CN102395146B - 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 towards multiple target point monitoring, or rather, the present invention relates to a kind of covering and corresponding topological construction method of wireless sense network multiple target point.Belong to wireless sense network field.
Background technology
The general solution flow process of the Construct question 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, the factor that generally need to consider comprises the energy response of coverage rate and active node integral body.First require the active node of selecting can cover all impact points, secondly require the active node of selecting to possess the characteristic that equalizing self-adapting network energy consumes.In second step, need to consider how active node builds a data channel of leading to Sink node.
1), for first step, the support of coverage rate is easier to realize; Adaptive energy characteristic requires node according to the Energy Expenditure Levels of interdependent node, to determine the operating state of self.
Generally, impact point and sensor node are all laid at random, suppose that sensor node meets the 0/1 monitoring sensor model distributing simultaneously, and network carries out dispatching management in the mode of wheel.Think and only have the sensor node that can monitor impact point just can regularly produce valid data.Due to the random inhomogeneities of laying, each impact point may be covered by a plurality of sensor nodes, if we use a plurality of sensor nodes to monitor an impact point simultaneously, these sensor nodes will produce identical data so, and this is a kind of waste to sensor resource.In network, other nodes may need to coordinate this sensor node to complete the uploading operation of data simultaneously, and this has also aggravated the communications burden of other nodes in network simultaneously.Effective mode is to make the sensor node of redundancy enter a resting state, often takes turns only limited Activity On the Node, but will complete the task of target monitoring simultaneously, realizes the equilibrium of nodes energy consumption.Therefore the selection problem of active node, in fact 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 by a jumping route and certain, or can pass through one and jump route directly and Sink node communication, thereby our target lock-on is covered in scheduling problem at multiple target point.
Comprehensive above description, can find out that multiple target point monitoring perception topology constructing problem can dispatch by reasonably optimizing sensor node, and adaptive control sensor node on off state solves, and the route in network can be take active node and be built as basis.
Related Sensor Network multiple target point monitoring problem above, has had research report:
1) mode of research is not distinguish a multiple covering for 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] concept of maximum Coverage Control collection (Maximum Set Covers) has early been proposed in article for K target monitoring, be called for short MSC, author represents the covering relation between sensor node and impact point with bipartite graph, and two solutions have been provided from the angle of linear programming, respectively the Greedy MSC of didactic LP-MSC and Greedy, by building the method for a plurality of non-non-intersect covering collection, reach the target of maximization network life cycle, in addition author go back valid certificates MSC problem itself be a np complete problem.This article author, in demonstration and the process of emulation, only supposes the sensor node life cycle of only working, and 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, once think that node has covered certain impact point, will the cycle produce data, so for the impact point of multiple covering, must have the problem of a data redundancy.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] be that multiobject covering scheduling problem is summed up as to minimal weight transducer covering problem (MWSCP), and the covering scheduling of consideration under Probability Coverage Model, by building an integral linear programming model, author uses respectively ant group algorithm and particle cluster algorithm to solve this problem.In this article, author is only divided into operating conditions and non-operation node area, and no matter thinks how many impact points of coverage, and its energy consumption is in working order all identical.This hypothesis possesses certain reasonability, but still is not enough.
2) in addition, a kind of report of research is that the multiple coverage condition of impact point is considered, thinks that node energy consumption geometric ratio is in its responsible impact point number.
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 of 09 year, this problem definition, be multiple target covering problem (Multiple target coverage), be called for short MTC.Propose one and considered balancing energy sexual enlightenment formula algorithm MTCP.This algorithm has been considered the transmission energy consumption of node simultaneously and has been re-covered the impact point of lid, for the impact point of each multiple covering, chooses a suitable responsibility point, makes an 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 to consider.Defect for this article, Kim H and for example, Han Y-H, people [Maximum lifetime scheduling for target coverage in wireless sensor networks[C] the .Proceedings of the 6th International Wireless Communications and Mobile Computing Conference.Caen such as Min S-G., France:ACM, 2010.] reported multiple target monitoring problem has been done to further solution, on the basis of forefathers' heuritic approach, do further improvement, proposed MLS algorithm.This algorithm, when considering to re-cover and covering destination node problem, is considered the dump energy of sensor node.Emulation shows, after the problem of considering aspect above-mentioned two, compares MSC, MTCP algorithm in the past, and this algorithm can be found out the more covering collection 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 more than 50% energy, a lot of nodes have been used whole energy; Use MCTP to cause not consumes energy of a lot of nodes, and only have minority node to use whole energy.
