CN114928653B - Data processing method and device for crowd sensing - Google Patents
Data processing method and device for crowd sensing Download PDFInfo
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- G06F9/00—Arrangements for program control, e.g. control units
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- G06F9/46—Multiprogramming arrangements
- G06F9/48—Program initiating; Program switching, e.g. by interrupt
- G06F9/4806—Task transfer initiation or dispatching
- G06F9/4843—Task transfer initiation or dispatching by program, e.g. task dispatcher, supervisor, operating system
- G06F9/4881—Scheduling strategies for dispatcher, e.g. round robin, multi-level priority queues
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F9/00—Arrangements for program control, e.g. control units
- G06F9/06—Arrangements for program control, e.g. control units using stored programs, i.e. using an internal store of processing equipment to receive or retain programs
- G06F9/46—Multiprogramming arrangements
- G06F9/50—Allocation of resources, e.g. of the central processing unit [CPU]
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Abstract
The embodiment of the invention discloses a data processing method and a device for crowd sensing, wherein the data processing method for crowd sensing comprises the following steps: acquiring perception task data to be processed of the terminal equipment i; determining a task segmentation strategy according to reference conditions, wherein the reference conditions comprise at least one of the following: the terminal equipment i, the edge equipment and the cloud center are busy, task processing speed and data transmission rate respectively; the control terminal equipment i segments the perception task data according to a segmentation strategy; and controlling at least one of the terminal equipment i, the edge equipment and the cloud center to process the segmented data according to the segmentation condition of the perception task data. The invention solves the technical problem of slower processing speed when processing the perception data in the related technology.
Description
Technical Field
The invention relates to the field of crowd sensing, in particular to a data processing method and device for crowd sensing.
Background
With the rapid popularization of crowd-sourced mobile devices and the exponential growth of IOT-aware devices, the variety and data volume of crowd-sourced (MCS: mobile Crowd Sensing) tasks has increased, particularly with the advent and rapid increase of computationally intensive and delay-sensitive awareness tasks, such as analysis of driving road conditions, AR/VR, modeling of disaster site information such as earthquake and flood fires, and the like. Resulting in the problem of excessive computational delay. And (3) a situation that the allocation of the computing resources is unreasonable.
In the related art, in the process of performing the perception task processing, when a large amount of calculation is faced, the data of the end device is completely unloaded to the edge device or the cloud side, and the means for improving the calculation speed is not different from the consideration of increasing the calculation performance of the end device or increasing the number and performance of edge nodes. However, the task allocation is unreasonable, so that the computing resources of each end-edge cloud cannot be fully utilized, not only is the resource wasted, but also the processing speed of the crowd sensing task is slow.
In view of the above problems, no effective solution has been proposed at present.
The above information disclosed in the background section is only for enhancement of understanding of the background of the technology described herein. Accordingly, the background art may contain some information that is not otherwise known to those of skill in the art.
Disclosure of Invention
The embodiment of the invention provides a data processing method and device for crowd sensing, which at least solve the technical problem of slower processing speed when sensing data is processed in the related technology.
According to a first aspect of the embodiment of the present invention, there is provided a data processing method for crowd sensing, including: acquiring perception task data to be processed of the terminal equipment i; determining a task segmentation strategy according to reference conditions, wherein the reference conditions comprise at least one of the following: the terminal equipment i, the edge equipment and the cloud center are busy, task processing speed and data transmission rate respectively; the control terminal equipment i segments the perception task data according to a segmentation strategy; and controlling at least one of the terminal equipment i, the edge equipment and the cloud center to process the segmented data according to the segmentation condition of the perception task data.
Further, the reference condition further includes the power of the terminal device iAnd the total energy consumption E of the terminal device i for processing the acquired perception task data i 。
Further, determining the task segmentation strategy according to the reference condition includes: according to the objectives and constraintsDetermining the segmentation proportion of the perception task data; wherein (1)>T i L The total delay of processing the perceived task data acquired by the terminal device i itself is +.>Processing the total time delay of the perception task data acquired by the terminal equipment i for the edge equipment j, T i C For the total time delay of the perception task data acquired by the cloud center processing terminal equipment i, pi is an unloading decision set pi= { piof all the terminal equipment i ,i∈N}，∏ i For the terminal equipment i to unload decision vector of the acquired perception task data per se, pi i ＝{x i ,y i,j ,z i }，x i For the proportion of perceived task data divided into terminal i, y i,j For the proportion of perceived task data divided into edge device j, z i For the proportion of the perceived task data divided into the cloud center, N is the total number of terminal devices, f is the computing resource allocation set f= { f of all edge devices i,j ,i∈N,j∈M}，f i,j For computing resource allocation vectors for edge devices j handling aware task data from terminal device i, M is the total number of edge devices.
