Accurately Tracking a Smart Device Using Non-GPS Based Sensor Data
Princeton Docket # 16-3275
With the rapid proliferation of smart devices such as wearables, cell phones, and self-driving cars, high-accuracy location-tracking services have become increasingly important. Previous high-accuracy techniques have used GPS data, but this can carry significant downsides such as high battery usage, potential service blackout, and the risk of malicious GPS spoofing.
Inventors at Princeton University in the Department of Electrical Engineering have developed a novel user-location algorithm named PinMe. This mechanism utilizes non-sensory/sensory data stored on the smartphone (e.g., the environment’s air pressure and device’s time zone) along with publicly available auxiliary information (e.g., elevation maps) to accurately estimate the user’s location when all location services (e.g., GPS) are turned off. While each such signal may not be highly discriminative by itself, the inventors demonstrated that a collection of such signals, along with robust machine learning, can provide location accuracy similar to traditional GPS services. Unlike competitors in this space, PinMe neither requires any prior knowledge about the user nor a training dataset on specific routes. The algorithm can discriminate between four activities (walking, traveling on a train, driving, and traveling on a plane), and accurately estimates the user’s location during each activity.
This technology has immediate applications as a supplement to current GPS navigation software. It would provide continuous high-accuracy location services in areas of where satellite coverage is disrupted such as in tunnels, buildings, and cities. In the context of self-driving cars, where loss of service is a critical safety concern, PinMe provides a valuable backup method and is not vulnerable to GPS spoofing attacks.
• Does not rely on GPS signal or external sensor data
• Can operate offline after downloading publicly available auxiliary information
• Resistant to security attacks
• Comparable location quality to GPS
• Smart devices including cell phones, wearables, GPS navigation devices
• Self-driving cars
A. M. Nia, X. Dai, P. Mittal, and N. K. Jha, “PinMe: Tracking a smartphone user around the world”, Submitted to IEEE Trans. Multi-scale Computing, Nov. 2016.
Arsalan Mosenia received his B.S. degree in Computer Engineering from Sharif University of Technology, Tehran, Iran, in 2012, and M.A. degree in Electrical Engineering from Princeton, NJ, in 2014. He is currently pursuing a Ph.D. degree in Electrical Engineering at Princeton University, NJ. His research interests include Internet of Things, information security, mobile computing, distributed computing, and machine learning.
Xiaoliang Dai received the B.Physics degree from Peking University, China, in 2014. He is currently a Ph.D. student in the Electrical Engineering Department at Princeton University. His research interests include machine learning for healthcare and security, Internet of Things, and novel mathematical models for TCAD simulations.
Prateek Mittal is an assistant professor in the department of Electrical Engineering at Princeton University. His research aims to build secure andprivacy-preservingcommunicationsystems. Hisresearchinterestsincludethedomainsofprivacy enhancing technologies, trustworthy social systems, and Internet/network security. His work has inﬂuenced the design of several widely used anonymity systems, and is the recipientofseveralawardsincludinganACMCCS outstanding paper. He served as the program co-chair for the FOCI and the HotPETs workshops. He is the recipient of the NSF CAREER Award, the Google Faculty Research Award, the M. E. Van Valkenburg research Award, and Princeton Engineering Commendation List for Outstanding Teaching. Prior to joining Princeton University, he was a postdoctoral scholar at UniversityofCalifornia,Berkeley.HeobtainedhisPh.D.inElectricaland Computer Engineering from University of Illinois at Urbana-Champaign in 2012.
Niraj K. Jha, Professor of Electrical Engineering
Professor Niraj K. Jha completed his doctoral studies in Electrical Engineering at the University of Illinois at Urbana-Champaign in 1985. He holds a M.S. in Electrical Engineering from the State University of New York at Stony Brook and a B.Tech. in Electronics and Electrical Communication Engineering from the Indian Institute of Technology, Kharagpur. He joined Princeton University in 1987, achieving the rank of Professor in 1998.
Prof. Jha is a fellow of IEEE and the Association for Computing Machinery (ACM) and has served as the Editor-in-Chief of IEEE Transactions on VLSI Systems, and as an Associate Editor of several journals. He has been the recipient of the AT&T Foundation Award, NEC Preceptorship Award for Research Excellence, the NCR Award for Teaching Excellence, and the Princeton University Graduate Mentoring Award. He has co-authored four books, in addition to authoring or co-authoring 15 book chapters and more than 410 technical papers, and received nine best paper awards. In addition, his papers have been selected for “The Best of ICCAD: A collection of the best IEEE International Conference on Computer-Aided Design papers of the past 20 years,” by IEEE Micro Magazine as top picks from the 2005 and 2007 Computer Architecture conferences, and two were included among the most influential papers of the last 10 years at the IEEE Design Automation and Test in Europe Conference. He holds 16 U.S. patents. The research interests of the Jha lab include power- and temperature-aware chip multiprocessor (CMP) and multiprocessor system-on-chip (MPSoC) design, design algorithms and tools for FinFETs, three-dimensional monolithic integrated circuit (3D IC) design, embedded system analysis and design, field-programmable gate arrays (FPGAs), digital system testing, computer security, quantum circuit design, and energy-efficient buildings.
Intellectual Property Status
Patent applications are pending. Princeton is seeking industrial collaborators for further development and commercialization of this technology.
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