean square error and unbiased estimationiOptimizing to obtain final average hop moment c 'of each anchor node'i；
In the traditional three-dimensional DV-Hop positioning method, when the average Hop distance of each anchor node is calculated, the linear distance d between each anchor node and another anchor node is firstly calculatedijAnd using the linear distance and the corrected minimum inter-node hop count value h'ijDividing to obtain the average jump distance c of the anchor nodeiAnd solving the minimum mean square error according to a calculation formula for calculating the average hop moment value of the node:
f∈＝∑(dij-cih′ij)2
according to the unbiased estimation, to minimize the minimum mean square error value, derivative is taken and made 0, then:
2∑(dij-cih′ij)h′ij＝0
and calculating to obtain the average hop moment of the anchor node i as follows:
Figure FDA0002202609020000022
an averaging process is carried out on the average hop distance values of the anchor nodes obtained in the formula so as to reduce errors caused by the fact that one of the average hop distance values of the anchor nodes is randomly selected and is brought into calculation, namely:
Figure FDA0002202609020000023
m is the number of anchor nodes communicating with the anchor node to be solved, and the thought that the unknown node is used for carrying out coordinate positioning in a three-dimensional space relating to an x axis, a y axis and a z axis is combined, so that m is determined to be 3, and the final calculation formula of the average jump distance of the anchor node i is as follows:
Figure FDA0002202609020000024
step5, and the corrected minimum inter-node hop count value h 'obtained in Step 3'ijAnd the average hop moment value c 'of each anchor node obtained at Step 4'iThe two can be multiplied to obtain the linear distance D between the unknown node D to be solved and the anchor node i which is close to the unknown node DiNamely:
di＝h′ij×c′i
and then constructing a distance calculation equation set containing coordinate parameters x, y and z of the unknown nodes according to the known distance values between the unknown nodes and the surrounding anchor nodes:
Figure FDA0002202609020000031
in the above formula (x)1，y1，z1)，(x2，y2，z2)，(x3，y3，z3)，…，(xn，yn，zn) (x, y, z) known and unknown parameter values for n anchor nodes and unknown nodes D on the coordinate axes x, y, z, respectively, D1，d2，d3，…，dnThe corrected distance value between the unknown node and the known node is obtained;
the 1 st to n-1 st equations are respectively calculated by difference with the last equation: and (3) converting AX (x) b into a coordinate value calculation matrix of the unknown node D in the three-dimensional space by a matrix solving method:
X＝(ATA)-1ATb
wherein A is a coordinate difference parameter matrix, b is a coordinate squared difference parameter matrix, ATA transpose matrix which is a coordinate difference parameter matrix;
Figure FDA0002202609020000032
Figure FDA0002202609020000033
step6, and the estimated coordinate values (x, y, z) of the unknown nodes calculated in Step5 and the initial coordinate values (x'i、y′i、z′i) Calculating the average positioning accuracy values accurve of all unknown nodes to prove that the method can greatly reduce the positioning error of the unknown nodes:
Figure FDA0002202609020000034
in formula (II), x'i、y′i、z′iThe initial known coordinate values of the unknown node i on the x axis, the y axis and the z axis are respectively, n is the number of the unknown nodes, and R is the communication radius of the unknown nodes.
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