Further, the method comprises the steps of,1-μ i1 CPU cycle number duty ratio, K for processing self acquired perception task data for terminal equipment i i Acquisition for processing terminal device iThe total number of CPU cycles, f, required for sensing task data i The calculation force distribution quantity when the terminal equipment i processes the acquired sensing task data; />For edge device m i Delay of transmission of perceived task data acquired by terminal device i to edge device j,/for the perceived task data acquired by terminal device i>Calculating the time delay of the perception task data acquired by the terminal equipment i for the edge equipment j; />T c For the time delay of the perception task data acquired by the terminal equipment i, the preset edge equipment transmits the perception task data to the cloud center, < +.>Calculating the time delay of the perception task data acquired by the terminal equipment i for the cloud center;calculates the energy consumption of the perceived task data acquired by the terminal device i for the terminal device i,for terminal equipment i to edge equipment m i And transmitting the energy consumption of the acquired sensing task data.
Further, under the energy consumption constraint, according to the target and the constraintDetermining a segmentation scale of the perception task data, comprising: at->In the case of (2), then determine x i ＝1，y i,j ＝0，z i =0; wherein C is i Computing for terminal device i its own acquired awarenessCalculation rate at task data, +.>For terminal equipment i to edge equipment m i Transmission rate at which the perceived task data acquired by itself is transmitted.
Further, under the energy consumption constraint, according to the target and the constraintDetermining a segmentation ratio of the perception task data, further comprising: />If the data size of the task to be processed of the edge device j is less than or equal to B i Then determine x i ＜1，y i,j ＜0，z i =0; wherein (1)>C i,j And calculating the calculation rate of the perception task data acquired by the terminal equipment i for the edge equipment j.
Further, under the energy consumption constraint, according to the target and the constraintDetermining a segmentation ratio of the perception task data, further comprising: at->And the data volume of the task to be processed of the edge equipment j is larger than B i In the case of (2) according to the formula->Calculate X i 、Y i 、Z i Then according to X i 、Y i 、Z i Determining a segmentation proportion; wherein X is i For the data quantity of the perception task data divided into the terminal device i, Y i For the data volume of the perception task data divided to the edge device j, Z i Is divided intoData volume X of perception task data cut to cloud center i +Y i +Z i ＝L i ，x i :y i :z i ＝X i :Y i :Z i ，L i To perceive the total amount of task data.
Further, under the energy consumption constraint, according to the target and the constraintDetermining a segmentation ratio of the perception task data, further comprising: in case of busy terminal device i, according to the formula +.>Calculating a data quantity threshold X divided into terminal equipment i Ti The method comprises the steps of carrying out a first treatment on the surface of the And/or, in case the edge device j is busy, according to the formulaCalculating the data quantity threshold Y divided to the edge device j Ti The method comprises the steps of carrying out a first treatment on the surface of the According to X Ti And Y Ti Determining a segmentation ratio by at least one of the sensing task data and the total amount of the sensing task data; wherein (1)>For the time that needs to wait on terminal device i; />Indicating the time to wait on edge device j, +.>For the time calculated at terminal i, < >>For terminal equipment i to edge equipment m i Transmission rate when transmitting self-acquired perception task data, C i,j And calculating the calculation rate of the perception task data acquired by the terminal equipment i for the edge equipment j.
According to a second aspect of the embodiments of the present invention, there is also provided a data processing apparatus oriented to crowd sensing, including: the acquisition unit is used for acquiring perception task data to be processed by the terminal equipment; a determining unit, configured to determine a task segmentation strategy according to a reference condition, where the reference condition includes at least one of: the terminal equipment, the edge equipment and the cloud center are busy, task processing speed and data transmission rate respectively; the segmentation unit is used for controlling the terminal equipment to segment the perception task data according to a segmentation strategy; and the control unit is used for controlling at least one of the terminal equipment, the edge equipment and the cloud center to process the segmented data according to the segmentation condition of the perception task data.
