Application-Controlled Granularity for Power-Efficient Context Classification
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Solution Overview
Problem
Current mobile devices face inefficiencies in power consumption and resource usage due to high classification precision requirements for context awareness, as different applications demand varying levels of granularity in context classification, leading to suboptimal power management and resource allocation.
Innovation Solution
Implementing a method and apparatus for application-controlled context classification with adjustable granularity, allowing applications to specify desired classification precision levels, which in turn adjusts resource usage levels by selecting appropriate sensor features, classification techniques, and duty cycles, enabling power-efficient context classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high classification precision is used for context awareness, then context classification accuracy is improved, but power consumption and resource usage increase
Solution Approach 1:
The system dynamically adjusts the classification granularity based on application requirements. Applications can specify their desired granularity level (e.g., coarse, medium, fine), and the context classification system adapts its precision accordingly. This dynamic adjustment allows the system to use high precision only when necessary while conserving power for applications that tolerate lower precision, directly resolving the contradiction between accuracy and power consumption.
Solution Approach 2:
The system changes the granularity parameter of context classification to match application needs. By offering multiple granularity levels (coarse, medium, fine), the system can modify the classification parameter to achieve the optimal balance between accuracy and resource usage for each application, thereby reducing overall power consumption while maintaining sufficient classification accuracy.
2Measurement precision
If high classification precision is used for context awareness, then context classification accuracy is improved, but resource usage increases
Solution Approach 1:
The system dynamically adjusts computational resources allocated to context classification based on the granularity level required by each application. For coarse-grained classification, simpler and less resource-intensive algorithms are used, while fine-grained classification activates more complex models. This dynamic resource allocation reduces overall device complexity and resource usage while maintaining high accuracy where needed.
Solution Approach 2:
By changing the granularity parameter, the system controls the complexity of classification operations. Coarse granularity uses fewer computational resources and simpler processing, while fine granularity engages more robust but resource-intensive methods. This parameter-based control allows the system to optimize resource usage according to actual application requirements.
3Measurement precision
If uniform high granularity classification is applied to all applications, then classification accuracy is improved, but power efficiency decreases
Solution Approach 1:
The system applies different classification granularities to different applications based on their specific requirements. Instead of using uniform high granularity for all applications, each application receives the appropriate level of precision it needs. This localized approach ensures that power is not wasted on applications that do not require high precision, thereby improving overall power efficiency while maintaining necessary accuracy for each individual application.
Solution Approach 2:
The system performs partial classification action by offering multiple granularity levels (coarse, medium, fine) and allowing applications to select the appropriate level. Applications that can tolerate lower precision use coarse or medium granularity, consuming less power, while only applications requiring high precision use fine granularity. This partial action approach eliminates excessive power consumption associated with uniform high-granularity classification across all applications.
Data Source
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AI summary
Systems and methods for providing application-controlled, power-efficient context (state) classification are described herein. An apparatus for performing context classification with adjustable granularity as described herein includes a classifier controller configured to receive a request for a context classification and a granularity input associated with the request; and a context classifier communicatively coupled to the classifier controller and configured to receive the request and the granularity input from the classifier controller, to select a resource usage level for the context classification based on the granularity input, wherein a granularity input indicating a higher granularity level is associated with a higher resource usage level and a granularity input indicating a lower granularity level is associated with a lower resource usage level, and to perform the context classification at the selected resource usage level.