A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges

Maritime activities represent a major domain of economic growth with several emerging maritime Internet of Things use cases, such as smart ports, autonomous navigation, and ocean monitoring systems. The major enabler for this exciting ecosystem is the provision of broadband, low-delay, and reliable wireless coverage to the ever-increasing number of vessels, buoys, platforms, sensors, and actuators. Towards this end, the integration of unmanned aerial vehicles (UAVs) in maritime communications introduces an aerial dimension to wireless connectivity going above and beyond current deployments, which are mainly relying on shore-based base stations with limited coverage and satellite links with high latency. Considering the potential of UAV-aided wireless communications, this survey presents the state-of-the-art in UAV-aided maritime communications, which, in general, are based on both conventional optimization and machine-learning-aided approaches. More specifically, relevant UAV-based network architectures are discussed together with the role of their building blocks. Then, physical-layer, resource management, and cloud/edge computing and caching UAV-aided solutions in maritime environments are discussed and grouped based on their performance targets. Moreover, as UAVs are characterized by flexible deployment with high re-positioning capabilities, studies on UAV trajectory optimization for maritime applications are thoroughly discussed. In addition, aiming at shedding light on the current status of real-world deployments, experimental studies on UAV-aided maritime communications are presented and implementation details are given. Finally, several important open issues in the area of UAV-aided maritime communications are given, related to the integration of sixth generation (6G) advancements

Uplink NOMA for UAV-Aided Maritime Internet-of-Things

Maritime activities are vital for economic growth, being further accelerated by various emerging maritime Internet of Things (IoT) use cases, including smart ports, autonomous navigation, and ocean monitoring systems. However, broadband, low-delay, and reliable wireless connectivity to the ever-increasing number of vessels, buoys, platforms and sensors in maritime communication networks (MCNs) has not yet been achieved. Towards this end, the integration of unmanned aerial vehicles (UAVs) in MCNs provides an aerial dimension to current deployments, relying on shore-based base stations (BSs) with limited coverage and satellite links with high latency. In this work, a maritime IoT topology is examined where direct uplink communication with a shore BS cannot be established due to excessive pathloss. In this context, we employ multiple UAVs for end-to-end connectivity, simultaneously receiving data from the maritime IoT nodes, following the non-orthogonal multiple access (NOMA) paradigm. In contrast to other UAV-aided NOMA schemes in maritime settings, dynamic decoding ordering at the UAVs is used to improve the performance of successive interference cancellation (SIC), considering the rate requirements and the channel state information (CSI) of each maritime node towards the UAVs. Moreover, the UAVs are equipped with buffers to store data and provide increased degrees of freedom in opportunistic UAV selection. Simulations reveal that the proposed opportunistic UAV-aided NOMA improves the average sum-rate of NOMA-based maritime IoT communication, leveraging the dynamic decoding ordering and caching capabilities of the UAVs

Hybrid clouds for data-Intensive, 5G-Enabled IoT applications: an overview, key issues and relevant architecture

Hybrid cloud multi-access edge computing (MEC) deployments have been proposed as efficient means to support Internet of Things (IoT) applications, relying on a plethora of nodes and data. In this paper, an overview on the area of hybrid clouds considering relevant research areas is given, providing technologies and mechanisms for the formation of such MEC deployments, as well as emphasizing several key issues that should be tackled by novel approaches, especially under the 5G paradigm. Furthermore, a decentralized hybrid cloud MEC architecture, resulting in a Platform-as-a-Service (PaaS) is proposed and its main building blocks and layers are thoroughly described. Aiming to offer a broad perspective on the business potential of such a platform, the stakeholder ecosystem is also analyzed. Finally, two use cases in the context of smart cities and mobile health are presented, aimed at showing how the proposed PaaS enables the development of respective IoT applications.Peer ReviewedPostprint (published version

Learning to Fulfill the User Demands in 5G-enabled Wireless Networks through Power Allocation: a Reinforcement Learning approach

The goal of the study presented in this paper is to evaluate the performance of a proposed Reinforcement Learning (RL) power allocation algorithm. The algorithm follows a demand-driven power adjustment approach aiming at maximizing the number of users inside a coverage area that experience the requested throughput to accommodate their needs. In this context, different Quality of Service (QoS) classes, corresponding to different throughput demands, have been taken into account in various simulation scenarios. Considering a realistic network configuration, the performance of the RL algorithm is tested under strict user demands. The results suggest that the proposed modeling of the RL parameters, namely the state space and the rewarding system, is promising when the network controller attempts to fulfill the user requests by regulating the power of base stations. Based on comparative simulations, even for strict demands requested by multiple users (2.5 – 5 Mbps), the proposed scheme achieves a performance rate of about 96%

Essential role of the EF-hand domain in targeting sperm phospholipase Cζ to membrane phosphatidylinositol 4,5-bisphosphate (PIP2)

