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Antonio Albanese, Vincenzo Sciancalepore, Xavier Costa-Pérez: “SARDO: An Automated Search-and-Rescue "Drone-based Solution for Victims Localization", accepted to IEEE Transactions on Mobile Computing

Paper Details

Natural disasters affect millions of people every year. Finding missing persons in the shortest possible time is of crucial importance to reduce the death toll. This task is especially challenging when victims are sparsely distributed in large and/or difficult-to-reach areas and cellular networks are down. In this paper, we present SARDO, a drone-based search and rescue solution that leverages the high penetration rate of mobile phones in the society to localize missing people. SARDO is an autonomous, all-in-one drone-based mobile network solution that does not require infrastructure support or mobile phones modifications. It builds on novel concepts such as pseudo-trilateration combined with machine-learning techniques to efficiently locate mobile phones in a given area. Our results, with a prototype implementation in a field-trial, show that SARDO rapidly determines the location of  mobile phones (~3 min/UE) in a given area with an accuracy of few tens of meters and at a low battery consumption cost (~5%). State-of-the-art localization solutions for disaster scenarios rely either on mobile infrastructure support or exploit onboard cameras for human/computer vision, IR, thermal-based localization. To the best of our knowledge, SARDO is the first drone-based cellular search-and-rescue solution able to accurately localize missing victims through mobile phones.

Accepted to:          IEEE Transactions on Mobile Computing

To be published:    2021

U. Fattore, M. Liebsch, C. Bernardos, “UPFlight – An enabler for Avionic MEC in a Drone-extended 5G Mobile Network”, VTC 2020, May 2020

D. Bega, M. Gramaglia, M. Fiore, A. Banchs, X. Costa, “AZTEC: Anticipatory Capacity Allocation for Zero-Touch Network Slicing”, INFOCOM 2020, November 2019

F. Devoti, V. Sciancalepore, I. Filippini, X. Costa Pérez: "PASID: Exploiting Indoor mmWave Deployments for Passive Intrusion Detection", INFOCOM 2020, November 2019

JA. Ayala-Romero, A. Garcia-Saavedra, M.Gramaglia, X. Costa-Perez, A. Banchs, J.J. Alcaraz: “vrAIn: A Deep Learning Approach Tailoring Computing and Radio Resources in Virtualized RANs”, ACM MOBICOM 2019. July 2019

C. Marquez, M. Gramaglia, M. Fiore, A. Banchs, X. Costa-Perez, “Resource Sharing Efficiency in Network Slicing”, IEEE Transactions on Network and Service Management. June 2019

L. Zanzi, F. Cirillo, S. Mangiante, V. Sciancalepore, F. Giust, X CostaPerez and G. Klas, “Evolving Multi-Access Edge Computing to support enhanced IoT deployments”,  IEEE Communications Standards Magazine. May 2019

D.Bega, M.Gramaglia, A.Banchs, V.Sciancalepore, X.Costa-Perez, “A Machine Learning approach to 5G Infrastructure Market optimization” IEEE Transactions Journal on Mobile Computing (TMC). Accepted Date March 2019

T. Taleb, A. Ibrahim, S. Konstantinos, F. Zarrar Yousaf, “On Multi-domain Network Slicing Orchestration Architecture & Federated Resource Control”, IEEE Network Magazine, Feb 2019

B. Han, V. Sciancalepore, D. Feng, X. Costa-Perez, H. D. Schotten, "A Utility-driven MultiQueue Admission Control Solution for Network Slicing", IEEE INFOCOM 2019 Accepted Date December 2018

D. Bega, M. Gramaglia, M. Fiore, A. Banchs, X. Costa-Perez, "DeepCog: Cognitive Network Management in Sliced 5G Networks with Deep Learning", IEEE INFOCOM 2019 Accepted Date December 2018

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