RSSI Snapshot Analysis for Wireless Resource Detection
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Solution Overview
Problem
Conventional methods for determining the availability of local wireless resources, such as Wi-Fi hotspots, are energy-intensive and inefficient, leading to battery drain and sub-optimal power usage in mobile devices, as they often require continuous scanning or reliance on location-centric data maps.
Innovation Solution
The system employs RSSI snapshot analysis, which captures and compares received signal strength indicator (RSSI) information to historical data to determine the probability of a local wireless resource's availability, allowing mobile devices to selectively activate or deactivate wireless radios based on this probability, independent of location information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If continuous scanning or location-centric data maps are used to determine wireless resource availability, then the accuracy of resource availability detection is improved, but energy consumption increases significantly
Solution Approach 1:
The system performs RSSI measurements at periodic intervals rather than continuously, capturing signal strength snapshots at predetermined times. This periodic sampling approach maintains adequate detection accuracy while significantly reducing the energy consumption associated with continuous scanning operations.
Solution Approach 2:
The system creates a copy of the historical RSSI data and compares it with current measurements to determine wireless resource availability. By using historical data copies instead of continuous real-time scanning, the system maintains detection accuracy while minimizing energy expenditure.
2Loss of time
If continuous scanning is performed to detect local wireless resources, then the timeliness of resource availability information is improved, but battery life is reduced
Solution Approach 1:
The system implements periodic RSSI snapshot measurements at predetermined intervals, capturing signal strength information at specific moments rather than continuously. This approach provides timely updates on wireless resource availability while preserving battery life through reduced operational duration of the scanning mechanism.
Solution Approach 2:
The system performs preliminary comparisons of current RSSI snapshots with historical data to quickly determine resource availability. By using pre-stored historical RSSI information, the system can make rapid availability assessments without requiring prolonged scanning operations.
3Area of stationary object
If location-centric data maps are used to determine wireless resource availability, then the coverage area is improved, but device complexity increases
Solution Approach 1:
The system extracts and compares only the relevant RSSI snapshot data from historical records against current measurements, rather than processing entire location maps. This extraction approach maintains broad wireless resource coverage detection while simplifying the data processing requirements and reducing device complexity.
Solution Approach 2:
The system uses simplified copies of historical RSSI data rather than complete location-centric data maps. By working with condensed RSSI snapshot information, the system achieves wide coverage area detection with reduced computational complexity and simpler data structures.
4Use of energy by moving object
If RSSI snapshot analysis with historical data comparison is used, then energy consumption is reduced, but the complexity of data analysis increases
Solution Approach 1:
The system compares current RSSI snapshots with copied historical RSSI data to determine wireless resource availability. By using pre-stored historical snapshots, the analysis complexity is managed while maintaining low energy consumption through efficient data reuse rather than repeated measurements.
Solution Approach 2:
The system uses disposable RSSI snapshot comparisons rather than maintaining complex continuous analysis structures. Each snapshot comparison is a simple, low-cost operation that can be performed periodically, avoiding the need for complex long-running analysis processes.
Data Source
AI summary
The disclosed subject matter relates to received signal strength indicator (RSSI) snapshot analysis. RSSI snapshot analysis can be independent of determining location/map information. An RSSI snapshot can be analyzed in view of historic RSSI information to determine a probability that a local wireless resource correlated with the historical RSSI information is within the service area of the user equipment. Machine learning can be employed to train an inference component to facilitate in determining the probability. In an aspect, the state of a wireless radio can be controlled based on the probability, which can reduce the energy consumption of the user equipment by facilitating selective enablement of a wireless radio.