CN102238561A - Node deployment method for energy efficient hierarchical collaboration coverage model - Google Patents

Abstract

The invention discloses a node deployment method for an energy efficient hierarchical cooperative coverage model, and belongs to the technical field of wireless sensor networks. The method specifically comprises the following steps of: (1) establishing the coverage model of a target area, and layering the target area to fulfill the aim of saving energy consumption; (2) solving a heuristic factor so as to greatly increase the rate of convergence when the model is solved by using an ant colony algorithm; (3) solving an upper node number limit and a lower node number limit to reduce the number of iterations when the model of the solved by using the ant colony algorithm; (4) selecting a node position deployment strategy; and (5) solving the optimal solution of the hierarchical collaboration coverage model by utilizing the ant colony algorithm. The method has the advantage of meeting the node deployment of areas with specific area shapes.

Description

Node deployment method based on the layering of Energy Efficient cooperation overlay model

Technical field

The present invention relates to a kind of node deployment method of the layering cooperation overlay model based on Energy Efficient; Belong to the wireless sensor network technology field.

Background technology

Wireless sensor network is the computer system that a kind of intelligent radio sensing node constitutes, and the natural ability of human mobile perception has been expanded in its appearance, has changed the mode of people's managing families, factory and environment.Wireless sensor network is disposed flexibly with it, and characteristics with low cost have obtained in fields such as military surveillance, traffic monitoring, intelligent industrial controls using widely.

The characteristics of wireless sensing node comprise: resource-constrained, and network coverage is big, and node is intensive, dynamic topology.Generally speaking, for a certain measurement task, the layout quantity of sensing node all will surpass its requirement.Do like this and can remedy the problem that the not high certainty of measurement that causes of sensing node positioning accuracy reduces on the one hand; Also can further improve System Fault Tolerance on the other hand.But wireless sensing node all is subjected to strict constraint on size, weight, energy and bandwidth, these will influence the availability of Internet resources.Therefore, how to guarantee simultaneously under the situation that consumes least energy that network coverage is the focus of studying at present.

The network coverage is a kind of tolerance of wireless sensor network to its target monitoring regionally detecting, is a major issue in the wireless sensor network research contents.Overlay model can be divided into the certainty covering according to configuration mode and randomness covers.The research contents that certainty covers is the state relative fixed of target area, determines network topology structure or sets the node density of subregion as required according to pre-configured node location.The research contents that random node covers is to finish covering to the target area in node random distribution and node location condition of unknown.The main standard of estimating the overlay model quality has level of coverage, energy efficiency, the accuracy of algorithm and the robustness of network topology of network to the target area.In wireless sensor network, because node energy is limited, actual application environment is complicated and in most cases do not allow the node battery is changed, and the useful life and the network lifetime that therefore prolong node to greatest extent become research contents important in the wireless sensor network.

Traditional overlay model commonly used has boolean's model, general overlay model, cooperation overlay model and Probability Coverage Model etc.Boolean's model is comparatively simple, but the detectivity that can not express transducer increases the characteristics that reduce with distance; General overlay model has then only been considered the covering power of single-sensor; Though cooperation overlay model and Probability Coverage Model have been considered the contribution of a plurality of transducers to covering, and do not study from the angle of node energy consumption.