The data processing method facing crowd sensing of the embodiment of the invention comprises the following steps: acquiring perception task data to be processed of the terminal equipment i; determining a task segmentation strategy according to reference conditions, wherein the reference conditions comprise at least one of the following: the terminal equipment i, the edge equipment and the cloud center are busy, task processing speed and data transmission rate respectively; the control terminal equipment i segments the perception task data according to a segmentation strategy; and controlling at least one of the terminal equipment i, the edge equipment and the cloud center to process the segmented data according to the segmentation condition of the perception task data. By adopting the processing mode, the task segmentation strategy is determined according to at least one of the busy condition, the task processing speed condition and the data transmission speed condition of the terminal equipment i, the edge equipment and the cloud center, task data are segmented according to the segmentation strategy, and at least one of the terminal equipment i, the edge equipment and the cloud center is controlled to process the segmented data according to the segmentation result of the task, so that the perception task data can be segmented and then processed respectively by combining the actual conditions of the terminal equipment i, the edge equipment and the cloud center, the computing power of the terminal equipment i, the edge equipment and the cloud center is fully utilized, the resource allocation is optimized, the crowd sensing platform is enabled to assign a resource scheduling scheme by a more efficient scheme, the crowd sensing task data can be processed by deepening the end-edge cloud cooperation more deeply, the processing speed of the crowd sensing task data can be effectively improved, the processing waiting time is shortened, and the problem of processing speed is low in processing the perception data in a related technology is solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this application, illustrate embodiments of the invention and together with the description serve to explain the invention and do not constitute a limitation on the invention. In the drawings:
fig. 1 is a flow chart of a data processing method for crowd sensing according to an embodiment of the present invention;
fig. 2 is a schematic diagram of a data processing device for crowd sensing according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and in the drawings are used for distinguishing between different objects and not for limiting a particular order.
Fig. 1 is a data processing method for crowd sensing according to an embodiment of the present invention, as shown in fig. 1, the method includes the following steps:
step S102, acquiring perception task data to be processed of a terminal device i;
step S104, determining a task segmentation strategy according to reference conditions, wherein the reference conditions comprise at least one of the following: the terminal equipment i, the edge equipment and the cloud center are busy, task processing speed and data transmission rate respectively;
step S106, the control terminal device i segments the perception task data according to a segmentation strategy;
step S108, at least one of the terminal equipment i, the edge equipment and the cloud center is controlled to process the segmented data according to the segmentation condition of the perception task data.
The data processing method for crowd sensing by adopting the scheme comprises the following steps: acquiring perception task data to be processed of the terminal equipment i; determining a task segmentation strategy according to reference conditions, wherein the reference conditions comprise at least one of the following: the terminal equipment i, the edge equipment and the cloud center are busy, task processing speed and data transmission rate respectively; the control terminal equipment i segments the perception task data according to a segmentation strategy; and controlling at least one of the terminal equipment i, the edge equipment and the cloud center to process the segmented data according to the segmentation condition of the perception task data. By adopting the processing mode, the task segmentation strategy is determined according to at least one of the busy condition, the task processing speed condition and the data transmission speed condition of the terminal equipment i, the edge equipment and the cloud center, task data are segmented according to the segmentation strategy, and at least one of the terminal equipment i, the edge equipment and the cloud center is controlled to process the segmented data according to the segmentation result of the task, so that the perception task data can be segmented and then processed respectively by combining the actual conditions of the terminal equipment i, the edge equipment and the cloud center, the computing power of the terminal equipment i, the edge equipment and the cloud center is fully utilized, the resource allocation is optimized, the crowd sensing platform is enabled to assign a resource scheduling scheme by a more efficient scheme, the crowd sensing task data can be processed by deepening the end-edge cloud cooperation more deeply, the processing speed of the crowd sensing task data can be effectively improved, the processing waiting time is shortened, and the problem of processing speed is low in processing the perception data in a related technology is solved.
Specifically, the reference condition further includes an electric quantity of the terminal device iAnd the total energy consumption E of the terminal device i for processing the acquired perception task data i 。
The electric quantity of the terminal equipment is included in the range of the reference quantity, so that the task segmentation can be better optimized by combining the energy consumption of the task data processing process and the electric quantity of the terminal equipment, the control of the energy consumption of the terminal equipment is facilitated, and the risk that the energy consumption of the terminal equipment is increased or even the electric quantity of the terminal equipment is insufficient due to unreasonable task segmentation is reduced.
