Data processing method for indoor positioning system with improved scalability

Abstract

A data processing method (600) for an indoor positioning system (100), comprising the steps of: providing (S601) configuration information to a mobile device (200); detecting (S602), by the mobile device (200), the raw localized data; upon determining (S603), by the mobile device (200), that the pre-processing is performed locally, pre-processing (S604) the raw location data by using the configuration information; and sending (S605) the preprocessed localized data to an internally deployed local server (400) or a cloud server (300).

Description

Technical Field

[0001] The present invention relates to the field of data processing for indoor positioning systems. More particularly, various devices, systems and methods related to improving the scalability of indoor positioning systems are disclosed herein. Background Art

[0002] In a system that enables indoor positioning, a mobile device running positioning software (e.g., IPSSDK) is able to detect the device location using various technologies including visible light communication, Bluetooth triangulation, etc. The software then exports the location of the device in an absolute manner (such as a geographic location) or a relative manner (such as a location on a floor plan map). In the former case, the floor plan map image is usually annotated with geographic reference points so that the pixel location on the floor plan can be mapped to an absolute geographic location described by latitude and longitude. The mobile device periodically sends the detected location to the cloud for further processing and analysis.

[0003] When indoor positioning systems were designed for indoor navigation applications, the expected number of mobile devices generating location data was limited. However, today the system is expanding this feature to also enable data analysis of how and where customers visit and stay in indoor spaces. As the customer base and number of mobile devices increase, a large amount of data will be sent to the cloud for processing. The network services that handle the location data need to be upgraded to cope with the volume of data, which will increase costs and reduce system stability.

[0004] US8396254B1 relates to a method and system for estimating the position of a robot, comprising a robot: capturing a range image indicating distances from the robot to multiple objects in an environment, transmitting a query to a server based on the range image, receiving a mapping of the environment from the server, and estimating the position of the robot. Summary of the invention

[0005] In indoor environments, using positioning-enabled mobile devices, the location of the device can be detected and provided to a local server or a remote server in the cloud. Using such location data, analysis of certain key performance indicators (such as passenger flow, density, and dwell time) can be calculated over space and time intervals, which can provide high commercial value. However, with the increase in the number of mobile devices and therefore the amount of location data, the data processing overhead on the server (especially the cloud server) may increase significantly and lead to higher cost and lower scalability of the system. In the present invention, a new processing method is proposed to reduce the load on the server and thereby increase the scalability of the system.

[0006] More particularly, the objects of the present invention are achieved by a data processing method for an indoor positioning system as claimed in claim 1 and by an indoor positioning system as claimed in claims 12-15, respectively.

[0007] According to a first aspect of the present invention, a data processing method is provided. A data processing method for indoor positioning, comprising the following steps: providing configuration information to a mobile device; detecting raw localization data by the mobile device; when the mobile device determines to perform preprocessing locally, preprocessing the raw localization data by using the configuration information; sending the preprocessed localization data to an on-premise local server or a cloud server; wherein the configuration information includes a size in pixels of a floor plan and a grid size in pixels or absolute length, and the grid size is configurable and defines the accuracy of the preprocessed localization data;

[0008] The pre-processing step further comprises:

[0009] - mapping the raw localization data to a grid index; wherein the pre-processed localization data comprises the grid index.

[0010] The mobile device may be a mobile phone, a tablet, a laptop, a wearable device, a handheld scanner, or another portable electronic device.

[0011] In order to process the raw localized data locally on the device, a certain amount of computing power is required. This may not be a problem for advanced devices such as smartphones, but it may be a burden for low-end devices such as handheld scanners in supermarkets. Moreover, even if the mobile device has computing power, it may not always be available for this task. For example, sometimes the mobile device may be busy with some other important tasks and therefore cannot allocate enough resources on this dedicated data processing task.

[0012] Therefore, it is beneficial that the mobile device can first evaluate its capabilities and / or status and decide whether to run the pre-processing step. If the mobile device determines not to perform pre-processing locally, it can simply send the raw localized data to a local server or a cloud server.

[0013] Advantageously, the mobile device determines to perform pre-processing locally only when there are sufficient processing resources on the mobile device to process the raw localized data. If the mobile device determines that it can perform pre-processing locally, it will first perform pre-processing locally and then send the pre-processed localized data to a local server or a cloud server.

