CN102143526A - Method for selecting sensor resource nodes based on energy balance and quality of service (QoS) constraints - Google Patents
Method for selecting sensor resource nodes based on energy balance and quality of service (QoS) constraints Download PDFInfo
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Abstract
The invention discloses a method for selecting sensor resource nodes based on energy balance and quality of service (QoS) constraints. The method comprises the following steps of: classifying the QoS constraints of a task in a wireless sensor grid environment according to characteristics of limited sensor node energy and resource scheduling in the wireless sensor grid environment; differentiating influence degree of different QoS constraints levels on resource scheduling; preferably scheduling the task with higher QoS constraint level for the tasks with different QoS constraints levels under the condition of according with QoS constraints; for the task with the same QoS constraint level, evaluating a consumption value of resource scheduling in each time when the resources are selected by using an evaluation function; and selecting the task and the resources with the minimum consumption value as a mapping result according to the balance between energy consumption of the sensor nodes and power consumption of each node. In the method, the influence of energy consumption and the QoS constraints in the wireless sensor grid on resource scheduling when resources scheduling is performed is taken into comprehensive consideration.
Description
Technical field
The present invention relates to the technical field that grid computing and wireless sensor network combine, be specifically related to a kind of sensor resource node selecting method based on balancing energy and QoS constraint.
Background technology
In this year, along with the high speed development of wireless technology and sensor technology, development of technology such as microelectronics, embedded system are promoting the fast development of wireless sensor network technology.Wireless sensor network is made of a series of sensor node, and each node all has environment sensing, data processing and wireless communication ability.Sensor node has powered battery, calculates the characteristics that storage capacity is limited, communication bandwidth is low, and this is restricted it when handling and utilizing the gained data.Grid computing with distribute on the geography, the various resources of isomery link together, and form a virtual high-performance supercomputer, be available anywhere and computing capability reliably for the user provides.Now, the grid with high-speed computational capability, flood tide storage capacity and high-speed communication bandwidth characteristics has become solved a standard mode large-scale distributed, that heterogeneous resource is shared in dynamic virtual community.
Along with going deep into of sensor technology and grid research, sensor node more and more is used as a kind of resource and has been incorporated in the grid, thereby grid and sensor technology can be combined, utilize sensor node to obtain resource in real time, share by grid platform; The mass data of utilizing computational resource that grid has and storage resources by technology such as data mining, data fusion, distributed data bases sensor node to be collected is handled, is analyzed and stores.
At present, the grid researcher puts forth effort on research grid resource scheduling (the selection problem of resource node), promptly for a gridding task, selects suitable gridding resource node to shine upon, and makes its maximizing the benefits in the process of scheduling.In recent years, the grid researcher has proposed a lot of Grid Resource Schedule Algorithms, but these dispatching algorithms are mainly designed at conventional mesh, and for the conventional mesh in the different field, the goal in research of its Grid Resource Schedule Algorithms may the emphasis difference.As resource there being the high-performance calculation of requirement, the matter of utmost importance that resource scheduling algorithm need be considered is QoS; The equipment limited along with various energy reserves more and more joins grid, an energy-optimised aspect that has also become Grid Resource Schedule Algorithms to pay close attention to.
Compare with scheduling of resource in the conventional mesh, sensor node energy limited and sensor grid task be to the qos requirement of resource node in the wireless senser grid, must take all factors into consideration in the wireless senser grid energy consumption and QoS constraint when making scheduling of resource to the influence of scheduling of resource.Therefore, be necessary to provide sensor resource node selecting method under a kind of suitable wireless sensor network lattice ring border.
Summary of the invention
The purpose of this invention is to provide the sensor resource node selecting method under a kind of suitable wireless sensor network lattice ring border, take all factors into consideration in the wireless senser grid constraint of energy consumption and QoS the influence of scheduling of resource.
