CN102395146A - Multiple-target monitoring oriented method for sensing topology construction in wireless sensor network - Google Patents
Multiple-target monitoring oriented method for sensing topology construction in wireless sensor network Download PDFInfo
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Abstract
The invention relates to a multiple-target monitoring oriented method for sensing topology construction in a wireless sensor network. The method is characterized in that: on the basis of a multiple-target point sensing coverage scheduling algorithm based on a bionic algorithm, a multiple target point monitoring sensing topology construction method is provided from the perspective of multiple target point coverage scheduling; the bionic algorithm takes a target point coverage rate as a design condition and sets network equalization as a design object as well as puts emphasis on solution of problems of data redundant collection and target point multiple coverage. An optimization algorithm of the bionic algorithm is utilized as well as a network energy variance, a target point coverage rate and the number of movable nodes are used as construction parameters of a fitness function, so that a reasonable movable node set is calculated; and according to the set, a topological structure and a route of the network are controlled. According to the invention, the method is suitable for a low-power mixing sensor network with medium and high speeds in random redundancy laying and corresponded M2M application.
Description
Technical field
The present invention relates to a kind of wireless sense network perception topological construction method, or rather, the present invention relates to a kind of covering and corresponding topological construction method of wireless sense network multiple target point towards the multiple target point monitoring.Belong to the wireless sense network field.
Background technology
The general solution flow process of the structure problem of multiple target point self adaptation perception topology roughly comprises two steps: the one, and the selection of active node; The 2nd, the structure of active node route.
In a first step, generally need the factor of consideration to comprise coverage rate and the whole energy response of active node.The active node that promptly at first requires to select can cover all impact points, and the active node that secondly then requires to select possesses the characteristic that the equalizing self-adapting network energy consumes.In second step, need to consider how active node makes up a data channel of leading to the Sink node.
1) for first step, the support of coverage rate is easier to realize; The adaptive energy characteristic then requires node to determine self working state according to the energy consumption situation of interdependent node.
Generally speaking, impact point and sensor node are all laid at random, suppose that simultaneously sensor node satisfies the monitoring sensor model of 0/1 distribution, and network carries out dispatching management with the mode of wheel.Think and have only the sensor node that can monitor impact point just can regularly produce valid data.Because the inhomogeneities of laying at random; Each impact point possibly covered by a plurality of sensor nodes; If we use a plurality of sensor nodes that an impact point is monitored simultaneously, these sensor nodes will produce identical data so, and this is a kind of waste to sensor resource.Other nodes possibly need to cooperate this sensor node to accomplish the uploading operation of data in the network simultaneously, and this has also aggravated the communications burden of other nodes in the network simultaneously.A kind of effective and efficient manner is to make redundant sensor node get into resting state, whenever takes turns an only limited Activity On the Node, but will accomplish the task of target monitoring simultaneously, realizes the equilibrium of node energy consumption in the network.Therefore in fact the selection problem of active node can be equivalent to multiple target point and cover scheduling problem.
2) for second step; Can simplify and think that active node can not cover the node communication of impact point through a jumping route and certain; Or can pass through one and jump the direct and Sink node communication of route, thereby our target lock-on is covered on the scheduling problem at multiple target point.
Comprehensive above the description can find out that multiple target point monitoring perception topology constructing problem can be through the scheduling of reasonably optimizing sensor node, and adaptive control sensor node on off state solves, and the route in the network then can be that the basis makes up with the active node.
More than related sensing net multiple target point monitoring problem, research report has been arranged:
1) a kind of mode of research is not distinguish the multiple covering of impact point, thinks that the energy geometric ratio of node consumption is counted or proportional with the time in the target of its covering.