[0014] The configuration information can be specific to the location and / or application of the site. Based on the configuration information, a pre-processing step is then used to extract more business-relevant localized data from the raw data. For example, the raw localized data can be re-expressed with a certain level of abstraction to produce statistics with higher business value. The absolute geographic location of the device can be expressed in a simplified format linked to a certain application or business.

[0015] Advantageously, the data processing method further comprises the step of sending the original localized data to a cloud server or a local server.

[0016] It may be useful to send the pre-processed localized data in combination with the original localized data. When necessary, the local server or cloud server can perform further calculations using the original localized data to derive additional information beyond the pre-processed data received from the mobile device. For example, the local server or cloud server can reorganize the data using different extractions and / or combine data obtained from different locations to derive other information of interest to the business.

[0017] Preferably, the configuration information includes the size in pixels of the plan view and the grid size in pixels or absolute length.

[0018] Using the dimensions in pixels of the floor plan, the mobile device can derive a mapping between the actual dimensions of the venue and the dimensions in pixels. Further information about the grid size can be conveyed in pixels or absolute lengths, such as in centimeters (cm), decimeters (dm), or meters.

[0019] In one example, the two-dimensional grid is transformed into a pixelated plan view, wherein each rectangular grid cell comprises a (same) integer number of pixels. Alternatively, each rectangular grid cell has the same length and width expressed in absolute length units.

[0020] The grid size defines the accuracy of the preprocessed localization data, which can be configured according to the application. This can be used to reduce the size or length of the localization data by keeping only the necessary accuracy of the localization data. As the size or length of the localization data is reduced, the computational overhead for deriving further information will also be reduced. This also saves communication resources used to transfer the localization data from the mobile device to a local server or cloud server.

[0021] In one example, the pre-processing step further includes mapping the raw localization data to pixel coordinates or grid indices; and the pre-processed localization data includes pixel coordinates or grid indices.

[0022] Advantageously, the configuration information also includes a region definition represented by a set of grid indices.

[0023] A region may be a minimum unit defined according to relevant applications for generating statistics on localized data.

[0024] In another example, the pre-processing step further includes the step of mapping the original localized data to the region index; wherein the pre-processed localized data includes the region index.

[0025] Advantageously, the areas are defined according to product categories or functional spaces.

[0026] Note that product categories can be defined according to different classifications. For example, in a supermarket, different areas can be defined for different product categories, such as fruits, vegetables, dairy products, meat, beverages, baked goods, etc. In another example, different areas can also be defined for subcategories of products (e.g., apples, bananas, oranges, etc. within the same fruit category).

[0027] In an office environment, different zones can be defined for individual offices, meeting rooms, and coffee corners.

[0028] Preferably, the configuration information further includes a region group definition represented by a region set.

[0029] With additional levels of abstraction, zone groups can combine sets of zones. And then local servers or cloud servers can use different levels of abstracted data for different applications.

[0030] In one example, the preprocessing step further includes the step of mapping the original localized data to a region group index, and the preprocessed localized data includes the region group index.

[0031] In a preferred arrangement, the mobile device detects raw localization data via visible light communication, Bluetooth low energy radio, ultra-wideband radio, or a combination thereof.

[0032] Advantageously, the configuration information is provided to the mobile device by a cloud server or a local server.

[0033] The local server is located inside. For example, it can be placed at the entrance of a building, or at the entrance of a floor in a building, or at the entrance of a large lobby. Configuration information related to the building, floor, or lobby is stored in the local server and provided to the mobile device (such as via short-range wireless communication technology or via a local area network) when the mobile device passes by the local server.

[0034] Alternatively, the configuration information may be stored in a cloud server and provided to the mobile device via a wireless network connected to the cloud.

[0035] According to a second aspect of the present invention, an indoor positioning system is provided. The indoor positioning system comprises: an internally deployed local server, which is configured to provide configuration information to a mobile device; and a mobile device, which is configured to detect raw localization data; when determining to perform preprocessing locally in the mobile device, preprocess the raw localization data by using the configuration information; and send the preprocessed localization data to the local server; wherein the configuration information comprises a size in pixels of a plan view and a grid size in pixels or absolute length, and the grid size is configurable and defines the accuracy of the preprocessed localization data;

[0036] The mobile device is further configured to map the original localized data to the grid index to pre-process the original localized data, and the pre-processed localized data includes the grid index.

[0037] The mobile device may be further configured to send the original localization data to the local server.

