CN109275099A - More AUV efficient data collection methods in underwater wireless sensor network based on VOI - Google Patents
More AUV efficient data collection methods in underwater wireless sensor network based on VOI Download PDFInfo
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/02—Services making use of location information
- H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04B—TRANSMISSION
- H04B13/00—Transmission systems characterised by the medium used for transmission, not provided for in groups H04B3/00 - H04B11/00
- H04B13/02—Transmission systems in which the medium consists of the earth or a large mass of water thereon, e.g. earth telegraphy
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
- H04W4/90—Services for handling of emergency or hazardous situations, e.g. earthquake and tsunami warning systems [ETWS]
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/02—Communication route or path selection, e.g. power-based or shortest path routing
- H04W40/20—Communication route or path selection, e.g. power-based or shortest path routing based on geographic position or location
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W40/00—Communication routing or communication path finding
- H04W40/24—Connectivity information management, e.g. connectivity discovery or connectivity update
- H04W40/32—Connectivity information management, e.g. connectivity discovery or connectivity update for defining a routing cluster membership
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- H—ELECTRICITY
- H04—ELECTRIC COMMUNICATION TECHNIQUE
- H04W—WIRELESS COMMUNICATION NETWORKS
- H04W84/00—Network topologies
- H04W84/18—Self-organising networks, e.g. ad-hoc networks or sensor networks
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
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- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D30/00—Reducing energy consumption in communication networks
- Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks
Abstract
The invention discloses a kind of more AUV efficient data collection methods in underwater wireless sensor network based on VOI, include the following steps: that (1) high VOI packet priority is transmitted to cluster head；(2) the mobile position the Sink determination of three-dimensional underwater environment and Spacial domain decomposition；(3) path planning of the AUV in subregion；(4) more AUV dynamic competitions handle emergency task.The present invention passes through the characteristics of spatial characteristics and resource distribution using more AUV, the complex data that can effectively solve in underwater wireless sensor network collects task, the data collection mode of more AUV has preferable fault-tolerant ability and improves system robustness, and highly reliable and low latency advantage is that single AUV system does not have.Three-dimensional underwater region division is with AUV path planning ining conjunction with, and using the processing emergency of more AUV dynamic competition mechanism, data in dynamic collection network, equilibrium network energy consumption extends network life.
Description
Technical field
The invention belongs to underwater wireless sensor network more AUV data collection techniques fields, and in particular to a kind of underwater nothing
More AUV efficient data collection methods in line sensor network based on VOI.
Background technique
In the interested monitoring region of application system, node passes through node random placement in underwater wireless sensor network
The mode of self-organizing forms underwater wireless sensor network.Data Collection task is executed in an underwater environment, is at least needed common
Three sensor node, aggregation node (Sink) and task management center modules.Ordinary node can be sharp by way of multi-hop
The data perceived are sent to Sink with acoustic communication, cluster interior nodes can also be collected using cluster head by clustering mechanism
Data, then Sink is sent to by multi-hop between cluster.Sink is sent the information of convergence in task management by wireless wave communication
The heart allows to make timely countermeasure.The multidate information in sensor node real-time monitoring interest region, when a certain region
Node perceived to a large amount of identical data or multiple nodes occur data overflow etc. overflow when, illustrate to have in the region underwater
Emergency occurs.
