IoT Sensor Data Prioritization Using Edge Variance Preprocessing
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Solution Overview
Problem
IoT environments generate overwhelming amounts of raw sensor data, leading to hindered or cumbersome decision-making in enterprise organizations.
Innovation Solution
A sensor computing system preprocesses sensor data using a simplified state estimation module to calculate variance, generates processing and prioritization codes, and sends data to a real-time processing platform for efficient handling based on these codes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If raw sensor data is transmitted and processed in real-time processing platforms, then complete data processing capability is achieved, but processing latency increases and decision-making becomes cumbersome
Solution Approach 1:
The patent applies preliminary action by performing preprocessing operations (noise filtering, data validation, feature extraction) at the edge device before data transmission. This advance processing reduces the computational burden on remote platforms and decreases overall processing latency, directly resolving the contradiction between processing completeness and speed.
Solution Approach 2:
The patent segments the data processing workflow into multiple stages: edge-level preprocessing, selective transmission of processed data, and remote platform analysis. This segmentation allows different processing depths at different locations, optimizing the balance between processing completeness and latency based on specific needs.
2Loss of information
If all sensor data is transmitted to remote processing platforms, then comprehensive analysis is possible, but network bandwidth consumption increases and processing efficiency decreases
Solution Approach 1:
The patent extracts and removes unnecessary data elements at the edge device through filtering and preprocessing. Only relevant, processed data is transmitted to remote platforms, reducing network bandwidth consumption while maintaining the comprehensiveness of analysis for critical parameters.
Solution Approach 2:
The patent applies local quality by performing different processing operations at different locations based on data characteristics and requirements. Edge devices perform lightweight preprocessing, while remote platforms conduct comprehensive analysis, optimizing the distribution of processing tasks to improve overall efficiency.
3Reliability
If complex preprocessing is performed at the edge device, then data quality improves, but device complexity and computational overhead increase
Solution Approach 1:
The patent applies partial action by implementing selective preprocessing operations at the edge device based on data type, sensor characteristics, and application requirements. Not all data receives the same level of processing, which maintains data quality for critical parameters while reducing overall device complexity and computational overhead.
Data Source
AI summary
Aspects of the disclosure relate to apparatuses, method steps, and systems for optimized Internet of Things (IoT) data processing for real-time decision support systems. The systems are used for real-time processing prioritization using a prioritization code and/or processing code. Edge devices may generate processing codes that are used in optimizing the data processing. For example, the system receives sensor data and preprocesses the sensor data with a simplified state estimation module to calculate a variance that is used to determine a processing code and/or a prioritization code.