CN104506576A - Wireless sensor network and node task migration method thereof - Google Patents

Abstract

The invention relates to a wireless sensor network and a node task migration method thereof. The wireless sensor network comprises a gateway node and a plurality of normal nodes. The wireless sensor network is characterized in that the gateway node and the normal nodes are linked in a wireless multi-hop way; power is supplied to the gateway node; no power is supplied to the normal nodes; and the normal nodes are distributed randomly, and are not moved once being distributed. The gateway node in the wireless sensor network is used for allocating interdependent sub-tasks based on a genetic algorithm, and an integer incentive system is introduced into an allocation algorithm, so that loads of the nodes are balanced, and the lifetime of the network is prolonged. Under the condition that the nodes are unstable, uncompleted tasks on unstable points can be migrated to other appropriate nodes, so that the tasks can be finished within a certain time limit; the task allocation efficiency is increased; and the task completion quality is enhanced.

Description

A kind of wireless sensor network and node tasks moving method thereof

Technical field

The invention belongs to wireless multimedia sensor network technical field, particularly a kind of node tasks moving method based on genetic algorithm and integration incentive mechanism.

Background technology

Along with wireless sensor network application requirement of real-time is more and more higher, it is the essential condition ensureing whole application real-time that the task that node distributes is successfully completed in time limit.But in wireless sensor network environment, radio node is easy to because of energy exhaustion or is subject to the attack of malicious node and lost efficacy, so when a node of executing the task is about to lose efficacy, or during death, how to find a kind of can fast, the low consumption task immigration method that success rate is high again, go to be very important by the task immigration on failure node to other nodes, this when respective nodes lost efficacy, can ensure the smooth execution of task.

Summary of the invention

Compared with prior art, gateway node of the present invention distributes complementary subtask based on genetic algorithm, and integration incentive mechanism is incorporated in allocation algorithm, thus balances each node load, extends network lifecycle; When node failure, by task immigration that failure node does not complete on other suitable nodes, can ensure that task completes in time limit, improve task matching efficiency and difficulty action accomplishment.Meanwhile, by Revised genetic algorithum, improve the space exploration ability of algorithm, accelerate evolutionary rate, the allocative decision of node can be obtained within a short period of time, improve the reaction time of wireless sensor network.

The invention provides a kind of wireless sensor network, comprise a gateway node and multiple ordinary node, it is characterized in that:

Described gateway node and ordinary node link in the mode of wireless multi-hop and form, and gateway node has supply of electric power, and ordinary node does not have supply of electric power, ordinary node random arrangement, once after arranging, just no longer move.

The invention still further relates to the method for allocating tasks based on genetic algorithm in a kind of wireless sensor network, it is characterized in that:

Step one, gateway node receive an application instruction, and the application in this instruction can be broken down into multiple complementary subtask, describes with DAG task image G=(T, E), the summit set T={T of DAG task image ₁, T ₂..., T _nrepresent, representative needs the subtask performed, and wherein n represents the number of subtask, and there is a time restriction deadline each subtask, and the execution of subtask must complete before the deadline specified, the limit E={E of DAG task image ₁, E ₂..., E _grepresent, represent the data dependence between subtask or control to rely on, wherein g represents the number on the limit of DAG task image, if from summit T _ito summit T _jthere is a directed edge E _ij, then subtask T is described _jexecution need subtask T _ioutput data; Gateway node adopts genetic algorithm manage the subtask in DAG task image and distribute, and concrete grammar is as follows:

(1) stochastic generation allocative decision and chromosome, builds chromosome congression S

Use S={C ₁, C ₂..., C _xrepresent all allocative decisions and chromosomal set, wherein C is a candidate scheme, and x is the number of all candidate schemes; Gateway node stochastic generation x allocative decision, each allocative decision is exactly a chromosome, and each chromosome 3 × n matrix C represents, n represents the total task number in DAG task image, (the T in Matrix C the first row ₁... T _i... T _n) be subtask to be allocated, its order from left to right determines according to tasks carrying order in DAG task image, Matrix C second row (V ₁... V _j... V _m) represent subtask map node, Matrix C the third line (ω ₁... ω _i... ω _n) representing the amount of calculation of subtask, chromosome Matrix C is as follows:

$C=\left[\begin{array}{ccccc}{T}_1& ...& T_i& ...& T_n\\ V_1& ...& Vj& ...& V_m\\ {\mathrm{\ω}}_1& ...& {\mathrm{\ω}}_i& ...& {\mathrm{\ω}}_n\end{array}\right]$

