CN113347707B - DV-Hop node positioning method based on weighted iteration and double selection - Google Patents

DV-Hop node positioning method based on weighted iteration and double selection Download PDF

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CN113347707B
CN113347707B CN202110619018.XA CN202110619018A CN113347707B CN 113347707 B CN113347707 B CN 113347707B CN 202110619018 A CN202110619018 A CN 202110619018A CN 113347707 B CN113347707 B CN 113347707B
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陈天飞
侯帅鑫
孙丽君
牛海旭
赵志鹏
赵攀
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Henan University of Technology
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    • H—ELECTRICITY
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Abstract

The invention provides a DV-Hop node positioning method based on weighted iteration and double selection, which comprises the following steps: firstly, iteratively calculating the average hop distance of the anchor node by using a weighting mode, comparing the average hop distance error obtained each time, and taking the average hop distance with the minimum error as the optimal average hop distance of the anchor node; then, reducing the calculation steps in the standard DV-Hop, and solving the estimated distance between the unknown node and the anchor node by using the optimal average Hop distance of the anchor node; and finally, grouping the anchor nodes according to the distance between the anchor nodes and the unknown nodes, and eliminating the potential error of the least square method by transforming the reference nodes. And calculating the optimal unknown node coordinate of each anchor node group, and then selecting the coordinate with the minimum error as the final coordinate of the unknown node. The invention can improve the average jump distance of the anchor nodes and the accuracy of the distance from the unknown nodes to the anchor nodes, and has better node positioning precision and positioning stability.

Description

DV-Hop node positioning method based on weighted iteration and double selection
Technical Field
The invention relates to the technical field of node positioning in a wireless sensor network, in particular to a DV-Hop node positioning method based on weighted iteration and double selection.
Background
Wireless Sensor Networks (WSNs), as a new information acquisition and processing technology, are widely used in many fields, such as: military reconnaissance, medical treatment and health, urban traffic, precision agriculture, forest fire monitoring and the like. For wireless sensor network applications, it is often necessary to know the specific location of the event in order to make the corresponding processing. Therefore, the method has very important research significance for acquiring the accurate position information of the sensor node.
The research directions for node location techniques can be mainly divided into two categories: ranging-based positioning methods and non-ranging-based positioning methods. A common ranging and positioning method includes: time difference of arrival (TDOA), Received Signal Strength (RSSI), time of arrival (TOA), etc. The positioning method based on the distance measurement has higher positioning accuracy, but the angle, the distance, the signal strength and the like between nodes are measured by extra hardware, so that the cost for building a network is increased. The DV-Hop location method, which is one of typical non-ranging location methods, calculates an estimated distance between nodes by an unknown node through its average Hop distance and the minimum number of hops to an anchor node, and then calculates a position coordinate of the unknown node by a multilateration method or a maximum likelihood estimation method. However, due to the complexity of the network and the limitation of the algorithm, the DV-Hop positioning method has lower positioning accuracy, which limits the application of some scenes with higher requirements on node accuracy.
The standard DV-Hop location method calculates the average Hop distance of the anchor node by using an unbiased estimation method, and the result obtained by the method usually has a large error from the average Hop distance in a real network (reference [1] algorithm). Chen et al propose a method based on the minimum mean square error criterion to calculate the average hop distance of anchor nodes in consideration of the distribution rule of errors (reference [2] algorithm), which improves the average hop distance accuracy of anchor nodes to some extent, but does not consider the degree of mutual influence among anchor nodes. In the standard DV-Hop positioning method, the unknown node only refers to the average Hop distance of the nearest anchor node as the average Hop distance of the unknown node, and the actual average Hop distance of the unknown node cannot be reflected. Zhao et al introduces a weighting factor to weight the average hop distance of the anchor node, and the average hop distance of the unknown node is obtained by weighting the average hop distances of the three anchor nodes closest to the unknown node (reference [3] algorithm). In order to improve the accuracy of the distance between an unknown node and an anchor node, Li and the like adopt a double-communication-radius mode to calculate the hop count between the nodes, refine the communication radius between the nodes and improve the positioning precision of the unknown node to a certain extent (reference [4] algorithm), but because the communication radius needs to be broadcasted for the second time, the energy consumption of the sensor node is increased, and the service life of the node is reduced.
The current DV-Hop positioning method adopts various different schemes to improve the accuracy of the average Hop distance of the anchor nodes, but does not consider the mutual influence among the anchor nodes, and different anchor nodes should have different contribution degrees to the calculation of the average Hop distance. Meanwhile, when the coordinates of the unknown nodes are calculated, all anchor nodes participate in the calculation process, but if the distance error between a certain anchor node and the unknown nodes is large, the positioning result is influenced. In addition, when the least square method is adopted to solve the unknown node coordinates, potential errors generated by the equation set cannot be eliminated.
Disclosure of Invention
Aiming at the technical problems that the existing DV-Hop positioning method is low in positioning accuracy and potential errors cannot be eliminated when the least square method is used for solving the coordinates of unknown nodes, the invention provides the DV-Hop positioning method based on weighted iteration and double selection.
