KR20230039871A - Apparatus and method for localization in wireless sensor networks - Google Patents

Abstract

An embodiment of the present specification relates to a sensor node and a method for estimating the position of a sensor node in a wireless sensor network. The sensor node includes: a communication module that transmits and receives signals with other nodes; a memory in which a routing tree generation program is stored; and a processor that executes the program. According to the execution of the program, the processor calculates the movement direction and movement speed of a normal node based on the signals, measures the coordinate position and calculates an error range based on the movement direction and movement speed, and separates a defective node from other nodes based on machine learning.

Description

Apparatus and method for localization in wireless sensor networks}

The present invention relates to an apparatus and method for estimating a position of a wireless sensor network.

A wireless sensor network (WSN) is a network composed of a sensor node, a processor that processes information collected from the sensor node, and a wireless transceiver that transmits the processed information. It enables not only communication but also automated remote information collection, so it is widely used in various application developments such as tracking, monitoring, and control of sensor nodes in the network.

In a wireless sensor network, finding the location of a node that generated data is the most important information regardless of its purpose. Due to the large number of nodes, only a small percentage of nodes could be placed in the set positions at the beginning of node deployment. These nodes are so-called anchors or beacons. The other nodes are called normal nodes, and know a number of distances between them and their neighbors, and a number of distances to anchors. They must use localization algorithms to indicate their own location.

A factor to be considered for location estimation technology in a wireless sensor network is, firstly, the speed of location estimation technology. The speed of location estimation technology means the execution time required for the location value of the sensor node to be located to be given as a result value. Here, the execution time varies depending on the type of service, but can be treated as more important in a network that must be operated in real time. This is because the importance of data generated before position estimation by one node is ambiguous until the corresponding node finishes position estimation. Second, the accuracy of location data. Location estimation technology with low accuracy of location data is not necessary. Thirdly, it is the weight of position estimation on the entire wireless sensor network. The degree of weight for the entire system of location estimation is how much the system resources of the node are occupied by the location estimation technology when performing a service in one node. This means that location estimation technology becomes an issue related to node resource allocation in implementing a service.

In a conventional wireless network there is a trilateration. The three-line method is the most representative method of distance-based location estimation, and it is convenient in location estimation because it only needs to solve as many linear equations as the number of reference nodes considered. That is, the three-line method has a fast calculation speed and a low weight of the wireless sensor network. There is a limit reflected in the error of the actual position estimation.

In addition, there is a multidimensional scaling (MDS) as a position estimation technique in a conventional wireless network. The multidimensional survey method (MDS) calculates location information using a matrix having distance information for all pairs of neighboring nodes of one node, so it has an advantage of high accuracy even in a situation where there is a lot of distance measurement error. On the other hand, the multi-dimensional surveying method (MDS) has a disadvantage in that the number of distance information used for calculation is too high, so the complexity is high and the amount of calculation is large. Since this causes a problem of increasing the load of the wireless sensor network, there is a limit that is difficult to apply in practice.

In order to solve the aforementioned problems, a method based on a location estimation algorithm for accurately and quickly estimating the location of a mobile general node within a wireless sensor network and a sensor node executing the method are provided.

A sensor node in a wireless sensor network according to an embodiment of the present invention includes a communication module for transmitting and receiving signals to and from other nodes, a memory in which a routing tree creation program is stored, and a processor executing the program, wherein the processor comprises the program According to the execution of, calculating the movement direction and movement speed of the normal node based on the signal, measuring the coordinate position and calculating the error range based on the movement direction and movement speed, and calculating the error range of the defective node based on machine learning. It is characterized in that it detects and separates it from other nodes.

The communication module according to an embodiment is characterized in that it transmits and receives signals with other nodes located within the transmission range of the 3D wireless sensor network.

The processor according to an embodiment is characterized in that the location of the mobile node is identified based on a Gaussian kernel function.

The processor according to an embodiment may calculate a transmission range based on a signal transmission radius, and calculate a distance between a normal node and a sensor node based on the transmission range.

