CN106507374A - The WSN fence intensifying methods of secondary deployment - Google Patents

The WSN fence intensifying methods of secondary deployment Download PDF

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CN106507374A
CN106507374A CN201610917720.3A CN201610917720A CN106507374A CN 106507374 A CN106507374 A CN 106507374A CN 201610917720 A CN201610917720 A CN 201610917720A CN 106507374 A CN106507374 A CN 106507374A
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node
fence
weak spot
deployment
energy
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CN106507374B (en
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毛科技
方飞
孙俊生
方凯
施伟元
李鹏欢
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Zhejiang University of Technology ZJUT
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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W16/00—Network planning, e.g. coverage or traffic planning tools; Network deployment, e.g. resource partitioning or cells structures
    • H04W16/18—Network planning tools
    • 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
    • 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
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • 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

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Signal Processing (AREA)
  • Alarm Systems (AREA)
  • Management, Administration, Business Operations System, And Electronic Commerce (AREA)

Abstract

The WSN fence intensifying methods of secondary deployment, including：Step 1, initial fence point：(1) Robustness Analysis are carried out to the fence after initial fence and reinforcing according to node energy consumption sensor model, (2) cause the perception radius to reduce according to energy consumption consumption carries out weak spot lookup；Step 2, secondary deployment：The method that the present invention proposes secondary deployment, deployment static node, builds fence using static node, disposes mobile node again along the fence having been built up for the second time for the first time, is repaired using mobile node or reinforcing fence；Step 3, strengthens fence：The displacement summation of research and utilization mobile node reinforcing fence weak spot reaches minimum.