In order to achieve the purpose, the technical scheme of the invention is realized as follows: a DV-Hop node positioning method based on weighted iteration and double selection comprises the following steps:
the method comprises the following steps: calculating the optimal average hop distance of the anchor node by adopting a weighted iteration strategy: firstly, solving the initial average hop distance of an anchor node by using a minimum mean square error criterion, and then calculating the error value of each hop from the anchor node to other anchor nodes; the error value is used as a weight coefficient of the anchor node, and the average hop distance of the anchor node is recalculated by using the weighted minimum mean square error criterion; comparing the error of the average jump distance of the anchor nodes obtained after each weighted iteration, and if the error of the average jump distance of the anchor nodes obtained by the current iteration is smaller than the error of the last time, continuing the iteration; otherwise, finishing iteration, and taking the average hop distance of the anchor node obtained last time as the optimal average hop distance of the anchor node;
step two: calculating the estimated distance between the unknown node and the anchor node by using the optimal average hop distance of the anchor node obtained in the step one;
step three: and (3) solving the coordinates of the unknown nodes by adopting a double selection scheme: grouping the anchor nodes according to the distances from different anchor nodes to the unknown nodes, taking each anchor node in the anchor node group as a primary reference node, calculating the coordinates of the unknown nodes by using a least square method, and selecting the coordinate with the minimum error as the optimal unknown node coordinate of the anchor node group; and finally, comparing the error of the optimal coordinates obtained by different anchor node groups, and selecting the coordinate with the minimum error as the final coordinate of the unknown node.
The method for calculating the optimal average hop distance of the anchor node by adopting the weighted iteration strategy in the first step comprises the following steps:
step 1: initialization of the network: all anchor nodes broadcast data packets to the network in a flooding mode, and receiving nodes obtain the minimum hop count among the nodes in the network by changing hop count values and transmitting the data packets;
and 2, step: endowing all anchor nodes with the same weight, and solving the initial average hop distance of each anchor node by using the minimum hop count and the minimum mean square error criterion:
Figure BDA0003098943610000031
wherein AHDi initIs the initial average hop distance, Dis, of the anchor node iijRepresenting the actual distance, Hop, between anchor node i and anchor node jijRepresenting the minimum hop number from the anchor node i to the anchor node j, wherein n is the total number of the anchor nodes in the network;
and step 3: calculating the error of the initial average hop distance of the anchor node by using the initial average hop distance calculated in the step 2:
Figure BDA0003098943610000032
wherein eta isi initError, AHD, representing initial average hop distance of anchor node ii init·HopijThe estimated distance from the anchor node i to the anchor node j;
and 4, step 4: average jump distance AHD of anchor node obtained by step 2i initCalculating an estimated distance between anchor nodes, and calculating an error value of each hop between the anchor nodes by referring to an actual distance between the anchor nodes and the minimum hop count:
Figure BDA0003098943610000033
wherein, EHijAn error value representing each hop from anchor node i to anchor node j;
and 5: and assigning weights to corresponding anchor nodes by using error values of each hop between the anchor nodes:
Figure BDA0003098943610000034
wherein alpha isijRepresenting the weight given to the anchor node j when calculating the average hop distance of the anchor node i;
and 6: and 5, calculating the weighted average jump distance of the anchor nodes according to the weighted minimum mean square error criterion by using the weight values between the anchor nodes obtained in the step 5:
Figure BDA0003098943610000035
wherein AHDi (m)The average hop distance of the anchor node i obtained after the mth weighted iteration is obtained;
and 7: calculating the error of the weighted average jump distance of the anchor nodes according to the weighted average jump distance of the anchor nodes obtained in the step 6:
Figure BDA0003098943610000036
wherein eta isi (m)Represents the anchor node i obtained after the mth weighting iterationError of the average hop distance of (1);
and 8: if the current weighting iteration number m is 1, comparing the error eta of the average jump distance of the anchor nodes obtained after the first weightingi (1)Error eta of initial average jump distance of anchor nodei initIf ηi (1)<ηi initAnd continuing to carry out weighted iterative solution on the average hop distance of the anchor node, returning to the step 4, and obtaining the average hop distance AHD of the anchor node by using the current iterationi (1)As initial average hop distance AHD in step 4i initRecalculating error per hop among anchor nodes, and iteratively calculating the average hop distance of the anchor nodes again by taking the error value per hop as a weight; if etai (1)>ηi initEnding the iterative calculation process, and obtaining the initial average jump distance AHD in the step 2i initAs the optimal average hop for anchor node i;
if the current weighted iteration number m>1, comparing the error eta of the average jump distance of the anchor nodes obtained after the m-1 weightingi (m-1)Error eta of anchor node average jump distance obtained by current iterationi (m)If ηi (m)<ηi (m-1)And continuing to carry out weighted iterative solution on the average hop distance of the anchor node, returning to the step 4, and obtaining the AHD by using the current iterative weightingi (m)As initial average hop distance AHD in step 4i initRecalculating error per hop among anchor nodes, and iteratively calculating the average hop distance of the anchor nodes again by taking the error value per hop as a weight; if etai (m)>ηi (m-1)Ending iterative calculation process, and obtaining average jump distance AHD of anchor node in last iterationi (m-1)As the optimal average hop distance for anchor node i.
The method for calculating the estimated distance from the unknown node to the anchor node in the second step comprises the following steps: d is a radical ofui＝AHDi best·Hopui；
Wherein d isuiEstimation for unknown node u to anchor node iDistance measurement, AHDi bestRepresenting the optimal average hop distance of the anchor node i; hopuiRepresents the minimum hop count of the unknown node u to the anchor node i, and u is 1.
