IEEE Fellow, AAIA Fellow Prof. Shiwen Mao
Auburn University, USA
SHIWEN MAO is a professor and Earle C. Williams Eminent Scholar Chair, and Director of the Wireless Engineering Research and Education Center (WEREC) at Auburn University. His research interest includes wireless networks, multimedia communications, and smart grid. He is a Distinguished Lecturer of IEEE Communications Society and the IEEE Council of RFID, and is on the Editorial Board of IEEE TWC, IEEE TNSE, IEEE TMC, IEEE IoT, IEEE TCCN, IEEE OJ-ComSoc, IEEE/CIC China Communications, IEEE Multimedia, IEEE Network, IEEE Networking Letters, and ACM GetMobile. He received the IEEE ComSoc TC-CSR Distinguished Technical Achievement Award in 2019 and NSF CAREER Award in 2010. He is a co-recipient of the 2021 Best Paper Award of Elsevier/KeAi Digital Communications and Networks Journal, the 2021 IEEE Communications Society Outstanding Paper Award, the IEEE Vehicular Technology Society 2020 Jack Neubauer Memorial Award, the 2004 IEEE Communications Society Leonard G. Abraham Prize in the Field of Communications Systems, and several conference best paper/demo awards. He is a Fellow of the IEEE.
Speech Title: Indoor Radio Map Construction and Location Estimation with a Deep Gaussian Process Approach
With the increasing demand for location-based service, WiFi-based localization has become one of the most popular methods due to the wide deployment of WiFi and its relatively low cost. In this talk, we present a deep Gaussian process based indoor radio map construction and location estimation system. Received signal strength (RSS) samples, as well earth magnetic field readings, are used to generate accurate and fine-grained radio maps with confidence intervals using deep Gaussian process, while the model parameters are optimized with an offline Bayesian training method. Utilizing the maps, an LSTM based location prediction model is pre-trained with the artificial trajectory data and then fine-tuned with the signal measurements collected by the mobile device to be localized. Our extensive experiments demonstrate the excellent performance of the proposed system.