Machine Learning Network Controller for Wireless Power Management
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Solution Overview
Problem
Mobile computing devices often unintentionally remain disconnected from reliable Wi-Fi networks, leading to increased battery consumption and potential unintended usage surcharges from cellular data networks, due to users forgetting to re-enable Wi-Fi connections after avoiding low-quality or malicious networks.
Innovation Solution
An apparatus with a network interface, detector, and processor that uses a machine learning engine to determine when to activate or deactivate the network interface based on predefined rules and training data, ensuring efficient power management and network connectivity by distinguishing between powered and power-saving states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Use of energy by moving object
If the network interface is kept deactivated to conserve battery power, then power consumption is reduced, but the device may fail to connect to reliable networks when needed
Solution Approach 1:
The system dynamically adjusts the network interface state between deactivated and activated based on real-time contextual factors. The machine learning model continuously evaluates current context (location, time, user activity, network availability) and transitions the network interface state accordingly, allowing the system to adapt to changing conditions rather than maintaining a fixed state
Solution Approach 2:
The system implements feedback loops where the network detector continuously monitors for available networks and the machine learning model receives feedback about connection outcomes, user preferences, and contextual changes. This feedback informs future decisions about when to activate the network interface, improving the reliability of connecting to reliable networks while maintaining power savings
2Reliability
If the network interface is kept activated to ensure connectivity, then network connection reliability is improved, but battery power is consumed unnecessarily
Solution Approach 1:
The system transitions from a static activated state to a dynamic state that adjusts based on contextual needs. The machine learning model evaluates multiple contextual factors and dynamically determines the optimal state, activating the network interface only when reliable networks are available and needed, thereby maintaining connectivity reliability while minimizing unnecessary power consumption
Solution Approach 2:
The system changes the operational parameters of the network interface by transitioning between deactivated and activated states based on contextual evaluation. The machine learning model adjusts the activation threshold and timing parameters dynamically, allowing the system to optimize the balance between connection reliability and power consumption for each specific situation
3Object-affected harmful factors
If users manually deactivate network access to avoid malicious networks, then security is improved, but users may forget to reconnect leading to unintended cellular data usage
Solution Approach 1:
The system performs self-service by automatically detecting available networks, evaluating their reliability through machine learning analysis, and activating the network interface without user intervention. The system learns from user preferences and connection outcomes to autonomously manage network connectivity, eliminating the need for users to manually remember to reconnect while maintaining security against malicious networks
Solution Approach 2:
The machine learning model acts as an intermediary between the user's security concerns and the network connection needs. It analyzes contextual factors, network characteristics, and user preferences to make intelligent decisions about when to connect, serving as a mediator that balances security requirements with connectivity needs without requiring direct user involvement
4Productivity
If the device automatically connects to available networks, then connectivity is improved, but the device may connect to low-quality or malicious networks
Solution Approach 1:
The system performs preliminary evaluation of available networks before establishing connections. The machine learning model analyzes network characteristics, historical performance data, and contextual factors in advance to predict network quality and reliability. This preliminary assessment allows the system to avoid low-quality or malicious networks before connection attempts occur, ensuring both connectivity and reliability
Solution Approach 2:
The system implements feedback mechanisms where connection outcomes, network performance, and user preferences are continuously monitored and fed back to the machine learning model. This feedback loop enables the system to learn from past connections and improve its ability to identify reliable networks, ensuring that automatic connections are made only to high-quality networks while maintaining good connectivity
Data Source
AI summary
An example of an apparatus including a memory to store training data and rules. The apparatus includes a network interface to communicate with a wireless network. The apparatus also include a network detector to detect a presence of the wireless network. The apparatus includes a machine learning engine in communication with the memory. The machine learning engine is to use the training data to generate rules to determine if the network interface is to be switched from the power-saving state to the powered state in the presence of the wireless network. The apparatus also includes a processor to switch the network interface from the power-saving state to the powered state based on the rules.