Search Results (18)

Bolstered by emerging sixth-generation (6G) communication technology, space–air–ground– sea integrated networks (SAGSINs) are reshaping edge computing through the synergistic use of space, aerial, terrestrial, and maritime platforms. However, in such highly dynamic and heterogeneous network environments, long-term energy-efficient computation offloading in drone-enabled SAGSINs has not yet been thoroughly explored, particularly when dynamic task demands from user equipment (UE) are served under the constraints of energy-limited drones. To fill this gap, the problem of service node association and computing-frequency allocation under dynamic computation offloading demands at the edge of the networks is studied in this paper. However, the related problem turns out to be a stochastic optimization problem. To this end, through in-depth mathematical analysis based on the Lyapunov optimization method, it is found that the multi-time-slot long-term optimization problem can be transformed into several single-time-slot optimization problems, which enables an efficient solution to the original problem. The single time-slot problem is solved using graph theory, convex optimization, and optimization theory, where Lyapunov optimization is utilized to achieve queue stability and energy efficiency. Simulation results demonstrate that the proposed strategy effectively reduces energy consumption, ensures low delay, and maintains long-term queue stability in drone-enabled SAGSINs under dynamic task demands from UE. Full article

Against the backdrop of intensifying global climate change and water eutrophication, the increasing occurrence of Harmful Algal Blooms (HABs) poses a significant threat to aquatic ecosystems, human health, and socio-economic activities. The occurrence and development of HABs are complex processes governed by the interaction of physical, chemical, and biological factors. Therefore, timely and accurate monitoring is essential for early warning and scientific research. This paper comprehensively reviews recent advances in HAB monitoring technologies, with a focus on two core components: sensors and monitoring platforms. First, organized around key environmental parameters, it summarizes the principles, applications, and limitations of in situ sensors, such as multi-parameter water quality sondes, Imaging Flow Cyto-bots (IFCB), and Environmental Sample Processors (ESP), as well as laboratory-based analytical techniques such as HPLC-MS for measuring physical, chemical, and biological indicators. Second, it compares the technical characteristics of three major monitoring platforms (including field surveys, remote sensing, and autonomous systems) and discusses their potential for synergistic application. Finally, this review proposes a future framework for an integrated “Space–Air–Ground–Sea” intelligent monitoring network and explores possible pathways to address current challenges through cross-platform data fusion, sensor miniaturization, intelligentization, and artificial intelligence-driven decision support. This review aims to provide a comprehensive reference for the optimization and innovation of HAB monitoring technologies and to promote the development of the field toward greater integration, intelligence, and real-time monitoring capability. Full article

To support intelligent maritime applications, space–air–ground–sea integrated networks (SAGSINs) have been introduced in maritime communications to provide wide coverage and reliable network services. In unmanned aerial vehicle (UAV)-assisted SAGSIN architectures, UAVs can flexibly extend coverage and provide on-demand communication and computing support. However, due to the high mobility of low Earth orbit (LEO) satellites and the limited endurance of UAVs, single-platform deployment strategies struggle to provide both flexibility and scalability in maritime communication networks. To mitigate the service instability caused by satellite orbital dynamics and limited UAV endurance, we propose a Hybrid Multi-Agent Proximal Policy Optimization (H-MAPPO)-based joint satellite–UAV deployment scheme for UAV-assisted SAGSIN systems. The proposed method optimizes joint UAV positioning and resource allocation to enhance communication coverage while reducing overall operational cost. By incorporating satellite orbital dynamics and UAV mobility into a multi-agent reinforcement learning (MARL) framework, adaptive resource scheduling can be achieved under time-varying maritime demands. Simulation results show that the proposed H-MAPPO algorithm achieves superior convergence performance, higher user coverage, and lower total system cost compared with learning-based, random, and heuristic methods while maintaining stable and robust performance under varying user densities and network scales. Full article

