CN107343283A - A kind of three-dimensional static radio sensing network dispositions method based on genetic algorithm - Google Patents
A kind of three-dimensional static radio sensing network dispositions method based on genetic algorithm Download PDFInfo
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Abstract
The invention discloses a kind of three-dimensional static radio sensing network dispositions method based on genetic algorithm, solve wireless sensor network disposition of the prior art, coverage rate, poor connectivity, energy consumption, the problem of cost is high, belong to radio sensing network deployment techniques field.The three dimensions of target area is divided into several regions by the present invention；The topology that three-dimensional static wireless sensor network is carried out using genetic algorithm to each region is disposed, and is communicated with each other between the wireless sensor node in topology deployment, is obtained regional connectivity；The minimum spanning tree for generating each regional connectivity finds optimum base station position；Using being interconnected between the optimum base station position of all areas, 100% connection of the wireless senser of forest three dimensions is completed.The present invention is used to be modeled three-dimensional static wireless sensor network topology in wide area.
Description
Technical field
A kind of three-dimensional static radio sensing network dispositions method based on genetic algorithm, for three-dimensional in wide area
Static wireless sensor network topology is modeled, and belongs to radio sensing network deployment techniques field.
Background technology
Wireless Sensor Networks three dimensions covers to be proposed with the perspective treatise of deployment including Wang professors Yun
On the research [4] for the wireless sensor network coverage rate uniformly disposed in three dimensions, Taiwan university of communications is yellow strange auxiliary etc.
What the polynomial time algorithm [5] delivered for 2004 and Ravelomanana the same years of the university of Paris, FRA the 13rd proposed
EXCHANGEID and ASSIGNCODE agreements [6], but [5] [6] assume that monitoring event is uniformly distributed in three dimensions and node is quiet
Only.Alam of Cornell Univ USA in 2006 etc. proposes the three dimensions node deployment plan using Vorinoi polygon diagrams
Slightly, the covering of 100% space had not only been ensured but also had made nodes minimum, and the strategy can be applied to the fixed static and mobile feelings of section simultaneously
Condition [7], but this strategy assumes that the monitoring range of all nodes is the same.
(1) bit plane of mobile wireless sensor network two deployment based on fictitious force
The node motion diffusion method [1] based on potential field is initially proposed by University of Southern California of U.S. Howard etc., is a kind of
The incremental deploying algorithm of mobile wireless sensor network.Its main thought be by node one by one be deployed to one it is unknown
In region, then each node determines its deployed position using the information for the node collection disposed before.The design of the algorithm
Purpose is the coverage rate of maximization network, while ensures that node keeps the sight relation with another node.The algorithm is by virtual
Strength (VF) algorithm supports that, however, in VF algorithms, fictitious force caused by static wireless sensor node can hinder to move
The movement of wireless sensor node.Particle group optimizing (PSO) is introduced into as another Dynamical Deployment algorithm, but in such case
Under, the required calculating time is maximum bottleneck.Tsing-Hua University Wang Xue [3] etc. proposes a kind of Dynamical Deployment algorithm life for 2007
Entitled " coevolution particle group optimizing fictitious force guiding " (VFCPSO), because the algorithm combines coevolution particle group optimizing
(multiple) VF algorithms, i.e., again using the speed of the multiple groups of different assembly synergistic Dynamical Deployments for optimizing solution vector and each particle of renewal
Not only history part and globally optimal solution, and the strength of virtual radio sensor node.Simulation result shows, is proposed
VFCPSO can effectively realize WSNs Dynamical Deployment, and than VF, PSO and VFPSO in terms of time and validity is calculated
Algorithm has better performance.These researchs provide the thinking much referred to for the deployment issue of wireless sensor network,
But these are only for two-dimensional space, and what is considered mostly in practice is three dimensions, therefore its practical value is also waited to excavate.
(2) polynomial time algorithm
In the article on polynomial time algorithm of Huang Qifu professors [5], formulate wireless sensor network disposition and ask
Topic be used as a decision problem, its target be to determine the wireless sensor network of each point in coverage cover at least α without
Line sensor, α are the balls (being not necessarily identical radius) of the sensitive zones modeling of a given parameter and wireless senser.
