CN102869090B - AUV (autonomous underwater vehicle)-assisted based underwater wireless sensor network positioning method - Google Patents

Abstract

The invention discloses an underwater wireless sensor network positioning method based on AUV assistance. The method of the present invention first uses the centroid algorithm to locate the unknown node once, estimates the positioning accuracy of the unknown node according to the number of beacon nodes around the unknown node, the distribution of the beacon nodes, and the distance between the beacon node and the unknown node, and screens out the nodes For some nodes with low positioning accuracy, AUV is used to assist these nodes in the second positioning. This method consumes less calculation and communication, and can make full use of AUV to help nodes improve positioning accuracy and reduce positioning errors. It is suitable for underwater wireless sensor networks. Positioning of unknown nodes.

Description

A localization method for underwater wireless sensor network based on AUV assistance

technical field

The invention relates to a wireless sensor network positioning method, in particular to an underwater wireless sensor network positioning method, which utilizes AUVs to assist in realizing accurate positioning of unknown nodes in the network.

Background technique

The underwater environment is relatively complex, and positioning is limited by various aspects. Many wireless sensor network positioning technologies on the ground are not suitable for underwater. Common underwater wireless sensor network positioning algorithms mainly include buoy-based positioning algorithms, AUV-based positioning algorithms, USP (distributed Underwater Sensor Positioning framework) positioning algorithms, SBRAL (Surface-Based Reflection Anchor-free Localization) positioning algorithms, SLMP ( Scalable Localization scheme with Mobility Prediction) positioning algorithm, underwater ASP (Ad-hoc Position System) positioning system and magnetometer-based positioning algorithm, etc.

The positioning algorithm based on smart buoys includes buoy nodes equipped with GPS equipment that can float on the water surface. These buoy nodes obtain their own accurate coordinates through GPS, and other nodes calculate their own coordinates by communicating with at least three buoy nodes. This method is only suitable for small-scale UWSNs. Then the hierarchical positioning algorithm proposed on this basis contains three kinds of nodes in the positioning system: buoy nodes, beacon nodes and unknown nodes. The beacon node first uses the buoy node to obtain its own coordinates, and then the unknown node communicates with the beacon node, using a similar trilateration method for positioning, thereby reducing the amount of communication, suitable for large-scale positioning systems, but this positioning algorithm uses an iterative algorithm, There will be cumulative errors.

The USP positioning algorithm uses projection technology to convert underwater three-dimensional positioning into two-dimensional positioning. In this positioning method, the depth of the node is obtained using a pressure sensor, and three beacon nodes that are not on the same straight line are projected to the plane where the unknown node is located, and then the coordinates of the unknown node are calculated using a similar trilateration method. However, the positioning coverage of the USP algorithm has a great relationship with the node connectivity, and the large amount of candidate location information generated by the algorithm will increase the storage burden of the nodes.

Lloyd Emokpae et al. proposed a positioning algorithm for non-beacon nodes based on water surface reflection. The SBRAL positioning algorithm first establishes a communication link reflected by the water surface, and then establishes a relative coordinate system. Each node obtains information of other nodes by changing the angle of the transmitted signal, and the intersection point of the reflection point in the water surface grid is used as a reference. Nodes, using the trilateration method to calculate the node position. This algorithm gets rid of the dependence on the positioning scene and fixed beacon nodes, but the positioning accuracy of SBRAL is greatly affected by the frequency of water waves on the water surface.

In the SLMP positioning algorithm, positioning is divided into beacon node positioning and ordinary node positioning. The beacon node uses the buoy node to locate first, and the ordinary node locates according to the beacon node. Each node predicts its own position according to the historical coordinate data of its own node. Mobile mode, estimate the current position coordinates based on the mobile model and past coordinates. The introduction of the mobile model can reduce the communication traffic consumption and simplify the node positioning process, but the positioning error is significantly affected by the node density, prediction model, and information value settings.

