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1. Development and Testing of an OFDM Physical Layer for the DESERT Simulator

   Falleni, S ; Saha, D ; Haque, I ; Melodia, T ; Basagni, S. ( September 2021 , MTS/IEEE OCEANS 2021)

   The realization of efficient, robust, and adaptable applications for the emergent Internet of Underwater Things enables the sustainable and effective conservation and exploitation of our oceans and waterways. Recent advances have fo- cused on Orthogonal Frequency-Division Multiplexing (OFDM) physical layers for supporting applications requiring high data rates and swift adaptation to changing underwater conditions. This prompts the need of tools for testing new OFDM-enabled underwater solutions. To this aim, this paper presents the implementation and evaluation of an OFDM-based physical layer module for the popular underwater network simulator DESERT. We aim at modeling the flexibility of the software-defined acoustic SEANet modem by realizing OFDM features that can vary in time, including the number and the selection of subcarriers and their modulation on a per-transmission basis. We demonstrate the usage of the proposed module through the DESERT-based simulation of three simple OFDM-enabled cross-layer MAC protocols in underwater acoustic networks of different sizes. The diverse and detailed set of results are obtained by using our physical layer module simply and swiftly. Our results also confirm the advantages of using the OFDM technology in solutions for underwater networking in challenging environments.
2. Evaluation of Physical Layer Secret Key Generation for IoT Devices

   https://doi.org/10.1109/WAMICON.2019.8765465  

   Jacovic, Marko ; Kraus, Martin ; Mainland, Geoffrey ; Dandekar, Kapil R. ( April 2019 , 2019 IEEE 20th Wireless and Microwave Technology Conference (WAMICON))

   As aspects of our daily lives become more interconnected with the emergence of the Internet of Things (IoT), it is imperative that our devices are reliable and secure from threats. Vulnerabilities of Wi-Fi Protected Access (WPA/WPA2) have been exposed in the past, motivating the use of multiple security techniques, even with the release of WPA3. Physical layer security leverages existing components of communication systems to enable methods of protecting devices that are well-suited for IoT applications. In this work, we provide a low-complexity technique for generating secret keys at the Physical layer to enable improved IoT security. We leverage the existing carrier frequency offset (CFO) and channel estimation components of Orthogonal Frequency Division Multiplexing (OFDM) receivers for an efficient approach. The key generation algorithm we propose focuses on the unique CFO and channel experienced between a pair of desired nodes, and to the best of our understanding, the combination of the features has not been examined previously for the purpose of secret key generation. Our techniques are appropriate for IoT devices, as they do not require extensive processing capabilities and are based on second order statistics. We obtain experimental results using USRP N210 software defined radios and analyze the performancemore »of our methods in post-processing. Our techniques improve the capability of desired nodes to establish matching secret keys, while hindering the threat of an eavesdropper, and are useful for protecting future IoT devices.« less
3. Ion Dynamics in the Meso-scale 3-D Kelvin–Helmholtz Instability: Perspectives From Test Particle Simulations

   https://doi.org/10.3389/fspas.2021.758442  

   Ma, Xuanye ; Delamere, Peter ; Nykyri, Katariina ; Burkholder, Brandon ; Eriksson, Stefan ; Liou, Yu-Lun ( November 2021 , Frontiers in Astronomy and Space Sciences)

