CN105933397B - The improvement distribution auction Ik-SAAP method that task is distributed in WSAN - Google Patents
The improvement distribution auction Ik-SAAP method that task is distributed in WSAN Download PDFInfo
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Abstract
The invention discloses the improvement distribution auction Ik-SAAP algorithms that task in a kind of WSAN is distributed, comprising the following steps: 1) establishes the mathematical model of the task distribution of actuator cooperation；2) initialization network parameter；3) decision node is chosen, auction node is initiated；4) leapfrog number is set；5) value of utility of auction node is calculated, and passes back to decision node；6) judge whether decision node meets the condition of best actuator node, if not satisfied, then return step 4), if satisfied, subtask distribution terminates；7) selected actuator node executes current task;8) Ik-SAAP algorithm is executed 200 times, emulation obtains data packet forwarding number comparison diagram, dump energy variance comparison diagram, average task completion time comparison diagram.This method can reduce the quantity of actuator node data packet forwarding under the premise of guaranteeing that task apportionment ratio is high, shorten task completion time, balanced WSAN network energy consumption.
Description
Technical field
The present invention relates to distributed information processing, cooperative cooperating technology, intelligent Computation Technology fields, are especially
Task Allocation Problem in WSAN in actuator and actuator cooperation, belongs to sensor network technology field.
Background technique
Wireless sensor and actor network (Wireless Sensor And Actor Networks, WSAN) is by a large amount of
Sensor node and the actuator node composition that moves on a small quantity, they are by interconnected with wireless network, for perceiving real world,
Perception data is handled, and carries out decision and action when event occurs.Wherein collaborative and real-time are that WSAN should meet
Two unique requirements, wherein between actuator and actuator cooperate (Actor to Actor Coordination) main mesh
Be completion task effective distribution.Wireless channel transmission data can consume big energy, therefore when having task in WSAN
When, it selects suitable actuator node that can reduce communication energy consumption, improves response speed, to the real-time and energy for guaranteeing network
Harmony, extend network life have important meaning.
For the Task Allocation Problem in WSAN, forefathers have proposed some schemes, are broadly divided into centralized and distributed
Algorithm.The advantages of centralized algorithm, which refers to, to be collected the information of all nodes by a central node and then carries out decision, this scheme
It is that can select optimal actuator node, still, this method communication overhead is big, Shi Yanchang, and error rate is high.
Distributed-solution is to carry out decision using local nodes information, and the quantity for generating data packet reduces, entirely
The stability of network improves.Tommaso Melodia et al. proposes distributed auction algorithm to solve between actuator node
Cooperation problem, but the algorithm is only applicable to the actuator node of overlapping region, and the actuator node in Non-overlapping Domain can be
The event in overlapping region can just be made a response after executing existing task, therefore shadow will necessarily be caused to the real-time of network
It rings.Ivan Mezei et al. proposes k-SAAP (k-hop Simple Auction Aggregation Protocol) algorithm,
K-SAAP obtains the information of local k-hop neighborhood by one response tree of construction, to determine to be executed by which actuator node
Current task, this algorithm do not account for the state of actuator node, can make that data packet is forwarded between actuator node
Number increases, load imbalance between actuator node, and network life shortens.It can guarantee real-time by the improvement to algorithm
On the basis of reduce data packet forward number, improve task apportionment ratio, reduce communication overhead, the energy consumption of uniform network.
Summary of the invention
Technical problem to be solved by the invention is to provide the task allocation algorithms to cooperate between actuator in a kind of WSAN,
Data packet is reduced on the basis of guaranteeing real-time and forwards number, is improved task apportionment ratio, is reduced communication overhead, the energy of uniform network
Consumption.
In order to solve the above technical problems, the present invention provides a kind of improvement distribution auction Ik- of task distribution in WSAN
SAAP algorithm, comprising the following steps:
1) mathematical model of the task distribution of actuator cooperation is established；
2) initialization network parameter；
Initialization network parameter includes: initial leapfrog number k；What network range, each task assignment procedure were randomly generated appoints
Be engaged in number, the number of actuator node, actuator node executes maximum task number, actuator section in a task assignment procedure
The communication radius of point communication radius, sensor node；The movement speed of actuator node；
3) decision node is chosen；The probability that one actuator node is selected as decision node is Pi, can be counted according to formula (1)
Calculate its probability:
D=1/d1+1/d2+...+1/dn
Pi=1/ (D*di) (1)
Wherein diIt is actuator node i with a distance from task generation, n is the number of actuator node in network；dnIt is n-th
A node is with a distance from task generation.
