School of Information Science&Technologe
Research Interest:
My research interests include the design and experimental analysis of architectures, protocols, and applications for next-generation wireless communication and sensing systems with a focus on millimeter-wave and terahertz networks, the Internet of Things, robotic wireless networks, and wireless security. On these topics, my lab’s research spans from theoretical analysis and modeling to hardware implementations and experimental evaluations.
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Liyao Li, Bozhao Shang, Yun Wu, Jie Xiong, Xiaojiang Chen, Yaxiong Xie
NSDI’24 (CCF-A) 2024
Intraocular pressure (IOP), commonly known as eye pressure, is a critical physiological parameter related to health. Contact lens-based IOP sensing has garnered significant attention in research. Existing research has been focusing on developing the sensor itself, so the techniques used to read sensing data only support a reading range of several centimeters, becoming the main obstacle for real-world deployment. This paper presents Cyclops, the first battery-free IOP sensing system integrated into a contact lens, which overcomes the proximity constraints of traditional reading methods. Cyclops features a three-layer antenna comprising two metallic layers and a nanomaterial-based sensing layer in between. This innovative antenna serves a dual purpose, functioning as both a pressure sensor and a communication antenna simultaneously. The antenna is connected to an RFID chip, which utilizes a low-power self-tuning circuit to achieve high-precision pressure sensing, akin to a 9-bit ADC. Extensive experimental results demonstrate that Cyclops supports communication at meter-level distances, and its IOP measurement accuracy surpasses that of commercial portable IOP measurement devices.
Xinyi Li, Chao Feng, Xiaojing Wang, Yangfan Zhang, Yaxiong Xie, Xiaojiang Chen
NSDI '23 (CCF-A) 2023
Offloading the beamforming task from the endpoints to the metasurface installed in the propagation environment has attracted significant attention. Currently, most of the metasurface-based beamforming solutions are designed and optimized for operation on a single ISM band (either 2.4 GHz or 5 GHz). In this paper, we propose RF-Bouncer, a compact, low-cost, simple-structure programmable dual-band metasurface that supports concurrent beamforming on two Sub-6 ISM bands. By configuring the states of the meta-atoms, the metasurface is able to simultaneously steer the incident signals from two bands towards their desired departure angles. We fabricate the metasurface and validate its performance via extensive experiments. Experimental results demonstrate that RF-Bouncer achieves 15.4 dB average signal strength improvement and a 2.49× throughput improvement even with a relatively small 16 × 16 array of meta-atoms.
Binbin Xie, Jie Xiong, Xiaojiang Chen, Eugene Chai, Liyao Li, Zhanyong Tang, Dingyi Fang
SenSys’19 (CCF-B) 2019
Material sensing is an essential ingredient for many IoT applications. While hyperspectral camera, infrared, X-Ray, and Radar provide potential solutions for material identification, high cost is the major concern limiting their applications. In this paper, we explore the capability of employing RF signals for fine-grained material sensing with commodity RFID device. The key reason for our system to work is that the tag antenna's impedance is changed when it is close or attached to a target. The amount of impedance change is dependent on the target's material type, thus enabling us to utilize the impedance-related phase change available at commodity RFID devices for material sensing. Several key challenges are addressed before we turn the idea into a functional system: (i) the random tag-reader distance causes an additional unknown phase change on top of the phase change caused by the target material; (ii) the tag rotations cause phase shifts and (iii) for conductive liquid, there exists liquid reflection which interferes with the impedance-caused phase change. We address these challenges with novel solutions. Comprehensive experiments show high identification accuracies even for very similar materials such as Pepsi and Coke.