KR101993083B1 - R-tree based task management method in space crowd sourcing system - Google Patents

Abstract

Disclosed is an R-Tree based task management technique in a space crowdsourcing system. According to one embodiment of the present invention, a task management method performed in a crowdsourcing server comprises the following steps: constructing an R-Tree based on the density of sensing data and crowdsourcing region information in accordance with reception of a crowdsourcing request; and using minimum bounding rectangle (MBR) data of a leaf node in the R-Tree to assign the task to preregistered participants.

Description

{R-TREE BASED TASK MANAGEMENT METHOD IN SPACE CROWD SOURCING SYSTEM}

The following discussion is about R-Tree based task management in a space crowd sourcing system.

With the development of sensors and data communication technologies, real-time environmental information can be collected by smart devices. As a result, the paradigm of information systems is changing from existing service centers to data centers. Because data-centric paradigms are becoming increasingly important to analyze real-time data and data, research is currently exploring a variety of methods for data collection.

In existing sensor networks, in order to collect real-time environmental information, many sensors had to be placed in areas requiring measurement. This conventional sensor network method has a disadvantage of large maintenance cost. The crowdsourcing system can collect the necessary data directly from the applicant's smart device and send it to the crowdsourcing server. This makes it easier to collect data in a short period of time, while achieving less cost and higher accuracy than existing sensor networks.

Spatial crowdsourcing collects sensor information using a smart device's GPS or other location sensors. As a result, most of the crowdsourcing results include location data, and the collected information is analyzed from a map-based perspective that focuses on the characteristics of the local space. In addition, the visibility of the analysis results can be improved in a space crowd sourcing system.

Enough participants are the most important factor in the service of space crowdsourcing applications. Most crowdsourcing applications are now based on volunteer applicants. When a volunteer is selected as a participant, he / she consumes his or her own resources such as battery, computing performance, etc., and discloses his / her location, thereby causing leakage of personal information. Zhao, Li and Ma have worked to allow general participants to choose problem-solving solutions. They define the participants 'consuming models and the value model of participants' contributions to support the selection of suppliers and company participants. The system assigns tasks based on balance between participant consumption and service provider spending.

Resources: KR10-2014-0145033

We can provide a method to manage R-Tree based tasks in a space crowd sourcing system.

The task management method performed by the crowd sourcing server is to build an R-tree based on the crowdsensing data density and the crowd sourcing region information upon receipt of the crowd sourcing request ; And assigning the task to the pre-registered participant using the Minimum Bounding Rectangle (MBR) data of the leaf node in the al-tree.

The step of assigning the task to the registered participant using the minimum bounding rectangle (MBR) data of the leaf node in the al-tree may include a step of performing data sensing when the distance between the boundary of the participant and the MBR is less than a preset reference And structuring the detected target data into a data package list as the data sensing starts, wherein data sensing is continued while the participant is within the boundary of the MBR data, Wherein the first data package includes location data, time data, and sensing data, the first data package included in the data package list means the first data package including the MBR data and existing in the data package list, The remaining data packages, except for the package, And it may include anonymous location data associated with it.

)을 통해 계산되고,

는 참가자의 밀도에 따라 계산되고, 상기 MBR 데이터의 밀도가 기 설정된 기준 이하이면,

가 기 설정된 기준 보다 커지고, 상기 MBR 데이터의 밀도가 기 설정된 기준 이상이면,

가 기 설정된 기준보다 작아질 수 있다. The step of assigning a task to a registered participant using the minimum bounding rectangle (MBR) data of the leaf node in the al-tree is characterized in that the MBR data is [( x ₁ , y ₁ ), ( x ₂ , y ₂ ) And the anonymous location data is denoted by Ap , the anonymous location data of the MBR data is expressed by Equation 1 (

), ≪ / RTI >

Is calculated according to the density of the participant, and if the density of the MBR data is less than a preset reference,

If the density of the MBR data is greater than a preset reference,

May be smaller than a predetermined standard.

The step of assigning a task to a registered participant using minimum bounding rectangle (MBR) data of a leaf node in the al-tree may include: assigning a task to a participant registered in the al- Searching for a leaf node, and inserting the first data package into a leaf node of the searched al-tree.

The step of assigning a task to a registered participant using the minimum bounding rectangle (MBR) data of the leaf node in the al-tree may include detecting a distance between the boundary of the participant and the MBR data, And selecting at least one participant closest to the target data based on the target data, wherein the nodes of the al-tree are separated as the number of the participants increases, The node may include a task ID associated with the task.

