CN107548029B - Seawater layering-based AUV data collection method in underwater sensor network - Google Patents

Seawater layering-based AUV data collection method in underwater sensor network Download PDF

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CN107548029B
CN107548029B CN201710717156.5A CN201710717156A CN107548029B CN 107548029 B CN107548029 B CN 107548029B CN 201710717156 A CN201710717156 A CN 201710717156A CN 107548029 B CN107548029 B CN 107548029B
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CN107548029A (en
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韩光洁
沈松杰
王皓
刘立
江金芳
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Hohai University HHU
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Abstract

本发明公开了一种基于海水分层的水下传感网络中AUV数据收集方法，包括：运用海洋学模型，进行水下传感网络分层，针对不同的分层采用不同的数据收集策略；在移动性较强的表层，AUV按照预定路径巡游，传感器节点通过转发概率、剩余能量等因素在每一跳处动态的选择转发集进行数据转发，以增强移动节点数据转发的可靠性。同时在下层网络中，节点通过先聚类再分簇的初始化操作，形成AUV停驻点，采用最短路径算法规划AUV轨迹。利用AUV作为辅助工具，收集网络中的数据，同时考虑AUV能耗，使得整个算法更加符合实际情况，通过合理规划AUV巡游路径，减少了数据转发的跳数，降低了网络的能量消耗，部分解决能耗不均的问题，延长了网络的生存时间。

Figure 201710717156

The invention discloses a method for collecting AUV data in an underwater sensor network based on seawater layering, comprising: using an oceanographic model to perform layering of an underwater sensor network, and adopting different data collection strategies for different layers; On the surface layer with strong mobility, the AUV patrols according to the predetermined path, and the sensor node dynamically selects the forwarding set at each hop for data forwarding through factors such as forwarding probability and remaining energy, so as to enhance the reliability of data forwarding of the mobile node. At the same time, in the lower network, the nodes form AUV parking points through the initialization operation of first clustering and then clustering, and use the shortest path algorithm to plan the AUV trajectory. Using AUV as an auxiliary tool to collect data in the network, and considering the energy consumption of AUV at the same time, makes the whole algorithm more in line with the actual situation. By rationally planning the AUV cruise path, the number of hops for data forwarding is reduced, and the energy consumption of the network is reduced. Partial solution The problem of uneven energy consumption prolongs the lifetime of the network.

Figure 201710717156

Description

Seawater layering-based AUV data collection method in underwater sensor network
Technical Field
The invention belongs to the field of technology, and particularly relates to an AUV data collection method in an underwater sensor network based on seawater stratification.
Background
In recent years, Underwater Sensor Networks (UWSNs) have gained increasing attention from researchers. The seawater occupies 70.8% of the earth surface, and the underwater sensor network shows excellent performance in various underwater applications, such as underwater environment detection, underwater disaster early warning, military monitoring, underwater resource detection and the like. The application is mainly based on data acquisition of sensor nodes, and the data can be simple binary data or complex big data such as audio, images, videos and the like. Therefore, how to collect the collected data to the corresponding device for processing to obtain the required information is an important issue. Meanwhile, due to the particularity of the underwater environment, such as dynamic topology, low delivery rate, large propagation delay, difficult node energy supplement and the like, the land sensor network data collection algorithm cannot be directly applied to the underwater environment. Therefore, designing an underwater data collection algorithm to achieve energy consumption balance and energy efficiency of the network and prolonging the service life of the underwater sensor network is a great challenge.
At present, there are three main types of underwater data collection schemes, which are a multi-hop method, an AUV assistance method, and a hybrid method, specifically:
the first type is a multi-hop forwarding mode, and a source node forwards a data packet through a relay node in a multi-hop manner through a series of forwarding mechanisms such as greedy strategies until the data packet is transmitted to a sink on the water surface.
Haitao Yu et al published in Ad Hoc Networks of 15 years "An adaptive routing protocol in An adaptive router adaptive sensor Networks" and proposed An improved routing mechanism based on vector forwarding: AHH-VBF. The scheme is an improvement of hop-by-hop VBF (HH _ VBF), the radius of a pipeline is changed according to the distribution position of adjacent nodes, the power level is adjusted hop-by-hop according to the distribution of the adjacent nodes in a local area to reduce forwarding power, and the maintaining time of a data packet is calculated according to the distance from a current node to a target node to reduce the forwarding of the data packet. The scheme reduces energy consumption and data redundancy and reduces delay by adjusting the self-adaptive communication pipeline and the transmission radius and setting the data packet maintaining time. The defects are also obvious, the data in the pipeline is transmitted in a broadcasting mode, the energy consumption is relatively large, and the energy consumption is unbalanced; a16-year 'Wireless Networks' publication of Faiza AlSalti et al, "EMGGR", an energy-efficient multi-path grid-based geographic routing protocol for underservers Wireless sensor Networks, proposes a multi-path routing forwarding protocol based on geographic information and grids, sets weights for each grid based on information such as residual energy, and selects gateway nodes according to the weights; then the destination node is mapped to the same plane of the source node, the source node and the gateway node construct a forwarding path from multiple directions to the mapped destination node, and finally the forwarding path is vertically forwarded to the real destination node. According to the scheme, through selection of the nodes in the 3D grid, the problem of uneven grid energy consumption is effectively reduced, and meanwhile, multipath routing is adopted, so that the transmission reliability is enhanced. Accordingly, the end-to-end delay is relatively high, and thus is not suitable for large-scale networks.
The second type is to use AUV to assist in data collection policy, but traversing each node is impractical, so usually partitioning or clustering the grid, only traversing the center is needed, and path length is greatly reduced. Further reduction of the tour path is a typical TSP problem. The problem of uneven network energy consumption is effectively reduced through AUV assistance, and the service life of the network is prolonged.