The processor according to an embodiment may calculate a coordinate value of a normal node using a distance between a normal node and a sensor node, calculate a root mean square error, and estimate a position of each node by applying the error.

A method for estimating a position in a wireless sensor network according to another embodiment of the present invention includes receiving a signal of a normal node; calculating a moving direction and a moving speed of a general node based on the signal; measuring a coordinate position based on the moving direction and moving speed, and calculating an error range; and determining the defective node based on machine learning and separating it from other nodes.

According to another embodiment of the present invention, a method for estimating a position in a wireless sensor network, a computer readable recording medium having a program recorded thereon, includes receiving a signal of a normal node; calculating a moving direction and a moving speed of a general node based on the signal; measuring a coordinate position based on the moving direction and moving speed, and calculating an error range; and determining the defective node based on machine learning and separating it from other nodes on a computer.

According to an embodiment disclosed in the present invention, there is an effect of improving the accuracy of a location estimation method in a wireless sensor network and reducing energy consumption by lowering computational complexity compared to the prior art.

1 is a diagram for explaining a 3D mobility model according to an embodiment of the present invention.
2 is a flowchart illustrating a method of a manifold and machine learning-based position estimation algorithm according to an embodiment of the present invention.
3 is a diagram for explaining a transmission range-based measurement method according to an embodiment of the present invention.
4 is a flowchart illustrating a 3D position estimation method according to an embodiment of the present invention.
A sensor node located in a wireless sensor network according to an embodiment of the present invention will be described with reference to FIG. 5 .
6 is a diagram showing an example of a simulation experiment environment according to an embodiment of the present invention.
7 is a graph showing an average localization error with respect to node density according to an embodiment of the present invention.
8 is a graph showing the relationship within a computed position and the average localization error according to one embodiment of the present invention.
9 is a graph showing position estimation errors of normal nodes according to an embodiment of the present invention.
10 is a graph showing the total time taken to identify localization errors with the total amount of sensor nodes for which the total time is the minimum value according to an embodiment of the present invention.
11 is a graph showing RMSE values versus standard deviations according to an embodiment of the present invention.

Since the present invention can have various changes and various embodiments, specific embodiments will be described in detail with reference to the drawings. However, this is not intended to limit the present invention to specific embodiments, and should be understood to include all modifications, equivalents, and substitutes included in the spirit and scope of the present invention. Like reference numerals have been used for like elements throughout the description of each figure.

Terms such as first, second, A, and B may be used to describe various components, but the components should not be limited by the terms. These terms are only used for the purpose of distinguishing one component from another. For example, a first element may be termed a second element, and similarly, a second element may be termed a first element, without departing from the scope of the present invention. The term and/or includes a combination of a plurality of related items or any one of a plurality of related items.

Terms used in this application are only used to describe specific embodiments, and are not intended to limit the present invention. Singular expressions include plural expressions unless the context clearly dictates otherwise. In this application, the terms "include" or "have" are intended to designate that there is a feature, number, step, operation, component, part, or combination thereof described in the specification, but one or more other features It should be understood that the presence or addition of numbers, steps, operations, components, parts, or combinations thereof is not precluded.

Throughout the specification and claims, when a part includes a certain component, it means that it may further include other components, not excluding other components unless otherwise stated.

1 is a diagram for explaining a 3D mobility model according to an embodiment of the present invention.

Referring to FIG. 1 , it can be seen that a sensor node N moves randomly in a transmission range of a wireless sensor network (WSN). The movement speed of all sensor nodes can be identified using the direction and speed of the previous period. The moving speed is calculated by Equation 1.

[Equation 1]

인 직선 운동을 나타낸다. 이동 노드의 방향은 수학식 2와 같이 계산된다. Here, ω _p denotes the moving speed of the sensor node in period p, and ω _p+1 denotes the moving speed of the sensor node in period p+1. α is a random variable that satisfies 0≤α≤1, and when α is 0, the sensor node's mobility indicates the completion of random movement, whereas when α is 1, the sensor node's movement speed is

represents linear motion. The direction of the mobile node is calculated as in Equation 2.