Underwater wireless sensor network is in sides such as area monitoring, natural resources discovery, submarine target tracking and enemy army's investigations
Face has effect difficult to the appraisal.Under water in the research of wireless sensor network, it rough can be divided into data collection, node
The research directions such as positioning, network topology control, safety encryption and node charging, and data collection techniques are as basic research contents
In key technology, in depth being studied in conjunction with underwater environment feature it has far-reaching significance.Underwater wireless sensor
The distinguishing feature of network has:
(1) network size is big.Since underwater environment is not static environment, it can not plan specific zone boundary, be difficult
Go the size of control underwater wireless sensor network.Meanwhile the sensing range of ordinary node is limited with data transmission range, needs
A large amount of sensor node is disposed in the environment；
(2) node energy is limited.It is deployed in underwater node to be restricted by cost and volume, the electricity of node can only be by electricity
Pond provides, and replaces battery and supplement electricity is all highly difficult.Node energy has consumed i.e. death, is easy to cause not noticeable routing empty
Hole influences the performance of network；
(3) decaying of underwater sound signal is serious.Since the complex characteristic and sound wave of underwater environment exist in communication process
Absorption loss water and divergence loss, therefore, it is more serious that acoustic signal transmits its remoter decaying.This characteristic strongly limits underwater logical
The optional frequency of letter, the problem of inevitably bringing high time delay and low transmission success rate；
(4) node locating problem.It is different from land sensor node, it is influenced by water flow and sea wind, underwater sensor position
It is dynamic change, there is uncertainty, be difficult to position by GPS, and there are precision for the positioning method based on acoustic signal
The problems such as not high and consuming overlong time.
With the continuous development of science and technology, it has been introduced for the research of underwater wireless sensor network more advanced soft or hard
Part equipment.Autonomous Underwater Vehicle (AUV) is one kind of underwater unmanned vehicle (UUV), has currently combined artificial intelligence
Can be with other advanced computing techniques, data collection field plays irreplaceable role under water.Dispose underwater AUV by
The features such as sufficient, small by water currents in energy, can cruise access ordinary node according to certain path.More AUV more can be with
It is cooperated by the way of centralized and distributed, greatly improves the efficiency of data collection.The network of deployment AUV has relatively strong
Scalability complete task often through different working methods is arranged in face of different application environment and demand.
Underwater wireless sensor network has very high researching value, to comprehensive research ocean characteristic, exploitation and protection sea
Ocean has a very important significance.In nearest international research progress, a kind of new sphere nodes can control the side of signal
For formula come depth where making a reservation for, AUV can carry underwater node as underwater mobile node, can continuously collect underwater letter
Breath.China has begun marine environment further investigation, but the research in terms of wireless sensor network just starts to walk under water,
Main research institution has Acoustical Inst., Chinese Academy of Sciences, the Institute of Oceanology of the Chinese Academy of Sciences, Chinese Academy of Sciences's automation to grind
Institute, Harbin Engineering University, Xiamen University and Chinese Marine University etc. are studied carefully, mainly for water sound communication technique, Networking protocol, body
The expansion such as architecture research.
Summary of the invention
In view of the above-mentioned problems, the present invention proposes more AUV efficient datas based on VOI in a kind of underwater wireless sensor network
Collection method, in the underwater wireless sensor network of random placement, the packet priority of high VOI is transferred to cluster head, by area
Domain divides and path planning, collects the data of subregion using more AUV, and balanced network energy extends network life.
It realizes above-mentioned technical purpose, reaches above-mentioned technical effect, the invention is realized by the following technical scheme:
A kind of more AUV efficient data collection methods based on VOI in underwater wireless sensor network, comprising the following steps:
(1) high VOI packet priority is transmitted to cluster head
Underwater wireless sensor network node random placement and cluster after the endogenous node perceived to data of cluster, calculate first
The information value VOI of data packet, next-hop node compare itself VOI of storing data packet and the VOI of received data packet,
The data packet of preferential forwarding VOI high.The higher packet priority of VOI is transferred to cluster head in cluster, waits the collection of AUV；
(2) the mobile position the Sink determination of three-dimensional underwater environment and Spacial domain decomposition
In three-dimensional underwater environment, underwater data is executed using more AUV and collects task, to keep the energy and flow of more AUV
Load balancing, while ensuring that AUV will be collected into data and be timely transmitted to Sink.Pass through region partitioning algorithm, it is first determined mobile
The attributes such as the vertical underway position of Sink, then integration node quantity, node density and node depth draw hydrospace region
It is divided into multiple subregions.Each sub-regions are made of multiple clusters, and are responsible for Data Collection task by the same AUV；
(3) path planning of the AUV in subregion
After the completion of sub-zone dividing, each AUV is responsible for the Data Collection task of a sub-regions, completes within a certain period of time
The data collection of multiple clusters in subregion.The nearest cluster of the vertical navigation area of the mobile Sink of distance in subregion is set as AUV mono-
The termination of secondary collecting path accesses cluster, and randomly chooses initial access cluster.By the dynamic rewards that Q-learning algorithm is arranged
Function come control AUV access cluster quantity and required time, sample path is learnt, dynamic establish a return it is highest
Access path；
(4) more AUV dynamic competitions handle emergency task
When the emergency tasks such as data spilling occur for the cluster head of certain sub-regions, which will by way of multi-hop between cluster
Data emergency collects solicited message and is sent to mobile Sink, and mobile Sink assesses the current state of AUV, more AUV with
Emergency task distance, dump energy and execution task status etc. are at war with, and the AUV for competing triumph obtains emergency task
Disposal right simultaneously pauses immediately Current data collection task, be switched to emergency task processing status and go to emergency task place, AUV
Atomic region is returned after the completion of processing, is continued to complete remaining data and is collected task.
