Localized Sensor Quality Control to Reduce IoT Bandwidth Consumption
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Solution Overview
Problem
The proliferation of IoT devices leads to inefficient and costly long-haul network communications for quality control and calibration of sensors, as raw data and control instructions often need to be transmitted via the Internet to cloud-computing devices, consuming significant bandwidth.
Innovation Solution
Implementing localized sensor quality analysis and control, where sensors with similar functions communicate via short-range protocols to perform quality control and calibration among themselves, reducing reliance on cloud-based systems and utilizing nearby sensors' data for calibration and quality control analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If cloud-based quality control systems are used for sensor analysis, then comprehensive quality control can be achieved, but bandwidth consumption increases significantly
Solution Approach 1:
The patent implements localized quality control by deploying edge computing devices at regional levels that perform quality control analysis on sensor data locally, rather than transmitting all raw data to centralized cloud systems. This local processing reduces bandwidth consumption while maintaining comprehensive quality control through hierarchical analysis across multiple levels (sensor, edge, cloud).
Solution Approach 2:
The quality control system is segmented into multiple hierarchical levels: sensor-level preliminary filtering, edge-level regional analysis, and cloud-level comprehensive processing. This segmentation allows each level to handle appropriate data volumes, reducing overall bandwidth consumption while maintaining thorough quality control through distributed processing.
2Reliability
If all sensor data is transmitted to cloud systems for analysis, then complete quality control can be performed, but operational costs increase
Solution Approach 1:
The patent extracts and performs essential quality control functions at the edge computing level, removing the need to transmit all raw sensor data to expensive cloud systems. Only processed results and anomaly detections are transmitted upward, significantly reducing operational costs while maintaining comprehensive quality control through the hierarchical architecture.
3Measurement precision
If centralized cloud systems are used for sensor calibration, then uniform standards can be applied, but processing delays increase
Solution Approach 1:
The patent implements preliminary quality control filtering and preprocessing at the sensor and edge levels before data reaches cloud systems. This preliminary action reduces the volume of data requiring centralized processing, maintaining uniform calibration standards through hierarchical consistency while minimizing processing delays through early-stage data reduction.
4Measurement precision
If comprehensive quality control analysis is performed on all sensor data, then measurement accuracy improves, but system complexity increases
Solution Approach 1:
The patent applies partial quality control analysis at lower hierarchical levels (sensor and edge), performing comprehensive analysis only on data that requires it. This approach maintains high measurement accuracy through targeted comprehensive analysis while reducing overall system complexity by avoiding unnecessary comprehensive processing of all data at all levels.
Data Source
AI summary
An apparatus includes a memory comprising executable instructions and a processor coupled to the memory and configured to execute the instructions. Executing the instructions causes the processor to receive a broadcast including measurement data captured by a first sensor, verify integrity of the measurement data, perform a quality control analysis on the measurement data by analyzing the measurement data using a quality control algorithm to form an analysis result, generate a response for broadcasting the analysis result, and broadcast the analysis result. The response comprises a determination of pass when the quality control analysis indicates that the measurement data passes the quality control analysis and a determination of fail, a suspected cause of the failure, and a recommended resolution for the suspected cause of the failure when the quality control analysis indicates that the measurement data fails the quality control analysis.