Cloud-Based M2M Sensor Calibration Without Local Computing
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Solution Overview
Problem
Existing M2M systems face challenges in efficiently calibrating sensors due to variations in sensor characteristics and the need for local computing power and memory for calibration, which increases costs and complicates sensor design.
Innovation Solution
A cloud-based calibration method and apparatus that transmits calibration parameters and rules from a cloud to a calibration application, adjusting sensor data using these values and rules, eliminating the need for local computing resources in the sensor.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensor calibration is performed locally using computing power and memory in the sensor device, then calibration accuracy can be maintained, but device complexity and manufacturing cost increase
Solution Approach 1:
The calibration computing resources are extracted from the sensor device and relocated to an external calibration server. The sensor device only retains the minimal functionality to communicate calibration data, while the complex calibration computations are performed externally, thus reducing device complexity while maintaining calibration accuracy.
Solution Approach 2:
A calibration server acts as an intermediary between the sensor device and the calibration process. The server receives raw sensor data, performs the complex calibration computations, and returns calibrated results, thereby eliminating the need for complex local computing resources while preserving calibration accuracy.
2Measurement precision
If sensor calibration is performed locally with embedded computing resources, then real-time calibration is achieved, but manufacturing cost increases
Solution Approach 1:
Computing resources are extracted from the sensor device and consolidated on external calibration servers. This allows sensor manufacturing to focus only on basic sensing capabilities, significantly reducing manufacturing costs while calibration accuracy is maintained through sophisticated external processing.
Solution Approach 2:
Instead of embedding expensive computing resources in each sensor, the calibration computation capability is replicated across multiple cloud-based servers. This allows any sensor to access calibration services without requiring local computing hardware, reducing per-unit manufacturing costs.
3Ease of operation
If calibration parameters are stored locally in the sensor, then calibration can be performed independently, but sensor size and cost increase
Solution Approach 1:
Calibration parameter storage is extracted from the sensor device and relocated to external servers. The sensor device only stores minimal identification information, while all calibration parameters are maintained externally, dramatically reducing sensor size while preserving independent calibration capability through cloud connectivity.
4Ease of manufacture
If cloud-based calibration is implemented, then sensor size and cost are reduced, but dependency on cloud connectivity increases
Solution Approach 1:
Calibration parameters are pre-computed and stored on cloud servers before the sensor needs them. The sensor device only needs to retrieve these pre-computed parameters, reducing the need for complex real-time computation and minimizing cloud dependency to simple data retrieval operations, thereby improving reliability.
Data Source
AI summary
The present disclosure relates to device calibration based on a cloud in a machine-to-machine (M2M) system, and a method for calibration based on a cloud in an M2M system may include transmitting a calibration parameter stored in the cloud to a calibration application, receiving a calibration value and a calibration rule from the calibration application, and adjusting data received from a device based on the calibration value and the calibration rule