Hierarchical Sensor Data Aggregation for Efficient Storage
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Solution Overview
Problem
Current sensor data processing systems face inefficiencies in data transmission and storage, particularly in resource-limited applications, as they often require transferring and storing entire datasets for evaluation, leading to high costs and limited real-time analysis capabilities.
Innovation Solution
The iDataStore system stores and processes sensor data hierarchically near the data source, allowing for selective transmission and aggregation of data based on interest, using methods like temporal subsampling and compression, enabling efficient data navigation and analysis without transferring raw data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If the entire dataset is transferred and stored for evaluation, then complete data availability for analysis is achieved, but data transmission and storage costs increase significantly
Solution Approach 1:
The system extracts only the necessary evaluation results and aggregated data from the complete dataset, transmitting only these extracted portions to the evaluation device. This eliminates the need to transfer the entire raw dataset while maintaining data availability for evaluation purposes.
Solution Approach 2:
The data is segmented into different levels of aggregation (raw data, aggregated data, evaluation results). The system transmits only the relevant segments needed for evaluation rather than the complete dataset, reducing transmission volume while preserving necessary information.
2Measurement precision
If data is stored and transmitted in high resolution, then analysis precision is improved, but resource consumption increases
Solution Approach 1:
The system applies different quality levels to different parts of the data processing pipeline. Raw data is stored in high resolution locally at the sensor, while only aggregated summaries are transmitted. This allows precise local analysis without the resource cost of transmitting high-resolution data.
Solution Approach 2:
The system performs partial aggregation of data before transmission, sending only the essential summarized information needed for evaluation. This partial action maintains sufficient precision for analysis while dramatically reducing resource consumption compared to transmitting complete high-resolution datasets.
3Speed
If real-time evaluation is implemented, then response speed is improved, but data processing complexity increases
Solution Approach 1:
The system performs preliminary aggregation of data at the sensor level before transmission. This preliminary action reduces the data volume that needs to be processed in real-time at the evaluation device, enabling faster response without increasing overall system complexity.
Data Source
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AI summary
The invention relates to a method for the evaluation and the aggregated storage of data, in particular multivariable time series such as sensor and vital data, wherein the data is acquired through at least one sensor (2) and stored by the computer unit (3) of a user's device (4) and aggregated in chronologically hierarchical resolution stages. The stored data can be retrieved selectively by an external evaluation device (5). The special feature lies in the provision of both high-resolution data (possibly raw data) - if there is latent interest in retrospective analysis (and the provision of training data for machine learning methods) - and also of multi-scale overview data for integral observations, ad hoc analyses and explorative data mining.