[Equation 2]

The position of a sensor node in a contiguous period can be represented by the position, direction and velocity of the previous value. The location coordinates (a _p+1 , b _p+1 ) may be calculated by Equations 3 and 4.

[Equation 3]

[Equation 4]

Whenever the sensor node moves in the 3D space, two angles γ _p and η _p exist at the movement position of the velocity-based coordinate value, and these values are calculated by Equations 5, 6, and 7.

[Equation 5]

[Equation 6]

[Equation 7]

The position of the mobility modeling in the adjacent period can be calculated by the previous position. do. The coordinate values of the sensor node locations at all locations can be calculated using Equations 8, 9, and 10.

[Equation 8]

[Equation 9]

[Equation 10]

The WSN location is measured in a 3-dimensional area, and the sensor node is accessed randomly as shown in FIG. 1 with the 3-dimensional coordinate point (a _p , b _p , c _p ) as a starting point. The sensor node moves while transmitting signals at preset cycles based on mobility, speed, and transmission radius. The mobility of the sensor node is measured up to a preset period.

2 is a flowchart illustrating a method of a manifold and machine learning-based position estimation algorithm according to an embodiment of the present invention.

The inputs and outputs of the manifold and machine learning-based localization algorithm being executed are as follows.

를 갖는 위치 행렬 LM=(lm₁, lm₂, ...lm_m,0,0.0) : 거리 백터의 집합

Input: sensor node

position matrix with LM=(lm ₁ , lm ₂ , ...lm _m ,0,0.0) : set of distance vectors

Here, the distance vector manages distance information to individual networks included in the entire network.

Output: estimated positions for unknown nodes

Referring to FIG. 2, the execution steps of the following manifold and machine learning-based position estimation algorithm are described.

In step 110, the geospatial matrix GS=(gs _ij ) of sensor nodes within the transmission range Trans _range is calculated as follows.

When Si ≤ Trans _range , gs _ij =Si _ij ,

In other cases, gs _ij = min{ gs _ij, gs _il + gs _lj }.

In step 120, a weighting matrix wt and a Laplacian matrix lm are generated as follows. A weighting matrix Wt is calculated, and a Laplacian matrix is generated based on the weighting matrix.

Trans _range =sum(wt);

Lm=trans _range -wt

In step 130, a sensor node for generating n-dimensional coordinates generates a matrix X = G(n, Si).

In step 140, a polynomial matrix is calculated based on the generated matrix as follows.

PM _ij = X _ij gs _ij

In step 150, a normal node, which is an unknown node, is identified by generating polynomial coefficients as follows.

= X_ij PM_ij

= X _ij PM _ij

Since the wireless sensor network (WSN) according to an embodiment of the present invention can efficiently show geometric properties of measurement information, it is easy to implement a manifold and machine learning-based position estimation technology.

In one embodiment, a kernel function may be used to identify similarities within the sensors and mobile node location of the WSN. In one embodiment, a Gaussian kernel function is adopted.

In one embodiment, a receiver signal strength indicator (RSSI) based communication range technique may be employed to calculate the energy loss of the signal propagation with the signal quality transmitted to the sensor node and to calculate the distance value from the energy loss parameter. can In the RSSI-based communication range technology, the relationship between distance and signal strength is calculated by Equation 11.

[Equation 11]

where dis is the distance of the receiving sensor node, dis ₀ is the distance of the transmitting sensor node, n is the channel quality, and Noise _z is an arbitrary value of the noise-based standard deviation z. In the graph of the relationship between distance and signal strength, the density function is calculated by Equation 12. In this graph, the X axis is the value of the signal strength, which is a random variable, and the Y axis is the average of the signal strength as the probability that the value of the random variable is included in a certain interval.

[Equation 12]

The value of y is calculated by Equation 13.

[Equation 13]

The standard deviation z is calculated by Equation 14.

[Equation 14]

In Equations 12 to 14, x _i represents the value of the signal strength, k represents the total amount of the experiment, and the transmission range function calculates the distance between the sender and the receiver in consideration of the signal strength. The transmission range for position estimation may be limited in some real-time application programs because the sender and the receiver must broadcast signals in similar periods when signals are generated.

