Device Selection for IoT Media Sensing
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Solution Overview
Problem
IoT services using battery-powered devices, such as smartphones and tablets, face interruptions due to battery exhaustion, leading to potential service disruptions and reduced media data quality when agent devices take over sensing tasks.
Innovation Solution
A device selection method that estimates service continuation time and battery continuation time based on historical data, prioritizing the user device to maintain high-quality media data acquisition until battery exhaustion, and switching to agent devices only when necessary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If battery-powered devices are used for IoT services, then device portability and user convenience are improved, but service continuity is compromised due to battery exhaustion
Solution Approach 1:
The system performs preliminary estimation of service continuation time based on historical data before the battery actually exhausts. By predicting how long the service can continue with remaining battery power, the system proactively prepares for the battery exhaustion event and schedules agent device takeover in advance, ensuring seamless service continuity without interruption.
2Reliability
If agent devices take over sensing tasks when user devices run out of battery, then service continuity is maintained, but media data quality deteriorates
Solution Approach 1:
The system dynamically determines the timing of agent device takeover based on estimated service continuation time. Instead of fixed or immediate takeover, the system optimizes the transition timing to maintain user device involvement as long as possible (preserving quality) while ensuring takeover occurs before battery exhaustion (maintaining continuity). This dynamic approach balances both requirements.
3Duration of action of moving object
If service continuation time is extended beyond battery capacity, then service availability improves, but energy consumption increases beyond sustainable levels
Solution Approach 1:
The system uses historical service continuation time data as feedback to estimate future service duration. By analyzing past performance and battery consumption patterns, the system predicts how long the service can continue with current battery levels, enabling informed decisions about service extension and agent device activation timing that optimize both service availability and energy efficiency.
Data Source
AI summary
A device selection method includes receiving a start demand of a service from a first device, acquiring positional-information of the first device, estimating a service continuation-time of the service, based on a history of the service continuation-time corresponding to an area to which the positional-information belongs by referring to the history of the service continuation-time for which the service is continuously provided for each area, estimating a battery continuation-time of the first device, based on a remaining battery quantity of the first device and a battery consumption rate of the first device at a time of receiving the start demand, selecting the first device or a second device installed in the area to which the positional-information belongs, based on the service continuation-time and the battery continuation-time, and causing a device selected between the first device and the second device to execute sensing of media data used for the service.