This problem has been proposed an effective polynomial time algorithm [8] in 2d spaces.Professor Huang Qifu demonstrates under study for action
It is still feasible in polynomial time to solve this problem in a three dimensions.And propose a scheme can be very
Wireless sensor network disposition problem is easily converted into a kind of effective polynomial time distributorship agreement.The program can be used for
Wireless senser is disposed in three dimensions, and reduces the usage time of wireless senser, extends network life.
(3) EXCHANGEID and ASSIGNCODE agreements
The purpose of the article of Ravelomanana professors is the effect for studying randomness in wireless sensor network, and
Design and analysis to the appropriate agreement of these networks.On the basis of some typical random wireless sensor network behaviors,
Propose two kinds of distributed mechanisms.First problem is related to for the identifier of node being distributed to their neighbours, and second
Agreement solves code assignment problem.There is high probability, two kinds of agreements can realize theirs in the time slot of more logarithms
Task.
(4) the three dimensions node deployment strategy of Vorinoi polygon diagrams
Alam of Cornell Univ USA in 2006 etc. proposes the three dimensions node using Vorinoi polygon diagrams
Administration's strategy, it was demonstrated that it is the method for most reasonably solving this problem that the octahedron being truncated in three dimensions, which is inlayed,.Wen Zhongding
One measurement for being referred to as volume business of justice, it is the measurement of space-filling polyhedron quality.This article demonstrates the octahedral being truncated
Body result is optimal selection, and volume business is 0.68329, all more much better than the every other volume factor that may be selected (to be respectively
The hexagon prism and granatohedron of optimization, its volume factor is 0.477, and 0.36755) cube only has.Big 3D is empty
Between cover the Voronoi tessel ablation in the space that required number of nodes depends on being created by these nodes and created
The shape of unit.If the shape of each cell is a space-filling polyhedron with higher capacity business, then node
Number is just smaller.For example, granatohedron or the nodes needs of hexagon prism arrangement are more than the octahedron placement being truncated
Go out 43.25% node.After optimal Placement Strategy is found, connection sex chromosome mosaicism is checked, it is found that optimal Placement Strategy (is cut
Tail is octahedra) require that transmission range is at least 1.7889 times, to keep complete connection.This article is wireless sensing in three dimensions
A kind of deployment thinking of device, but it is uniformly distributed in space only for wireless senser, and wireless senser specification is consistent
Problem, significant limitation in actual applications also be present.
Some researchs to wireless sensor network disposition problem both at home and abroad are enumerated above, when majority research is all based on
Theoretical research, seldom with reference to practical application, considers how solve the problems, such as a specific wireless sensor network disposition.One
More comprehensively, while coverage rate is considered, connective, energy consumption, the solution party of the wireless sensor network disposition problem of factor such as cost
Case is the target of Future Development.
The content of the invention
It is an object of the invention to：Solve wireless sensor network disposition of the prior art, coverage rate, poor connectivity,
Limited by topographic irregularity, energy consumption, the problem of cost is high, there is provided a kind of three-dimensional static wireless sensing based on genetic algorithm
Network deployment method.
The technical solution adopted by the present invention is as follows：
A kind of three-dimensional static radio sensing network dispositions method based on genetic algorithm, it is characterised in that：Following steps；
(1) three dimensions of target area is divided into several regions；
(2) topology for being carried out three-dimensional static wireless sensor network using genetic algorithm to each region is disposed, topological portion
Communicated with each other between wireless sensor node in administration, obtain regional connectivity；
(3) minimum spanning tree for generating each regional connectivity finds optimum base station position；
(4) using being interconnected between the optimum base station position of all areas, the wireless biography of forest three dimensions is completed
100% connection of sensor.
Further, step (2) concretely comprise the following steps：
(21) region of M wireless senser of N number of dispensing is randomly generated as primary sample；
(22) optimal sample is selected in primary sample by natural selection function；
(23) parental generation sample is obtained with primary sample, new M wireless senser of N number of dispensing is produced by heredity and variation
Area sample, i.e., sample of future generation；
(24) the optimal sample added in sample of future generation in primary sample selects optimal sample of future generation；
(25) judge whether optimal sample of future generation meets to require, if it is satisfied, then exporting optimal sample, go to step
(26), if be unsatisfactory for, go to step (23) and carry out sample iteration of future generation；
(26) judge whether optimal sample connects, if do not connected, increase extra wireless senser number △ i as measurement
Connective index, obtains increased wireless sensor node number, and the region that obtaining wireless senser can be interconnected connects
It is logical.
