Artificial Intelligence for Enhanced Mobility and 5G Connectivity in UAV-Based Critical Missions

Autor(es)

Lins, Silvia ^{[1, 2]} ; Cardoso, Kleber Vieira ^[3] ; Both, Cristiano Bonato ^[4] ; Mendes, Luciano ^[5] ; de Rezende, Jose F. ^[6] ; Silveira, Antonio ^[1] ; Linder, Neiva ^{[2, 7]} ; Klautau, Aldebaro ^[1]

Afiliação do(s) autor(es)

^[1] Fed Univ UFPA, LASSE 5G & IoT Res Grp, BR-66075110 Belem, Para - Brazil

^[2] Ericsson Res Brazil, BR-13337300 Indaiatuba - Brazil

^[3] Univ Fed Goias UFG, Inst Informat INF, BR-74690900 Goiania, Go - Brazil

^[4] Univ Vale Rio Sinos UNISINOS, Appl Comp Grad Program, BR-93022750 Sao Leopoldo - Brazil

^[5] Natl Inst Telecommun Mate, BR-37540000 Santa Rita Do Sapucai - Brazil

^[6] Fed Univ Rio Janeiro UFRJ, Lab Modeling Anal & Dev Networks & Comp Syst LAND, BR-21941901 Rio De Janeiro - Brazil

^[7] Ericsson Res, S-16480 Stockholm - Sweden

• Tipo de documento: Artigo Científico
• Fonte: IEEE ACCESS; v. 9, p. 111792-111801, 2021.
• Citações Web of Science: 0

Resumo

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. (AU)

• Processo FAPESP: 18/23097-3 - SFI2: fatiamento de infraestruturas de internet do futuro
• Beneficiário: Tereza Cristina Melo de Brito Carvalho
• Modalidade de apoio: Auxílio à Pesquisa - Temático