The step three of obtaining the coordinates of the unknown node by adopting a double selection strategy comprises the following steps:
s1: by utilizing the distances from different anchor nodes to unknown nodes, as at least three anchor nodes are needed for positioning the unknown nodes, the anchor nodes are divided into n-2 different anchor node groups, namely an anchor node set ANSET＜k＞K is 3, …, n; wherein, ANSET＜k＞Representing k anchor nodes closest to the unknown node;
s2: aggregating anchor nodes into ANSET＜k＞Each anchor node in the system is used as a primary reference anchor node to calculate the coordinates of an unknown node;
s3: according to the estimated distance between the unknown node and the anchor node calculated in the step two, a least square method and an anchor node set ANSET are utilized＜k＞The anchor nodes in the system respectively calculate corresponding unknown node candidate coordinates (x)u,yu) And u ═ 1,. k;
s4: calculating the error of each unknown node candidate coordinate according to the coordinates of the unknown nodes and the coordinates of the anchor nodes:
Figure BDA0003098943610000051
wherein (x)u,yu) Estimated coordinates representing unknown node u, (x)i,yi) Coordinates representing anchor node i;
s5: comparing each set of anchor nodes ANSET＜k＞The candidate coordinate with the minimum error is used as the optimal candidate coordinate obtained by the anchor node set according to the obtained error of the candidate coordinate;
s6: and comparing the error of the optimal candidate coordinates obtained under different anchor node sets, and selecting the optimal candidate coordinates with the minimum error as the final coordinates of the unknown nodes.
Calculating candidate coordinates (x) of the unknown node by using a least square method in the step S3u,yu) Comprises the following steps:
s31: obtaining an estimated distance from an unknown node to each anchor node:
let the coordinates of the unknown node be (x)u,yu) The coordinates of the anchor node are (x)i,yi) 1,2, …, k, unknown node-to-anchor node set ANSET＜k＞The estimated distances of the inner anchor nodes are respectively du1,du2,…,dukThen, the following equation set is given:
Figure BDA0003098943610000052
s32: converting the nonlinear equation set into a linear equation set to solve the coordinates of the unknown node:
Figure BDA0003098943610000053
s33: expressing the linear equation set in a matrix form of GX ═ H, where:
Figure BDA0003098943610000054
solving the coordinates of the unknown nodes by using a least square method:
X＝(GTG)-1GTH；
wherein G, H is a matrix representation of the linear equation set in step S32, and X represents the coordinate of the obtained unknown node.
Compared with the prior art, the invention has the beneficial effects that: obtaining initial average hop distance of the anchor node by using a method based on a minimum mean square error criterion, then calculating the error of each hop from the anchor node to other anchor nodes, taking the error as a weight factor, endowing different weights to each anchor node, and then calculating the average hop distance of the anchor node by using the minimum mean square error criterion based on weighting; and comparing the average hop distance error obtained this time with the average hop distance error obtained last time by using the difference between the actual distance and the estimated distance between the anchor nodes as an evaluation index of the average hop distance error of the anchor nodes, and if the average hop distance error is smaller than the average hop distance error obtained last time, continuing the weighted iterative calculation, otherwise, using the average hop distance obtained last time as the optimal average hop distance of the anchor nodes. The method adopts the optimal average Hop distance of the anchor node to calculate the estimated distance between the unknown node and the anchor node, thereby not only reducing the error of the distance between the nodes, but also reducing the process of broadcasting the average Hop distance of the anchor node in the standard DV-Hop positioning method and reducing the energy consumption of the nodes. According to the method, the position coordinates of the unknown nodes are obtained by adopting a dual selection strategy, the anchor nodes are divided into different anchor node sets in consideration of the distance between the anchor nodes and the unknown nodes, and meanwhile, each anchor node in the anchor node sets is used as a primary reference anchor node, so that the potential error of an equation set can be effectively reduced, and the positioning accuracy of the nodes is improved. Finally, simulation experiments show that the method has higher precision in each step of solving the unknown node coordinates, not only reduces the accumulation of errors in the calculation process, but also obviously improves the positioning precision and the stability. The invention can improve the average jump distance of the anchor nodes and the accuracy of the distance from the unknown nodes to the anchor nodes, and has better node positioning precision and positioning stability.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present invention, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of the present invention.
Fig. 2 is a flowchart of calculating the optimal average hop distance of the anchor node according to the weighted iteration strategy of the present invention.
FIG. 3 is a comparison graph of the average hop distance calculation accuracy of the anchor nodes of the present invention and the comparison algorithm.
FIG. 4 is a graph comparing unknown node-to-anchor node distance accuracy with a comparison algorithm of the present invention.
FIG. 5 is a comparison graph of unknown node positioning accuracy for different node counts for the present invention and comparison algorithm.
FIG. 6 is a comparison graph of unknown node location accuracy for different anchor node ratios of the present invention and comparison algorithm.
