Adaptive Speed Data Collection for Mobile QoS Monitoring
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Solution Overview
Problem
Conventional quality of service (QoS) data collection in wide area networks, such as cellular networks, follows a schedule-based approach that consumes battery power and processor cycles even when the collected data is redundant or less valuable, leading to inefficient energy usage and unnecessary data collection.
Innovation Solution
Implementing an adaptive speed data collection methodology that dynamically adjusts the time duration between QoS data collection acts based on triggers and rules, such as cell identifier changes, signal strength, and user activity, to reduce unnecessary data collection and conserve energy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If schedule-based data collection is used, then data collection frequency is maintained, but battery power consumption increases and redundant data is collected
Solution Approach 1:
The patent applies dynamics by making the data collection interval adaptive rather than fixed. The background service dynamically adjusts the time duration between data collection acts based on triggers (e.g., cell identifier changes, signal strength variations, user activity changes) and rules that evaluate current network and device conditions. This allows the system to collect data more frequently when conditions change (maintaining productivity) while extending intervals when conditions are stable (reducing energy consumption).
Solution Approach 2:
The patent changes the parameter of data collection interval from a static schedule-based value to a dynamic adaptive value. The system evaluates triggers and rules to determine appropriate sample time values, which are then used to adjust the collection frequency. This parameter change enables the system to optimize between data collection frequency and energy consumption based on actual network conditions and device state.
2Reliability
If schedule-based data collection is used, then consistent monitoring is maintained, but processor cycles are wasted on redundant data collection
Solution Approach 1:
The system dynamically adjusts monitoring intervals based on evaluated triggers and rules. When network conditions or device state change (indicated by triggers), the system increases monitoring frequency to maintain reliability. When conditions are stable, it reduces frequency to conserve processor cycles. This dynamic approach maintains monitoring consistency where needed while reducing unnecessary processing.
Solution Approach 2:
The background service autonomously evaluates triggers and rules to determine appropriate data collection timing without requiring external scheduling instructions. The service self-adjusts its operation based on current conditions, making intelligent decisions about when to collect data to maintain reliability while minimizing processor usage.
3Measurement precision
If data collection frequency is increased, then data accuracy is improved, but energy consumption increases
Solution Approach 1:
The system changes the data collection interval parameter dynamically based on evaluated triggers and rules. When significant changes in network conditions or device state are detected (indicating potential data accuracy issues), the system increases collection frequency. When conditions are stable, it reduces frequency to conserve energy. This parameter adjustment optimizes the trade-off between data accuracy and energy consumption.
Solution Approach 2:
The patent applies dynamics by making the collection frequency adaptive rather than fixed. The system responds to changing conditions by adjusting the sample time value, collecting data more frequently when accuracy is critical (e.g., during network transitions) and less frequently when conditions are stable, thereby optimizing the balance between measurement precision and energy usage.
Data Source
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AI summary
A mobile device is arranged to perform an adaptive speed data collection method. A host application running on the mobile device cooperates with a background service also running on the mobile device. The background service is arranged as a state machine. On each pass through the state machine, quality of service (QoS) data associated with a particular wide area network, such as a cellular network, is collected. Also on each pass through the state machine, a sample time value is calculated based on a plurality of asserted triggers and rules applied to the asserted triggers. An alarm is loaded with the sample time value, and the background service is suspended until the alarm expires or an interrupt is asserted. The asserted interrupt begins a new sequential pass through the state machine.