[0038] According to another aspect of the present invention, an indoor positioning system is provided. The indoor positioning system includes: a cloud server configured to provide configuration information to a mobile device; and a mobile device configured to detect raw localization data; when determining to perform preprocessing locally in the mobile device, preprocess the raw localization data by using the configuration information; and send the preprocessed localization data to the cloud server; wherein the configuration information includes a size in pixels of a plan view and a grid size in pixels or absolute length, and the grid size is configurable and defines the accuracy of the preprocessed localization data;

[0039] The mobile device is further configured to map the original localized data to the grid index to pre-process the original localized data, and the pre-processed localized data includes the grid index.

[0040] The mobile device may be further configured to send the original localization data to the cloud server.

[0041] According to another aspect of the present invention, an indoor positioning system is provided. The indoor positioning system includes: a local server configured to provide configuration information to a mobile device; a cloud server; and a mobile device configured to detect raw localization data; when determining to perform preprocessing locally in the mobile device, preprocess the raw localization data by using the configuration information; and send the preprocessed localization data to the cloud server; wherein the configuration information includes a size in pixels of a plan view and a grid size in pixels or absolute length, and the grid size is configurable and defines the accuracy of the preprocessed localization data;

[0042] The mobile device is further configured to map the original localized data to the grid index to pre-process the original localized data, and the pre-processed localized data includes the grid index.

[0043] The mobile device may be further configured to send the original localization data to the cloud server.

[0044] According to another aspect of the present invention, an indoor positioning system is provided. The indoor positioning system includes: a cloud server configured to provide configuration information to a mobile device; an internally deployed local server; and a mobile device configured to detect raw localization data; when determining to perform preprocessing locally in the mobile device, preprocess the raw localization data by using the configuration information; and send the preprocessed localization data to the local server; wherein the configuration information includes a size in pixels of a plan view and a grid size in pixels or absolute length, and the grid size is configurable and defines the accuracy of the preprocessed localization data;

[0045] The mobile device is further configured to map the original localized data to the grid index to pre-process the original localized data, and the pre-processed localized data includes the grid index.

[0046] Advantageously, the cloud server is configured to schedule computing resources on the cloud server to handle the localized data submitted by the mobile device according to the processing capability of the mobile device and / or the business hours of the site where the mobile device is located.

[0047] A cloud server may have to handle large amounts of data from multiple sites within different regions. It may be more efficient if computing resources are scheduled based on demand from individual sites. For example, the cloud server may take into account the typical processing power of mobile devices at the site, and / or take into account the business hours of the site. When during business hours, more resources will be reserved for the site to handle localized data submitted by mobile devices in the site. When it is non-business hours, the cloud server may release the resources reserved for the site and reallocate them to other sites. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In the drawings, similar reference numerals are used throughout the different Figure 1 Also, the drawings are not necessarily to scale, emphasis instead generally being placed upon illustrating the principles of the invention.

[0049] Figure 1 A flow chart showing a data processing method;

[0050] Figure 2 An example of an indoor positioning system according to the present invention is illustrated;

[0051] Figure 3 Another example of an indoor positioning system according to the present invention is illustrated; and

[0052] Figure 4 A further example of the indoor positioning system according to the present invention is illustrated. DETAILED DESCRIPTION

[0053] The embodiments set forth below represent information that enables those skilled in the art to practice these embodiments. After reading the following description in light of the accompanying drawings, those skilled in the art will understand the concepts of the present disclosure and will recognize the applications of these concepts, which are not particularly set forth herein. It should be understood that these concepts and applications fall within the scope of the present disclosure.

[0054] In a system that enables indoor positioning, a mobile device can detect the device location with the help of different sensing technologies and positioning software (e.g., IPSSDK). The location information of the device can be expressed in an absolute manner (such as geographic location) or in a relative manner (such as location on a floor plan map). In the former case, the floor plan map image is usually annotated with geographic reference points so that the pixel location on the floor plan can be mapped to the absolute geographic location described by latitude and longitude. The mobile device periodically sends the detected location to the cloud for further processing and analysis.

[0055] Professional users such as facility managers of buildings define regions, each of which is a polygon with several points on a floor plan image. A region can represent a category of product shelves in a supermarket, or a functional space in a warehouse or office. Statistics on foot traffic, dwell time, and density at facility locations can provide information such as how the space is used, which categories of products are most attractive to customers, etc.

