IP Library › Granted Patent US 12,381,796
Granted Patent B2
US 12,381,796 · App. 17/968,304 · Granted Aug 5, 2025

Sampling configurations for environmental sensors

Inventors: Peiman Amini (Fremont, CA); Vishal Satyendra Desai (San Jose, CA); Ardalan Alizadeh (Milpitas, CA)
Assignee: Cisco Technology, Inc.
H04L43/062H04L67/12
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,381,796
App. No.
17/968,304
Granted
Aug 5, 2025
Kind
B2
Abstract

In one embodiment, a device may obtain sensor measurements from an environmental sensor of an access point. The device may obtain traffic telemetry data regarding network traffic handled by the access point. The device may generate, based on the sensor measurements and the traffic telemetry data, a sampling configuration of the environmental sensor of the access point. The device may cause the access point to collect additional sensor measurements from its environmental sensor according to the sampling configuration.

Claims (38)

1. A method comprising:

obtaining, by a device, sensor measurements from an environmental sensor of an access point;

obtaining, by the device, traffic telemetry data regarding network traffic handled by the access point that is indicative of a type or an activity of clients associated to the access point, wherein the type comprises a device type of a given client and the activity comprises an application or process executing on the given client;

generating, by the device and based on the sensor measurements and the traffic telemetry data, a sampling configuration of the environmental sensor of the access point, wherein the sampling configuration controls transitions among power states of the access point; and

causing, by the device, the access point to collect additional sensor measurements from its environmental sensor according to the sampling configuration.

2. The method as in claim 1 , wherein generating the sampling configuration includes determining an adjustment to a sampling rate or a sampling resolution of the environmental sensor.

3. The method as in claim 1 , wherein the traffic telemetry data includes an indication of a type of traffic handled by the access point, wherein the type of traffic is indicative of a type of user activity.

4. The method as in claim 1 , wherein the access point comprises the environmental sensor and the environmental sensor is a temperature sensor, a relative humidity sensor, or a gas sensor.

5. The method as in claim 1 , wherein the sampling configuration causes the access point to leave a power saving mode when its environmental sensor collects the additional sensor measurements.

6. The method as in claim 1 , wherein the traffic telemetry data is indicative of a number of clients associated to the access point.

7. The method as in claim 1 , wherein the sampling configuration causes the access point to reduce a sampling rate of its environmental sensor and is based further on a correlation between the sensor measurements obtained from the access point and sensor measurements from one or more access points within a defined proximity of the access point.

8. The method as in claim 1 , wherein the sampling configuration causes the access point to increase a sampling rate of its environmental sensor and is based further on an event detected by another access point within a defined proximity of the access point.

9. The method as in claim 1 , further comprising:

determining that the sensor measurements are anomalous and providing an indication that the sensor measurements are anomalous to a user interface.

10. The method as in claim 1 , wherein performing anomaly detection further comprises:

determining that the sensor measurements are anomalous and adjusting the sensor measurements using an offset that makes them non-anomalous.

11. An apparatus, comprising:

one or more network interfaces;

a processor coupled to the one or more network interfaces and configured to execute one or more processes; and

a memory configured to store a process that is executable by the processor, the process when executed configured to:

obtain sensor measurements from an environmental sensor of an access point;

obtain traffic telemetry data regarding network traffic handled by the access point that is indicative of a type or an activity of clients associated to the access point, wherein the type comprises a device type of a given client and the activity comprises an application or process executing on the given client;

generate, based on the sensor measurements and the traffic telemetry data, a sampling configuration of the environmental sensor of the access point, wherein the sampling configuration controls transitions among power states of the access point; and

cause the access point to collect additional sensor measurements from its environmental sensor according to the sampling configuration.

12. The apparatus as in claim 11 , wherein to generate the sampling configuration includes determining an adjustment to a sampling rate or a sampling resolution of the environmental sensor.

13. The apparatus as in claim 11 , wherein the traffic telemetry data includes an indication of a type of traffic handled by the access point, wherein the type of traffic is indicative of a type of user activity.

14. The apparatus as in claim 11 , wherein the access point comprises the environmental sensor and the environmental sensor is a temperature sensor, a relative humidity sensor, or a gas sensor.

15. The apparatus as in claim 11 , wherein the sampling configuration causes the access point to leave a power saving mode when its environmental sensor collects the additional sensor measurements.

16. The apparatus as in claim 11 , wherein the traffic telemetry data is indicative of a number of clients associated to the access point.

17. The apparatus as in claim 11 , wherein the sampling configuration causes the access point to reduce a sampling rate of its environmental sensor and is based further on a correlation between the sensor measurements obtained from the access point and sensor measurements from one or more access points within a defined proximity of the access point.

18. The apparatus as in claim 17 , wherein the sampling configuration causes the access point to increase a sampling rate of its environmental sensor and is based further on an event detected by another access point within a defined proximity of the access point.

19. The apparatus as in claim 11 , further comprising:

determining that the sensor measurements are anomalous and providing an indication that the sensor measurements are anomalous to a user interface.