Sperm-specific phospholipase C-ζ (PLCζ) is widely considered to be the physiological stimulus that triggers intracellular Ca2+ oscillations and egg activation during mammalian fertilization. Although PLCζ is structurally similar to PLCδ1, it lacks a pleckstrin homology domain, and it remains unclear how PLCζ targets its phosphatidylinositol 4,5-bisphosphate (PIP2) membrane substrate. Recently, the PLCδ1 EF-hand domain was shown to bind to anionic phospholipids through a number of cationic residues, suggesting a potential mechanism for how PLCs might interact with their target membranes. Those critical cationic EF-hand residues in PLCδ1 are notably conserved in PLCζ. We investigated the potential role of these conserved cationic residues in PLCζ by generating a series of mutants that sequentially neutralized three positively charged residues (Lys-49, Lys-53, and Arg-57) within the mouse PLCζ EF-hand domain. Microinjection of the PLCζ EF-hand mutants into mouse eggs enabled their Ca2+ oscillation inducing activities to be compared with wild-type PLCζ. Furthermore, the mutant proteins were purified, and the in vitro PIP2 hydrolysis and binding properties were monitored. Our analysis suggests that PLCζ binds significantly to PIP2, but not to phosphatidic acid or phosphatidylserine, and that sequential reduction of the net positive charge within the first EF-hand domain of PLCζ significantly alters in vivo Ca2+ oscillation inducing activity and in vitro interaction with PIP2 without affecting its Ca2+ sensitivity. Our findings are consistent with theoretical predictions provided by a mathematical model that links oocyte Ca2+ frequency and the binding ability of different PLCζ mutants to PIP2. Moreover, a PLCζ mutant with mutations in the cationic residues within the first EF-hand domain and the XY linker region dramatically reduces the binding of PLCζ to PIP2, leading to complete abolishment of its Ca2+ oscillation inducing activity

Antigen unmasking enhances visualization efficacy of the oocyte activation factor, phospholipase C zeta, in mammalian sperm

Study Question Is it possible to improve clinical visualization of phospholipase C zeta (PLCζ) as a diagnostic marker of sperm oocyte activation capacity and male fertility? Summary Answer Poor PLCζ visualization efficacy using current protocols may be due to steric or conformational occlusion of native PLCζ, hindering antibody access, and is significantly enhanced using antigen unmasking/retrieval (AUM) protocols. What is Known Already Mammalian oocyte activation is mediated via a series of intracellular calcium (Ca2+) oscillations induced by sperm-specific PLCζ. PLCζ represents not only a potential clinical therapeutic in cases of oocyte activation deficiency but also a diagnostic marker of sperm fertility. However, there are significant concerns surrounding PLCζ antibody specificity and detection protocols. Study Design, Size Duration Two PLCζ polyclonal antibodies, with confirmed PLCζ specificity, were employed in mouse, porcine and human sperm. Experiments evaluated PLCζ visualization efficacy, and whether AUM improved this. Antibodies against two sperm-specific proteins [post-acrosomal WW-binding protein (PAWP) and acrosin] were used as controls. Participants/Materials, Setting, Methods Aldehyde- and methanol-fixed sperm were subject to immunofluorescence analysis following HCl exposure (pH = 0.1–0.5), acid Tyrode's solution exposure (pH = 2.5) or heating in 10 mM sodium citrate solution (pH = 6.0). Fluorescence intensity of at least 300 cells was recorded for each treatment, with three independent repeats. Main Results and the Role of Chance Despite high specificity for native PLCζ following immunoblotting using epitope-specific polyclonal PLCζ antibodies in mouse, porcine and human sperm, immunofluorescent visualization efficacy was poor. In contrast, sperm markers PAWP and acrosin exhibited relatively impressive results. All methods of AUM on aldehyde-fixed sperm enhanced visualization efficacy for PLCζ compared to visualization efficacy before AUM (P < 0.05 for all AUM interventions), but exerted no significant change upon PAWP or acrosin immunofluorescence following AUM. All methods of AUM enhanced PLCζ visualization efficacy in mouse and human methanol-fixed sperm compared to without AUM (P < 0.05 for all AUM interventions), while no significant change was observed in methanol-fixed porcine sperm before and after. In the absence of aldehyde-induced cross-linkages, such results suggest that poor PLCζ visualization efficacy may be due to steric or conformational occlusion of native PLCζ, hindering antibody access. Importantly, examination of sperm from individual donors revealed that AUM differentially affects observable PLCζ fluorescence, and the proportion of sperm exhibiting detectable PLCζ fluorescence in sperm from different males. Limitations, Reasons for Caution Direct correlation of fertility outcomes with the level of PLCζ in the sperm samples studied was not available. Such analyses would be required in future to determine whether the improved methodology for PLCζ visualization we propose would indeed reflect fertility status. Wider Implications of the Findings We propose that AUM alters conformational interactions to enhance PLCζ epitope availability and visualization efficacy, supporting prospective application of AUM to reduce misinterpretation in clinical diagnosis of PLCζ-linked male infertility. Our current results suggest that it is perhaps prudent that previous studies investigating links between PLCζ and fertility parameters are re-examined in the context of AUM, and may pave the way for future work to answer significant questions such as how PLCζ appears to be kept in an inactive form in the sperm

