Mobile Context Collection via Energy-Aware Sensor Selection
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Solution Overview
Problem
Mobile devices face a challenge in determining context while minimizing battery runtime due to the energy consumption of various sensors used for context determination, which reduces user satisfaction and device efficiency.
Innovation Solution
A method that selectively activates sensors based on energy cost and information gain, using a model to determine the most beneficial sensors to use, allowing for accurate context determination with reduced energy consumption by minimizing unnecessary sensor data collection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple sensors are activated to determine context, then context determination accuracy is improved, but energy consumption increases
Solution Approach 1:
The system dynamically selects and activates sensors based on current context requirements and energy cost considerations. Instead of statically activating all sensors, the system adapts sensor activation to the specific situation, activating only the necessary subset of sensors to achieve the required context determination accuracy while minimizing energy consumption.
Solution Approach 2:
The system changes the operational parameters of sensor activation by introducing energy cost values associated with each sensor. This allows the system to evaluate and select sensors based on their energy consumption characteristics, transitioning from a state where all sensors are activated to a state where only cost-effective sensors are activated for each context determination task.
2Loss of information
If all sensors are activated continuously, then context information completeness is improved, but battery runtime decreases
Solution Approach 1:
The system applies partial action by activating only the necessary subset of sensors required for adequate context determination rather than all sensors continuously. By selecting sensors based on energy cost values and information gain, the system achieves sufficient context information completeness with a partial set of sensor activations, thereby preserving battery runtime.
Solution Approach 2:
The system performs preliminary evaluation of sensor energy costs and information gains before activating sensors. This preliminary action allows the system to pre-determine the optimal sensor subset for each context determination task, ensuring that context information completeness is maintained while avoiding unnecessary sensor activations that would deplete battery runtime.
3Measurement precision
If high-precision sensors are used, then context accuracy is improved, but energy cost increases
Solution Approach 1:
The system introduces energy cost values as a new parameter for sensor evaluation, changing the decision-making criteria from purely accuracy-based to a balanced consideration of accuracy and energy cost. This parameter change enables the system to select sensors that provide adequate context accuracy at acceptable energy cost levels.
Solution Approach 2:
The system applies different quality levels of sensor activation locally to different sensing needs. Instead of uniformly using high-precision sensors for all context determination tasks, the system selectively activates sensors with appropriate precision levels based on the specific context requirements and energy cost considerations for each situation.
Data Source
AI summary
This disclosure relates to determining a context of a mobile device. A processor receives from one or more first sensors first sensor data in relation to the context. The processor then selects from multiple second sensors one or more selected sensors based on the first sensor data and based on an energy cost value associated with each of the multiple second sensors, the energy cost value being indicative of energy consumed when requesting data from each of the multiple second sensors. Next, the processor receives from the one or more selected sensors second sensor data and determines the context of the mobile device based on the first sensor data and the second sensor data. This reduces the overall energy consumption and therefore extends the battery life of the mobile device while still providing an accurate context determination.