Infrastructure for location discovery

• US 9,759,800 B2
• Filed: 07/22/2015
• Issued: 09/12/2017
• Est. Priority Date: 03/31/2009
• Status: Expired due to Fees

• Current Assignee: Empire Technology Development LLC (Allied Inventors Management, LLC), Regents of the University of California (University of California)
• Original Assignee: Empire Technology Development LLC (Allied Inventors Management, LLC)
• Inventors: Potkonjak, Miodrag
• Primary Examiner(s): Elhag, Magdi
• Application Number:
  US14/805,870
• Publication Number:
  US 20160021544A1
• Time in Patent Office: 783 Days
• Field of Search: 455446-450, 4554561, 4554042, 455 412
• CPC Class Codes: G01S 1/00 Beacons or beacon systems transmitting signals having a characteristic or characteristics capable of being detected by non-directional receivers and defining directions, positions, or position lines fixed relatively to the beacon transmitters; Receivers co-operating therewith position fixing by co-ordinating a plurality of determinations of direction or position lines G01S5/00
• CPC Class Codes: G01S 1/02 using radio waves G01S19/00 takes precedence
• CPC Class Codes: G01S 1/022 Means for monitoring or calibrating
• CPC Class Codes: G01S 1/024 of beacon transmitters
• CPC Class Codes: G01S 1/026 of associated receivers
• CPC Class Codes: G01S 1/028 Simulation means, e.g. of beacon signals therefor for teaching or training purposes G09B9/00
• CPC Class Codes: H04L 41/145 involving simulating, designing, planning or modelling of a network
• CPC Class Codes: H04W 16/18 Network planning tools
• CPC Class Codes: H04W 16/20 for indoor coverage or short range network deployment
• CPC Class Codes: H04W 16/28 using beam steering
• 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 64/003 locating network equipment
• CPC Class Codes: H04W 72/542 using measured or perceived quality
• CPC Class Codes: H04W 84/18 Self-organising networks, e.g. ad-hoc networks or sensor networks
• CPC Class Codes: H04W 84/20 Master-slave selection or change arrangements

Abstract

Techniques are generally described for determining locations of a plurality of communication devices in a network. In some examples, methods for creating a location discovery infrastructure (LDI) for estimating locations of one or more of a plurality of communication nodes may comprise one or more of determining a plurality of locations in the terrain to place a corresponding plurality of beacon nodes, determining a plurality of beacon node groups for the placed beacon nodes, and determining a schedule for the placed beacon nodes to be active. Additional variants and embodiments are also disclosed.

Claims

1. A method comprising:

detecting, by a communication node, a signal from a beacon node;

determining, by the communication node, a signal strength of the detected signal;

determining, by the communication node, a proximity of the communication node to the beacon node based at least in part on the signal strength of the detected signal; and

determining, by the communication node, an accuracy of the proximity determination based at least in part on the signal strength of the detected signal, wherein the determining includes calculating a margin of error based on at least one of an indoor terrain model or outdoor terrain model that includes obstacles between the communication node and the beacon node, in the margin of error calculation,

wherein the at least one of the indoor terrain model or the outdoor terrain model is generated to maintain a specified density of the obstacles and create a specified level of clustering of the obstacles.

2. The method of claim 1, wherein the determining the accuracy of the proximity determination further includes calculating a margin of error based on a distance measurement error model.

3. The method of claim 2, wherein the distance measurement error model includes accounting for obstacles attenuating the signal strength of the detected signal between the communication node and the beacon node.

4. The method of claim 1, further comprising determining when the communication node enters a region proximate to the beacon node based on the communication node receiving the signal.

5. The method of claim 1, wherein the signal includes beacon identification information.

6. The method of claim 1, wherein the signal includes location identification information.

7. The method of claim 1, further comprising determining a location of the communication node based on proximity determinations relative to a plurality of beacon nodes in a region proximate to the respective beacon nodes.

8. The method of claim 1, further comprising determining an accuracy of the proximity determination based on a distance measurement error model.

9. The method of claim 1, wherein the at least one of the indoor terrain model or the outdoor terrain model are scalable both in terms of size and resolution.

10. A mobile device, comprising:

a memory configured to store a plurality of programming instructions; and

a processor, coupled to the memory, the processor configured to execute the plurality of programming instructions, to enable the mobile device to;

detect a signal from a beacon node,

determine a signal strength of the detected signal,

determine a proximity of the mobile device to the beacon node based at least in part on the signal strength of the detected signal,

determine an accuracy of the proximity determination based on a distance measurement error model that includes calculation of a margin of error based on at least one of an indoor terrain model or outdoor terrain model that includes obstacles between the mobile device and the beacon node, in the margin of error calculation,

wherein the at least one of the indoor terrain model or the outdoor terrain model is generated to maintain a specified density of the obstacles and create a specified level of clustering of the obstacles, and

determine when the mobile device enters a region proximate to the beacon node based on the mobile device detecting the signal.

11. The mobile device of claim 10,

wherein the distance measurement error model includes accounting for one or more obstacles attenuating the signal strength between the mobile device and the beacon node.

12. The mobile device of claim 10, wherein the detected signal includes:

beacon identification information, and

location identification information.

13. The mobile device of claim 10, wherein the processor is further configured to execute the plurality of programming instructions, to enable the mobile device to:

determine a location of the mobile device based on proximity determinations relative to a plurality of beacon nodes in the region proximate to the beacon node.

14. A non-transitory computer-readable medium storing executable instructions that, in response to being executed, cause a communication node to perform or control performance of operations, wherein the operations comprise:

detect a signal from a beacon node;

determine a signal strength of the detected signal;

determine a proximity of the communication node to the beacon node based at least in part on the signal strength of the detected signal;

determine an accuracy of the determined proximity based at least in part on the signal strength of the detected signal that includes calculation of a margin of error based on at least one of an indoor terrain model or outdoor terrain model that includes obstacles between the communication node and the beacon node, in the margin of error calculation,

wherein the at least one of the indoor terrain model or the outdoor terrain model is generated to maintain a specified density of the obstacles and create a specified level of clustering of the obstacles; and

determine whether the communication node enters a region proximate to the beacon node based on the communication node detecting the signal.

15. The non-transitory computer-readable medium of claim 14,

wherein the determination of the accuracy of the determined proximity includes calculation of a margin of error based on a distance measurement error model.

16. The non-transitory computer-readable medium of claim 14, wherein the signal includes:

beacon identification information, and

location identification information.

17. The non-transitory computer-readable medium of claim 14, wherein the operations further comprise:

determine a location of the communication node based on the determined proximity relative to a plurality of respective beacon nodes in the region proximate to the respective beacon nodes.