20. A tangible, non-transitory, computer-readable medium storing program instructions that cause a device to execute a process comprising:

obtaining, by the device, sensor measurements from an environmental sensor of an access point;

obtaining, by the device, traffic telemetry data regarding network traffic handled by the access point that is indicative of a type or an activity of clients associated to the access point, wherein the type comprises a device type of a given client and the activity comprises an application or process executing on the given client;

generating, by the device and based on the sensor measurements and the traffic telemetry data, a sampling configuration of the environmental sensor of the access point, wherein the sampling configuration controls transitions among power states of the access point; and

causing, by the device, the access point to collect additional sensor measurements from its environmental sensor according to the sampling configuration.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Oct 18, 2022
From: AMINI, PEIMAN; DESAI, VISHAL SATYENDRA; ALIZADEH, ARDALAN
To: CISCO TECHNOLOGY, INC.
Reel/Frame 061457/0161 →
Continuity (1)
Related Publication 20240129210A1 · Apr 18, 2024
References Cited (37)
US 7318010B2 · Anderson · 2008 [cited by examiner]
US 8983460B2 · Trethewey · 2015 [cited by examiner]
US 9325516B2 · Pera et al. · 2016 [cited by applicant]
US 10257236B2 · Emmanuel · 2019 [cited by examiner]
US 10382840B2 · Branch · 2019 [cited by examiner]
US 10887001B2 · Tofighbakhsh · 2021 [cited by examiner]
US 11221249B2 · Gray, Jr. · 2022 [cited by examiner]
US 11275366B2 · Mirfakhraei et al. · 2022 [cited by applicant]
US 11659025B2 · Po · 2023 [cited by examiner]
US 11678262B2 · Fuleshwar Prasad · 2023 [cited by examiner]
US 11811721B2 · Pai · 2023 [cited by examiner]
US 20190280940A1 · Furuichi · 2019 [cited by examiner]
US 20200305049A1 · Vasseur · 2020 [cited by examiner]
US 20200357270A1 · Oga · 2020 [cited by examiner]
US 20210289493A1 · Choi · 2021 [cited by examiner]
US 20210344738A1 · Petrie · 2021 [cited by examiner]
US 20220030334A1 · Stamatakis et al. · 2022 [cited by applicant]
US 20220182451A1 · Wang · 2022 [cited by examiner]
KR 20100090489 · 2010 [cited by applicant]
WO 2022045851 · 2022 [cited by applicant]
Algabroun, Hatem, “Dynamic sampling rate algorithm (DSRA) implemented in selfa-daptive software architecture: a way to reduce the energy consumption of wireless sensors through event-based sampling”, Microsystem Technol… [cited by applicant]
Monteiro Santos, Ivairton, “Dynamic definition of the sampling rate of data in Wireless Sensor Network with Adaptive Automata”, IEEE Latin America Transactions (vol. 9, Issue: 6, Oct. 2011), pp. 963-968, IEEE. [cited by applicant]
Sood, et al., “Green Cloud Computing: Green Indexing of Cloud Network Resources”, Technical Disclosure Commons, Defensive Publication Series, Aug. 2021, 16 pages. [cited by applicant]
Bruno, Luigi, “Multisensor Systems with Variable Sampling Rate”, IASTED Signal and Image Processing and Application, Jun. 2011, 8 pages. [cited by applicant]
Sun, et al., “Optimum sampling in spatial-temporally correlated wireless sensor networks”, EURASIP Journal on Wireless Communications and Networking 2013, 2013:5, 18 pages, Springer. [cited by applicant]
Bandyopadhyay, et al., “Spatio-Temporal Sampling Rates and Energy Efficiency in Wireless Sensor Networks”, IEEE/ACM Transactions on Networking, vol. 13, No. 6, Dec. 2005, pp. 1339-1352. [cited by applicant]
Giouroukis, et al., “A Survey of Adaptive Sampling and Filtering Algorithms for the Internet of Things”, DEBS '20: The 14th ACM International Conference on Distributed and Event-based Systems, Jul. 2020, 12 pages. [cited by applicant]
“Automatically Generating Notifications of Emergency Medical Events”, Technical Disclosure Commons, Defensive Publication Series, Aug. 2020, 9 pages. [cited by applicant]
Price, Thomas, “Machine Learning to Identify Vehicle Maintenance Needs”, Technical Disclosure Commons, Defensive Publications Series, Dec. 2017, 42 pages. [cited by applicant]
Felker, Nick, “Correcting Image Anomalies Using Machine Learning”, Technical Disclosures Commons, Defensive Publications Series, Dec. 2017, 36 pages. [cited by applicant]
Florabäck, Johan, “Anomaly Detection in Logged Sensor Data”, Chalmers University of Technology, Department of Applied Mechanics, Masters Thesis in Complex Adaptive Systems, 2015, 52 pages, Sweden. [cited by applicant]
Vanem, et al., “Unsupervised anomaly detection based on clustering methods and sensor data on a marine diesel engine”, Jun. 2019, 24 pages. [cited by applicant]
Hill, et al., “Real-Time Bayesian Anomaly Detection for Environment Sensor Data”, online: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.329.8262&rep=rep1&type=pdf, accessed May 17, 2022, 10 pages. [cited by applicant]
Conde, Erick F., “Environmental Sensor Anomaly Detection Using Learning Machines”, Utah State University Digital Commons, All Graduate Theses and Dissertations, Dec. 2011, 95 pages. [cited by applicant]
Kim, et al., “Anomaly Detection of Environmental Sensor Data using Recurrent Neural Network at the Edge Device”, 2020 International Conference on Information and Communication Technology Convergence (ICTC), Oct. 2020, p… [cited by applicant]
Hayes, et al., “Contextual Anomaly Detection in Big Sensor Data”, Western University, Electrical and Computer Engineering Publications, Jun. 2014, 9 pages, IEEE Big Data. [cited by applicant]
Hill, et al., “Anomaly detection in streaming environmental sensor data: A data-driven modeling approach”, Environmental Modelling & Software 25 (2010), pp. 1014-1022, Elsevier. [cited by applicant]