Enabling Adaptive Deep Neural Networks for Video Surveillance in Distributed Edge Clouds In the field of video surveillance, the demands of intelligent video analysis services based on Deep Neural Networks (DNNs) have grown rapidly. Although most existing studies focus on the performance of DNNs predeployed at remote clouds, the network delay caused by computation offloading from network cameras to remote clouds is usually long and sometimes unbearable. Edge computing can enable rich services and applications in close proximity to the network cameras. However, owing to the limited computing resources of distributed edge clouds, it is challenging to satisfy low latency and high accuracy requirements for all users, especially when the number of users surges. To address this challenge, we first formulate the intelligent video surveillance task scheduling problem that minimizes the average response time while meeting the performance requirements of tasks and prove that it is NPhard. Second, we present an adaptive DNN model selection method to identify the most effective DNN model for each task by comparing the feature similarity between the input video segment and prestored training videos. Third, we propose a twostage delayaware graph searching approach that presents a beneficial tradeoff between network delay and computing delay. Experimental results demonstrate the efficiency of our approach.

A Support Infrastructure for Machine Learning at the Edge in Smart City Surveillance Nowadays, the massive usage of mobile and IoT applications generate large amounts of data. Due to several reasons, including latency and bandwidth, it is not practical to send all generated data to the cloud. Recent standardization efforts, namely, Fog computing and the Multiaccess Edge Computing (MEC), provide an extension of Cloud computing storage and network resources placed in a geographically distributed manner at the edge of the network closer to mobiles and IoT devices. These paradigms allow low latency, high bandwidth, and locationbased awareness. In this paper, we present an infrastructure to support distributed Machine Learning (ML) by enabling edge devices to collaboratively learn a shared model while keeping local knowledge stored at the edge of the network. In addition, we claim the possibility of improving the model through the cloud that acts as a supervisor of the system that contains the global knowledge of the entire system through the integration of local edge models. We describe our architectural proposal and analyze a case study, namely video streaming processing for face recognition, deployed in a collaborative edge network. Finally, we report experimental results that show the potential advantages of using our approach instead of ML algorithms completely expected at the cloud infrastructure.