(2) communication matrix E is built

Data transmission relations between task 3 × g matrix E represents i.e. communication matrix, and g is the sum on the limit of DAG task image, each row first element T in matrix E _iexpression task transmit leg, second element T _jfor task recipient, the 3rd element l _ijfor task T _iand T _jbetween transmit the size of data, wherein row of communication matrix E are as follows:

$E=\left[\begin{array}{c}{T}_i\\ T_j\\ l_{\mathrm{ij}}\end{array}\right]$

(3) the total reward points of chromosome is calculated

The reward points that every bar chromosome produces refer to that gateway node is by certain chromosome C _kwhen carrying out task matching, complete the summation of the reward points paid needed for all subtasks in DAG task image:

${\mathrm{Point}}_{\mathrm{total}}^{C_k}=\underset{T_i\∈T,V_j\∈C_k}{\mathrm{\Σ}}{\mathrm{Point}}_{\mathrm{reward}}^{V_j}\left(T_i\right)$

Wherein, T _i∈ T represents all subtasks in DAG task image, V _j∈ C _krepresent chromosome C _kin involved all ordinary nodes, for node V _jfinish the work T _irequired reward points;

(4) the chromosome deadline is calculated

Chromosome deadline WT (C _k) refer to that gateway node is by certain chromosome C _kwhen carrying out task matching, complete the time span required for all subtasks in DAG task image;

(5) construct fitness function, chromosome performance is assessed

Fitness represents chromosomal quality, fitness is higher, this chromosome is more excellent, then chromosome survival probability is higher, chromosomal fitness is calculated by structure fitness function, the structure target of fitness function finds total reward points little, the chromosome that the deadline is short, and fitness function is as follows:

$\mathrm{fit}\left(C_k\right)=\frac{\mathrm{MIN}\_{\mathrm{Point}}_{\mathrm{total}}^S}{{\mathrm{Point}}_{\mathrm{total}}^{C_k}}+\mathrm{\β}\frac{\mathrm{MIN}\_\mathrm{WT}\left(S\right)}{\mathrm{WT}\left(C_k\right)}$

Wherein, fit (C _i) be chromosome C _ifitness, be the minimum value of total reward points in chromosome congression S, MIN_WT (S) is the minimum value of deadline in chromosome congression S, and β is customized parameter, regulates total reward points and the deadline weight in fitness function;

Calculate each chromosomal fitness, be stored in performance rate table by the fitness of chromosomal for x bar No. ID and correspondence and be used for classification and identifying, the descending by adaptive value in performance rate table sorts, and the chromosome that fitness is high comes the top of table;

(6) genetic manipulation is carried out to chromosome

1) operation is inherited

In performance rate table, before x chromosome, y% inherits in chromosome congression of future generation, and all the other x × (1-y%) bar chromosome produces through selection, intersection, variation step, and y% represents chromosomal excellent rate, wherein y ∈ [1-100];

2) operation is selected:

In performance rate table, select the interlace operation that two chromosomes carry out below, thus produce new chromosome, adopt the mode of roulette, chromosomal fitness is higher, higher by the probability selected;

3) interlace operation

Two the chromosome Matrix C selected ₁and C ₂as former generation's chromosome, interlace operation is to former generation's chromosome Matrix C ₁and C ₂carry out part restructuring, produce offspring's chromosome C ₃and C ₄, in interlace operation, chromosome matrix the first row remains unchanged, constant to ensure tasks carrying order, in former generation's chromosome Matrix C ₁and C ₂second row is selected a bit as crosspoint, Matrix C ₁and C ₂part behind second row crosspoint exchanges, thus produces offspring's chromosome Matrix C ₃and C ₄, calculate offspring's chromosome Matrix C ₃and C ₄fitness, and be stored in the relevant position of performance rate table;

4) mutation operation

Comprise two kinds of mutational formats, mode is a) sudden change of task based access control, and each chromosome has λ probability, by Stochastic choice duty mapping on another node; Or adopt mode b) based on chromosomal sudden change, the new chromosome that each chromosome has λ probability to be randomly generated is completely alternative, and wherein λ represents mutation rate, λ ∈ (0-1);

(7) said process is after the iterative operation of certain number of times, and gateway node selects the chromosome being in top as current allocative decision in performance rate table;

The ordinary node related in the allocative decision that step 2, gateway node are selected is active node, and active node obtains associated quad according to the nodal integration exchange rate and energy consumption of finishing the work;

