CN113573407A - MSVR-DV-Hop positioning algorithm based on beacon screening - Google Patents

MSVR-DV-Hop positioning algorithm based on beacon screening Download PDF

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CN113573407A
CN113573407A CN202110849530.3A CN202110849530A CN113573407A CN 113573407 A CN113573407 A CN 113573407A CN 202110849530 A CN202110849530 A CN 202110849530A CN 113573407 A CN113573407 A CN 113573407A
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beacon
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张德敬
张向成
董恩清
解锋锋
王传哲
徐文
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Shandong University
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W64/00—Locating users or terminals or network equipment for network management purposes, e.g. mobility management
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04L—TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
    • H04L41/00—Arrangements for maintenance, administration or management of data switching networks, e.g. of packet switching networks
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
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Abstract

The invention discloses an MSVR-DV-Hop algorithm based on beacon screening, which is characterized in that RSSI (received signal strength indicator) -based Hop number refinement is adopted, the Hop number between nodes after grading is obtained, the distance information between an unknown node and a beacon node is calculated by combining MSVR, and the beacon node with smaller error is screened by introducing verification error of the beacon node aiming at the phenomenon that the error of the MSVR-based algorithm is obviously increased when the beacon node is less; and finally, calculating the coordinates of the unknown nodes by using a weighted least square method. The algorithm precision of the invention is obviously improved, and the invention is less influenced by the network topology, and is suitable for the anisotropic network; the method is not easily influenced by the network scale, still shows higher precision and has higher stability in a small-scale anisotropic network; the method also shows good performance in the scene of a small number of beacon nodes.