FIG. 7 is a comparison graph of unknown node positioning accuracy under different communication radii of the present invention and the comparison algorithm.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
A DV-Hop node positioning method based on weighted iteration and double selection has the conception that: the method adopts a weighted iteration mode of average jump distance of the anchor nodes, calculates the error of each jump from the anchor nodes to other anchor nodes by using the average jump distance of the initial anchor nodes, takes the error value as a weight coefficient, endows different weights to each anchor node, and then calculates the average jump distance of the anchor nodes by adopting a weighted minimum mean square error rule. If the average jump distance error obtained this time is smaller than the last time, continuing iterative computation; otherwise, taking the average hop distance obtained last time as the optimal average hop distance of the anchor node. Then, the calculation process of the standard DV-Hop method is reduced, and the estimated distance between the nodes is obtained by the optimal average Hop distance of the anchor nodes. And finally, solving the final coordinate of the unknown node by adopting a double selection strategy. The invention considers the mutual influence among anchor nodes, and the average hop distance obtained by the weighted iteration strategy is closer to the real average hop distance in the network. Meanwhile, the calculation steps of the standard DV-Hop positioning method are reduced, and the dual-selection strategy is adopted to calculate the coordinates, so that the service life of the sensor node can be effectively prolonged, and the positioning accuracy of the node can be improved.
As shown in fig. 1, after obtaining the minimum hop count between anchor nodes by using a flooding manner, the present invention calculates the optimal average hop distance of each anchor node by using a weighted iteration strategy, then calculates the distance between the anchor node and an unknown node by referring to the relatively accurate optimal average hop distance of the anchor node obtained in the previous step, and finally selects a candidate coordinate with the minimum error as the final coordinate of the unknown node by using a dual selection strategy, which specifically comprises the following steps:
the method comprises the following steps: calculating the optimal average hop distance of the anchor node by adopting a weighted iteration strategy: firstly, solving the initial average hop distance of an anchor node by using a minimum mean square error criterion, and then calculating the error value of each hop from the anchor node to other anchor nodes; the error value is used as a weight coefficient of the anchor node, and the average hop distance of the anchor node is recalculated by using the weighted minimum mean square error criterion; comparing the error of the average jump distance of the anchor nodes obtained after each weighted iteration, and if the average jump distance error of the anchor nodes obtained by the current iteration is smaller than the error of the last time, continuing the iterative calculation process; otherwise, finishing iteration, and taking the average hop distance obtained last time as the optimal average hop distance of the anchor node.
As shown in fig. 2, the weighted iteration strategy is a flowchart for calculating the average hop distance of the anchor node, and the calculation method is as follows:
step 1: initialization of the network: all anchor nodes broadcast data packets to the network in a flooding mode, and receiving nodes obtain the minimum hop count among the nodes in the network by changing the hop count value and transmitting the data packets.
The initialization of the network is the same as that of a standard DV-Hop method, firstly, an anchor node broadcasts a data packet (comprising the number, the coordinate and the Hop value with the initial value of 0) to the network in a flooding mode, a receiving node judges whether the data packet from the same anchor node is received or not after receiving the data packet, and if the data packet is received, the Hop value of the newly received data packet is compared with the Hop value recorded. If the number of the recorded hops is smaller than the recorded hop number, modifying the recorded hop number to be smaller, and transmitting the data packet to other neighbor nodes; otherwise, the packet is discarded. In this way, the nodes in the network can all obtain a value for the minimum number of hops to other nodes.
Step 2: endowing all anchor nodes with the same weight, and solving the initial average hop distance of each anchor node by using the minimum mean square error criterion:
Figure BDA0003098943610000071
wherein AHDi initIs the initial average hop distance, Dis, of the anchor node iijRepresenting the actual distance, Hop, between anchor node i and anchor node jijRepresenting the minimum number of hops from anchor node i to anchor node j, and n is the total number of anchor nodes in the network.
Assuming that the influence degrees of all anchor nodes are the same, all anchor nodes are given the same weight (the weight values are all set to be 1), and the initial average hop distance of each anchor node can be obtained by using the minimum error criterion. In an actual network, since nodes are randomly distributed and the mutual influence among the nodes is different, different weights should be given to different anchor nodes. The initial average hop distance of the anchor node can provide a basis for the calculation of subsequent weights.
And step 3: calculating the error of the initial average hop distance of the anchor node by using the initial average hop distance calculated in the step 2:
Figure BDA0003098943610000081
wherein eta isi initError, AHD, representing initial average hop distance of anchor node ii init·HopijIs the estimated distance from anchor node i to anchor node j.
Because the anchor nodes are provided with the GPS device, the specific position coordinates of the anchor nodes can be obtained, and the real distance between the anchor nodes is
Figure BDA0003098943610000082
And calculating to obtain the estimated distance between the anchor nodes by utilizing the obtained average jump distance of the anchor nodes as follows: AHDi init·HopijAnd comparing the actual distance with the average jump distance to obtain the error of the average jump distance of the anchor node, namely the accuracy.
And 4, step 4: utilizing the average jump distance AHD of the anchor nodes obtained in the step 2i initCalculating an estimated distance between anchor nodes, and calculating an error value of each hop between the anchor nodes by referring to a real distance between the anchor nodes and the minimum hop count:
Figure BDA0003098943610000083
wherein, EHijAn error value representing each hop from anchor node i to anchor node j.
And 5: and assigning weights to corresponding anchor nodes by using error values of each hop between the anchor nodes:
Figure BDA0003098943610000084
wherein alpha isijWhich represents the weight given to anchor node j when calculating the average hop distance of anchor node i.
The square of the reciprocal of the error value of each hop among the anchor nodes is taken as a weight, so that the influence degree of each anchor node on the average hop distance can be reflected. If the error of each hop to a certain anchor node is larger, a smaller weight is allocated so as to weaken the influence of the anchor node on the calculation of the average hop distance result. In contrast, a larger weight is assigned.