Further, in the step (22), the specific formula of natural selection function is as follows：
f1=Vcover/ V,
△ i=∑sj△ij,
In formula, Rs is the radius that wireless senser detects spheroid, and b, c represent the weighted value of spreadability and connectedness, Vcover
For total space scope, i.e., the union of each wireless senser coverage, viThe inspection that can be covered for single wireless senser
Scope is surveyed, [] represents to round downwards, and Rc is the connection radius of wireless senser, and △ i are that network-in-dialing needs increased wireless sensing
Device quantity, the i.e. nodes of the network minimum spanning tree and the difference of existing wireless senser number, L are more than the road of connection radius
The length in footpath is, j represents line number in minimum spanning tree, and V is mesh point points, when gridding draw it is very thin when, with coated
Lid points divided by total points represent coverage rate, and V calculating is to make use of the thought of infinitesimal, by region with intensive mesh generation,
Grid node is monitoring point.
Further, step (23) comprise the following steps that：
(231) after selecting a worst sample from primary sample, worst sample is replaced with optimal sample, it is optimal with one
Sample and other samples match as parental generation sample two-by-two, and the wireless senser for randomly selecting half respectively from parental generation sample enters
Row location swap, generation are middle for sample；
(232) fine and closely woven grid is drawn for sample areas to centre, travels through all grid nodes；
(233) the grid node set A and the grid that one and only one wireless senser covers in intermediate sample are recorded
Wireless sensor node set B corresponding to node, the contribution that the wireless senser in B once covers to network is counted, and to every
Individual wireless senser is voted, poll value minimum, as the minimum wireless senser of occurrence number, as redundant node,
It is deleted, deletes a redundant wireless sensor and be increased by a wireless senser；
(234) for the grid node set C not covered in sample by any wireless senser among record, enumerating will be new
Increase the number that wireless senser is placed on grid coverage node in optional position in C, find maximized grid node locations, be
The position of newly-increased wireless senser, after increasing wireless senser newly in this position, generates sample of future generation.
Further, step (3) comprise the following steps that：
(31) minimum spanning tree of wireless sensor network is found using KRUSCAL algorithms；
(32) leafy node of minimum spanning tree is found as outmost turns, and marks leafy node；
(33) leaf node of mark and side are removed from minimum spanning tree, modification generates new minimum spanning tree；
(34) leafy node is found from new minimum spanning tree again, as secondary outer ring；
(35) repeat step (32)-step (34), until minimum spanning tree nodes are 0, that is, base station location is found, or time
Find there is a line segment and its two end points after going through, just taking up an official post to take in line segment is a little used as base station location.
In summary, by adopting the above-described technical solution, the beneficial effects of the invention are as follows：
First, the present invention considers wireless senser isomery in actual conditions, and landform, barrier, forest volume are irregular, tree
The influence of the multi-party factor such as wooden Density inhomogeneity, with reference to genetic algorithm exactly with genetic algorithm wireless biography static to 3-D wireless
Sensor network is modeled, and has high value of practical；
2nd, in genetic Algorithm Design, 3D region wireless senser coverage rate is counted using the mode of discrete cultellation；Enumerate
The effect of increase node in white space, row variation adjustment is entered to wireless senser local topology, effectively accelerates convergence；
3rd, the algorithm traveled through from leafy node to minimum spanning tree inside, the network hops of 3D region are ensured most
It is small, find optimum base station position so that network energy consumption is minimum.
4th, the concept of the small base station of middle-high density is studied with reference to 5G, the wireless sensor network of forest three dimensions is entered
Row hierarchical design, using being interconnected between small base station, 100% connection between wireless senser is realized, is 5G correlation techniques
Research provide technical support.
5th, consider that the multi-party factor such as wireless senser quantity, energy consumption, forest landform and barrier integrates shadow to network topology
Ring, compared with prior art, restrictive condition of the present invention is few, and such as sensing radius is unrestricted, and coverage rate, connected ratio ratio are not
It is restricted.
