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theses

With the advancement of mobile sensing and pervasive computing, extensive research is being carried out in various application domains of Internet of Things (IoT), such as smart home, smart healthcare, connected vehicles, and their security issues. My research work explores the power of pervasive sensing technologies to benefit people's daily lives and make impacts on society advancement, especially in the emerging areas of smart healthcare, IoT security and IoT embedded system communications. In this dissertation, I mainly study the following topics: (1) how to perform vital signs monitoring during sleep towards smart healthcare; (2) how to conduct user authentication on any solid surface for IoT applications; (3) IoT security: side-channel security leakage of typing with a nearby phone; and (4) high-throughput and inaudible acoustic communication for IoT applications.

We first propose to track the vital signs of both breathing rate and heart rate during sleep by using off-the-shelf WiFi without any wearable or dedicated devices. Our system reuses existing WiFi network of IoT and exploits the fine-grained channel information to capture the minute movements caused by breathing and heart beats. Our system thus has the potential to be widely deployed and perform continuous long-term monitoring. Our extensive experiments demonstrate that our system can accurately capture vital signs during sleep under realistic settings and achieve comparable or even better performance comparing to traditional and existing approaches, which is a strong indication of providing noninvasive, continuous fine-grained vital signs monitoring without any additional cost.

Moreover, we propose VibWrite that extends finger-input authentication beyond touch screens to any solid surface for smart access or IoT systems (e.g., access to apartments, vehicles or smart appliances). It integrates passcode, behavioral and physiological characteristics, and surface dependency together to provide a low-cost, tangible and enhanced security solution. VibWrite builds upon a touch sensing technique with vibration signals that can operate on surfaces constructed from a broad range of materials. It is significantly different from traditional password-based approaches, which only authenticate the password itself rather than the legitimate user, and the behavioral biometrics-based solutions, which usually involve specific or expensive hardware (e.g., touch screen or fingerprint reader), incurring privacy concerns and suffering from smudge attacks. VibWrite discriminates fine-grained finger inputs and supports three independent passcode secrets including PIN number, lock pattern, and simple gestures by extracting unique features to capture both behavioral and physiological characteristics such as contacting area, touching force, and etc. Our extensive experiments demonstrate that VibWrite can authenticate users with high accuracy, low false positive rate and is robust to various types of attacks.

In addition, we explore the limits of audio ranging on mobile devices in the context of a keystroke snooping scenario. we show that mobile audio hardware advances of mobile and IoT devices can be exploited to discriminate mm-level position differences and that this makes it feasible to locate the origin of keystrokes from only a single phone behind the keyboard. The technique clusters keystrokes using time-difference of arrival measurements as well as acoustic features to identify multiple strokes of the same key. It then computes the origin of these sounds precise enough to identify and label each key. By locating keystrokes this technique avoids the need for labeled training data or linguistic context. Experiments with three types of keyboards and off-the-shelf smartphones demonstrate that our system can recover 94% of keystrokes, which to our knowledge, is the first single-device technique that enables acoustic snooping of passwords.

Finally, we design the first acoustic communication system, which achieves high- throughput and inaudibility at the same time. The highest throughput we achieve is over 17x higher than the state-of-the-art acoustic communication systems, which could facilitate various IoT applications. Particularly, we theoretically model the non- linearity of the mobile device's inbuilt microphone and use orthogonal frequency division multiplexing (OFDM) technique together with the non-linearity model to transmit data bits over multiple orthogonal channels with an ultrasound frequency carrier. Extensive evaluations under various realistic settings demonstrate that our inaudible acoustic communication system achieves throughput as high as 47.49kbps.

ETD_10084

Ph.D.

Includes bibliographical references

doi:10.7282/t3-k31g-sz74

ETD doctoral

The author owns the copyright to this work.