Step 6: and 5, calculating the weighted average jump distance of the anchor nodes according to the weighted minimum mean square error criterion by using the weight values between the anchor nodes obtained in the step 5:
Figure BDA0003098943610000091
wherein AHDi (m)And the average hop distance of the anchor node i obtained after the mth weighting iteration is obtained.
The weight value is used as a weighting coefficient, the average jump distance of the anchor node is calculated by utilizing a weighted minimum mean square error criterion mode, the contribution degrees of different anchor nodes are considered, and the accuracy of the average jump distance of the anchor node is improved.
And 7: calculating the error of the weighted average jump distance of the anchor nodes according to the weighted average jump distance of the anchor nodes obtained in the step 6:
Figure BDA0003098943610000092
wherein eta isi (m)And representing the error of the average hop distance of the anchor node i obtained after the mth weighting iteration.
And 8: if the current weighting iteration number m is 1, comparing the error eta of the average jump distance of the anchor nodes obtained after the first weightingi (1)And the error eta of the initial average jump distance of the anchor node obtained in the step 3i initIf ηi (1)<ηi initAnd continuing to carry out weighted iterative solution on the average hop distance of the anchor node, returning to the step 4, and utilizing the AHD obtained by the current iterationi (1)Average hop distance AHD as anchor node in step 4i initRecalculating error per hop among anchor nodes, and iteratively calculating the average hop distance of the anchor nodes again by taking the error value per hop as a weight; if etai (1)>ηi initEnding the iterative calculation process to obtain the AHD in step 2i initOptimal average hop distance (denoted AHD) as anchor node ii best)。
The step has the advantage that whether iterative computation needs to be continued or not can be judged by comparing the error of the average jump distance of the anchor nodes obtained by the first iteration with the error of the average jump distance of the initial anchor nodes.
And step 9: if the current weighted iteration number m>1, comparing the error eta of the average jump distance of the anchor nodes obtained after the m-1 weightingi (m-1)Error eta of anchor node average jump distance obtained by current iterationi (m)If ηi (m)<ηi (m-1)Continuously carrying out weighted iterative solution on the average hop distance of the anchor node; returning to the step 4, the AHD obtained by the current iteration weighting is utilizedi (m)Average hop distance AHD as anchor node in step 4i initRecalculating error per hop among anchor nodes, and iteratively calculating the average hop distance of the anchor nodes again by taking the error value per hop as a weight; if etai (m)>ηi (m-1)Ending the iterative calculation process, and obtaining AHD by the iteration of the last timei (m-1)Optimal average hop distance (denoted AHD) as anchor node ii best)。
According to the invention, the weight distribution is recalculated by using the newly obtained average hop distance of the anchor nodes, the weighted iterative calculation is repeatedly carried out on the average hop distance of the anchor nodes, and the error of the average hop distance obtained each time is compared, so that the optimal average hop distance of each anchor node can be obtained. The weighted iteration scheme adopted by the invention can iteratively refine the average hop distance of the anchor node, so that the obtained average hop distance is closer to the average hop distance in a real network, and the accumulation of errors in the positioning process can be effectively reduced.
Step two: and calculating the estimated distance between the unknown node and the anchor node by using the optimal average hop distance of the anchor node obtained in the step one.
The specific calculation method of the estimated distance from the unknown node to the anchor node comprises the following steps: dui＝AHDi best·Hopui；
Wherein d isuiFor the estimated distance, AHD, from unknown node u to anchor node ii bestRepresenting the optimal average hop distance of the anchor node i. HopuiRepresenting the minimum number of hops from unknown node u to anchor node i. And u 1., N is the total number of unknown nodes in the network.
The method has the advantages that the calculation process of the average Hop distance of the unknown node in the standard DV-Hop positioning method is reduced, the anchor node does not need to broadcast again after obtaining the optimal average Hop distance, the energy consumption of the sensor node is reduced, and the service life of the node is prolonged. Meanwhile, the estimated distance is obtained by utilizing the optimal average hop distance of the anchor node, and the Euclidean distance in a real network can be more approximate.
Step three: and (3) solving the coordinates of the unknown nodes by adopting a double selection scheme: grouping the anchor nodes according to the distances from different anchor nodes to the unknown nodes, taking each anchor node in the anchor node group as a primary reference node, calculating the coordinates of the unknown nodes by using a least square method, and selecting the coordinates with the minimum error as the optimal unknown node coordinates of the anchor node group; and finally, comparing the error of the optimal coordinates obtained by different anchor node groups, and selecting the coordinate with the minimum error as the final coordinate of the unknown node.
The implementation steps of obtaining the coordinates of the unknown node by adopting the double selection strategy are as follows:
s1: and (4) preferentially considering the anchor node closest to the unknown node to participate in calculation by utilizing the distances from different anchor nodes to the unknown node. Since at least three anchor nodes are required to locate an unknown node, the anchor nodes can be divided into n-2 different sets of anchor nodes, denoted ANSET＜k＞K is 3, …, n; wherein, ANSET＜k＞Representing the k anchor nodes closest to the unknown node.
Since the anchor node farther away from the unknown node has a larger corresponding minimum hop count value, the probability that a large error exists in the estimated distance from the unknown node to the anchor node is also higher. If an anchor node with a large distance error exists in the positioning process, the precision of the coordinates of the unknown node is influenced. Therefore, the anchor node closest to the unknown node is preferentially involved in positioning, and the possibility of error generation can be reduced to some extent.