Description

MSVR-DV-Hop positioning algorithm based on beacon screening
Technical Field
The invention belongs to the field of wireless sensor network node positioning, and particularly relates to a MSVR-DV-Hop positioning algorithm based on beacon screening.
Background
As a system-level project, the wireless sensor network is divided into multiple fields for research, wherein, since many location-aware protocols and applications need to obtain support for location information, determining the location of an unknown node is a very critical technology and has been a research hotspot. According to the principle of whether distance measurement is carried out or not during positioning, WSN positioning algorithms can be divided into two types of distance measurement-based algorithm and distance measurement-free algorithm, a DV-Hop algorithm is used as a classical distance measurement-free algorithm and is widely applied due to the advantages of small network communication overhead, small energy loss and the like, but the DV-Hop algorithm generates error accumulation during calculation of minimum Hop count and average Hop distance and estimation of unknown node coordinates, and therefore algorithm errors are large. Therefore, reducing the accumulated positioning error of the DV-Hop positioning algorithm is one of the important problems for accurate positioning.
Many studies have proposed improved methods at various stages of the algorithm for this problem, and in the stage of obtaining the hop count between nodes, the communication distance is usually used to correct the hop count, and furthermore, the rssi (received Signal Strength indication) -based improvements are more adopted in the studies. The RSSI is a method for obtaining distance information from received signal strength according to the characteristic that a wireless signal attenuates along with the distance, and is a common distance-combining improvement method in DV-Hop improvement due to low cost and easy realization. In the stage of obtaining the distance from an unknown node to a beacon node, the improvement method mainly carries out different types of weighted correction on the average hop distance. In the stage of estimating the coordinates of the unknown nodes, because the coordinates of the unknown nodes are estimated by using the least square method in the traditional DV-Hop algorithm, the estimation result is extremely susceptible to the error of the estimated distance between the beacon nodes and the unknown nodes. The main improved methods comprise a weighted least square method, a two-dimensional hyperbolic algorithm, a differential evolution algorithm, a simulated annealing algorithm, a particle swarm optimization algorithm and the like. In addition, in recent years, researchers propose to combine a machine learning theory with a positioning algorithm, and an lmsvr (localization Based on multimedia Support Vector regression) estimates the coordinates of an unknown node through Hop count information between nodes.
Disclosure of Invention
The invention provides a beacon screening-based MSVR-DV-Hop node positioning method, which can effectively improve positioning accuracy and has low requirement on network topology distribution.
The invention adopts the following technical scheme:
a MSVR-DV-Hop node positioning method based on beacon screening comprises the following steps:
step 1: each node acquires the minimum hop count among the nodes, and the beacon node sends the hop count table to the sink node;
step 2: the sink node trains an MSVR model;
and step 3: the sink node calculates a beacon verification error;
and 4, step 4: the sink node broadcasts the model parameters and the beacon verification errors to each node;
and 5: and the unknown nodes screen the available beacon nodes and calculate the coordinates of the unknown nodes.
Wherein, the hop count obtained in the step 1 adopts RSSI-based first hop hierarchical refinement,
the first jump is divided into n grades, and the grading mode is as follows:
Figure BDA0003181899000000021
the first hop hierarchical refinement h is expressed as:
Figure BDA0003181899000000022
wherein d is the distance between two nodes, R is the communication radius, n is the first-hop series, i is the ith stage of the n stages, A is the strength value of the wireless signal received by the receiving node when the wireless transceiving node is 1m away, and k is the strength value of the wireless signal received by the receiving node when the wireless transceiving node is 1m away0Is a propagation factor, P, of a radio signalrIs the measured RSSI power at distance d.
In said step 2The MSVR model utilizes pairs of training data (h) for the beacon nodesi，Ii) Constructing a regression model and outputting estimated coordinates of unknown nodes by using the model, wherein hiAs node-to-beacon hop vectors, IiIs a node coordinate; when constructing a regression model from a training data set, the multidimensional regression function is
Figure BDA0003181899000000023
Where ω is a weight matrix, b is a two-dimensional regression deviation vector,
Figure BDA0003181899000000024
and n is the number of beacon nodes as a non-linear function.
Defining an epsilon-insensitive quadratic loss function LεIs composed of
Figure BDA0003181899000000025
Where z is the function argument and ε is the insensitivity.
The transition variable alpha is
Figure BDA0003181899000000031
The training process can be equivalent to an optimization problem
Figure BDA0003181899000000032
The constraint condition is
Figure BDA0003181899000000033
ξi≥0
Wherein C is a soft boundary parameter ξiIs a variable of relaxationRegularization parameter λ 1/C, hiAs node-to-beacon hop vectors, IiAs node coordinates, k (h)i,hj) For the kernel function, i, j is the node index.
The optimal solution of alpha and xi can be obtained through the equivalent optimization problem and is used as a model parameter.