Guaranteeing that sensing network has under the situation of certain covering power to the target area, the main means that reduce node energy consumption have under the situation that interstitial content is determined optimizes network topology, under the situation that node redundancy is disposed, carry out dormancy dispatching, optimize interstitial content and node location, optimize the position of sink node etc.Covering research at Energy Efficient in the radio sensing network at present focuses mostly in preceding two fields.People such as K.Shashi Prab have proposed a kind of distributed hexagon covering algorithm, and this algorithm is realized the network coverage of Energy Efficient by selecting optimized neighbor node.People such as Paul Sounak have proposed the balanced dormancy dispatching strategy of dynamic power under the isomorphism meshed network environment, this algorithm determines that by three factors such as residue energy of node the dormancy of node reaches the purpose of saving energy, but this algorithm is not supported multihop network.People such as Costa have proposed the multipath selection algorithm of topological sensing node refusal, and this algorithm can prolong the life span of network by investigating node and covering power and selecting the balance node energy consumption by multipath by this selection algorithm.People such as Martins have then proposed to solve to be had under the situation of node failure, and the dynamic recovery that the zone covers has proposed the dynamic covering algorithm MGoDA of Energy Efficient.Minimum covering set approximate data based on greedy method can go out minimum covering set in linear Time Calculation, promptly uses minimum node coverage goal zone, thereby allows more node enter dormancy dispatching, reaches the purpose of saving node energy consumption.Above algorithm all is that in the past two angles are analyzed and optimized the network node energy consumption.In multihop network, most nodes need carry out routing forwarding by via node could send to data the sink node, and via node also need be transmitted the Monitoring Data of other node except sending self Monitoring Data, and the node energy loss is very fast.Therefore, the loss rate that is in the node energy of diverse location in network is not quite similar.If via node death is too fast, the monitor data of ordinary node can't be transmitted by via node, and network just can't provide normal service, and the energy of ordinary node fails to be fully used.And in a lot of the application, the node that many positions are important is bigger because dispose difficulty, should guarantee at first that the energy of these nodes is fully used, as the front node in the data monitoring of battlefield, and mine bottom node in the coal information monitoring or the like.Therefore only consider coverage rate and ignore the node death faster that causes the energy loss model can make some position because of node location is different, thereby reduce the life span of whole network.Therefore need be when node deployment at diverse location, zones of different is laid the node of different densities, to reach balancing energy, prolongs the purpose of network lifetime.

People such as calendar year 2001 Seapahn have set up cooperation overlay model (CCM), reflect the coverage condition of network at this point by defining certain any covering index.The covering index definition is:

$I(p,K)=\underset{k=1}{\overset{K}{\mathrm{\Σ}}}S(p,s_k)---\left(1\right)$

Wherein

People such as 2007 Nian Yang point out on the basis of cooperation overlay model, are guaranteeing under the condition that the zone is covered fully that optimum node distributes and is: any 3 constitute an equilateral triangle, and the leg-of-mutton length of side is (R is the maximum communication radius of node), all nodes become cellular coverage goal zone.As shown in Figure 1, region S is become cellular covering by 14 nodes.

The cooperation overlay model is only investigated the coverage of node to the target area from the angle of overlay area, and does not have to add the analysis to the energy loss of node.In multihop network, except will sending self monitor data, also need to transmit data from the nearer relatively node of sink node from sink node node far away, sending under the identical situation of data volume, energy loss is very fast relatively.In single-hop networks, the energy that sends data degradation from sink node node far away is more relatively.Therefore, the covering of network should only not considered area coverage, also will consider diverse location, the energy loss of the node of different levels.

Summary of the invention

The object of the present invention is to provide a kind of node deployment method of the layering cooperation overlay model based on Energy Efficient, be implemented in to satisfy to cover and make the minimum power consumption of node under the situation about requiring.

The present invention takes following technical scheme to realize: a kind of node deployment method of the layering cooperation overlay model based on Energy Efficient may further comprise the steps:

1) set up the overlay model of target area, this model reaches the purpose of saving energy consumption by the target area being carried out layering.

2) find the solution the heuristic factor, make that convergence rate is accelerated greatly when finding the solution this model with ant group algorithm.

3) solution node lower limit quantitatively makes when finding the solution this model with ant group algorithm, and iterations reduces.

4) choose the position deployment strategy of node.

5) utilize ant group algorithm to find the solution the optimal solution of layering cooperation overlay model.

Described step 1 specifically may further comprise the steps:

11. draw out the drawing of target area, and calculate the area of target area.

12., choose the level granularity of division according to the size of coverage requirement and target area.

13. set up the layering cooperation overlay model of target area.

Described step 3 is specially: at first set up the target function of model, this target function is the life span maximum that makes the layer of the life span minimum behind the score layer.Its less important constraints of setting up this model: one, every node layer quantity sum equals total number of nodes.Two, the coverage rate of every node layer is greater than the coverage rate of requirement.