Description

The WSN fence intensifying methods of secondary deployment
Technical field
The invention mainly relates to wireless sensor network fence Covering domain, is related to the WSN fence reinforcing side of secondary deployment Method.
Background technology
It is one of main overlay model of wireless sensor network field that fence is covered, and which is mainly studied and tries when monitoring objective Figure is detected problem when passing through wireless sensor network disposition region.Need to expend larger resource due to building a fence, Therefore the robustness for how improving fence is key issue.Wireless sensor network fence is covered extensive purposes, such as exists Fence is deployed in the invasion that enemy can be detected in position forward position by military aspect.In terms of environmental protection, fence is deployed in polluter Surrounding can detect the spread condition of pollutant.In terms of Market Economy, fence is deployed in forest fire scene and can detect fire Spread situation etc..
The field for covering in wireless sensor network fence both at home and abroad at present has been achieved for abundant achievement, document 《Command control for many-robot systems》With regard to robot coverage, propose what fence was covered earliest Concept.Kumar et al. proposes the concept of k- fence covering, and the problems such as condition of the survey region needed for the covering of K- fence, Optimal Sleep-Wakeup fence dispatching algorithms are proposed simultaneously in the literature, the algorithmic dispatching fence forms strong K- grid Hurdle so that the energy of fence is fully utilized, so that reach the maximization of fence life span.Anwar Saipulla et al. are carried Line-based dispositions methods are gone out, uniformly the mode such as deployment is higher for the likelihood ratio of the method formation fence, and proposes base Send mobile node to repair fence gap in the maximum-flow algorithm of weight directed graph, make total displacement optimum.Habib Mostafaei et al. proposes the K- Barrier Coverage Problems that a kind of distributed self-learning algorithm solves static node, and should The network lifetime and document of algorithm《Optimal Sleep-Wakeup Algorithms for Barriers of Wireless Sensors》In Optimal Sleep-Wakeup algorithms contrasted.Xu B et al. are have studied using invasion The priori conditions that the historical data of person is strengthened as follow-up fence, predict in subsequent time period fence most easily by intruder attack Node, and mobile node moved to the method at most vulnerable node, the method make the fence will not be by rapid damage.Li Et al. et al. have studied one kind and dispatch fence under conditions of intrusion detection rate is met so that the most long side of fence life span Method.Chen J et al. propose, using probability sensor model, to invade speed as restrictive condition, sensor network problem to be converted into Maximum flow problem, and according to euclidean distance between node pair and dump energy, propose a kind of bounded fence building method.Keung et al. etc. People have studied static sensor node and form the number of nodes that strong K- fence needs in deployment region.
Above-mentioned method is all that the fence for building is studied, but these fence are very fragile, with node energy Consumption, the perception radius can be gradually reduced, and fence interior joint sensing range overlaps few part and gap easily occurs.
Content of the invention
The invention solves the problems that the disadvantages mentioned above of prior art, it is proposed that a kind of fence intensifying method of secondary deployment, the party Weak spot in fence is strengthened by method by disposing less mobile node again so that fence is not easy gap occur, greatly The survival ability of fence is strengthened greatly.
The WSN fence intensifying methods of secondary deployment of the present invention, comprise the steps：
Step 1, initial fence analysis；
Step 11, energy consumption sensor model；
There is relation with the energy of node itself storage in step 111, the perception radius of sensor node, this method is proposed Shown in relational model such as formula (1)：
E in formula (1)kFor the energy that node is consumed, c1 represents perception constant, rkRepresent the perception radius variable quantity；
Step 112, the gross energy of sensor node is E, the perception radius as shown in formula (2), when node consumed energy is e Shown in r such as formulas (3)：
R in formula (2) and (3) is maximum the perception radius, and e is the energy for consuming, and E is the gross energy of sensor node；
Step 12, weak spot are searched；
Step 121, traversal fence in all of sensor node, calculate adjacent node apart from adjacent node in d, fence Between the collection of distance be combined into D={ d1,d2,d3...ds-1(s is sensor node quantity)；
Step 122, the distance set D between the static sensor node of random placement meet Gauss distribution, such as formula (4) institute Show:
In formula (4) S represents that distance set size, this method will be gathered Element d in DiMidpoint w between ∈ [μ+σ, 2R] corresponding adjacent nodenAs weak spot, weak point set W={ w is set up1,w2, w3...wn(numbers of the n for weak spot)；
Step 2, secondary deployment；
Step 21, the method that the present invention proposes secondary deployment, deployment static node, is built using static node for the first time Fence, disposes mobile node again along the fence having been built up for the second time, is repaired using mobile node or reinforcing fence, specifically Step is as follows:
Step 211, according to the environment and requirement deployment static sensor node of deployment region, is then built using fence and is calculated Method build fence, with the consumption of node energy, easily there is gap, so as to cause the monitoring function of fence to fail, therefore I Carry out step 212, deployment mobile node is used for strengthening fence；
Step 212, the removable node of the fence deployment built along in step 211 using line-based methods；
Step 3, strengthens fence；
Step 31, the weak spot quantity that can strengthen；