Cardei M, Thai MT, Yingshu L; Et al. " [Energy-efficient target coverage in wireless sensor networks, " Proc.IEEE Infocom Conference, vol.1973; PP.1976-1984; 2005] in article, early be directed against the notion that the K target monitoring has proposed maximum Coverage Control collection (Maximum Set Covers), be called for short MSC, the author representes the covering relation between sensor node and the impact point with bipartite graph; And two solutions have been provided from the angle of linear programming; Be respectively the Greedy MSC of didactic LP-MSC and greedy formula, reach the target of maximization network life cycle through the method that makes up a plurality of non-non-intersect covering sets, the author has proved effectively that also the MSC problem itself is a np complete problem in addition.This article author only supposes the sensor node life cycle of only working in demonstration and process of simulation, when expanding to multi-lifecycle, the method for document proposition can not effectively be considered the dump energy of node; In addition, this article author does not pay close attention to the impact point of multiple covering, thinks that in a single day node has covered certain impact point, will the cycle produce data, so for the impact point of multiple covering, must have the redundant problem of data.Document [Jiming C; Junkun L, Shibo H, et al.Energy-Efficient Coverage Based on Probabilistic Sensing Model in Wireless Sensor Networks [J] .Communications Letters; IEEE; 2010,14 (9): 833-835] then be to be summed up as minimal weight transducer covering problem (MWSCP) to multiobject covering scheduling problem, and consider the covering scheduling under Probability Coverage Model; Through making up an integral linear programming model, the author uses ant group algorithm and particle cluster algorithm that this problem is found the solution respectively.In this article, the author only is divided into operating conditions and non-operation to node area, and thinks no matter how many impact points node covers, and its energy consumption in working order is all identical.This hypothesis possesses certain reasonability, but still is not enough.
2) in addition, a kind of report of research then is that the multiple coverage condition of impact point is considered, thinks that the node energy consumption geometric ratio is in its impact point number of being responsible for.
As; Sung-Yeop P and Dong-Ho C. [Power-saving scheduling for multiple-target coverage in wireless sensor networks [J] .Communications Letters; IEEE; 2009,13 (2): 130-132] in the ieee communication news flash in 09 year, be this problem definition multiple target covering problem (Multiple target coverage), be called for short MTC.A balanced heuritic approach MTCP of Considering Energy has been proposed.This algorithm has been considered the transmission energy consumption of node and the impact point that heavily covers simultaneously, chooses a suitable responsibility point for the impact point of each multiple covering, makes the only corresponding sensor node of each impact point, thereby has reduced the redundancy of data.But document author does not have to consider the energy feature of sensor node is considered.Defective to this article; Kim H and for example; Han Y-H; People [Maximum lifetime scheduling for target coverage in wireless sensor networks [C] .Proceedings of the 6th International Wireless Communications and Mobile Computing Conference.Caen, France:ACM, 2010.] such as Min S-G. have reported the multiple target monitoring problem have been done further solution; On the basis of forefathers' heuritic approach, do further improvement, proposed the MLS algorithm.This algorithm is considered the dump energy of sensor node when considering heavy coverage goal node problems.Emulation shows, after the problem of considering aspect above-mentioned two, compares in the past MSC, MTCP algorithm, and this algorithm can be found out the more covering set of more number, so network lifecycle is longer.Under identical network scene and energy consumption model prerequisite, use MLS can make most of nodes use the energy more than 50%, a lot of nodes have used whole energy; Use MCTP then to cause not consumes energy of a lot of nodes, and only have the minority node to use whole energy.
It is overall goal that the present invention intends with maximization network life-span and effective monitoring objective point; Can carry out Classification and Identification to the impact point that self covers with sensor node and be the basis; Consider residue energy of node, impact point heavily covers redundancy, on the basis that guarantees coverage rate, optimizes network energy efficient; Through being modeled as problem the combinatorial optimization problem of discrete space, use bionic Algorithm to find the solution then.
Summary of the invention
The object of the present invention is to provide a kind of wireless sense network perception topological construction method towards the multiple target point monitoring.
Sensor node in the existing application at present can be discerned the impact point in the perception radius, and the data total amount geometric ratio that sensor node perceives is in the number of the impact point of its actual monitoring.This means that the impact point number of sensor node monitoring is many more, its energy of uploading energy consumption is many more.Shown in figure (2), can find out that each sensor node possibly cover a plurality of impact points, for example, S
1Sensing node covers T
1, T
2, T
3And T
54 impact points, the S2 sensing node covers T
1, T
3And T
43 destination node.
Therefore, technical problem to be solved by this invention is:
Under the situation that impact point heavily covers; How to pass through the dormancy dispatching of multisensor node; Obtain the maximum node covering set, realize that the self adaptation of multiobject effective monitoring and perception topology makes up, make network in the big as far as possible time cycle, adopt the mode of balanced energy consumption to move simultaneously as possible.
The foundation of construction method of the present invention comprises:
1) the energy variance of sensor node is as much as possible near null value;
2) make the energy variance toward developing to little direction as far as possible;
3) amplitude that develops to worse direction of control energy variance.
Through effective enforcement of these measures, guarantee that to a certain extent network reaches the energy consumption balance state.Select suitable responsibility node through dynamic wake-up and for each impact point, be implemented in the life cycle of maximization network under the situation that satisfies all standing of K target.