Described step 5 specifically may further comprise the steps:

51. the parameter of using in the initialization algorithm comprises the number N of total node, the primary power of node, the maximum communication radius of node. in ant group algorithm, the number of ant, the pheromones amount τ on the ij of limit _IjBe initially 0, the relative significance level α of pheromones, the relative significance level β of the heuristic factor, heuristic factor η _IjIn constant M=1, W=1, ant ring constant Q=10.

52. beginning outermost layer iteration for every ant, adds the taboo table with present node.

53. calculate the heuristic factor.

54., and choose next node by the transition probability on pheromones and every limit of heuristic factor calculating.

55. choose the node location deployment strategy.

56. carry out the multi-hop routing algorithm, the computing network life span.

57. carrying out pheromones upgrades.

Described step 55 is specially: the area according to the overlay area obtains each orthohexagonal area size divided by the node number, thereby obtains the length of side and center.Each sensor node just is placed on orthohexagonal center, and this length of side is smaller or equal to the orthohexagonal length of side of general overlay model.

Layering cooperation overlay model based on Energy Efficient provided by the invention, the distribution of the node location that it takes into full account is for the influence of network lifetime, realized satisfying under the situation that covers requirement the maximization of the life span of network by the target area being carried out hierarchy optimization.Find the solution this model by ant group algorithm and can reach quick convergence, avoid the situation of global optimum.The present invention can satisfy the node deployment in the zone with specific region shape.

Further specify the present invention below in conjunction with drawings and Examples.

Description of drawings

Fig. 1 is the schematic diagram of the layering cooperation overlay model that adopts of the present invention.

Fig. 2 is the conversion method of the ant group algorithm that adopts of the present invention.

Embodiment

As shown in Figure 1, the present invention at first will set up the overlay model of target area in the specific implementation.The layering cooperation overlay model Energy Efficient Hierarchical Collaboration CoverageModel (EEHCCM) of Energy Efficient finds the solution the maximum of network lifetime exactly under certain constraints.The target function of this model is:

${}^T\mathrm{Sur}=\underset{i=\mathrm{1,2}L}{\min }{}_l{}^T\mathrm{Sur}_i---\left(3\right)$

Constraints is:

$\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{b}_i=N---\left(4\right)$

N＞N′ (5)

S _monitor≥S _area (6)

The implication of formula 4 is that the number sum of every node layer equals to sum up and counts out, and formula 5 and formula 6 are that the deployment of requirement node covers whole target monitoring zone, wherein S _MonitorBe the area in node coverage area territory, S _AreaIt is the area of target area.The problem that EEHCC solved is in a certain monitored area, sums up under the known situation of number of spots, realizes the node energy equilibrium by the node density of optimizing diverse location, prolongs network lifetime.

After modelling is intact, model is carried out ant group algorithm find the solution, the parameter of before finding the solution, using in this algorithm of setting and the selection rule of the heuristic factor.The selection rule of the heuristic factor of the ant colony optimization algorithm of EEHCCM is:

If i＞j

If i＜j

Wherein: η _Ij(k) expression is selected the heuristic factor of k bar limit to layer j from layer i,

The optimum number of nodes of presentation layer i estimates that M and W are arbitrarily positive constants.

In each iterative process, therefore every node layer quantity exist a bound owing to be subjected to the restriction of coverage rate, and the following of every node layer quantity is limited to:

L _iBe limited on the node layer quantity:

Because finding the solution of EEHCC model is the NP difficulty, therefore there is not the derivation algorithm of polynomial time.When the application ant group algorithm is found the solution EEHCCM, need change, make the input parameter type satisfy the form of ant group algorithm this problem.If the set L that level is divided regards the set C in the city in the TSP problem as, and every layer node manifold is closed N and is regarded the distance set D between the city in the TSP problem as, and then this problem is converted into the class TSP problem of a multipath.Shown in Figure 2, the circle among the figure is represented certain one deck in the area dividing, and corresponding to the city in the TSP problem, the camber line between the circle is represented the quantity n of every node layer _l, corresponding to intercity in the TSP problem apart from d _{Ci, cj}, network lifetime T _kCorresponding to path L _k, selecting k bar limit to transfer to a layer j definition of probability from layer i is a _Ij(k), the pheromone concentration on this limit is τ _Ij(k, τ), k represents the number of nodes among the layer i, and the heuristic factor between layer i and the layer j on the k bar limit is η _Ij(k).When algorithm was carried out, ant carried out random walk between each layer, determine to shift by transition probability, and the pheromones on the new route more, finally reach convergence.Therefore EEHCCM is converted in the set of paths of each circle in traversing graph search and makes the number of nodes compound mode that network lifetime is the longest.