Melike Erol proposed a positioning algorithm based on self-moving nodes, using an AUV (Autonomous underwater vehicle, autonomous underwater vehicle) to help node positioning, when the AUV is floating, it obtains its own position coordinates by receiving GPS signals, and then moves along the Moving along the preset route, the AUV can calculate its own coordinates according to the moving route. When the AUV moves into the node deployment area, it broadcasts an information packet. After receiving the information packet, the node obtains the coordinates of the AUV and the distance from the AUV. After reaching at least four groups, calculate its own coordinates according to the trilateration method. This algorithm does not require beacon nodes, and can obtain better positioning coverage when the AUV broadcast message grouping frequency is high, but the positioning accuracy is greatly affected by the AUV's movement trajectory, and in addition, the nodes need to monitor the wake-up packets sent by the AUV during the positioning process And send the request packet to the AUV, which increases the energy consumption and communication overhead of the node. On this basis, Hanjiang Luo and others proposed the LDB (Localization with Directional Beacons) positioning algorithm. The AUV installs a directional transceiver and emits a cone-shaped sound wave, which can improve the accuracy of distance calculation. Only two beams are needed for positioning calculation. AUV message package, the node only needs to receive the message sent by the AUV, and does not need to send a request message, which reduces the energy and communication overhead, but this algorithm is only applicable when the AUV moves along a straight line, and the position of the node in the positioning will cause uncertainty .

Contents of the invention

The technical problem to be solved by the present invention is to overcome the deficiencies of existing underwater wireless sensor network positioning methods, and provide an underwater wireless sensor network positioning method based on AUV assistance. Position again to improve positioning accuracy.

The present invention specifically adopts the following technical solutions:

An underwater wireless sensor network positioning method based on AUV assistance, in the underwater wireless sensor network

Setting up multiple beacon nodes includes the following steps:

Step A, each beacon node sends a beacon message containing its own coordinate information; the unknown node initially determines its own position coordinates by using the centroid algorithm according to the coordinates of the beacon node in the received beacon message;

Step B, use AUV to assist in the secondary positioning of unknown nodes, specifically according to the following methods:

Step B1, establish a spatial Cartesian coordinate system with the unknown node as the origin, divide the communication range of the unknown node into 8 areas, each hexagram limit corresponds to an area, and count the number of beacon nodes in each area;

Step B2, moving at least one AUV to an area with the fewest number of beacon nodes, and its distance from the unknown node is equal to the average distance between the unknown node and each beacon node within its communication range;

Step B3, using the AUV as a new beacon node, using the centroid algorithm to re-determine the coordinates of the unknown node.

When using beacon nodes for the initial positioning of unknown nodes, the accuracy of positioning is greatly affected by the number of beacon nodes around the unknown node, the distribution of beacon nodes, and the distance between beacon nodes and unknown nodes. The distribution of marker nodes is different. During the initial positioning, there will be some unknown nodes with high positioning accuracy, and there is no need for subsequent AUV-assisted relocation. For this reason, according to the number of beacon nodes around the unknown nodes, the distribution of beacon nodes and The distance between the beacon node and the unknown node is used to estimate the positioning accuracy of the unknown node, and several nodes with low node positioning accuracy are screened out, and AUV is used to assist these nodes in the second positioning. This method consumes less calculation and communication, and can fully Use AUV to help nodes improve positioning accuracy and reduce positioning errors. The present invention further adopts the following preferred technical solutions:

As mentioned above, based on the AUV-assisted underwater wireless sensor network positioning method, in step B, the first k unknown nodes with the lowest AUV-assisted positioning accuracy or the unknown nodes whose positioning accuracy is less than a preset threshold are used for secondary positioning, and the positioning accuracy of the unknown node i is Calculate according to the following formula:

In the formula, is the number of beacon nodes within the communication range of unknown node i ; is the average distance between unknown node i and each beacon node within its communication range; is the variance of the distance between unknown node i and each beacon node within its communication range; is the distribution variance of beacon nodes within the communication range of unknown node i , and its expression is , establish a spatial Cartesian coordinate system with the unknown node i as the origin, divide the communication range of the unknown node i into 8 areas, and each hexagram limit corresponds to an area, is the average number of beacon nodes in 8 regions, is the number of beacon nodes in the jth area; α , β , ε , θ ∈(0,1], are weight parameters.

This method first uses the centroid algorithm to locate the unknown node, and estimates the positioning accuracy of the unknown node according to the number of beacon nodes around the unknown node, the distribution of the beacon nodes, and the distance between the beacon node and the unknown node, and screens out the node positioning accuracy. For some nodes with low precision, use AUV to assist these nodes in the second positioning. This method consumes less calculation and communication, and can make full use of AUV to help nodes improve positioning accuracy and reduce positioning errors. It is suitable for underwater wireless sensor networks. Node positioning.

