Data Collection Device Scheduling for Network Congestion
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Solution Overview
Problem
Existing data collection systems face network congestion due to fluctuations in device performance and network environment, leading to delays and throughput issues when periodically requesting data from distributed devices.
Innovation Solution
A data collection device that schedules data transmission requests into groups based on predicted delay and throughput values, clustering requests to minimize dispersion and optimize transmission timing, thereby reducing network congestion and enhancing real-time performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data transmission requests are periodically issued to distributed devices, then data collection completeness is improved, but network congestion occurs due to concentration of data reception
Solution Approach 1:
The patent segments the periodic data collection requests into multiple groups based on predicted delay and throughput characteristics. Each group is processed separately with optimized timing, preventing the concentration of all requests at once and thereby avoiding network congestion while maintaining complete data collection from all distributed devices.
Solution Approach 2:
The patent dynamically adjusts the timing and grouping of data requests based on real-time network conditions and device performance predictions. By making the request schedule adaptive rather than static, the system optimizes data collection completeness while preventing network congestion through dynamic load distribution.
2Reliability
If requests are periodically issued to the device, then data collection coverage is improved, but delay and throughput fluctuate depending on device performance and network environment
Solution Approach 1:
The patent performs preliminary prediction of delay and throughput for each distributed device before issuing data requests. This advance assessment allows the system to plan optimal request timing and grouping, ensuring comprehensive data collection coverage while minimizing delays and throughput fluctuations by pre-coordinating requests based on predicted performance characteristics.
Solution Approach 2:
The patent changes the parameters of data requests by grouping them according to predicted delay and throughput values. This parameter-based grouping transforms the uniform periodic request pattern into a differentiated scheduling approach, maintaining data collection coverage while reducing time loss through optimized request timing for each group.
3Device complexity
If data transmission requests are issued without optimization, then system simplicity is maintained, but network load concentration reduces real-time processing efficiency
Solution Approach 1:
The patent implements a feedback mechanism that uses predicted delay and throughput information to optimize request scheduling. This feedback loop enables the system to automatically adjust request timing and grouping based on performance predictions, improving real-time processing efficiency without requiring complex manual configuration or optimization.
Data Source
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AI summary
A data collection device includes an acquisition unit configured to acquire, for a plurality of data transmission requests, first predicted values of a delay from transmission of the respective data transmission requests to a start of reception of data and second predicted values of a throughput regarding the reception of the data, a classification unit configured to classify the plurality of data transmission requests into groups based on first dispersion of the first predicted values and second dispersion of the second predicted values, and a determination unit configured to determine transmission timings of the respective data transmission requests in a unit of the group.