Providing visualization data to a co-located plurality of mobile devices

• US 10,674,328 B2
• Filed: 05/23/2019
• Issued: 06/02/2020
• Est. Priority Date: 09/09/2016
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Hwang, Inseok, Kim, Myung-Chul, Taban, Gelareh, Yoo, Chungkuk
• Primary Examiner(s): Schwartz, Joshua L
• Application Number:
  US16/421,341
• Publication Number:
  US 20190281425A1
• Time in Patent Office: 376 Days
• CPC Class Codes: G06F 3/00 Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
• CPC Class Codes: G06Q 30/02 Marketing; Price estimation or determination; Fundraising
• CPC Class Codes: H04W 4/021 Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences
• CPC Class Codes: H04W 4/08 User group management
• CPC Class Codes: H04W 4/21 for social networking applications
• CPC Class Codes: H04W 64/00 Locating users or terminals or network equipment for network management purposes, e.g. mobility management
• CPC Class Codes: H04W 84/18 Self-organising networks, e.g. ad-hoc networks or sensor networks

Abstract

A computer-implemented method according to one embodiment includes identifying a plurality of devices, determining a relative location of each of the plurality of devices, and assigning visualization data to each of the plurality of devices, based on the relative location of each of the plurality of devices, wherein determining the relative location of each of the plurality of devices includes performing relative localization on each of the plurality of devices by performing a constrained nonlinear optimization in order to reconstruct relative locations of each of the plurality of devices, using distributed observations received from the plurality of devices in order to determine a co-located group of the devices.

Claims

1. A computer-implemented method, comprising:

identifying a plurality of devices;

determining a relative location of each of the plurality of devices; and

assigning visualization data to each of the plurality of devices, based on the relative location of each of the plurality of devices;

wherein determining the relative location of each of the plurality of devices includes performing relative localization on each of the plurality of devices by performing a constrained nonlinear optimization in order to reconstruct relative locations of each of the plurality of devices, using distributed observations received from the plurality of devices in order to determine a co-located group of the devices.

2. The computer-implemented method of claim 1, wherein each of the plurality of devices is in a possession of a unique individual located within a single crowd of people.

3. The computer-implemented method of claim 1, wherein the visualization data includes textual and graphical output that is pixelated into a matrix of tiles within a cloud computing environment.

4. The computer-implemented method of claim 1, wherein results of a constrained optimization are used to reconstruct relative locations of each of the plurality of devices with respect to each other, resulting in reconstructed locations for each of the plurality of devices.

5. The computer-implemented method of claim 1, wherein the visualization data represents one or more of textual and graphical output that is pixelated into a matrix of tiles.

6. The computer-implemented method of claim 1, wherein performing constrained nonlinear optimization includes utilizing a linearly constrained nonlinear programming algorithm within a cloud computing environment to formulate a constrained optimization problem on the distributed observations and solve the constrained optimization problem.

7. The computer-implemented method of claim 1, further comprising accelerating the constrained nonlinear optimization by:

formulating a linearly constrained quadratic polynomial programming problem,

solving the problem within a cloud computing environment, utilizing a quadratic programming algorithm, to obtain an approximate solution, and

sending the approximate solution as initial values to the constrained nonlinear optimization.

8. The computer-implemented method of claim 1, further comprising:

resampling the visualization data to fit a number of the plurality of devices in both horizontal and vertical directions,

quantizing the relative locations of each of the plurality of devices to snap to a virtual uniform grid, and

determining unit grid spacing within the virtual uniform grid.

9. The computer-implemented method of claim 1, wherein assigning visualization data to each of the plurality of devices includes:

pixelating the visualization data into a matrix of tiles, and

assigning each tile within the matrix of tiles to one of the plurality of devices matching a respective quantized location within the matrix of tiles.

10. The computer-implemented method of claim 1, further comprising:

identifying a new device within the plurality of devices; and

initiating spatially-isolated relative localization only for a subgroup of the plurality of devices that are within a vicinity of the new device.

11. A computer program product for providing visualization data to a plurality of devices, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, wherein the computer readable storage medium is not a transitory signal per se, the program instructions executable by a processor to cause the processor to perform a method comprising:

identifying a plurality of devices, utilizing the processor;

determining a relative location of each of the plurality of devices, utilizing the processor; and

assigning, utilizing the processor, visualization data to each of the plurality of devices, based on the relative location of each of the plurality of devices;

wherein determining the relative location of each of the plurality of devices includes performing relative localization on each of the plurality of devices by performing a constrained nonlinear optimization in order to reconstruct relative locations of each of the plurality of devices, using distributed observations received from the plurality of devices in order to determine a co-located group of the devices.

12. The computer program product of claim 11, wherein each of the plurality of devices is in a possession of a unique individual located within a single crowd of people.

13. The computer program product of claim 11, wherein the visualization data includes textual and graphical output that is pixelated into a matrix of tiles within a cloud computing environment.

14. The computer program product of claim 11, wherein results of a constrained optimization are used to reconstruct relative locations of each of the plurality of devices with respect to each other, resulting in reconstructed locations for each of the plurality of devices.

15. The computer program product of claim 11, wherein the visualization data represents one or more of textual and graphical output that is pixelated into a matrix of tiles.

16. The computer program product of claim 11, wherein performing constrained nonlinear optimization includes utilizing a linearly constrained nonlinear programming algorithm within a cloud computing environment to formulate a constrained optimization problem on the distributed observations and solve the constrained optimization problem.

17. The computer program product of claim 11, wherein the method further comprises accelerating the constrained nonlinear optimization by:

formulating a linearly constrained quadratic polynomial programming problem,

solving the problem within a cloud computing environment, utilizing a quadratic programming algorithm, to obtain an approximate solution, and

sending the approximate solution as initial values to the constrained nonlinear optimization.

18. The computer program product of claim 11, wherein the method further comprises:

resampling the visualization data to fit a number of the plurality of devices in both horizontal and vertical directions,

quantizing the relative locations of each of the plurality of devices to snap to a virtual uniform grid, and

determining unit grid spacing within the virtual uniform grid.

19. The computer program product of claim 11, wherein assigning visualization data to each of the plurality of devices includes:

pixelating the visualization data into a matrix of tiles, and

assigning each tile within the matrix of tiles to one of the plurality of devices matching a respective quantized location within the matrix of tiles.

20. The computer program product of claim 11, wherein the method further comprises:

identifying a new device within the plurality of devices; and

initiating spatially-isolated relative localization only for a subgroup of the plurality of devices that are within a vicinity of the new device.