For ease of understanding the present invention, the implication of planning in the form of presentation is explained as follows earlier:
Effective node: to the arbitrary node in the network, when its appreciable impact point number more than or equal to 1 the time, think that promptly this node is effective node.
Effective set of node: the set for effective nodes all in the sensor node S set promptly is effective set of node.
Activate set of node: in certain is taken turns, be in the set of effective node of state of activation.
After network was laid at random, node can arbitrarily not be moved, and part of nodes is not owing to covering impact point, so not in monitoring range of the present invention.So the present invention only handles effective set of node.
The present invention is a kind of wireless sense network that relates to towards the multiple target monitoring, that is must consider what multiple target point heavily covered, and waking up with dynamic dormancy is the dynamic topology construction method of means, specifically may further comprise the steps:
Step 1: obtain each node self-position information in network through methods such as GPS; And through flooding or mode such as directed pathfinding route is informed network center's Control Node; Can obtain relevant informations such as effective node location, energy in the network in view of the above; Effectively the node ratio is lower than preset threshold value in network, and then algorithm stops.
Step 2: coding, for unified describing mode, adopt simple 0/1 model that bionical individuality is encoded, the scheduling method that on behalf of a lower whorl, each bionical individuality possibly adopt.Bionical individual lengths equals the effective node number in the network.
The available following formula of bionical individuality is explained:
X＝[x
1，x
2，x
3，…，x
i，…，x
V-1，x
V]
Wherein V is effective node total number, x
iBe Boolean variable, its value can only get 0 or 1.
Work as x
i, be illustrated in that i number effective node is state of activation in the lower whorl at=1 o'clock.
Step 3: according to the operative norm flow process of bionic Algorithm, be guidance, the feasible solution at random behind the coding is carried out standard operation, separate up to reaching maximum iteration time, providing finally with the fitness function.The design of fitness function is that target is carried out with the energy consumption balance.In line with the principle of the balancing energy characteristic that guarantees effective node to greatest extent, should be with three parts that are designed to of fitness function:
(1) choosing of responsibility node: the responsibility node to each impact point is effectively judged, is principle to the maximum with the energy variance with all effective nodes, and the impact point that has multiple covering perception in effective set of node is chosen its corresponding sensor node.
(2) separate the superiority-inferiority assessment: epicycle energy variance is fitness valuation functions parameter with difference, the current bionical individuality of the lower whorl energy variance of estimating to the number of the coverage rate of impact point, employed sensor node.
(3) final assessment result correction:
The parameter of described assessment comprises:
1) epicycle energy variance V
CurWith the lower whorl energy variance V that estimates
PreDifference V
Dif
2) current weeds individuality is to the coverage rate C of impact point
t
3) number N of employed sensor node
v
The fitness valuation functions that is made up by above parameter is:
V
dif＝V
pre-V
cur
C
t＝C(X)/M
C (X) wherein: the corresponding impact point number that covers of current bionical individual X;
M: impact point sum;
C wherein
t, N
v, V
DifAll relevant with current bionical individual X.
(3) final assessment result correction: the result according to the responsibility point is chosen, rest and reorganize to bionical individuality, only selecting the corresponding bit position 1 of sensor node that is used as responsibility point.
Step 4: if discontented foot-eye point all standing of final bionical individuality or the above target that covers of special ratios; The bionical individual coding item that finishing is corresponding; It is changed with the minimum code position satisfy all standing, it is decoded can obtain the maximum node covering set under the current network scene.For poor excessively finally the separating of coverage rate, adopt deletion action, if the deletion action number of times less than N, then returns step 4.Otherwise, need report through center control nodes and carry out artificial treatment.
Step 5: according to the result of calculation of step 4, forming with the center control nodes is the tree network topology of root.Change deletion action number of times is zero.
Step 6:, return step 1 according to preset network topology reconstitution time.
Preferred version as bionic Algorithm: can choose in genetic algorithm and binary particle swarm algorithm, the binary system invasive weed algorithm any.
The employing of bionic Algorithm makes the problem that originally can't in polynomial time, achieve a solution that reliable solution arranged.So in construction method; Only need be concerned about core point; Be final design target: energy consumption balance, in case network can be realized energy consumption balance, network must have the sensing node set of maximum numbers so; Must increase the life cycle of network like this, the network valid data amount that finally can get access to is inevitable so also can increase to maximum.And for energy consumption balance the realization means of corresponding preferred plan, then can under the search mechanisms that configures, give bionic Algorithm itself and solve.