The information value VOI of data packet is made of event significance level EIP and information concentration degree ICN in above-mentioned steps (1).
EIP describes the correlation and timeliness of data and the required data of application that node is collected into, calculation formula Ek,i(t)=
αkFk+(1-αk)f(t-tk,i), wherein α k is the constant between 0 to 1, and t is current time, whereinWherein αkFor the constant between 0 to 1, t is current time, FkIndicate the data packet of node i perception
The correlation of k and the required data of application, what X was indicated is the physical signal using required data, and K is the physics letter that node perceived arrives
Number.In EIPIndicate information only has the attribute of value to decision within a certain period of time, declines
Subtract coefficientηkWith FkIt is different and different.Since nodes different in cluster generate identical data packet, this association when perceiving similar events
The value of data packet is affected, ICN is the intensity for indicating data packet k in cluster, and calculation formula isWherein TicIndicate that data packet k is transferred to cluster head needs from node i
Time, n indicate perception identical data packet k quantity.tendIndicate that data packet last time is transferred to the time point of cluster head,
tstartIndicate the time point that the data packet is collected by cluster head for the first time.Temporal information in ICN obtains by historical information, cluster
Interior nodes are by obtaining relevant information with the regular command interaction of cluster head.Thus information value VOI is defined as V=γkEk+(1-
γk)Ik, γkFor the constant between 0 to 1.
Cluster in network is mapped as two-dimensional surface first with the position of cluster head by the region partitioning algorithm in above-mentioned steps (2)
On particle.Make Voronoi Diagram using these particles, passes through formula first
CHi∈ CH, i=(1,2,3...n) establish the specific location that mobile Sink is vertically moved.Thiessen polygon where each particle
Referred to as matter region, wherein dSink,centerMatter regional center is at a distance from two-dimensional surface center where mobile sink, di,centerFor
Matter region i is at a distance from two-dimensional surface center, Nerbor (CHi) indicate the quantity in the adjacent matter region in the matter region.For
One matter region of Voronoi diagram, there are a variety of attributes such as number of nodes, cluster internal segment dot density, the mean depth etc. of cluster node,
The form of the attribute vector of each matter region i is expressed as Pi=[pi1,pi2,L,pim]T, for m attribute, it is defined on i-th
The similarity of subregion s behind a matter region and multiple matter region merging techniques is fi(psm,pim), with Sim (Ps,Pi) indicate more attributes
Similarity Sim (Ps,Pi)=[fi(ps1,pi1),fi(ps2,pi2),L,fi(psm,pim)], the attributes similarity in matter region calculates
Formula isMatter all in Voronoi diagram region is calculated
The growth standard of attribute m isWhereinAny selection is more
A matter region carries out random synchronism growth to adjacent matter region, in the case where meeting W, when each matter region is all located at a son
When the s of region, region partitioning algorithm stops.
