Underwater wireless charging offers an effective solution to extend the operational time of autonomous underwater vehicles (AUVs). Both mobile and static wireless charging systems have been employed to support these operations. AUV swarms can significantly enhance the efficiency of remote underwater task execution. However, assigning tasks and planning recharging for AUV swarms in a distributed manner, especially under local communication constraints, presents a significant challenge. Unlike terrestrial environments, underwater wireless charging stations can only be deployed in specific locations. This paper introduces a model-based auction algorithm that jointly optimizes task assignment and AUV recharging schedules. The proposed algorithm intelligently plans recharging by accounting for factors such as the distance to charging stations and the queue length at charging stations. The developed solution is highly generic as it is independent of task location, task number, and task completion time which makes it adaptable to a wide range of operational scenarios.

Video communication is the critical enabler of the metaverse and eXtended Reality (XR), where emerging technologies such as 360-degree videos, holograms, and point cloud videos impose substantial bandwidth demands. Although deep learning-based video compression and advanced codecs can significantly compress video data, Generative Artificial Intelligence (GAI) provides a new solution to generate videos under the supervision of minimal real-time streaming control data, thereby further reducing communication overhead. This paper proposes a video communication system using diffusion model-based GAI and topological data analysis. The GAI is used to generate videos using a limited number of video frames. Topological features extracted from video frames using topological data analysis are employed to ensure the quality of the generated video. This paper uses a mobile edge computing (MEC) system and presents its operational architecture. Experimental results on three different datasets demonstrate that the future frames generated at the edge server are of high quality compared to HEVC-encoded frames while also achieving significant compression. Furthermore, the proposed system maintains high fidelity in frames at the edge server, even under noisy conditions. Additionally, to validate real-world applicability, we implement a 5G testbed, where experimental results closely align with simulation outcomes, confirming the practicality and scalability of our approach.

Extending the range of Near-Field Communication (NFC) is critical to enabling battery-free wireless sensing and Internet of Things (IoT), such as smart home, underground IoT, and smart agriculture. This poster demonstrates a practical and theoretically supported method to extend the NFC communication range beyond 1 meter using customized sensor antennas and a lumped-element matching network. We design and fabricate two variants of large and small passive NFC tags/sensors, each matched to 13.56 MHz, and validate their effective range in standalone and arrayed configurations. Single large-antenna sensors reach 1.02 m, while small-antenna sensors reach 0.55 m. Further, two-sensor arrays exhibit enhanced coupling, achieving up to 1.27 m with large antennas and 0.80 m with small antennas. These results are consistent with an equivalent theoretical model and illustrate how inter-sensor mutual inductance in spatially arranged arrays can form a virtual magnetic waveguide for passive NFC sensors.

Near Field Communication (NFC) is widely used in security applications such as door access systems and ID cards. However, clone attacks can replicate digital information, enabling unauthorized access. RF fingerprinting offers a robust defense by extracting unique physical-layer features from NFC cards that cannot be cloned. While RF fingerprinting has been extensively applied to Internet of Things (IoT) device authentication, NFC tags present distinct characteristics that require specialized approaches. This paper focuses on RF fingerprinting for the ISO15693 NFC tag, which is a widely used international standard, by leveraging multi-channel, multi-rate data sampling to enhance accuracy. Deep learning and Random Forest models are employed to identify NFC tags, while uncertainty quantification, particularly Conformal Prediction, accelerates the identification process with high confidence and precision. A software-defined radio (SDR) testbed is developed to transmit customized commands and collect multi-channel multi-rate NFC signals. The multi-channel multi-rate NFC signals are progressively collected to ensure fast and accurate identification. Experimental results demonstrate that the proposed system achieves high accuracy by adaptively utilizing the optimal combination of NFC signals. The developed solution is model-agnostic which can be utilized for any machine learning-based NFC tag identification.

The growing prominence of eXtended Reality (XR), holographic-type communications, and metaverse demands truly immersive user experiences by using many sensory modalities, including sight, hearing, touch, smell, taste, etc. Additionally, the widespread deployment of sensors in areas such as agriculture, manufacturing, and smart homes is generating diverse sensory data. A new media format known as multisensory media (mulsemedia) has emerged, which incorporates many sensory modalities beyond the traditional visual and auditory media. 6 G wireless systems are envisioned to support the Internet of Senses, making it crucial to explore effective data fusion and communication strategies for mulsemedia. In this paper, we introduce a task-oriented multi-task mulsemedia communication system named MuSeCo, which is developed using unified Perceiver models and Conformal Prediction. This unified model can accept any sensory input and efficiently extract latent semantic features, making it adaptable for deployment across various Artificial Intelligence of Things (AIoT) devices. Conformal Prediction is employed for modality selection and combination, enhancing task accuracy while minimizing data communication overhead. The model is trained using six sensory modalities across four classification tasks. Simulations and experiments demonstrate that it can effectively fuse sensory modalities, significantly reduce end-to-end communication latency and energy consumption, and maintain high accuracy in communication-constrained systems.