The step of assigning a task to a registered participant using minimum bounding rectangle (MBR) data of a leaf node in the al-tree may include: obtaining a sensing result obtained by sensing the target data in pre- Into the node of the MBR data.

You can reduce the computing cost of a crowdsourcing server while protecting your personal information.

A large number of participants can be recruited and the amount of crowd sensing data can be increased.

FIG. 1 is a view for explaining a task allocation process based on R-Tree spaceclocking in a crowd sourcing server according to an embodiment.
2 is a block diagram illustrating a configuration of a crowd sourcing server according to an exemplary embodiment of the present invention.
3 is a flowchart illustrating a method of managing an R-Tree-based task in a crowd sourcing server according to an exemplary embodiment of the present invention.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings.

In the following embodiments, a method of allocating a task based on R-Tree space clocking in a cloud sourcing system (server) will be described. In addition, incentive mechanisms, absence of real-world data sets, and privacy concerns are also considered. A space crowd sourcing server assigns tasks to a large number of participants based on real-time cost. At this time, the task of the requester is automatically assigned to the participant, and the automatic task assignment can minimize the work cost of the participant while preserving the participant position data. This eliminates the need for the participant to worry about where he or she should go or approve the authorization.

A method of allocating a task based on R-tree space clocking is to assign a task to a participant using MBR data instead of location-point data so that data sensing starts automatically when a participant approaches the MBR boundary, This lasts while the participant is in the MBR boundary. The whole process is done automatically, so you can recruit more participants and increase the amount of crowdsensing data. When a sensing result related to crowd sensing data (hereinafter referred to as sensing data) is transferred to the crowd sourcing server, a data package list including position data, time data, and sensing data is transmitted. To protect the participant's location data, the local position associated with the MBR is used within this data package, and the MBR data is transmitted only in the first package. This method increases the amount of sensing data while protecting the participant's location data. MBR data can be used for R-tree-based data storage of a crowd sourcing server to reduce data insertion cost.

FIG. 1 is a view for explaining a task allocation process based on R-Tree spaceclocking in a crowd sourcing server according to an embodiment.

The task allocation method based on R-tree space clocking for a space crowd sourcing system will be described in detail. At this time, the task of the requester is registered in the crowd sourcing server, and it is assumed that the task is automatically assigned to the participant registered in advance in the crowd sourcing server.

The information the requester needs in the crowd sourcing server includes crowd sourcing domain information, persistence of collection time, and density data of the sensing data. In this case, the crowd sourcing area may be an area for collecting crowd sourcing data to be processed in relation to the task, and the data may be acquired from the participant in the crowd sourcing area. The crowd sourcing server can build an R-tree based on density data and crowd sourcing domain information. A task can be assigned to a registered participant using the leaf node's MBR data in a crowd sourcing server al-tree (R-tree). In the embodiment, the MBR data is used instead of the data for the location point.

As the participant approaches the boundary of the MBR data, the data sensing is interrupted and the target data can be automatically detected. Alternatively, when the participant approaches the boundary of the MBR data, the data sensing can be automatically started, and the target data can be automatically detected. The detected target data can be structured into a data package list and sent to the crowd sourcing server. At this time, data sensing can be continued until the participant leaves the MBR boundary.

In the data package, MBR data is included in the first data package, and only anonymous local location data related to the MBR data can be used to protect the participant's location information in the data package except the first data package.

는 참가자의 밀도에 따라 결정된다. 대상 MBR 데이터의 밀도가 기 설정된 기준보다 낮을 경우,

가 기 설정된 기준보다 클 것이고, 대상 MBR 데이터의 밀도가 기 설정된 기준보다 높을 경우,

가 기 설정된 기준보다 작을 것이다. 이런 종류의 익명의 데이터는 크라우드소싱 결과의 정확성에 영향을 미칠 수 있다. 그러나 크라우드소싱의 문제가 특정 지역의 노이즈를 수집하는 것과 같이 영역 기반 요청인 경우라면, 그 효율성은 매우 낮다.If the MBR data is expressed as [( x ₁ , y ₁ ), ( x ₂ , y ₂ )] and the anonymous location data is Ap , the anonymous location data for the MBR data is calculated by the following formula . In Equation (1)

Is determined according to the density of the participant. If the density of the target MBR data is lower than the preset reference,

If the density of the target MBR data is higher than the preset reference,

Will be less than the predetermined standard. This kind of anonymous data can affect the accuracy of crowd sourcing results. However, if the problem of crowd sourcing is area-based requests, such as collecting noise from a particular area, the efficiency is very low.