The path planning stage in above-mentioned steps (3) establishes the reward matrix that AUV is shifted between cluster, in each sub-district
In domain, set reward matrix that AUV is shifted between cluster asInitializing Q matrix is 0.Reward function
Selection can determine Q-learning convergence speed of the algorithm and degree, in order to enable AUV in the condition of not repeated accesses cluster
All clusters in lower traversal subregion, the reward setting of each cluster is as follows
Wherein, α is the constant between 0 to 1, and C is range of reward adjustment parameter, and f is the access times of j-th of cluster, VjCluster is gone to for AUV
J total VOI obtained, dijIt (f) is the linear distance of cluster i and cluster j.AUV is learnt by the way of random walk, until obtaining
Stable Q matrix is obtained, the calculation formula of Q matrix is Q (s, a)=R+ γ maxa'Q (s', a'), γ are adjustment parameter, and s is current
State.AUV is learnt using above-mentioned algorithm, and experience is equivalent to primary training each time.In training each time, intelligent body is to environment
It is explored.AUV finds the highest route of return and reaches termination access cluster, and sends data to mobile Sink.
When having the emergencies such as multiple data spillings to generate in above-mentioned steps (4), mobile Sink can carry out multiple events
Assessment, priority processing data overflow the high data collection request information of degree.Multiple AUV to the disposal right of current emergency into
Row competition, mobile Sink, which calculates the AUV in network according to following formula, competes winning valueWherein α is the constant between 0 to 1, and M is net
The boundary of network, d are AUV at a distance from emergency event place, and E is the surplus capacity of AUV, and e is that AUV navigation unit distance needs disappear
The energy of consumption, v are the average speed of AUV.When AUV is when executing emergency processing task, winning value is competedCalculating
Formula isWherein, tiIt indicates
Time required for the current emergency event of AUV processing, dnextIt (i) is the linear distance of present emergency a to event.It obtains
The AUV of victory can be obtained disposal right and go to relatively point progress data collection immediately.
Beneficial effects of the present invention:
The present invention passes through the characteristics of spatial characteristics and resource distribution using more AUV, can effectively solve underwater
Complex data in wireless sensor network collects task, and the data collection mode of more AUV has preferable fault-tolerant ability and mentions
High system robustness, highly reliable and low latency advantage is that single AUV system does not have.Three-dimensional underwater region division with
AUV path planning combines, and handles emergency using more AUV dynamic competition mechanism, and the data in dynamic collection network are balanced
Network energy consumption, extends network life.
Detailed description of the invention
Fig. 1 is the network model figure of an embodiment of the present invention；
Fig. 2 is the Voronoi illustraton of model of an embodiment of the present invention；
Fig. 3 is the zoning plan of an embodiment of the present invention；
Fig. 4 is that the AUV of an embodiment of the present invention transmits datagram to mobile sink.
Specific embodiment
In order to make the objectives, technical solutions, and advantages of the present invention clearer, with reference to embodiments, to the present invention
It is further elaborated.It should be appreciated that described herein, specific examples are only used to explain the present invention, is not used to limit
The fixed present invention.
Application principle of the invention is explained in detail with reference to the accompanying drawing.
When disposing underwater wireless sensor network, due to the complex characteristics of underwater environment, the distribution situation of node is often
It is random and density unevenness.In order to reduce the energy consumption of node, extend the service life of network, it is more using specific mechanism sub-clustering
The cluster head data being collected into are sent to mobile Sink by AUV.This network is subjected to modeling processing, as shown in Figure 1.