Brief description of the drawings
Fig. 1 is wireless senser and base station schematic diagram in forest in the present invention；
Fig. 2 is the flow chart of the topology deployment that genetic algorithm carries out three-dimensional static wireless sensor network in invention；
Fig. 3 is the schematic diagram of the primary sample generated at random in the present invention, and each point represents a sensor；
Fig. 4 is the schematic diagram of the primary sample generated at random in the present invention；Each ball representative sensor overlay area, covering
Rate：70.2% is connective：△ i=2；
Fig. 5 is optimal sample wireless senser distribution schematic diagram after the 8th iteration in the present invention；
Fig. 6 is sensor footprint domain schematic diagram, coverage rate after the 8th iteration in the present invention：81.88% is connective：△i
=1；
Fig. 7 is optimal sample wireless senser distribution schematic diagram after the 10th iteration in the present invention；
Fig. 8 is the optimal sample area of coverage schematic diagram after the 10th iteration, coverage rate in the present invention：87.71% is connective：
△ i=0, without repairing；
Fig. 9 is the minimum spanning tree schematic diagram of optimal sample after 10 iteration in the present invention；
Figure 10 is base station location schematic diagram in the present invention；
Figure 11 for the present invention in primary sample coverage rate 71.39%, △ i=2 schematic diagrames；
Figure 12 is optimization sample coverage rate 92.56%, △ i=0 schematic diagrames in the present invention.
Embodiment
In order to make the purpose , technical scheme and advantage of the present invention be clearer, it is right below in conjunction with drawings and Examples
The present invention is further elaborated.It should be appreciated that specific embodiment described herein is only to explain the present invention, not
For limiting the present invention.
Because the area coverage of forest is very wide, therefore forest is carried out region segmentation by us, and each region is carried out
Genetic algorithm, generation minimum spanning tree finds optimum base station position after genetic algorithm, gets up to make ours with base station connection
Studying complexity reduces, while reaches preferable forest monitoring purpose.
(1) according to the geographical environment of forest, such as barrier, the density of trees, landform factor are by the three of target wood land
Dimension space is divided into several regions, as shown in Figure 1；
(2) topology for being carried out three-dimensional static wireless sensor network using genetic algorithm for each region is disposed, and is adjusted
Whole wireless senser quantity so that the one's respective area network coverage can connect close to 90% each other between wireless sensor node
It is logical；As shown in Fig. 2 concretely comprise the following steps：
(21) region of M wireless senser of N number of dispensing is randomly generated as primary sample；
(22) optimal sample is selected in primary sample by natural selection function；The specific formula of natural selection function is such as
Under：
Spreadability：In this research, it is boolean models, i.e. wireless senser detection range that we, which take wireless senser,
The spheroid for being Rs for radius.The detection range that then single wireless senser can cover isThe total wireless biography of definition
The coverage of sensor isThe union of i.e. each wireless senser coverage.V is mesh point points, when picture net
Lattice draw it is very thin when, represent coverage rate with capped points divided by total points, V calculating is to make use of the thought of infinitesimal, by area
The intensive mesh generation in domain, grid node is monitoring point.Then define the coverage rate f of the network of wireless senser1=
Vcover/V.I.e. in order to calculate with simplifying irregular size, in actual emulation, fine and closely woven grid is innovatively drawn to a certain region,
And ensure that mesh width is far smaller than wireless senser detection radius.Whether all mesh points are by wireless senser in zoning
Covering, if no matter capped how many times are also only designated as being capped, therefore, in coverage rate available capped grid node and region
The ratio of all grid nodes characterizes.
It is connective：We set the connection radius of wireless senser as Rc, i.e., wireless senser can be less than with the distance to it
Other wireless sensers connection equal to Rc.Because we will ensure that all wireless sensers in given range can phase
It is intercommunicated, thus we by the use of can make real-time performance be interconnected needed for increased extra wireless senser number △ i as measurement
Connective index.
Specifically calculating △ i way is：The minimum spanning tree that existing node can be formed is found, is found big in minimum spanning tree
In the path of connection radius.Assuming that the length more than the path of connection radius is L, then increased node is needed on this paths
Number isSo △ i=∑sj△ij, [] represents to round downwards, and j represents line number in minimum spanning tree.
We first assume that the trees in forest are uniformly distributed, and in a small pieces square infinitesimal, (infinitesimal just refers to following discussion
The small size block of wireless senser distribution) wireless senser deployment issue in wood land.