Wherein, integration is used to the history performance that measurement node is finished the work, and for increasing the participation that node is executed the task, carry out quantizing examination with integration to the performance that node is finished the work, line item of going forward side by side, integration comprises reward points with punishment integration wherein T _i∈ T represents all subtasks in DAG task image; If active node is successfully completed task, corresponding reward points can be obtained finish the work if unsuccessful, can be deducted and punish integration accordingly the integration summation that active node is got up by accumulation of finishing the work is total mark total mark ordinary node V _jthe integration summation that accumulation of finishing the work is got up, namely all reward points sums deduct all punishment integration sums: ${\mathrm{Point}}_{\mathrm{wealth}}^{V_j}=\underset{T_s\∈V_j\left(T\right)}{\mathrm{\Σ}}{\mathrm{Point}}_{\mathrm{reward}}^{V_j}\left(T_s\right)-\underset{T_s\∈V_j\left(T\right)}{\mathrm{\Σ}}{\mathrm{Point}}_{\mathrm{punish}}^{V_j}\left(T_f\right),$ Wherein, V _j(T) be ordinary node V _jall tasks of upper distribution, T _sordinary node V _jon the task of being successfully completed, T _fordinary node V _jupper unsuccessful completing of task;

The described integration exchange rate is that node consumption unit energy should obtain integration, and it embodies the cost performance that node is finished the work, and uses represent, wherein for ordinary node V _jdump energy, for node V _jtotal mark;

If step 3 active node itself fail or be subject to other malicious nodes attack, it is then unstable node, if this unstable node has not complete task, need the cooperation being exchanged for other ordinary nodes by the mode of redeem points, thus by task immigration on other ordinary nodes;

If step 4 active node is due to energy exhaustion or communication link fails can not communicate with other node, then this active node is death nodes, gateway node can find death nodes immediately, and not finishing the work on death nodes is re-assigned on other ordinary nodes.

Accompanying drawing explanation

Fig. 1 is wireless sensor network structure chart;

Fig. 2 is gateway node allocating task flow chart;

Fig. 3 is ordinary node task immigration flow chart.

Embodiment

Below in conjunction with the drawings and specific embodiments, the present invention is further detailed explanation, is not practical range of the present invention is confined to this.As shown in Figure 1:

Wireless sensor network generally includes a gateway node and some ordinary nodes, is formed by wireless multi-hop link.Gateway node disposal ability is powerful, has enough supplies of electric power.Ordinary node finite energy, does not have supply of electric power.Ordinary node is isomery, shows as processing speed difference, and node energy is different, but transmission characteristic is thought identical.Ordinary node random arrangement, once after arranging, just no longer moves.

Fig. 2,3 shows the method for allocating tasks based on genetic algorithm and integration incentive mechanism based on above-mentioned wireless sensor network, wherein gateway node exchanges the service of ordinary node for by being supplied to ordinary node reward on total mark, each subtask is made to be assigned on most suitable node, under the prerequisite meeting time limit task time, total number of points that gateway node is spent is minimum, thus complete all subtasks, specifically comprise following steps:

Step one, gateway node receive an application instruction, and the application in this instruction can be broken down into multiple complementary subtask, describes with directed acyclic graph DAG task image G=(T, E), the summit set T={T of DAG task image ₁, T ₂..., T _nrepresent, representative needs the subtask performed, and n represents the number of subtask, and there is a time restriction deadline each subtask, and the execution of subtask must complete before the deadline specified, the limit E={E of DAG task image ₁, E ₂..., E _grepresent, represent the data dependence between subtask or control to rely on, g represents the number on the limit of DAG task image, if from summit T _ito summit T _jthere is a directed edge E _ij, then subtask T is described _jexecution need subtask T _ioutput data; Gateway node adopt genetic algorithm the subtask in DAG task image is managed and distributes, concrete task management and distribution method as follows:

(1) stochastic generation allocative decision and chromosome, builds chromosome congression S

Use S={C ₁, C ₂..., C _xrepresent all allocative decisions and chromosomal set, wherein C is a candidate scheme, and x is the number of all candidate schemes; Gateway node stochastic generation x allocative decision, each allocative decision is exactly a chromosome, and each chromosome 3 × n matrix C represents, n represents the total task number in DAG task image, (the T in Matrix C the first row ₁... T _i... T _n) representing subtask to be allocated, its order from left to right determines according to tasks carrying order in DAG task image, Matrix C second row (V ₁... V _j... V _m) represent subtask map node, Matrix C the third line (ω ₁... ω _i... ω _n) representing the amount of calculation of subtask, chromosome Matrix C is as follows:

$C=\left[\begin{array}{ccccc}{T}_1& ...& T_i& ...& T_n\\ V_1& ...& Vj& ...& V_m\\ {\mathrm{\ω}}_1& ...& {\mathrm{\ω}}_i& ...& {\mathrm{\ω}}_n\end{array}\right]$