An AEDG Data collection method is proposed by An article "An Efficient Data-Gathering Protocol for An underswater Wireless Sensor Networks" published by Nadeem Javaid et al in Sensors 2015. According to the scheme, an AUV moves according to a preset elliptical track, hello is broadcasted, a Gateway Node (GN) is selected based on an RSSI value and residual energy, a member node (CN) is related to the gateway node through a shortest path, and then the gateway node transmits the gateway node to the AUV. The method can effectively reduce node energy consumption, but the fixed AUV path easily causes the problem of hot area.
An AUV cooperative transmission Data collection algorithm based on network partitioning is proposed by an article ' Data-collecting SchemeUsing AUVs in Large-Scale Underwater Sensor Networks ' published by Jawaad Ullah Khan et al 2016 in Sensors, A Multihop Approach '. The Sink divides the whole network plane into a plurality of Thiessen polygons, each polygon area is allocated with an AUV, path planning is carried out in the area according to clusters, and simultaneously each area carries out AUV cooperative transmission through set proxy nodes.
The third type is a hybrid mode, and combines multi-hop and AUV cooperation, so that the problem of uneven energy consumption can be solved, and transmission delay is reduced. An article "Data gathering protocol with the Data aggregation and correlation in an underlying water Wireless Sensor Networks" published by Journal of Network and computer applications of 2017 by Chien-Fu Cheng proposes a hybrid Data collection method, which defines the importance of Data, forwards the important Data to a corresponding layer through multiple hops, reduces the delay of the important Data, and uses an AUV (autonomous Underwater vehicle) to move from bottom to top to collect Data in a spiral track. The scheme can balance the energy consumption of the whole network node, improve the service life of the network and reduce the delay of important data; the disadvantage is that the proportion of important data is, after all, a few, and the energy consumption and delay impact on the whole network is not as great as imaginable.
Disclosure of Invention
In order to solve the defects of data collection in an underwater sensor network and comprehensively consider the advantages of multi-hop and AUV assistance, the invention provides an AUV data collection method in an underwater sensor network based on seawater stratification, the underwater sensor network is stratified according to seawater characteristics, different data collection strategies are adopted for different strata, the AUV moves in a surface layer network according to a preset track to form a data collection area, the surface layer network is meshed, and a sensor node in the surface layer network judges whether the sensor node is in a data collection area or not, wherein the sensor node in the data collection area sends data to the AUV after waiting for the AUV to arrive nearby; the sensor nodes outside the collection area calculate the grids according to the coordinates of the sensor nodes, and forward the data to the collection area by using a forwarding set dynamic routing method to wait for AUV collection; and when the AUV enters a lower-layer network, planning an AUV path through k-means + + clustering and node density clustering, considering that the AUV has limited energy, floating to supplement energy when the AUV residual energy is less than a set threshold, and returning along the original path of the floating path after the energy supplement is finished until complete network data is collected.
The technical purpose is achieved, the technical effect is achieved, and the invention is realized through the following technical scheme:
the first step is as follows: by introducing an oceanographic model and taking the Ackerman depth as a boundary, the whole underwater sensing network is divided into a surface network and a lower network. The surface network has high water flow speed, correspondingly causes high node mobility, deflects the flow direction along with the increase of the depth, reduces the flow speed, and when the depth is larger than the Eckmann depth, the water flow speed is about 4% of the surface speed, and the sensor nodes can be approximately considered to be static in consideration of the volume and the mass of the underwater sensor nodes. The Eckman depth is expressed as:
Figure GDA0002413948950000031
wherein H is the depth of the Ackerman, AZFor the turbulent viscosity coefficient in the z-axis direction, 0.01 is taken according to the literature,
Figure GDA0002413948950000032
is dimension, interval [0 °,90 ° ]]W is the rotational angular velocity of the earth, and has a value of 7.29 · 10-5。
The second step is that: the AUV broadcasts in the surface network according to a preset tour track, a node transmission radius and the length, the width and the height of the surface network, which are acquired from the sink node, and simultaneously forms a cylindrical data collection area along a preset vertical path, nodes outside the data collection area forward data to the data collection area in a multi-hop mode, and nodes in the data collection area forward the data to the AUV when the AUV reaches the vicinity of the nodes; and simultaneously gridding the surface layer network, then moving along a preset tour path, and sending a broadcast control packet to each sensor node of the surface layer network.
The third step: the sensor node in the surface layer network can calculate the coordinates of the position of the sensor node according to the existing positioning method, after the sensor node receives a broadcast control packet broadcasted by the AUV, the sensor node determines the horizontal distance from the sensor node to the straight line of the center of the data collection area, judges whether the sensor node is located in the data collection area, and if the sensor node is located in the data collection area, waits for the AUV to reach the communication range of the sensor node and directly forwards the data to the AUV; otherwise, the sensor node forwards the data to the data collection area direction in a multi-hop mode, and selects the sensor node closest to the last hop in the data collection area as a target node, and the specific process is as follows:
3.1: in a source node communication range, a source node sends a message data packet and receives a message returned by an adjacent node, acquires residual energy information and forwarding times of the adjacent node, calculates the distance between the source node and the adjacent node and the forwarding probability, establishes a weight formula and an adjacent node weight table, arranges the adjacent node weight table in a descending order, selects the first nodes with proper number in the adjacent node weight table as a forwarding set, and forwards the data to a sensor node with the maximum weight in the forwarding set;
3.2: after data forwarding, the receiving node (i.e. the node with the largest weight in the forwarding set) and other nodes in the forwarding set both need to recalculate the grid coordinates of the receiving node itself, and if the grid to which the receiving node itself belongs is closer to the data collection area than the grid to which the source node belongs, it indicates that data forwarding is successful, and the other nodes in the forwarding set no longer serve as the forwarding set nodes after monitoring the message;
3.3: when the receiving node fails to receive data or moves to a grid far away from the collection area due to factors such as mobility and the like, the source node takes the unaffected suboptimal weight node in the forwarding set as a data forwarding point, and when all nodes in the forwarding set cannot serve as the data forwarding point (namely all nodes in the forwarding set are not in the priority grid), the source node reselects the forwarding set.