Specifically, determining the task segmentation strategy according to the reference condition includes: according to the objectives and constraintsDetermining the segmentation proportion of the perception task data; wherein (1)>T i L The total delay of processing the perceived task data acquired by the terminal device i itself is +.>Processing the total time delay of the perception task data acquired by the terminal equipment i for the edge equipment j, T i C For the total time delay of the sensing task data acquired by the cloud center processing terminal equipment i, pi is an unloading decision set pi= { pi of all the terminal equipment i ,i∈N}，Π i For the terminal equipment i to unload decision vector of the acquired perception task data per se, pi i ＝{x i ,y i,j ,z i }，x i For the proportion of perceived task data divided into terminal i, y i,j For the proportion of perceived task data divided into edge device j, z i For the proportion of the perception task data divided to the cloud center, N is the total number of terminal devices, and f is all edgesComputing resource allocation set f= { f of device i,j ,i∈N,j∈M}，f i,j For computing resource allocation vectors for edge devices j handling aware task data from terminal device i, M is the total number of edge devices.
By setting the targets and the constraints, reasonable task segmentation conditions are designed, and all segmentation conditions conforming to the targets and the constraints can well optimize the processing process of the perception task data, so that the processing speed of the perception task data is improved.
In particular, the method comprises the steps of,1-μ i1 CPU cycle number duty ratio, K for processing self acquired perception task data for terminal equipment i i CPU total cycle number f required for processing perception task data acquired by terminal equipment i i The calculation force distribution quantity when the terminal equipment i processes the acquired sensing task data; />For edge device m i Delay of transmission of perceived task data acquired by terminal device i to edge device j,/for the perceived task data acquired by terminal device i>Calculating the time delay of the perception task data acquired by the terminal equipment i for the edge equipment j; />T c For the time delay of the perception task data acquired by the terminal equipment i, the preset edge equipment transmits the perception task data to the cloud center, < +.>Calculating the time delay of the perception task data acquired by the terminal equipment i for the cloud center;calculates the energy consumption of the perceived task data acquired by the terminal device i for the terminal device i,for terminal equipment i to edge equipment m i And transmitting the energy consumption of the acquired sensing task data.
In actual implementation, the terminal device i is transmitting the perception task data to the edge device m i After that, edge device m i There may be different busy states if edge device m i If the queued task exceeds the threshold, the edge device m is set i The received data is transmitted to the edge device j. Of course, edge device m i And edge device j may be the same device, edge device m i The data to be processed of the terminal equipment i does not exceed a threshold value, the data to be processed of the terminal equipment i is processed by the terminal equipment i, data transmission is not needed between the terminal equipment i,i.e. 0.
In a specific embodiment, under energy consumption constraints, according to the objectives and constraintsDetermining a segmentation scale of the perception task data, comprising: at->In the case of (2), then determine x i ＝1，y i,j ＝0，z i =0; wherein C is i Calculating rate when calculating the perceived task data acquired by itself for terminal device i, +.>For terminal equipment i to edge equipment m i Transmission rate at which the perceived task data acquired by itself is transmitted.
Under the constraint of energy consumption, according to the target and the constraintDetermining perceptual arbitraryThe division ratio of the business data further comprises: at->If the data size of the task to be processed of the edge device j is less than or equal to B i Then determine x i ＜1，y i,j ＜0，z i =0; wherein (1)>C i,j And calculating the calculation rate of the perception task data acquired by the terminal equipment i for the edge equipment j.
Specifically, under the energy consumption constraint, according to the objective and the constraintDetermining a segmentation ratio of the perception task data, further comprising: at->And the data volume of the task to be processed of the edge equipment j is larger than B i In the case of (2) according to the formula->Calculate X i 、Y i 、Z i Then according to X i 、Y i 、Z i Determining a segmentation proportion; wherein X is i For the data quantity of the perception task data divided into the terminal device i, Y i For the data volume of the perception task data divided to the edge device j, Z i X is the data volume of the perception task data divided into the cloud center i +Y i +Z i ＝L i ，x i :y i :z i ＝X i :Y i :Z i ，L i To perceive the total amount of task data.
