Beacon Scanning Context Detection Energy Optimization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing location-based technologies fail to accurately determine a user's context, such as environment or activity, beyond raw location data, which limits the effectiveness of location-based functionalities provided by beacon devices.
Innovation Solution
A computer-implemented method that compares location information with beacon data to infer user context by detecting changes in beacon data and location, adjusting scan rates, and providing notifications based on inferred contexts like transportation mode or urban settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If beacon devices continuously broadcast and user devices continuously scan for beacon data, then location accuracy and context detection capability are improved, but energy consumption increases
Solution Approach 1:
The system implements periodic beacon data scanning instead of continuous scanning. The user device scans for beacon data at predetermined intervals, which reduces energy consumption while still maintaining adequate location accuracy and context detection capability. This periodic action allows the device to balance between measurement precision and energy usage by adjusting the scan interval based on movement detection.
Solution Approach 2:
The system uses the user device's own movement status to dynamically adjust scanning behavior. When movement is detected, the device increases scanning frequency to maintain location accuracy; when stationary, it reduces scanning frequency to save energy. This self-adjusting mechanism allows the device to serve its own energy optimization needs based on real-time context.
2Reliability
If beacon data scanning frequency is increased to improve context detection accuracy, then user context inference reliability is improved, but device complexity and processing load increase
Solution Approach 1:
The system dynamically adjusts beacon data scanning frequency based on detected user movement. When movement is detected, scanning frequency increases to capture context changes; when stationary, scanning frequency decreases. This dynamic adjustment maintains context detection reliability while avoiding unnecessary processing complexity during periods of no change.
Solution Approach 2:
The system extracts only the essential information needed for context determination from beacon data, rather than processing all available data continuously. By focusing on key parameters such as beacon presence/absence and comparing them against movement status, the system achieves reliable context detection with reduced processing complexity.
3Adaptability or versatility
If continuous location tracking and beacon monitoring are performed to improve context awareness, then location-based functionality effectiveness is improved, but loss of time and computational resources increase
Solution Approach 1:
The system performs location tracking and beacon monitoring at periodic intervals rather than continuously. By scanning for beacon data at predetermined intervals and comparing current beacon data with previous data, the system maintains context awareness while significantly reducing the time and computational resources required for processing.
Solution Approach 2:
The system performs partial monitoring by only processing beacon data when changes are detected or when movement is observed. Rather than analyzing all beacon data continuously, it focuses on detecting changes in beacon presence or identity, which provides sufficient context awareness with reduced processing overhead.
Data Source
Figure 1
Figure 2
Figure 3
AI summary
Systems and methods are provided for determining a user context based on one or more wireless signals. In particular, location data can be determined by a user device. The user device can then detect beacon data broadcast by a first set of beacon devices, and subsequent to detecting the first beacon data, second beacon data broadcast by a second set of beacon devices. The location data can be compared with the first beacon data and the second beacon data to determine a user context. In particular, a context can be determined based at least in part on whether the location data is indicative of a changing location of a user, and whether the second beacon data corresponds to a change in beacon data from the first beacon data.