With the deployment and application of the Fifth-Generation (5G) mobile communication technologies and the ongoing research and development of the Sixth-Generation (6G) mobile communication technologies, the space–air–ground–sea integrated network has become the core development vision for future communications. As aerial nodes, Unmanned Aerial Vehicles (UAVs) can be applied in a wide range of scenarios, including emergency rescue, surveying and mapping, environmental monitoring, and communication coverage enhancement. In terms of communication coverage enhancement, the space–air–ground integrated network, with UAVs as a key component, can provide seamless communication coverage for the full-domain three-dimensional space such as remote areas, deserts, and oceans. Benefiting from advantages such as low cost and high flexibility, UAVs have become a critical research focus, and the one-hop Base Station (BS)–relay UAV–slave UAV architecture for communication coverage enhancement has emerged as an important development direction. However, the high mobility and wide coverage characteristics of UAVs also pose significant synchronization challenges. Aiming at the uplink synchronization problem on the sidelink between slave UAVs and the relay UAV, a two-step random-access scheme based on Asynchronous Non-Orthogonal Multiple Access (A-NOMA) is designed to mitigate the Doppler Frequency Offset (DFO), improve access efficiency, reduce resource consumption, and accommodate the asynchrony among different users. This scheme leverages the existing preamble sequences of the Physical Random Access Channel (PRACH) and realizes DFO estimation in combination with the pairing index. On this basis, a Successive Interference Cancellation (SIC) algorithm based on DFO and phase compensation is designed to complete the demodulation of user data. For the downlink synchronization problem on the sidelink between slave UAVs and the relay UAV, the frequency offset estimation performance is improved by redesigning the resource allocation scheme of the Sidelink Synchronization Signal Block (S-SSB). Meanwhile, considering the energy constraint of UAVs, a downsampling-based detection scheme is designed to reduce UAV power consumption, and a full-link algorithm is developed to support the practical implementation of the proposed scheme. Full article

Federated learning enables privacy-preserving distributed intelligence but faces challenges in balancing computation, communication, and privacy in heterogeneous networks. To address these issues, this paper proposes a privacy-preserving U-shaped split federated learning (USFL) framework for space–air–ground–sea integrated networks. The proposed architecture combines split learning and federated learning in a U-shaped structure, ensuring that both raw data and labels remain localized at client devices. In addition, a differential privacy mechanism is introduced to perturb intermediate features during transmission, enhancing resistance to inference attacks. A mathematical framework is established to model the learning process under resource constraints, and the convergence behavior and privacy loss are theoretically analyzed. Experimental results on the SeaShips dataset demonstrate that the proposed method achieves competitive accuracy compared with centralized and existing distributed approaches, while reducing communication overhead and improving privacy protection. These results validate the effectiveness of the proposed framework for secure and efficient distributed learning in complex network environments. Full article

Unmanned Aerial Vehicle (UAV)-assisted mobile edge computing is pivotal for the Space–Air–Ground–Sea Integrated Network (SAGSIN) to support heterogeneous task offloading. However, the inherent resource constraints of UAVs limit their ability to support intensive and concurrent task processing in dynamic environments. In such complex scenarios, the dual requirements of discrete model partitioning and continuous bandwidth allocation make it difficult for traditional reinforcement learning algorithms to achieve optimal resource matching. Therefore, in this paper, we design a joint optimization framework based on Asynchronous Advantage Actor-Critic (A3C) and proximal policy optimization (PPO). Specifically, the model partitioning strategy is learned through PPO, which utilizes a clipped objective function to ensure training stability and generalization across complex Deep Neural Network (DNN) structures. Moreover, the framework leverages the asynchronous multi-threaded architecture of A3C to dynamically allocate bandwidth, effectively accommodating rapid fluctuations in terminal access. Finally, to prevent resource monopolization and ensure fairness, a weighted priority scheduling mechanism based on task urgency and computation time is introduced. Extensive simulations show that the proposed algorithm outperforms existing approaches in terms of task completion rate, task processing latency, and resource utilization under dynamic SAGSIN scenarios. Full article

Space–Air–Ground–Sea Integrated Networks (SAGSINs) are emerging as a key enabling architecture for broadband maritime connectivity, where heterogeneous access tiers (shore, aerial, and satellite) jointly support delay-sensitive and mission-critical uplink traffic such as alarms, telemetry, and surveillance video. However, uplink resource scheduling in maritime SAGSINs remains challenging due to time-varying channels, locally bursty traffic, and intense contention, while centralized optimization is ill-suited, as global information collection is often delayed, incomplete, and inconsistent over long-haul maritime links. Therefore, this paper investigates distributed uplink scheduling in maritime SAGSINs, where maritime nodes jointly select the access tier, spectrum slice, and transmit power under interference, spectrum, deadline, and energy constraints. Specifically, we formulate the uplink resource scheduling as a cumulative value of information (VoI) maximization problem, and develop a game-theoretic distributed multi-agent reinforcement learning algorithm, termed GTMARL. Therein, maritime nodes learn transmission policies from local observations, coordinated through congestion prices broadcast by access nodes. These prices are derived from Lagrangian relaxation and act as coordination signals that align individual decisions with global objectives. To ensure stable operation, a two-timescale mechanism is adopted, where maritime nodes make fast slot-level transmission decisions, while access nodes adapt and broadcast congestion prices on a slower timescale. Extensive experiments demonstrate that GTMARL achieves up to 90% of the idealized upper bound, significantly outperforming baselines in deadline satisfaction, throughput, delay, energy efficiency and fairness under varying traffic loads and network densities. Full article