The present invention intends take the maximization network life-span and effective monitoring objective point is overall goal, the impact point that the sensor node of take can cover self carries out Classification and Identification as basis, consider residue energy of node, impact point re-covers and covers redundancy, optimized network energy efficiency on the basis of ensuring coverage rate, by problem being modeled as to the combinatorial optimization problem of discrete space, then use bionic Algorithm to solve.
Summary of the invention
The object of the present invention is to provide a kind of wireless sense network perception topological construction method towards multiple target point monitoring.
For the sensor node in existing application at present, can identify the impact point in 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, the impact point number of sensor node monitoring is more, and its energy of uploading energy consumption is more.As shown in figure (2), can find out, each sensor node may cover a plurality of impact points, for example, S
1sensing node covers T
1, T
2, T
3and T
54 impact points, S2 sensing node covers T
1, T
3and T
43 destination node.
Therefore, technical problem to be solved by this invention is:
At impact point, re-cover cover in the situation that, how to pass through the dormancy dispatching of multisensor node, obtain maximum node and cover collection, the self adaptation that realizes multiobject effective monitoring and perception topology builds, and makes as possible network within the large as far as possible time cycle, adopt the mode of energy balance to move simultaneously.
Construction method of the present invention according to comprising:
1) the energy variance of sensor node is as much as possible near null value;
2) make energy variance past as far as possible to little future development;
3) control energy variance to the amplitude of worse future development.
By effective enforcement of these measures, guarantee that to a certain extent network reaches energy consumption balance state.By dynamic wake-up and for each impact point, select suitable responsibility node, realize the life cycle of maximization network in the situation that meeting all standing of K target.
For ease of understanding the present invention, the implication of planning in form of presentation is first expressed as follows:
Effective node: to the arbitrary node in network, when its appreciable impact point number is more than or equal to 1, think that this node is effective node.
Effective set of node: the set for effective nodes all in sensor node S set is effective set of node.
Activate set of node: in certain is taken turns, the set of the effective node in state of activation.
After the random laying of network, node can arbitrarily not be moved, and part of nodes is owing to not covering impact point, so not in monitoring range of the present invention.Therefore the present invention only processes effective set of node.
The present invention is a kind of wireless sense network relating to towards multiple target monitoring, that is must consider that multiple target point re-covers lid, and the dynamic dormancy of take wakes the dynamic topology construction method as means up, specifically comprises the following steps:
Step 1: obtain each node self-position information in network by methods such as GPS, and by flooding or the mode such as directed pathfinding route informs that network center controls node, can obtain accordingly in network effectively the relevant informations such as node location, energy, in network, effectively node ratio is lower than default threshold value, and algorithm stops.
Step 2: coding, for unified describing mode, adopt simple 0/1 model to encode to bionical individuality, each bionical individuality represents the scheduling method that a lower whorl may adopt.Bionical individual lengths equals the effective node number in 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
ifor Boolean variable, its value can only get 0 or 1.
Work as x
i, show that No. i effective node is state of activation in lower whorl at=1 o'clock.
Step 3: according to the operative norm flow process of bionic Algorithm, take fitness function as guidance, the random feasible solution after coding is carried out to standard operation, until reach maximum iteration time, provide final solution.The design of fitness function, the energy consumption balance of take carries out as target.In line with the principle that guarantees to greatest extent the balancing energy characteristic of effective node, should be by 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, chooses its corresponding sensor node to having the impact point of multiple covering perception in effective set of node.
(2) separate superiority-inferiority assessment: epicycle energy variance and the difference of the lower whorl energy variance of estimating, current bionical individuality are fitness evaluating function parameter to the number of the coverage rate of impact point, the sensor node that uses.
(3) final reassessment:
The parameter of described assessment comprises:
1) epicycle energy variance V
curwith the lower whorl energy variance V estimating
predifference V
dif;
2) the coverage rate C of current weeds individuality to impact point
t;
3) number N of the sensor node using
v.
The fitness evaluating function being built by above parameter is:
V
dif＝V
pre-V
cur
C
t＝C(X)/M
C (X) wherein: the corresponding impact point number covering of current bionical individual X;
M: impact point sum;
C wherein
t, N
v, V
difall relevant to current bionical individual X.
(3) final reassessment: the result of choosing according to responsibility point, bionical individuality is rested and reorganized, only selecting bit position 1 corresponding to sensor node that is used as responsibility point.