4) generate auction node tree: initial leapfrog number k adds 1, according to the quantity of current leapfrog number limitation auction node；
5) it calculates auction node value of utility and returns, the actuator node for receiving mission bit stream message marks oneself state
After starting state, calculates value of utility and this value is back to decision node；
6) the decision node decision phase carries out analysis comparison to the utility information message of return, selects best actuator section
Point goes to execute current task.And each actuator node is sent along spanning tree by decision information decision information；
7) selected actuator node executes current task；
8) Ik-SAAP algorithm is executed 200 times, emulation obtains data packet forwarding number comparison diagram, dump energy variance pair
Than figure, average task completion time comparison diagram.
Above-mentioned steps 1) in, the foundation that task distributes mathematical model includes following 2 steps:
11) Task Allocation Problem describes: having n in assigning processtA task, naA actuator node.Each task has oneself
DurationWith deadline dj；Each actuator node has NiA available time slot.Guarantee task is complete within deadline
Under the premise of, a task can only be completed by an actuator node, and can be only done a task in each time slot；
12) guarantee that the reasonable distribution of task will meet following constraint condition:
Wherein fijValue is 1 or 0, and task T is indicated when being 1jBy actuator node AiIt executes.
Above-mentioned steps 11) in, Task Allocation Problem will meet the following conditions:
111) sensor node is static, and actuator node can move and have data calculating and analysis ability；
112) sensor node all in network is synchronous with actuator node clock；
113) actuator node can be divided into two classes: decision node and auction node, and all there are four types of states for both nodes.
The state demarcation of decision node is Idle state, initial state, waiting state, decision state；Competitive bidding node state divides are as follows: Idle state opens
Dynamically, waiting state, execution state；
114) research environment is the Cluster Networks structure using actuator node as cluster head, has task when monitoring in region
When, report request can be sent to the actuator node of this cluster by sensor node.
Above-mentioned steps 2) in, initialization network parameter are as follows: initial leapfrog number k=0；Network range be 500m*500m just
Square region；40 tasks are randomly generated in each task assignment procedure；The number of actuator node is 10；One subtask was distributed
It is 5 that actuator node, which executes maximum task number, in journey；The communication radius of actuator node is 50m；Sensor node leads to
News radius is 10m；The movement speed of actuator node is 10m/s.
Above-mentioned steps 3) in, there are two types of states for decision node:
31) it is Idle state that not selected actuator node, which marks the state of itself,；
32) auction message is added to surrounding broadcast in the actuator node for being selected as decision node, and oneself state is marked
For initial state.
Above-mentioned steps 4) in, generate auction node tree the following steps are included:
41) auction node tree is generated by root node of decision node, and successively passes to child node for auction message is added；
42) actuator node for participating in auction message is received by oneself state labeled as starting state；
43) labeled as dynamic actuator node is opened, mission bit stream message will be received along spanning tree and successively return to decision
Node；
44) mission bit stream is successively transmitted to every height section by the decision node for receiving reception mission bit stream message along spanning tree
Point, and be waiting state by oneself state information flag, wait the passback of actuator node value of utility.
Above-mentioned steps 5) in, the calculating and passback of value of utility the following steps are included:
51) determination of utility function, utility function need to weigh the dump energy (A of actuator nodeej), actuator node
(d at a distance from the region j and event iij), in actuator node task queue task quantity (Asj), the movement of actuator node
Speed (vj) this four parameters.The value of utility that actuator node executes corresponding task can be calculated according to formula (5).
aij=α1vj+α2Aej-α3Asj-α4dij (5)
Wherein parameter alphai, i=1,2,3,4 be a constant and can reflect out this four parameters determine competitive bidding task
Marked price value when significance level；
52) oneself state is labeled as decision state by the decision node for receiving utility information message, and will receive utility information
Message is sent to every sub- actuator node along spanning tree；
53) actuator node for receiving utility information message is received by oneself state labeled as waiting state, waits decision section
The decision information of point.
Above-mentioned steps 6) in, the decision phase includes following two situation:
If 61) have suitable actuator node in auction node tree, decision information message is sent to this by decision node
Actuator node；
If 62) return to step 5) without suitable actuator node in auction node tree.
Above-mentioned steps 61) in, there are two types of states for the actuator node in auction node tree:
If 611) best actuator node, then oneself state is labeled as execution state by the actuator node, and will be received
Decision information message is sent to decision node along spanning tree, and oneself state is labeled as Idle state by decision node；
612) if not best actuator node, then oneself state is labeled as Idle state by the actuator node, and will be connect
It receives decision information message and is sent to decision node along spanning tree, oneself state is labeled as Idle state by decision node.