The present invention relates to a kind of wireless sense network perception topological construction method that detects towards multiple target point; It is characterized in that covering the angle of scheduling from multiple target point; Cover dispatching algorithm with the multiple target point perception based on bionic Algorithm and be the basis, provide a kind of to multiple target point monitoring perception topological construction method, described bionic Algorithm is a design premises with the impact point coverage rate; The network energy consumption balance is a design object, and considers the redundant problem of gathering of the heavy cover data of impact point emphatically.Use bionics algorithm optimizes algorithm; Energy variance, impact point coverage rate, active node number with network are the constructing variable of fitness function; Calculate rational active node set, and, topology of networks and route are controlled according to this set.The present invention is applicable to that the redundant at random high speed of laying, low-power consumption hybrid sensor network and corresponding M2M use.
Description of drawings
Fig. 1 is a multiple target point self adaptation topology constructing flow chart.
Fig. 2 is that the multiple target point perception covers sketch map.
Embodiment
Practical implementation is following:
The network scenarios size is 400 * 400m, and sensor node perception radius is 100m, and the maximum communication radius is 2 times of perception radius.Aggregation node is positioned at the center of network scenarios, and sensor node possesses the power control ability.
Sensor node and impact point all adopt random fashion to lay, and impact point adds up to 20, and sensor node adds up to and can be set to 30～80.
Bionic Algorithm then can be selected binary system invasive weed algorithm for use, and the flow process of corresponding binary system invasive weed algorithm can find in disclosed document.The individual available following formula of weeds is explained:
X＝[x
1，x
2，x
3，…，x
i，…，x
V-1，x
V]
Wherein V is effective node total number, x
iBe Boolean variable, its value can only get 0 or 1.
Work as x
i, be illustrated in that i number effective node is state of activation in the lower whorl at=1 o'clock.
Concrete binary system invasive weed algorithm parameter disposes as follows:
In the last table, the dimension of problem should be in the running of algorithm adaptive determining, the Dim value equals the effective node sum under the current network running status.Can find out that thus for the network of laying at random of imperial scale, this simple coded system is unaccommodated.Need disassemble problem, make that the dimension of problem is suitable.The sensor node number that is provided with in this embodiment is all in a suitable dimension scope.
At first obtain each node self-position information through methods such as GPS; And through flooding or mode such as directed pathfinding route is informed aggregation node (network center's Control Node); Can obtain relevant informations such as effective node location, energy in the network in view of the above; Effectively the node ratio is lower than preset threshold value in network, and then algorithm stops.
The overall situation of placement algorithm deletion number of times is zero in aggregation node, adopts 0/1 model that bionical individuality is encoded, the scheduling method that on behalf of a lower whorl, each bionical individuality possibly adopt.Bionical individual lengths equals the effective node number in the network.
The available following formula of bionical individuality is explained:
X＝[x
1，x
2，x
3，…，x
i，…，x
V-1，x
V]
Wherein V is effective node total number, x
iBe Boolean variable, its value can only get 0 or 1.
Work as x
i, be illustrated in that i number effective node is state of activation in the lower whorl at=1 o'clock.
Next, carry out fitness function and make up, and the assessment that circulates, provide finally and separate.
At first obtain the corresponding activation node set of the individual X of current weeds, the corresponding bit position is that 1 node promptly is to activate node.Find out the matrix of the corresponding impact point of this node set, record format is:
[target_id，sensor_valid_id，energy_var]
Wherein target_id is the impact point numbering, and sensor_valid_id is the overall situation numbering of effective node, and energy_var is the current energy variance yields of effective node.
To impact point, add up the number N of each impact point corresponding sensor node with multiple covering perception in the upper set
t, press N
tOrder from small to large sorts to impact point; Successively the impact point after the ordering is chosen its corresponding sensor node; The principle of choosing adopts the maximum principle of corresponding energy variance, in case after choosing, promptly to the pairing energy variance of this sensor node in the above-mentioned set; Be that the energy_var item upgrades, amplitude of variation is 1.If the energy variance of a plurality of sensor nodes that certain impact point is corresponding is identical, then choose minimum its corresponding monitoring point of conduct of node serial number.
After completion responsibility node is chosen, just can assess the individual corresponding effective matching relationship of current weeds.
The parameter of assessment comprises:
1) epicycle energy variance V
CurWith the lower whorl energy variance V that estimates
PreDifference V
Dif
2) current weeds individuality is to the coverage rate C of impact point
t
3) number N of employed sensor node
v
The fitness valuation functions is:
V
dif＝V
pre-V
cur
C
t＝C(X)/M
C (X): the corresponding impact point number that covers of the individual X of current weeds;
M: impact point sum;
C wherein
t, N
v, V
DifAll relevant with the individual X of current weeds.