The ant group derivation algorithm model of EEHCCM can be expressed as:

$a_{\mathrm{ij}}\left(k\right)=\frac{{\left[{\mathrm{\τ}}_{\mathrm{ij}}\left(k\right)\right]}^{\mathrm{\α}}{\left[{\mathrm{\η}}_{\mathrm{ij}}\left(k\right)\right]}^{\mathrm{\β}}}{\underset{l=1}{\overset{N}{\mathrm{\Σ}}}{\left[{\mathrm{\τ}}_{\mathrm{ij}}\left(l\right)\right]}^{\mathrm{\α}}{\left[{\mathrm{\η}}_{\mathrm{ij}}\left(l\right)\right]}^{\mathrm{\β}}}\∀i\≠j\∈L---\left(11\right)$

τ _ij(τ，k)＝ρτ _ij(τ-l，k)+Δτ _ij (12)

${\mathrm{\Δ\τ}}_{\mathrm{ij}}\left(k\right)=\underset{a=1}{\overset{m}{\mathrm{\Σ}}}{\mathrm{\Δ\τ}}_{\mathrm{ij}}^a\left(k\right)---\left(13\right)$

Need the parameter used in the initialization algorithm before algorithm is carried out, comprise the number N of total node, the primary power of node, the maximum communication radius of node. in ant group algorithm, the number of ant, the pheromones amount τ on the ij of limit _IjBe initially 0, the relative significance level α of pheromones, the relative significance level β of the heuristic factor, heuristic factor η _IjIn constant M=1, W=1, ant ring constant Q=10

It is as follows to utilize ant group algorithm to find the solution the algorithm steps of layering coordination model of Energy Efficient:

Algorithm can converge on optimal solution through after the several times iteration, and this optimal solution has indicated the optimal number ratio of every node layer, determined every layer number of nodes after, need the position or the distribution of specified node.The mode that the present invention takes is that the area according to the overlay area obtains each orthohexagonal area size divided by the node number, thereby obtains the length of side and center.Each sensor node just is placed on orthohexagonal center, and this length of side is smaller or equal to the orthohexagonal length of side of general overlay model.

Claims (9)

1. node deployment method based on the layering of Energy Efficient cooperation overlay model, it is characterized in that: concrete steps are as follows:

1) set up the overlay model of target area, this model reaches the saving energy consumption by layering is carried out in the target area;

2) find the solution the heuristic factor, make that convergence rate is accelerated greatly when finding the solution this model with ant group algorithm;

3) solution node lower limit quantitatively makes when finding the solution this model with ant group algorithm, and iterations reduces;

4) choose the position deployment strategy of node;

5) utilize ant group algorithm to find the solution the optimal solution of layering cooperation overlay model.

2. the node deployment method of the layering cooperation overlay model based on Energy Efficient according to claim 1 is characterized in that the concrete grammar that described step 1) is set up the overlay model of target area is:

11. draw out the drawing of target area, and calculate the area of target area;

12., choose the level granularity of division according to the size of coverage requirement and target area;

13. set up the layering cooperation overlay model of target area.

3. the node deployment method of the layering cooperation overlay model based on Energy Efficient according to claim 1, it is characterized in that, the described step 3) solution node quantitatively concrete grammar of lower limit is: at first set up the target function of model, this target function is the life span maximum that makes the layer of the life span minimum behind the score layer; Next sets up the constraints of this model: one, every node layer quantity sum equals total number of nodes; Two, the coverage rate of every node layer is greater than the coverage rate of requirement.