Underground in-situ sensing plays a vital role in precision agriculture and infrastructure monitoring. While existing sensing systems utilize wires to connect an array of sensors at various depths for spatial-temporal data collection, wireless underground sensor networks offer a cable-free alternative. However, these wireless sensors are typically battery-powered, necessitating periodic recharging or replacement. This paper proposes a battery-free sensor array which can be used for wireless multi-depth in-situ sensing. Utilizing Near Field Communication (NFC)-which can penetrate soil with negligible signal power loss-this sensor array can form a virtual magnetic waveguide, achieving long communication ranges. An analytical model has been developed to offer insights and determine opti-mal design parameters. Moreover, a prototype, constructed using off-the-shelf NFC sensors, was tested to validate the proposed concept. While this system is primarily designed for underground applications, it holds potential for other multi-depth in-situ sensing scenarios, including underwater environments.

The tremendous potential of sensing and communication technologies has been explored and implemented for different remote event monitoring applications over the past two decades. However, the applicability of sensing and communication technologies is not necessarily limited to aboveground environments—it is also implementable and applicable for subterranean, underground scenarios. However, as opposed to air medium, underground communication medium is quite harsh due to the presence of heterogeneous underground materials along with underground aqueous components. In this article, we provide a technical overview of different underground wireless communication technologies, namely radio, acoustic, magnetic, and visible light, along with their potentials and challenges for several underground applications. We also lay out a detailed comparison among these technologies along with their pros and cons using detailed experimental results.

Sensing and communication technology has been used successfully in various event monitoring applications over the last two decades, especially in places where long-term manual monitoring is infeasible. However, the major applicability of this technology was mostly limited to terrestrial environments. On the other hand, underwater wireless sensor networks (UWSNs) opens a new space for the remote monitoring of underwater species and faunas, along with communicating with underwater vehicles, submarines, and so on. However, as opposed to terrestrial radio communication, underwater environment brings new challenges for reliable communication due to the high conductivity of the aqueous medium which leads to major signal absorption. In this paper, we provide a detailed technical overview of different underwater communication technologies, namely acoustic, magnetic, and visual light, along with their potentials and challenges in submarine environments. Detailed comparison among these technologies have also been laid out along with their pros and cons using real experimental results.

The UnderWater Acoustic (UWA) communication is the foundation for oceanic information applications. However, existing UWA systems suffer from low data rate problem. Although the Multiple Input Multiple Output (MIMO) scheme is proved to be able to increase channel capacity in many terrestrial scenarios, the high cost and complexity of acoustic MIMO significantly limit its usage. Recently, the acoustic reconfigurable intelligent surfaces (acoustic RIS) concept has been proposed to address the aforementioned problem. In this paper, with the real-world constraints taken into consideration, three key components of the acoustic RIS are designed to realize the underwater RIS concept, including the new acoustic RIS hardware, the ultra-wideband (UWB) beamforming, and practical operation protocol. Specifically, a completely new hardware design of acoustic RIS is first provided, since existing electromagnetic RIS designed for terrestrial environments do not work for underwater acoustic waves. Then, the UWB beamforming solution is developed, since underwater acoustic RIS need to control the acoustic signals, whose bandwidth is comparable to its carrier frequency. Finally, the practical operation protocol is developed to realize the acoustic RIS functionalities in complex underwater environment. The acoustic RIS design is validated through both COMSOL multiphysics simulations and end-to-end Bellhop-based simulations.

Underwater real-time video streaming is a challenging problem due to the limited bandwidth of acoustic communications. Although underwater optical communication promises high data rates, its performance is limited by the short communication range. This paper employs a multi-robot system to form a multi-hop relay network for high-speed long-range underwater communication, which can be used for underwater real-time monitoring. The impact of dynamic underwater environment on wireless optical communication is considered. A centralized optimization-based solution is derived by considering the existence of a powerful controller. Then, a distributed low-complexity algorithm is designed, which can also be adaptive to dynamic underwater environmental change. The performance of the proposed approach is evaluated in various underwater environments. The results show that the distributed algorithm can reliably obtain optimal trajectories and locations for underwater robots without knowing global environmental information.

Underwater Visible Light Communication (VLC) uses lasers or LEDs can be used for wireless sensor networks and robotic networks. Compared with underwater acoustic communication and radio frequency communication, VLC has its unique advantages in high data rates and low latency. In this paper, a fully reconfigurable low-cost VLC testbed is designed for underwater communication. The testbed uses a green laser as the transmitter. USRP is utilized to collect the received signals, which are processed in MATLAB. A demodulation algorithm is designed and Bit-Error-Rate (BER) is measured under various conditions. The results indicate that reliable communication can be achieved in the absence of some high-cost elements from typical VLC testbeds. The communication system provides an average BER of 5.5×10−3with a data rate of 4 Mbps.