The beneficial effects obtained by the present invention are as follows as follows:
1. the present invention improves on the basis of k-SAAP algorithm, compared with k-SAAP algorithm, in the initial modelling phase
The duration and deadline of each task are set, and task will be completed before deadline as constraint condition,
Meet requirement of the WSAN network to real-time；
2. the thought for being stepped up leapfrog number is introduced into the generation phase of auction node tree by the present invention, leapfrog number gradually
Best actuator node is found during increased and executes task, if it is found, then leapfrog number is not further added by, this method is reduced
Total leapfrog number reduces the quantity of data packet forwarding, shortens network delay, reduce energy consumption；
3. the present invention considers task quantity in actuator node task queue in decision process, and as utility function
A dimension, this method can load between efficient balance actuator node and network energy consumption, extend network life.
Detailed description of the invention
Fig. 1 Ik-SAAP algorithm flow chart；
Fig. 2 task apportionment ratio comparison diagram；
Fig. 3 data packet forwards number comparison diagram；
Fig. 4 actuator node dump energy variance comparison diagram；
Fig. 5 is averaged task completion time comparison diagram.
Specific embodiment
The present invention will be further explained with reference to the accompanying drawing.
System block diagram of the invention is as shown in Figure 1.The improvement distribution auction Ik- of task distribution in a kind of WSAN of the present invention
SAAP algorithm, including following part: establishing the mathematical model of task distribution；Initialization network parameter；Choose decision node；It generates
Auction node tree；It calculates auction node value of utility and returns；Decision node decision；Task distribution terminates, selected actuator
Node executes current task.
Step 1 establishes the mathematical model of the task distribution of actuator cooperation；
The foundation that task distributes mathematical model includes following 2 steps:
11) Task Allocation Problem describes: having n in assigning processtA task, naA actuator node；Each task has oneself
DurationWith deadline dj；Each actuator node has NiA available time slot；Guarantee task is complete within deadline
Under the premise of, a task can only be completed by an actuator node, and can be only done a task in each time slot；
12) guarantee that the reasonable distribution of task will meet following constraint condition:
Wherein fijValue is 1 or 0, and task T is indicated when being 1jBy actuator node AiIt executes.
In step 11), Task Allocation Problem will meet the following conditions:
111) sensor node is static, and actuator node can move and have data calculating and analysis ability；
112) sensor node all in network is synchronous with actuator node clock；
113) actuator node is divided into two classes: decision node and auction node, and all there are four types of states for both nodes；
The state demarcation of decision node is Idle state, initial state, waiting state, decision state；
Competitive bidding node state divides are as follows: Idle state, starting state, waiting state, execution state；
114) research environment is the Cluster Networks structure using actuator node as cluster head, has task when monitoring in region
When, report request can be sent to the actuator node of this cluster by sensor node.
Step 2 initialization network parameter: its initialization network parameter is as shown in table 1:
1 emulation experiment parameter of table
Step 3 chooses decision node: the probability that an actuator node is selected as decision node is Pi, according to formula (1)
Its probability can be calculated:
D=1/d1+1/d2+...+1/dn
Pi=1/ (D*di) (1)
Wherein diIt is actuator node i with a distance from task generation, n is the number of actuator node in network；dnIt is n-th
A node is with a distance from task generation.
Actuator node has two classes at this time:
1) it is Idle state that not selected actuator node, which marks the state of itself,；
2) auction message is added to surrounding broadcast in the actuator node for being selected as decision node, and oneself state is labeled as
Initial state.
Step 4 generates auction node tree: leapfrog number k adds 1, and the quantity of auction node is limited according to current leapfrog number；It is competing
Clap node tree generation the following steps are included:
1) auction node tree is generated by root node of decision node, and successively passes to child node for auction message is added；
2) actuator node for participating in auction message is received by oneself state labeled as starting state；
3) labeled as dynamic actuator node is opened, mission bit stream message will be received and successively return to decision section along spanning tree
Point；
4) mission bit stream is successively transmitted to every height section by the decision node for receiving reception mission bit stream message along spanning tree
Point, and be waiting state by oneself state information flag, wait the passback of actuator node value of utility.