Isomorphism node random placement is in interest region, and energy and communication range are limited, known to geographical location.Node is according to spy
Determine mechanism cluster.Since acoustic signal transmission speed is slow, bandwidth is lower, and is influenced by underwater routing cavity, the application of more AUV
Data collection effort for such common situation provides possibility.Specific position of the mobile Sink as convergence center, in region
Do the vertical movement of constant speed.Application system can data total flow to this network carry out predictive estimation, and according to network
Distributed areas size and the data total flow of network determine the deployment quantity of more AUV.Therefore, the present invention proposes a kind of underwater
The efficient data collection method of more AUV in wireless sensor network based on VOI, comprising the following steps:
Step 1: high VOI packet priority is transmitted to cluster head
Underwater wireless sensor network node random placement and cluster after the endogenous node perceived to data of cluster, calculate first
The information value VOI of data packet, next-hop node compare itself VOI of storing data packet and the VOI of received data packet,
The data packet of preferential forwarding VOI high.The higher packet priority of VOI is transferred to cluster head in cluster, waits the collection of AUV.Data packet
Information value VOI be made of event significance level EIP and information concentration degree ICN.EIP describe data that node is collected into
Using the correlation and timeliness of required data, calculation formula Ek,i(t)=αkFk+(1-αk)f(t-tk,i), wherein αkIt is 0
Constant between to 1, t are current time,FkIndicate data packet k and the application of node i perception
The correlation of required data, what X was indicated is the physical signal using required data, and K is the physical signal that node perceived arrives.EIP
InIndicate information only has the attribute of value, attenuation coefficient to decision within a certain period of time
ηkWith FkIt is different and different.Since nodes different in cluster generate identical data packet when perceiving similar events, this association is affected
The value of data packet, ICN are the intensity for indicating data packet k in cluster, and calculation formula isWherein TicIndicate that data packet k is transferred to cluster head needs from node i
Time, n indicate perception identical data packet k quantity.tendIndicate that data packet last time is transferred to the time point of cluster head,
tstartIndicate the time point that the data packet is collected by cluster head for the first time.Temporal information in ICN obtains by historical information, cluster
Interior nodes are by obtaining relevant information with the regular command interaction of cluster head.Thus information value VOI is defined as V=γkEk+(1-
γk)Ik, γkFor the constant between 0 to 1.
Step 2: the mobile position the Sink determination of three-dimensional underwater environment and Spacial domain decomposition
In three-dimensional underwater environment, underwater data is executed using more AUV and collects task, to keep the energy and flow of more AUV
Load balancing, while ensuring that AUV will be collected into data and be timely transmitted to Sink.The present invention is by region partitioning algorithm, first really
Surely the attributes such as the vertical underway position of Sink, then integration node quantity, node density and node depth are moved, by hydrospace
Region division is multiple subregions.Each sub-regions are made of multiple clusters, and are responsible for Data Collection task by the same AUV.
Cluster in network is mapped as the particle on two-dimensional surface first with the position of cluster head by region partitioning algorithm, as shown in Figure 2.It utilizes
These particles make Voronoi Diagram, pass through formula first
CHiIt is vertical that ∈ CH, i=(1,2,3...n) establish mobile Sink
Mobile specific location.Thiessen polygon where each particle is known as matter region, wherein dSink,centerWhere mobile sink
Matter regional center is at a distance from two-dimensional surface center, di,centerIt is matter region i at a distance from two-dimensional surface center, Nerbor
(CHi) indicate the quantity in the adjacent matter region in the matter region.For a matter region of Voronoi diagram, there are a variety of attributes as saved
Point quantity, the form of cluster internal segment dot density, the mean depth etc. of cluster node, the attribute vector of each matter region i are expressed as Pi
=[pi1,pi2,L,pim]T, for m attribute, it is defined on i-th of matter region and the subregion s's after multiple matter region merging techniques
Similarity is fi(psm,pim), with Sim (Ps,Pi) indicate multiattribute similarity
Sim(Ps,Pi)=[fi(ps1,pi1),fi(ps2,pi2),L,fi(psm,pim)], the attributes similarity in matter region calculates
Formula isλ1+λ2+…+λm=1.Matter all in Voronoi diagram region is calculated and is belonged to
The growth standard of property m isWherein
As shown in Fig. 2, it is random arbitrarily to select multiple matter regions to carry out to adjacent matter region
Synchronous growth, in the case where meeting W, when each matter region is all located at a sub-regions s, region partitioning algorithm stops.