(2) communication matrix E is built

Data transmission relations between task 3 × g matrix E represents i.e. communication matrix, and g is the sum on the limit of DAG task image, each row first element T in matrix E _iexpression task transmit leg, second element T _jfor task recipient, the 3rd element l _ijfor task T _iand T _jbetween transmit the size of data, a certain row of communication matrix E are as follows:

$E=\left[\begin{array}{c}{T}_i\\ T_j\\ l_{\mathrm{ij}}\end{array}\right]$

(2) the total reward points of chromosome is calculated

The reward points that every bar chromosome produces refer to that gateway node is by certain chromosome C _kwhen carrying out task matching, complete the summation of the reward points paid needed for all subtasks in DAG task image:

${\mathrm{Point}}_{\mathrm{total}}^{C_k}=\underset{T_i\∈T,V_j\∈C_k}{\mathrm{\Σ}}{\mathrm{Point}}_{\mathrm{reward}}^{V_j}\left(T_i\right)$

Wherein, T _i∈ T represents all subtasks in DAG task image, V _j∈ C _krepresent chromosome C _kin involved all ordinary nodes. for node V _jfinish the work T _irequired reward points;

(3) the chromosome deadline is calculated

Chromosome deadline WT (C _k) refer to that gateway node is by certain chromosome C _kwhen carrying out task matching, complete the time span required for all subtasks in DAG task image.

(4) construct fitness function, chromosome performance is assessed

Fitness represents chromosomal quality, fitness is higher, this chromosome is more excellent, then chromosome survival probability is higher, chromosomal fitness is calculated by structure fitness function, the structure target of fitness function finds total reward points little, the chromosome that the deadline is short, and fitness function is as follows:

$\mathrm{fit}\left(C_k\right)=\frac{\mathrm{MIN}\_{\mathrm{Point}}_{\mathrm{total}}^S}{{\mathrm{Point}}_{\mathrm{total}}^{C_k}}+\mathrm{\β}\frac{\mathrm{MIN}\_\mathrm{WT}\left(S\right)}{\mathrm{WT}\left(C_k\right)}$

Wherein, fit (C _i) be chromosome C _ifitness, be the minimum value of total reward points in chromosome congression S, MIN_WT (S) is the minimum value of deadline in chromosome congression S, and β is customized parameter, regulates total reward points and the deadline weight in fitness function.

Calculate each chromosomal fitness, be stored in performance rate table by the fitness of chromosomal for x bar No. ID and correspondence and be used for classification and identifying, the descending by adaptive value in performance rate table sorts, and the chromosome that fitness is high comes the top of table;

(5) genetic manipulation is carried out to chromosome

1) operation is inherited

In performance rate table, before x chromosome, y% inherits in chromosome congression of future generation, and all the other x × (1-y%) bar chromosome produces through selection, intersection, variation step, and y% represents chromosomal excellent rate, wherein y ∈ [1-100];

2) operation is selected:

In performance rate table, select the interlace operation that two chromosomes carry out below, thus produce new chromosome, adopt the mode of roulette, chromosomal fitness is higher, higher by the probability selected;

3) interlace operation

Two the chromosome Matrix C selected ₁and C ₂as former generation's chromosome, interlace operation is to former generation's chromosome Matrix C ₁and C ₂carry out part restructuring, produce offspring's chromosome C ₃and C ₄, in interlace operation, chromosome matrix the first row remains unchanged, constant to ensure tasks carrying order, in former generation's chromosome Matrix C ₁and C ₂second row is selected a bit as crosspoint, Matrix C ₁and C ₂part behind second row crosspoint exchanges, thus produces offspring's chromosome Matrix C ₃and C ₄, calculate offspring's chromosome Matrix C ₃and C ₄fitness, and be stored in the relevant position of performance rate table;

4) mutation operation

Comprise two kinds of mutational formats, mode is a) sudden change of task based access control, and each chromosome has λ probability, by Stochastic choice duty mapping on another node; Or adopt mode b) based on chromosomal sudden change, the new chromosome that each chromosome has λ probability to be randomly generated is completely alternative, and wherein λ represents mutation rate, λ ∈ (0-1);

(5) said process is after the iterative operation of certain number of times, and gateway node selects the chromosome being in top as current allocative decision in performance rate table;

The ordinary node related in the allocative decision that step 2, gateway node are selected is active node, and active node, according to the nodal integration exchange rate and energy consumption of finishing the work, obtains associated quad; The described integration exchange rate is used represent, be that node consumption unit energy should obtain integration, embody the cost performance that node is finished the work; Described integration is used to the history performance that measurement node is finished the work, and can increase the participation that node is executed the task, carry out quantizing examination, line item of going forward side by side with integration to the performance that node is finished the work, and be divided into reward points and punishment integration, described reward points is used represent, punishment integration is used represent; if the task of being successfully completed; corresponding reward points can be obtained; if finish the work unsuccessful, can be deducted and punish integration accordingly, described reward points refers to that node can obtain corresponding integration after being successfully completed task; described punishment integration refers to that node is unsuccessful and finishes the work; deduct corresponding integration, the integration summation that node is got up by accumulation of finishing the work is called total mark, uses represent;