Assuming that the piece wood land is the cube that a volume is V (a*a*a), the wireless senser in the panel region
Quantity M is definite value, and the determination of M values herein is relevant with the density of trees in forest.More nothing can be disposed in intensive forest
Line sensor, sparse forest then dispose less wireless senser, can so reach the purpose to economize on resources.
We solve this 30 wireless sensers in cube infinitesimal wood land using above-mentioned genetic algorithm
In distribution problem.
We obtain 21 first can launch wireless senser region and launching at random the sample of M wireless senser.So
A natural selection function is constructed afterwards.This function should both include coverage information or including communication information.Here the letter that we construct
Number isB, c represents the weighted value of spreadability and connectedness, and △ i is so that network-in-dialing still need to increase
The wireless senser quantity added, the i.e. nodes of the network minimum spanning tree and the difference of existing wireless senser number.To make company
The general character and spreadability can preferably embody, Wo Menqu
(23) parental generation sample is obtained with primary sample, new M wireless senser of N number of dispensing is produced by heredity and variation
Area sample, i.e., sample of future generation；
By natural selection function, we select an optimal sample f from 21 samplesmax, a worst sample fmin。
Then after replacing worst sample (to increase convergence rate) with optimal sample, an optimal sample and 18 general sample groups are selected
Into 20 samples match two-by-two randomly selected respectively from parental generation sample as parental generation sample half wireless senser carry out
Location swap, obtain intermediate sample.This is a genetic process, by first time genetic process, can obtain second generation heredity sample
This.
In order to accelerate preferably to be distributed, we carry out favourable variation to it.Specific mutation process is as follows：Each
Mutation process we the wireless senser of a most waste of resource is all moved to a most spacious place.Concrete methods of realizing
For：Fine and closely woven grid is drawn to intermediate sample first, travels through all mesh points.Record the wireless biography of one and only one in intermediate sample
Wireless sensor node set B corresponding to the grid point set A and the mesh point of sensor covering, it is minimum to count occurrence number in B
Node be redundant node (this operation purpose be to find the contribution that each wireless senser once covers to network, we
A monitoring point is not intended to be covered by multiple wireless sensers.For example wireless senser has 32, then monitoring point in traversal A,
Look at that monitoring point is covered by that wireless senser, and the wireless senser is voted, count and add one.Last basis
The gained vote numerical value of each wireless senser, it is determined that each contribution of the wireless senser to once covering, the few wireless sensing of numerical value
Device is exactly redundant wireless sensor.), it is deleted.The grid node set C not covered by any wireless senser is recorded,
The number that newly-increased wireless senser is placed on to grid coverage node in optional position in set C is enumerated, finds maximized grid
Node location, the position of as newly-increased wireless senser, after increasing wireless senser newly in this position, generates sample of future generation.This
The processing mode of kind variation can greatly speed up the convergence rate of genetic algorithm, and avoid being absorbed in locally optimal solution, efficiently change
In generation, goes out global optimum's topology.
(24) the optimal sample added in sample of future generation in primary sample selects optimal sample of future generation；
(25) judge whether optimal sample of future generation meets to require, if it is satisfied, then optimal sample is exported, by many times
Heredity and variation, we can obtain a coverage rate and reach 90%, and connective also good sample, go to step
(26), if be unsatisfactory for, go to step (23) and carry out sample iteration of future generation；
(26) judge whether optimal sample connects, if do not connected, increase extra wireless senser number △ i as measurement
Connective index, obtains increased wireless sensor node number, and the region that obtaining wireless senser can be interconnected connects
It is logical.Even if we have obtained the sample of comparison optimization, coverage rate reaches 90%, connective also relatively good, and we are not yet
It can guarantee that the wireless senser in this region realizes connection completely.To obtain a full communicating network, we also need pair
Optimal sample obtained by us is repaired.Specific practice is：The minimum spanning tree that existing node is formed is found, more than covering
Uniformly increase on the path L of lid radiusIndividual wireless senser.
So that a volume is V (a*a*a) square wood land as an example.Assuming that trees are distributed in this region.Wirelessly
Sensor can adhere on trees.In this region, distance creates grid as step-length between trees, and the position of wireless senser can be with
For arbitrary mess node, as shown in Fig. 3-Fig. 8, coverage rate is obtained：87.71% is connective：△ i=0 are without repairing.