To achieve these goals, the invention provides a kind of sensor resource node selecting method, comprise the steps: based on balancing energy and QoS constraint
(1) qos requirement of submitting to according to task is tested the QoS service that all available resources provide, and obtains the QoS constraint rank of each task, and described QoS constraint rank comprises rigid QoS constraint, soft level QoS constraint, level QoS constraint as possible;
(2) qos requirement of submitting to according to task is tested the QoS service that all available resources provide, and judges whether each task can be carried out on each resource, wherein can carry out the qos requirement that the expression resource satisfies task;
(3) obtain the quantity of the efficient resource of intrafascicular approximately each the task correspondence of each rank QoS;
(4) task of the rigid QoS constraint of scheduling earlier, dispatch the task of soft level QoS constraint again, scheduling at last is the task of level QoS constraint as possible, task for same rank QoS constraint, the task of efficient resource minimum number in this rank of priority scheduling QoS constraint task, for the identical task of efficient resource quantity, consider the energy consumption of node and the balancing energy between each node simultaneously, adopt valuation functions to assess the consumption figures that the examination on each resource of each task scheduling is carried out, use the Min-min algorithm to seek task, have the Task Distribution of minimum minimal consumption value on the resource that obtains this minimum minimal consumption value described with minimum minimal consumption value.
Compared with prior art, the present invention is based on balancing energy and QoS the constraint the sensor resource node selecting method have following advantage:
(1) to the QoS of task constraint having carried out classification, be respectively rigid level QoS constraint, soft level QoS constraint and level QoS constraint as possible, distinguish the priority of task, the priority of task scheduling that QoS constraint rank is high according to the influence degree of different QoS constraint rank degree of exchanging;
(2) on the basis of the QoS constraint of satisfying task, the energy consumption of consideration task in scheduling process assessed as an aspect of valuation functions the energy consumption of node to scheduling consumption each time, reduce the energy consumption of resource node as far as possible;
(3) when consideration is energy-optimised, the equilibrium that each node energy is consumed is also as a factor, in the process of scheduling, select the more resource node of those dump energies as far as possible, thereby reach the equalization that the whole sensor network node energy consumes, improve the reliability and the life cycle of sensor network.
By following description also in conjunction with the accompanying drawings, it is more clear that the present invention will become, and these accompanying drawings are used to explain embodiments of the invention.
Description of drawings
Fig. 1 is the flow chart that the present invention is based on the sensor resource node selecting method of balancing energy and QoS constraint.
Embodiment
With reference now to accompanying drawing, describe embodiments of the invention, the similar elements label is represented similar elements in the accompanying drawing.
The present invention is based on before the sensor resource node selecting method of balancing energy and QoS constraint the relevant parameter in the sensor resource scheduling problem under the multidimensional QoS constraint that elder generation's this method of explanation relates to, the energy consumption equalization problem under the sensor grid environment, sensor grid scheduling of resource environment description, the dispatching method in explanation.
Sensor resource scheduling problem under the multidimensional QoS constraint
Under the sensor grid scheduling of resource environment of multidimensional QoS constraint, task is many-sided to the qos requirement of resource, only considers that the QoS constraint of one dimension or apteryx obviously is the scheduling requirement that does not meet task.So in the process that sensor resource is dispatched, must take into full account the problem of multi-QoS constraint.
To in the sensor grid resource there being the high-performance calculation of requirement, the submission task that is the user has more qos requirement to sensor node, therefore the QoS (Quality of Service, service quality) that takes into full account task and resource requires particularly important in dispatching algorithm.Under the sensor grid environment, according to task to QoS constraint the carrying out classification of the qos requirement difference of resource to task::
(1) rigid level QoS: the QoS constraint to resource system platform, CPU and bandwidth etc. all belongs to rigid level QoS constraint;
(2) soft level QoS: expense, reliability etc. all belongs to soft level QoS constraint;
(3) a level QoS as possible: remove QoS rigid level QoS constraint and the soft level QoS constraint and retrain and belong to all as possible that grade QoS retrains.
By top principle of classification as can be known, rigid level QoS is the highest to resource requirement among all rank QoS, to the scheduling of resource decisive role, the scheduling that only meets rigid level QoS is only effectively, and the task with this rigid level QoS could mapped execution.: if task satisfies soft level QoS, and then scheduling is effective, and can make maximizing the benefits; If task does not satisfy soft level QoS, then scheduling is effectively, but benefit reduces.Level QoS constraint is less to the influence of scheduling of resource as possible, and the QoS of the level as possible constraint of task will realize as far as possible and satisfy.