In the case of busy terminal and/or edge devices, under energy consumption constraints, according to the objectives and constraintsDetermining a segmentation ratio of the perception task data, further comprising: in case of busy terminal device i, according to the formula +.>Calculating a data quantity threshold X divided into terminal equipment i Ti The method comprises the steps of carrying out a first treatment on the surface of the And/or, in case the edge device j is busy, according to the formula +.>Calculating the data quantity threshold Y divided to the edge device j Ti The method comprises the steps of carrying out a first treatment on the surface of the According to X Ti And Y Ti Determining a segmentation ratio by at least one of the sensing task data and the total amount of the sensing task data; wherein (1)>For the time that needs to wait on terminal device i; />Indicating the time to wait on edge device j, +.>For the time calculated at terminal i, < >>For terminal equipment i to edge equipment m i Transmission rate when transmitting self-acquired perception task data, C i,j And calculating the calculation rate of the perception task data acquired by the terminal equipment i for the edge equipment j.
Next, as shown in fig. 2, an embodiment of the present invention further provides a data processing apparatus facing crowd sensing, which includes: the acquisition unit is used for acquiring perception task data to be processed by the terminal equipment; a determining unit, configured to determine a task segmentation strategy according to a reference condition, where the reference condition includes at least one of: the terminal equipment, the edge equipment and the cloud center are busy, task processing speed and data transmission rate respectively; the segmentation unit is used for controlling the terminal equipment to segment the perception task data according to a segmentation strategy; and the control unit is used for controlling at least one of the terminal equipment, the edge equipment and the cloud center to process the segmented data according to the segmentation condition of the perception task data.
Specifically, the reference condition further includes an electric quantity of the terminal device iAnd the total energy consumption E of the terminal device i for processing the acquired perception task data i 。
The electric quantity of the terminal equipment is included in the range of the reference quantity, so that the task segmentation can be better optimized by combining the energy consumption of the task data processing process and the electric quantity of the terminal equipment, the control of the energy consumption of the terminal equipment is facilitated, and the risk that the energy consumption of the terminal equipment is increased or even the electric quantity of the terminal equipment is insufficient due to unreasonable task segmentation is reduced.
Specifically, the determining unit is configured to: according to the objectives and constraintsDetermining the segmentation proportion of the perception task data; wherein (1)>T i L The total delay of processing the perceived task data acquired by the terminal device i itself is +.>Processing the total time delay of the perception task data acquired by the terminal equipment i for the edge equipment j, T i C For the total time delay of the perception task data acquired by the cloud center processing terminal equipment i, pi is an unloading decision set pi= { pi of all terminal equipment i ,i∈N}，П i Unloading decision vector II of perception task data acquired by terminal equipment i aiming at terminal equipment i i ＝{x i ,y i,j ,z i }，x i For the proportion of perceived task data divided into terminal i, y i,j For the proportion of perceived task data divided into edge device j, z i For the proportion of the perceived task data divided into the cloud center, N is the total number of terminal devices, f is the computing resource allocation set f= { f of all edge devices i,j ,i∈N,j∈M}，f i,j For computing resource allocation vectors for edge devices j handling aware task data from terminal device i, M is the total number of edge devices.
By setting the targets and the constraints, reasonable task segmentation conditions are designed, and all segmentation conditions conforming to the targets and the constraints can well optimize the processing process of the perception task data, so that the processing speed of the perception task data is improved.
In particular, the method comprises the steps of,1-μ i1 CPU cycle number duty ratio, K for processing self acquired perception task data for terminal equipment i i CPU total cycle number f required for processing perception task data acquired by terminal equipment i i The calculation force distribution quantity when the terminal equipment i processes the acquired sensing task data; />For edge device m i Delay of transmission of perceived task data acquired by terminal device i to edge device j,/for the perceived task data acquired by terminal device i>Calculating the time delay of the perception task data acquired by the terminal equipment i for the edge equipment j; />T c For the time delay of the perception task data acquired by the terminal equipment i, the preset edge equipment transmits the perception task data to the cloud center, < +.>Calculating the time delay of the perception task data acquired by the terminal equipment i for the cloud center;for the terminal i, calculating the energy consumption of the perceived task data acquired by itself,/for the terminal i>For terminal equipment i to edge equipment m i And transmitting the energy consumption of the acquired sensing task data.