4. the node deployment method of the layering cooperation overlay model based on Energy Efficient according to claim 1 is characterized in that, the optimal solution concrete grammar that described step 5) is found the solution layering cooperation overlay model is:

51. the parameter of using in the initialization algorithm comprises the number N of total node, the primary power of node, the maximum communication radius of node. in ant group algorithm, the number of ant, the pheromones amount τ on the ij of limit _IjBe initially 0, the relative significance level α of pheromones, the relative significance level β of the heuristic factor, heuristic factor η _IjIn constant M=1, W=1, ant ring constant Q=10;

52. beginning outermost layer iteration for every ant, adds the taboo table with present node;

53. calculate the heuristic factor;

54., and choose next node by the transition probability on pheromones and every limit of heuristic factor calculating;

55. choose the node location deployment strategy;

56. carry out the multi-hop routing algorithm, the computing network life span;

57. carrying out pheromones upgrades.

5. the node deployment method of the layering cooperation overlay model based on Energy Efficient according to claim 4, it is characterized in that, described step 55) choosing the node location deployment strategy is specially: the area according to the overlay area obtains each orthohexagonal area size divided by the node number, thereby obtains the length of side and center; Each sensor node just is placed on orthohexagonal center, and this length of side is smaller or equal to the orthohexagonal length of side of general overlay model.

6. the node deployment method of the layering cooperation overlay model based on Energy Efficient according to claim 1 is characterized in that the described overlay model of setting up the target area adopts layering cooperation overlay model, and be specially: the target function of this model is:

${}^T\mathrm{Sur}=\underset{i=\mathrm{1,2}L}{\min }{}_l{}^T\mathrm{Sur}_i$

Constraints is:

$\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{b}_i=N$

N＞N′

S _monitor≥S _area

T wherein _SurRepresent network lifetime, N represents that the quantity of total node in the network is, the required minimum interstitial content of N ' representative cooperation overlay model under all standing condition, S _MonitorBe the area in node coverage area territory, S _AreaIt is the area of target area.The problem that model solved is in a certain monitored area, sums up under the known situation of number of spots, realizes the node energy equilibrium by the node density of optimizing diverse location, prolongs network lifetime.

7. the node deployment method of the layering cooperation overlay model based on Energy Efficient according to claim 1 is characterized in that described step 2 solving model adopts ant group algorithm, is specially:

$a_{\mathrm{ij}}\left(k\right)=\frac{{\left[{\mathrm{\τ}}_{\mathrm{ij}}\left(k\right)\right]}^{\mathrm{\α}}{\left[{\mathrm{\η}}_{\mathrm{ij}}\left(k\right)\right]}^{\mathrm{\β}}}{\underset{l=1}{\overset{N}{\mathrm{\Σ}}}{\left[{\mathrm{\τ}}_{\mathrm{ij}}\left(l\right)\right]}^{\mathrm{\α}}{\left[{\mathrm{\η}}_{\mathrm{ij}}\left(l\right)\right]}^{\mathrm{\β}}}\∀i\≠j\∈L$

τ _ij(τ，k)＝ρτ _ij(τ-l，k)+Δτ _ij

${\mathrm{\Δ\τ}}_{\mathrm{ij}}\left(k\right)=\underset{a=1}{\overset{m}{\mathrm{\Σ}}}{\mathrm{\Δ\τ}}_{\mathrm{ij}}^a\left(k\right)$

A wherein _IjBe the transition probability of node i to node j, τ _IjBe the pheromone concentration on the ij of path, η _IjBe the heuristic factor on the ij of path, ρ is the pheromones evaporation coefficient, and Q is pheromones quality coefficient (for any one positive constant), L _kBe path.

8. the node deployment method of the layering cooperation overlay model based on Energy Efficient according to claim 4 is characterized in that the heuristic factor computational methods of using are in derivation algorithm:

If i＞j

If i＜j

Its η _Ij(k) expression is selected the heuristic factor of k bar limit to layer j from layer i,

The optimum number of nodes of presentation layer i estimates that M and W are arbitrarily positive constants.

9. the node deployment method of the layering cooperation overlay model based on Energy Efficient according to claim 1, it is characterized in that described step 3) solving model has used the number of nodes constraint, if li layer guarded region area is Sli, wherein a is the node maximum communication distance, to guarantee that then the following of every node layer quantity is limited under the prerequisite of all standing:

If determined that the set of the layer of number of nodes is P, do not determine that the set of the layer of number of nodes is Q, be limited on the li node layer quantity:

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