According to the classification of top three kinds of QoS constraint, consider the validity of resource to task, the sensor grid resource scheduling under multidimensional QoS is retrained is summed up as the selection problem of task, the i.e. issue of priority of task choosing in the process of scheduling
Energy consumption equalization problem under the sensor grid environment
Because the sensor resource node in the sensor grid is arranged in special occasions usually, use powered battery, power supply is non-exchange, so the power saving of sensor node becomes extremely important; Because the approach exhaustion of some sensor node energy, may have influence on the service efficiency of other sensor node in the whole zone, therefore the problem of sensor node energy consumption equilibrium also must be considered (like this under the situation of the service efficiency that does not influence sensor node, submit on the basis of task finishing the user, prolong the life cycle of grid as far as possible, promptly before the energy consumption of sensor resource node is intact, make its maximization of utility).
Owing to needing to consider the energy-conservation of sensor node and energy consumption equilibrium in the process of carrying out resource selection, therefore the sensor grid resource scheduling can be converted into the selection problem of resource, promptly, how to select optimum resource to shine upon and make sensor node on energy-conservation and balancing energy, reach balance for a gridding task.
Sensor grid scheduling of resource environment description
Consider that the sensor resource node has distributivity, isomerism, characteristics such as wireless, sensor grid scheduling of resource environment is as follows:
(1) each gridding task to be scheduled all is an independent task, and free of data relies on or communication between the task;
(2) resource nodes can only be carried out a task at synchronization, finish up to this task and could carry out other tasks, and promptly task is monopolized a resource up to finishing;
(3) energy of each sensor resource node is limited, the energy initial value difference of different sensors resource node;
There is not energy consumption when (4) the sensor resource node is in idle condition;
(5) energy consumption of sensor resource node is only limited to task execution energy consumption;
(6) resource is to describe its available QoS method of service issue, and task is submitted to describe its QoS service manner that needs.
Relevant parameter in the dispatching method
Retrain relevant parameter-definition with QoS:
(1) set T={t
1, t
2..., t
mThe individual independently task t of expression m
i(i=1,2 ..., set m);
(2) set R={r
1, r
2..., r
nThe expression grid environment under n heterogeneous resource node r
i(i=1,2 ..., set n);
(3) the expection time of implementation matrix of task
Element et wherein
I, jWhen the QoS service that the qos requirement that expression is submitted to according to task provides all available resources is tested, testing out of task t
iAt resource r
jOn time of implementation, if resource satisfies the qos requirement of task, write down its expection time of implementation, if resource r
jDo not meet task t
iQos requirement, et then
I, jValue defined be ∞, promptly resource can not satisfy the qos requirement of task, task t
iCan not be at resource r
jLast execution;
(4) matrix carried out of task
Element c wherein
I, jExpression task t
iAt resource r
jOn whether can carry out (be resource r
jWhether satisfy task t
iQos requirement), if can carry out then c
I, jValue be 1, expression resource r
jMeet appointed task t
iQoS constraint, the QoS constraint of this task may be rigid level QoS, soft level QoS or any among the level QoS as possible; Can not carrying out then, its value is 0, represent that this task is that rigid QoS constraint and corresponding resource can't satisfy the QoS constraint of task (Matrix C the carried out E reflection resource of task is for the validity situation of task, can draw according to expection time of implementation matrix ETC, each element among the Ergodic Matrices ETC successively, if its value is numeral, then can carries out element corresponding among the Matrix C E and be changed to 1; If the element value among the ETC is ∞, then can carry out element corresponding among the Matrix C E and be changed to 0, when certainly the Matrix C the carried out E of the task QoS service that also can provide all available resources according to the qos requirement that task is submitted to is tested, judge whether each task can be carried out to draw on each resource);
(5) QoS of task constraint rank array QoL=(m
1, m
2..., m
m) represent that the QoS of the individual independently task of m retrains rank, according to the qos requirement that task is submitted to the QoS service that all available resources provide is tested, obtain the QoS constraint rank of each task, obtain the QoS constraint rank array QoL of task, its intermediate value is to be expressed as rigid QoS constraint at 2 o'clock, being the soft QoS constraints of 3 expressions, is 4 expressions level QoS constraints as possible;
(6) task priority vector NoR1=(n
1, n
2..., n
n) represent that QoS constraint rank is the efficient resource quantity of the task correspondence of rigid QoS, as n
iExpression QoS constraint rank is that the quantity of efficient resource of the task i correspondence of rigid QoS is n
iIf the QoS rank of corresponding task is for rigid QoS then its value defined is ∞, i.e. the maximum of system definition; Task priority vector NoR2=(n in like manner
1, n
2..., n
n) represent that QoS constraint rank is the efficient resource quantity of the task correspondence of soft QoS, task priority vector NoR3=(n
1, n
2..., n
n) represent that QoS retrains the efficient resource quantity of rank for the task correspondence of the level QoS that does the best, each component NoR of NoR vector
i(i=1,2,3) can be drawn by the Matrix C the carried out E of task, each component of NoR vector equal to carry out Matrix C E row and.