The detailed process obtaining integration is as follows:

First, ordinary node V _jfinish the work T in mandatory period deadline _i, think ordinary node V _jbe successfully completed task T _i, ordinary node V _jautomatic acquisition individual reward points, and upgrade ordinary node V _jdump energy, total mark and the integration exchange rate, return to gateway node together with task result.Gateway node upgrades the dump energy of this ordinary node in nodal integration table, total mark and the integration exchange rate;

Secondly, ordinary node V _jfail to finish the work T in mandatory period deadline _i, think ordinary node V _jfail the T that finishes the work _i, ordinary node V _jautomatically deduct in original total mark individual punishment integration, ordinary node V _jupgrade dump energy, total mark and the integration exchange rate, send gateway node to by regular reporting, gateway node upgrades the dump energy of this ordinary node in nodal integration table, total mark and the integration exchange rate;

The concrete computational process of integration is as follows:

First, the computing node integration exchange rate represent ordinary node V _joften consume the energy of a joule, the reward points that should obtain, relevant with residue energy of node, node total mark, the nodal integration exchange rate is expressed as: ${\mathrm{Point}}_{\mathrm{rate}}^{V_j}=\frac{1}{E_{\mathrm{residual}}^{V_j}}\×{\mathrm{Point}}_{\mathrm{wealth}}^{V_j},$ Wherein for ordinary node V _jdump energy, for node V _jtotal mark;

Secondly, computing node is finished the work energy consumption, comprises and calculates energy consumption and communication energy consumption;

$C_{\mathrm{total}}^{V_j}\left(T_i\right)=C_{\mathrm{comp}}^{V_j}\left(T_i\right)+C_{\mathrm{comm}}^{V_{\text{j}}}\left(T_i\right);$

Wherein, expression task T _iat ordinary node V _jon wastage in bulk or weight, expression task T _iat ordinary node V _jon calculating consumption, expression task T _iat ordinary node V _jon communication consumption;

Calculate and consume: represent ordinary node V _javerage energy consumption, expression task T _iat ordinary node V _jon time of implementation, expression task T _iamount of calculation, represent ordinary node V _jexecution speed;

Communication consumes: wherein, represent ordinary node V _jfor the T that finishes the work _ienergy consumption needed for transmission packet, represent ordinary node V _jfor the T that finishes the work _ireceive energy consumption needed for packet.

$C_{\mathrm{tran}}^{V_j}\left(T_i\right)=\left\{\begin{array}{c}({\mathrm{\&xi;}}_{\mathrm{elec}}+{\mathrm{\&xi;}}_{\mathrm{fs}}*d^2)*l,d<d_0\\ ({\mathrm{\&xi;}}_{\mathrm{elec}}+{\mathrm{\&xi;}}_{\mathrm{mp}}*d^4)*l,d\&GreaterEqual;d_0\end{array}\right.$

$C_{\mathrm{rece}}^{V_j}\left(T_i\right)={\mathrm{\&xi;}}_{\mathrm{elec}}*l$

L represents the data package size of transmission, and d represents the distance of sending node and receiving node, ξ _elec, ξ _fs, ξ _mphardware-related parameter, d ₀for preset parameter;

Again, node tasks reward points i.e. ordinary node V _jbe successfully completed task T _ithe integration that should obtain is:

${\mathrm{Point}}_{\mathrm{teward}}^{V_j}\left(T_i\right)={\mathrm{point}}_{\mathrm{rate}}^{V_j}\&times;C_{\mathrm{total}}^{V_j}\left(T_i\right)$

Wherein for ordinary node V _jthe integration exchange rate, for ordinary node V _jfinish the work T _itotal power consumption;

Again, node tasks punishment integration i.e. ordinary node V _jthe unsuccessful T that finishes the work _ithe integration that should deduct is:

${\mathrm{Point}}_{\mathrm{punish}}^{V_j}\left(T_i\right)=\mathrm{\&beta;}\&times;{\mathrm{Point}}_{\mathrm{reward}}^{V_j}\left(T_i\right)$

Wherein β is customized parameter;

Finally, computing node total mark ordinary node V _jthe integration summation that accumulation of finishing the work is got up, namely all reward points sums deduct all punishment integration sums;