Step 311, the present invention propose a kind of maximum-flow algorithm based on set and improve under conditions of accuracy rate is ensured The efficiency of algorithm, it is known that weak point set is W, the collection of removable node is combined into M={ m1,m2,m3...mn, mobile node can Displacement is md, works as wi(wi∈ W) and mi(mi∈ M) distance be less than md, then miThe neighbor node of referred to as weak spot, concrete walks Suddenly it is：
Step 3111, calculates the set of the neighboring mobile node of weak spot, and obtains after public-neighbor is distinguished The new neighbor node subclass n of weak spot；
Step 3112, closes number of elements in n with the new neighbor Node subsets of each weak spot and, as weighted value, sets up directed graph G；
Step 3113, using maximum-flow algorithm calculate figure G max-flow, when max-flow be equal to weak spot quantity, then this When weak spot all can be strengthened, otherwise there is the weak spot that can not be reinforced, because it may move neighbor node；
Step 32, reinforcing strategy；
Step 321, it is whole neighbors set N to merge multiple subclass n, and the removable node in set N is to its neighbour The distance set for occupying weak spot is MD={ md1,md2,md3,...mdnum|mdi≤ md } (num is the individual of all mobile nodes Number), set MD' is obtained to set MD ascending sorts, the present invention searches for md using binary chopoptimum, mdmin＜ mdoptimum ＜ mdmax(mdminAnd mdmaxThe minima obtained for ascending order and maximum) so that meet set ED={ MD'| mdi≤ mdoptimumMobile node can strengthen all of weak spot just, then now mobile node movement is minimum apart from summation, two Point-score algorithm is concretely comprised the following steps：
Step 3211, initialization
Step 3212, updates the neighbours subclass n of weak spot, will be more than md apart from weak spotoptimumNode from neighbours Set is removed, and updates the weight and topology of directed graph G；
Step 3213, calculates the max-flow of directed graph G after updating, when max-flow is less than weak spot quantity, thenWhen max-flow is equal to weak spot quantity, And | L-optimum | ＜ ε, export mdoptimum, algorithm terminates, otherwise execution step 3214；
Step 3124, rebound step 3212.
It is an advantage of the invention that：
(1) weak spot in fence is strengthened by the present invention by disposing less mobile node again so that fence is not Easily there is gap, greatly reinforced the survival ability of fence；
(2) present invention proposes the deployment way of secondary deployment, can greatly improve the utilization rate of mobile node, while utilizing Maximum-flow algorithm based on set calculates the quantity that can strengthen node, is then ensureing to maximize reinforcing using binary search method Cause the displacement summation of mobile node minimum on the premise of weak spot quantity.
Description of the drawings
The weak point diagram of Fig. 1 present invention
The secondary deployment diagram of Fig. 2 present invention
The weight directed graph of Fig. 3 present invention
Specific embodiment
The present invention is further illustrated below in conjunction with the accompanying drawings.
The WSN fence intensifying methods of secondary deployment of the present invention, comprise the steps：Step 1, initial fence point Analysis；
After certain sensor node is disposed in deployment region, be built into initial fence, but adjacent segments in fence The sensing range lap of point is not of uniform size, and with the consumption of node energy, sensing range is gradually decreased, therefore sensing range Easily there is fence gap where lap is few, monitoring objective just can pass through fence by gap and not be monitored to. Deployment WSN fence needs to expend substantial amounts of resource, causes fence dead too early if as the presence of some weak spots in fence Die be a kind of greatly waste, therefore find out the weak spot in fence and then carry out strengthening meaning using mobile node very big.
Step 11, energy consumption sensor model；
Step 111, the perception radius of sensor node have relation with the energy of node itself storage, when node energy abundance When, the perception radius are maximum, and with energy expenditure, the perception radius are gradually reduced.The perception radius and energy consumption relation generally adopt document 《Energy-efficient connected coverage of discrete targets in wireless sensor networks》In perception energy consumption model calculate, according to the model, the present invention proposes the relation that the perception radius change with energy consumption Model.
E in formula (1)kFor the energy that node is consumed, c1 represents perception constant, rkRepresent the perception radius variable quantity.The present invention is adopted Follow-up study is carried out with relational model (a).
Step 112, it is assumed that the gross energy of sensor node is E, as shown in formula (2), when node perceived radius is 0, section The energy of point just runs out of.When node consumed energy is e, shown in the perception radius r such as formula (3) of node.
In formula (2,3), R is node maximum the perception radius.Assume that the energy of node is mainly consumed by node perceived, pass through Node energy consumption sensor model can carry out Robustness Analysis to the fence after initial fence and reinforcing.
Step 12, weak spot are searched；
Step 121, the sensor node in the present invention obtain coordinate by location technology.The fence section of initial construction Point energy is sufficient, and the perception radius are R, and as the consumption of energy, the perception radius reduce, in fence, between adjacent node, distance is close to 2R Part easily there is gap.Using the foundation that euclidean distance between node pair d is searched as weak spot, d is bigger, and the fence at this more easily goes out Existing gap, fence weak spot is as shown in figure 1, the coordinate of weak spot is the midpoint of node n1 and n2.
Traversal fence in all of sensor node, calculate adjacent node apart from d, it is assumed that fence is by s sensor section Point is constituted, and in fence, between adjacent node, the collection of distance is combined into D={ d1,d2,d3...ds-1, diLess, fence is more firm, otherwise Fence is more fragile.