3.3: the steps are repeated for each hop until the data is forwarded to the node closest to the last hop in the data collection area as the destination node.
The fourth step: and the AUV reaches a lower-layer network, clustering all nodes of the lower-layer network by using a k-mean + + method according to the node deployment condition of the lower-layer network obtained from the sink, determining the coordinates of a clustering center, planning a tour path by using a shortest path algorithm, and reaching the first clustering center. Because the cluster size is not controllable, all nodes can not transmit data to the AUV in one hop, each cluster is clustered again, the cluster is divided by the density of adjacent nodes, and when the AUV collects data, a plurality of node data can be collected at one time.
4.1: specifically clustering: clustering each sensor node in the cluster according to the density of adjacent nodes, and establishing an adjacent node table by taking the communication range of the sensor nodes as the cluster radius and the adjacent nodes as cluster members;
4.2: arranging according to the quantity of the adjacent nodes in a descending order, selecting the sensor node with the largest quantity of the adjacent nodes as a cluster head, and taking the adjacent node as a cluster member; deleting the cluster head and the cluster members from the adjacent node list L, clustering by analogy in sequence to obtain a plurality of sub-clusters, traversing all the sub-clusters by the AUV through the shortest path, and forwarding data between the cluster center and the cluster members by adopting TDMA.
The method comprises the steps that the AUV calculates energy consumed by reaching a first cluster center and a residual energy threshold value in a clustering center, whether the residual energy is larger than the residual energy threshold value after the AUV reaches the first cluster center is judged through calculation (the residual energy threshold value is an energy value required by returning to an energy supplement center from the cluster center), if so, the AUV reaches the first cluster center for data collection, otherwise, the AUV stops moving forwards, floats upwards for energy supplement, returns according to an original path of a floating path, collects new surface layer data at the same time, and repeats the same steps every time the AUV reaches a next cluster center or the clustering center until data of a complete lower layer network are collected. And traversing the cluster center and the cluster center according to a shortest path algorithm.
4.3: the AUV floats at every time and records and stores the floating position of the AUV, when next round of data collection is carried out, the AUV needs to float at the same position, the AUV deflects along a clock direction, and floats after horizontally moving by taking the transmission radius of the sensor node as a distance, so that a new data collection area can be formed on the surface layer when the AUV returns according to a floating path, and the phenomenon that the nodes in the data collection area are constant and the energy consumption is overlarge and the nodes die too early is avoided.
The invention has the beneficial effects that:
the invention provides an AUV data collection method in an underwater sensor network based on seawater stratification, which is characterized in that the underwater sensor network is stratified according to seawater characteristics, different data collection strategies are adopted for different strata, the AUV moves in a surface network according to a preset track to form a data collection area, the surface network is gridded at the same time, a sensor node in the surface network judges whether the sensor node is in the data collection area, the sensor node in the data collection area sends data to the AUV after the AUV reaches the communication range of the sensor node, the sensor node outside the collection area calculates the affiliated grid according to own coordinates, and a forwarding set dynamic routing method is used for forwarding the data to the collection area to wait for the AUV to collect; and when the AUV enters a lower-layer network, planning an AUV path through k-means + + clustering and node density clustering, considering that the AUV has limited energy, floating to supplement energy when the AUV residual energy is less than a set threshold, and returning along the original path of the floating path after the energy supplement is finished until complete network data is collected.
Drawings
FIG. 1 is a schematic diagram of a network model according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of surface meshing according to an embodiment of the present invention;
FIG. 3 is a schematic diagram of the relationship between the node communication range and the 3D grid side length;
FIG. 4 is a schematic diagram of a data forwarding process of a sensor node located at a surface layer;
FIG. 5 is a flow chart of the operation of AUV in the surface layer;
FIG. 6 is a flow chart of the operation of a sensor node located at the surface;
FIG. 7 is a flowchart of the AUV's work in the lower layer;
fig. 8 is a flowchart of the operation of the sensor node located at the lower layer.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
According to the invention, the characteristics of underwater layering are considered, and the underwater sensing network is layered to obtain a surface layer network and a lower layer network. Different data collection strategies are respectively applied to the surface layer network and the lower layer network, specifically: the AUV moves on the surface layer network according to a preset track, and because the mobility of a sensor node positioned on the surface layer network is large, judgment is needed at each hop, a multi-path forwarding set is adopted for data forwarding, and the reliability of forwarding is enhanced; after the AUV enters a lower-layer network, the shortest path algorithm is used for reducing the path length, reducing the AUV energy consumption and reducing the problems of large node energy consumption and uneven energy consumption through clustering and clustering.
The present invention will be further described with reference to the accompanying drawings.
As shown in fig. 1, the underwater sensor network is a schematic diagram of an underwater sensor network model, the underwater sensor network is an M × M three-dimensional region, the underwater sensor network includes a sink node, an AUV and a plurality of sensor nodes, the sink node is located in the center of the surface layer network, and is used for providing the AUV with information of the global underwater sensor network and a predetermined tour path of the AUV in the surface layer network, and providing energy for the AUV; in a three-dimensional area of the underwater sensor network, a three-dimensional coordinate system is established by taking the upper left corner of the surface of the network as an origin, horizontally rightwards in the positive direction of an x axis, horizontally forwards in the positive direction of a y axis and vertically downwards in the positive direction of a z axis.