${\mathrm{Point}}_{\mathrm{wealth}}^{V_j}=\underset{T_s\&Element;V_j\left(T\right)}{\mathrm{\&Sigma;}}{\mathrm{Point}}_{\mathrm{reward}}^{V_j}\left(T_s\right)-\underset{T_s\&Element;V_j\left(T\right)}{\mathrm{\&Sigma;}}{\mathrm{Point}}_{\mathrm{punish}}^{V_j}\left(T_f\right)$

Wherein, V _j(T) be ordinary node V _jall tasks of upper distribution, T _sordinary node V _jon the task of being successfully completed, T _fordinary node V _jupper unsuccessful completing of task;

If step 3 active node itself fail or be subject to other malicious nodes attack, namely unstable node is thought, if unstable node has not complete task, need the cooperation being exchanged for other ordinary nodes by the mode of redeem points, thus by task immigration on other ordinary nodes;

The process that unstable node redeem points seeks the cooperation of other ordinary nodes adopts auction mechanism, and improve auction formats, specific tasks transition process is as follows:

First, executing the task when the ordinary node discovery oneself of body is for unstable node when having, can not continue to perform the task of having distributed, so initiate the successor node that task is found in an auction, unstable node V _failurerepresent, the task T that unstable node does not complete _failurerepresent; Unstable node V _failuretender Tender (T is sent to other ordinary nodes as bid node _failure, deadline, Point _budget), include task description in bidding documents, mandatory period, and this node is the maximum budget Point that this task is paid _budget, maximum budget equals the total mark of this node

Secondly, when ordinary node receives tender Tender (T _failure, deadline, Point _budget) after, just according to the requirement in tender, own situation is passed judgment on, determine whether participating in competitive bidding, if do not participate in just refusing action, if participate in just providing quotation according to own situation; Determine that the process whether participating in competitive bidding is as follows:

First competitive bidding node considers time limit task time factor, if competitive bidding node has had original task of distributing, and the Estimated Time Of Completion of new task is greater than official hour time limit deadline in bidding documents, then do not participate in competitive bidding;

If competitive bidding node Estimated Time Of Completion is less than or equal to official hour time limit deadline in bidding documents, then calculate this task integration price ${\mathrm{Point}}_{\mathrm{price}}^{V_j}\left(T_{\mathrm{failure}}\right)={\mathrm{Point}}_{\mathrm{rate}}^{V_j}\&times;C_{\mathrm{total}}^{V_j}\left(T_{\mathrm{failure}}\right),$ Wherein for competitive bidding node V _jthe integration exchange rate, for competitive bidding node V _jfinish the work T _failuretotal power consumption;

If the integration price that competitive bidding node is finished the work is greater than the budget in bidding documents, namely then this competitive bidding node does not participate in competitive bidding,

If the integration price that competitive bidding node is finished the work is less than or equal to the budget in bidding documents, namely ${\mathrm{Point}}_{\mathrm{price}}^{V_j}\left(T_{\mathrm{failure}}\right)\&le;{\mathrm{Point}}_{\mathrm{budget}},$ Then this competitive bidding node can participate in competitive bidding, and as tender price;

Again, the competitive bidding node that bid sensor selection problem tender price is minimum is as acceptance of the bid node.After acceptance of the bid node is determined, bid node V _failureby task T _failureacceptance of the bid node is given in migration, deducts in total mark individual integration.And report Report using the total mark after renewal, the integration exchange rate, acceptance of the bid node, migration mission bit stream as accident _emergencybe uploaded to gateway node;

Again, gateway node upgrades nodal integration table again;

Again, acceptance of the bid node is executed the task, and obtains after being successfully completed task individual reward points, and task result is returned to gateway node;

Finally, if without ordinary node acceptance of the bid, unstable node V _failureby task T _failureas accident report Report _emergencybe uploaded to gateway node, by gateway node by task T _failureassignment of allocation on other ordinary nodes, unstable node V _failurededuction punishes integration accordingly;

If step 4 active node is due to energy exhaustion or communication link fails can not communicate with other ordinary node, then this active node is called death nodes, gateway node can find death nodes immediately, and is re-assigned on other ordinary nodes by not finishing the work on death nodes;

Detailed process is as follows:

First, ordinary node needs regularly to send regular reporting Report to gateway node node _periodic, comprise dump energy, total mark, integration exchange rate information, gateway node is according to regular reporting Report _periodic, regular update nodal integration table;

Secondly, if at the appointed time, the regular reporting Report of certain ordinary node is not received _periodic, do not receive the accident report Report of this ordinary node yet _emergency, gateway node assert that this ordinary node is dead;

Finally, gateway node judges whether this death nodes there is not completing of task, if do not had, just directly upgrades nodal integration table, is deleted by this node from table; If there is not completing of task, after first this not being finished the work and is redistributed, then upgrade nodal integration table.

