CN106209261B - The mobile data collection method of three-dimensional UASNs based on probability neighborhood grid - Google Patents
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Abstract
The invention discloses a kind of mobile data collection methods of the three-dimensional UASNs based on probability neighborhood grid, including：According to the characteristic of 3D underwater sound Sensor Networks, the factors such as sound wave decline, ocean current surface activity, turbulence noise, wind, thermal noise are considered, build the probability traffic model of 3D underwater sound Sensor Networks；The probability traffic model of 3D underwater sound Sensor Networks based on structure, partitions the network into probability neighborhood grid；The data collection path that AUV is planned based on probability neighborhood grid, carries out the data collection of the whole network.Therefore, beneficial effects of the present invention are：Using probability underwater sound communication model, data collection distance can be adjusted flexibly according to probability demand；Data collection is carried out using AUV, the energy consumption that sensor node carries out data transmission is effectively reduced, extends network life；By partitioning the network into small grid, AUV only needs traversal grid element center position that data collection can be completed, can be efficiently applied to the unknown water sound sensor network of node deployment information；Pass through change data transmission success ratepValue and data transmission wheel number, provide a kind of solution of effective equalization information gain and data delay.
Description
Technical field
The invention belongs to water sound sensor network fields, and in particular to a kind of three-dimensional UASNs based on probability neighborhood grid
Mobile data collection method.
Background technology
Underwater data collect to water sound sensor network (underwater acoustic sensor networks,
UASNs application) has vital meaning, the either monitoring of underwater environment and management or underwater disaster monitoring pre-
Alert, people are required for collecting the interest message for getting perception monitoring region using UASNs, then carry out analyzing processing to information
It the operations such as excavates with storage, finally can just make reasonable effective decision.In many applications of UASNs, data collection needs
A large amount of perception data is transmitted, and a large amount of perception datas transmit in a network, will produce mass communication expense.Further, since section
Point energy be the battery supplied of limited electricity rather than sustainable supply, in order to obtain more inspections in detection zone
Measured data ensures the validity of network, extends the task that network life is exactly primary.Therefore, how to ensure information gain
In the case of, extend network life simultaneously as much as possible, is an extremely challenging problem.
Currently, as follows to the research papers of water sound sensor network method of data capture：
1, Wang et al. was in 2008《International Conference on Distributed
Computing Systems Workshops》On article " the Data Collection with Multiple Mobile that deliver
Actors in Underwater Sensor Networks " propose one and use multiple mobile actors to obtain height
The underwater data collection scheme of time precision data.The program includes mainly three algorithms：Region division and actors dispersions are calculated
Method, subregion optimization algorithm and virtual clustering algorithm.The program partitions the network into 4 according to boundary node position firstn
A region, the collection time delay of every sub-regions is estimated further according to node number, and is optimized to subregion, then will be according to one
Actors is deployed to each sub-regions by fixed position strategy, is established virtual cluster and is carried out data collection.
2, Domingo et al. was in 2011《Wireless Personal Communications》On the article delivered
“A Distributed Energy-Aware Routing Protocol for Underwater Wireless Sensor
Networks " proposes a kind of distribution of energy efficient and clusters scheme DUCS, and the program passes through sub-clustering and data aggregated data
Redundancy is eliminated, achievees the purpose that reduce network energy consumption with this.Although sub-clustering is to optimize one kind of catenet total energy consumption
Effective ways, but this method can cause the problem of leader cluster node energy consumption unevenness.
3, Hollinger et al. was in 2012《IEEE Journal on Selected Areas in
Communications》On article " the Underwater Data Collection using Robotic Sensor that deliver
Networks " proposes a scheme that water sound sensor network data collection is carried out using AUV.The program will plan the roads AUV
Under the problem of diameter carries out underwater data collection, maximum spends data collection while minimizing routing cost is defined as communication constraint
Data gathering problem (CC-DCP), and by CC-DCP problem formulations, it is proposed that a heuristic approximate data, it is final to propose
Three kinds of 2D heuristic path programmes for being suitable for different scenes.
4, Ilyas et al. was in 2015《Procedia Computer Science》On the article " AEDG that delivers:
AUV-aided Efficient Data Gathering Routing Protocol for Underwater Wireless
Sensor Networks " propose the method for data capture AEDG of AUV auxiliary, and its object is to realize the reliable number in UASNs
According to collection.In AEDG, gateway uses shortest path tree algorithm collector node data, and AUV is along preset elliptical path later
From gateway collection data.This method can effectively equilibrium energy consume, and extend Network morals, however AEDG is to be based on
Deterministic communication model, and in the practical application of UASNs, data transmission success is reduced with distance.