S2: in order to eliminate the inherent error existing in the least square method, the anchor nodes are assembled into ANSET＜k＞Each anchor node in the set is used as a primary reference anchor node to calculate the coordinates of an unknown node, if the set ANSET of the anchor nodes＜k＞If k anchor nodes are stored, candidate coordinates of k unknown nodes can be obtained.
When the system of equations is solved by the least square method, the last equation is subtracted from the other equations, so that the nonlinear system of equations becomes a linear system of equations. However, if the distance error of the last equation is large, the solution result of the whole equation set is affected. Therefore, each equation can be used as the last equation at a time, and the optimal solution can be selected from the equations, which can effectively eliminate the potential errors generated by the linear equation set.
S3: according to the estimated distance between the unknown node and the anchor node calculated in the step two, a least square method and an anchor node set ANSET are utilized＜k＞The anchor nodes in the system respectively calculate corresponding unknown node candidate coordinates (x)u,yu) And u is 1.
Calculating candidate coordinates (x) of unknown nodes by using least square methodu,yu) Comprises the following steps:
s31: obtaining an estimated distance from an unknown node to each anchor node:
let the coordinates of the unknown node be (x)u,yu) The coordinates of the anchor node are (x)i,yi) 1,2, …, k, unknown node-to-anchor node set ANSET＜k＞The estimated distances of the inner anchor nodes are respectively du1,du2,…,dukThen, the following equation set is given:
Figure BDA0003098943610000111
the method has the advantages that the existing anchor node information and the obtained estimated distance can be combined, and the coordinates of the unknown nodes can be obtained by solving an equation set.
S32: converting the nonlinear equation set into a linear equation set to solve the coordinates of the unknown node:
Figure BDA0003098943610000112
the above set of equations may be expressed in matrix form as GX ═ H, where:
Figure BDA0003098943610000113
and finally, solving the coordinates of the unknown node by using a least square method:
X＝(GTG)-1GTH；
g, H is a matrix representation of a linear equation system, and X represents the coordinate of the unknown node. And the candidate coordinate obtained by calculation is used as one of the alternatives of the final coordinate of the unknown node.
The method has the advantages that the nonlinear equation set can be converted into the linear equation set by subtracting the equation set, and then the least square method is used for solving, so that the solving process is simplified.
S4: calculating the error of each unknown node candidate coordinate according to the coordinates of the unknown nodes and the coordinates of the anchor nodes:
Figure BDA0003098943610000114
wherein (x)u,yu) Estimated coordinates representing unknown node u, (x)i,yi) The coordinates representing the anchor node i are,
Figure BDA0003098943610000121
and calculating the error of the candidate coordinates of the unknown node.
And calculating the Euclidean distance to each anchor node by using the candidate coordinates, and comparing the Euclidean distance with the estimated distance obtained by using the average hop distance of the optimal anchor node, so that the error of the candidate coordinates of the unknown node can be obtained, and the optimal candidate coordinates of the unknown node can be conveniently obtained.
S5: and comparing the error of the candidate coordinates obtained under each anchor node set, and taking the candidate coordinate with the minimum error as the optimal candidate coordinate obtained by the anchor node set.
S6: and comparing the error of the optimal candidate coordinates obtained under different anchor node sets, and selecting the optimal candidate coordinate with the minimum error as the final coordinate of the unknown node.
The method comprises the steps of firstly, iteratively calculating the average hop distance of an anchor node by using a weighting mode, comparing the average hop distance error obtained each time, and taking the average hop distance with the minimum error as the optimal average hop distance of the anchor node; then, reducing the calculation steps in the standard DV-Hop, and solving the estimated distance between the unknown node and the anchor node by using the optimal average Hop distance of the anchor node; and finally, grouping the anchor nodes according to the distance between the anchor nodes and the unknown nodes, eliminating the potential error of the least square method by transforming the reference nodes, calculating the optimal unknown node coordinate of each anchor node group, and selecting the coordinate with the minimum error as the final coordinate of the unknown node.
According to the method, 100 sensor nodes are randomly distributed in an area of 100m multiplied by 100m, the total number of anchor nodes in a network is kept unchanged at 30, the total number of unknown nodes is 70, and the maximum communication radius of the nodes is 30 m; to avoid the chance of simulation results, all experimental results are the mean of 500 results under the same experimental conditions.
Fig. 3 is a comparison graph of average hop accuracy of anchor nodes. The result obtained by the reference [2] based on the minimum mean square error criterion is superior to the standard DV-Hop method in the reference [1], and the average jump distance precision of the anchor nodes obtained by adopting the weighted iteration scheme is superior to the results obtained in the reference [1] and the reference [2 ].
FIG. 4 is a comparison graph of unknown node-to-anchor node distance accuracy. The result obtained by weighting the average jump distance of the anchor nodes in the reference document [3] is superior to the standard DV-Hop method in the reference document [1], and the result obtained by the distance calculation method designed by the invention is most accurate.
FIG. 5 is a comparison graph of unknown node positioning accuracy for different node counts. The total number of the nodes in the simulation experiment condition is constantly changed, and other conditions are kept unchanged. The result shows that the method still has higher positioning accuracy under different total node numbers.
