Real-time adaptive resource management for high-resolution computer vision over private 5G networks

The advancements in wireless networking technologies empower new opportunities and applications. However, these advanced networks are increasingly consuming more energy to provide higher performance. In line with the United Nations sustainable development goals and the need to reduce networking energy consumption, this paper presents an efficient dynamic slicing and antenna control architecture for 5G networks. The architecture was then applied to a smart port scenario, where a pre-gate is used to control the entrance of trucks into the port with the aid of a camera, with the video stream being analyzed in a datacenter to detect the presence of a truck and access its details. Experimental results showed that the architecture was able to potentially reduce the energy expenditure of the considered scenario in 2.26 MWh in a year, considering a real statistical number of trucks entering the Portuguese Sines Port in 2017. Moreover, it reduced the energy consumption of the User Equipment, computing infrastructure, and wireless network in 9%, 23% and 14% respectively.