RL Sensor Data Management via Action Space Abstraction
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Solution Overview
Problem
Existing sensor data management systems face challenges in efficiently managing sensor data under resource constraints, particularly due to the large and complex action space associated with numerous sensors, which hinders effective learning and data synchronization.
Innovation Solution
A reinforcement learning-based sensor data management system that utilizes virtualized objects and a data orchestrator to abstract the action space, calculate scores based on position relationships, and assign priorities to sensors for efficient data updating, thereby addressing the complexity of large action spaces.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the number of sensors is increased to improve data quality and coverage, then the sensing capability is improved, but the action space complexity increases making reinforcement learning difficult
Solution Approach 1:
The patent segments the large action space into multiple smaller action spaces by dividing sensors into different groups or categories. Each group has its own simplified action space, allowing reinforcement learning to be applied effectively to each segment while maintaining overall system performance with numerous sensors
Solution Approach 2:
The patent transforms the high-dimensional action space problem by introducing abstraction layers that map the complex multi-sensor action space into a lower-dimensional representation, enabling reinforcement learning agents to operate efficiently even with many sensors
2Productivity
If resource constraints are reduced to improve learning efficiency, then the learning performance is improved, but the system becomes less adaptable to real-world limited resources
Solution Approach 1:
The patent implements dynamic resource allocation where the system adapts its resource consumption based on current learning needs and system state. The action space abstraction is dynamically adjusted to balance learning efficiency with resource constraints, allowing the system to perform well both during training and in resource-limited deployment scenarios
3Reliability
If the update frequency of sensors is increased to improve data synchronization, then the data quality is improved, but the resource consumption increases
Solution Approach 1:
The patent applies partial action by selectively updating only the most critical or changed sensor data rather than all sensors at every time step. The action space abstraction identifies which sensors require updates based on current system state and data quality requirements, reducing unnecessary updates and resource consumption while maintaining adequate synchronization
Data Source
AI summary
A reinforcement learning-based sensor data management system includes a processor configured to: manage virtualized objects that correspond to sensors included in a sensor network to update data received from each sensor and queries representing a data quality requested by an application; calculate an abstracted action that abstracts a size of an action space of the sensor network based on present state information of the virtualized objects and the queries; calculate scores for virtualized objects based on position relationships between the calculated abstracted action the virtualized objects; and assign priorities to the virtualized objects based on the calculated scores to update data received from the sensors to the virtualized objects according to the priorities.