SARDO: An Automated Search-and-Rescue Drone-based Solution for Victims Localization

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 exploits 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.

[1]  Raj Jain,et al.  A Quantitative Measure Of Fairness And Discrimination For Resource Allocation In Shared Computer Systems , 1998, ArXiv.

[2]  J. Paradells,et al.  Performance evaluation of a TOA-based trilateration method to locate terminals in WLAN , 2006, 2006 1st International Symposium on Wireless Pervasive Computing.

[3]  Rui Zhang,et al.  Wireless communications with unmanned aerial vehicles: opportunities and challenges , 2016, IEEE Communications Magazine.

[4]  Halim Yanikomeroglu,et al.  Efficient 3-D placement of an aerial base station in next generation cellular networks , 2016, 2016 IEEE International Conference on Communications (ICC).

[5]  Antonio Torralba,et al.  RF-based 3D skeletons , 2018, SIGCOMM.

[6]  Ilan Noy,et al.  NATURAL DISASTERS , 2011 .

[7]  Kate A. Remley,et al.  Propagation and detection of radio signals before, during, and after the implosion of a 13-story apartment building , 2005 .

[8]  M. Musavi,et al.  Localization using neural networks in wireless sensor networks , 2008, MOBILWARE.

[9]  Pawel Dabrowski,et al.  Comparative analysis of positioning accuracy of GNSS receivers of Samsung Galaxy smartphones in marine dynamic measurements , 2019, Advances in Space Research.

[10]  M. Pachter,et al.  GPS estimation algorithm using stochastic modeling , 1998, Proceedings of the 37th IEEE Conference on Decision and Control (Cat. No.98CH36171).

[11]  Haitao Zhao,et al.  Deployment Algorithms for UAV Airborne Networks Toward On-Demand Coverage , 2018, IEEE Journal on Selected Areas in Communications.

[12]  Emre Ozen,et al.  An efficient approach for trilateration in 3D positioning , 2008, Comput. Commun..

[13]  Ying Liu,et al.  Prospective Positioning Architecture and Technologies in 5G Networks , 2017, IEEE Network.

[14]  Xinyu Zhang,et al.  Enabling High-Precision Visible Light Localization in Today's Buildings , 2017, MobiSys.

[15]  Shafique Ahmad Chaudhry,et al.  A cooperative trilateration technique for object localization , 2018, 2018 20th International Conference on Advanced Communication Technology (ICACT).

[16]  Sampath Rangarajan,et al.  TrackIO: Tracking First Responders Inside-Out , 2019, NSDI.

[17]  Stig Fr. Mjølsnes,et al.  Easy 4G/LTE IMSI Catchers for Non-Programmers , 2017, MMM-ACNS.

[18]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[19]  Injong Rhee,et al.  SLAW: A New Mobility Model for Human Walks , 2009, IEEE INFOCOM 2009.

[20]  Emidio DiGiampaolo,et al.  Experimental Characterization of Electromagnetic Propagation Under Rubble of a Historic Town After Disaster , 2015, IEEE Transactions on Vehicular Technology.

[21]  Stefania Sesia,et al.  LTE - The UMTS Long Term Evolution, Second Edition , 2011 .

[22]  Siyang Cao,et al.  Study of portable infrastructure-free cell phone detector for disaster relief , 2016, Natural Hazards.

[23]  Jiliang Wang,et al.  RainbowLight: Towards Low Cost Ambient Light Positioning with Mobile Phones , 2018, MobiCom.

[24]  Sampath Rangarajan,et al.  SkyRAN: a self-organizing LTE RAN in the sky , 2018, CoNEXT.

[25]  Hyundong Shin,et al.  Machine Learning for Wideband Localization , 2015, IEEE Journal on Selected Areas in Communications.

[26]  Kandeepan Sithamparanathan,et al.  Optimal LAP Altitude for Maximum Coverage , 2014, IEEE Wireless Communications Letters.

[27]  Dragan Stokic,et al.  Control and Stability , 1990 .

[28]  Mort Naraghi-Pour,et al.  A Novel Algorithm for Distributed Localization in Wireless Sensor Networks , 2014, TOSN.

[29]  Ronald Raulefs,et al.  Survey of Cellular Mobile Radio Localization Methods: From 1G to 5G , 2018, IEEE Communications Surveys & Tutorials.

[30]  Reg Austin Control and Stability , 2010 .

[31]  Lei Jing,et al.  Design of a 3D localization method for searching survivors after an earthquake based on WSN , 2011, 2011 3rd International Conference on Awareness Science and Technology (iCAST).

[32]  Luiz A. DaSilva,et al.  UAVs as Mobile Infrastructure: Addressing Battery Lifetime , 2018, IEEE Communications Magazine.

[33]  Kamesh Namuduri,et al.  Flying cell towers to the rescue , 2017, IEEE Spectrum.

[34]  J. Perelló,et al.  Active and reactive behaviour in human mobility: the influence of attraction points on pedestrians , 2015, Royal Society Open Science.

[35]  Rade Stanojevic,et al.  From Cells to Streets: Estimating Mobile Paths with Cellular-Side Data , 2014, CoNEXT.

[36]  Borut Zalik,et al.  The stochastic walk algorithms for point location in pseudo-triangulations , 2011, Adv. Eng. Softw..

[37]  Cristina Cano,et al.  srsLTE: an open-source platform for LTE evolution and experimentation , 2016, WiNTECH@MobiCom.

[38]  Pan Li,et al.  Channel State Information Prediction for 5G Wireless Communications: A Deep Learning Approach , 2020, IEEE Transactions on Network Science and Engineering.