FIG. 6 is a comparison graph of unknown node location accuracy for different anchor node populations. The total number of anchor nodes in the simulation experiment condition is constantly changed, and other conditions are kept unchanged. The positioning accuracy obtained by the present invention is far superior to the methods in reference [1] and reference [4 ].
FIG. 7 is a comparison graph of unknown node positioning accuracy under different communication radiuses. The communication radius of the nodes in the simulation experiment condition is constantly changed, and other conditions are kept unchanged. The precision of the unknown node coordinate obtained by the method still has relatively high advantage and is closer to the real coordinate of the unknown node.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents, improvements and the like that fall within the spirit and principle of the present invention are intended to be included therein.

Claims (5)

1. A DV-Hop node positioning method based on weighted iteration and double selection is characterized by comprising the following steps:
the method comprises the following steps: calculating the optimal average hop distance of the anchor node by adopting a weighted iteration strategy: firstly, solving the initial average hop distance of an anchor node by using a minimum mean square error criterion, and then calculating the error value of each hop from the anchor node to other anchor nodes; the error value is used as a weight coefficient of the anchor node, and the average hop distance of the anchor node is recalculated by using the weighted minimum mean square error criterion; comparing the error of the average jump distance of the anchor nodes obtained after each weighted iteration, and if the error of the average jump distance of the anchor nodes obtained by the current iteration is smaller than the error of the last time, continuing the iteration; otherwise, finishing iteration, and taking the average hop distance of the anchor node obtained last time as the optimal average hop distance of the anchor node;
when the average hop distance of the anchor node i is calculated, the weight given to the anchor node j is as follows:
Figure FDA0003608317420000011
wherein, EHijAn error value representing each hop from anchor node i to anchor node j;
step two: calculating the estimated distance between the unknown node and the anchor node by using the optimal average hop distance of the anchor node obtained in the step one;
step three: and (3) solving the coordinates of the unknown nodes by adopting a double selection scheme: grouping the anchor nodes according to the distances from different anchor nodes to the unknown nodes, taking each anchor node in the anchor node group as a primary reference node, calculating the coordinates of the unknown nodes by using a least square method, and selecting the coordinate with the minimum error as the optimal unknown node coordinate of the anchor node group; finally, comparing the error of the optimal coordinate obtained by different anchor node groups, and selecting the coordinate with the minimum error as the final coordinate of the unknown node;
the anchor nodes closest to the unknown nodes are preferentially participated in positioning in the grouping, and the anchor nodes are divided into n-2 different anchor node groups, namely an anchor node set ANSET＜k＞K is 3, …, n; wherein, ANSET＜k＞Representing k anchor nodes closest to the unknown node; n is the total number of anchor nodes in the network.
2. The DV-Hop node positioning method based on weighted iteration and double selection according to claim 1, wherein the method for calculating the optimal average Hop distance of the anchor node by using the weighted iteration strategy in the first step is as follows:
step 1: initialization of the network: all anchor nodes broadcast data packets to the network in a flooding mode, and receiving nodes obtain the minimum hop count among the nodes in the network by changing hop count values and transmitting the data packets;
and 2, step: endowing all anchor nodes with the same weight, and solving the initial average hop distance of each anchor node by using the minimum hop count and the minimum mean square error criterion:
Figure FDA0003608317420000012
wherein AHDi initIs the initial average hop distance, Dis, of the anchor node iijRepresenting the actual distance, Hop, between anchor node i and anchor node jijRepresenting the minimum hop number from the anchor node i to the anchor node j, wherein n is the total number of the anchor nodes in the network;
and step 3: calculating the error of the initial average hop distance of the anchor node by using the initial average hop distance calculated in the step 2:
Figure FDA0003608317420000021
wherein eta isi initError, AHD, representing initial average hop distance of anchor node ii init·HopijThe estimated distance from the anchor node i to the anchor node j;
and 4, step 4: average jump distance AHD of anchor nodes obtained by step 2i initCalculating an estimated distance between anchor nodes, and calculating an error value of each hop between the anchor nodes by referring to an actual distance between the anchor nodes and the minimum hop count:
Figure FDA0003608317420000022
wherein, EHijAn error value representing each hop from anchor node i to anchor node j;
and 5: endowing weight alpha to corresponding anchor node by using error value of each hop between anchor nodesij：
Step 6: and 5, calculating the weighted average jump distance of the anchor nodes according to the weighted minimum mean square error criterion by using the weight values between the anchor nodes obtained in the step 5:
Figure FDA0003608317420000023
wherein AHDi (m)The average hop distance of the anchor node i obtained after the mth weighted iteration is obtained;
and 7: calculating the error of the weighted average jump distance of the anchor nodes according to the weighted average jump distance of the anchor nodes obtained in the step 6:
Figure FDA0003608317420000024
wherein eta isi (m)Representing the error of the average hop distance of the anchor node i obtained after the mth weighting iteration;
and 8: if the current weighting iteration number m is 1, comparing the error eta of the average jump distance of the anchor nodes obtained after the first weightingi (1)Error eta of initial average jump distance of anchor nodei initIf ηi (1)<ηi initAnd continuing to carry out weighted iterative solution on the average hop distance of the anchor node, returning to the step 4, and obtaining the average hop distance AHD of the anchor node by using the current iterationi (1)As initial average hop distance AHD in step 4i initRecalculating error per hop among anchor nodes, and iteratively calculating the average hop distance of the anchor nodes again by taking the error value per hop as a weight; if etai (1)>ηi initEnding the iterative calculation process, and obtaining the initial average hop distance AHD in the step 2i initAs the optimal average hop of the anchor node i;
if the current weighted iteration number m>1, comparing the error eta of the average jump distance of the anchor nodes obtained after the m-1 weightingi (m-1)Error eta of anchor node average jump distance obtained by current iterationi (m)If ηi (m)<ηi (m-1)And continuing to carry out weighted iterative solution on the average hop distance of the anchor node, returning to the step 4, and obtaining the AHD by using the current iterative weightingi (m)As initial average hop distance AHD in step 4i initRecalculating error per hop among anchor nodes, and iteratively calculating the average hop distance of the anchor nodes again by taking the error value per hop as a weight; if etai (m)>ηi (m-1)Ending iterative calculation process, and obtaining average jump distance AHD of anchor node in last iterationi (m-1)As the optimal average hop distance for anchor node i.