[0056] In existing systems, the location of a device is expressed in latitude and longitude to maintain sufficient accuracy. The cloud service divides the floor plan into non-overlapping grids of square shapes with predefined sizes. When receiving the location of the mobile device, the cloud service first determines which grid each reported location belongs to, and then uses the center of such a grid to represent the location to reduce the amount of data to be processed in subsequent stages.

[0057] When the indoor positioning system was designed for indoor navigation applications, the expected number of mobile devices generating location data was limited. However, the system expands this feature, thereby also enabling data analysis of how and where customers visit and stay in indoor spaces. As the customer base and the number of mobile devices increase, a large amount of data will be sent to the cloud for processing. The network service that handles the location data needs to be upgraded to cope with the amount of data, which will increase costs and reduce system stability.

[0058] Considering that each mobile device sends its location (including latitude, longitude, altitude and metadata) every second, this will result in 60 locations per mobile device per minute. In one example, there are 200 sites and each site has 100 mobile devices, a total of 8 million locations will need to be processed in the cloud.

[0059] As an example, for each location data received in the cloud, the following processing may be performed:

[0060] 1. Divide the plan into grids of fixed size (e.g. 30×30 cm);

[0061] 2. Calculate which grid the location belongs to;

[0062] 3. Determine which region and region group the location belongs to;

[0063] 4. Calculate the total customer traffic dwell time for each area and area group, considering whether it is the same customer journey;

[0064] 5. Generate a density map by counting the number of locations in each grid.

[0065] The present invention proposes to distribute the pre-processing of location data on a large number of mobile devices, thereby reducing the amount of central computing in local servers or remote servers in the cloud. When the cloud service receives the location data, it also provides the results of the pre-processing, thereby making the calculation of grid groups, passenger flow and dwell time straightforward.

[0066] Figure 1 A flow chart of a data processing method 600 is shown. The data processing method 600 for the indoor positioning system 100 includes the following steps: providing configuration information to the mobile device 200 in step S601; detecting raw localization data by the mobile device 200 in step S602; when the mobile device 200 determines to perform preprocessing locally in step S603, preprocessing the raw location data by using the configuration information in step S604; and sending the preprocessed localization data to the internally deployed local server 400 or the cloud server 300 in step S605.

[0067] In order to enable data preprocessing on the mobile device, the local server, remote server or cloud service needs to pass configuration information to the mobile device, such as:

[0068] 1. The dimensions in pixels (width x height) of the building floor plan image(s). Note that for navigation purposes, a true floor plan map may already be available on the mobile device, typically in vector format. Here, the mobile device only needs the floor plan image dimensions to avoid blurring introduced by scaling.

[0069] 2. A reference point (usually 3) that maps pixel positions to physical latitude and longitude, and vice versa.

[0070] 3. The grid size expressed in pixels or absolute length units (such as cm, dm, etc.).

[0071] 4. Region and region group definition. Region definition can be represented by a collection of grid indices, while a region group is a collection of regions. Grid indices can be defined in a sequential manner starting from the top left of the floor plan.

[0072] 5. When defining regions and region groups using graphical tools, the boundaries of the regions should be aligned with mesh vertices to avoid one mesh belonging to multiple regions.

[0073] This above listed information can be transmitted to the mobile device from a local server or from a remote server in the cloud during the initial handshake phase between the two devices.An example of an existing application programming interface (API) function is a GetVenue API call of a device to obtain information about the building it is in.

[0074] As described above, when the mobile device begins handshaking with the remote server in the cloud or the local server, information about the building floor plan will be transmitted as part of the response of the Get Venue API. An example of such data in JSON format is as follows:

[0075]

[0076]

[0077] The mobile device can use different indoor positioning technologies (such as visible light communication (VLC), Bluetooth low energy (BLE), ultra-wideband (UWB), or a combination of multiple technologies) to obtain positioning information. The location data reported by the mobile device to the cloud may look like the following JSON format, which means that the mobile device is at position (latitude, longitude) = (51.40675248055913, 5.460239092416265) at time 2021-06-23T09:01:11, with an accuracy of 0.4862756 meters.

[0078]

[0079] Using the additional information provided during the handshake phase between the mobile device and the cloud service, the mobile device can map the above position (latitude, longitude) to a pixel position on the plane (x, y). The pixel position can be converted to a grid index using the grid size.

[0080]

[0081] Here GSx and GSy are the grid sizes on the x and y axis. W is the x dimension of the floor plan image.