Step 3: path planning of the AUV in subregion
After the completion of sub-zone dividing, each AUV is responsible for the Data Collection task of a sub-regions, completes within a certain period of time
The data collection of multiple clusters in subregion.The nearest cluster of the vertical navigation area of the mobile Sink of distance in subregion is set as AUV mono-
The termination of secondary collecting path accesses cluster, and randomly chooses initial access cluster.By the dynamic rewards that Q-learning algorithm is arranged
Function come control AUV access cluster quantity and required time, sample path is learnt, dynamic establish a return it is highest
Access path.The path planning stage establishes the reward matrix that AUV is shifted between cluster, and in each sub-regions, setting AUV exists
The reward matrix shifted between cluster isInitializing Q matrix is 0.The selection of reward function can determine
Q-learning convergence speed of the algorithm and degree, in order to enable AUV to traverse subregion under conditions of not repeated accesses cluster
Interior all clusters, the reward setting of each cluster is as followsWherein, 0 α
Constant between to 1, C are range of reward adjustment parameter, and f is the access times of j-th of cluster, VjGo to cluster j obtained for AUV
Total VOI, dijIt (f) is the linear distance of cluster i and cluster j.AUV is learnt by the way of random walk, until obtaining stable Q
Matrix, the calculation formula of Q matrix are Q (s, a)=R+ γ maxa'Q(s',a')；γ is adjustment parameter, and s is current state.AUV benefit
Learnt with above-mentioned algorithm, experience is equivalent to primary training each time.In training each time, intelligent body explores environment.
Trained purpose is enhancing AUV brain, and more training results will lead to more preferably Q matrix, and AUV can find the highest road of return
Line, which reaches, terminates access cluster, and sends data to mobile Sink, as shown in Figure 4.
Step 4: more AUV dynamic competitions handle emergency task
When the emergency tasks such as data spilling occur for the cluster head of certain sub-regions, which will by way of multi-hop between cluster
Data emergency collects solicited message and is sent to mobile Sink, and mobile Sink assesses the current state of AUV, more AUV with
Emergency task distance, dump energy and execution task status etc. are at war with, and the AUV for competing triumph obtains emergency task
Disposal right simultaneously pauses immediately Current data collection task, be switched to emergency task processing status and go to emergency task place, AUV
Atomic region is returned after the completion of processing, is continued to complete remaining data and is collected task.There are the emergencies such as multiple data spillings to generate
When, mobile Sink can assess multiple events, and priority processing data overflow the high data collection request information of degree.It is multiple
AUV is at war with to the disposal right of current emergency, and mobile Sink is excellent to the AUV calculating competition in network according to following formula
Victory valueWherein α is the constant between 0 to 1, M
For the boundary of network, d is AUV at a distance from emergency event place, and E is the surplus capacity of AUV, and e is that AUV navigation unit distance needs
The energy to be consumed, v are the average speed of AUV.When AUV is when executing emergency processing task, winning value is competedMeter
Calculating formula isWherein, tiIt indicates
Time required for the current emergency event of AUV processing, dnextIt (i) is the linear distance of present emergency a to event.It obtains
The AUV of victory can be obtained disposal right and go to relatively point progress data collection immediately.
In summary:
The invention discloses a kind of more AUV efficient data collection methods in underwater wireless sensor network based on VOI, first
First, invention elaborates the element of packet information value VOI, in importance, timeliness and the correlation of comprehensive perception data
Under the conditions of property etc., the size of VOI is determined by event significance level EIP and information concentration degree ICN.Then, in three-dimensional underwater environment
The middle multinomial attributive character according to different clusters determines position that Sink is vertically moved using Voronoi diagram and carries out hydrospace
The division in region makes AUV when the underwater data for executing subregion collects task, reaches the load balancing of energy and flow.Cluster
Distance between the size and cluster of interior VOI is the major influence factors of the subregion path planning of AUV, and therefore, the present invention is by setting
The dynamic rewards function of Q-learning algorithm is set to control the quantity and required time of AUV access cluster, difference is can adapt to and answers
Demand accelerates data collection efficiency.Finally, more AUV are competing by dynamic when the emergencies such as data spilling occur in cluster
Mechanism is striven to obtain event handling permission, after the completion of event handling, AUV returns to atomic region and continues data collection effort.