Description of drawings

FIG. 1 is a schematic diagram of a beacon message grouping structure in the present invention;

Fig. 2 is a schematic diagram of a node message grouping record table of the present invention;

Figure 3 is a schematic diagram of the distribution of beacon nodes within the communication range of unknown nodes;

Figure 4 is a schematic diagram of the distribution of beacon nodes within the communication range of unknown nodes after adding AUV.

Detailed ways

The technical scheme of the present invention is described in detail below in conjunction with accompanying drawing:

The present invention first uses the centroid algorithm to locate the unknown node, and then estimates the positioning accuracy of the unknown node according to the number of beacon nodes around the unknown node, the distribution of the beacon nodes and the distance between the beacon node and the unknown node, and screens out the nodes For some nodes with low positioning accuracy, AUV is used to assist these nodes in the second positioning. AUV is a form of UUV (Unmanned Underwater Vehicle) that integrates artificial intelligence, depth vehicles, computer software, sensors and mission controllers with advanced technologies. Different AUV systems have different designs and functional divisions. The usual AUV system includes the following parts: power monitoring and management system, environmental detection system, obstacle detection system, navigation system, planning and control system, power system, communication system and task management systems. AUV can use its own equipment to achieve precise positioning. For example, it can float AUV to the water surface, receive GPS signals through its own GPS antenna, obtain the AUV's water surface position, and then obtain the vertical position through the pressure sensor, so as to realize the precise positioning of AUV.

The positioning method of the present invention specifically includes the following steps:

Step 1. The beacon node sends a beacon message packet with the maximum transmission power. The structure of the message packet in this specific embodiment is shown in Figure 1: it includes information such as beacon node ID, beacon node coordinates, and sending time.

Step 2. The unknown node establishes a beacon message record table, and records the received message grouping sent by the beacon node, as follows:

Step 2-1. Set the record table in the unknown node. Its structure is shown in Figure 2. The record table includes the beacon node ID, the coordinates of the beacon node, the sending time of the beacon message packet, the arrival time of the message packet, and the node connectivity , the distance between the beacon node and the unknown node;

Step 2-2. After receiving the beacon message, the unknown node compares whether the message group of the beacon node is included in the record table, and if so, replaces the original beacon message; otherwise, directly adds it to the record table as a new entry, and Add 1 to the connectivity value (that is, the number of beacon nodes within the communication range of the unknown node).

Step 3, the unknown node uses the centroid algorithm to perform the first positioning calculation according to the coordinates of each beacon node in the record table:

; ;

Among them, X _i , Y _i , Z _i are the three-dimensional coordinates of unknown node i , x _j , y _j , z _j are the coordinates of the jth beacon node within the communication range of unknown node i ;

Step 4. The unknown node records its own coordinates and calculates its own positioning accuracy, which specifically includes the following steps:

Step 4-1. Each unknown node calculates the distance d _j from the jth beacon node within its communication range to the unknown node according to the time difference of arrival time of the information in the record table: d _j = ( T ₂ -T ₁ ) *V , where V is the sound wave propagation speed in water, T ₂ is the arrival time of the beacon message, and T ₁ is the sending time of the beacon message;

Step 4-2. According to the distance between each beacon node and _the unknown node in the record table, calculate the average distance D i = , where N _i is the number of beacon nodes within the communication range of node i , and the distance variance B _i = , the smaller the variance, it means that the distance between all beacon nodes and unknown nodes is smaller, so the positioning is more accurate;

Step 4-3: Establish a space Cartesian coordinate system with the unknown node i as the origin, divide the space into eight regions, and map it to eight hexagrams in the space Cartesian coordinate system. The distribution of beacon nodes in this space is shown in Figure 3 , to count the number of beacon nodes within each hexagram limit in the beacon record table, respectively recorded as M ₁ , M ₂ , M ₃ , M ₄ , M ₅ , M ₆ , M 7 _, M ₈ ; For the number of beacon nodes, the following method can be used: calculate the coordinate difference between the coordinates of the beacon node and the unknown node i using the centroid algorithm for the first time (△ x, △ y , △ z ), according to △ x , △ y , △ z To determine which area the beacon node belongs to, add 1 to the number of beacon nodes in the corresponding area. For example, if △ x , △ y , △ z are all greater than zero, then the beacon node is in the first hexagram limit, and the first hexagram limit will be The number of beacon nodes M ₁ plus 1;

Step 4-4, calculate the average number of beacon nodes in the above eight areas E _i = , and use the variance of the number of beacon nodes to represent the distribution of beacon nodes, and the variance is calculated as R _i = , the smaller the variance, the more uniform the distribution of beacon nodes;

Step 4-5, use the following formula to calculate the positioning accuracy L _i of the unknown node i :

L _i =N _i ^α /( D _i ^β × B _i ^ε × R _i ^θ )                

Among them, α , β , ε , θ represent weights. In different application environments, each factor has different influence on the positioning accuracy, and their values can be set according to the actual situation. α , β , ε , θ ∈ (0,1 ]. The greater the positioning accuracy, the more accurate the node’s positioning.