Above execution mode is only for illustration of the present invention; and be not limitation of the present invention; person skilled in the relevant technique; when not departing from the inventive method and scope; can also make a variety of changes; therefore all equivalent technical schemes also belong to category of the present invention, and scope of patent protection of the present invention should be defined by the claims.

Claims (2)

1. a wireless sensor network, comprises a gateway node and multiple ordinary node, it is characterized in that:

Described gateway node and ordinary node link in the mode of wireless multi-hop and form, and gateway node has supply of electric power, and ordinary node does not have supply of electric power, ordinary node random arrangement, once after arranging, just no longer move.

2. in wireless sensor network as claimed in claim 1 based on a task immigration method for genetic algorithm and integration incentive mechanism, it is characterized in that:

Step one, gateway node receive an application instruction, and the application in this instruction can be broken down into multiple complementary subtask, describes with DAG task image G=(T, E), the summit set T={T of DAG task image ₁, T ₂..., T _nrepresent, representative needs the subtask performed, and wherein n represents the number of subtask, and there is a time restriction deadline each subtask, and the execution of subtask must complete before the deadline specified, the limit E={E of DAG task image ₁, E ₂..., E _grepresent, represent the data dependence between subtask or control to rely on, wherein g represents the number on the limit of DAG task image, if from summit T _ito summit T _jthere is a directed edge E _ij, then subtask T is described _jexecution need subtask T _ioutput data; Gateway node adopts genetic algorithm manage the subtask in DAG task image and distribute, and concrete grammar is as follows:

(1) stochastic generation allocative decision and chromosome, builds chromosome congression S

Use S={C ₁, C ₂..., C _xrepresent all allocative decisions and chromosomal set, wherein C is a candidate scheme, and x is the number of all candidate schemes; Gateway node stochastic generation x allocative decision, each allocative decision is exactly a chromosome, and each chromosome 3 × n matrix C represents, n represents the total task number in DAG task image, (the T in Matrix C the first row ₁... T _i... T _n) be subtask to be allocated, its order from left to right determines according to tasks carrying order in DAG task image, Matrix C second row (V ₁... V _j... V _m) represent subtask map node, Matrix C the third line (ω ₁... ω _i... ω _n) representing the amount of calculation of subtask, chromosome Matrix C is as follows:

$C=\left[\begin{array}{ccccc}{T}_1& ...& T_i& ...& T_n\\ V_1& ...& V_j& ...& V_m\\ {\mathrm{\&omega;}}_1& ...& {\mathrm{\&omega;}}_i& ...& {\mathrm{\&omega;}}_n\end{array}\right]$

(2) communication matrix E is built

Data transmission relations between task 3 × g matrix E represents i.e. communication matrix, and g is the sum on the limit of DAG task image, each row first element T in matrix E _iexpression task transmit leg, second element T _jfor task recipient, the 3rd element l _ijfor task T _iand T _jbetween transmit the size of data, wherein row of communication matrix E are as follows:

$E=\left[\begin{array}{c}{T}_i\\ T_j\\ l_{\mathrm{ij}}\end{array}\right]$

(3) the total reward points of chromosome is calculated

The reward points that every bar chromosome produces refer to that gateway node is by certain chromosome C _kwhen carrying out task matching, complete the summation of the reward points paid needed for all subtasks in DAG task image:

${\mathrm{Point}}_{\mathrm{total}}^{C_k}=\underset{T_i\&Element;T,V_j\&Element;C_k}{\mathrm{\&Sigma;}}{\mathrm{Point}}_{\mathrm{reward}}^{V_j}\left(T_i\right)$

Wherein, T _i∈ T represents all subtasks in DAG task image, V _j∈ C _krepresent chromosome C _kin involved all ordinary nodes, for node V _jfinish the work T _irequired reward points;

(4) the chromosome deadline is calculated

Chromosome deadline WT (C _k) refer to that gateway node is by certain chromosome C _kwhen carrying out task matching, complete the time span required for all subtasks in DAG task image;

(5) construct fitness function, chromosome performance is assessed

Fitness represents chromosomal quality, fitness is higher, this chromosome is more excellent, then chromosome survival probability is higher, chromosomal fitness is calculated by structure fitness function, the structure target of fitness function finds total reward points little, the chromosome that the deadline is short, and fitness function is as follows:

$\mathrm{fit}\left(C_k\right)=\frac{\mathrm{MIN}\_{\mathrm{Point}}_{\mathrm{total}}^S}{{\mathrm{Point}}_{\mathrm{total}}^{C_k}}+\mathrm{\&beta;}\frac{\mathrm{MIN}\_\mathrm{WT}\left(S\right)}{\mathrm{WT}\left(C_k\right)}$