This paper considers the problem of localizing a static transmitter using a robot with a single receiving antenna and a single communication channel in unknown complex environments. Existing solutions using Time-of-Arrival (TOA) and Angle-of-Arrival (AOA) rely on complex wireless communication systems with multiple receive antennas or multiple communication channels, which are not available for robots with off-the-shelf low-cost radios. This paper develops a localization framework using Received Signal Strength (RSS) to estimate unknown channel model parameters considering multipath fading and spatial-correlated shadowing effects. The robot moves along a predefined trajectory to collect RSS data. AOA information is also estimated and integrated with the robot SLAM (Simultaneous Localization and Mapping) results to improve the localization accuracy. Numerical simulations and experiments in an indoor environment are conducted. Results show that 90% of the estimation error is smaller than 2 m to localize a randomly placed transmitter in a 10 × 10 m2area.

Tiny batteryless sensors are desirable since they create negligible impacts on the operation of the system being monitored or the surrounding environment. Wireless energy transfer for batteryless sensors is challenging since they cannot cooperate with the charger due to the lack of energy. In this paper, a Magnetic Blind Beamforming (MagBB) algorithm is developed for wireless energy transfer for batteryless sensors in inhomogeneous media. Batteryless sensors with randomly orientated coils may experience significant orientation losses and they may not receive any energy from the charger. MagBB uses a set of optimized current vectors to generate rotating magnetic fields which can ensure that coils on batteryless sensors with arbitrary orientations can receive sufficient voltages for charging. It does not require any information regarding the batteryless sensor's coil orientation or location. The efficiency of MagBB is proven by extensive numerical simulations.

Swarm robotics is envisioned to automate a large number of dirty, dangerous, and dull tasks. Robots have limited energy, computation capability, and communication resources. Therefore, current swarm robotics have a small number of robots, which can only provide limited spatio-temporal information. In this paper, we propose to leverage the mobile edge computing to alleviate the computation burden. We develop an effective solution based on a mobility-aware deep reinforcement learning model at the edge server side for computing scheduling and resource. Our results show that the proposed approach can meet delay requirements and guarantee computation precision by using minimum robot energy.

Compared with a single robot, the wireless robotic network provides more reliable and efficient services. When tasks are not independent and the movement of robots is constrained by wireless connectivity, coordination and cooperation are required to efficiently allocate tasks. Traditionally, the task allocation problem is formulated as a mixed-integer quadratically constrained quadratic programming, which is difficult to solve and the solution is not scalable. This paper studies the sequential task allocation for wireless robotic networks, where robots are subject to wireless connectivity constraints and the tasks are stochastic. The objective of this paper is to reduce the task completion time by using multiple robots. A one-dimensional motion along a straight line with applications for pipeline monitoring and tunnel exploration is considered. First, a baseline is developed for sequential task allocation using the greedy algorithm. Then, a deep reinforcement learning model with offline training is introduced, which can efficiently reduce the task completion time. To further improve the performance, the online rollout for reinforcement learning is employed. Wireless communication protocols and lower bounds of task completion time are also developed. The results show that robots can gradually learn the optimal policy and efficiently address the sequential task allocation problem.

Underwater magnetic induction communication (UWMIC) provides a low-power and high-throughput solution for autonomous underwater vehicles (AUVs). UWMIC with tri-axis coils increases the reliability of wireless channel by exploring the coil orientation diversity. However, the UWMIC channel is different from typical fading channels and the mutual inductance information (MII) is not always available. It is not clear the performance of the tri-axis coil MIMO without MII. Also, its performances with multiple users have not been investigated. In this paper, we analyze the reliability and multiplexing gain of UWMIC with tri-axis coils by using coil selection. We optimally select the transmit and receive coils to reduce the computation complexity and power consumption and explore the diversity for multiple users. We find that without using all the coils and MII, we can still achieve reliability. Also, the multiplexing gain of UWMIC without MII is 5 dB smaller than typical terrestrial fading channels. The results of this paper provide a more power-efficient way to use UWMIC with tri-axis coils.

While underwater wireless communications have been investigated and implemented for decades, existing solutions still have difficulties in establishing reliable and low-delay wireless underwater links among small-size devices. The Magnetic Induction (MI) communication technique is among the promising solutions due to its advantages in low propagation delay and less susceptibility to the transmission environments. To date, existing MI models cannot accurately characterize the complex underwater MI channels, especially in the shallow water with omnidirectional antennas. In this paper, an analytical channel model is developed for underwater MI communication system with Tri-directional coil (TD coil), which is derived based on the rigorous electromagnetic field analysis. The MIMO channel between the tri-directional coil antennas are characterized under the complex influences from the water absorption as well as the surface reflection and lateral waves. To validate the channel model, we compare the theoretical results with simulations derived by the COMSOL Multiphysics simulation tool. The developed channel model confirms the feasibility and lays the foundation of reliable MI underwater communications.