(3) minimum spanning tree of one's respective area connection is obtained using KRUSCAL algorithms, the minimum spanning tree is traveled through,
Find optimum base station.After the distribution of the wireless sensor network in an infinitesimal is determined, it would be desirable to search out one most preferably
Base station location.Theory analysis shows that the utilization rate of energy of wireless sensor network is quick with the increase of SINK node hop counts
Monotone decreasing.SINK nodes are that we need to place the position of base station.So in order to improve the utilization of wireless sensor network
Rate, extend the Web vector graphic life-span, it would be desirable to find the position of a SINK node, network maximum hop count is reached most as far as possible
It is small.As shown in Fig. 9,10, comprise the following steps that：
(31) minimum spanning tree of wireless sensor network is found using KRUSCAL algorithms；
(32) leafy node of minimum spanning tree is found as outmost turns, and marks leafy node；
(33) leaf node of mark and side are removed from minimum spanning tree, modification generates new minimum spanning tree；
(34) leafy node is found from new minimum spanning tree again, as secondary outer ring；
(35) repeat step (32)-step (34), until minimum spanning tree nodes are 0, that is, base station location is found, or time
Find there is a line segment and its two end points after going through, just taking up an official post to take in line segment is a little used as base station location.
In Figure 10, the side of same color is same jump, has 33 wireless sensers on the minimum spanning tree, totally 11 is jumped.It is logical
Simulation result is crossed it will be seen that by the heredity and variation of 10 times, we can obtain a coverage rate as 87.71%,
And full communicating wireless sensor network can be realized.This result has reached our expected requirement, and than primary sample
Optimize many.Then we can find its root node by drawing the minimum spanning tree of wireless sensor network, determine base
Stand place position.
(4) using being interconnected between the optimum base station of all areas, the wireless senser of forest three dimensions is completed
100% connection.
This algorithm strictly considers the distribution of actual landform landforms and foreign object and covers spy to the connection of wireless senser in itself
Property caused by influence.On the other hand, the countermeasure that we are taken is the variation of infinitesimal block.Main influence of the landform for this model is every
Individual infinitesimal will no longer be regular square.For this problem, we only need to launch wireless sensing in control initially random
During device, wireless senser is set not fall in the region that can not be thrown.
This method can be very good solve influence of the topography and geomorphology influence to script scheme.And for relatively common in forest
Barrier, such as more sturdy branch, big stone etc., the covering that these will be to whole network make with characteristic is connected
Into influence.Such as Figure 11 primary samples coverage rate 71.39%, △ i=2, such as Figure 12 optimization samples coverage rate 92.56%, △ i=0.
Consider the influence of various common barriers and landform to wireless sensor network, be summarized as follows at 2 points.If barrier
Hinder can not be placed at thing wireless senser only need in cultellation avoiding obstacles position, then take with topography and geomorphology influence as
Method handled.If it can not be connected between the wireless senser that barrier can cause to connect originally, such as barrier
The factors such as dielectric constant, shape have impact on the propagation of external electromagnetic field, it is possible to cause the connective quilt between wireless senser
Destroy.When the connection between two wireless sensers is blocked, we first judge whether the connection to whole network of throwing the net causes for it
Influence.If whole network of throwing the net still connects, need not be repaired.If whole network of throwing the net does not connect, we are by hindered two
The distance of individual wireless senser is set to infinitely great, and whole network-in-dialing can be made most by then finding one using KRUSCL algorithms
Small spanning tree, the minimum point of increase on the path of one times of connection radius is more than in this tree and causes whole network-in-dialing.This solution
The certainly algorithm of barrier, while be also applied for when wireless sensor network has node damage, influence whole network connectivty
Situation.Increase and the algorithm scalability is high to be caused to the processing links of the factors such as landform, barrier, trees distribution, adapt to dynamic
The topological structure of change.