Basic principles and main features and advantages of the present invention of the invention have been shown and described above.The skill of the industry
Art personnel it should be appreciated that the present invention is not limited to the above embodiments, the above embodiments and description only describe
The principle of the present invention, without departing from the spirit and scope of the present invention, various changes and improvements may be made to the invention, these
Changes and improvements all fall within the protetion scope of the claimed invention.The claimed scope of the invention by appended claims and
Its equivalent thereof.
Claims (5)
1. a kind of more AUV efficient data collection methods in underwater wireless sensor network based on VOI, which is characterized in that including
Following steps:
(1) high VOI packet priority is transmitted to cluster head
Underwater wireless sensor network node random placement and cluster, after the endogenous node perceived to data of cluster, calculating data first
The information value VOI of packet, next-hop node compare itself VOI of storing data packet and the VOI of received data packet, preferentially
Forward the data packet of VOI high；The higher packet priority of VOI is transferred to cluster head in cluster, waits the collection of AUV；
(2) the mobile position the Sink determination of three-dimensional underwater environment and Spacial domain decomposition
In three-dimensional underwater environment, underwater data is executed using more AUV and collects task, to keep the energy and flow load of more AUV
Equilibrium, while ensuring that AUV will be collected into data and be timely transmitted to Sink；Pass through region partitioning algorithm, it is first determined mobile Sink
Vertical underway position, hydrospace region division is by then the attribute of integration node quantity, node density and node depth
Multiple subregions；Each sub-regions are made of multiple clusters, and are responsible for Data Collection task by the same AUV；
(3) path planning of the AUV in subregion
After the completion of sub-zone dividing, each AUV is responsible for the Data Collection task of a sub-regions, completes sub-district within a certain period of time
The data collection of multiple clusters in domain；The nearest cluster of the vertical navigation area of the mobile Sink of distance in subregion is set as AUV once to receive
The termination for collecting path accesses cluster, and randomly chooses initial access cluster；By the dynamic rewards function that Q-learning algorithm is arranged
The quantity and required time of AUV access cluster are controlled, sample path is learnt, dynamic establishes the highest access of return
Path；
(4) more AUV dynamic competitions handle emergency task
When emergency task occurs for the cluster head of certain sub-regions, which is asked data emergency collection by way of multi-hop between cluster
Ask information to be sent to mobile Sink, mobile Sink assesses the current state of AUV, more AUV with emergency task distance, surplus
It complementary energy and executes task status etc. and is at war with, the AUV for competing triumph obtains the disposal right of emergency task and temporary immediately
Ready preceding Data Collection task is switched to emergency task processing status and goes to emergency task place, returns after the completion of AUV processing
Atomic region continues to complete remaining data and collects task.
2. the more AUV efficient data collection methods of water in lower wireless sensor network according to claim 1 based on VOI,
It is characterized by: the information value VOI of data packet is by event significance level EIP and information concentration degree ICN group in the step (1)
At；EIP describes the correlation and timeliness of data and the required data of application that node is collected into, calculation formula Ek,i(t)
=αkFk+(1-αk)f(t-tk,i), wherein αkFor the constant between 0 to 1, t is current time,Fk
Indicate the data packet k and the correlation of the required data of application of node i perception, what X was indicated is the physical signal using required data,
K is the physical signal that node perceived arrives；In EIPIndicate that information is only fought to the finish within a certain period of time
Plan has valuable attribute, attenuation coefficient ηkWith FkIt is different and different；ICN indicates intensity of the data packet k in cluster, meter
Calculating formula isWherein TicIndicate that data packet k is transferred to from node i
The time that cluster head needs, n indicate the quantity of perception identical data packet k；tendIndicate that data packet last time is transferred to cluster head
Time point, tstartIndicate the time point that the data packet is collected by cluster head for the first time；Temporal information in ICN is by historical information
It obtains, cluster interior nodes are by obtaining relevant information with the regular command interaction of cluster head；Information value VOI is defined as V=γkEk+
(1-γk)Ik, γkFor the constant between 0 to 1.