An AUV data collection method in an underwater sensor network based on seawater stratification specifically comprises the following steps:
(1) the wind force acts on the sea surface or generates drift, so the sea surface is called Eckmann drift. As the depth increases, the flow deflects and the flow decreases, and at a certain depth, the flow decreases to 4% of the surface, which is the ekman depth, which is related to factors such as latitude, rotational angular velocity of the earth, and the like. Therefore, in the embodiment of the invention, the water is divided into the surface layer and the lower layer by taking the Ackerman depth as a boundary, the flow velocity of the surface layer is 2m/s, the flow velocity of the lower layer which is larger than the depth is about 8cm/s, the movement characteristic of the sensor node positioned on the surface layer is set to be random movement, and the sensor node positioned on the lower layer can be regarded as a static state relative to the self volume mass under the influence of the flow velocity.
(2) The main purpose of the dynamic data forwarding stage of the sensor nodes in the surface layer network is as follows: and when the AUV reaches the communication range of the sensor nodes in the data collection area, the sensor nodes directly forward the data to the AUV, the sensor nodes in the surface network have high mobility and are set to be in a random moving state, and the maximum moving distance is one grid length. The sensor nodes located in the surface layer network firstly judge whether the sensor nodes are located in a collection area, if so, the sensor nodes wait for arrival of an AUV, if the sensor nodes move out of the data collection area before the AUV arrives or are not located in the data collection area originally, the sensor nodes are arranged in adjacent or same grids according to the grid coordinates to which the sensor nodes belong in a descending order according to the weight, and a proper number of nodes are selected as a forwarding set. After the sensor receiving nodes (namely the sensor nodes in the forwarding set) receive the data, recalculating the grids to which the sensor receiving nodes belong, and if the sensor receiving nodes belong to the priority network (namely the grid center is closer to the center of the data collection area), continuing to forward the data according to the steps; otherwise, the forwarding centralized sensor node monitors forwarding failure information, the forwarding centralized node recalculates the grid coordinates to which the forwarding centralized node belongs, the node with suboptimal weight in the forwarding centralized node is selected to continue the forwarding, and once all the nodes in the forwarding centralized node are affected (namely, the nodes are not in the priority grid), the forwarding centralized node is reselected until the data are forwarded to the nodes in the data collection area; as shown in fig. 4, the method specifically includes the following steps:
(2.1) the AUV acquires a preset tour route of the AUV in the surface network from the Sink node, and calculates a data collection area, wherein in the embodiment of the invention, the AUV is performed in the surface network according to a preset vertical track, the surface network is gridded, the AUV moves in the surface network along the preset tour route, and broadcasts a control packet to each sensor node of the surface network through the surface network, specifically:
the coordinates in the AUV sinking process are as follows:
Figure GDA0002413948950000061
wherein H1Is the height of the surface layer, v is the movement speed of the AUV, t is the time for the AUV to move from the surface of the surface layer network to the bottom of the surface layer network, wherein z is0＝vt，x0，y0，z0AUV position coordinates;
the entire data collection area coordinate range is:
(X-x0)2+(Y-y0)2≤r2(0≤Z≤H1)
wherein r is the communication radius of the sensor node, H1Is the height, x, of the surface network0，y0The horizontal and vertical coordinates in the AUV diving process are represented, and X, Y and Z are variables and represent the coordinates of the sensor nodes.
The total data collection area volume was:
V1＝π·r2·H1
the AUV divides the entire surface layer network into a plurality of corresponding 3D cubic grids of the same size by calculation, the side length of each 3D cubic grid is D, fig. 2 is a schematic diagram of surface layer network grid division, and the specific calculation method is as follows:
height of surface network H1The 3D grid has a side length of D, so the whole network can be divided into
Figure GDA0002413948950000071
A 3D cubic grid;
setting two adjacent grid distances as the maximum communication range of the sensor node, and one adjacent grid distance as the data forwarding range, fig. 3 is a schematic diagram showing the relationship between the transmission radius (i.e. communication range) r of the sensor node and the 3D grid side length D, that is, the relationship is
Figure GDA0002413948950000072
Thus, the node can remain in communication with the source node even if it moves out of data forwarding range.
The AUV generates a broadcast control packet according to the mesh division information of the surface network, the moving speed of the AUV, the AUV scheduled tour route and the range of the data collection area formed according to the scheduled tour route, and then broadcasts the generated control packet to the whole surface network.
(2.2) the sensor node in the surface layer network utilizes the formula (X-X) according to the received broadcast control packet0)2+(Y-y0)2≤r2Judging whether the AUV is located in a data collection area or not, and when the AUV is judged to be located in the data collection area, directly forwarding the data after the AUV arrives; otherwise, data forwarding is carried out on the data collection area through a forwarding set dynamic routing method, and the sensor nodes in the data collection area forward the data to the AUV to complete the collection of the surface layer network data; specifically, the method comprises the following steps:
after receiving the control packet, the sensor node determines its own coordinate, its grid coordinate, the center distance from the data collection area, the remaining energy, etc., and simultaneously, each node presets a Count parameter, which is initially 0, and counts + + every time a data packet is sent or forwarded.
The sensor node obtains its own position coordinates (a, b, c) by using a positioning algorithm, and the grid coordinates to which the sensor node belongs are
Figure GDA0002413948950000073
The maximum integer is taken in the expression of | · | |, and the boundary value of the grid coordinate is:
Figure GDA0002413948950000081
the specific process of forwarding data to the data collection area by the forwarding set dynamic routing method is as follows:
traversing all the neighbor nodes in the grids within the communication range of 2d by the sensor node, obtaining the coordinates of the neighbor nodes and the corresponding grid coordinates, forwarding times and residual energy, and calculating the horizontal distance between the neighbor nodes and the central straight line of the collection area and the horizontal distance to the central straight line of the collection areaThe forwarding probability of the adjacent node, setting weight w,
Figure GDA0002413948950000082
and establishing an adjacent node table, arranging the adjacent node table in a descending order according to the weight, and selecting a proper number of nodes as a forwarding set.
Wherein α, gamma is a weight coefficient, which represents the importance degree of the corresponding index in the system, and ppackageIs the forwarding probability of a sensor node, EresFor sensor node residual energy, E0For initial energy of sensor node, DmaxFor the maximum distance of the network, here the maximum length M, D of the networkcThe horizontal distance between the adjacent node and the straight line where the center of the data collection area is located is shown, and the Count is the recorded forwarding times.
