We study the problem of optimal sampling in an edge-based video analytics system (VAS), where sensor samples collected at a terminal device are offloaded to a back-end server that processes them and generates feedback for a user. Sampling the system with the maximum allowed frequency results in the timely detection of relevant events with minimum delay. However, it incurs high energy costs and causes unnecessary usage of network and compute resources via communication and processing of redundant samples. On the other hand, an infrequent sampling result in a higher delay in detecting the relevant event, thus increasing the idle energy usage and degrading the quality of experience in terms of responsiveness of the system. We quantify this sampling frequency trade-off as a weighted function between the number of samples and the responsiveness. We propose an energy-optimal aperiodic sampling policy that improves over the state-of-the-art optimal periodic sampling policy. Numerically, we show the proposed policy provides a consistent improvement of more than 10$\mathbf{\%}$ over the state-of-the-art.

翻译：我们研究了边缘视频分析系统（VAS）中的最优采样问题。在该系统中，终端设备收集的传感器样本被卸载至后端服务器进行处理，并生成用户反馈。以最大允许频率对系统进行采样，能以最小延迟及时检测相关事件，但这会带来高能耗代价，且因传输和处理冗余样本导致网络与计算资源的非必要消耗。反之，低频采样会延迟相关事件的检测，从而增加空闲能耗，并因系统响应性下降而导致用户体验质量降低。我们将这一采样频率权衡量化为样本数量与响应性之间的加权函数，并提出一种能量最优的非周期采样策略，该策略优于当前最优的周期采样策略。数值结果表明，所提策略相较于现有最优方案可实现超过10$\mathbf{\%}$的持续性能提升。