In the step 3, in the sink node, the beacon node i is sequentially selected, and the actual distance vector from the beacon node i to other beacon nodes is (d)1,d2,...,dn) Training the MSVR model by using hop count and distance information of the beacon nodes except i, and predicting the predicted distance vector from i to other beacon nodes to be
Figure BDA0003181899000000034
And accumulating the absolute value of the difference value between the actual distance vector and the predicted distance vector to obtain the verification error of each beacon node.
In the step 5, an equation is constructed by using the available beacon nodes and is solved by using a weighted least square method,
Figure BDA0003181899000000035
where (x, y) is the coordinate to be estimated, (x)i,yi) The coordinate of the ith beacon node is, n is the number of available beacon nodes, and the weight formula is as follows:
Figure BDA0003181899000000036
in the step (5), when the distance from the unknown node to a certain beacon and the verification error of the beacon are both greater than the average value of the list where the reference quantity is located, the unknown node does not adopt the distance information of the beacon to solve an equation when estimating the coordinate of the unknown node, and the positioning deviation caused by a few high-error beacon nodes is corrected.
The invention has the beneficial effects that: the method adopts the method of RSSI Hop number hierarchical refinement and combination of an n-dimensional MSVR model and a DV-Hop algorithm, predicts the distance from an unknown node to each beacon based on the refined Hop number between beacon nodes and a distance training regression model, and calculates the coordinate of the unknown node by combining the DV-Hop algorithm so as to fully utilize potential information in a network and achieve good positioning accuracy. The positioning accuracy of the node is obviously improved by applying the method, the algorithm is less influenced by the network topology, and the method is suitable for the anisotropic network; the method is not easily influenced by the network scale, still shows higher precision in a small-scale anisotropic network and has high stability; the method has good performance in the scene with a small number of beacon nodes.
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FIG. 1 is a schematic diagram of a sensor node distribution, wherein (a), (b), (C), (d) and (e) are isotropic, X-type, H-type, S-type and C-type networks.
FIG. 2 is a simulation comparison diagram of the influence of communication radius on positioning error under different network types, wherein (a), (b), (C), (d) and (e) are isotropic, X-type, H-type, S-type and C-type networks respectively.
FIG. 3 is a simulation comparison diagram of the influence of the number of beacons on the positioning error under different network types, wherein (a), (b), (C), (d) and (e) are isotropic, X-type, H-type, S-type and C-type networks respectively.
FIG. 4 is a simulation comparison diagram of the influence of the total number of nodes on the positioning error under different network types, wherein (a), (b), (C), (d) and (e) are isotropic, X-type, H-type, S-type and C-type networks respectively.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings and specific embodiments.
A MSVR-DV-Hop node positioning method based on beacon screening comprises the following steps:
step 1: each node acquires the minimum hop count between each other, and the beacon node sends the hop count table to the sink node.
Each anchor node broadcasts a data packet to adjacent nodes, and each node acquires the minimum hop value of the anchor node through a forwarding mechanism. In the forwarding process, the communication radius is divided into n levels by using the RSSI value, single-hop refinement is firstly carried out between nodes according to the received RSSI value of the adjacent node, and then the refined single-hop is accumulated and updated into a forwarded data packet. And the sink node receives the hop count and the distance between the beacon nodes according to the ID of the data packet and stores the hop count and the distance as a hop count matrix and a distance matrix.
And acquiring hop count by adopting RSSI-based first hop hierarchical refinement. The first hop is divided into n grades by using the relation between the distance d between two nodes and the communication radius R, and the grading mode is as follows
Figure BDA0003181899000000051
The received RSSI value is used for representing the distance between two nodes, the power of Gaussian noise is far less than the signal strength received by a first-hop neighbor node, so that the Gaussian noise can be ignored, and the first-hop hierarchical refinement can be represented as:
Figure BDA0003181899000000052
step 2: and the sink node trains the MSVR model.
And the sink node trains the MSVR model by taking the hop number matrix as input and the distance matrix as output, and calculates the optimal model parameters.
MSVR model utilizes pairs of training data (h) for beacon nodesi，Ii) Constructing a regression model and outputting estimated coordinates of unknown nodes by using the model, wherein hiAs node-to-beacon hop vectors, IiAre the coordinates of the nodes. When constructing a regression model from a training data set, the multidimensional regression function is
Figure BDA0003181899000000053
Where ω is a weight matrix, b is a two-dimensional regression deviation vector, φ is a nonlinear function, and n is the number of beacon nodes.
Defining an epsilon-insensitive quadratic loss function LεIs composed of
Figure BDA0003181899000000054
Where z is the function argument and ε is the insensitivity.
The transition variable alpha is
Figure BDA0003181899000000055
The training process can be equivalent to an optimization problem
Figure BDA0003181899000000061
