Fog Computing and Efficient Resource Management in the era of Internet-of-Video Things (IoVT)

Internet-of-Things (IoT) consists of interconnected devices with sensing, monitoring and processing functionalities that work in a cooperative way to offer services. Smart buildings, self-driving cars, house monitoring and management, city electricity and pollution monitoring are some examples where IoT systems have been already deployed. Amongst different kinds of devices in IoT, cameras have a key role, since they can capture rich and resourceful content. The number of embedded cameras is rising rapidly establishing the term Internet-of-Video Things (IoVT). However, since multiple IoT devices share the same gateway, the data that is produced from high definition cameras struggle the network and the available computational resources, resulting in Quality-of-Service degradation regarding visual content. In this work, we propose a methodology that tries to balance the content generation rate of cameras in an IoT environment. Specifically, the targeted use case is face recognition for video surveillance under local storage, network utilization and computational constraints while achieving the highest possible accuracy.