Dynamic Data Collection for IoT Resource Optimization
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Solution Overview
Problem
The increasing volume of data generated by low-cost, network-connected IoT devices poses challenges in efficient data collection, analysis, and storage, as existing methods do not effectively manage peak data generation patterns or optimize resource usage such as electricity and network bandwidth.
Innovation Solution
A dynamic data collection method that determines data generation temporal patterns, creates a data collection strategy, generates a data collection policy based on infrastructure evaluation, and schedules data transfers to optimize resource usage and reduce costs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data is collected continuously from IoT devices, then data completeness is improved, but resource consumption (electricity and network bandwidth) increases
Solution Approach 1:
The patent implements dynamic data collection strategies that adjust collection frequency and methods based on real-time conditions, infrastructure capabilities, and data priorities. This allows the system to collect sufficient data for analysis while adapting resource usage to actual needs, preventing both over-collection and under-collection.
Solution Approach 2:
The system changes key parameters such as data collection frequency, transmission timing, and compression levels based on infrastructure evaluation results. By dynamically adjusting these parameters, the system optimizes the balance between data completeness and resource consumption according to current network and computing conditions.
2Measurement precision
If data collection frequency is increased to capture peak generation patterns, then data quality is improved, but network bandwidth and electricity costs increase
Solution Approach 1:
The system performs preliminary evaluation of data infrastructure capabilities and peak generation patterns before implementing data collection. This advance planning allows optimization of collection frequency to match actual peak patterns, capturing necessary data quality information while avoiding excessive collection during non-peak periods when resources are more costly.
Solution Approach 2:
The patent implements feedback mechanisms that monitor data generation patterns, infrastructure performance, and resource consumption. This feedback loop enables continuous adjustment of collection frequency to maintain data quality while optimizing resource usage based on observed patterns and actual costs.
3Reliability
If data is stored locally at edge devices, then data availability for analysis is improved, but device storage requirements and complexity increase
Solution Approach 1:
The patent segments data storage and management across multiple levels: local edge device storage for immediate availability, regional aggregation points for intermediate processing, and centralized cloud storage for long-term retention. This segmentation distributes storage requirements and management complexity across the hierarchy rather than concentrating it at single points.
Solution Approach 2:
The system introduces intermediary components such as edge gateways and regional aggregation servers that mediate between local devices and centralized systems. These intermediaries handle storage management, data filtering, and transfer coordination, reducing the complexity burden on individual edge devices while maintaining data availability.
Data Source
AI summary
Disclosed embodiments provide techniques for dynamic data collection. The dynamic data collection includes determining a data generation temporal pattern. Based on the data generation temporal pattern, a data collection strategy is created. The data collection strategy can be based on one or more data collection goals. The data collection strategy can contain specific details on how data is to be collected. A data infrastructure evaluation is performed, which provides pricing models for resources such as electricity and/or network bandwidth. A data collection policy is created based on the data collection strategy and the data infrastructure evaluation. The data collection policy can contain specific details on when data is to be collected and what strategy to use for the collection. A data transfer schedule is created based on the data collection policy. The data transfer schedule determines when to collect data from one or more data source devices.