Wherein, fit (C _i) be chromosome C _ifitness, be the minimum value of total reward points in chromosome congression S, MIN_WT (S) is the minimum value of deadline in chromosome congression S, and β is customized parameter, regulates total reward points and the deadline weight in fitness function;

Calculate each chromosomal fitness, be stored in performance rate table by the fitness of chromosomal for x bar No. ID and correspondence and be used for classification and identifying, the descending by adaptive value in performance rate table sorts, and the chromosome that fitness is high comes the top of table;

(6) genetic manipulation is carried out to chromosome

1) operation is inherited

In performance rate table, before x chromosome, y% inherits in chromosome congression of future generation, and all the other x × (1-y%) bar chromosome produces through selection, intersection, variation step, and y% represents chromosomal excellent rate, wherein y ∈ [1-100];

2) operation is selected:

In performance rate table, select the interlace operation that two chromosomes carry out below, thus produce new chromosome, adopt the mode of roulette, chromosomal fitness is higher, higher by the probability selected;

3) interlace operation

Two the chromosome Matrix C selected ₁and C ₂as former generation's chromosome, interlace operation is to former generation's chromosome Matrix C ₁and C ₂carry out part restructuring, produce offspring's chromosome C ₃and C ₄, in interlace operation, chromosome matrix the first row remains unchanged, constant to ensure tasks carrying order, in former generation's chromosome Matrix C ₁and C ₂second row is selected a bit as crosspoint, Matrix C ₁and C ₂part behind second row crosspoint exchanges, thus produces offspring's chromosome Matrix C ₃and C ₄, calculate offspring's chromosome Matrix C ₃and C ₄fitness, and be stored in the relevant position of performance rate table;

4) mutation operation

Comprise two kinds of mutational formats, mode is a) sudden change of task based access control, and each chromosome has λ probability, by Stochastic choice duty mapping on another node; Or adopt mode b) based on chromosomal sudden change, the new chromosome that each chromosome has λ probability to be randomly generated is completely alternative, and wherein λ represents mutation rate, λ ∈ (0-1);

(7) said process is after the iterative operation of certain number of times, and gateway node selects the chromosome being in top as current allocative decision in performance rate table;

The ordinary node related in the allocative decision that step 2, gateway node are selected is active node, and active node obtains associated quad according to the nodal integration exchange rate and energy consumption of finishing the work;

Wherein, integration is used to the history performance that measurement node is finished the work, and for increasing the participation that node is executed the task, carry out quantizing examination with integration to the performance that node is finished the work, line item of going forward side by side, integration comprises reward points with punishment integration wherein T _i∈ T represents all subtasks in DAG task image; If active node is successfully completed task, corresponding reward points can be obtained finish the work if unsuccessful, can be deducted and punish integration accordingly the integration summation that active node is got up by accumulation of finishing the work is total mark total mark ordinary node V _jthe integration summation that accumulation of finishing the work is got up, namely all reward points sums deduct all punishment integration sums: ${\mathrm{Point}}_{\mathrm{wealth}}^{V_j}=\underset{T_s\&Element;V_j\left(T\right)}{\mathrm{\&Sigma;}}{\mathrm{Point}}_{\mathrm{reward}}^{V_j}\left(T_s\right)-\underset{T_s\&Element;V_j\left(T\right)}{\mathrm{\&Sigma;}}{\mathrm{Point}}_{\mathrm{punish}}^{V_j}\left(T_f\right),$ Wherein, V _j(T) be ordinary node V _jall tasks of upper distribution, T _sordinary node V _jon the task of being successfully completed, T _fordinary node V _jupper unsuccessful completing of task;

The described integration exchange rate is that node consumption unit energy should obtain integration, and it embodies the cost performance that node is finished the work, and uses ${\mathrm{Point}}_{\mathrm{rate}}^{V_j}=\frac{1}{E_{\mathrm{residual}}^{V_j}}\&times;{\mathrm{Point}}_{\mathrm{wealth}}^{V_j}$ Represent, wherein for ordinary node V _jdump energy, for node V _jtotal mark;

If step 3 active node itself fail or be subject to other malicious nodes attack, it is then unstable node, if this unstable node has not complete task, need the cooperation being exchanged for other ordinary nodes by the mode of redeem points, thus by task immigration on other ordinary nodes;

If step 4 active node is due to energy exhaustion or communication link fails can not communicate with other node, then this active node is death nodes, gateway node can find death nodes immediately, and not finishing the work on death nodes is re-assigned on other ordinary nodes.