Adaptable Sensor Data Collection Framework for Edge Computing
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Solution Overview
Problem
Existing edge computing systems face challenges in optimizing sensor data collection due to static configurations and resource constraints, leading to inefficiencies in data quality and network throughput, particularly in scenarios requiring real-time processing and security, especially in multi-tenant and resource-constrained environments.
Innovation Solution
Implementing a framework that uses statistical or artificial intelligence architectures to dynamically adjust sensor data gathering based on feedback from consumers, optimizing the combination of sensors and data quality configurations to meet specific consumer needs, while ensuring secure and efficient resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If sensor data collection is configured statically, then device complexity is reduced and ease of operation is improved, but data quality and network throughput cannot be optimized dynamically
Solution Approach 1:
The patent implements dynamic sensor data collection by allowing the sensor manager to adjust data gathering parameters in real-time based on consumer feedback and system conditions. The framework transitions from static configuration to dynamic adaptation, where collection frequency, sensor selection, and data quality settings are continuously optimized according to current network conditions, battery status, and consumer requirements.
Solution Approach 2:
The system establishes a feedback loop where data consumers provide information about their requirements and performance metrics back to the sensor manager. This feedback mechanism enables the framework to learn from actual usage patterns and optimize sensor data collection strategies, adjusting parameters such as sampling rate and data precision to meet consumer needs while conserving resources.
2Measurement precision
If more sensor data is collected to improve data quality, then measurement precision is improved, but energy consumption and network throughput are reduced
Solution Approach 1:
The framework dynamically adjusts data collection parameters including sampling frequency, data precision, and sensor selection based on current conditions. When battery life is critical, the system reduces sampling rates or lowers data precision while maintaining acceptable quality levels. When energy is abundant, the system increases collection intensity to improve data quality, thus optimizing the trade-off between measurement precision and energy consumption.
Solution Approach 2:
The system collects data at varying intensities rather than maintaining constant maximum collection. It uses partial action by reducing data collection to minimum necessary levels during low-energy states and employs excessive action by intensifying collection when energy is abundant and high data quality is required, optimizing the balance between data quality and battery life.
3Productivity
If sensor data collection is optimized for specific consumers, then productivity is improved, but device complexity and orchestration difficulty increase
Solution Approach 1:
The sensor manager framework implements a universal interface that can serve multiple data consumers with different requirements through a single unified system. Rather than creating separate optimized collection mechanisms for each consumer, the framework provides multi-functional capabilities that adapt to various consumer needs through parameter adjustment, thus improving network throughput while avoiding proportional increases in orchestration complexity.
Solution Approach 2:
The system optimizes productivity by dynamically changing collection parameters based on consumer-specific requirements such as data precision, update frequency, and quality thresholds. The sensor manager adjusts these parameters per consumer while maintaining a unified orchestration layer, enabling tailored optimization without linearly increasing system complexity through parameter-based differentiation rather than structural multiplication.
Data Source
AI summary
Various systems and methods for providing adaptable sensor data collection are described herein. A system, includes: a sensor interface to receive sensor data; processing circuitry to: process the sensor data according to a configuration to generate processed sensor data; and transmit the processed sensor data to a plurality of data consumer devices; and feedback circuitry to: receive feedback from the plurality of data consumer systems; and revise the configuration based on the feedback from the plurality of data consumer systems, to modify subsequent processing of sensor data before transmitting to the plurality of data consumer systems.