IoT Sensor Data Compression via Mathematical Function Modeling

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Solution Overview

Problem

Current IoT solutions face high operational costs and energy consumption due to high-frequency data collection and transmission, requiring powerful network, processing, and storage capabilities, and often result in redundant data storage and loss of information in time-series analysis.

Innovation Solution

Implementing a method where IoT end devices generate a mathematical function to describe the behavior of measurement data, sending compact encoded messages only when the behavior changes, allowing for efficient data reconstruction and reduced transmission, processing, and storage resources.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If high-frequency data collection and transmission is implemented, then data accuracy and completeness are improved, but energy consumption and operational costs increase

Engineering Contradiction:

Improvedata accuracyVSAvoidenergy consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

The patent extracts only the essential information from raw sensor data by generating mathematical functions that describe data behavior patterns. Instead of transmitting complete raw datasets, the system extracts behavioral characteristics (trends, patterns, anomalies) and transmits only these extracted insights, significantly reducing energy consumption while preserving data accuracy for analysis purposes

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The patent transforms raw sensor data into different parameter representations through mathematical functions. By changing the parameter form from raw measurement values to behavioral descriptors (slopes, intercepts, correlation coefficients), the system maintains analytical accuracy while reducing transmission frequency and energy requirements

Inventive Principle:

Principle #35Parameter changes

2Loss of information

If all measurement data is transmitted and stored, then data completeness is improved, but network traffic and storage resources increase

Engineering Contradiction:

Improvedata completenessVSAvoiddata volume

Core Design Contradiction:

Loss of informationVSQuantity of substance

Solution Approach 1:

The patent creates simplified copies of the original data in the form of mathematical function parameters. These function copies (linear, quadratic, exponential models) reproduce the essential behavior of the original sensor data without requiring storage of every raw measurement point, thus maintaining data completeness for analysis while dramatically reducing data volume

Inventive Principle:

Principle #26Copying

Solution Approach 2:

Instead of compressing raw data to reduce volume, the patent inverts the approach by generating mathematical functions that model the data behavior. The system works backwards from the need for complete data to a solution where function parameters represent the complete behavioral information, eliminating the need to store and transmit redundant raw data points

Inventive Principle:

Principle #13The other way round (Inversion)

3Speed

If frequent data transmission is performed, then real-time monitoring capability is improved, but network resources and processing power increase

Engineering Contradiction:

Improvereal-time monitoring capabilityVSAvoidprocessing power

Core Design Contradiction:

SpeedVSPower

Solution Approach 1:

The patent implements periodic transmission based on behavior changes rather than fixed time intervals. The system continuously monitors data behavior and triggers transmissions only when mathematical functions detect significant changes in patterns or anomalies, maintaining real-time monitoring capability while reducing unnecessary periodic transmissions that consume network and processing resources

Inventive Principle:

Principle #19Periodic action

Solution Approach 2:

The patent introduces mathematical functions as intermediary processors between raw sensor data and transmission. These functions act as mediators that analyze local data behavior and determine transmission necessity, filtering out redundant data before it reaches the network, thus reducing processing power requirements at all system levels while maintaining monitoring responsiveness

Inventive Principle:

Principle #24Intermediary (Mediator)

Data Source

PatentUS10855566B2Efficiency and performance in Internet-of-Things scenarios

Publication Date: 2020.12.01 SAP SE

AI summary

A set of measurement data is gathered by a network-enabled end device over a period of time. A mathematical function is generated to describe behavior of the set of measurement data. A compact, encoded message is generated representing the behavior of the gathered set of measurement data and the compact, encoded message is transmitted for storage in a data store associated with a backend server. Responsive to a received data analysis request, particular compact, encoded messages stored in the data store and applicable to the data analysis request are decoded. Time series data reconstructing the measurement data based on the decoded messages is generated.