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 PDFInfo
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Abstract
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 the trip according to a preset path, and the sensor node dynamically selects a forwarding set at each hop according to factors such as forwarding probability and residual energy to forward data 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.
Description
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:
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,
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:
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
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
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
The maximum integer is taken in the expression of | · | |, and the boundary value of the grid coordinate is:
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,
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:
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
SNR represents the signal-to-noise ratio of the underwater acoustic communication;
the energy consumption model of the sensor nodes is as follows:
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:
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
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
The remaining energy when the AUV is ready to reach the next destination location can be pre-calculated as
EtravelConsuming energy per unit distance, D1Distance between AUV current position and upcoming next position, where D1Can be calculated as:
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.
According to
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:
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. An AUV data collection method in an underwater sensor network based on seawater stratification is characterized by comprising the following steps:
(1) layering an underwater sensing network according to the oceanography model to obtain a surface layer network and a lower layer network, wherein the lower layer network is positioned below the surface layer network;
(2) the method comprises the steps that an AUV obtains a preset tour path of the AUV in a surface layer network, the length, the width and the height of the surface layer network and the transmission radius of sensor nodes, calculates a data collection area, simultaneously grids the surface layer network, moves along the preset tour path, and sends a broadcast control packet to each sensor node of the surface layer network;
(3) the sensor node positioned in the surface layer network judges whether the sensor node is positioned in the data collection area according to the received broadcast control packet, and selects a set data forwarding mode to forward the data to the AUV according to the judgment result to finish the collection of the surface layer network data;
(4) the AUV carries out clustering division by using a k-means + + algorithm according to the acquired sensor node deployment information positioned in the lower-layer network, further obtains a plurality of clustering centers, simultaneously broadcasts all the clustering center information to inform all the sensor nodes of the lower-layer network, and traverses all the clustering centers by using a shortest path algorithm to obtain a basic tour path of the AUV;
(5) constructing an adjacent node table for the sensor nodes in each cluster, clustering according to the adjacent node density to obtain a plurality of sub-clusters, and traversing the centers of all the sub-clusters by the AUV (autonomous Underwater vehicle) by using a shortest path algorithm;
(6) when the energy of the AUV is lower than a dynamic threshold value, floating and recording the floating position, returning along the original path of the floating path after the energy is supplemented, and simultaneously performing surface network data collection until the data collection of the whole lower network is completed; when the next round of data collection is carried out, if the floating position of the AUV is detected to be the same as the floating position in the previous round of collection, the AUV rotates clockwise by a set arrival angle, then horizontally moves by taking the transmission radius of the sensor node as the distance, and then floats upwards; and completing the AUV data collection method in the underwater sensing network based on seawater stratification.
2. The AUV data collection method in the underwater sensor network based on seawater stratification according to claim 1, characterized in that: and the Eckman depth is used as a boundary between the surface network and the lower network.
3. The AUV data collection method in the underwater sensor network based on seawater stratification according to claim 1, characterized in that: the step (2) of meshing the surface layer network specifically comprises the following steps:
setting the height of the surface network to be H1And the side length of each 3D cubic grid is D, the whole surface layer network can be divided into
3D cubic grids with the same size;
setting two adjacent grid distances as sensor nodesThe maximum communication range of (3D) is defined as the data forwarding range, the distance between adjacent grids is defined as the data forwarding range, and the relation between the 3D grid side length D and the transmission radius r of the sensor node is defined as
M is the maximum length of the network.
4. The AUV data collection method in the underwater sensor network based on seawater stratification according to claim 1, characterized in that: the broadcast control in the step (2) comprises grid division information, data collection area coordinate information and an AUV predetermined tour route; the sensor node located in the surface layer network judges whether the sensor node is located in the data collection area, a set data forwarding mode is selected according to the judgment result to forward the data to the AUV, and the collection of the surface layer network data is completed, and the specific process is as follows:
2.1: each sensor node positioned in the surface layer network obtains the position coordinate of the sensor node by using a positioning algorithm;
2.2: comparing the position coordinates of the user with the received coordinate information of the data collection area, and judging whether the user is in the data collection area;
2.3: when the AUV is judged to be 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 located in the data collection area forward the data to the AUV.