Equation 1:

As the first data package is inserted into the crowd sourcing server, the crowd sourcing server can search the leaf node of the R-tree and insert the first data package into the leaf node of the discovered R-tree. If the crowd sourcing server receives the result data and one position data of the same participant, the data package is considered to be one of the sensing results of the previous MBR data node. In this case, the data package can be inserted directly into the leaf node without any pruning, thereby reducing the data insertion cost of the R-tree.

S ₁ , S ₂ , S _3, etc.) after the task is divided into a plurality of sub-tasks, if the task area <S> . At this time, the main task becomes the parent of the subtree, and the subtask becomes the child node of the corresponding subtree.

This reduces the integration cost of the sensing results. The size of the task area is defined by task registration. If the number of participants is greater than TA - Limitation , the task area is separated. At this time, the TA - Limitation is determined by the performance of the server computer. For example, TA - can be set to Limitation <100 - Limitation of the range of 10 <TA.

Referring to FIG. 1, a structure based on R-tree spatial cloaking is shown. When a crowd sourcing request is registered with a crowd sourcing server, an R-tree is estimated using the crowd sourcing domain and density data. After that, the task selects a previously registered participant. It is possible to select at least one participant who exists at a distance less than the preset reference with the MBR data. In other words, as the distance between the boundary of the participant and the MBR data is determined to be equal to or less than a preset reference, at least one participant who is close to the sensed target data can be selected. For example, the task in the R7 area can select participant 1 near the target data (area). Further, the identification information of the task (for example, the task ID) and the selected participant can be separately managed. At this time, when the number of participants increases, the R-tree node is separated and the parent node includes task IDs. At the previous leaf node level, each parent node has only one task ID to reduce battery working time.

When the participant approaches the R7 area, the task can automatically detect the target data in P1, P2, P3, P4, and P5 position data. In this way, the sensing results can be inserted directly into the R7 node without battery operation. In the conventional conventional R-tree method, when the task area (search area S) is too large, the task area is automatically divided into a plurality of sub areas, and the divided areas are included in other task sub areas. In contrast, in the embodiment, the subtrees of one task are managed independently. In this way, it is possible to increase the amount of sensing data, to protect the personal information of the participants, and to reduce the data insertion cost of the crowd sourcing server.

FIG. 2 is a block diagram illustrating a configuration of a crowd sourcing server according to an embodiment. FIG. 3 is a flowchart illustrating a method of managing a task based on an R-Tree in a crowd sourcing server according to an exemplary embodiment.

The crowd sourcing server 100 may include a construction unit 210 and an allocation unit 220. These components may be representations of different functions performed by the processor in accordance with control commands provided by the program code stored in the crowd sourcing server 100. [ The components can control the crowd sourcing server 100 to perform the steps 310 to 320 included in the R-Tree based task management method of FIG. At this time, the components may be implemented to execute an instruction according to code of an operating system and code of at least one program included in the memory.

The processor may load the program code stored in the file of the program for the R-Tree based task management method into the memory. For example, when a program is executed in the crowd sourcing server 100, the processor can control the server to load the program code from the file of the program into the memory under the control of the operating system. At this time, each of the construction unit 210 and the allocation unit 220 included in the processor and the processor includes a processor for executing a command of a corresponding one of the program codes loaded in the memory and executing the steps 310 to 320 thereafter They can be different functional expressions.

In step 310, the constructing unit 210 may construct an R-Tree based on the density of the sensing data and the crowd sourcing area information upon receipt of the request for crowdsourcing.

In step 320, the assigning unit 220 may assign a task to a previously registered participant using the minimum bounding rectangle (MBR) data of the leaf node in the tree. The allocating unit 220 may start data sensing when the distance between the boundary of the participant and the MBR is less than a preset reference and may structure the detected target data into the data package list as the data sensing starts. At this time, the data sensing can be continued while the participant exists within the boundary of the MBR data.

Assignment section 220 is the MBR data through the _{[(x 1, y 1)} , (x 2, y 2)] represented and when Ap anonymous location data LA, Equation anonymous location data MBR data 1 to Can be calculated.

Equation 1:

는 참가자의 밀도에 따라 계산되고, MBR 데이터의 밀도가 기 설정된 기준 이하이면,

가 기 설정된 기준 보다 커지고, MBR 데이터의 밀도가 기 설정된 기준 이상이면,

가 기 설정된 기준보다 작아질 수 있다. At this time,

Is calculated according to the density of the participant, and if the density of the MBR data is less than a predetermined standard,

Is greater than a preset reference, and the density of the MBR data is equal to or greater than a preset reference,

May be smaller than a predetermined standard.

The allocating unit 220 may search the leaf node of the al-tree corresponding to the first data package as the first data package is inserted and insert the first data package into the leaf node of the searched al-tree.