The calculation formula of the horizontal distance between the adjacent node and the straight line where the center of the data collection area is located is as follows:
Figure GDA0002413948950000083
a1，b1is the x, y coordinates of the neighboring node;
the forwarding probability calculation formula of the sensor node is as follows:
ppackage＝1-(1-pe)N
wherein p iseIs bit error rate, and
Figure GDA0002413948950000084
SNR represents the signal-to-noise ratio of the underwater acoustic communication;
the energy consumption model of the sensor nodes is as follows:
Figure GDA0002413948950000085
Erx＝L·εelec
wherein EtxEnergy consumed for transmitting Lbit data, L is the packet size, εelecTransmission loss between transmission node and receiving node, s being nodeDistance between points, s0Distance threshold, f is the node power, a (f) is the absorption coefficient, here taken 1.001, E according to the literaturerxThe energy consumed to receive the Lbit data.
The sensor node residual energy is:
Eres＝E0-(Etx+Erx)·Count
wherein E is0For initial energy of sensor nodes, EtxEnergy consumed for node transmission, ErxEnergy consumed by receiving data for a node;
total energy E consumed by AUV in surface layer networktopThe calculation formula is as follows:
Figure GDA0002413948950000091
wherein E istravelEnergy consumption for AUV movement per unit distance, EcontrolConsuming energy for broadcasting control packets;
to sum up: as shown in fig. 5, the specific working process of the AUV in the surface network is as follows:
501) the AUV carries out mesh division of a surface layer network;
502) the AUV sends a broadcast control packet to each sensor node located in the surface layer network through the surface layer network, wherein the broadcast control packet comprises: movement parameters (i.e., the moving speed of the AUV, a predetermined cruising path (i.e., a moving trajectory) of the AUV, and the range of the data collection area formed by the predetermined cruising path) and meshing information;
503) the AUV moves along a preset tour path and receives data forwarded by the sensor nodes in the data collection area;
504) and entering a lower-layer network.
Fig. 6 is a flowchart of the work of a sensor node located in a surface layer network, which specifically includes the following steps:
601) the sensor node receives the broadcast control packet;
602) the sensor node obtains the position coordinate of the sensor node by using a positioning algorithm, and calculates the grid coordinate of the sensor node according to the position coordinate of the sensor node;
603) the sensor node judges whether the sensor node is in a data collection area formed by the AUV, if so, the step goes to 608), and if not, the step is executed 604;
604) and in the distance communication range of one adjacent grid (namely 26 adjacent grids), arranging the nodes in a descending order according to the weight, and selecting a proper number of nodes as a forwarding set.
605) And the receiving node recalculates the grid coordinates to which the receiving node belongs due to the movement.
606) Determine if the receiving node has moved to a more optimal grid area (i.e., the grid center is closer to the data collection area) and if at least one node is, repeat 604) -605) until the data reaches the collection area. Otherwise 607) is executed;
607) the forwarding set nodes communicate and select the node in which the location is relatively optimal to perform 604) -605).
608) Waiting for the AUV to reach the communication range at the node in the data collection area;
609) if the receiving node moves out of the data collection area due to movement before the AUV arrives, 604) -608) are repeated.
610) The data is forwarded to the AUV.
(3) After the AUV reaches the lower layer, the AUV carries out clustering by using a K-means + + algorithm according to the deployment conditions of all sensor nodes in the lower layer network acquired from the sink node, K node coordinates serving as clustering centers and corresponding clusters are acquired, then a control packet containing the node coordinates of the clustering centers and corresponding clustering information is broadcast, and the sensor nodes in the lower layer network mark which cluster the sensor nodes belong to according to the received control packet; the sensor nodes in each cluster are clustered according to the node density, the communication range of the nodes is taken as the cluster radius, the adjacent nodes are taken as cluster members, an adjacent node table is established, the sensor nodes are arranged in a descending order according to the number of the adjacent nodes, the sensor nodes with the largest number of the adjacent nodes are selected as cluster heads, the adjacent nodes are taken as cluster members, the cluster heads and the cluster members are deleted from the adjacent node table L, clustering is carried out by analogy, isolated nodes do not participate in the process and are taken as independent clusters to obtain a plurality of sub-clusters, the AUV traverses all the sub-clusters by using shortest paths, and TDMA (time division multiple address) is adopted between the cluster center and the cluster members for data forwarding.
The AUV adopts a shortest path algorithm to plan a path in a lower layer network, firstly plans a basic path, namely the shortest path traverses all cluster centers, secondly, sensor nodes in the clusters need to be clustered again because the cluster size is uncontrollable, preferably clustering is carried out according to the node density, and thus when the AUV reaches a sub-cluster, a plurality of node data are collected once, and the shortest path traverses all sub-cluster centers in each cluster. In the process, a dynamic residual energy threshold value is set, the state of the dynamic residual energy is checked in real time, the energy is floated upwards to supplement energy once the energy shortage of the next stage is predicted, the surface layer network data collection is carried out while returning according to the original path, the floating position is stored, the next round of data collection floats upwards at the same position, the data deflects theta (theta is more than 0 degrees and less than 45 degrees) along one clock direction, the data floats upwards after horizontally moving r, a new data collection area is formed on the surface layer, and the situation that nodes in the data collection area are constant, so that the energy consumption is overlarge and the death is early is avoided. The cycle ends after traversing the entire network.
In the K-means + + algorithm, the coordinates of two sensor nodes are set as1,b1,c1) And (a)2,b2,c2) The degree of identity is expressed as
Figure GDA0002413948950000101
The smaller the Sa value is, the higher the recognition degree is, and the process of clustering by using the K-means + + algorithm in the invention is the prior art and is not described herein.