In actual implementation, the terminal device i is transmitting the perception task data to the edge device m i After that, edge device m i There may be different busy states if edge device m i If the queued task exceeds the threshold, the edge device m is set i The received data is transmitted to the edge device j. Of course, edge device m i And edge device j may be the same device, edge device m i The data to be processed of the terminal equipment i does not exceed a threshold value, the data to be processed of the terminal equipment i is processed by the terminal equipment i, data transmission is not needed between the terminal equipment i,i.e. 0.
In a specific embodiment, the determining unit comprises, under energy consumption constraints, a first determining module: the first determining module is used forIn the case of (2), then determine x i ＝1，y i,j ＝0，z i =0; wherein C is i Calculating rate when calculating the perceived task data acquired by itself for terminal device i, +.>For terminal equipment i to edge equipment m i Transmission rate at which the perceived task data acquired by itself is transmitted.
The determining unit further comprises a second determining module for determining, under the energy consumption constraint, thatIf the data size of the task to be processed of the edge device j is less than or equal to B i Then determine x i ＜1，y i,j ＜0，z i =0; wherein (1)>C i,j And calculating the calculation rate of the perception task data acquired by the terminal equipment i for the edge equipment j.
Specifically, under the energy consumption constraint, the determining unit further includes a third determining module: the third determining module is used forAnd the data volume of the task to be processed of the edge equipment j is larger than B i In the case of (2), according to the formulaCalculate X i 、Y i 、Z i Then according to X i 、Y i 、Z i Determining a segmentation proportion; wherein X is i For the data quantity of the perception task data divided into the terminal device i, Y i For the data volume of the perception task data divided to the edge device j, Z i X is the data volume of the perception task data divided into the cloud center i +Y i +Z i ＝L i ，x i :y i :z i ＝X i :Y i :Z i ，L i To perceive the total amount of task data.
In case the terminal device and/or the edge device are busy, the determining unit is further adapted to, under the energy consumption constraint: in case the terminal device i is busy, according to the formulaCalculating a data quantity threshold X divided into terminal equipment i Ti The method comprises the steps of carrying out a first treatment on the surface of the And/or, in case the edge device j is busy, according to the formula +.>Calculating the data quantity threshold Y divided to the edge device j Ti The method comprises the steps of carrying out a first treatment on the surface of the According to X Ti And Y Ti Determining a segmentation ratio by at least one of the sensing task data and the total amount of the sensing task data; wherein (1)>For the time that needs to wait on terminal device i; />Indicating the time to wait on edge device j, +.>For the time calculated at terminal i, < >>For terminal equipment i to edge equipment m i Transmission rate when transmitting self-acquired perception task data, C i,j And calculating the calculation rate of the perception task data acquired by the terminal equipment i for the edge equipment j.
The data processing method for crowd sensing of the present invention is described below with reference to a specific embodiment, and includes the following steps:
step one: recording all information on a cloud by using the attributes of a sensing task, a network and equipment;
step two: computing perception task data Q i Energy consumption and time delay for processing at the end node:
the total delay of the local processing is
Wherein 1-mu i1 CPU cycle number duty ratio, K for processing self acquired perception task data for terminal equipment i i The total number of CPU cycles required for processing the perceived task data acquired by terminal device i.
Local processing Q i Is of (1)The consumption of the quantity is expressed as
Step three: computing perception task data Q i The total delay of computation and transmission on the delay and transmission power consumption execution device processed on the edge node j isThe energy consumption of data transmission is:
step four: computing perception task data Q i Total time delay processed on cloud center;
step five: combining the above steps to obtain Q i The total time delay and total energy consumption of the treatment are as follows:
through the above description, the partial unloading decision vector of the device i is recorded as pi i ＝{x i ,y i,j ,z i An unloading decision set of all devices is pi= { pi } i I e N, the computing resource allocation vector for all devices is denoted as f= { f i,j I epsilon N, j epsilon M, the battery capacity of the device isThe goals and constraints of the resource allocation scheme under partial offloading are expressed as follows:
step six: determination of a partial segmentation threshold
First, the segmentation threshold of the end device and the edge node, ci represents the processing rate of the task on the end device i, A i Representing the local maximum computable amount of data when calculated locally only, formulated as(/>For transmission rate, f i To distribute local force) is simplified as:
the default time delay is not counted when the transmission connection and the receiving are established, and the method meets the energy consumption constraint on the premise of ensuring that the local equipment i can normally process all tasksThe formula shows that if the processing rate of the task data of device i is +.>It chooses to compute locally under energy consumption constraints, otherwise it is partially offloaded or transmitted to the edge node for processing.