3. more AUV efficient data collection methods in underwater wireless sensor network according to claim 1 based on VOI,
It is characterized by: the cluster in network is mapped as two dimension first with the position of cluster head by the region partitioning algorithm in the step (2)
Particle in plane；Make Voronoi Diagram using these particles, passes through formula firstCHi∈ CH, i=(1,2,3...n) establish the specific position that mobile Sink is vertically moved
It sets；Thiessen polygon where each particle is known as matter region, wherein dSink,centerFor matter regional center where mobile sink with
The distance at two-dimensional surface center, di,centerIt is matter region i at a distance from two-dimensional surface center, Nerbor (CHi) indicate the matter area
The quantity in the adjacent matter region in domain；For a matter region of Voronoi diagram, there are a variety of attributes such as number of nodes, cluster interior nodes
The form of density, the mean depth etc. of cluster node, the attribute vector of each matter region i is expressed as Pi=[pi1,pi2,L,pim]T,
For m attribute, the similarity of the subregion s after being defined on i-th of matter region and multiple matter region merging techniques is fi(psm,
pim), with Sim (Ps,Pi) indicate multiattribute similarity Sim (Ps,Pi)=[fi(ps1,pi1),fi(ps2,pi2),L,fi(psm,
pim)], the attributes similarity calculation formula in matter region isFor
The growth standard of all matter region computation attribute m is in Voronoi diagramWhereinIt arbitrarily selects multiple matter regions to carry out random synchronism growth to adjacent matter region, is meeting W's
In the case of, when each matter region is all located at a sub-regions s, region partitioning algorithm stops.
4. more AUV efficient data collection methods in underwater wireless sensor network according to claim 1 based on VOI,
It is characterized by: the path planning stage in the step (3) establishes the reward matrix that AUV is shifted between cluster, at each
In subregion, set reward matrix that AUV is shifted between cluster asInitializing Q matrix is 0；It will
The reward setting of each cluster is as followsWherein, α is the constant between 0 to 1, C
For range of reward adjustment parameter, f is the access times of j-th of cluster, VjCluster j total VOI obtained, d are gone to for AUVijIt (f) is cluster
The linear distance of i and cluster j；AUV is learnt by the way of random walk, until obtaining stable Q matrix, the meter of Q matrix
Calculation formula is Q (s, a)=R+ γ maxa'Q (s', a'), γ are adjustment parameter, and s is current state；AUV utilizes above-mentioned algorithm
It practises, experience is equivalent to primary training each time；In training each time, intelligent body explores environment；AUV finds return most
High route, which reaches, terminates access cluster, and sends data to mobile Sink.
5. more AUV efficient data collection methods in underwater wireless sensor network according to claim 1 based on VOI,
It is characterized by: mobile Sink assesses multiple events, preferentially when having multiple emergencies generation in the step (4)
It handles data and overflows the high data collection request information of degree；Multiple AUV are at war with to the disposal right of current emergency, move
Dynamic Sink, which calculates the AUV in network according to following formula, competes winning valueWherein α is the constant between 0 to 1, and M is net
The boundary of network, d are AUV at a distance from emergency event place, and E is the surplus capacity of AUV, and e is that AUV navigation unit distance needs disappear
The energy of consumption, v are the average speed of AUV；When AUV is when executing emergency processing task, winning value is competedCalculating
Formula isWherein, tiIt indicates
Time required for the current emergency event of AUV processing, dnextIt (i) is the linear distance of present emergency a to event；It obtains
The AUV of victory can be obtained disposal right and go to relatively point progress data collection immediately.
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