IP Library Granted Patent US 12,436,525
Granted Patent B2
US 12,436,525 · App. 17/927,231 · Granted Oct 7, 2025

Sensor control system for controlling a sensor network

Inventors: Zsófia Kallus (Budapest, HU); Péter Hága (Budapest, HU); Máté Szebenyei (Budapest, HU)
Assignee: Telefonaktiebolaget LM Ericsson (publ)
G05B19/4185G05B19/4183
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 12,436,525
App. No.
17/927,231
Granted
Oct 7, 2025
Kind
B2
Abstract

A sensor control system ( 202 ) for managing at least a first set of one or more sensors ( 101 ) for monitoring a first domain of an industrial process and a second set of one or more sensors ( 102 ) for monitoring a second domain of the industrial process, wherein the sensor control system ( 202 ) comprises at least a first reinforcement learning, RL, agent (A 1 ) and a second RL agent (A 2 ), wherein the first and second RL agents were trained using reinforcement learning and a process graph ( 196 ) representing the industrial process.

Claims (57)

1. A method performed by a sensor control system for managing at least a first set of one or more sensors for monitoring a first domain of an industrial process and a second set of one or more sensors for monitoring a second domain of the industrial process, wherein the sensor control system comprises at least a first reinforcement learning (RL) agent and a second RL agent, wherein the first and second RL agents were trained using reinforcement learning and a process graph representing the industrial process, the method comprising:

the sensor control system receiving sensor data from the first set of one or more sensors;

the sensor control system using the received sensor data and the process graph to decide whether or not to reconfigure the first set of sensors and/or the second set of sensors; and

the sensor control system providing configuration information to the first set of sensors and/or the second set of sensors as a result of the sensor control system deciding to reconfigure the first set of sensors and/or the second set of sensors,

wherein the first and second domains are defined based on sensor locations; or

wherein the first and second domains are defined based on functional similarities.

2. The method of claim 1 , wherein

the first set of sensors are configured to monitor a first workstation, and

the second set of sensors are configured to monitor a second workstation.

3. The method of claim 2 , wherein receiving sensor data from the first set of one or more sensors comprises the first RL agent receiving the sensor data from the first set of sensors.

4. The method of claim 3 , wherein using the received sensor data and the process graph to decide whether or not to reconfigure the first set of sensors and/or the second set of sensors comprises:

the first RL agent detecting an anomaly with respect to the first workstation based on the received sensor data; and

the first RL agent, as a result of detecting the anomaly with respect to the first workstation, obtaining information about a third workstation that is monitored by a third set of sensors.

5. The method of claim 4 , further comprising the first RL agent using the obtained information about the third workstation to decide whether or not to reconfigure the second set of sensors.

6. The method of claim 1 , further comprising:

training the first RL agent using: i) the process graph, ii) sensor data, and iii) communication capacity information; and

training the second RL agent using: i) the process graph, ii) the sensor data, and iii) the communication capacity information.

7. The method of claim 6 , wherein training the first and second RL agents comprises:

performing a first training phase where the first RL agent is trained to optimize a first local optimization function and the second RL agent is trained to optimize a second local optimization function; and

performing a second training phase where the first and second RL agents are trained to optimize a predefined weighted sum of local objective functions.

8. The method of claim 1 , wherein the first and second domains are defined based on sensor locations.

9. The method of claim 1 , wherein the first and second domains are defined based on functional similarities.

10. The method of claim 1 , wherein providing the configuration information to the first set of sensors and/or the second set of sensors comprises transmitting the configuration information to a sensor gateway that is configured to relay the configuration information to the first set of sensors and/or the second set of sensors.

11. A non-transitory computer readable storage medium storing a computer program comprising instructions which when executed by processing circuitry of a sensor control system causes the sensor control system to perform the method of claim 1 .

12. A sensor control system for managing at least a first set of one or more sensors for monitoring a first domain of an industrial process and a second set of one or more sensors for monitoring a second domain of the industrial process, the sensor control system comprising:

a first reinforcement learning, (RL) agent; and

a second RL agent, wherein

the first and second RL agents were trained using reinforcement learning and a process graph representing the industrial process, and

the sensor control system is operable to:

i) receive sensor data from the first set of one or more sensors;

ii) use the received sensor data and the process graph to decide whether or not to reconfigure the first set of sensors and/or the second set of sensors; and

iii) provide configuration information to the first set of sensors and/or the second set of sensors as a result of deciding to reconfigure the first set of sensors and/or the second set of sensors.