AUV initial energy of EinitThe broadcast control packet consumes EcontrolThe residual energy of AUV to reach the lower layer is
Figure GDA0002413948950000102
The remaining energy when the AUV is ready to reach the next destination location can be pre-calculated as
Figure GDA0002413948950000103
EtravelConsuming energy per unit distance, D1Distance between AUV current position and upcoming next position, where D1Can be calculated as:
Figure GDA0002413948950000104
wherein (x)1,y1,z1) Is the current coordinate of AUV, (x)2,y2,z2) For the next position coordinate to be reached by the AUV, the AUV residual energy threshold is dynamically changed, EThreshold＝D2·Etravel，D2The next destination node to surface AUV home distance.
Figure GDA0002413948950000105
According to
Figure GDA0002413948950000106
And judging whether the AUV can reach the next position or not, and if not, floating to form a data collection area.
The data collection area formed by the AUV ascent stage can be expressed as:
Figure GDA0002413948950000111
wherein (x)1,y1,z1) Is the current AUV position, (x)0,y00) initial coordinates of the surface of the water in preparation for the AUV to submerge, X, Y, z are arguments, the above formula represents a straight line between the current AUV position and the initial surface position, X, Y are arguments, and the entire formula represents the resulting data collection area, and is an inclined cylinder.
To sum up: fig. 7 is a flowchart of the work of AUV in the lower layer network, and the specific steps are as follows:
701) the AUV uses k-means + + to perform clustering division according to the acquired deployment information of all the sensor nodes in the lower-layer network, and simultaneously informs the information to all the sensor nodes in the lower-layer network in a broadcast control packet mode;
702) the AUV reaches a certain clustering center by using a shortest path algorithm;
703) the AUV firstly judges whether all clusters in the whole lower-layer network are traversed, if so, the step is carried out 709), and if not, the step is carried out 704);
704) predicting whether the AUV residual energy is smaller than a set residual energy threshold value in the next process, if so, turning to 705), otherwise, executing 706);
705) floating to a sink node for energy supplement and data uploading, and simultaneously recording and storing a floating position;
706) the AUV reaches the first cluster center after clustering according to the node density, and a shortest path algorithm is also adopted;
707) the AUV continuously traverses, whether all sub-clusters in the current cluster are traversed or not is judged by next data collection, and if yes, the cluster jumps to 708), and if not, the cluster repeats 706);
708) predicting whether the AUV residual energy can support the AUV to reach the next clustering center, yes to 702) repeating the previous operation, no to 705)
709) And finishing the collection of the current round, floating upward and waiting for the start of the next round.
Fig. 8 is a lower node work flow chart, which includes the following steps:
801) a sensor node receives a control packet broadcasted by an AUV;
802) marking the cluster to which the label belongs;
803) the sensor nodes establish an adjacent node table and are clustered according to the node density;
804) and the AUV reaches the center of the sub-cluster, and all nodes in the cluster use the TDMA to carry out data forwarding operation in order to avoid interference.
In summary, the following steps:
the invention discloses an AUV data collection method in an underwater sensor network based on seawater stratification, which comprises the following steps: layering an underwater sensing network by using an oceanography model, and adopting different data collection strategies aiming at different layers; on the surface layer with strong mobility, the AUV patrols according to a preset path, and the sensor node dynamically selects a forwarding set at each hop through factors such as forwarding probability and residual energy to perform multi-path data forwarding so as to enhance the reliability of data forwarding of the mobile node. Meanwhile, in the lower layer network, the nodes form AUV (autonomous Underwater vehicle) parking points through the initialization operation of clustering first and then clustering, and the AUV track is planned by adopting a shortest path algorithm. The AUV is used as an auxiliary tool to collect data in the network, and the energy consumption of the AUV is considered at the same time, so that the whole algorithm is more in line with the actual situation, the hop count of data forwarding is reduced, the energy consumption of the network is reduced, the problem of uneven energy consumption is partially solved, and the survival time of the network is prolonged.
The foregoing shows and describes the general principles and broad features of the present invention and advantages thereof. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are described in the specification and illustrated only to illustrate the principle of the present invention, but that various changes and modifications may be made therein without departing from the spirit and scope of the present invention, which fall within the scope of the invention as claimed. The scope of the invention is defined by the appended claims and equivalents thereof.