Secondly, under the condition that the edge node and the cloud center cooperate, B i The perception task data representing the maximum data amount which can be processed by the edge node when the cloud is not needed to perform cooperative calculation meets the formula on the basis of minimizing the time delayIgnoring the effects of network instabilityThe delay of the network to establish the connection can be reduced to:
B i ·C i,j ＝T C '·f i,j the method comprises the steps of carrying out a first treatment on the surface of the Wherein T is C ‘＝T C Is a known quantity, thus B in critical state can be determined i Further determining a task division strategy;
again in a local busy state, i.e. tasks are waiting for local processing, the splitting of the task needs to take into account the waiting time of the queue, which is similar to the case when the edge node is busy. The task process split in the local busy state is formulated as follows:
wherein X is Ti A data amount threshold representing offloading in a local busy state;indicating the time to wait on the local device i; />Indicating the time that task data needs to wait on the edge node.
Again, the analysis of the situation in the busy state of the edge node satisfies the formulaThe formula needs to ensure that the data is just completely transmitted from the local device to the edge node, and the local device starts to calculate the divisor A i Is included in the data of the remaining perception data of the (b).
Wherein the method comprises the steps ofRepresenting the time at which the computation is performed locally on the data, Y Ti A data amount threshold representing the offloading of the data portion in the busy state of the edge node.
Finally, under the condition of the cooperative work of the end-edge clouds, the partial unloading condition of the cooperative calculation of the end-edge clouds is as follows:
and it also satisfies:
X i +Y i +Z i ＝L i ，x i :y i :z i ＝X i :Y i :Z i ；
calculating the data quantity threshold X under each condition according to the above formula i 、Y i And Z i The unloading scheme and the execution scheme of each task in the whole partial unloading system can be obtained.
Step seven: and then dividing the perception task processing process according to the dividing threshold value, and then using an optimized ADMM algorithm to partially unload the perception data, so that the total time delay can be minimized.
Therefore, the minimum time delay of partial unloading under the cooperation of the crowd sensing end edge cloud can be realized, and the end and the local refer to the terminal.
The foregoing embodiment numbers of the present invention are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments. Moreover, the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that herein.
In the foregoing embodiments of the present invention, the descriptions of the embodiments are emphasized, and for a portion of this disclosure that is not described in detail in this embodiment, reference is made to the related descriptions of other embodiments.
In the several embodiments provided in the present application, it should be understood that the disclosed technology content may be implemented in other manners. The above-described embodiments of the apparatus are merely exemplary, and the division of the units, for example, may be a logic function division, and may be implemented in another manner, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be through some interfaces, units or modules, or may be in electrical or other forms.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units may be implemented in hardware or in software functional units.
The integrated units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied essentially or in part or all of the technical solution or in part in the form of a software product stored in a storage medium, including instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a removable hard disk, a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely a preferred embodiment of the present invention and it should be noted that modifications and adaptations to those skilled in the art may be made without departing from the principles of the present invention, which are intended to be comprehended within the scope of the present invention.
