Mobile Camera Video Analysis with Adaptive Edge Offloading
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Solution Overview
Problem
Server-edge collaborative data analysis performance is hindered by deteriorating network quality in mobile devices due to movement, particularly in non-line-of-sight environments, leading to degraded offloading-based video analysis performance.
Innovation Solution
A mobile device with a processor that determines an offloading policy based on wireless link status, using an online learning algorithm to minimize per-frame energy consumption and maintain video analysis delay within acceptable thresholds by selectively offloading tasks to an edge server or performing them locally.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If server-edge collaborative data analysis is performed, then video analysis performance is improved, but network stability requirement increases
Solution Approach 1:
The system dynamically adjusts the offloading policy based on real-time wireless link status. The processor monitors link quality metrics and adaptively determines whether to perform video analysis locally or offload to the edge server, making the system flexible to changing network conditions rather than relying on fixed thresholds
Solution Approach 2:
The system implements a feedback mechanism where the processor continuously evaluates wireless link status and video analysis performance outcomes. Based on this feedback, the offloading policy is updated and optimized over time, allowing the system to learn from past performance and adjust to maintaining reliable operation under varying network conditions
2Use of energy by moving object
If offloading is performed continuously, then energy consumption is reduced, but video analysis delay increases
Solution Approach 1:
The system changes the offloading parameter (decision variable) based on wireless link status. When link quality is good, offloading is performed to reduce energy consumption; when link quality deteriorates, local processing is performed to minimize delay. This parameter adjustment resolves the contradiction by adapting to real-time conditions
Solution Approach 2:
The offloading policy is made dynamic rather than static. The processor continuously monitors both energy consumption metrics and delay metrics, adjusting the offloading decision in real-time to balance these competing objectives based on current system state and network conditions
3Device complexity
If offloading policy is fixed, then system complexity is reduced, but adaptability to network changes deteriorates
Solution Approach 1:
The system performs self-service by automatically learning and optimizing the offloading policy without requiring manual configuration or complex external control. The processor autonomously monitors performance metrics, learns from outcomes, and adjusts the policy adaptively, achieving high adaptability through a relatively simple implementation
Solution Approach 2:
A feedback loop enables the system to adapt to network changes without increasing complexity significantly. The processor receives feedback on video analysis performance and wireless link status, then automatically adjusts the offloading policy accordingly, providing adaptability through continuous learning rather than complex pre-programming
Data Source
AI summary
Provided is a mobile device according to one embodiment of the present disclosure. The mobile device includes a camera module, a communication module providing a wireless link for communication with a server, and a processor functionally connected to the camera module and the communication module, wherein the processor collects video data through the camera module, and determines whether to perform an offloading function for analyzing the video data by utilizing computing resources of the server according to a wireless link status of the communication module based on an offloading policy, and the offloading policy is determined by learning a change in video analysis performance of the mobile device and the server according to a change in the wireless link status.