Step 5. Arrange the positioning accuracy of the unknown nodes in ascending order, and select the top k unknown nodes with the smallest positioning accuracy; or use the preset positioning accuracy threshold to filter out the unknown nodes with lower positioning accuracy.

Step 6. For the selected unknown nodes with low positioning accuracy, move at least one AUV to its communication range. The number of AUVs required can be determined according to the required positioning accuracy. The higher the positioning accuracy required, the more AUVs should be set. ; Concretely include the following steps:

Step 6-1, select the area with the least number of beacon nodes in the area divided by the unknown node as the center;

Step 6-2. Move the AUV to this area, and the distance from the unknown node is the average distance between all beacon nodes within the communication range of the unknown node and the unknown node, that is, the AUV moves to the unknown node as the center of the sphere and the radius is Di _'s Any position in the spherical area.

Step 7. Use AUV as a new beacon node (the spatial distribution of beacon nodes after adding an AUV is shown in Figure 4), and assist unknown nodes to relocate using the centroid algorithm:

; ;

Among them, X _i ' , Y _i ' , Zi _' are the repositioned coordinates of the unknown node i , and x _a , y _a , z _a are the coordinates of the set AUV.

Claims (4)

1., based on the underwater wireless sensor network localization method that AUV assists, be provided with multiple beaconing nodes in described underwater wireless sensor network, it is characterized in that, comprise the following steps:

Steps A, each beaconing nodes send the beacon message comprising self coordinate information; Unknown node, according to the beaconing nodes coordinate in received beacon message, utilizes centroid algorithm tentatively to determine the position coordinates of self;

Step B, the secondary utilizing Autonomous Underwater Vehicle AUV assistance to carry out unknown node are located, specifically in accordance with the following methods:

Step B1, be that initial point sets up rectangular coordinate system in space with unknown node, the communication range of this unknown node is divided into 8 regions, the corresponding region of each octant, adds up the quantity of beaconing nodes in each region;

Step B2, at least one AUV is moved to the minimum region of anchor node number, and the distance of itself and unknown node equals the mean value that this unknown node communicates with each beaconing nodes spacing in scope;

Step B3, using AUV as new beaconing nodes, centroid algorithm is utilized to redefine the coordinate of unknown node.

2. as claimed in claim 1 based on the underwater wireless sensor network localization method that AUV assists, it is characterized in that, before utilizing AUV to assist positioning precision minimum in step B kthe unknown node that individual or positioning precision is less than a predetermined threshold value carries out secondary location, unknown node ipositioning precision according to following formulae discovery:

In formula, for unknown node ithe quantity of beaconing nodes in communication range; for unknown node icommunicate with the mean value of each beaconing nodes spacing in scope; for unknown node icommunicate with the variance of each beaconing nodes spacing in scope; for unknown node ibeaconing nodes distribution variance in communication range, its expression formula is , with unknown node ifor initial point sets up rectangular coordinate system in space, by unknown node icommunication range be divided into 8 regions, the corresponding region of each octant, be the mean value of anchor node number in 8 regions, be janchor node number in individual region; α, β, ε, θ∈ (0,1], be weight parameter.

3. as claimed in claim 1 based on the underwater wireless sensor network localization method that AUV assists, it is characterized in that, described beacon message comprises beaconing nodes ID mark, beaconing nodes coordinate and transmitting time.

4. as claimed in claim 1 based on the underwater wireless sensor network localization method that AUV assists, it is characterized in that, the spacing of described unknown node and beaconing nodes obtains in accordance with the following methods: unknown node is poor for the time of advent according to beacon message, use acoustic communication calculates, specifically according to following formula

d _j =( T ₂ -T ₁) *V

In formula, d _j represent that unknown node communicates with the in scope jindividual beaconing nodes spacing, t ₁, t ₂be respectively jindividual beaconing nodes send transmitting time, the time of advent of beacon message, vfor sound wave is in water transmission speed.