Step 4: if the discontented foot-eye point all standing of final bionical individuality or the above target covering of special ratios, the bionical individual coding that finishing is corresponding, make it with minimum code position, change and meet all standing, its maximum node that can obtain under current network scene of decoding is covered to collection.For the excessively poor final solution of coverage rate, adopt deletion action, if deletion action number of times is less than N, return to step 4.Otherwise, need report and carry out artificial treatment by center control nodes.
Step 5: according to the result of calculation of step 4, form and take the tree network topology that center control nodes is root.Change deletion action number of times is zero.
Step 6: according to default network topology reconstitution time, return to step 1.
Preferred version as bionic Algorithm: can choose any in genetic algorithm and binary particle swarm algorithm, binary system invasive weed algorithm.
The employing of bionic Algorithm, makes the problem that originally cannot achieve a solution in polynomial time have reliable solution.Therefore in construction method, only need to be concerned about core point, it is final design target: energy consumption balance, once 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 valid data amount that network finally can get so must also can increase to maximum.And for energy consumption balance the means that realize of corresponding preferred plan, can, under the search mechanisms setting, give bionic Algorithm itself and solve.
The present invention relates to a kind of wireless sense network perception topological construction method detecting towards multiple target point, it is characterized in that covering from multiple target point the angle of scheduling, the multiple target point sensor coverage dispatching algorithm of take based on bionic Algorithm is basis, provide a kind of to multiple target point monitoring perception topological construction method, described bionic Algorithm be take impact point coverage rate as design premises, network energy consumption balance is design object, and considers that emphatically impact point re-covers the problem that data redundancy gathers of covering.Use bionics algorithm optimizes algorithm, the constructing variable that energy variance, impact point coverage rate, the active node number of network of take is fitness function, calculate rational active node set, and according to this set, topology of networks and route are controlled.The present invention is applicable to high speed, low-power consumption hybrid sensor network and the corresponding M2M application that random redundancy is laid.
Accompanying drawing explanation
Fig. 1 is that multiple target point adaptive topology builds flow chart.
Fig. 2 is multiple target point sensor coverage schematic diagram.
Embodiment
Specifically be implemented as follows:
Network scenarios size is 400 * 400m, and sensor node perception radius is 100m, and maximum communication radius is 2 times of perception radius.Aggregation node is positioned at the center of network scenarios, and sensor node possesses 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 can be selected binary system invasive weed algorithm, 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
ifor Boolean variable, its value can only get 0 or 1.
Work as x
i, show that No. i effective node is state of activation in lower whorl at=1 o'clock.
Concrete binary system invasive weed algorithm parameter configuration is as follows:
In upper table, the dimension of problem should be in the running of algorithm adaptive determining, Dim value equals the effective node sum under current network running status.Can find out thus, for the random distributing network of imperial scale, this simple coded system is unaccommodated.Problem need to be disassembled, make the dimension of problem suitable.The sensor node number arranging in the present embodiment is all within the scope of a suitable dimension.
First by methods such as GPS, obtain each node self-position information, and by flooding or the mode such as directed pathfinding route is informed aggregation node (network center controls node), can obtain accordingly in network effectively the relevant informations such as node location, energy, in network, effectively node ratio is lower than default threshold value, and algorithm stops.
In aggregation node, the overall situation of placement algorithm deletion number of times is zero, adopts 0/1 model to encode to bionical individuality, and each bionical individuality represents the scheduling method that a lower whorl may adopt.Bionical individual lengths equals the effective node number in 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
ifor Boolean variable, its value can only get 0 or 1.
Work as x
i, show that No. i effective node is state of activation in lower whorl at=1 o'clock.
Next, carry out fitness function structure, and the assessment that circulates, final solution provided.
First obtain activation node set corresponding to the individual X of current weeds, the node that corresponding bit position is 1 is to activate node.Find out the matrix of impact point corresponding to this node set, record format is:
[target_id，sensor_valid_id，energy_var]
Wherein target_id is impact point numbering, the overall situation numbering that sensor_valid_id is effective node, the current energy variance yields that energy_var is effective node.
To the impact point with multiple covering perception in upper set, add up the number N of the sensor node that each impact point is corresponding
t, by N
torder from small to large sorts to impact point, successively the impact point after sequence is chosen to its corresponding sensor node, the principle of choosing adopts the principle of corresponding energy variance maximum, once after choosing, to the corresponding energy variance of this sensor node in above-mentioned set, be that 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, choose its corresponding monitoring point of conduct of node serial number minimum.
After completing responsibility node selection, 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 estimating
predifference V
dif;
2) the coverage rate C of current weeds individuality to impact point
t;
3) number N of the sensor node using
v.
