ML-Based Access Point Scanning for Battery and Connection Trade-offs
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Solution Overview
Problem
Modern computing devices face significant power consumption issues due to frequent scanning for access points, leading to rapid battery drain, and often experience inconsistent network connections, especially in mobile environments where transient networks cause frequent connection and reconnection.
Innovation Solution
A computerized method utilizing machine learning (ML) to collect profile data and generate scan patterns that dictate scan frequency, iteration count, and channel hints, optimizing access point scanning based on user mobility patterns and network history, thereby reducing unnecessary scanning and improving connection consistency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If scanning for access points is performed frequently to ensure identification of available networks, then network connection availability is improved, but battery power consumption increases rapidly
Solution Approach 1:
The patent implements dynamic scanning behavior that adapts to device state and environment. The system adjusts scanning frequency based on whether the device is stationary or moving, using sensors to detect motion state and modify scanning intensity accordingly. This dynamic approach maintains connection reliability when needed while conserving battery during transit.
Solution Approach 2:
The system changes scanning parameters (frequency, duration, channels) based on contextual conditions. When the device detects it is in motion beyond a threshold speed, it modifies scanning parameters to reduce intensity. The system also adjusts parameters based on current network connection quality and historical scanning results, optimizing the balance between connection availability and power consumption.
2Use of energy by moving object
If scanning for access points is limited to conserve battery power, then battery life is extended, but network connection consistency deteriorates
Solution Approach 1:
The system performs preliminary scanning actions at reduced intensity to predict future network availability. By analyzing historical scanning data and current motion state, it anticipates when access points will become available and prepares accordingly. This allows the system to maintain connection consistency without requiring continuous high-intensity scanning.
Solution Approach 2:
The system implements feedback mechanisms that monitor scanning results, connection quality, and battery status. This feedback is used to continuously adjust scanning behavior, ensuring that scanning intensity is optimized to maintain connection consistency while conserving battery power. The system learns from past scanning outcomes to improve future scanning decisions.
3Speed
If scanning frequency is increased to improve network detection, then access point identification speed is improved, but battery drain accelerates
Solution Approach 1:
The system implements periodic scanning with variable intervals rather than continuous scanning. Scanning is performed at specific intervals that adapt based on device motion state and network conditions. When the device is stationary, scanning intervals are shorter to maintain quick access point identification. When moving, intervals are extended to reduce battery drain while maintaining sufficient detection capability.
Data Source
AI summary
The methods described herein are configured to collect profile data on a device, scan for access points based on the profile data, and update a machine learning (ML) component based on feedback from the scan. Profile data is collected on a device as input to the ML component and a scan pattern is generated by the ML component based on the collected profile data, the scan pattern including a scan frequency, a scan iteration count, and a channel hint. A scan for access points is run in accordance with the generated scan pattern and the ML component receives feedback including a scanning result based on the scan for access points. ML component is then updated based on the scanning result, the scan pattern, and the profile data. Improving the ML component and thereby, the scanning efficiency of the device provides consistent network connection and improved battery performance.