Whether contrast considers two kinds of situations of processing links of the factors such as landform, barrier, trees distribution：
| Sample number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| Coverage rate/% | 87.89 | 86.83 | 78.25 | 83.12 | 76.04 | 81.56 | 84.56 | 82.16 | 82.38 | 79.93 |
| Degree of communication | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 |
Table 1. considers the optimal topology in wood land of the influence of topography
| Sample number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 |
| Coverage rate/% | 87.71 | 85.06 | 87.71 | 82.15 | 86.41 | 86.41 | 86.11 | 86.06 | 85.99 | 85.35 |
| Degree of communication | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
Table 2. does not consider the optimal topology of the wood land of landform
Algorithm processing performance in the case of two kinds of contrast, it can find the network coverage opening up in 90% or so, 100% connection
Flutter.Therefore, the influence scheme of the factor to wireless sensor network topology such as this algorithm process landform, barrier, trees distribution is
Feasible.
The present invention is used for known four communication paths by (four of line) are blocked, and this solves the algorithm of barrier, together
When be also applied for, when wireless sensor network has node damage, influenceing the situation of whole network connectivty.The algorithm is expansible
Property it is high, adapt to the topological structure of dynamic change.
The present invention innovatively utilize genetic algorithm and graph theory correlation theory, complete based on cover with the three-dimensional static that connects without
The modeling of line sensor network and the solution of optimal topological project.This programme fast convergence rate, utilize small number of wireless biography
Sensor, realize the index of the network coverage 90% and degree of communication 100%.
The present invention considers wireless senser isomery in actual conditions, and landform, barrier, forest volume are irregular, trees
The influence of the multi-party factor such as Density inhomogeneity so that optimal case has preferable use value.
The present invention by way of locally increasing node, is entered by monitoring network in real time to the region for the communication barrier occur
Row is repaired, and this programme scalability is high, adapts to the topological structure of dynamic change.
The present invention combines the concept of the small base station of 5G research middle-high densities, according to the density of trees, by forest zoning,
Small base station is set up in each region.Wireless senser-wireless senser or wireless senser-base station communication are based in region,
It is interregional to be based on base station-base station communication.During cleverly with the base-station node that is actually needed improveing whole termination there may be
Unconnectedness, that is, solve the connectedness between block and block, and also have to a certain degree for coverage rate in theory
On gain.Also, by optimizing base station location, reduce the average number of hops in each region so that network power consumption is as far as possible uniform, prolongs
The life-span of long whole wireless sensor network.
The foregoing is merely illustrative of the preferred embodiments of the present invention, is not intended to limit the invention, all essences in the present invention
All any modification, equivalent and improvement made within refreshing and principle etc., should be included in the scope of the protection.
Claims (5)
- A kind of 1. three-dimensional static radio sensing network dispositions method based on genetic algorithm, it is characterised in that：Following steps；(1) three dimensions of target area is divided into several regions；(2) topology for being carried out three-dimensional static wireless sensor network using genetic algorithm to each region is disposed, in topology deployment Wireless sensor node between communicate with each other, obtain regional connectivity；(3) minimum spanning tree for generating each regional connectivity finds optimum base station position；(4) using being interconnected between the optimum base station position of all areas, the wireless senser of forest three dimensions is completed 100% connection.
- 2. a kind of three-dimensional static radio sensing network dispositions method based on genetic algorithm according to claim 1, it is special Sign is；Step (2) concretely comprise the following steps：(21) region of M wireless senser of N number of dispensing is randomly generated as primary sample；(22) optimal sample is selected in primary sample by natural selection function；(23) parental generation sample is obtained with primary sample, the area of new M wireless senser of N number of dispensing is produced by heredity and variation Domain sample, i.e., sample of future generation；(24) the optimal sample added in sample of future generation in primary sample selects optimal sample of future generation；(25) judge whether optimal sample of future generation meets to require, if it is satisfied, then exporting optimal sample, go to step (26), If be unsatisfactory for, go to step (23) and carry out sample iteration of future generation；(26) judge whether optimal sample connects, if do not connected, increase extra wireless senser number △ i and connected as measurement The index of property, obtains increased wireless sensor node number, obtains the regional connectivity that wireless senser can be interconnected.
