Cloud-Based Video Monitoring Framework: An Approach Based on Software-Defined Networking for Addressing Scalability Problems

Abstract

Abstract. Closed-circuit television (CCTV) and Internet protocol (IP) cameras have been applied to a surveillance or monitoring system, from which users can remotely monitor video streams. The system has been employed for many applications such as home surveillance, traffic monitoring, and crime prevention. Currently, cloud computing has been integrated with the video monitoring system for achieving value-added services such as video adjustment, encoding, image/video recognition, and backup services. One of the challenges in this integration is due to the size and geographical scalability problems when video streams are transferred to and retrieved from the cloud services by numerous cameras and users, respectively. Unreliable network connectivity is a major factor that causes the problems. To deal with the scalability problems, this paper proposes a framework designed for a cloud-based video monitoring (CVM) system. In particular, this framework applies two major approaches, namely stream aggregation (SA) and software-defined networking (SDN). The SA approach can reduce the network latency between cameras and cloud services. The SDN approach can achieve the adaptive routing control which improves the network performance. With the SA and SDN approaches applied by the framework, the total latency for transferring video streams can be minimized and the scalability of the CVM system can be significantly enhanced.

What the Fog? Edge Computing Revisited: Promises, Applications and Future Challenges

“…An extensive survey about the IoT and its enabling technologies can be found in [241]. The data volume and latency requirements of future IoT devices will likely be challenging to transfer and process at central clouds [242], [243]. In this section, we focus on IoT deployments in three settings: Homes and buildings (Section IV-C1), industrial applications (Section IV-C2), and agriculture and farming (Section IV-C3).…”

“…These devices and building systems produce large amounts of sensitive personal data streams [8]. One classic example of a smart home task is the aggregation and pre-processing of home video surveillance streams [243]*. Such a case was examined by Abdallah et al [245]* in a prototype system.…”