Auburn researchers awarded IEEE Jack Neubauer Memorial Award
Published: Dec 15, 2020 4:23 PM
By Alyssa Turner
An Auburn professor and his former students have received the IEEE Vehicular Technology Society’s 2020 Jack Neubauer Memorial Award for their paper published in the IEEE Transactions on Vehicular Technology. The award is presented to the team with the best systems paper published in the past five years.
The team of authors, Shiwen Mao, Xuyu Wang, Lingjun Gao and Santosh Pandey, received the award for their paper, “CSI-Based Fingerprinting for Indoor Localization: A Deep Learning Approach.”
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| Clockwise from top left: Xuyu Wang, Lingjun Gao, Santosh Pandey and Shiwen Mao. |
“It is a great honor to receive the award, which is also a recognition of Auburn’s wireless research program,” said Mao, an electrical and computer engineering professor and director of the Wireless Engineering Research and Education Center. “It was fortunate for me to work with the smart and hard-working Auburn students, and to have the strong internal support for our research work."
The primary focus of the paper is to confirm that Wi-Fi channel state information (CSI) can be exploited to effectively reduce location error in indoor environments through a novel deep learning-based indoor fingerprinting system, termed DeepFi, which integrates machine learning, signal processing, wireless communications and networking. The results are demonstrated through experiments in representative indoor environments and compared with three state-of-the-art methods.
Because of GPS’s inability to provide accurate results indoors, next-generation positioning, navigation and timing (PNT) systems are in great demand, and the use of fingerprinting to track indoor positioning is paramount due to its accuracy and the wide availability of the Wi-Fi infrastructure.
The award was sponsored by an endowment from Matsushita Communication Industrial Corp. Funded by a gift from Cisco and then by an NSF grant, the paper has generated high interest, recognizable through its high download and citation numbers. Since publication, it has become the most downloaded piece of IEEE Transactions on Vehicular Technology for over two years and received several “best paper” awards.
The team presented the paper and received the award at the VTC2020 Fall Conference.
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