Result according to the responsibility point is chosen rests and reorganizes to the weeds individuality, only selecting the corresponding bit position 1 of sensor node that is used as responsibility point.So final assessment result comprises two parts: 1. the individual X ' of weeds later rests and reorganizes; 2. superiority-inferiority evaluation result fit.
Repeating algorithm is separated up to reaching greatest iteration, providing finally.
When binary system invasive weed algorithm obtain finally separate the special ratios coverage rate that reaches more than 95% time; Think that promptly separating of its acquisition is one and effectively separates; Can effectively separate the repair operation that covers to this, thereby make its requirement of satisfying all standing, can obtain the maximum node covering set under the current network scene after the decoding; Make up tree topology with this, and be handed down to other nodes in the scene with the high power broadcast mode by aggregation node.And for poor excessively finally the separating of coverage rate; Less than 50% impact point; We then adopt deletion action, finally separate calculating again, and increase by 1 to a deletion number of times if search N (desirable 5～7) still can't satisfy condition after inferior; Corresponding deletion this moment number of times then need report through center control nodes and carry out artificial treatment greater than preset value N.
Effectively finally separate according to what binary system invasive weed algorithm obtained, forming with the center control nodes is the tree network topology of root.
After arriving the network topology reconstitution time of regulation, need according to above-mentioned steps, to recomputate effectively finally and separate again according to node location in the current network and energy information.In effective interstitial content can not overlay network during 50% destination node, need report and carry out artificial treatment through center control nodes.
Claims (7)
1. wireless sense network perception topological construction method towards multiple target point monitoring is characterized in that following steps:
Step 1: obtain each node self-position information in network, and inform network center's Control Node, thereby obtain effective node location and energy-related information in the network through general confession or directed pathfinding routing mode;
Step 2: coding, adopt 0/1 model that bionical individuality is encoded, the scheduling method that on behalf of a lower whorl, each bionical individuality possibly adopt, bionical individual lengths equals the effective node number in the network;
Step 3: according to the operative norm flow process of bionic Algorithm, be guidance, provide finally and separate with the fitness function; The design of fitness function is that target is carried out with the energy consumption balance; Comprise choosing of (1) responsibility node; (2) separate the superiority-inferiority assessment; (3) final assessment result correction;
Step 4: if discontented foot-eye point all standing of final bionical individuality or the above target that covers of special ratios; The bionical individual coding item that finishing is corresponding; It is changed with the minimum code position satisfy all standing, it is decoded can obtain the maximum node covering set under the current network scene; For poor excessively finally the separating of coverage rate, adopt deletion action, if the deletion action number of times less than N, then returns step 4; Otherwise, need report through center control nodes and carry out artificial treatment;
Step 5: according to the result of calculation of step 4, forming with the center control nodes is the tree network topology of root, and change deletion action number of times is zero;
Step 6:, return step 1 according to preset network topology reconstitution time.
2. method according to claim 1 is characterized in that being lower than preset threshold value like effective node ratio in the network in the step 1, then stops.
3. method according to claim 1 is characterized in that the design of the described fitness function of step 3 is target with the balancing energy, and design principle comprises:
A) the energy variance of sensor node is as much as possible near null value;
B) make the energy variance develop toward as far as possible little direction;
C) amplitude that develops to worse direction of control energy variance.
4. method according to claim 1 is characterized in that with epicycle energy variance V
CurWith the lower whorl energy variance V that estimates
PreDifference V
Dif, current bionical individuality is to the coverage rate C of impact point
t, employed sensor node number N
vBe fitness valuation functions parameter.
The fitness valuation functions is:
V
dif＝V
pre-V
cur
C
t＝C(X)/M
Wherein, the corresponding impact point number that covers of the individual X of the current weeds of C (X) expression, M is the impact point sum; C
t, N
v, V
DifAll relevant with the individual X of current weeds.
5. method according to claim 1 is characterized in that step 5 comprises finishing operation that the target that is higher than specific coating ratio is separated and to the poor excessively deletion action of separating of coverage rate.
6. according to claim 1 or 3 described methods, it is characterized in that each sensor node covers a plurality of impact points.
7. method according to claim 1 is characterized in that this method is applicable to that high speed, low-power consumption, sensing net topology structure that both mix mutually and corresponding M2M use.
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