5. The AUV data collection method in the seawater stratification-based underwater sensor network according to claim 4, characterized in that: the data forwarding to the data collection area by the forwarding set dynamic routing method specifically includes:
3.1: the source sensor node calculates the grid coordinate according to the coordinate position of the source sensor node and the received grid division information, then calculates the weights w of all sensor nodes in the communication range r, establishes a node weight table, arranges the node weight table in a descending order, selects the nodes with the preset number in the weight table as a forwarding set, and can monitor the respective data forwarding conditions among the forwarding set nodes;
3.2: defining a grid with a distance from the data collection area smaller than or equal to the distance from the grid where the source node is located to the data collection area as a priority grid;
3.3: the source node firstly forwards the data to the receiving node with the maximum weight w, the receiving node recalculates the mesh to which the receiving node belongs, if the receiving node calculates the mesh as the priority mesh, the data is continuously forwarded according to the steps 3.1 and 3.2, after other nodes in the forwarding set monitor the successful data forwarding information, the nodes do not serve as forwarding set nodes, and finally the data is forwarded to the nearest node in the data collection area, namely the destination node; otherwise, the receiving node is not positioned in the priority grid any more due to the movement of the receiving node, or the receiving node is not positioned in the priority grid, the forwarding concentrated sensor node monitors the forwarding failure information, the sensor node with the suboptimal forwarding concentrated weight is selected to continue data forwarding, the forwarding concentrated node recalculates the grid coordinate to which the forwarding concentrated node belongs, and once all the nodes in the forwarding concentrated node are not positioned in the priority grid, the forwarding concentrated node is reselected until the nodes in the data collection area are reached.
6. The AUV data collection method in the underwater sensor network based on seawater stratification according to claim 1, characterized in that: the step (4) is specifically as follows:
and selecting k clustering centers, clustering by taking Euclidean distance as similarity, and taking the shortest path formed by each clustering center as a basic tour path of the AUV.
7. The AUV data collection method in the underwater sensor network based on seawater stratification according to claim 1, characterized in that: the step (5) is specifically as follows:
5.1: each sensor node establishes an adjacent node table and summarizes the adjacent node table as a total adjacent node table L;
5.2: arranging according to the number of the adjacent nodes in a descending order, selecting the sensor node with the largest number of the adjacent nodes as a cluster head, and taking the adjacent node as a cluster member;
5.3: 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.
8. The AUV data collection method in the seawater stratification-based underwater sensor network according to claim 1 or 7, wherein: the step (6) is specifically as follows:
when the AUV reaches the nearest cluster center, firstly judging whether the residual energy of the AUV can reach the nearest sub-cluster, if the AUV cannot reach the nearest sub-cluster, stopping turning forward, directly floating up to supplement energy, returning along the original path of a floating up path, and meanwhile, collecting surface network data; if the situation that the data can be acquired is judged, the AUV uses a shortest path algorithm to reach the first sub-cluster for data acquisition, the residual energy threshold value is reset according to the energy required by returning to the energy supplementing point from the next sub-cluster before reaching the next sub-cluster, once the residual energy of the AUV is smaller than the preset residual energy threshold value, forward turning to floating up for supplementing energy is stopped, and a new round of surface network data acquisition is executed while returning along the original path of the floating up path; this process is repeated until all the sub-clusters in the cluster are traversed, returning to the point where the cluster can be replenished, and starting the next round.
9. The AUV data collection method in the seawater stratification-based underwater sensor network according to claim 8, wherein: and (3) simultaneously performing surface network data collection in the step (6), wherein the collection process is the same as that of the surface network data collection in the step (3).
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