The allocating unit 220 may select at least one participant closest to the target data based on the detected target data as the distance between the participant and the boundary of the MBR data is determined to be less than a preset reference. At this time, as the number of participants increases, the nodes of the al-tree are separated, and in the node of the separated al-tree, the parent node can include the task ID associated with the task. The assigning unit 220 may insert the sensing result obtained in the task into the node of the MBR data as the task senses the target data in the pre-designated position data.

The apparatus described above may be implemented as a hardware component, a software component, and / or a combination of hardware components and software components. For example, the apparatus and components described in the embodiments may be implemented within a computer system, such as, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate array (FPGA) , A programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For ease of understanding, the processing apparatus may be described as being used singly, but those skilled in the art will recognize that the processing apparatus may have a plurality of processing elements and / As shown in FIG. For example, the processing unit may comprise a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as a parallel processor.

The software may include a computer program, code, instructions, or a combination of one or more of the foregoing, and may be configured to configure the processing device to operate as desired or to process it collectively or collectively Device can be commanded. The software and / or data may be in the form of any type of machine, component, physical device, virtual equipment, computer storage media, or device As shown in FIG. The software may be distributed over a networked computer system and stored or executed in a distributed manner. The software and data may be stored on one or more computer readable recording media.

The method according to an embodiment may be implemented in the form of a program command that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, and the like, alone or in combination. The program instructions to be recorded on the medium may be those specially designed and configured for the embodiments or may be available to those skilled in the art of computer software. Examples of computer-readable media include magnetic media such as hard disks, floppy disks and magnetic tape; optical media such as CD-ROMs and DVDs; magnetic media such as floppy disks; Magneto-optical media, and hardware devices specifically configured to store and execute program instructions such as ROM, RAM, flash memory, and the like. Examples of program instructions include machine language code such as those produced by a compiler, as well as high-level language code that can be executed by a computer using an interpreter or the like.

While the present invention has been particularly shown and described with reference to exemplary embodiments thereof, it is to be understood that the invention is not limited to the disclosed exemplary embodiments. For example, it is to be understood that the techniques described may be performed in a different order than the described methods, and / or that components of the described systems, structures, devices, circuits, Lt; / RTI &gt; or equivalents, even if it is replaced or replaced.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (6)

A method of managing a task performed by a crowd sourcing server,
Constructing an R-Tree based on the density of the sensing data and the crowd sourcing region information upon receipt of the crowdsourcing request; And
Assigning a task to a pre-registered participant using minimum bounding rectangle (MBR) data of the leaf node in the al-tree
Lt; / RTI &gt;
The step of assigning a task to a registered participant using minimum bounding rectangle (MBR) data of a leaf node in the al-
The data sensing is started when the distance between the boundary of the participant and the MBR is determined to be less than a preset reference, the target data detected as the data sensing is started is structured into a data package list, and the MBR data is [( x ₁ , y ₁ ), ( x ₂ , y ₂ )] and the anonymous location data is denoted Ap , the anonymous location data of the MBR data is expressed by Equation 1

), &Lt; / RTI &gt;

Is calculated according to the density of the participant, and if the density of the MBR data is less than a preset reference,

If the density of the MBR data is greater than a preset reference,

Becomes smaller than a predetermined standard,
The data sensing is continued while the participant is present within the boundary of the MBR data,
Wherein the data package list includes position data, time data, and sensing data,
Wherein the first data package included in the data package list includes the MBR data and the first data package exists in the data package list,
The data package excluding the first data package includes anonymous location data related to the MBR data
Task management method. delete delete The method according to claim 1,
The step of assigning a task to a registered participant using minimum bounding rectangle (MBR) data of a leaf node in the al-
Searching for a leaf node of an al-tree corresponding to the first data package as the first data package is inserted, and inserting the first data package into a leaf node of the searched al-
The task management method comprising: The method according to claim 1,
The step of assigning a task to a registered participant using minimum bounding rectangle (MBR) data of a leaf node in the al-
Selecting at least one participant closest to the target data based on the sensed target data as the distance between the participant and the boundary of the MBR data is determined to be less than a preset reference
Lt; / RTI &gt;
As the number of participants increases, the nodes of the al-tree are separated, and at the node of the separated al-tree, the parent node includes the task ID associated with the task
The task management method comprising: The method according to claim 1,
The step of assigning a task to a registered participant using minimum bounding rectangle (MBR) data of a leaf node in the al-
Inserting the obtained sensing result into the node of the MBR data as the task senses target data in pre-designated position data
The task management method comprising:

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