5, Khan et al. was in 2015《Sensors》On article " the A Distributed Data- that deliver
Gathering Protocol Using AUV in Underwater Sensor Networks " propose a distributed number
According to collection scheme AUV-PN.In this scenario, AUV executes two stages：The trip of travel (NPT) and data collection of network division
(DGT).In the NPT stages, whole network is divided into multiple clusters by AUV first, and each cluster selects a cluster head section according to LEACH agreements
Point CH；Then, cluster is further divided into multiple submanifolds by CH, and specifies a path-node (PN) to collect for each submanifold
The local data of member node MN in submanifold.After having divided network, AUV starts to execute DGT.In this scenario, AUV only needs to access
CH and PN, so that it may acquire whole network data, be effectively shortened data collection time.However, in this scenario, PN needs to collect son
All data in cluster, and the selection of PN only considered the total energy consumption expense that data upload in submanifold, not consider that dump energy is asked
Topic, additional communication overhead can lead to PN premature deaths, influence the life cycle of whole network.
6, Jalaja et al. was in 2015《Lecture Notes in Computer Science》On the article delivered
“Adaptive data collection in sparse underwater sensor networks using mobile
Elements " proposes the self-adapting data collection method of mobile auxiliary, and this method reduces network by using mobile element
Energy consumption reduces data delay by a kind of polling mechanism.However, due in this method mobile element need to be moved to all nodes
Data collection, therefore polling mechanism despite the use of are carried out, data delay is still very big.
In conclusion common problem when at present in water sound sensor network based on mobile element progress data acquisition
It is：
1) design of most of water sound sensor network data collection plans is all based on ideal certainty underwater sound communication
Model, and in practical applications, the data transmission success of underwater acoustic channel be with range attenuation, when data transmission fails,
Data collection will be unable to complete；
2) energy consumption of leader cluster node can be made to increase based on the method for data capture to cluster, eventually leads to network energy consumption not
, network life is reduced；
3) most of methods of data capture based on mobile auxiliary all assume that sensor node deployment in approximately the same plane,
3D water environments cannot be effectively applied to；
4) it is highly dependent on node deployment information, for the network that node deployment information is unknown, method of data capture can not
It realizes.
Invention content
In order to solve problems and deficiency present in existing water sound sensor network data collection techniques, the present invention
A kind of three-dimensional UASNs mobile data collections method based on probability neighborhood grid is proposed, it is mainly general by partitioning the network into
Rate neighborhood grid, the center for reaching each probability neighborhood grid by AUV are loaded come collector node data with active balance, drop
Low node energy consumption extends network life.
It realizes above-mentioned technical purpose, reaches above-mentioned technique effect, the invention is realized by the following technical scheme：
A kind of mobile data collection method of the three-dimensional UASNs based on probability neighborhood grid includes specifically four steps：
(1) the probability traffic model structure of network：According to the characteristic of three-dimensional UASNs, sound wave decline, ocean current table are considered
The factors such as face activity, turbulence noise, wind, thermal noise, the probability traffic model of structure three-dimensional UASNs；
(2) network divides：The data transmission success p of probability traffic model and needs based on structure draws network
It is divided into the probability neighborhood grid of same size；
(3) trellis traversal path planning：Based on ready-portioned probability neighborhood grid, using Layered-Scan methods
Each probability neighborhood grid is successively traversed, determines the path of AUV；
(4) data collection：AUV starts data-gathering process along all probability neighborhood grids of traversal path determined, when
When AUV is close to the center of small grid, the data of current probability neighborhood grid are collected using scheduling protocol.
The method of data capture can be widely used in the unknown three-dimensional UASNs of node deployment information.
In the probability traffic model of water sound sensor network described in step (1), data transmission success is with transmission distance
From increase and decay.
Probability neighborhood definition described in step (2) is：Probability neighborhood ΨnTo arrive position x in three-dimensional UASNsvData pass
Defeated success rate P (xv,xnAll position x of) >=pvSet.
It is not less than p for the data Successful transmissions rate of the arbitrary node in guarantee probability neighborhood grid, described in step (2)
Probability neighborhood grid be completely contained in the inscribed regular hexahedron of probability neighborhood.