[0082] Additional information about which zone and zone group the location belongs to can also be derived based on the definition of zones and zone groups provided in the configuration information. The location data reported by the mobile device to the cloud service is then as follows:

[0083]

[0084] }

[0085] Figure 2 An example of an indoor positioning system 100 according to the present invention is illustrated. The indoor positioning system 100 includes: an internally deployed local server 400, which is configured to provide configuration information to a mobile device 200; and the mobile device 200, which is configured to: detect raw location data; when determining to perform preprocessing locally in the mobile device 200, preprocess the raw localization data by using the configuration information; and send the preprocessed localization data to the local server 400. Figure 2 As shown, there may be multiple mobile devices 200 on site. Each mobile device 200 may be a mobile phone, a tablet, a laptop, a wearable device, a handheld scanner, or another portable electronic device. Different mobile devices 200 may have different computing capabilities, and then each of the mobile devices 200 may determine whether to perform pre-processing on the raw localization data depending on its own capabilities and status.

[0086] Figure 3 Another example of the indoor positioning system 100 according to the present invention is illustrated. The indoor positioning system 100 includes: a cloud server 300 configured to provide configuration information to a mobile device 200; and a mobile device 200 configured to detect raw location data; when determining to perform preprocessing locally in the mobile device 200, preprocess the raw location data by using the configuration information; and send the preprocessed localized data to the cloud server 300.

[0087] Figure 4 Another example of the indoor positioning system 100 according to the present invention is illustrated. The indoor positioning system 100 includes: a local server 400, which is configured to provide configuration information to the mobile device 200; a cloud server 300; and the mobile device 200, which is configured to: detect raw localization data; when determining to perform preprocessing locally in the mobile device 200, preprocess the raw location data by using the configuration information; and send the preprocessed localization data to the cloud server 300.

[0088] When receiving localized data from one or more mobile devices, the local server or cloud server can calculate traffic, density, and dwell time by aggregating related data, such as grouping data from mobile devices in the same location, same area, or same group of areas.

[0089] Preprocessing data locally in a mobile device may not be a problem for advanced devices such as high-end mobile phones, but may be a burden for low-end devices such as handheld scanners in supermarkets. In addition, sometimes the mobile device may be busy with some other important tasks in the background and therefore cannot allocate enough resources on this dedicated data processing task. Therefore, it is beneficial that the mobile device can first evaluate its capabilities and / or status and decide whether to run the preprocessing step. If the mobile device determines not to perform preprocessing locally, it can simply send the raw localized data to a local server or cloud server. If the mobile device determines that it is able to perform preprocessing locally, it will first preprocess locally and then send the preprocessed localized data to a local server or cloud server. The preprocessed localized data may include derived grid indices, regions and region groups, or a subset of information. The choice of data may depend on the application requirements.

[0090] It is also beneficial to send the pre-processing results (e.g., grid index, region, region group) in combination with the original localized data. The local server or cloud server can still be able to perform calculations based on the original localized data to derive some additional information in addition to the grid index, region, region group, or derive the grid index, region, region group in the case where one or more of the grid index, region, region group is missing in the data received from the mobile device.

[0091] The cloud server can also try to optimize its computing resources so that the cloud server can schedule computing resources based on some prior knowledge about the device and the site, such as the processing power of the mobile device and the business hours of the site where the mobile device is located. For example, it is known that a handheld scanner may not be able to perform calculations locally. By knowing the business hours of the device, more cloud computing resources can be scheduled for data preprocessing in the corresponding time period.

[0092] The method according to the invention can be implemented on a computer as a computer-implemented method, or in dedicated hardware, or in a combination of both.

[0093] The executable code of the method according to the invention may be stored on a computer / machine readable storage device. Examples of computer / machine readable storage devices include non-volatile storage devices, optical storage media / devices, solid-state media, integrated circuits, servers, etc. Preferably, the computer program product comprises non-transitory program code means stored on a computer readable medium for performing the method according to the invention when the program product is executed on a computer.

[0094] Methods, systems, and computer-readable media (transitory and non-transitory) may also be provided to implement selected aspects of the above-described embodiments.

