Internet of Things (IoTs) was rapidly developed during past years. Smart devices, such as smartphones, smartwatches, and smart assistants, which are equipped with smart chips as well as sensors, provide users with many easy used functions and lead them to a more convenient life. In this dissertation, we carefully studied the birefringence of the transparent tape, the nonlinear effects of the microphone, and the phase characteristic of the reflected ultrasound, and make use of such effects to design three systems, RainbowLight, Patronus, and BreathPass, to provide users with accurate localization, privacy protection, and authentication, respectively. RainbowLight leverages observation direction-varied spectrum generated by a polarized light passing through a birefringence material, i.e., transparent tape, to provide localization service. We characterize the relationship between observe direction, light interference and the special spectrum, and using it to calculate the direction to a chip after taking a photo containing the chip. With multiple chips, RainbowLight designs a direction intersection based method to derive the location. In this dissertation, we build the theoretical basis of using polarized light and birefringence phenomenon to perform localization. Based on the theoretical model, we design and implement the RainbowLight on the mobile device, and evaluate the performance of the system. The evaluation results show that RainbowLight achieves 1.68 cm of the median error in the X-axis, 2 cm of the median error in the Y-axis, 5.74 cm of the median error in Z-axis, and 7.04 cm of the median error with the whole dimension. It is the first system that could only use the reflected lights in the space to perform visible light positioning. Patronus prevents unauthorized speech recording by leveraging the nonlinear effects of commercial off-the-shelf microphones. The inaudible ultrasound scramble interferes recording of unauthorized devices and can be canceled on authorized devices through an adaptive filter. In this dissertation, we carefully studied the nonlinear effects of ultrasound on commercial microphones. Based on the study, we proposed an optimized configuration to generate the scramble. It would provide privacy protection againist unauthorized recordings that does not disturb normal conversations. We designed, implemented a system including hardware and software components. Experiments results show that only 19.7% of words protected by Patronus' scramble can be recognized by unauthorized devices. Furthermore, authorized recordings have 1.6x higher perceptual evaluation of speech quality (PESQ) score and, on average, 50% lower speech recognition error rates than unauthorized recordings. BreathPass uses speakers to emit ultrasound signals. The signals are reflected off the chest wall and abdomen and then back to the microphone, which records the reflected signals. The system then extracts the fingerprints from the breathing pattern, and use these fingerprints to perform authentication. In this dissertation, we characterized the challenge of conducting authentication with the breathing pattern. After addressing these challenges, we designed such a system and implemented a proof-of-concept application on Android platform. We also conducted comprehensive experiments to evaluate the performance under different scenarios. BreathPass achieves an overall accuracy of 83%, a true positive rate of 73%, and a false positive rate of 5%, according to performance evaluation results. In general, this dissertation provides an enhanced ranging and versatile authentication systems of Internet of Things.
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