Claims (2)
1. The data processing method for crowd sensing is characterized by comprising the following steps of:
acquiring perception task data to be processed of the terminal equipment i;
determining a task segmentation strategy according to reference conditions, wherein the reference conditions comprise at least one of the following: the terminal equipment i, the edge equipment and the cloud center are busy, task processing speed and data transmission rate respectively;
controlling the terminal equipment i to divide the sensing task data according to the division strategy;
controlling at least one of the terminal equipment i, the edge equipment and the cloud center to process the segmented data according to the segmentation condition of the perception task data;
the reference condition also comprises the electric quantity of the terminal equipment iAnd the terminal device i processes the total energy consumption E of the perception task data acquired by the terminal device i i ；
Determining a task segmentation strategy according to the reference condition comprises:
according to the objectives and constraintsDetermining the segmentation proportion of the perception task data;
wherein,T i L processing for said terminal device i the total delay of said perceived task data acquired by itself,/->Processing the terminal for edge device jTotal time delay of the perception task data acquired by the equipment i is T i C Processing the total time delay of the sensing task data acquired by the terminal equipment i for the cloud center, wherein pi is an unloading decision set of all the terminal equipment> Unloading decision vector for the perceived task data acquired by the terminal device i for itself,/->x i For the proportion of the perceived task data divided into the terminal devices i, y i,j For the proportion of the perceived task data segmented to the edge device j, z i For the ratio of the perceived task data divided into the cloud center, N is the total number of the terminal devices, and f is the computing resource allocation set f= { f of all the edge devices i,j ,i∈N,j∈M}，f i,j A computing resource allocation vector for the edge device j processing the perceived task data from the terminal device i, M being the total number of the edge devices;
1-μ i1 CPU cycle number duty ratio, K for processing the acquired perception task data for the terminal equipment i i The total CPU cycle number f required for processing the perception task data acquired by the terminal equipment i i Calculating force distribution quantity when the terminal equipment i processes the self-acquired sensing task data; /> For edge device m i Delay of transmitting the perceived task data acquired by the terminal device i to the edge device j, +.>Calculating the time delay of the sensing task data acquired by the terminal equipment i for the edge equipment j; />T c For the preset time delay of the edge device to transmit the perception task data acquired by the terminal device i to the cloud center, the user is in the step of +.>Calculating the time delay of the perception task data acquired by the terminal equipment i for the cloud center; /> Calculating for said terminal device i the energy consumption of said perceived task data it acquired by itself,/->For the terminal device i to the edge device m i Transmitting the energy consumption of the sensing task data acquired by the self;
under the constraint of energy consumption, according to the target and the constraintDetermining the segmentation proportion of the perception task data comprises the following steps:
at the position ofIs the case of (2)Next, determine x i ＝1，y i,j ＝0，z i ＝0；
Wherein C is i The calculation rate when calculating the sensing task data acquired by the terminal equipment i for the terminal equipment i,for the terminal device i to the edge device m i The transmission rate of the sensing task data acquired by the self is transmitted;
under the constraint of energy consumption, according to the target and the constraintDetermining the segmentation proportion of the perception task data further comprises:
at the position ofIf the data size of the task to be processed of the edge device j is less than or equal to B i Then determine x i ＜1，y i,j ＜0，z i ＝0；
Wherein,C i,j calculating the calculation rate of the sensing task data acquired by the terminal equipment i for the edge equipment j;
under the constraint of energy consumption, according to the target and the constraintDetermining the segmentation proportion of the perception task data further comprises:
at the position ofAnd the data volume of the task to be processed of the edge equipment j is larger than B i In the case of (2), according to the formulaCalculate X i 、Y i 、Z i Then according to X i 、Y i 、Z i Determining the segmentation proportion;
wherein X is i For dividing the data volume of the perception task data to the terminal device i, Y i For dividing the data volume of the perception task data to the edge device j, Z i X is the data volume of the perception task data divided into the cloud center i +Y i +Z i ＝L i ，x i :y i :z i ＝X i :Y i :Z i ，L i A total amount of the perception task data;
under the constraint of energy consumption, according to the target and the constraintDetermining the segmentation proportion of the perception task data further comprises:
in case the terminal device i is busy, according to the formulaCalculating a data quantity threshold X divided into the terminal equipment i Ti The method comprises the steps of carrying out a first treatment on the surface of the And/or, in case said edge device j is busy, according to the formula +.>Calculating a data quantity threshold Y segmented to the edge device j Ti ；
According to X Ti And Y Ti Determining the segmentation ratio based on at least one of the perception task data and the total amount of the perception task data;
wherein,for the time that needs to wait at the terminal device i; />Indicating the time to wait on said edge device j,/->For the time calculated at the terminal device i,/-a time calculated at the terminal device i>For the terminal device i to the edge device m i Transmission rate C when transmitting the self-acquired perception task data i,j And calculating the calculation rate of the sensing task data acquired by the terminal equipment i for the edge equipment j.
2. An apparatus for performing the method of claim 1, comprising:
the acquisition unit is used for acquiring perception task data to be processed by the terminal equipment;
a determining unit, configured to determine a task segmentation strategy according to a reference condition, where the reference condition includes at least one of: the terminal equipment, the edge equipment and the cloud center are busy, task processing speed and data transmission rate respectively;
the segmentation unit is used for controlling the terminal equipment to segment the perception task data according to the segmentation strategy;
and the control unit is used for controlling at least one of the terminal equipment, the edge equipment and the cloud center to process the segmented data according to the segmentation condition of the perception task data.
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