Network Interface Activation Timeout Management
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Solution Overview
Problem
Existing computing devices struggle to efficiently manage network interface activation, leading to unnecessary power consumption and potential missed calls or network-dependent activities, as they lack dynamic adjustment mechanisms based on user usage patterns.
Innovation Solution
A computing device that dynamically adjusts the activation of network interfaces by using a machine learning model to analyze historical usage patterns and recommend a timeout value, allowing the device to optimize power usage and network connectivity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the network interface is activated immediately upon disconnection from the companion device, then network connectivity is ensured for timely communication, but power consumption increases unnecessarily
Solution Approach 1:
The system dynamically adjusts the timeout value based on usage patterns learned by the machine learning model. Instead of a fixed timeout, the delay before activating the network interface varies adaptively according to historical usage data, allowing optimal balance between connectivity reliability and power consumption for each specific scenario
Solution Approach 2:
The machine learning model continuously learns from historical usage information and provides feedback to optimize the timeout value. The system monitors actual usage patterns and adjusts the network interface activation timing accordingly, creating a closed-loop system that improves both connectivity reliability and power efficiency over time
2Use of energy by moving object
If the network interface is activated after a long timeout period, then power consumption is reduced, but the likelihood of missing important network events increases
Solution Approach 1:
The timeout period before network interface activation is made dynamic rather than static. The machine learning model adjusts this timeout value based on learned usage patterns, ensuring that for users who frequently need immediate network access, the timeout is shorter, while for users with less urgent needs, the timeout can be longer to save power
Solution Approach 2:
The system changes the timeout parameter adaptively based on historical usage data. By analyzing patterns in when the wearable device was disconnected from the companion device and when network access was actually needed, the system optimizes the timeout parameter to prevent missing important events while minimizing unnecessary power consumption
3Productivity
If the machine learning model continuously updates based on additional usage information, then the timeout value optimization improves over time, but computational resources and processing time are consumed
Solution Approach 1:
The system performs incremental updates to the machine learning model using only new usage data that arrives, rather than reprocessing all historical data each time. This partial action approach allows continuous improvement of optimization accuracy while minimizing the computational time and resources required for each update cycle
Data Source
AI summary
A computing device is described that includes at least one processor, a network interface, and a storage device that stores instructions executable by the at least one processor to obtain a usage profile generated by at least applying a machine learning model to historical feature usage information of the computing device collected while the computing device was wirelessly connected to a companion computing device. The instructions may further cause the one or more processors to determine a timeout value based on the usage profile. The instructions may further cause the one or more processors to initiate a connection to a network using the network interface responsive to determining that the computing device is no longer wirelessly connected to the companion computing device and after an amount of time specified by the timeout value has elapsed.