M2M Server Dynamic Data Collection Interval Control
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Solution Overview
Problem
In machine-to-machine (M2M) communication, existing technologies face challenges in efficiently controlling the data collection interval of M2M devices, particularly in scenarios where power consumption is critical and data dependency between devices is not adequately managed, leading to potential power wastage and reduced data quality.
Innovation Solution
A method is introduced where an M2M service server determines data dependency between M2M devices by calculating a correlation coefficient value based on collected sensing data, classifies devices as reference and control devices based on power source and battery level, and adjusts the data collection interval of control devices based on the variation in sensing data from the reference devices, optimizing power usage and data quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If data collection interval is reduced to improve data quality, then data reliability is improved, but power consumption increases
Solution Approach 1:
The patent implements dynamic adjustment of data collection intervals based on real-time analysis of data dependency between devices. The server continuously monitors correlation coefficients and modifies collection frequencies adaptively, transitioning from static to dynamic control to optimize both data quality and power consumption.
Solution Approach 2:
The system changes the time parameter (data collection interval) based on calculated correlation coefficients between devices. When correlation is high, the interval is extended; when correlation is low, the interval is reduced, thereby adjusting the parameter to balance data quality and energy consumption.
2Loss of energy
If data collection interval is increased to reduce power consumption, then energy efficiency is improved, but data quality deteriorates
Solution Approach 1:
The server implements a feedback mechanism where it analyzes sensing data from multiple devices, calculates correlation coefficients, and uses this information to adjust data collection intervals. This closed-loop feedback ensures that power consumption is reduced only when data dependency analysis confirms it will not compromise data quality.
Solution Approach 2:
The system performs preliminary analysis of data dependency and correlation between devices before adjusting collection intervals. By pre-calculating the relationship between devices, the system can proactively extend intervals for highly correlated devices without risking data quality degradation.
3Loss of information
If data collection is performed frequently for all devices, then data completeness is improved, but system energy consumption increases
Solution Approach 1:
The patent segments devices into different groups based on their data dependency relationships and correlation coefficients. Instead of applying a uniform collection strategy to all devices, the system divides them into segments with different optimal collection intervals, reducing overall system energy consumption while maintaining data completeness for each segment.
Solution Approach 2:
The system applies different data collection strategies to different devices based on their local characteristics and relationships with other devices. Each device receives a customized collection interval determined by its specific data dependency pattern, rather than a blanket approach, optimizing both completeness and energy efficiency.
Data Source
AI summary
Described embodiments relate to a machine to machine (M2M) server for controlling a data collection interval of a M2M device. The M2M server may be configured to determine data dependency between a first M2M device and a second M2M device based on sensing data collected from the first M2M device and the second M2M device and to control a data collection interval of at least one of the first M2M device and the second M2M device based on sensing data variation of one of the first M2M device and the second M2M device.