Papers
12
Total Citations
146
H-Index
8
About
Tao Jiang is a prominent researcher at the intersection of wireless communications, robotics, and the Internet of Things (IoT), whose work has significantly advanced how robotic networks sense, communicate, and secure themselves in complex environments. His most recognized contribution lies in backscatter localization for IoT applications, developing robot-assisted and zero-start-up-cost techniques that enable accurate tracking of low-power tags essential for smart cities and smart homes — work that has garnered over 40 citations across related publications. Jiang has also made notable strides in mmWave radar sensing, pioneering rough-relay-surface scattering approaches for non-line-of-sight detection critical to autonomous vehicles and unmanned robots. His research extends into multi-robot network security, where he has proposed innovative methods for detecting and repelling Sybil attackers using backscatter signals and multipath manipulation. Beyond security, Jiang investigates edge intelligence for heterogeneous robot networks and discrete-time flocking control under realistic wireless link failures, addressing fundamental coordination challenges in robotic swarms. More recently, his work on Tactile Internet quality metrics and deep learning-aided FBMC systems demonstrates a broadening vision toward next-generation machine-type communications, cementing his reputation as a versatile and impactful contributor to intelligent networked systems.
Research Focus
Key Achievements
Top Papers
- 1Robot-Assisted Backscatter Localization for IoT Applications36 citations · 2020
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- 5Detecting Colluding Sybil Attackers in Robotic Networks Using Backscatters12 citations · 2021
- 6Lightweight Sybil-Resilient Multi-Robot Networks by Multipath Manipulation11 citations · 2020
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- 10Localizing Backscatters by a Single Robot with Zero Start-Up Cost5 citations · 2019