Dynamic Media Selection for Visual IoT Services
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Solution Overview
Problem
Existing IoT service technologies primarily consider network-level QoS attributes, failing to provide services that are optimally suited to users in physical IoT environments, as they do not account for physical effects such as visual or auditory experiences.
Innovation Solution
An electronic device equipped with a processor and memory that monitors users in IoT service environments, predicts visual service effects of service media, and dynamically selects the most effective media using reinforcement learning to provide continuous and optimal physical effects, minimizing handovers between media.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If service selection is based only on network-level QoS attributes, then service provision is simplified, but service suitability for users in physical IoT environments deteriorates
Solution Approach 1:
The patent implements dynamic service medium selection that adapts to changing user conditions and environmental factors. The system continuously monitors user state, physical environment, and service effects, then dynamically selects optimal service media rather than using static selection based solely on network QoS. This dynamic adaptation resolves the contradiction by making the system complex enough to handle physical environment nuances while maintaining adaptability to user needs.
Solution Approach 2:
The patent introduces feedback mechanisms where the system monitors the actual service effects on users and uses this information to adjust service medium selection. By incorporating user responses and service effect measurements into the selection process, the system improves service suitability without requiring complete redesign of the service provision architecture, thus balancing complexity and adaptability.
2Device complexity
If service medium is statically selected, then system complexity is reduced, but continuity of physical effect generation deteriorates
Solution Approach 1:
The system implements dynamic service medium selection that continuously adapts to changing conditions while maintaining service continuity. Rather than static selection, the system monitors user state and environmental factors in real-time, dynamically switching between service media to ensure continuous physical effect generation. This dynamic approach maintains service continuity without requiring overly complex manual intervention systems.
Solution Approach 2:
The patent ensures continuous service delivery by implementing mechanisms that maintain uninterrupted physical effects on users. The system monitors service effectiveness and proactively switches between service media before service interruption occurs, ensuring continuous useful action. This approach maintains duration of service without requiring excessive system complexity through automated continuous monitoring and seamless transition protocols.
3Productivity
If frequent handovers between service media are performed, then service optimization is improved, but service stability deteriorates
Solution Approach 1:
The system implements selective handover between service media based on actual service effect thresholds rather than attempting constant optimization. By applying partial action - only switching when necessary based on measured service quality - the system achieves sufficient service optimization while avoiding excessive handovers that would destabilize the service. This balanced approach maintains stability while still improving service where needed.
Solution Approach 2:
The patent uses feedback mechanisms to monitor service effects and trigger handovers only when performance thresholds are breached. This feedback-controlled approach prevents unnecessary frequent switching by basing handover decisions on actual service quality measurements rather than arbitrary time intervals. The system achieves optimization through targeted handovers while maintaining stability through evidence-based switching criteria.
Data Source
AI summary
An electronic device and an operating method thereof relate to effect-driven dynamic media selection for visual Internet of things (IoT) service using reinforcement learning, and may be configured to monitor a user in an Internet of things (IoT) service environment, predict a visual service effect of at least one service medium related to the user in the IoT service environment, select one of the at least one service medium based on the visual service effect, and provide service for the user through the selected service medium.