The constraint condition is
Figure BDA0003181899000000062
ξi≥0
Wherein C is a soft boundary parameter ξiFor relaxation variables, the regularization parameter λ is 1/C, hiAs node-to-beacon hop vectors, IiAs node coordinates, k (h)i,hj) For the kernel function, i, j is the node index.
The optimal solution of alpha and xi can be obtained through the equivalent optimization problem and is used as a model parameter.
And step 3: a beacon validation error is calculated.
And the sink node trains the MSVR model by using the hop number matrix and the distance matrix after the single beacon node is removed respectively, and then the removed single node is used for verification to obtain the verification error of each beacon node.
The beacon's authentication error is used to correct for positioning bias due to a small number of high error beacons. In the sink node, selecting a beacon node i, wherein the distance vector from the beacon node i to other beacon nodes is (d)1,d2,…,dn) Training the MSVR model by using hop count and distance information of the beacon nodes except i, and predicting the distance vector from i to other beacon nodes
Figure BDA0003181899000000063
The verification error is calculated by comparing the actual distance vector with the predicted distance vector as follows.
Figure BDA0003181899000000064
And 4, step 4: the sink node broadcasts the model parameters and beacon validation errors to each node.
And 5: and the unknown nodes screen the available beacon nodes and calculate the coordinates of the unknown nodes.
After the unknown node receives the broadcast packet of the convergent node, the hop number matrix from the node to each beacon is used as input, the distance from the node to each beacon is predicted through the MSVR model, the verification error of each beacon is integrated, and when the distance from the node to the beacon with the large verification error is far, the node is marked as an unavailable beacon. And the unknown node calculates the coordinates of the node by using the distance between the unknown node and the available beacon nodes and the coordinate solution weighted least square equation of the available beacon.
After the unknown nodes screen the beacon nodes, an equation is constructed by using the available beacon nodes, and a weighted least square method is used for solving.
Figure BDA0003181899000000065
Where (x, y) is the coordinate to be estimated, (x)i,yi) And n is the number of available beacon nodes for the ith beacon coordinate. In the algorithm proposed herein, the weighting mode adopted is distance-based weighting, i.e. the weight formula is
Figure BDA0003181899000000071
The invention relates to a beacon screening-based MSVR-DV-Hop algorithm, wherein RSSI (received Signal Strength indicator) graded refinement is introduced in a Hop count acquisition stage so as to acquire more accurate Hop count information, and data which is more in line with actual conditions is provided for a training set when an MSVR model is trained; in the distance calculation stage, the distance between an unknown node and a beacon node is obtained by using the multi-dimensional MSVR, the process of estimating the coordinates of the node by using the hop count is divided into two parts, and the error caused by the MSVR model in the subsequent algorithm is further corrected so as to increase the accuracy of the algorithm; in the coordinate estimation stage, a beacon node verification error is designed according to mutual verification among beacon nodes, and the unknown node calculates the estimated coordinate of the unknown node based on the beacon node verification error and the beacon nodes with larger distance elimination errors from the unknown node to the beacon nodes by using a weighted least square method. By screening the beacon nodes, the large-error beacon nodes do not participate in equation solution, so that the influence of the beacon node errors in the coordinate equation solution is reduced.
In the embodiment, 5 network types including an isotropic network, an X-type network, an H-type network, an S-type network and a C-type network are respectively used for carrying out comparative research on a classical DV-Hop algorithm, an LMSVR algorithm, a DV-Hop based on a simulated annealing algorithm and 4 algorithms including an MSVR-DV-Hop algorithm based on beacon screening, wherein the node distribution of the 5 network types is shown in figure 1, and the comparative results of three aspects including the variation positioning error of the communication radius (shown in figure 2), the variation positioning error of the number of beacon nodes (shown in figure 3) and the variation positioning error of the total number of nodes (shown in figure 4) are known, so that the method can better mine and utilize hidden network information, and has more advantages when an unknown anisotropic network is needed in actual conditions; the influence of network topology is small, and high precision can be achieved under complex conditions; the method still shows higher precision in the small-scale anisotropic network, improves the problem of serious precision reduction caused by beacon reduction when only an LMSVR algorithm is adopted, is not easily influenced by the network scale, and has higher stability.
Finally, it should be noted that the above embodiments are only used for illustrating the technical solutions of the present invention and not for limiting, and although the present invention is described in detail with reference to the preferred embodiments, the protection scope of the present invention is not limited thereto, and any modifications or equivalent substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention disclosed herein without departing from the spirit and scope of the technical solutions of the present invention should be covered within the protection scope of the present invention.