The parameter-definition relevant with energy consumption:
(1) energy parameter matrix
This matrix is a n * 2 matrixes, wherein e
I, 1The residual energy value of expression resource i, e
I, 2The execution energy expenditure rate (energy value that resource i executes the task and consumed in the unit interval) of expression resource i;
(2) task executions energy consumption EoC
I, jTask executions energy consumption EoC
I, jBe task t
iAt resource r
jOn time of implementation et
I, jExecution energy expenditure rate e with resource j
J, 3Product, as shown in the formula:
EoC
i，j＝et
i，j*e
j，3 (4-1)
Resource r
jExecute task t
iAfter dump energy be resource r
jResidual energy value e
J, 1With task executions energy consumption EoC
I, jDifference, use difference e
J, 1-EoC
I, jReplace e among the energy parameter matrix E
J, 1Value.When the different resource node of task choosing, the dump energy of sensor resource node will promptly preferentially select the maximum resource of dump energy as mapping result as the reference foundation.If have the dump energy of a plurality of sensor resource nodes when identical, the resource of preferentially selecting the energy consumption minimum is as mapping result, thereby reaches purpose of energy saving.
The parameter-definition relevant with optimum span:
(1) expection deadline matrix
Element ct wherein
I, jExpression task t
iAt resource r
jOn the expection deadline, promptly from resource r
jResource r begins to execute the task
jExecute task t
iThe time that is spent;
(2) the maximum execution time Makespan of first resource scheduling.The maximum execution time Makespan of first resource scheduling equals the difference between the zero-time of maximum deadline of task and scheduling, promptly begins to executing last task institute's time spent from first task.
Suppose task t
iAt resource r
jOn the beginning time of implementation be d
j, the expection time of implementation of task is et
I, j, task t then
iAt resource node r
jOn deadline ct
I, jFor:
ct
i，j＝d
j+et
i，j (4-2)
Can draw expection deadline matrix ECT according to formula (4-2).If task t
iDistributed to resource r
jCarry out, then resource r
jT finishes the work
iTime of implementation ct
i=et
I, jIf zero-time is 0, the maximum execution time Makespan=max (ct of first resource scheduling then
i), t
i∈ T.
Take all factors into consideration the time loss that satisfies task QoS constraint in the once scheduling of energy consumption and formula (4-2) reflection in the once scheduling of formula (4-1) reflection, propose valuation functions TF (Tradeoff Function):
TF(i，j，qol)＝1/qol×ct
i，j+1/qol×EoC
i，j-(1-2/qol)×e
j，1
Wherein, i represents task number, and j represents resource number, and qol represents the Qos constraint rank of task i, TF (i, j, qol) expression task t
iBe assigned to resource r
jEnergy consumption when last and the integrated value of time loss use this integrated value to estimate the reasonability of scheduling of resource.
The QoS constraint rank of task is high more, and the proportion of optimum span (Optimal Makespan) in once dispatching is big more; The Qos constraint rank of task is low more, and the node of sensor resource node and the energy consumption equilibrium proportion in once dispatching is big more.For any other task of QoS confinement level, the optimum span ct of task
I, jWith energy consumption EoC
I, jProportion in valuation functions TF is identical; And other descends along with the QoS confinement level, the dump energy e of sensor resource node
J, 1Proportion in valuation functions TF increases, thereby the equilibrium of energy consumption between the outstanding sensor node promptly preferentially selects the more sensor node of dump energy as mapping result.
