Context-Based Sensor Data Recording for IoT Storage Efficiency
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Solution Overview
Problem
Internet of Things devices collect vast amounts of sensor data, including irrelevant information that occupies storage space and is not of value to users, leading to inefficiencies in storage, processing, and power usage.
Innovation Solution
An apparatus and method for context-based sensor data recording, which includes sensors, a processor, and memory that detect and record data only when the context is designated as interesting to the user, using modules to determine context of interest and activate or record relevant information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If sensor data is recorded continuously without context filtering, then complete data coverage is achieved, but storage space is wasted on irrelevant data
Solution Approach 1:
The system performs preliminary context detection and analysis before recording sensor data. The context detection module evaluates whether the current context matches user-defined contexts of interest, and only then triggers the recording action. This preliminary filtering prevents unnecessary data storage while ensuring relevant data is captured.
Solution Approach 2:
The context detection module acts as an intermediary between the sensors and the storage system. It receives raw sensor data, evaluates it against contextual criteria, and selectively passes only relevant data to the storage component. This intermediary layer resolves the contradiction by filtering data based on contextual relevance.
2Loss of information
If all sensor data is processed and stored, then no information is lost, but processing time and power consumption increase
Solution Approach 1:
The system extracts and processes only the contextual information necessary for decision-making, rather than processing all sensor data uniformly. The context detection module identifies and isolates relevant data patterns, processing only those that match predefined contexts of interest, thereby reducing overall processing time while retaining important information.
Solution Approach 2:
Instead of processing all sensor data equally, the system applies partial processing focused specifically on contextual evaluation. The context detection module performs selective processing on data streams that are likely to be relevant, using heuristics and patterns to identify which data requires full processing versus which can be filtered more aggressively.
3Reliability
If sensors operate continuously to capture all events, then complete monitoring is achieved, but power consumption increases
Solution Approach 1:
The sensors operate in periodic cycles rather than continuously. The context detection module monitors data streams at intervals, evaluating contextual conditions periodically. When a context of interest is detected, the system intensifies monitoring; otherwise, it reduces sensor activity to conserve power while maintaining monitoring coverage.
Solution Approach 2:
The system uses the sensor data itself to control sensor operation. The context detection module analyzes incoming data to determine whether continued high-power sensing is necessary, creating a self-regulating mechanism where data quality and relevance feedback control power consumption while maintaining monitoring reliability.
Data Source
AI summary
Apparatuses, methods, systems, and program products are disclosed for context-based sensor data recording. A method includes detecting, by a processor, a current context of an information handling device based on information sensed using one or more sensors of the information handling device. The method includes determining that the current context comprises a context that has been designated as a context of interest to a user. The method includes recording the information that is sensed using the one or more sensors in a storage volume in response to determining that the current context comprises a context of interest to the user.