Therefore, the computational methods of probability neighborhood grid are：The maximal side of probability neighborhood grid isWherein
D_p is the corresponding transmission ranges of data transmission success p；The three-dimensional UASNs is divided into k*k*k probability neighborhood grid,
WhereinWherein L is the network length of side；Final probability neighborhood side length of element is
Data collection scheduling protocol described in step (4) is based on time division multiple access scheme, includes specifically three phases：
(4-1) starting stage：All nodes disposed in network are all in an inactive state, when AUV is close to probability neighborhood
When the center of grid, AUV broadcasts a high power Wake-up control packet comprising node initial schedule information, the Gao Gong
The node that rate Wake-up control packets can trigger in current probability neighborhood grid enters active state；
(4-2) scheduling phase：The node for receiving Wake-up packets judges oneself whether in current probability neighborhood grid,
If so, switching to active state, and according to the time slot distributed in Wake-up packets, sequentially replys AUV mono- and confirm packet ACK, it
Afterwards, AUV redistributes time slot according to the information of each node feedback, and new transmitting scheduling information is sent to node；
(4-3) data transfer phase：According to new transmitting scheduling information, the data packet respectively stored is transferred to by node
AUV, after the data transfer ends of all nodes, AUV reschedules Data Transport Protocol for the data transmission of next round
Until all transmission wheel is counted up into.
In the data transfer phase, data transmission wheel number is pre- according to the information gain and data delay requirement of user
It first sets, by increasing data transmission wheel number, information gain can be improved in the case where keeping postponing compared with small data.
Compared with existing water sound sensor network method of data capture, good effect is possessed by the present invention：
(1) use probability underwater sound communication model, data collection distance that can be adjusted flexibly according to probability demand；
(2) data collection is carried out using AUV, effectively reduces the energy consumption that sensor node carries out data transmission, extends
Network life；
(3) by partitioning the network into small grid, AUV only needs traversal grid element center position that data collection can be completed, can
To be efficiently applied to the unknown water sound sensor network of node deployment information；
(4) by the value and data transmission wheel number of change data transmission success rate p, a kind of effective equalization information is provided
The solution of gain and data delay.
Description of the drawings
Fig. 1 is the flow diagram of entire method of data capture in the present invention；
Fig. 2 is probability neighborhood grid computing schematic diagram in the present invention；
Fig. 3 is mesh generation schematic diagram in the present invention；
Fig. 4 is the path schematic diagram that AUV traverses probability neighborhood grid in the present invention；
Fig. 5 is the circuit theory schematic diagram of data dispatch agreement in the present invention.
Specific implementation mode
The invention will be further described below in conjunction with the accompanying drawings.
It is a kind of flow chart of the mobile data collection method of the three-dimensional UASNs based on probability neighborhood grid as shown in Figure 1,
Include specifically following four steps：
(1) the probability traffic model structure of network：According to the characteristic of three-dimensional UASNs, sound wave decline, ocean current table are considered
The factors such as face activity, turbulence noise, wind, thermal noise, the probability traffic model of structure three-dimensional UASNs；
(2) network divides：Probability traffic model based on structure and data transmission success p, partition the network into phase
With the probability neighborhood grid of size；
(3) trellis traversal path planning：Based on ready-portioned probability neighborhood grid, using Layered-Scan methods
Each probability neighborhood grid is successively traversed, determines the path of AUV；
(4) data collection：AUV starts data-gathering process along all probability neighborhood grids of traversal path determined, when
When AUV is close to the center of small grid, the data of current probability neighborhood grid are collected using scheduling protocol.
In the probability traffic model of water sound sensor network described in step (1), data transmission success is with transmission distance
From increase and decay.
It is illustrated in figure 2 the schematic diagram for calculating probability neighborhood sizing grid.Wherein, probability neighborhood definition is：Probability neighborhood
ΨnTo arrive position x in three-dimensional UASNsvData transmission success P (xv,xnAll position x of) >=pvSet.
It is not less than p, probability neighborhood grid for the data Successful transmissions rate of the arbitrary node in guarantee probability neighborhood grid
It must completely include in the probability neighborhood ball of grid element center position, grid is up to the inscribed regular hexahedron of probability neighborhood ball.
Therefore, the circular of probability neighborhood grid is：The maximal side of probability neighborhood grid is
Wherein d_p is the corresponding transmission ranges of data transmission success p；
The 3D water sound sensor networks are divided into k*k*k probability neighborhood grid, wherein
Wherein L is the network length of side；
Final probability neighborhood side length of element is
It is illustrated in figure 3 mesh generation schematic diagram, entire three-dimensional UASNs is finally evenly divided into the k*k*k length of side
For the probability neighborhood grid of l.
Such as the traverse path that Fig. 4 is probability neighborhood grid.By the way that network is divided into k layers, every layer can be considered as a 2D
Plane, AUV are finally moved on heart position according to the paths Scan in each layer.