Step 5 calculates auction node value of utility and simultaneously returns: receiving the actuator node of mission bit stream message for oneself state
After starting state, calculates value of utility and this value is back to decision node, this process comprising the following three steps:
1) determination of utility function, utility function need to weigh the dump energy (A of actuator nodeej), actuator node j
With (d at a distance from the region event iij), in actuator node task queue task quantity (Asj), the mobile speed of actuator node
Spend (vj) this four parameters.The value of utility that actuator node executes corresponding task can be calculated according to formula (5).
aij=α1vj+α2Aej-α3Asj-α4dij (5)
Wherein parameter alphai, i=1,2,3,4 be a constant and can reflect out this four parameters determine competitive bidding task
Marked price value when significance level；
2) decision node for receiving utility information message disappears oneself state labeled as decision state, and by utility information is received
Breath is sent to every sub- actuator node along spanning tree；
3) actuator node for receiving utility information message is received by oneself state labeled as waiting state, waits decision node
Decision information.
Step 6 decision node decision: analysis comparison is carried out to the utility information message of return, selects best actuator node
It goes to execute current task.This process has following two situation:
If 1) there is suitable actuator node in auction node tree, decision information message is sent to this and held by decision node
Row device node；There are two types of states for actuator node at this time:
If 11) best actuator node, then oneself state is labeled as execution state, and decision information message edge will be received
Spanning tree is sent to decision node, and oneself state is labeled as Idle state by decision node；
12) if not best actuator node, then be labeled as Idle state for oneself state, and will receive decision information message
It is sent to decision node along spanning tree, oneself state is labeled as Idle state by decision node.
If 2) return to step 5) without suitable actuator node in auction node tree.
The selected actuator node of step 7 executes current task.
Step 8 executes Ik-SAAP algorithm 200 times, and emulation obtains data packet forwarding number comparison diagram, dump energy variance
Comparison diagram, average task completion time comparison diagram.It is illustrated in figure 2 the task apportionment ratio of Ik-SAAP algorithm and k-SAAP algorithm
Comparison diagram；It is illustrated in figure 3 the data packet forwarding number comparison diagram of Ik-SAAP algorithm and k-SAAP algorithm, Fig. 4 show Ik-
The dump energy variance comparison diagram of SAAP algorithm and k-SAAP algorithm, Fig. 5 are being averaged for Ik-SAAP algorithm and k-SAAP algorithm
Task completion time comparison diagram.
Comparative analysis Fig. 2 and Fig. 3 can be seen that Ik-SAAP algorithm under conditions of guaranteeing that task apportionment ratio is high, and forwarding is more
Few data packet；From fig. 4, it can be seen that the dump energy variance between each actuator node of Ik-SAAP algorithm is smaller, network energy
More evenly, network life is longer for consumption；From fig. 5, it can be seen that the average task completion time of Ik-SAAP algorithm is less.Comprehensive point
Analysis is it is found that the method in the case where guaranteeing that task apportionment ratio is high, reduces the quantity of data packet forwarding, balanced network energy
Consumption, reduces average task completion time.
Claims (8)
1. a kind of improvement distribution auction Ik-SAAP method that task is distributed in WSAN, which comprises the following steps:
1) mathematical model of the task distribution of actuator cooperation is established；
2) initialization network parameter；
Initialization network parameter include: initial leapfrog number k, network range, each task assignment procedure be randomly generated number of tasks,
It is logical to execute maximum task number, actuator node for actuator node in the number of actuator node, a task assignment procedure
Interrogate the movement speed for communicating radius, actuator node of radius, sensor node；
3) decision node is chosen；
The probability that one actuator node is selected as decision node is Pi, its probability is calculated according to formula (1):
Wherein diIt is actuator node i with a distance from task generation, n is the number of actuator node in network；dnFor n-th of section
Point is with a distance from task generation；
4) generate auction node tree: initial leapfrog number k adds 1, and the quantity of auction node is limited according to current leapfrog number；
Generate auction node tree the following steps are included:
41) auction node tree is generated by root node of decision node, and successively passes to child node for auction message is added；
42) actuator node that auction message is added is received by oneself state labeled as starting state；
43) labeled as dynamic actuator node is opened, mission bit stream message will be received along spanning tree and successively return to decision node；
44) mission bit stream is successively transmitted to each child node along spanning tree by the decision node for receiving reception mission bit stream message,
And by oneself state information flag be waiting state, wait actuator node value of utility passback；
5) calculate auction node value of utility and return: oneself state is labeled as opening by the actuator node for receiving mission bit stream message
After dynamic, calculates value of utility and this value is back to decision node；
6) the decision node decision phase carries out analysis comparison to the utility information message of return, selects best actuator node and go
Current task is executed, and sends each actuator node along spanning tree for decision information；
7) selected actuator node executes current task；
8) Ik-SAAP algorithm is executed 200 times, emulates and obtains data packet forwarding number comparison diagram, dump energy variance comparison diagram,
Average task completion time comparison diagram.
