Estimating Application Data Traffic to Condition Wireless Channels
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Solution Overview
Problem
Wireless communication devices face inefficiencies in power consumption and network utilization due to limited knowledge of application data traffic characteristics, leading to unnecessary prolonged power usage or rapid connection re-establishment, which affects battery life and network performance.
Innovation Solution
The device detects data source additions and deletions to estimate data traffic patterns, adjusts communication components based on traffic class states, and uses a traffic class policy to optimize power consumption and network resource allocation by transitioning between active and sleep states in discontinuous transmission/reception modes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If wireless circuitry remains powered longer than necessary, then data communication reliability is improved, but power consumption increases
Solution Approach 1:
The system performs preliminary actions by detecting socket open/close events and estimating future data traffic patterns before actually needing to maintain powered state. This allows the wireless circuitry to proactively adjust its power state based on predicted traffic characteristics rather than reacting to actual traffic after the fact.
Solution Approach 2:
The system implements feedback mechanisms by monitoring actual data traffic patterns and comparing them against estimated patterns. This feedback loop allows the system to refine its traffic predictions and adjust power management decisions accordingly, improving both reliability and power efficiency over time.
2Use of energy by moving object
If wireless circuitry responds rapidly by removing connections, then power consumption is reduced, but network signaling overhead increases
Solution Approach 1:
The system performs preliminary analysis of traffic patterns and socket events before making connection decisions. By estimating future traffic characteristics in advance, the system can make more informed decisions about when to maintain connections versus when to safely close them, avoiding premature disconnections that would generate unnecessary reconnection signaling.
Solution Approach 2:
The system dynamically adjusts connection management behavior based on real-time traffic conditions and predicted patterns. This dynamic approach allows the system to adapt its connection retention strategy to current network conditions, balancing power savings with the need to maintain reliable data communication.
3Device complexity
If the system lacks knowledge of application data traffic characteristics, then device complexity is reduced, but power management efficiency deteriorates
Solution Approach 1:
The system extracts and processes only the most critical information needed for power management decisions - specifically socket open/close events and basic traffic pattern characteristics. By focusing on extracting only essential data rather than attempting to analyze all application details, the system maintains low complexity while achieving effective power management.
Solution Approach 2:
The system uses self-service mechanisms where the socket layer automatically provides traffic event notifications to the power management component. This self-service approach eliminates the need for complex manual monitoring and analysis of application traffic patterns, allowing the system to manage power efficiently through automated event-driven processing.
Data Source
AI summary
Apparatus and methods for estimating data traffic characteristics for applications to condition data communication channels that support data packet transfer for the applications in wireless communication devices are disclosed. Data connections to support different applications and/or daemon software processes can be established and subsequently adjusted based on data traffic characteristics for data that the different applications/daemons generate and/or consume. Traffic flows for the applications/daemons are classified into traffic classes based on likely data traffic patterns. When data sources are added or deleted, changes to a traffic class state can be determined, and wireless circuitry adjusted based at least in part on the traffic class state and a traffic class policy. Adjusting parameters that affect the periodicity and/or length of active time periods and sleep periods in accordance with estimated data traffic patterns for applications/daemons that use the data connections can reduce power consumption by the wireless circuitry.