   Over three decades of in-situ observations illustrate that the Kelvin–Helmholtz (KH) instability driven by the sheared flow between the magnetosheath and magnetospheric plasma often occurs on the magnetopause of Earth and other planets under various interplanetary magnetic field (IMF) conditions. It has been well demonstrated that the KH instability plays an important role for energy, momentum, and mass transport during the solar-wind-magnetosphere coupling process. Particularly, the KH instability is an important mechanism to trigger secondary small scale (i.e., often kinetic-scale) physical processes, such as magnetic reconnection, kinetic Alfvén waves, ion-acoustic waves, and turbulence, providing the bridge for the coupling of cross scale physical processes. From the simulation perspective, to fully investigate the role of the KH instability on the cross-scale process requires a numerical modeling that can describe the physical scales from a few Earth radii to a few ion (even electron) inertial lengths in three dimensions, which is often computationally expensive. Thus, different simulation methods are required to explore physical processes on different length scales, and cross validate the physical processes which occur on the overlapping length scales. Test particle simulation provides such a bridge to connect the MHD scale to the kinetic scale. This study applies different testmore »particle approaches and cross validates the different results against one another to investigate the behavior of different ion species (i.e., H+ and O+), which include particle distributions, mixing and heating. It shows that the ion transport rate is about 10 25  particles/s, and mixing diffusion coefficient is about 10 10  m 2  s −1 regardless of the ion species. Magnetic field lines change their topology via the magnetic reconnection process driven by the three-dimensional KH instability, connecting two flux tubes with different temperature, which eventually causes anisotropic temperature in the newly reconnected flux.« less
4. In-Band Full-Duplex Underwater Acoustic Communication Measurements: Self-interference

   https://doi.org/10.21227/ecjt-ze13  

   Guo, Zheng ; Song, Aijun ( January 2021 )

   Abstract

   The presented data contain recordings of underwater acoustic transmissions collected from a field experiment whose goal was to characterize self-interference for in-band full-duplex underwater acoustic communications. The experiment was conducted in the Lake of Tuscaloosa in July 2019. A single transmission-receiving line was deployed off a boat that was moored in the center of the lake. The transmission-receiving line had one acoustic transmitter and eight hydrophone receivers. Two types of signals, binary phase-shift keying (BPSK) and orthogonal frequency-division multiplexing (OFDM), were transmitted at the center frequency of 28 kHz. The receptions were recorded in .wav audio files by eighter high-precision digital hydrophones. In addition to the acoustic data, a complete set of source information, environmental measurements, and processed impulse responses are included in the data package. Matlab programs are also provided to retrieve the data and facilitate further analysis.
5. Machine Learning for Improving Surface-Layer-Flux Estimates

   https://doi.org/10.1007/s10546-022-00727-4  

   McCandless, Tyler ; Gagne, David John ; Kosović, Branko ; Haupt, Sue Ellen ; Yang, Bai ; Becker, Charlie ; Schreck, John ( September 2022 , Boundary-Layer Meteorology)

   Flows in the atmospheric boundary layer are turbulent, characterized by a large Reynolds number, the existence of a roughness sublayer and the absence of a well-defined viscous layer. Exchanges with the surface are therefore dominated by turbulent fluxes. In numerical models for atmospheric flows, turbulent fluxes must be specified at the surface; however, surface fluxes are not known a priori and therefore must be parametrized. Atmospheric flow models, including global circulation, limited area models, and large-eddy simulation, employ Monin–Obukhov similarity theory (MOST) to parametrize surface fluxes. The MOST approach is a semi-empirical formulation that accounts for atmospheric stability effects through universal stability functions. The stability functions are determined based on limited observations using simple regression as a function of the non-dimensional stability parameter representing a ratio of distance from the surface and the Obukhov length scale (Obukhov in Trudy Inst Theor Geofiz AN SSSR 1:95–115, 1946),$$z/L$$$z/L$. However, simple regression cannot capture the relationship between governing parameters and surface-layer structure under the wide range of conditions to which MOST is commonly applied. We therefore develop, train, and test two machine-learning models, an artificial neural network (ANN) and random forest (RF), to estimate surface fluxes of momentum, sensible heat, and moisturemore »based on surface and near-surface observations. To train and test these machine-learning algorithms, we use several years of observations from the Cabauw mast in the Netherlands and from the National Oceanic and Atmospheric Administration’s Field Research Division tower in Idaho. The RF and ANN models outperform MOST. Even when we train the RF and ANN on one set of data and apply them to the second set, they provide more accurate estimates of all of the fluxes compared to MOST. Estimates of sensible heat and moisture fluxes are significantly improved, and model interpretability techniques highlight the logical physical relationships we expect in surface-layer processes.

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