It is illustrated in figure 5 the circuit theory schematic diagram of data dispatch agreement, data collection scheduling protocol is more based on the time-division
Location mechanism, include specifically three phases：
1) starting stage：All nodes disposed in network are all in an inactive state, when AUV is close to probability neighborhood grid
Center when, AUV broadcasts a high power Wake-up control packet comprising node initial schedule information, the high power
The node that Wake-up control packets can trigger in current probability neighborhood grid enters active state；
2) scheduling phase：The node for receiving Wake-up packets judges oneself whether in current probability neighborhood grid, if
It is then to switch to active state, and according to the time slot distributed in Wake-up packets, sequentially reply AUV mono- and confirm packet ACK, later,
AUV redistributes time slot according to the information of each node feedback, and new transmitting scheduling information is sent to node；
3) data transfer phase：According to new transmitting scheduling information, the data packet respectively stored is transferred to AUV by node,
After the data transfer ends of all nodes, AUV reschedule Data Transport Protocol for next round data transmission until
All transmission wheels are counted up into.
Wherein, in the data transfer phase, data transmission wheel number is the information gain and data delay according to user
Demand is preset, by increasing data transmission wheel number, can improve information in the case where keeping postponing compared with small data and increase
Benefit.
Claims (7)
1. a kind of mobile data collection method of the three-dimensional UASNs based on probability neighborhood grid, which is characterized in that include specifically four
A step：
(1) the probability traffic model structure of network：According to the characteristic of three-dimensional UASNs, sound wave decline is considered, ocean current surface is lived
Dynamic, turbulence noise, wind, thermal noise factor, the probability traffic model of structure three-dimensional UASNs；
(2) network divides：The data transmission success p of probability traffic model and needs based on structure, partitions the network into
The probability neighborhood grid of same size；The probability neighborhood definition is：Probability neighborhood ΨnTo arrive position in 3D underwater sound Sensor Networks
xvData transmission success P (xv,xnAll position x of) >=pvSet；
(3) trellis traversal path planning：Based on ready-portioned probability neighborhood grid, successively using Layered-Scan methods
Each probability neighborhood grid is traversed, determines the path of AUV；
(4) data collection：AUV starts data-gathering process, works as AUV along all probability neighborhood grids of traversal path determined
When close to the center of small grid, the data of current probability neighborhood grid are collected using scheduling protocol.
2. the mobile data collection method of the three-dimensional UASNs according to claim 1 based on probability neighborhood grid, feature
It is, the method for data capture is suitable for the unknown network of node deployment information.
3. the mobile data collection method of the three-dimensional UASNs according to claim 1 based on probability neighborhood grid, feature
It is, the probability traffic models of UASNs are characterized in that：Data transmission success decays with distance.
4. the mobile data collection method of the three-dimensional UASNs according to claim 1 based on probability neighborhood grid, feature
It is, the probability neighborhood grid described in step (2) is characterized as：Probability neighborhood grid is to be completely contained in being inscribed for probability neighborhood
Regular hexahedron.
5. the mobile data collection method of the three-dimensional UASNs according to claim 4 based on probability neighborhood grid, feature
It is, the computational methods of the probability neighborhood grid are：
The maximal side of probability neighborhood grid isWherein d_p is the corresponding transmission distances of data transmission success p
From；
The three-dimensional UASNs networks are divided into k*k*k probability neighborhood grid, whereinWherein L
For the network length of side；
Final probability neighborhood side length of element is
6. the mobile data collection method of the three-dimensional UASNs according to claim 1 based on probability neighborhood grid, feature
It is, the data collection scheduling protocol described in step (4) is based on time division multiple access scheme, includes specifically three phases：
(4-1) starting stage：All nodes disposed in network are all in an inactive state, when AUV is close to probability neighborhood grid
Center when, AUV broadcasts a high power Wake-up control packet comprising node initial schedule information, the high power
The node that Wake-up control packets can trigger in current probability neighborhood grid enters active state；
(4-2) scheduling phase：The node for receiving Wake-up packets judges oneself whether in current probability neighborhood grid, if so,
Then switch to active state, and according to the time slot distributed in Wake-up packets, sequentially replys AUV mono- and confirm packet ACK, later, AUV
Time slot is redistributed according to the information of each node feedback, and new transmitting scheduling information is sent to node；
(4-3) data transfer phase：According to new transmitting scheduling information, the data packet respectively stored is transferred to AUV by node, when
After the data transfer ends of all nodes, AUV reschedules Data Transport Protocol for the data transmission of next round until institute
There is data transmission wheel to count up into.
7. the mobile data collection method of the three-dimensional UASNs according to claim 6 based on probability neighborhood grid, feature
It is, the data transmission wheel number is preset according to the demand of user, by increasing data transmission wheel number, is being kept
In the case of postponing compared with small data, information gain is improved.
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| CN108684005B (en) * | 2018-04-02 | 2021-02-09 | 河海大学常州校区 | SOM-based multi-AUV efficient data collection method in underwater sensor network |
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