Explanation now the present invention is based on the sensor resource system of selection of balancing energy and QoS constraint.With reference to figure 1, described method comprises the steps:
Step S1 tests the QoS service that all available resources provide according to the qos requirement that task is submitted to, if resource r
jSatisfy task t
iQos requirement, write down its expection time of implementation, if resource r
jDo not meet task t
iQos requirement, expect that then the time of implementation is recorded as ∞, expection time of implementation of record is recorded among the expection time of implementation matrix ETC of task;
Step S2, each element of the expection time of implementation matrix ETC among the traversal step S1 successively, its value then is changed to 1 with the element of the correspondence position of the Matrix C the carried out E of task for numerical value, otherwise the element of correspondence position is changed to 0;
Step S3, when the QoS service that the qos requirement of submitting to according to task in step S1 provides all available resources is tested, obtain the QoS constraint rank of each task, and the QoS of each task constraint rank is recorded among the QoS constraint rank array QoL, described QoS constraint rank comprises rigid QoS constraint, soft level QoS constraint, level QoS constraint as possible;
Step S4, determine the quantity of the efficient resource of intrafascicular approximately each the task correspondence of each rank QoS according to the Matrix C the carried out E of task, the quantity of the efficient resource of intrafascicular approximately each the task correspondence of rigid QoS is recorded among the task priority vector NoR1, the quantity of the efficient resource of intrafascicular approximately each the task correspondence of soft level QoS is recorded task priority vector NoR2, the quantity of the efficient resource of intrafascicular approximately each the task correspondence of level QoS is as possible recorded among the vectorial NoR3 of task priority;
Step S5 carries out non-descending to task priority vector NoR1, NoR2, NoR3;
Step S6, task among the scheduler task priority vector NoR1, during scheduling, (component value is exactly the some values in the vector to the component value minimum among the priority scheduling task priority vector NoR1, S4 obtains by step) task, then adopt valuation functions to assess each task scheduling consumption figures that examination is carried out on each resource if having the identical task of a plurality of component values, use the Min-min algorithm to seek the task of minimal consumption value (to each task with minimum, in the consumption figures of its corresponding all resources, find out the minimal consumption value of this task, choose the task of minimum minimal consumption value correspondence, Here it is Min-min algorithm);
Step S7 has the Task Distribution of minimum minimal consumption value on the resource that obtains this minimum minimal consumption value with described;
Step S8, the described task of deletion from task priority vector NoR1, updating task priority vector NoR1 with minimum minimal consumption value;
Step S9 judges whether task among the task priority vector NoR1 all finishes scheduling (be among the task priority vector NoR1 task all delete), if not, changes step S6, if, continuation step S10;
Step S10, task among the scheduler task priority vector NoR2, during scheduling, the task of component value minimum among the priority scheduling task priority vector NoR2, adopt valuation functions to assess the consumption figures that the examination on each resource of each task scheduling is carried out, use the Min-min algorithm to seek task with minimum minimal consumption value;
Step S11 has the Task Distribution of minimum minimal consumption value on the resource that obtains this minimum minimal consumption value with described;
Step S12, the described task of deletion from task priority vector NoR2, updating task priority vector NoR2 with minimum minimal consumption value;
Step S13 judges whether task among the task priority vector NoR2 all finishes scheduling (be among the task priority vector NoR2 task all delete), if not, changes step S10, if, continuation step S14;
Step S14, task among the scheduler task priority vector NoR3, during scheduling, the task of component value minimum among the priority scheduling task priority vector NoR3, adopt valuation functions to assess the consumption figures that the examination on each resource of each task scheduling is carried out, use the Min-min algorithm to seek task with minimum minimal consumption value;
Step S15 has the Task Distribution of minimum minimal consumption value on the resource that obtains this minimum minimal consumption value with described;
Step S16, the described task of deletion from task priority vector NoR3, updating task priority vector NoR3 with minimum minimal consumption value;
Step S17 judges whether task among the task priority vector NoR3 all finishes scheduling (be among the task priority vector NoR3 task all delete), if not, changes step S14, if, end.
As shown from the above technical solution, the present invention is based on balancing energy and QoS the constraint the sensor resource system of selection have following characteristics:
1, considers the multidimensional QoS constraint of task, the energy-optimised and balancing energy of node, be applicable to the scheduling of resource under the sensor grid environment;
2, when dispatching, the qos requirement of task is carried out classification, the QoS constraint of task is divided into rigid level QoS, soft level QoS and the level QoS that does the best, task to different stage QoS constraint, in the process of scheduling of resource, according to the influence degree of QoS constraint to scheduling of resource, QoS retrains the high more task of rank priority scheduling more, that is: to having the task of rigid QoS constraint, preferentially select resource preferentially to shine upon; To having the task of soft level QoS constraint, time preferential mapping of inferior preferential selection resource; To having the task of level QoS constraint as possible, select resource to shine upon at last at last;
3, retraining of task for same rank QoS, in the process of scheduling of resource, select the fewer priority of task scheduling of those available resources nodes, and take all factors into consideration the energy consumption and the balancing energy of sensor node, use valuation functions to assess the consumption figures of first resource scheduling, comprise optimum span, energy consumption and balancing energy, using the min-min algorithm to seek the task with minimum minimal consumption value dispatches, can guarantee under the situation that satisfies the QoS constraint life cycle of maximized prolongation sensor grid;
4, taken all factors into consideration the QoS constraint of the energy consumption equalization and the task of sensor node: under the condition that satisfies the QoS constraint, consider the energy consumption problem of resource node in scheduling process, reduce the energy consumption in the resource node scheduling process as far as possible, in the process of scheduling, use the more sensor resource node of dump energy as far as possible, make the energy consumption equalization of sensor node, thereby satisfying the service time that increases each sensor node under the situation of task scheduling, prolonging the life cycle of sensor grid.