- 3. a kind of three-dimensional static radio sensing network dispositions method based on genetic algorithm according to claim 2, it is special Sign is；In the step (22), the specific formula of natural selection function is as follows：<mrow> <mi>f</mi> <mo>=</mo> <msub> <mi>bf</mi> <mn>1</mn> </msub> <mo>+</mo> <mi>c</mi> <mfrac> <mrow> <mi>&Delta;</mi> <mi>i</mi> </mrow> <mrow> <msup> <mrow> <mo>(</mo> <mfrac> <mi>a</mi> <mrow> <mi>R</mi> <mi>s</mi> </mrow> </mfrac> <mo>+</mo> <mn>1</mn> <mo>)</mo> </mrow> <mn>3</mn> </msup> <mo>-</mo> <mi>M</mi> </mrow> </mfrac> <mo>,</mo> </mrow>f1=Vcover/ V,<mrow> <msub> <mi>V</mi> <mrow> <mi>cov</mi> <mi>e</mi> <mi>r</mi> </mrow> </msub> <mo>=</mo> <munder> <mrow> <mi></mi> <mo>&cup;</mo> </mrow> <mi>i</mi> </munder> <msub> <mi>v</mi> <mi>i</mi> </msub> <mo>,</mo> </mrow><mrow> <msub> <mi>v</mi> <mi>i</mi> </msub> <mo>=</mo> <mfrac> <mn>4</mn> <mn>3</mn> </mfrac> <msup> <mi>&pi;Rs</mi> <mn>3</mn> </msup> <mo>,</mo> </mrow>△ i=∑sj△ij,<mrow> <msub> <mi>&Delta;i</mi> <mi>j</mi> </msub> <mo>=</mo> <mo>&lsqb;</mo> <mfrac> <mi>L</mi> <mrow> <mi>R</mi> <mi>c</mi> </mrow> </mfrac> <mo>&rsqb;</mo> <mo>,</mo> </mrow>In formula, Rs is the radius that wireless senser detects spheroid, and b, c represent the weighted value of spreadability and connectedness, VcoverTo be total Spatial dimension, i.e., the union of each wireless senser coverage, viThe detection model that can be covered for single wireless senser Enclose, [] represents to round downwards, and Rc is the connection radius of wireless senser, and △ i are that network-in-dialing needs increased wireless senser number Amount, the i.e. nodes of the network minimum spanning tree and the difference of existing wireless senser number, L are more than the path of connection radius Length is, j represents line number in minimum spanning tree, and V is mesh point points, when gridding draw it is very thin when, with capped point Number divided by total points represent coverage rate, and V calculating is to make use of the thought of infinitesimal, by region intensive mesh generation, grid Node is monitoring point.
- 4. a kind of three-dimensional static radio sensing network dispositions method based on genetic algorithm according to claim 2, it is special Sign is；Step (23) comprise the following steps that：(231) after selecting a worst sample from primary sample, worst sample is replaced with optimal sample, with an optimal sample Matched two-by-two as parental generation sample with other samples, the wireless senser for randomly selecting half respectively from parental generation sample enters line position Exchange is put, generation is middle for sample；(232) fine and closely woven grid is drawn for sample areas to centre, travels through all grid nodes；(233) the grid node set A and the grid node that one and only one wireless senser covers in intermediate sample are recorded Corresponding wireless sensor node set B, the contribution that the wireless senser in B once covers to network is counted, and to each nothing Line sensor is voted, poll value minimum, as the minimum wireless senser of occurrence number, as redundant node, by it Delete, delete a redundant wireless sensor and be increased by a wireless senser；(234) enumerated among record for the grid node set C not covered in sample by any wireless senser by newly-increased nothing Line sensor is placed on the number of grid coverage node in optional position in C, finds maximized grid node locations, as newly-increased The position of wireless senser, after increasing wireless senser newly in this position, generate sample of future generation.
- 5. a kind of three-dimensional static radio sensing network dispositions method based on genetic algorithm according to claim 3, it is special Sign is：Step (3) comprise the following steps that：(31) minimum spanning tree of wireless sensor network is found using KRUSCAL algorithms；(32) leafy node of minimum spanning tree is found as outmost turns, and marks leafy node；(33) leaf node of mark and side are removed from minimum spanning tree, modification generates new minimum spanning tree；(34) leafy node is found from new minimum spanning tree again, as secondary outer ring；(35) repeat step (32)-step (34), until minimum spanning tree nodes are 0, that is, after finding base station location, or traversal It was found that having a line segment and its two end points, just taking up an official post to take in line segment is a little used as base station location.
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