ECE PhD Proposal Review: Subhramoy Mohanti

PhD Proposal Review: Distributed Data and Energy Beamforming with Unmanned Vehicles for Wireless IoT : A Systems Perspective

Subhramoy Mohanti

Location: Teams Meeting

Abstract: The pervasive deployment of the wireless Internet of Things (IoT) has given rise to heterogeneous sensors and small form-factor computing devices in homes, offices, public spaces, manufacturing floors, among others. Such large number of connected devices require (i) simple ways of charging, so that they remain operationally available, and (ii) effective ways of sharing wireless spectrum, so that they continue to transmit and receive data amidst competing and interfering signals. This thesis focuses on the link and physical layer of the protocol stack to enable distributed beamforming as a key enabler for these two objectives. Specifically, we experimentally demonstrate how beamforming capability can address both wireless power transfer (WPT) needs and resilient communication in interference-challenged environments.
This thesis proposes a method for accessing and sharing the wireless channel for both regular data communication and WPT. This is the first work that accomplishes these dissimilar tasks within the constraints of the standard compliant IEEE 802.11 protocol, resulting in a practical and so called ‘WiFi-friendly Energy Delivery’ (WiFED). First, WiFED exploits the IEEE 802.11 supported protocol features to request energy and for energy transmitters to participate in energy transfer via beamforming. Second, it devises a controller-driven bipartite matching algorithm, assigning appropriate number of energy transmitters to sensors for efficient energy delivery. Thirdly, it detects outlier sensors, which have limited power reception from static energy transmitters and utilizes mobile energy transmitters to satisfy their charging cycles.
From a communication-only perspective that relies on distributed beamforming, this thesis presents AirBeam, a software-based approach that runs on Unmanned Aerial Vehicles (UAVs) to deliver on-demand data to sensors deployed in infrastructure constrained environments. We first show why this problem is difficult given the continuous hovering-related channel fluctuations, synchronizing the distributed transmit streams without a wired clock reference, the need to ensure timely feedback from the ground receiver due to the channel coherence time, and the size, weight, power, and cost (SWaP-C) constraints for UAVs. This work is extended further to consider realistic traffic patterns and packet arrival thresholds, involving dynamic grouping of transmitters to beamform towards target receivers at any given time. Again, we evaluate outcome both experimentally and in a virtual environment in Colosseum, the world’s largest RF emulator.
Since beamforming requires the action of multiple devices not directly connected to each other by wire, we introduce a security framework called AirID, which identifies authorized beamforming UAVs by learning their so called ‘RF fingerprints’. This step requires applying deep learning techniques on their received signals, with the goal of identifying discriminative features introduced by the transmitter due to process variations. Our approach involves intentionally inserting ‘signatures’ in the signals from each known UAV, which are detected through a deep convolutional neural network (CNN) at the physical layer, without affecting the ongoing UAV data communication process.
In the proposed work, we will explore optimized placement of UAVs, while also considering battery limits, to enhance beamforming performance. We will validate these outcomes in a testbed of 4-5 UAVs.