Edge Control Task Distribution for Low-Latency IoT Actuation
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Solution Overview
Problem
Conventional IoT systems face communication latency and bandwidth limitations between programmable logic controllers (PLCs) and edge computing devices, which restrict the temporal resolution of control signals and the types of control schemes that can be used for IoT-controlled devices.
Innovation Solution
A computing device is configured to receive sensor data from IoT devices, identify subsets for local and remote processing, generate control instructions based on this data, and transmit subsets to a remote computing device for further processing, allowing for real-time local control while offloading less time-sensitive tasks to a remote server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If all sensor data is processed locally at the edge computing device, then real-time control response is improved, but the computing device becomes overloaded and communication bandwidth is excessively consumed
Solution Approach 1:
The patent segments sensor data into two categories: time-sensitive data processed locally at the edge computing device for immediate control actions, and non-time-sensitive data transmitted to remote cloud servers for advanced processing. This segmentation allows the system to maintain real-time responsiveness while distributing computational complexity across multiple levels.
Solution Approach 2:
The edge computing device acts as an intermediary between IoT devices and remote cloud servers. It receives sensor data, performs preliminary processing and local control, and selectively transmits only necessary data to the cloud, thereby reducing communication bandwidth consumption and protecting cloud resources from being overwhelmed by raw data streams.
2Productivity
If sensor data is transmitted to remote computing device for processing, then computing capabilities are increased, but communication latency increases
Solution Approach 1:
The patent divides sensor data processing into two segments: time-sensitive data processed locally at the edge computing device to ensure immediate control responses, and non-time-sensitive data transmitted to remote cloud servers for computationally intensive processing. This segmentation ensures that critical control operations are not delayed by communication latency.
Solution Approach 2:
The system implements local quality processing by equipping the edge computing device with sufficient computational resources to handle time-sensitive data processing locally. This allows the system to maintain high-speed response for critical control functions while still utilizing remote cloud computing resources for less time-sensitive analytical tasks.
3Measurement precision
If communication bandwidth between PLC and edge computing device is increased, then temporal resolution of control signals is improved, but system cost and complexity increase
Solution Approach 1:
The patent extracts only the essential time-sensitive portions of sensor data for local processing at the edge computing device, while non-critical data is transmitted to remote servers. This extraction approach maintains high temporal resolution for control-critical signals without requiring excessive communication bandwidth for all data streams.
Solution Approach 2:
The system applies partial processing action by performing only the necessary local processing of time-sensitive data at the edge device, rather than processing all sensor data locally. This partial action approach achieves the required temporal resolution for control signals without the excessive bandwidth consumption and cost associated with processing all data at high speed.
Data Source
AI summary
A computing device, including a processor configured to receive sensor data from a control device. The control device may include a control processor configured to execute control instructions to control an actuator of a target electromechanical system and may further include one or more sensors. The processor may identify a first subset of the sensor data and a second subset of the sensor data. The processor may generate first control instructions based on the first subset and transmit the first control instructions to the control processor of the control device. The processor may transmit the second subset to a remote computing device. In response to transmitting the second subset to the remote computing device, the processor may receive a remote processing result from the remote computing device. The processor may generate second control instructions from the remote processing result and transmit the second control instructions to the control processor.