3 is a diagram for explaining a transmission range-based measurement method according to an embodiment of the present invention.

Referring to FIG. 3 , a signal transmission range between node 1 and node 2 can be confirmed. It can be seen that a delay occurs between transmitting a signal from node 1, receiving a signal from node 2, transmitting a response signal to the received signal, and receiving a response signal from node 1 to the transmitted signal.

In the example of FIG. 3 , the measured value of the transmission range may be calculated by Equation 15.

[Equation 15]

During calculation of transmission range, there may be slight deviations between the distance obtained and the estimated distance. As shown in Equation 16, the distance can be measured based on the transmission period and signal speed of the two nodes.

[Equation 16]

distance = speed × duration

Analysis of transmission range calculation, path loss value is obtained based on real-time environment. The measurement accuracy is high when the communication range is very small. When the normal node distance is higher than the sensor node, the accuracy of the transmission range is very large. Computing the communication range requires the highest precision compared to other calculations.

4 is a flowchart illustrating a 3D position estimation method according to an embodiment of the present invention.

At step 210, the WSN collects signals.

Wireless sensor nodes SN _i (i = 1,2,…N) are randomly placed in the three-dimensional wireless sensor network transmission range, and within the total sensor nodes of the network, there are S sensor nodes, NS general nodes A _j (j = 1,2 , … , S), and the general node transmits signals periodically using the mobile sensor node. At this time, the transmission range is calculated based on the transmission radius Rad.

In step 220, a distance-based transmission range is calculated.

It uses a distance-based transmission range technique to calculate the distance of a specific sensor node. Construct a log-based communication model to generate attenuation signal values. This value is replaced with a specific node value to generate the signal strength.

In the WSN transmission area, the distance between the normal node and the sensor node is calculated as the minimum value of the preset threshold value of the normal node within the transmission range.

In step 230, the distance from the sensor node to the general node is calculated based on the time-based transmission range.

In step S231, the distance between the sensor node and the general node in the transmission area of the wireless sensor network is compared with an expected threshold distance. In step S232, when the transmission range is greater than the threshold distance and the minimum value is greater than the transmission radius, the distance from the sensor node to the normal node is calculated in step S233.

In step S234, if the distance between the sensor node and the normal node in the transmission area of the wireless sensor network is smaller than the expected critical distance, the transmission range is stored, and the transmission range calculation is completed in step S235.

In step 240, location coordinates are estimated based on the transmission range.

Identification of the location estimation process, coordinate values are generated using general nodes, and distance values are measured through the 3D transmission area of WSN.

When generating coordinate values, the root mean square error is displayed to measure the accuracy of the location estimation problem and is calculated by Equation 17.

[Equation 17]

는 j번째 일반 노드에 대해 좌표값을 나타내고, 센서 노드는 위치 추정 절차를 수행하는 동안 노이즈 값을 식별하는 실시간 애플리케이션을 위해 복잡한 환경에 배치된다. here,

denotes coordinate values for the j-th normal node, and the sensor node is deployed in a complex environment for real-time applications to identify noise values during position estimation procedures.

By measuring the distance-based power attenuation, the path loss value is calculated by Equation 18.

[Equation 18]

Here, τ is a coefficient for the path loss value according to the 3D WSN environment, and dis ₀ is the closer distance from the source to the transmitter.

A sensor node located in a wireless sensor network according to an embodiment of the present invention will be described with reference to FIG. 5 .

5 is a block diagram of a sensor node 100 according to an embodiment of the present invention.

The sensor node 100 includes a communication module 110 , a memory 120 and a processor 130 .

The communication module 110 performs data communication with a plurality of previously connected sensor nodes. That is, the communication module 110 may transmit and receive messages with the plurality of sensor nodes 100 .

The memory 120 stores manifold and machine learning based position estimation algorithms. At this time, the memory 120 may collectively refer to a non-volatile storage device that continuously retains stored information even when power is not supplied and a volatile storage device that requires power to maintain stored information.

The processor 130 executes a program stored in the memory 120. At this time, the processor 130 receives a signal from a sensor node that transmits a signal through the communication module 110 according to the execution of the program.