Claims (5)

1. A MSVR-DV-Hop node positioning method based on beacon screening is characterized in that: the method comprises the following steps:
step 1: each node acquires the minimum hop count among the nodes, and the beacon node sends the hop count table to the sink node;
step 2: the sink node trains an MSVR model;
and step 3: the sink node calculates a beacon verification error;
and 4, step 4: the sink node broadcasts the model parameters and the beacon verification errors to each node;
and 5: the unknown nodes screen available beacon nodes and calculate self coordinates;
and obtaining the hop count in the step 1, and carrying out first hop hierarchical refinement based on RSSI.
2. The MSVR-DV-Hop node positioning method based on beacon screening according to claim 1, wherein:
in the step 1, the first jump is divided into n grades, and the grading mode is as follows:
Figure FDA0003181898990000011
the first hop hierarchical refinement h is expressed as:
Figure FDA0003181898990000012
wherein d is the distance between two nodes, R is the communication radius, n is the first-hop series, i is the ith stage of the n stages, A is the strength value of the wireless signal received by the receiving node when the wireless transceiving node is 1m away, and k is the strength value of the wireless signal received by the receiving node when the wireless transceiving node is 1m away0Is a propagation factor, P, of a radio signalrIs the measured RSSI power at distance d.
3. According to the claimsSolving 1 the MSVR-DV-Hop node positioning method based on beacon screening, which is characterized in that: in step 2, the MSVR model utilizes the training data pair (h) of the beacon nodei，Ii) Constructing a regression model and outputting estimated coordinates of unknown nodes by using the model, wherein hiAs node-to-beacon hop vectors, IiIs a node coordinate; when constructing a regression model from a training data set, the multidimensional regression function is
Figure FDA0003181898990000013
Where ω is a weight matrix, b is a two-dimensional regression deviation vector,
Figure FDA0003181898990000021
is a nonlinear function, n is the number of beacon nodes;
defining an epsilon-insensitive quadratic loss function LεIs composed of
Figure FDA0003181898990000022
Wherein z is a function argument and epsilon is insensitivity;
the transition variable alpha is
Figure FDA0003181898990000023
The training process can be equivalent to an optimization problem
Figure FDA0003181898990000024
The constraint condition is
Figure FDA0003181898990000025
ξi≥0
Wherein C is a soft boundary parameter ξiFor relaxation variables, the regularization parameter λ is 1/C, hiAs node-to-beacon hop vectors, IiAs node coordinates, k (h)i,hj) Is a kernel function, i, j is a node subscript; and obtaining the optimal solution of alpha and xi as model parameters through the equivalent optimization problem.
4. The MSVR-DV-Hop node positioning method based on beacon screening according to claim 1, wherein: in the step 3, in the sink node, the beacon node i is sequentially selected, and the actual distance vector from the beacon node i to other beacon nodes is (d)1,d2,...,dn) Training the MSVR model by using hop count and distance information of the beacon nodes except i, and predicting the predicted distance vector from i to other beacon nodes to be
Figure FDA0003181898990000026
And accumulating the absolute value of the difference value between the actual distance vector and the predicted distance vector to obtain the verification error of each beacon node.
5. The MSVR-DV-Hop node positioning method based on beacon screening according to claim 1, wherein: in the step 5, an equation is constructed by using the available beacon nodes and is solved by using a weighted least square method,
Figure FDA0003181898990000027
where (x, y) is the coordinate to be estimated, (x)i,yi) The coordinate of the ith beacon node is, n is the number of available beacon nodes, and the weight formula is as follows:
Figure FDA0003181898990000031
in the step (5), when the distance from the unknown node to a certain beacon and the verification error of the beacon are both greater than the average value of the list where the reference quantity is located, the unknown node does not adopt the distance information of the beacon to solve an equation when estimating the coordinate of the unknown node, and the positioning deviation caused by a few high-error beacon nodes is corrected.
CN202110849530.3A 2021-08-30 2021-08-30 MSVR-DV-Hop positioning algorithm based on beacon screening Withdrawn CN113573407A (en)

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Citations (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112888064A (en) * 2021-01-12 2021-06-01 上海海事大学 Improved wireless sensor network DV-HOP positioning optimization method based on M-CG

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* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112888064A (en) * 2021-01-12 2021-06-01 上海海事大学 Improved wireless sensor network DV-HOP positioning optimization method based on M-CG

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DEJING ZHANG: "Research on Location Algorithm Based on Beacon Filtering Combining DV-Hop and Multidimensional Support Vector Regression", 《SENSORS》 *

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Application publication date: 20211029