Sensor Data Transmission Using Prediction-Based Deviation Filtering
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Solution Overview
Problem
Existing home automation systems face network congestion and high energy consumption due to continuous data transmission from sensors, particularly those measuring high-frequency data like acceleration, which requires significant bandwidth and memory storage.
Innovation Solution
Implementing a method where sensors compute an indicator of deviation between new data and a predicted value from a prediction model, transmitting only data that deviates above a threshold, reducing the need for continuous data transmission and memory storage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensors transmit data continuously to the server, then the server can monitor the environment in real-time, but network congestion occurs and energy consumption increases
Solution Approach 1:
The patent extracts only the essential information (deviation from predicted value) rather than transmitting all raw sensor data. The sensor computes whether a measured value deviates from a predicted value, and only transmits when deviation exceeds a threshold, thereby reducing network traffic and energy consumption while maintaining monitoring reliability
Solution Approach 2:
The system performs preliminary computation at the sensor level by calculating predicted values and comparing them with actual measurements before transmission. This preliminary action filters out redundant data, reducing the burden on the network and server while preserving real-time monitoring capability for significant events
2Loss of information
If sensors transmit data continuously to the server, then complete data is available for analysis, but network bandwidth is excessively consumed
Solution Approach 1:
The patent extracts only the deviation information rather than transmitting complete raw data streams. By computing whether measured values deviate from predicted values and transmitting only when thresholds are exceeded, the system maintains analytical completeness for significant events while dramatically reducing network bandwidth consumption
Solution Approach 2:
The system transforms the transmission parameter from 'all raw sensor values' to 'deviation indicators'. This parameter change converts continuous high-volume data transmission into selective low-volume transmission, preserving information about significant events while reducing overall bandwidth usage
3Use of energy by moving object
If sensors store captured data in memory for later transmission, then network transmission frequency is reduced, but memory capacity requirements increase
Solution Approach 1:
The patent extracts only deviation information at the sensor level before potential storage or transmission. By computing predicted values and comparing them with actual measurements, the system identifies only significant deviations for storage or transmission, reducing memory requirements while maintaining energy efficiency benefits of reduced transmission frequency
4Measurement precision
If high-frequency data is measured and transmitted, then detailed environmental tracking is achieved, but network congestion occurs
Solution Approach 1:
The patent extracts only significant deviation events from high-frequency sensor data. By comparing actual measurements against predicted values and transmitting only when deviations exceed thresholds, the system preserves detailed environmental tracking capability for significant events while eliminating redundant data that causes network congestion
Solution Approach 2:
The system changes the transmission parameter from 'all high-frequency measurements' to 'deviation indicators above threshold'. This transformation maintains measurement precision for significant events while improving network throughput by filtering out predictable, non-critical data
Data Source
AI summary
A method for transmitting data collected by at least one sensor to a monitoring device. The method includes, upon acquisition of a new piece of data by the at least one sensor, acts of calculating a deviation indicator indicating a deviation between the value of the new piece of data and a value predicted for this piece of data by a prediction model representative of previously acquired data, and transmitting the new piece of data to the monitoring device when the deviation indicator is higher than a threshold. Also provided are a monitoring method on a monitoring device, a terminal implementing the transmission method and a server implementing the monitoring method.