As described with reference to FIGS. 1 to 4 , the processor 130 estimates the location of a general node by executing a manifold and machine learning-based location estimation algorithm based on the received signal.

First, the processor 130 calculates the movement direction and movement speed of the general node based on the signal according to the execution of the program, measures the coordinate position based on the movement direction and movement speed, calculates an error range, , it separates faulty nodes from other nodes based on machine learning.

In one embodiment, processor 130 identifies a mobile node location based on a Gaussian kernel function.

In one embodiment, the processor 130 calculates the transmission range based on the signal transmission radius and calculates the distance of the sensor node based on the transmission range.

In one embodiment, the processor 130 calculates coordinate values of normal nodes using the distances of sensor nodes, calculates root mean square errors, and estimates the location of each node by applying the errors.

6 to 10 are diagrams for explaining MATLAB simulation results for evaluating a position estimation algorithm according to an embodiment of the present invention.

In the MATLAB simulation, the conventional position estimation method, TOA [TOA-based localization algorithm], PDR [RSSI/PDR-based probabilistic position selection algorithm], and RSSI [RSSI-based indoor localization and identification] graph and FIG. The performance of graphs by the location estimation algorithm described with reference was compared.

6 is a diagram showing an example of a simulation experiment environment according to an embodiment of the present invention.

Referring to FIG. 6 , wireless sensor nodes are shown in three dimensions, and red dots represent sensor nodes SN and green dots represent normal nodes KN. As simulation parameters, the parameters shown in Table 1 were applied.

[Table 1]

The simulation experiment is performed with 100 iterations, and the average value is used as the final value.

In the WSN location estimation process, the WSN with the highest density of sensor nodes can effectively minimize the positioning error in the entire network transmission area. Additionally, sensor node densities are increased, generating huge amounts of repetitive data. Faulty nodes are identified within the sensor node and isolated from other nodes using machine learning techniques to improve efficiency.

According to an embodiment of the present invention, it was shown that the average position error is minimized as the node density (%) increases. Localization error was measured and related to the accuracy of localization identification. Localization measurements are related to energy consumption and transmission costs.

In the WSN transmission area, the larger the transmission radius, the more sensor nodes must be identified and the position estimation error of the subsequent nodes must be minimized.

7 is a graph showing an average localization error with respect to node density according to an embodiment of the present invention.

Referring to FIG. 7 , it can be seen that according to an embodiment of the present invention, the amount of average position estimation error decreases as the transmission radius increases and the node density increases compared to the prior art. The localization error affects the computational position of the sensor node and the localization error subsequently increases whenever the computational position distance increases.

8 is a graph showing relationships within a computing location and average location estimation error according to one embodiment of the present invention. According to an embodiment of the present invention, the average position estimation error is minimized compared to the prior art. The communication range of the sensor node is estimated as the probability of the communication range measured according to the measurement area identified by the collective coverage of the WSN node and the sensing capacity of the sensor node in the network. Mobility-based positioning problems are identified in cooperative three-dimensional wireless sensor networks where RSSI values are utilized to provide improved accuracy. The location estimation of all nodes is calculated by measuring the distance between normal *** nodes and sensor nodes according to node weights. The RMSE parameter is used to measure accuracy as the distance of a sensor node in the network.

9 is a graph showing position estimation errors of normal nodes according to an embodiment of the present invention.

Referring to FIG. 9 , although the average position estimation error increases as the total amount of sensor nodes increases, according to an embodiment of the present invention, it can be seen that the average position estimation error is smaller than that of the prior art.

10 is a graph showing the total time taken to identify localization errors with the total amount of sensor nodes having the minimum total time according to an embodiment of the present invention.

Referring to FIG. 10 , it can be seen that the time of the algorithm according to an embodiment of the present invention is reduced compared to the conventional algorithm. The calculated standard deviation value can be used for noise identification with reduced position estimation error.

11 is a graph showing RMSE values versus standard deviations according to an embodiment of the present invention. Referring to FIG. 11, it can be seen that the RMSE value of the algorithm according to an embodiment of the present invention is reduced compared to the conventional algorithm.