Claims (9)

1.一种基于海水分层的水下传感网络中AUV数据收集方法，其特征在于，包括：1. AUV data collection method in an underwater sensor network based on seawater layering, is characterized in that, comprising: (1)根据海洋学模型，进行水下传感网络分层，得到表层网络和下层网络，所述下层网络位于表层网络下方；(1) according to the oceanographic model, carry out the layering of the underwater sensor network to obtain a surface layer network and a lower layer network, and the lower layer network is located below the surface layer network; (2)AUV获取其在表层网络的预定巡游路径，表层网络长宽高以及传感器节点传输半径，计算出数据收集区域，同时将表层网络网格化，然后沿着预定巡游路径移动，并发送广播控制包至表层网络的各传感器节点；(2) The AUV obtains its predetermined cruising path in the surface network, the length, width and height of the surface network and the transmission radius of the sensor nodes, calculates the data collection area, meshes the surface network at the same time, and then moves along the predetermined cruising path and sends a broadcast Control packets to each sensor node of the surface network; (3)位于表层网络的传感器节点根据接收到的广播控制包判断其自身是否处于数据收集区域内，根据判断结果选择设定的数据转发方式将数据转发至AUV，完成表层网络数据的收集；(3) The sensor node located in the surface layer network judges whether it is in the data collection area according to the received broadcast control packet, selects the set data forwarding mode according to the judgment result and forwards the data to the AUV, and completes the collection of the surface layer network data; (4)AUV根据获取的位于下层网络的传感器节点部署信息，使用k-means++算法进行聚类划分，进而获得多个聚类中心，同时将所有的聚类中心信息广播告知下层网络的所有传感器节点，使用最短路径算法遍历各聚类中心，得到AUV的基本巡游路径；(4) AUV uses the k-means++ algorithm to perform clustering according to the acquired deployment information of sensor nodes located in the lower network, and then obtains multiple cluster centers, and broadcasts all the cluster center information to all sensor nodes in the lower network. , using the shortest path algorithm to traverse each cluster center to obtain the basic cruise path of the AUV; (5)对所述的各聚类中的传感器节点构建邻节点表，按照邻节点密度进行分簇，得到若干个子簇，AUV使用最短路径算法遍历所有子簇中心；(5) constructing a neighbor node table for the sensor nodes in each of the clusters, clustering according to the neighbor node density, to obtain several sub-clusters, and the AUV uses the shortest path algorithm to traverse the centers of all sub-clusters; (6)当AUV的能量低于动态阈值时，上浮并记录上浮位置，补充完能量后沿上浮路径原路返回，同时执行表层网络数据收集，直到完成整个下层网络的数据收集；当进入下一轮数据收集，在进行下层网络数据收集时，若检测出AUV的上浮位置与上一轮收集中的上浮位置相同时，则顺时针转动一个设定到达角度后以传感器节点传输半径为距离进行水平移动，然后再上浮；直到完成基于海水分层的水下传感网络中AUV数据收集方法。(6) When the energy of the AUV is lower than the dynamic threshold, it floats up and records the float position. After replenishing the energy, it returns to the original path along the float path. At the same time, the data collection of the surface network is performed until the data collection of the entire lower network is completed; when entering the next During the data collection of the lower layer network, if it is detected that the floating position of the AUV is the same as the floating position in the previous round of collection, then turn a set arrival angle clockwise and then use the transmission radius of the sensor node as the distance. Move, and then surface again; until the completion of the AUV data collection method in the underwater sensor network based on seawater stratification. 2.根据权利要求1所述的一种基于海水分层的水下传感网络中AUV数据收集方法，其特征在于：所述表层网络和下层网络之间以埃克曼深度为分界线。2 . The method for collecting AUV data in an underwater sensor network based on seawater layering according to claim 1 , wherein the Ekman depth is used as the dividing line between the surface network and the lower network. 3 . 3.根据权利要求1所述的一种基于海水分层的水下传感网络中AUV数据收集方法，其特征在于：所述步骤(2)中的将表层网络网格化，具体为：3. AUV data collection method in a kind of underwater sensor network based on seawater layering according to claim 1, is characterized in that: in the described step (2), the surface layer network is gridded, specifically: 设定表层网络的高度为H1，每个3D立方体网格的边长为d，则整个表层网络可以划分为
Figure FDA0002413948940000011
个大小相同的3D立方体网格；Set the height of the surface network as H 1 and the side length of each 3D cube mesh as d, then the entire surface network can be divided into
Figure FDA0002413948940000011
a mesh of 3D cubes of the same size; 设定相邻的两个网格距离作为传感器节点的最大通信范围，相邻的一个网格距离作为数据转发范围，3D网格边长d与传感器节点传输半径r的关系为
Figure FDA0002413948940000012
M为网络的最大长度。The distance between two adjacent grids is set as the maximum communication range of sensor nodes, and the distance between one adjacent grid is set as the data forwarding range. The relationship between the side length d of the 3D grid and the transmission radius r of the sensor node is:
Figure FDA0002413948940000012
M is the maximum length of the network. 4.根据权利要求1所述的一种基于海水分层的水下传感网络中AUV数据收集方法，其特征在于：所述步骤(2)中的广播控制包含有网格划分信息、数据收集区域坐标信息和AUV预定巡游路径；所述位于表层网络的传感器节点判断其自身是否处于数据收集区域内，根据判断结果选择设定的数据转发方式将数据转发至AUV，完成表层网络数据的收集，具体过程为：4. AUV data collection method in a kind of underwater sensor network based on seawater layering according to claim 1, is characterized in that: the broadcast control in described step (2) comprises grid division information, data collection Regional coordinate information and AUV scheduled cruise path; the sensor node located in the surface network judges whether it is in the data collection area, selects the set data forwarding method according to the judgment result and forwards the data to the AUV, and completes the collection of surface network data, The specific process is: 2.1：位于表层网络的各传感器节点利用定位算法得到自己的位置坐标；2.1: Each sensor node located in the surface network uses the positioning algorithm to obtain its own position coordinates; 2.2：将自己的位置坐标与接收到的数据收集区域坐标信息进行比对，判断是否处于数据收集区域内；2.2: Compare your own location coordinates with the received coordinate information of the data collection area to determine whether it is within the data collection area; 2.3：当判定处于数据收集区域内，则等待AUV到来后直接转发数据；否则通过转发集动态路由方法向数据收集区域进行数据转发，由位于数据收集区域内的传感器节点将数据转发给AUV。2.3: When it is determined to be in the data collection area, wait for the arrival of the AUV and forward the data directly; otherwise, forward the data to the data collection area through the forwarding set dynamic routing method, and the sensor nodes located in the data collection area forward the data to the AUV. 5.