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- Signal Processing and Machine Learning Approaches to Enabling Advanced Sensing and Networking Capabilities in Everyday Infrastructure and Electronics
- Ali, Kamran
- Electronic Theses & Dissertations
Mainstream commercial off-the-shelf (COTS) electronic devices of daily use are usually designed and manufactured to serve a very specific purpose. For example, the WiFi routers and network interface cards (NICs) are designed for high speed wireless communication, RFID readers and tags are designed to identify and track items in supply chain, and smartphone vibrator motors are designed to provide haptic feedback (e.g. notifications in silent mode) to the users. This dissertation focuses on...
Show moreMainstream commercial off-the-shelf (COTS) electronic devices of daily use are usually designed and manufactured to serve a very specific purpose. For example, the WiFi routers and network interface cards (NICs) are designed for high speed wireless communication, RFID readers and tags are designed to identify and track items in supply chain, and smartphone vibrator motors are designed to provide haptic feedback (e.g. notifications in silent mode) to the users. This dissertation focuses on revisiting the physical-layer of various such everyday COTS electronic devices, either to leverage the signals obtained from their physical layers to develop novel sensing applications, or to modify/improve their PHY/MAC layer protocols to enable even more useful deployment scenarios and networking applications - while keeping their original purpose intact - by introducing mere software/firmware level changes and completely avoiding any hardware level changes. Adding such new usefulness and functionalities to existing everyday infrastructure and electronics has advantages both in terms of cost and convenience of use/deployment, as those devices (and their protocols) are already mainstream, easily available, and often already purchased and in use/deployed to serve their mainstream purpose of use.In our works on WiFi signals based sensing, we propose signal processing and machine learning approaches to enable fine-grained gesture recognition and sleep monitoring using COTS WiFi devices. In our work on gesture recognition, we show for the first time thatWiFi signals can be used to recognize small gestures with high accuracy. In our work on sleep monitoring, we propose for the first time aWiFi CSI based sleep quality monitoring scheme which can robustly track breathing and body/limb activity related vital signs during sleep throughout a night in an individual and environment independent manner.In our work on RFID signals based sensing, we propose signal processing and machine learning approaches to effectively image customer activity in front of display items in places such as retail stores using commercial off-the-shelf (COTS) monostatic RFID devices (i.e. which use a single antenna at a time for both transmitting and receiving RFID signals to and from the tags). The key novelty of this work is on achieving multi-person activity tracking in front of display items by constructing coarse grained images via robust, analytical model-driven deep learning based, RFID imaging. We implemented our scheme using a COTS RFID reader and tags.In our work on smartphone’s vibration based sensing, we propose a robust and practical vibration based sensing scheme that works with smartphones with different hardware, can extract fine-grained vibration signatures of different surfaces, and is robust to environmental noise and hardware based irregularities. A useful application of this sensing is symbolic localization/tagging, e.g. figuring out whether a user’s device is in their hand, pocket, or at their bedroom table, etc. Such symbolic tagging of locations can provide us with indirect information about user activities and intentions without any dedicated infrastructure, based on which we can enable useful services such as context aware notifications/alarms. To make our scheme easily scalable and compatible with COTS smartphones, we design our signal processing and machine learning pipeline such that it relies only on builtin vibration motors and microphone for sensing, and it is robust to hardware irregularities and background environmental noises. We tested our scheme on two different Android smartphones.In our work on powerline communications (PLCs), we propose a distributed spectrum sharing scheme for enterprise level PLC mesh networks. This work is a major step towards using existing COTS PLC devices to connect different types of Internet of Things (IoT) devices for sensing and control related applications in large campuses such as enterprises. Our work is based on identification of a key weakness of the existing HomePlug AV (HPAV) PLC protocol that it does not support spectrum sharing, i.e., currently each link operates over the whole available spectrum, and therefore, only one link can operate at a time. Our proposed spectrum sharing scheme significantly boosts both aggregated and per-link throughputs, by allowing multiple links to communicate concurrently, while requiring a few modifications to the existing HPAV protocol.