Claims

1. A data processing method (600) for indoor positioning, The following steps are involved: - providing (S601) configuration information to the mobile device (200); - detecting ( S602 ) original localization data by the mobile device ( 200 ); - when it is determined (S603) by the mobile device (200) that pre-processing is performed locally, pre-processing (S604) the original localized data by using the configuration information; - Sending (S605) the pre-processed localized data to an internally deployed local server (400) or a cloud server (300); wherein the configuration information includes a size in pixels of the plan view and a grid size in pixels or absolute length, and the grid size is configurable and defines the accuracy of the pre-processed localization data; The pre-processing step (S604) further includes: - Map raw localization data to grid index; The pre-processed localization data includes the grid index.

2. The data processing method (600) according to claim 1 further comprises sending original localized data to a cloud server (300) or a local server (400).

3. The data processing method (600) according to claim 1 or 2, wherein the configuration information further comprises a region definition represented by a set of grid indices.

4. The data processing method (600) according to claim 3, wherein the preprocessing step further comprises: include: - Mapping raw localization data to region indexes; The pre-processed localized data includes the region index.

5. The data processing method (600) according to claim 3 or 4, wherein the area is defined according to product category or functional space.

6. The data processing method (600) according to any one of the preceding claims 3-5, wherein the configuration information further comprises a region group definition represented by a region set.

7. The data processing method (600) according to claim 6, wherein the preprocessing step further comprises: include: - Mapping raw localization data to zone group index; The pre-processed localization data includes the region group index.

8. The data processing method (600) according to any one of the preceding claims, wherein the raw localization data is detected by the mobile device (200) via visible light communication, Bluetooth low energy radio, ultra wideband radio or a combination thereof.

9. The data processing method (600) according to any one of the preceding claims, wherein the configuration information is provided to the mobile device (200) by a cloud server (300) or by a local server (400).

10. An indoor positioning system (100), include: - an internally deployed local server (400) configured to provide configuration information to the mobile device (200); and - A mobile device (200) configured to: oDetection of raw localization data; o when it is determined that the pre-processing is to be performed locally in the mobile device (200), pre-processing the raw localized data by using the configuration information; o Sending the pre-processed localized data to the local server (400); wherein the configuration information includes a size in pixels of the plan view and a grid size in pixels or absolute length, and the grid size is configurable and defines the accuracy of the pre-processed localization data; The mobile device (200) is further configured to map the original localized data to a grid index to pre-process the original localized data, and the pre-processed localized data includes the grid index.

11. An indoor positioning system (100), include: - a cloud server (300) configured to provide configuration information to the mobile device (200); and - A mobile device (200) configured to: oDetection of raw localization data; o when it is determined that the pre-processing is to be performed locally in the mobile device (200), pre-processing the raw localized data by using the configuration information; o Sending the pre-processed localized data to the cloud server (300); wherein the configuration information includes a size in pixels of the plan view and a grid size in pixels or absolute length, and the grid size is configurable and defines the accuracy of the pre-processed localization data; The mobile device (200) is further configured to map the original localized data to a grid index to pre-process the original localized data, and the pre-processed localized data includes the grid index.

12. An indoor positioning system (100), include: - an internally deployed local server (400) configured to provide configuration information to the mobile device (200); - cloud server (300); and - A mobile device (200) configured to: oDetection of raw localization data; o when it is determined that the pre-processing is to be performed locally in the mobile device (200), pre-processing the raw localized data by using the configuration information; o Sending the pre-processed localized data to the cloud server (300); wherein the configuration information includes a size in pixels of the plan view and a grid size in pixels or absolute length, and the grid size is configurable and defines the accuracy of the pre-processed localization data; The mobile device (200) is further configured to map the original localized data to a grid index to pre-process the original localized data, and the pre-processed localized data includes the grid index.

13. An indoor positioning system (100), include: - a cloud server (300) configured to provide configuration information to the mobile device (200); - an internally deployed local server (400); and - A mobile device (200) configured to: oDetection of raw localization data; o when it is determined that the pre-processing is to be performed locally in the mobile device (200), pre-processing the raw localized data by using the configuration information; o Sending the pre-processed localized data to the local server (400); wherein the configuration information includes a size in pixels of the plan view and a grid size in pixels or absolute length, and the grid size is configurable and defines the accuracy of the pre-processed localization data; The mobile device (200) is further configured to map the original localized data to a grid index to pre-process the original localized data, and the pre-processed localized data includes the grid index.

14. An indoor positioning system (100) according to claim 11 or 12, wherein the cloud server (300) is configured to schedule computing resources on the cloud server (300) according to the processing capacity of the mobile device (200) and / or the business hours of the site where the mobile device (200) is located to process the localization data submitted by the mobile device.

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