Federated Learning for Maritime Environments: Use Cases, Experimental Results, and Open Issues

Maritime transportation is crucial for global trade and responsible for the majority of goods movement worldwide. The optimization of maritime operations is challenged by the complexity and heterogeneity of maritime nodes. This paper presents the emerging deployment of federated learning (FL) in maritime environments to address these challenges. FL enables decentralized machine learning model training, ensuring data privacy and security while overcoming issues associated with non-i.i.d. data. This paper explores various maritime use cases, including fuel consumption reduction, predictive maintenance, and just-in-time arrival. Experimental results using real datasets demonstrate the superiority of FL in predicting the fuel consumption of large cargo ships in terms of accuracy and spatiotemporal complexity over traditional collaborative machine learning approaches. The findings indicate that FL can significantly improve the performance of fuel consumption models in a collaborative way, while ensuring data privacy preservation and no data transmission during the learning process. Finally, this paper discusses open issues and future research directions necessary for the widespread adoption of FL in maritime transportation and settings

Bandit-Based Learning-Aided Full-Duplex/Half-Duplex Mode Selection in 6G Cooperative Relay Networks

Publisher Copyright: AuthorsThe high level of autonomy and intelligence that is envisioned in sixth generation (6G) networks necessitates the development of learning-aided solutions, especially in cases in which conventional Channel State Information (CSI)-based network processes introduce high signaling overheads. Moreover, in wireless topologies characterized by fast varying channels, timely and accurate CSI acquisition might not be possible and the transmitters (CSIT) only have statistical CSI available. This work focuses on the appropriate selection of relaying mode in a cooperative network, comprising a single information source, one buffer-aided (BA) relay with full-duplex (FD) capabilities, and a single destination. Here, prior to each transmission, the relay should select to operate either in FD mode with power control, or, resort to half-duplex (HD) relaying when excessive self-interference (SI) arises. Targeting the selection of the best relaying mode, we propose an FD/HD mode selection mechanism, namely multi-armed bandit-aided mode selection (), relying on reinforcement learning and the processing of acknowledgements/negative-acknowledgements (ACK/NACK) packets for acquiring useful information on channel statistics. As a result, does not require continuous CSI acquisition and exchange and nullifies the negative effect of outdated CSI. The proposed algorithm&#x2019;s average throughput performance is evaluated, highlighting a performance-complexity trade-off over alternative solutions, based on pilot-based channel estimation that result in spectral and energy costs while obtaining instantaneous CSI.Peer reviewe

Essential Role of the EF-hand Domain in Targeting Sperm Phospholipase Cζ to Membrane Phosphatidylinositol 4,5-Bisphosphate (PIP2)

Sperm-specific phospholipase C-ζ (PLCζ) is widely considered to be the physiological stimulus that triggers intracellular Ca2+ oscillations and egg activation during mammalian fertilization. Although PLCζ is structurally similar to PLCδ1, it lacks a pleckstrin homology domain, and it remains unclear how PLCζ targets its phosphatidylinositol 4,5-bisphosphate (PIP2) membrane substrate. Recently, the PLCδ1 EF-hand domain was shown to bind to anionic phospholipids through a number of cationic residues, suggesting a potential mechanism for how PLCs might interact with their target membranes. Those critical cationic EF-hand residues in PLCδ1 are notably conserved in PLCζ. We investigated the potential role of these conserved cationic residues in PLCζ by generating a series of mutants that sequentially neutralized three positively charged residues (Lys-49, Lys-53, and Arg-57) within the mouse PLCζ EF-hand domain. Microinjection of the PLCζ EF-hand mutants into mouse eggs enabled their Ca2+ oscillation inducing activities to be compared with wild-type PLCζ. Furthermore, the mutant proteins were purified, and the in vitro PIP2 hydrolysis and binding properties were monitored. Our analysis suggests that PLCζ binds significantly to PIP2, but not to phosphatidic acid or phosphatidylserine, and that sequential reduction of the net positive charge within the first EF-hand domain of PLCζ significantly alters in vivo Ca2+ oscillation inducing activity and in vitro interaction with PIP2 without affecting its Ca2+ sensitivity. Our findings are consistent with theoretical predictions provided by a mathematical model that links oocyte Ca2+ frequency and the binding ability of different PLCζ mutants to PIP2. Moreover, a PLCζ mutant with mutations in the cationic residues within the first EF-hand domain and the XY linker region dramatically reduces the binding of PLCζ to PIP2, leading to complete abolishment of its Ca2+ oscillation inducing activity