2. the improvement distribution auction Ik-SAAP method that task is distributed in WSAN according to claim 1, feature exist
In: in the step 1), the foundation that task distributes mathematical model includes following 2 steps:
11) Task Allocation Problem describes: having n in assigning processtA task, naA actuator node；Each task has holding for oneself
The continuous timeWith deadline dj；Each actuator node has NiA available time slot；Guarantee task is completed within deadline
Under the premise of, a task can only be completed by an actuator node, and can be only done a task in each time slot；
12) guarantee that the reasonable distribution of task will meet following constraint condition:
Wherein fijValue is 1 or 0, and task T is indicated when being 1jBy actuator node AiIt executes.
3. the improvement distribution auction Ik-SAAP method that task is distributed in WSAN according to claim 2, feature exist
In: in the step 11), Task Allocation Problem will meet the following conditions:
111) sensor node is static, and actuator node can move and have data calculating and analysis ability；
112) sensor node all in network is synchronous with actuator node clock；
113) actuator node is divided into two classes: decision node and auction node, and all there are four types of states for both nodes；
The state demarcation of decision node is Idle state, initial state, waiting state, decision state；
Auction node state divides are as follows: Idle state, starting state, waiting state, execution state；
114) research environment is the Cluster Networks structure using actuator node as cluster head, when monitoring has task generation in region,
Report request can be sent to the actuator node of this cluster by sensor node.
4. the improvement distribution auction Ik-SAAP method that task is distributed in WSAN according to claim 1, feature exist
In: in the step 2), initialization network parameter are as follows: initial leapfrog number k=0；Network range is the square of 500m*500m
Region；40 tasks are randomly generated in each task assignment procedure；The number of actuator node is 10；In task assignment procedure
It is 5 that actuator node, which executes maximum task number,；The communication radius of actuator node is 50m；The communication of sensor node half
Diameter is 10m；The movement speed of actuator node is 10m/s.
5. the improvement distribution auction Ik-SAAP method that task is distributed in WSAN according to claim 1, feature exist
In: in the step 3), there are two types of states for decision node:
31) it is Idle state that not selected actuator node, which marks the state of itself,；
32) auction message is added to surrounding broadcast in the actuator node for being selected as decision node, and by oneself state labeled as just
Primary state.
6. the improvement distribution auction Ik-SAAP method that task is distributed in WSAN according to claim 1, feature exist
In: in the step 5), the calculating and passback of value of utility the following steps are included:
51) determination of utility function, utility function need to weigh the dump energy (A of actuator nodeej), actuator node j with
Distance (the d in the region event iij), in actuator node task queue task quantity (Asj), the movement speed of actuator node
(vj) this four parameters；The value of utility that actuator node executes corresponding task is calculated according to formula (5)；
aij=α1vj+α2Aej-α3Asj-α4dij(5)
Wherein parameter alphai, i=1,2,3,4 be a constant and can reflect out this four parameters determine competitive bidding task mark
Significance level when value；
52) oneself state is labeled as decision state by the decision node for receiving utility information message, and will receive utility information message
Every sub- actuator node is sent to along spanning tree；
53) actuator node for receiving utility information message is received by oneself state labeled as waiting state, waits decision node
Decision information.
7. the improvement distribution auction Ik-SAAP method that task is distributed in WSAN according to claim 1, feature exist
In: in the step 6), the decision phase includes following two situation:
If 61) there is suitable actuator node in auction node tree, decision information message is sent to the execution by decision node
Device node；
If 62) return to step 5) without suitable actuator node in auction node tree.
8. the improvement distribution auction Ik-SAAP method that task is distributed in WSAN according to claim 7, feature exist
In: in the step 61), there are two types of states for the actuator node in auction node tree:
If 611) best actuator node, then oneself state is labeled as execution state by the actuator node, and will receive decision
Informational message is sent to decision node along spanning tree, and oneself state is labeled as Idle state by decision node；
612) if not best actuator node, then oneself state is labeled as Idle state by the actuator node, and reception is determined
Plan informational message is sent to decision node along spanning tree, and oneself state is labeled as Idle state by decision node.
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| CN102014439A (en) * | 2010-12-22 | 2011-04-13 | 北京邮电大学 | Task allocation method and device for clustered wireless sensor network based on contract net |
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