IoT Sensor Network Dynamic Data Transmission Control
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Solution Overview
Problem
Traditional IoT environments face inefficiencies due to the transmission of large amounts of repetitive data from numerous sensors, which increases resource consumption and processing burdens on aggregators.
Innovation Solution
A system of peer devices that compare locally measured data with neighboring devices' data and adjust sampling and transmission frequencies based on correlation thresholds, reducing redundant data transmission and conserving resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large number of sensors are deployed to obtain more granular data, then measurement precision and data granularity are improved, but resource consumption and processing burden increase
Solution Approach 1:
The patent extracts only the essential and non-redundant data from the sensor network. By comparing data from multiple sensors and identifying correlations, the system extracts only unique information for transmission to the aggregator, eliminating repetitive data while preserving measurement precision.
Solution Approach 2:
The system dynamically changes transmission parameters based on data correlation. When sensors show high correlation, transmission frequency is reduced; when divergence is detected, transmission increases. This adaptive parameter adjustment optimizes resource consumption while maintaining data granularity.
2Loss of information
If all sensor data is transmitted to the aggregator, then data completeness is improved, but network traffic and processing load increase
Solution Approach 1:
The system implements feedback mechanisms where sensors compare their data with neighboring sensors and adjust their transmission behavior accordingly. This feedback loop ensures that only necessary data is transmitted, maintaining completeness while reducing volume.
Solution Approach 2:
Instead of transmitting all raw sensor data, the system creates selective copies of only the unique and relevant information. Sensors identify which data points differ from their neighbors and transmit only those differences, reducing data volume while preserving completeness.
3Loss of time
If sensors transmit data at high frequency, then data freshness is improved, but energy consumption and network overhead increase
Solution Approach 1:
The system makes transmission frequency dynamic rather than static. Transmission rate adjusts based on real-time conditions: high frequency when data changes significantly or correlations break, low frequency when data is stable and highly correlated with neighbors, optimizing both freshness and energy usage.
Solution Approach 2:
Sensors perform periodic correlation checks with neighboring sensors and adjust transmission accordingly. This periodic assessment allows the system to maintain data freshness when needed while conserving energy during stable periods, creating an efficient rhythm of measurement and transmission.
Data Source
AI summary
Methods, apparatus, systems and articles of manufacture to dynamically control devices based on distributed data are disclosed. An example apparatus includes a comparator to compare a first measurement measured by a first peer device to a second measurement, the second measurement being measured locally by the apparatus; and an operation adjuster to, when the comparison satisfies a threshold, adjust a measurement protocol of the first peer device.