13. A sensor control system for managing at least a first set of one or more sensors for monitoring a first domain of an industrial process and a second set of one or more sensors for monitoring a second domain of the industrial process, the sensor control system comprising:

a receiver for receiving sensor data from a first set of one or more sensors;

processing circuitry; and

a memory, the memory containing instructions executable by the processing circuitry, wherein the sensor control system is configured to:

use the received sensor data and a process graph representing the industrial process to decide whether or not to reconfigure the first set of sensors and/or a second set of sensors; and

provide configuration information to the first set of sensors and/or the second set of sensors as a result of the sensor control system deciding to reconfigure the first set of sensors and/or the second set of sensors,

wherein the first and second domains are defined based on sensor locations; or

wherein the first and second domains are defined based on functional similarities.

14. The sensor control system claim 13 , further comprising:

training a first reinforcement learning (RL) agent using: i) the process graph, ii) sensor data, and iii) communication capacity information; and

training a second RL agent using: i) the process graph, ii) the sensor data, and iii) the communication capacity information.

15. The sensor control system claim 14 , wherein training the first and second RL agents comprises:

performing a first training phase where the first RL agent is trained to optimize a first local optimization function and the second RL agent is trained to optimize a second local optimization function; and

performing a second training phase where the first and second RL agents are trained to optimize a predefined weighted sum of local objective functions.

16. The sensor control system claim 14 , wherein

the first set of sensors are configured to monitor a first workstation,

the second set of sensors are configured to monitor a second workstation,

receiving sensor data from the first set of one or more sensors comprises the first RL agent receiving the sensor data from the first set of sensors,

using the received sensor data and the process graph to decide whether or not to reconfigure the first set of sensors and/or the second set of sensors comprises:

the first RL agent detecting an anomaly with respect to the first workstation based on the received sensor data; and

the first RL agent, as a result of detecting the anomaly with respect to the first workstation, obtaining information about a third workstation that is monitored by a third set of sensors, and

the first RL agent is configured to use the obtained information about the third workstation to decide whether or not to reconfigure the second set of sensors.

17. The sensor control system claim 13 , wherein the first and second domains are defined based on sensor locations.

18. The sensor control system claim 13 , wherein the first and second domains are defined based on functional similarities.

19. The sensor control system claim 13 , wherein providing the configuration information to the first set of sensors and/or the second set of sensors comprises transmitting the configuration information to a sensor gateway that is configured to relay the configuration information to the first set of sensors and/or the second set of sensors.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jan 18, 2023
From: KALLUS, ZSÓFIA; HÁGA, PÉTER; SZEBENYEI, MÁTÉ
To: TELEFONAKTIEBOLAGET LM ERICSSON (PUBL)
Reel/Frame 062404/0048 →
Continuity (1)
Related Publication 20230213920A1 · Jul 6, 2023
References Cited (11)
US 20180165603A1 · Van Seijen et al. · 2018 [cited by applicant]
US 20190279076A1 · Hu et al. · 2019 [cited by applicant]
US 20200151562A1 · Pietquin et al. · 2020 [cited by applicant]
US 20210103286A1 · Wang · 2021 [cited by examiner]
US 20210174203A1 · Lee · 2021 [cited by examiner]
US 20210334592A1 · Taniai · 2021 [cited by examiner]
WO 2019155354A1 · 2019 [cited by applicant]
International Search Report and Written Opinion issued in International Application No. PCT/IB2020/054987 dated Feb. 16, 2021 (9 pages). [cited by applicant]
OpenAI Five, https://openai.com/blog/openai-five/ Aug. 16, 2020 (18 pages). [cited by applicant]
Berner, C et al., “Dota 2 with Large Scale Deep Reinforcement Learning”, Dec. 13, 2019 (66 pages). [cited by applicant]
OpenAI Five 2016-2019, https://openai.com/projects/five/ Jun. 10, 2020 (12 pages). [cited by applicant]