Fitness evaluating function is:
V
dif＝V
pre-V
cur
C
t＝C(X)/M
C (X): the corresponding impact point number covering of the individual X of current weeds;
M: impact point sum;
C wherein
t, N
v, V
difall relevant to the individual X of current weeds.
The result of choosing according to responsibility point, rests and reorganizes to weeds individuality, only selecting bit position 1 corresponding to sensor node that is used as responsibility point.Therefore final assessment result comprises two parts: 1. the individual X ' of the weeds later of resting and reorganizing; 2. superiority-inferiority evaluation result fit.
Repeating algorithm, until reach greatest iteration, provides final solution.
When the final solution of binary system invasive weed algorithm acquisition reaches more than 95% special ratios coverage rate, think that the solution of its acquisition is an effectively solution, the repair operation that can cover this efficient solution, thereby make it meet the requirement of all standing, the maximum node that can obtain after decoding under current network scene covers collection, with this, build tree topology, and in high power broadcast mode, be handed down to other nodes in scene by aggregation node.And for the excessively poor final solution of coverage rate, be less than 50% impact point, we adopt deletion action, re-starting final solution calculates, if and still cannot satisfy condition after deletion number of times increase by 1 search N (desirable 5～7) is inferior, now the corresponding number of times of deleting is greater than preset value N, needs to be reported and carried out artificial treatment by center control nodes.
Effectively final solution theing obtain according to binary system invasive weed algorithm, forms and take the tree network topology that center control nodes is root.
Arrive after the network topology reconstitution time of regulation, need again according to node location in current network and energy information, according to above-mentioned steps, recalculate effectively final solution.In effective interstitial content can not overlay network, during 50% destination node, need report and carry out artificial treatment by center control nodes.
Claims (6)
1. towards a wireless sense network perception topological construction method for multiple target point monitoring, it is characterized in that following steps:
Step 1: obtain each node self-position information in network, and inform that by general confession or directed pathfinding routing mode network center controls node, thereby obtain effective node location and energy-related information in network;
Step 2: coding, adopt 0/1 model to encode to bionical individuality, each bionical individuality represents the scheduling method that a lower whorl may adopt, bionical individual lengths equals the effective node number in network;
Step 3: according to the operative norm flow process of bionic Algorithm, take fitness evaluating function as guidance, provide final solution; The design of fitness function, the energy consumption balance of take carries out as target; Comprise choosing of (1) responsibility node; (2) separate superiority-inferiority assessment; (3) final reassessment;
Step 4: if the discontented foot-eye point all standing of final bionical individuality or the above target covering of special ratios, the bionical individual coding that finishing is corresponding, make it with minimum code position, change and meet all standing, its maximum node that can obtain under current network scene of decoding is covered to collection; For the excessively poor final solution of coverage rate, adopt deletion action, if deletion action number of times is less than N, return to step 4; Otherwise, need report and carry out artificial treatment by center control nodes; N gets 5-7;
Step 5: according to the result of calculation of step 4, form and take the tree network topology that center control nodes is root, change deletion action number of times is zero;
Step 6: according to default network topology reconstitution time, return to step 1;
Fitness evaluating function described in step 3 is by 1. epicycle energy variance V
curwith the lower whorl energy variance V estimating
predifference V
dif; 2. the coverage rate C of current bionical individuality to impact point
t; The number N of the sensor node that 3. used
v, three evaluate parameter structures; Described fitness evaluating function is:
V
dif=V
pre-V
cur
C
t=C(X)/M
Wherein, C (X) represents the corresponding impact point number covering of the individual X of current weeds, and M is impact point sum; C
t, N
v, V
difall relevant to the individual X of current weeds.
2. method according to claim 1, is characterized in that in step 1 as effective node ratio in network, lower than default threshold value, stops.
3. method according to claim 1, is characterized in that the design of the fitness evaluating function described in step 3 be take energy consumption balance as target, and design principle comprises:
A) the as far as possible little future development of energy variance of sensor node; As much as possible near null value;
B) control energy variance to the amplitude of worse future development.
4. method according to claim 1, is characterized in that step 5 comprises to the finishing operation of the target solution higher than specific coating ratio with to coverage rate to cross poor deletion action of separating.
5. according to the method described in claim 1 or 3, it is characterized in that each sensor node covers a plurality of impact points.
6. method according to claim 1, is characterized in that the method is applicable to high speed, low-power consumption, Sensor Network topological structure that both mix mutually.
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