根据权利要求4所述的一种基于海水分层的水下传感网络中AUV数据收集方法，其特征在于：所述通过转发集动态路由方法向数据收集区域进行数据转发，具体为：5. AUV data collection method in a kind of underwater sensor network based on seawater layering according to claim 4, is characterized in that: described carrying out data forwarding to data collection area by forwarding set dynamic routing method, specifically: 3.1：源传感器节点根据自己的坐标位置，结合接收到的网格划分信息，计算出所在网格坐标，然后计算其通信范围r内所有传感器节点权重w，建立节点权重表，并以降序排列，选取权重表中前设定个数个节点作为转发集，转发集节点之间能够监听到各自数据转发情况；3.1: The source sensor node calculates the grid coordinates based on its own coordinate position and the received grid division information, and then calculates the weight w of all sensor nodes within its communication range r, establishes a node weight table, and arranges them in descending order. Select the number of nodes previously set in the weight table as the forwarding set, and the forwarding set nodes can monitor their respective data forwarding situations; 3.2：定义与数据收集区域的距离小于或者等于源节点所在网格与的数据收集区域的距离的网格为优先网格；3.2: Define the grid with the distance from the data collection area less than or equal to the distance between the grid where the source node is located and the data collection area as the priority grid; 3.3：源节点首先将数据转发至权重w最大的接收节点，该接收节点重新计算其所属网格，若计算出为优先网格，则根据步骤3.1和3.2不断进行数据转发，转发集中其他节点监听到数据转发成功信息后，不再充当转发集节点，最终将数据转发到数据收集区域内最近的节点，称为目的节点；否则，由于接收节点移动导致其不再位于优先网格，或者接收接点本身就不处于优先网格内，转发集中传感器节点监听到转发失败信息，选择转发集中权重次优的传感器节点继续进行数据转发，转发集节点重新计算所属网格坐标，一旦转发集中所有节点均不处于优先网格内，则重新选择转发集，直到到达数据收集区域中的节点。3.3: The source node first forwards the data to the receiving node with the largest weight w. The receiving node recalculates the grid to which it belongs. If it is calculated as a priority grid, it will continue to forward the data according to steps 3.1 and 3.2, and other nodes in the forwarding set will monitor it. After the data forwarding is successful, it no longer acts as a forwarding set node, and finally forwards the data to the nearest node in the data collection area, which is called the destination node; otherwise, due to the movement of the receiving node, it is no longer located in the priority grid, or the receiving node It is not in the priority grid itself, and the sensor node in the forwarding set monitors the forwarding failure information, and selects the sensor node with the suboptimal weight in the forwarding set to continue data forwarding, and the forwarding set node recalculates the grid coordinates to which it belongs. Within the priority grid, the forwarding set is reselected until a node in the data collection area is reached. 6.根据权利要求1所述的一种基于海水分层的水下传感网络中AUV数据收集方法，其特征在于：所述步骤(4)具体为：6. AUV data collection method in a kind of underwater sensor network based on seawater layering according to claim 1, is characterized in that: described step (4) is specially: 选取k个聚类中心，以欧式距离作为相似度进行聚类，将各聚类中心形成的最短路径作为AUV的基本巡游路径。Select k cluster centers, use Euclidean distance as similarity for clustering, and take the shortest path formed by each cluster center as the basic cruise path of AUV. 7.根据权利要求1所述的一种基于海水分层的水下传感网络中AUV数据收集方法，其特征在于：所述步骤(5)具体为：7. AUV data collection method in a kind of underwater sensor network based on seawater layering according to claim 1, is characterized in that: described step (5) is specially: 5.1：每个传感器节点建立邻节点表，并汇总为总邻节点表L；5.1: Each sensor node establishes a neighbor node table and summarizes it into a total neighbor node table L; 5.2：按邻节点个数降序排列，选取邻节点个数最多的传感器节点作为簇头，其邻节点作为簇成员；5.2: Arrange in descending order of the number of adjacent nodes, select the sensor node with the largest number of adjacent nodes as the cluster head, and its adjacent nodes as the cluster members; 5.3：将簇头和簇成员从邻节点表L中删除，依次类推进行分簇，得到若干个子簇，AUV使用最短路径遍历所有子簇，簇中心与簇成员之间采用TDMA进行数据转发。5.3: Delete the cluster head and cluster members from the adjacent node table L, and then perform clustering by analogy to obtain several sub-clusters. AUV uses the shortest path to traverse all sub-clusters, and TDMA is used for data forwarding between the cluster center and the cluster members. 8.根据权利要求1或7所述的一种基于海水分层的水下传感网络中AUV数据收集方法，其特征在于：所述步骤(6)具体为：8. AUV data collection method in a kind of underwater sensor network based on seawater layering according to claim 1 or 7, is characterized in that: described step (6) is specifically: 当AUV到达最近的聚类中心后，首先判断AUV的剩余能量能否到达最近的子簇，若判定不能到达，则停止向前转而直接上浮进行能量补充，沿上浮路径原路返回，同时执行表层网络数据收集；若判定能到达，则AUV使用最短路径算法到达第一个子簇进行数据收集，每次到达下一子簇之前都根据从下一子簇返回至能量补充点所需的能量重新设定剩余能量阈值，一旦AUV的剩余能量小于预先设定的剩余能量阈值，则停止向前转而上浮补充能量，沿上浮路径原路返回同时执行新一轮表层网络数据收集；重复这一过程，直到遍历完所有聚类中的子簇，返回能够补充点，开始下一轮。When the AUV reaches the nearest cluster center, it first determines whether the remaining energy of the AUV can reach the nearest sub-cluster. If it is determined that it cannot reach it, it stops moving forward and directly floats up for energy replenishment, and returns along the original path of the floating path, and executes at the same time. Surface network data collection; if it is determined that it can be reached, the AUV uses the shortest path algorithm to reach the first sub-cluster for data collection, and each time it reaches the next sub-cluster according to the energy required to return from the next sub-cluster to the energy replenishment point Re-set the remaining energy threshold. Once the remaining energy of the AUV is less than the preset remaining energy threshold, it will stop moving forward and go up to replenish energy, and return to the original path along the up-floating path while performing a new round of surface network data collection; repeat this process. Process until all subclusters in the cluster are traversed, return the points that can be added, and start the next round. 9.根据权利要求8所述的一种基于海水分层的水下传感网络中AUV数据收集方法，其特征在于：所述步骤(6)中的同时执行表层网络数据收集，其收集过程同步骤(3)中的表层网络数据收集。9. AUV data collection method in a kind of underwater sensor network based on seawater stratification according to claim 8, is characterized in that: in described step (6), carry out surface layer network data collection simultaneously, and its collection process is the same as the Surface network data collection in step (3).
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