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In this paper, we introduce PriMO-5G which is an EU-Korea collaboration project studying the use of 5G technologies and unmanned aerial vehicles (UAVs) or drones to enhance the safety and efficiency of firefighting operations. We start by describing envisaged use cases of smart firefighting focusing on how the 5G communications with drones can help the firefighting. Inspired by the use cases, we identify several research challenges that call for new solutions in 5G radios and cores for mission-critical services. Then, a discussion of a new framework for defining key performance indicators (KPIs) follows. Finally, we introduce our effort and future plans for the demonstration of the technologies that the PriMO-5G develops.

Due to the edge's position between the cloud and the users, and the recent surge of deep neural network (DNN) applications, edge computing brings about uncertainties that must be understood separately. Particularly, the edge users' locally specific requirements that change depending on time and location cause a phenomenon called dataset shift, defined as the difference between the training and test datasets' representations. It renders many of the state-of-the-art approaches for resolving uncertainty insufficient. Instead of finding ways around it, we exploit such phenomenon by utilizing a new principle: AI model diversity, which is achieved when the user is allowed to opportunistically choose from multiple AI models. To utilize AI model diversity, we propose Model Diversity Network (MoDNet), and provide design guidelines and future directions for efficient learning driven communication schemes.

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