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1. QID: Identifying Mobile Devices via Wireless Charging Fingerprints

   https://doi.org/10.1109/IoTDI49375.2020.00009  

   Yang, Deliang; Xing, Guoliang; Huang, Jun; Chang, Xiangmao; Jiang, Xiaofan (April 2020, International Conference on Internet-of-Things Design and Implementation)

   Recent years have witnessed the increasing penetration of wireless charging base stations in the workplace and public areas, such as airports and cafeteria. Such an emerging wireless charging infrastructure has presented opportunities for new indoor localization and identification services for mobile users. In this paper, we present QID, the first system that can identify a Qi-compliant mobile device during wireless charging in real-time. QID extracts features from the clock oscillator and control scheme of the power receiver and employs light-weight algorithms to classify the device. QID adopts 2-dimensional motion unit to emulate a variety of multi-coil designs of Qi, which allows for fine-grained device fingerprinting. Our results show that QID achieves high recognition accuracy. With the prevalence of public wireless charging stations, our results also have important implications for mobile user privacy. 

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2. Extracting User Behavior at Electric Vehicle Charging Stations with Transformer Deep Learning Models

   https://doi.org/10.4995/carma2020.2020.11613  

   Marchetto, Daniel J; Ha, Sooji; Dharur, Sameer; Asensio, Omar Isaac (July 2020, CARMA 2020, 3rd International Conference on Advanced Research Methods and Analytics)

   Mobile applications have become widely popular for their ability to access real-time information. In electric vehicle (EV) mobility, these applications are used by drivers to locate charging stations in public spaces, pay for charging transactions, and engage with other users. This activity generates a rich source of data about charging infrastructure and behavior. However, an increasing share of this data is stored as unstructured text—inhibiting our ability to interpret behavior in real-time. In this article, we implement recent transformer-based deep learning algorithms, BERT and XLnet, that have been tailored to automatically classify short user reviews about EV charging experiences. We achieve classification results with a mean accuracy of over 91% and a mean F1 score of over 0.81 allowing for more precise detection of topic categories, even in the presence of highly imbalanced data. Using these classification algorithms as a pre-processing step, we analyze a U.S. national dataset with econometric methods to discover the dominant topics of discourse in charging infrastructure. After adjusting for station characteristics and other factors, we find that the functionality of a charging station is the dominant topic among EV drivers and is more likely to be discussed at points-of-interest with negative user experiences. 

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3. Energy-efficient Joint Wireless Charging and Computation Offloading In MEC Systems

   https://doi.org/10.1109/JSTSP.2021.3098963  

   Malik, Rafia; Vu, Mai (July 2021, IEEE Journal of Selected Topics in Signal Processing)

   null (Ed.)

   Wireless charging coupled with computation offloading in edge networks offers a promising solution for realizing power-hungry and computation intensive applications on user devices. We consider a mutil-access edge computing (MEC) system with collocated MEC servers and base-stations/access points (BS/AP) supporting multiple users requesting data computation and wireless charging. We propose an integrated solution for wireless charging with computation offloading to satisfy the largest feasible proportion of requested wireless charging while keeping the total energy consumption at the minimum, subject to the MEC-AP transmit power and latency constraints. We propose a novel nested algorithm to jointly perform data partitioning, time allocation, transmit power control and design the optimal energy beamforming for wireless charging. Our resource allocation scheme offers a minimal energy consumption solution compared to other schemes while also delivering a higher amount of wirelessly transferred charge to the users. Even with data offloading, our proposed solution shows significant charging performance, comparable to the case of charging alone, hence showing the effectiveness of performing partial offloading jointly with wireless charging. 

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4. Evaluating electric vehicle user mobility data using neural network-based language models

   Alvarez, K.* (January 2019, Proceedings of the Transportation Research Board (TRB) annual meeting)

   By displacing gasoline and diesel fuels, electric cars and fleets offer significant public health benefits by reducing emissions from the transportation sector. However, public confidence in the reliability of charging infrastructure remains a fundamental barrier to adoption. Using large-scale social data and machine learning based on 12,720 U.S. electric vehicle charging stations, we provide national evidence on how well the existing charging infrastructure is serving the needs of the expanding population of EV drivers in 651 core-based statistical areas in the United States. Contrary to predictions, we find that stations at private charging locations do not outperform public charging locations provided by government. We also find evidence of higher negative sentiment in the dense urban centers, where issues of charge rage and congestion may be the most prominent. Overall, 40% of drivers using mobility apps have faced negative experiences at EV charging stations, a problem that needs to be fixed as the market expands. 

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5. Enabling Magnetic Beamforming in MIMO Wireless Power Transfer Using Reconfigurable Metasurface

   https://doi.org/10.1109/GLOBECOM42002.2020.9322096  

   Li, Zhangyu; Sun, Zhi (December 2020, IEEE Globecom 2020)

   null (Ed.)

   Wireless power transfer (WPT) has been widely used in IoT applications, such as mobile device charging, biomedical implants communication, and RFID field. Maximizing the power transfer efficiency (PTE) becomes one of the most crucial problems for designing the WPT systems. Magnetic induction (MI) beamforming has been proposed recently to maximize the PTE for the near field MIMO WPT systems. However, conventional magnetic beamforming in WPT systems usually requires accurate magnetic channel estimation, both amplitude and phase control of the charging source, which can not be achieved in an extreme environment. In this paper, we propose a novel magnetic induction beamforming scheme in MIMO WPT system using a reconfigurable metasurface. Instead of controlling the source currents or voltages, the reconfigurable metasurface can achieve near field beamforming only by varying the capacitor and resistance in specific coil array units. The beamforming is modeled as a discrete optimization problem and solved by using the Simulate Anneal (SA) method. Through the analytical and COMSOL simulation results, our proposed beamforming scheme can achieve approximately two times PTE of the conventional beamforming method in a 40 cm charging distance. 

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