Edge Computing False Reading Detection
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Solution Overview
Problem
Existing edge computing systems face challenges in minimizing false alarms due to the heightened sensitivity of digital sensors, which often result in inaccurate readings, particularly in environments where dynamic conditions are monitored, leading to increased false alarms and data processing inefficiencies.
Innovation Solution
The implementation of an edge computing system with a sensor assembly comprising multiple sensors connected to local computing devices that can efficiently process data close to the sensor sources, allowing for preliminary analysis and identification of false readings before data is transmitted to downstream hosts, thereby optimizing data interpretation and reducing false alarms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If digital sensors are made highly sensitive to detect conditions in real-time, then measurement precision is improved, but false alarms increase
Solution Approach 1:
The patent combines multiple different sensors (first sensor and second sensor with different measurement capabilities) into a single sensor assembly. This merging allows the system to cross-validate readings and distinguish between actual conditions and false alarm sources, thereby maintaining high sensitivity while reducing false alarm rates.
Solution Approach 2:
The edge computing device acts as an intermediary between the sensor assembly and the downstream host. It processes sensor data locally, identifying false readings before transmission, thus mediating between the high-sensitive sensors and the final decision-making system to prevent false alarms from propagating.
2Device complexity
If sensor data is processed remotely at downstream hosts, then device complexity at sensor location is reduced, but data processing efficiency decreases
Solution Approach 1:
The patent segments the data processing function into two parts: preliminary processing at the edge computing device (identifying false readings) and final processing at the downstream host. This segmentation enables local efficiency improvements without significantly increasing sensor assembly complexity, as the edge device handles time-critical filtering operations.
Solution Approach 2:
The edge computing device performs preliminary action by processing and filtering sensor data before it reaches the downstream host. This preliminary identification of false readings reduces the burden on remote systems and improves overall processing efficiency by preventing unnecessary data transmission and remote analysis of invalid readings.
3Measurement precision
If all sensor data is transmitted to downstream hosts for analysis, then measurement precision is maintained, but data network bandwidth is wasted
Solution Approach 1:
The edge computing device extracts and removes false readings from the sensor data stream before transmission to the downstream host. This extraction process maintains the precision of valid readings while eliminating waste of network bandwidth on transmitting and processing inaccurate data at remote locations.
Data Source
AI summary
A multi-sensor edge computing system can have at least a sensor assembly connected to an edge computing device and a downstream host. The sensor assembly may have a first sensor and a second sensor with the respective sensors being different. The edge computing device can be configured to identify a false reading of the first sensor in response to data captured by the second sensor.