Step 122, deployment static sensor node is random placement, it is assumed that the element in set D meets Gauss distribution, As shown in formula (4).
In formula (4)
The present invention is by element d in set DiThe midpoint w of the corresponding adjacent node n1 and n2 of ∈ [μ+σ, 2R]iAs weak spot, Set up weak point set W={ w1,w2,w3...wn}.
Step 2, secondary deployment；
Step 21, has a lot of researchs all to repair fence, such as document using mobile node at present《Barrier coverage with line-based deployed mobile sensors》.These mobile nodes and static node are mixed in together once Property is deployed in monitored area, and this deployment way causes the probability that mobile node is utilized very low, causes a large amount of mobile nodes Can not be utilized, and mobile node is expensive, this deployment way undoubtedly considerably increases the cost of deployment fence.For The utilization rate of mobile node is improved, increases the robustness of fence, it is proposed that the method for secondary deployment, dispose static section for the first time Point, builds fence using static node, disposes mobile node again along the fence having been built up for the second time, using mobile node Repair or reinforcing fence, as shown in Figure 2.
Step 211, according to environment and the requirement of deployment region, can adopt Poisson deployment, random uniform deployment, Line- The deployment way such as based dispose static sensor node, then can adopt document《Based on the wireless senser for improving ant group algorithm Network fence coverage optimization is studied》Fence is built Deng fence developing algorithm.The fence poor robustness for now building, fence operation one Section the time after, with the consumption of node energy, easily there is gap, so as to cause the monitoring function of fence to fail, therefore we Step 2 is carried out, deployment mobile node is used for strengthening fence.
Step 212, disposes mode and the document of mobile node《Barrier Coverage of Line-Based Deployed Wireless Sensor Networks》Using Line-based methods similar, be specifically along in step one The fence deployment for having built may move sensor node.Secondary dispositions method causes mobile node to be targetedly deployed in fence Side, therefore can more fully be used for repairing and the forced working of fence.
Step 3, strengthens fence；
This part mainly calculates the weak spot quantity that can strengthen by the maximum-flow algorithm based on set, then will be removable Weak spot reinforcing fence of the dynamic node motion in fence.It is more sane that fence after reinforcing is compared with initial fence, with section Point energy expenditure, the fence after reinforcing are not easy gap occur.
Step 31, the weak spot quantity that can strengthen；
Step 311, at present a lot of researchs all solve to repair fence number of gaps this problem using maximum-flow algorithm, such as Document《Strengthening barrier-coverage of static sensor network with mobile sensor nodes》.Maximum can accurately be calculated by the method and can repair the quantity in fence gap, but the number of mobile node Amount is more, and the efficiency of algorithm is not high.Present invention reinforcing fence weak spot is similar to fence gap reparation problem, it is proposed that a kind of Maximum-flow algorithm based on set improves the efficiency of algorithm under conditions of accuracy rate is ensured.
Assume that the weak spot of fence has wi、wi+1、wi+2...wk, the collection of removable node is combined into M={ m1,m2,m3...mn, The movable distance of mobile node is md.Work as wiWith miDistance be less than md, then miThe neighbor node of referred to as weak spot.Algorithm has Body step is as follows：
Step 3111, with weak spot wi、wi+1、wi+2As a example by, calculate their neighboring mobile node, respectively set A, B, C, may include the public-neighbor of weak spot, then distinguish these public-neighbors in these set, to gather As a example by A, B, as shown in formula (5,6,7).
A=A-A ∩ B (5)
B=A ∩ B (6)
C=B-A ∩ B (7)
Wherein a is wiNeighbor node set, b be wi、wi+1Public-neighbor set, c is wi+1For wi+1Neighbours Node set.
Step 3112, with number of elements Na, Nb of set a, b, c, Nc as weighted value, sets up directed graph G, as shown in figure 3, Add start node u and end node v, quantity of the weighted value being connected with u for set element, the weighted value being connected with v in figure All it is 1, set d is wi+2Neighbor node set.
Step 3113, using maximum-flow algorithm calculate figure G max-flow, when max-flow be equal to weak spot quantity, then this When weak spot all can be strengthened, otherwise there is the weak spot that can not be reinforced, because it may move neighbor node.
Step 32, reinforcing strategy；
Step 321, the displacement summation of the main research and utilization mobile node reinforcing fence weak spot of this trifle are minimum.Logical The neighbor node set (a, b, c, d etc.) that 3.1 trifles have obtained all weak spots is crossed, merges neighbor node set N=a ∪ b ∪ c ∪ d ∪ ..., it is assumed that the distance set of the removable node in set N to its neighbours' weak spot is MD={ md1,md2, md3,...mdnum|mdi≤ md }, there is num element in set MD, set MD' is obtained to set MD ascending sorts, set MD''s First element is mdmin, last element is mdmax.Assume to exist one apart from mdoptimum, mdmin＜ mdoptimum＜ mdmaxSo that meet set ED={ MD'| mdi≤mdoptimumMobile node can strengthen all of weak spot just, then now It is minimum apart from summation that mobile node is moved.
The present invention searches for md using binary chopoptimum, ε is a threshold value, approaches for algorithm, algorithm concrete operations Shown in following steps：
Step 3211, initializationL=0,
Step 3212, updates neighborhood a, b, c, d of weak spot etc., will be more than md apart from weak spotoptimumNode Remove from neighborhood, and update the weight and topology of directed graph G.
Step 3213, calculates the max-flow of directed graph G after updating, if max-flow is less than weak spot quantity,L=optimum,Max-flow can not possibly be more than the number of weak spot Amount.If max-flow is equal to weak spot quantity, and L-optimum | ＜ ε, export mdoptimum, algorithm terminates, otherwise execution step 4.
Step 3214, rebound step 2.

