Key points are not available for this paper at this time.
In the context of Fifth Generation mobile networks (5G), Search and Rescue (SAR) missions using Unmanned Aerial Vehicles (UAVs) can benefit from a dynamic, intelligent, and autonomous placement of both Network Functions (NFs) and Artificial Intelligence (AI) systems to quickly adapt in minimal human intervention scenarios. This article examines current 5G architectures and timely standardization efforts within this context. The contribution of this work is to identify associated 5G components and propose AI modules that enable efficient UAV-based SAR missions: the System Intelligence (SI) and Edge Intelligence (EI) concepts. SI is conceived as the entity responsible for defining and orchestrating the placement and processing tasks of NFs and AI systems, while EI is responsible for the optimization of AI-based end-user applications. The article also presents an open-source virtualized testbed that enables a concrete example of SI and EI roles in a SAR mission based on object detection with Deep Neural Networks (DNNs). In this proof-of-concept, the DNN layers are partitioned and the tradeoffs between communication and computational costs are highlighted. For instance, the results indicate that the latency can severely degrade the UAV trajectory and different DNN partitioning options can reduce the required bit rate to transmit DNN scores by more than three times.
Lins et al. (Fri,) studied this question.