The interest in large scale automated video surveillance systems and the interest in using cloud in supporting such systems has increased dramatically. Unfortunately, building such a large system requires huge resources (for processing and storage) and very high network bandwidth. This paper, proposes a framework for resources efficient intelligent automated surveillance framework that utilizes edge servers. In this framework, multiple video sources capture and send videos to a an edge server which performs an intelligent computer vision based cross-layer optimization of the video sources hardware resources and the network bandwidth. The network can be a wireless network and the sources can be mobile. The proposed solution changes the application rates and the required link layer parameters (the transmission opportunities in our case study) of the sending video sources according to the dynamic network conditions to maximize the overall accuracy of the computer vision algorithm(s) of interest in the system. The proposed framework utilizes an enhanced effective airtime estimation algorithm utilizing a Proportional Integral Differential (PID) controller that measure the available useful bandwidth in the network. Furthermore, we propose a bandwidth pruning mechanism to reach any desired tradeoff between the computer vision algorithm accuracy and the energy consumption of the video sources. We evaluate and present the effectiveness of the proposed framework, the effective airtime estimation algorithm, and the proposed bandwidth pruning mechanism, through extensive experiments using OPNET.

对大规模自动化视频监控系统的兴趣以及对使用云技术支持此类系统的兴趣已大大增加。不幸的是，构建如此大的系统需要巨大的资源（用于处理和存储）和非常高的网络带宽。本文提出了一种利用边缘服务器的资源高效智能自动化监控框架。在此框架中，多个视频源捕获视频并将其发送到边缘服务器，该边缘服务器对视频源的硬件资源和网络带宽执行基于智能计算机视觉的跨层优化。该网络可以是无线网络，而源可以是移动的。所提出的解决方案根据动态网络条件更改发送视频源的应用速率和所需的链路层参数（在本案例研究中为传输机会），以最大程度地提高感兴趣的计算机视觉算法的整体准确性。系统。所提出的框架利用了增强的有效通话时间估计算法，该算法利用了比例积分差分（PID）控制器，用于测量网络中的可用可用带宽。此外，我们提出了一种带宽修剪机制，以在计算机视觉算法的准确性和视频源的能耗之间达到任何期望的折衷。通过使用OPNET进行的广泛实验，我们评估并提出了所提出的框架，有效的通话时间估计算法以及所提出的带宽修剪机制的有效性。