과 같다.Complexity analysis according to an embodiment of the present invention was measured using the configuration of the sensor network, calculation of the sensor network, and calculation of the distance between the sensor node and the unknown node. Localization accuracy is measured for every sensor node and localization error is also calculated. The computational complexity of the algorithm according to an embodiment of the present invention considering position estimation is measured as O, and the complexity of general node identification is

Same as

A location estimation method according to an embodiment of the present invention estimates a location based on a transmission range, thereby minimizing a transmission range error. In addition, since the location of the sensor node is identified in a 3D environment, accuracy is improved. By identifying faulty nodes based on machine learning, accuracy can be improved. This effect can be confirmed through simulation results with reference to FIGS. 6 to 10 .

An embodiment of the present invention may be implemented in the form of a recording medium including instructions executable by a computer, such as program modules executed by a computer. Computer readable media can be any available media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media. Also, computer readable media may include both computer storage media and communication media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Communication media typically includes computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave, or other transport mechanism, and includes any information delivery media.

Although the methods and systems of the present invention have been described with reference to specific embodiments, some or all of their components or operations may be implemented using a computer system having a general-purpose hardware architecture.

The above description of the present invention is for illustrative purposes, and those skilled in the art can understand that it can be easily modified into other specific forms without changing the technical spirit or essential features of the present invention. will be. Therefore, the embodiments described above should be understood as illustrative in all respects and not limiting. For example, each component described as a single type may be implemented in a distributed manner, and similarly, components described as distributed may be implemented in a combined form.

The scope of the present invention is indicated by the following claims rather than the detailed description above, and all changes or modifications derived from the meaning and scope of the claims and equivalent concepts should be construed as being included in the scope of the present invention. do.

110 --- communication module
120 --- memory
130 --- processor

Claims (12)

In a sensor node in a wireless sensor network,
A communication module that transmits and receives signals with other nodes;
The memory in which the routing tree generation program is stored and
A processor for executing the program;
The processor calculates a moving direction and a moving speed of a normal node based on the signal according to execution of the program,
A sensor node, characterized in that for measuring a coordinate position based on the moving direction and moving speed, calculating an error range, detecting a defective node based on machine learning, and separating it from other nodes. The sensor node according to claim 1, wherein the communication module transmits and receives signals with other nodes located within a transmission range of the 3D wireless sensor network. According to claim 1,
Wherein the processor identifies a mobile node location based on a Gaussian kernel function. According to claim 1,
Wherein the processor calculates a transmission range based on a signal transmission radius, and calculates a distance between a normal node and a sensor node based on the transmission range. According to claim 4,
The sensor node, characterized in that the processor calculates the coordinate value of the normal node using the distance between the normal node and the sensor node, calculates the root mean square error, and estimates the position of each node by applying the error. As a location estimation method in a wireless sensor network,
Receiving a signal of a normal node;
calculating a moving direction and a moving speed of a general node based on the signal;
measuring a coordinate position based on the moving direction and moving speed, and calculating an error range; and
Determining a defective node based on machine learning and separating it from other nodes
Location estimation method comprising a. [Claim 7] The method of claim 6, wherein the general node is located within a transmission range of the 3D wireless sensor network. According to claim 6,
The step of calculating the movement direction and movement speed of the general node
A location estimation method characterized by identifying a mobile node location based on a Gaussian kernel function. According to claim 6,
calculating a moving direction and a moving speed of the normal node;
A location estimation method comprising calculating a transmission range based on a signal transmission radius, and calculating a distance between a normal node and a sensor node based on the transmission range. According to claim 9,
The method of estimating a position, characterized in that by using the distance between the normal node and the sensor node, the coordinate value of the normal node is calculated, the root mean square error is calculated, and the position of each node is estimated by applying the error. According to claim 6,
Calculating the movement direction and movement speed of the general node based on the signal,
and calculating a distance to a normal node based on the signal strength of the received signal. A computer-readable recording medium recording a program for performing the method according to any one of claims 6 to 10 on a computer.

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