3. Weighted-based iteration according to claim 1 or 2And a double-selection DV-Hop node positioning method, wherein the calculation method of the estimated distance from the unknown node to the anchor node in the second step is as follows: dui＝AHDi best·Hopui；
Wherein d isuiFor the estimated distance, AHD, from unknown node u to anchor node ii bestRepresents the optimal average hop distance of the anchor node i; hopuiRepresents the minimum hop count of the unknown node u to the anchor node i, and u is 1.
4. The DV-Hop node positioning method based on weighted iteration and double selection according to claim 3, wherein in the third step, each anchor node in the anchor node group is used as a primary reference node, the coordinates of the unknown node are calculated by using a least square method, and the coordinate with the minimum error is selected as the optimal unknown node coordinate of the anchor node group; the method comprises the following steps of comparing the error of the optimal coordinate obtained by different anchor node groups, and selecting the coordinate with the minimum error as the final coordinate of the unknown node:
s1: aggregating anchor nodes into ANSET＜k＞Each anchor node in the system is used as a primary reference anchor node to calculate the coordinates of an unknown node;
s2: according to the estimated distance between the unknown node and the anchor node calculated in the step two, a least square method and an anchor node set ANSET are utilized＜k＞The anchor nodes in the system respectively calculate corresponding unknown node candidate coordinates (x)u,yu) And u ═ 1,. k;
s3: calculating the error of each unknown node candidate coordinate according to the coordinates of the unknown nodes and the coordinates of the anchor nodes:
Figure FDA0003608317420000031
wherein (x)u,yu) Estimated coordinates representing unknown node u, (x)i,yi) Coordinates representing anchor node i;
s4: ratio ofComparing per anchor node set ANSET＜k＞The candidate coordinate with the minimum error is used as the optimal candidate coordinate obtained by the anchor node set according to the obtained error of the candidate coordinate;
s5: and comparing the error of the optimal candidate coordinates obtained under different anchor node sets, and selecting the optimal candidate coordinates with the minimum error as the final coordinates of the unknown nodes.
5. The DV-Hop node location method based on weighted iteration and double selection according to claim 4, wherein the least square method in step S2 calculates candidate coordinates (x) of unknown nodeu,yu) The method comprises the following steps:
s31: obtaining an estimated distance from an unknown node to each anchor node:
let the coordinates of the unknown node be (x)u,yu) The coordinates of the anchor node are (x)i,yi) 1,2, …, k, unknown node-to-anchor node set ANSET＜k＞The estimated distances of the inner anchor nodes are respectively du1,du2,…,dukThen, the following equation set is given:
Figure FDA0003608317420000041
s32: converting the nonlinear equation set into a linear equation set to solve the coordinates of the unknown node:
Figure FDA0003608317420000042
s33: expressing the linear equation set in a matrix form of GX ═ H, where:
Figure FDA0003608317420000043
solving the coordinates of the unknown nodes by using a least square method:
X＝(GTG)-1GTH；
wherein G, H is a matrix representation of the linear equation set in step S32, and X represents the coordinates of the obtained unknown node.
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Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101004449A (en) * 2007-01-18 2007-07-25 北京航空航天大学 Weighted distance - vector method for positioning 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
CN108848449A (en) * 2018-06-25 2018-11-20 南京邮电大学 Based on the improved Localization Algorithm for Wireless Sensor Networks of DV-Hop
WO2019244104A1 (en) * 2018-06-22 2019-12-26 Humanitas Solutions Inc. Method and system for determining a position of a plurality of transmitting nodes
CN111556454A (en) * 2020-04-24 2020-08-18 河南工业大学 Weighted DV _ Hop node positioning method based on minimum mean square error criterion

Family Cites Families (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111479218B (en) * 2020-05-09 2022-09-06 上海海事大学 DQPSO algorithm-based positioning performance optimization method

Patent Citations (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN101004449A (en) * 2007-01-18 2007-07-25 北京航空航天大学 Weighted distance - vector method for positioning 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
WO2019244104A1 (en) * 2018-06-22 2019-12-26 Humanitas Solutions Inc. Method and system for determining a position of a plurality of transmitting nodes
CN108848449A (en) * 2018-06-25 2018-11-20 南京邮电大学 Based on the improved Localization Algorithm for Wireless Sensor Networks of DV-Hop
CN111556454A (en) * 2020-04-24 2020-08-18 河南工业大学 Weighted DV _ Hop node positioning method based on minimum mean square error criterion

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