Above invention has been described in conjunction with most preferred embodiment, but the present invention is not limited to the embodiment of above announcement, and should contain various modification, equivalent combinations of carrying out according to essence of the present invention.
Claims (1)
1. the sensor resource node selecting method based on balancing energy and QoS constraint comprises the steps:
According to the qos requirement that task is submitted to the QoS service that all available resources provide is tested, obtained the QoS constraint rank of each task, described QoS constraint rank comprises rigid QoS constraint, soft level QoS constraint, level QoS constraint as possible;
According to the qos requirement that task is submitted to the QoS service that all available resources provide is tested, judged whether each task can be carried out on each resource, wherein can carry out the qos requirement that the expression resource satisfies task;
Obtain the quantity of the efficient resource of intrafascicular approximately each the task correspondence of each rank QoS; The task of the rigid QoS constraint of scheduling earlier, dispatch the task of soft level QoS constraint again, scheduling at last is the task of level QoS constraint as possible, task for same rank QoS constraint, the task of efficient resource minimum number in this rank of priority scheduling QoS constraint task, for the identical task of efficient resource quantity, adopt valuation functions to assess the consumption figures that the examination on each resource of each task scheduling is carried out, use the Min-min algorithm to seek task, have the Task Distribution of minimum minimal consumption value on the resource that obtains this minimum minimal consumption value described with minimum minimal consumption value.
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| US9335816B2 (en) | 2011-12-23 | 2016-05-10 | Globalfoundries Inc. | System and method for efficient service-instance oriented energy management in the internet of things |
| CN108052097A (en) * | 2016-10-19 | 2018-05-18 | 小蚁科技（香港）有限公司 | For training the method for isomery sensing system and isomery sensing system |
| CN109618368A (en) * | 2018-12-28 | 2019-04-12 | 国网江苏省电力有限公司电力科学研究院 | A kind of wireless sense network data fusion dispatching algorithm of time delay and power consumption combined optimization |
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| CN101271405A (en) * | 2008-05-13 | 2008-09-24 | 武汉理工大学 | Bidirectional grade gridding resource scheduling method based on QoS restriction |
| CN101271407A (en) * | 2008-05-13 | 2008-09-24 | 武汉理工大学 | Gridding scheduling method based on energy optimization |
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| CN101271405A (en) * | 2008-05-13 | 2008-09-24 | 武汉理工大学 | Bidirectional grade gridding resource scheduling method based on QoS restriction |
| CN101271407A (en) * | 2008-05-13 | 2008-09-24 | 武汉理工大学 | Gridding scheduling method based on energy optimization |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
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| US9335816B2 (en) | 2011-12-23 | 2016-05-10 | Globalfoundries Inc. | System and method for efficient service-instance oriented energy management in the internet of things |
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| CN108052097A (en) * | 2016-10-19 | 2018-05-18 | 小蚁科技（香港）有限公司 | For training the method for isomery sensing system and isomery sensing system |
| CN108052097B (en) * | 2016-10-19 | 2021-04-09 | 小蚁科技（香港）有限公司 | Method for training heterogeneous sensing system and heterogeneous sensing system |
| CN109618368A (en) * | 2018-12-28 | 2019-04-12 | 国网江苏省电力有限公司电力科学研究院 | A kind of wireless sense network data fusion dispatching algorithm of time delay and power consumption combined optimization |
| CN109618368B (en) * | 2018-12-28 | 2021-02-09 | 国网江苏省电力有限公司电力科学研究院 | Wireless sensor network data fusion scheduling algorithm for time delay and power consumption joint optimization |
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