Optimizing Video Surveillance in IoT 6G with Edge/Fog Computing

Due to the rapid expansion of Internet of Things (IoT) devices, massive amounts of data are generated daily, and the number of IoT devices will continue to increase. Cloud computing provides storage, processing, and analysis services for managing such large amounts of data. However, there are limitations concerning the network connectivity between the cloud and IoT devices. Nonetheless, the cloud platform has evident concerns and limitations in terms of responsiveness, latency, and overall performance for processing and accessing IoT traffic data. This process takes time, especially for large datasets, as there is back-and-forth communication between the client and the cloud. This increase in latency and energy consumption is unacceptable for real-time applications such as online gaming, smart health, video surveillance, etc. Hence the possibility of using Edge/Fog computing for optimal resource allocation. Our work aims to propose a system model for surveillance video analysis in an IoT 6G environment based on enhanced cloud architecture, Edge/Fog computing while developing an analytical model to satisfy QoS requirements (latency, energy consumption, etc.).