Claims (1)

1. the WSN fence intensifying methods of two deployment, comprise the steps：
Step 1, initial fence analysis；
Step 11, energy consumption sensor model；
Step 111, the perception radius of sensor node are shown with the relational model such as formula (1) of the energy of node itself storage：
r k = e k c 1 ( a ) e k c 1 ( b ) - - - ( 1 )
E in formula (1)kFor the energy that node is consumed, c1 represents perception constant, rkRepresent the perception radius variable quantity；
Step 112, the gross energy of sensor node is E, and as shown in formula (2), the perception radius r when node consumed energy is e are such as Shown in formula (3)：
E = c 1 * R 2 2 - - - ( 2 )
r = R - 4 R 2 - 8 e c 1 2 - - - ( 3 )
R in formula (2) and (3) is maximum the perception radius, and e is the energy for consuming, and E is the gross energy of sensor node；
Step 12, weak spot are searched；
Step 121, traversal fence in all of sensor node, calculate adjacent node apart from adjacent node spacing in d, fence From collection be combined into D={ d1,d2,d3...ds-1(s is sensor node quantity)；
Step 122, the distance set D between the static sensor node of random placement meet Gauss distribution, as shown in formula (4):
f ( d i ) = 1 2 π σ exp ( - ( d i - μ ) 2 2 σ 2 ) - - - ( 4 )
In formula (4)S represents distance set size, by element d in set Di Midpoint w between ∈ [μ+σ, 2R] corresponding adjacent nodenAs weak spot, weak point set W={ w is set up1,w2,w3...wn}(n Number for weak spot)；
Step 2, secondary deployment；
Step 21, deployment static node, builds fence using static node, along the fence having been built up again for the second time for the first time Secondary deployment mobile node, is repaired using mobile node or reinforcing fence, is comprised the following steps that:
Step 211, according to the environment and requirement deployment static sensor node of deployment region, then adopts fence developing algorithm structure Fence is built, with the consumption of node energy, gap easily occurs, so as to cause the monitoring function of fence to fail, therefore walked Rapid 212, deployment mobile node is used for strengthening fence；
Step 212, the removable node of the fence deployment built along in step 211 using line-based methods；
Step 3, strengthens fence；
Step 31, the weak spot quantity that can strengthen；
Step 311, a kind of maximum-flow algorithm based on set, it is known that weak point set is W, the collection of removable node is combined into M= {m1,m2,m3...mn, the movable distance of mobile node is md, works as wi(wi∈ W) and mi(mi∈ M) distance be less than md, then mi The neighbor node of referred to as weak spot, concretely comprises the following steps：
Step 3111, calculates the set of the neighboring mobile node of weak spot, and obtains weakness after public-neighbor is distinguished The new neighbor node subclass n of point；
Step 3112, closes number of elements in n with the new neighbor Node subsets of each weak spot and, as weighted value, sets up directed graph G；
Step 3113, the max-flow for calculating figure G using maximum-flow algorithm are when max-flow is equal to the quantity of weak spot, then now thin Weakness all can be strengthened, and otherwise there is the weak spot that can not be reinforced, because it not may move neighbor node；
Step 32, reinforcing strategy；
Step 321, merges multiple subclass n for whole neighbors set N, and the removable node in set N is thin to its neighbours The distance set of weakness is MD={ md1,md2,md3,...mdnum|mdi≤ md } (num is the number of all mobile nodes), right Set MD ascending sorts obtain set MD', search for md using binary chopoptimum, mdmin＜ mdoptimum＜ mdmax, mdminWith mdmaxThe minima obtained for ascending order and maximum so that meet set ED={ MD'| mdi≤mdoptimumMobile node proper All of weak spot can be strengthened well, then now mobile node movement is minimum apart from summation, and two way classification algorithm is concretely comprised the following steps：
Step 3211, initializationL=0,
Step 3212, updates the neighbours subclass n of weak spot, will be more than md apart from weak spotoptimumNode from neighborhood move Remove, and update the weight and topology of directed graph G；
Step 3213, calculates the max-flow of directed graph G after updating, when max-flow is less than weak spot quantity, thenL=optimum,When max-flow is equal to weak spot quantity, and | L-optimum | ＜ ε, export mdoptimum, algorithm terminates, otherwise execution step 3214；
Step 3124, rebound step 3212.
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