Indoor localization has been an active research area given the popularity of Location-Based Services. The CSI fingerprinting based approach is one of the most practical and effective approaches since it can provide adequate accuracy with low overhead for users. The key drawback that limits its wide application is the huge amount of human effort required to build the fingerprint map. This paper is the first to explore addressing this limitation by automating CSI map construction using an Unmanned Aerial Vehicle (UAV). Given the limited battery capacity of commodity UAVs, it is extremely important yet challenging to optimize energy efficiency for the UAV during the CSI measurement task. To address this challenge, we formulate an energy optimization problem based on a novel graph model that includes the cost of possible actions for UAVs. We then transform the formulated problem to the classic Generalized Traveling Salesman Problem (GTSP), which can be solved efficiently. We implement the system on an off-the-shelf programmable drone equipped with a CSI measurement module. We achieve great energy efficiency improvement over the conventional coverage path planning algorithm. Meanwhile, accurate indoor localization can be achieved using the CSI data collected by our UAV system.

Voice, as a convenient and efficient way of information delivery, has a significant advantage over the conventional keyboard-based input methods, especially on small mobile devices such as smartphones and smartwatches. However, the human voice could often be exposed to the public, which allows an attacker to quickly collect sound samples of targeted victims and further launch voice impersonation attacks to spoof those voice-based applications. In this paper, we propose the design and implementation of a robust software-only voice impersonation defense system, which is tailored for mobile platforms and can be easily integrated with existing off-the-shelf smart devices. In our system, we explore magnetic field emitted from loudspeakers as the essential characteristic for detecting machine-based voice impersonation attacks. Furthermore, we use a state-of-the-art automatic speaker verification system to defend against human imitation attacks. Finally, our evaluation results show that our system achieves simultaneously high accuracy (100%) and low equal error rates (EERs) (0%) in detecting the machine-based voice impersonation attack on smartphones.

The wireless physical-layer identification (WPLI) techniques utilize the unique features of the physical waveforms of wireless signals to identify and classify authorized devices. As the inherent physical-layer features are difficult to forge, WPLI is deemed as a promising technique for wireless security solutions. However, as of today, it still remains unclear whether the existing WPLI techniques can be applied under real-world requirements and constraints. In this paper, through both theoretical modeling and experiment validation, the reliability and the differentiability of the WPLI techniques are rigorously evaluated, especially under the constraints of the state-of-the-art wireless devices, real operation environments, as well as wireless protocols and regulations. In particular, a theoretical model is first established to systematically describe the complete procedure of the WPLI. More importantly, the proposed model is then implemented to thoroughly characterize the various WPLI techniques that utilize the spectrum features coming from the nonlinear RF front-end, under the influences from different transmitters, receivers, and wireless channels. Subsequently, the limitations of the existing WPLI techniques are revealed and evaluated in details using both the developed theoretical model and the in-lab experiments. The real-world requirements and constraints are characterized along each step in WPLI, including: 1) the signal processing at the transmitter (device to be identified); 2) the various physical-layer features that originate from circuits, antenna, and environments; 3) the signal propagation in various wireless channels; 4) the signal reception and processing at the receiver (the identifier); and 5) the fingerprint extraction and classification at the receiver.