| Author: | Cui, Kaiyan |
| Title: | Interacting with ubiquitous IoT devices via RF sensing |
| Advisors: | Zheng, Yuan-qing (COMP) |
| Degree: | Ph.D. |
| Year: | 2023 |
| Subject: | Internet of things Ubiquitous computing Radio frequency identification systems Hong Kong Polytechnic University -- Dissertations |
| Department: | Department of Computing |
| Pages: | xviii, 137 pages : color illustrations |
| Language: | English |
| Abstract: | The world is seeing the Internet of Things (IoT) devices grow in volume and variety. From consumer smart home products and wearable devices, to retail and industrial IoT, to smart city applications, IoT devices have been widely integrated, making our life more convenient and efficient. As we gradually reach an IoT world, a fundamental question worth discussing is how to interact with these ubiquitous IoT devices. Besides traditional interaction methods, existing solutions mainly leverage cameras, acoustic sensors, and inertial measurement unit (IMU) sensors to enable human-IoT device interaction. Although effective, these solutions cannot be applied to IoT devices that do not have these specific sensors. Furthermore, vision-enabled interaction requires ideal lighting conditions, which are hard to guarantee in practice. Acoustic-based interaction still cannot handle some practical scenarios involving multiple speakers and background noise. IMU-based interaction is not user-friendly, as it generally requires users to carry some dedicated IMU devices. This thesis studies a novel human-IoT device interaction via RF sensing to complement and augment current interaction methods. We repurpose two RF technologies to realize a novel and user-friendly interaction by overcoming a series of technical challenges. Specifically, this thesis allows users to interact with two common types of IoT devices, Radio Frequency Identification (RFID) tags and millimeter wave (mmWave) radars in a contact-less interaction manner using their smartphones. The main contents are as follows. The first work aims to allow users to interact with ubiquitous RFID tags. Ultra-High Frequency (UHF) RFID technology has become increasingly popular in stores, since it can quickly query a large number of RFID tags from afar. The deployed RFID infrastructure, however, does not directly benefit smartphone users in stores, mainly because smartphones cannot read UHF RFID tags or fetch relevant information. In the first work, we propose ShakeReader to bridge the gap and allow users to 'read' UHF RFID tags using their smartphones, without any hardware modification to either deployed RFID systems or smartphone hardware. To 'read' an interested tag, a user makes a pre-defined smartphone gesture in front of the interested tag. The smartphone gesture causes changes in 1) RFID measurement data captured by RFID infrastructure, and 2) motion sensor data captured by the user's smartphone. Since the changes of the two data caused by the same gesture are synchronized, we can match the two data and pair the interested tag with the corresponding smartphone, thereby enabling the smartphone to indirectly 'read' the interested tag. Experimental results show that ShakeReader can accurately pair interested tags with their corresponding smartphones with an accuracy of >96.3%. To our best knowledge, ShakeReader is the first work that enables commodity smartphones to 'read' passive RFID tags without any hardware modification to either smartphones or RFID readers. The second work aims to allow users to interact with widely deployed mmWave radars. We present the design and implementation of mmRipple, which empowers commodity mmWave radars with communication capability through smartphone vibrations. In mmRipple, a smartphone (transmitter) sends messages by modulating smartphone vibrations, while a mmWave radar (receiver) receives the messages by detecting and decoding the smartphone vibrations with mmWave signals. By doing so, a smartphone user can not only be passively sensed by a mmWave radar, but also actively send messages to the radar using her smartphone without any hardware modifications to either the smartphone or the mmWave radar. mmRipple addresses a series of unique technical challenges, including vibration signal generation, tiny vibration sensing, multiple object separation, and movement interference mitigation. We implement and evaluate mmRipple using commodity mmWave radars and smartphones in different practical conditions. Experimental results show that mmRipple achieves an average vibration pattern recognition accuracy of 98.60% within a 2m communication range, and 97.74% within 3m on 11 different types of smartphones. The communication range can be further extended up to 5m with an accuracy of 91.67% with a line-of-sight path. To our best knowledge, mmRipple is the first work that allows smartphones to send data to COTS mmWave radars via smartphone vibrations without any hardware modification to either the smartphones or the mmWave radars. In conclusion, this thesis exploits RF sensing to complement and enhance current human-IoT interaction technologies, enabling users to interact with ubiquitous RFID tags and mmWave radars. Such technologies are promising to enable a wide variety of innovative applications such as product information inquiry, inventory management, customer behaviour analysis, access control,pedestrian-to-sensing-infrastructure interaction, and other near-field interaction. |
| Rights: | All rights reserved |
| Access: | open access |
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