Connected device region identification

Patent metadata

Field	Value
Publication number	US-2022014923-A1
Application number	US-202117484120-A
Country	US
Kind code	A1
Filing date	Sep 24, 2021
Priority date	Sep 24, 2021
Publication date	Jan 13, 2022
Grant date	—

Abstract

Various aspects of methods, systems, and use cases include region identification of a malicious device based on crowdsourced locations. A method may include generating a grid of bins of a local radio coverage region, receiving location data from a plurality of devices in the local radio coverage region, and classifying locations of the plurality of devices with respect to the bins. The method may include associating the classified locations of the plurality of devices to the received location data for corresponding devices of the plurality of devices, and generating a model, from the associated classified locations and the received location data.

First claim

What is claimed is:

1 . An edge device for region identification of a malicious device based on crowdsourced locations, the edge device comprising:
processing circuitry; and memory including instructions, which when executed by the processing circuitry, cause the processing circuitry to perform operations including: generating a grid of bins of a local radio coverage region; receiving location data from a plurality of devices in the local radio coverage region; classifying locations of each of the plurality of devices with respect to the bins using hypothesis testing; associating the classified locations of each of the plurality of devices to the received location data for corresponding devices of the plurality of devices; generating a model, from the associated classified locations and the received location data; and outputting the model for bin location identification of a received signal.

2 . The edge device of claim 1 , wherein each bin in the grid of bins is one square meter in size.

3 . The edge device of claim 1 , wherein each bin in the grid of bins has a respective unique bin identifier, and wherein the model is to output a unique bin identifier corresponding to the received signal.

4 . The edge device of claim 1 , wherein the grid of bins is a two-dimensional grid.

5 . The edge device of claim 1 , wherein the plurality of devices are devices that are trusted or known to the edge device.

6 . The edge device of claim 1 , wherein receiving the location data includes passively and periodically receiving the location data.

7 . The edge device of claim 1 , wherein receiving the location data includes actively receiving the location data on-demand based on a request.

8 . The edge device of claim 1 , wherein the location data includes reported cartesian coordinates of a respective device of the plurality of devices.

9 . The edge device of claim 1 , wherein the location data includes at least one of a timestamp of sent data, a received signal strength indicator (RSSI), a Reference Signal Received Power (RSRP), a Reference Signal Received Quality (RSRQ), a Power Headroom Report (PHR), or an interference level of a respective device of the plurality of devices.

10 . The edge device of claim 9 , wherein associating the classified locations includes associating the classified locations with a RSSI, RSRQ, or RSRP received from each device.

11 . The edge device of claim 1 , wherein the hypothesis testing includes testing what bin a location is in using a M-ary test where N corresponds to a number of bins in the grid of bins.

12 . The edge device of claim 1 , wherein the hypothesis testing includes testing whether a location is in a particular bin using a binary test corresponding to the particular bin.

13 . The edge device of claim 1 , wherein associating the classified locations includes using a trained neural network to map multipath channel gains with the bins of the grid of bins.

14 . The edge device of claim 1 , wherein the model includes a stochastic mapping of channel statistics to bins.

15 . The edge device of claim 1 , wherein the model is a neural network.

16 . The edge device of claim 15 , further comprising operations including updating the neural network based on neural network information received from a second edge device corresponding to a second grid generated at the second edge device.

17 . The edge device of claim 1 , further comprising operations including using the model to determine the bin location of the received signal, wherein the received signal is a potentially malicious signal.

18 . An apparatus for region identification of a malicious device based on crowdsourced locations, the apparatus comprising:
means for generating a grid of bins of a local radio coverage region; means for receiving location data from a plurality of devices in the local radio coverage region; means for classifying locations of each of the plurality of devices with respect to the bins using hypothesis testing; means for associating the classified locations of each of the plurality of devices to the received location data for corresponding devices of the plurality of devices; means for generating a model, from the associated classified locations and the received location data; and means for outputting the model for bin location identification of a received signal.

19 . The apparatus of claim 18 , wherein the plurality of devices are devices that are trusted or known to the edge device.

20 . The apparatus of claim 18 , wherein the location data includes at least one of a timestamp of sent data, a received signal strength indicator (RSSI), a Reference Signal Received Power (RSRP), a Reference Signal Received Quality (RSRQ), a Power Headroom Report (PHR), or an interference level of a respective device of the plurality of devices.

21 . The apparatus of claim 20 , wherein the means for associating the classified locations include means for associating the classified locations with a RSSI, RSRQ, or RSRP received from each device.

22 . A method for region identification of a malicious device based on crowdsourced locations, the method comprising:
generating, at an edge device, a grid of bins of a local radio coverage region; receiving, at the edge device, location data from a plurality of devices in the local radio coverage region; classifying locations of each of the plurality of devices with respect to the bins using hypothesis testing; associating the classified locations of each of the plurality of devices to the received location data for corresponding devices of the plurality of devices; generating a model for the edge device from the associated classified locations and the received location data; and outputting the model for bin location identification of a received signal.

23 . The method of claim 22 , wherein the model is a neural network, and further comprising updating the neural network based on neural network information received from a second edge device corresponding to a second grid generated at the second edge device.

24 . At least one machine-readable medium including instructions for region identification of a malicious device based on crowdsourced locations, which when executed by processing circuitry, cause the processing circuitry to perform operations comprising:
generating a grid of bins of a local radio coverage region; receiving location data from a plurality of devices in the local radio coverage region; classifying locations of each of the plurality of devices with respect to the bins using hypothesis testing; associating the classified locations of each of the plurality of devices to the received location data for corresponding devices of the plurality of devices; generating a model, from the associated classified locations and the received location data; and outputting the model for bin location identification of a received signal.

25 . The at least one machine-readable medium of claim 24 , further comprising operations including using the model to determine the bin location of the received signal, wherein the received signal is a potentially malicious signal.

Assignees

• Banjade Vesh Raj Sharma
• Jha Satish Chandra
• Sivanesan Kathiravetpillai
• Yang Liuyang Lily
• Smith Ned M
• Alam S M Iftekharul
• Merwaday Arvind
• Guim Bernat Francesc
• Doshi Kshitij Arun
• Chen Kuilin Clark

Inventors

• Banjade Vesh Raj Sharma
• Jha Satish Chandra
• Sivanesan Kathiravetpillai
• Yang Liuyang Lily
• Smith Ned M
• Alam S M Iftekharul
• Merwaday Arvind
• Guim Bernat Francesc
• Doshi Kshitij Arun
• Chen Kuilin Clark

Classifications

• G01S5/02524

  Creating or updating the radio-map · CPC title

• G06F18/24

  Classification techniques · CPC title

• G06N3/09

  Supervised learning · CPC title

• G06N3/0499

  Feedforward networks · CPC title

• G06N3/098

  Distributed learning, e.g. federated learning · CPC title