The rapid evolution of wireless communications has introduced new possibilities for the digital transformation of maritime operations. As 5G begins to take shape in selected nearshore and port environments, the forthcoming 6G promises to unlock transformative capabilities across the entire maritime domain, integrating Terrestrial/Non-Terrestrial Networks (TN/NTN) to form a space-air-ground-sea-underwater system. This paper presents a comprehensive review of how 6G-enabling technologies can be adapted to address the unique challenges of Maritime Communication Networks (MCNs). We begin by outlining a reference architecture for heterogeneous MCNs and reviewing the limitations of existing 5G deployments at sea. We then explore the key technical advancements introduced by 6G and map them to maritime use cases such as fleet coordination, just-in-time port logistics, and low-latency emergency response. Furthermore, the critical Artificial Intelligence/Machine Learning (AI/ML) concepts and algorithms are described to highlight their potential in optimizing maritime functionalities. Finally, we propose a set of resource optimization scenarios, including dynamic spectrum allocation, energy-efficient communications and edge offloading in MCNs, and discuss how AI/ML and learning-based methods can offer scalable, adaptive solutions. By bridging the gap between emerging 6G capabilities and practical maritime requirements, this paper highlights the role of intelligent, resilient, and globally connected networks in shaping the future of maritime communications. Full article

As sixth-generation mobile communication (6G) advances towards ultra-high speed and global coverage, visible light communication (VLC) has emerged as a crucial complementary technology due to its ultra-high bandwidth, low power consumption, and immunity to electromagnetic interference. Modulation format recognition (MFR) plays a vital role in the dynamic optimization and adaptive transmission of VLC systems, significantly influencing communication performance in complex channel environments. This paper systematically reviews the research progress in MFR for VLC, comparing the theoretical frameworks and limitations of traditional likelihood-based (LB) and feature-based (FB) methods. It also explores the advancements brought by deep learning (DL) technology, particularly in enhancing noise robustness, classification accuracy, and cross-scenario adaptability through automatic feature extraction and nonlinear mapping. The findings indicate that DL-based MFR substantially enhances recognition performance in intricate channels via multi-dimensional feature fusion, lightweight architectures, and meta-learning paradigms. Nonetheless, challenges remain, including high model complexity and a strong reliance on labeled data. Future research should prioritize multi-domain feature fusion, interdisciplinary collaboration, and hardware–algorithm co-optimization to develop lightweight, high-precision, and real-time MFR technologies that align with the 6G vision of space–air–ground–sea integrated networks. Full article

The Yellow River, China’s second-largest river, is renowned for its high sediment content. In response to the potential impacts of climate change on Yellow River water resources and water environmental management, an advanced monitoring and forecasting system for water and sediment throughout the entire Yellow River basin—from its source to the sea—is urgently needed. In this paper, based on the current status of water and sediment monitoring technologies, we proposed an integrated remote sensing monitoring network that combines satellite remote sensing, drone remote sensing, and ground-based wireless automatic monitoring networks, aiming to achieve the digital monitoring of water and sediment across the entire Yellow River basin, from its upper reaches to its estuary in the Bohai Sea. By utilizing ground-based in situ hyperspectral stations for sediment source areas in the upper reaches, such as the Qingshui River basin in Ningxia, and satellite remote sensing for midstream processes in the Xiaolangdi reservoir before the flood season in 2023, as well as downstream monitoring at the Yellow River estuary, this paper demonstrates the novelty and efficiency of the space–air–ground integrated remote sensing monitoring technology. Full article

A space–air–ground–sea integrated network (SAGSIN) is a promising heterogeneous network framework for the next generation mobile communications. Moreover, federated learning (FL), as a widely used distributed intelligence approach, can improve advanced network performance. In view of the combination and cooperation of SAGSINs and FL, an FL-based SAGSIN framework faces a number of unprecedented challenges, not only from the communication aspect but also on the security and privacy side. Motivated by these observations, in this article, we first give a detailed state-of-the-art review of recent progress and ongoing research works on FL-based SAGSINs. Then, the challenges of FL-based SAGSINs are discussed. After that, for different service demands, basic applications are introduced with their benefits and functions. In addition, two case studies are proposed, in order to improve SAGSINs’ communication efficiency under a significant communication latency difference and to protect user-level privacy for SAGSIN participants, respectively. Simulation results show the effectiveness of the proposed algorithms. Moreover, future trends of FL-based SAGSINs are discussed. Full article

Mobile crowdsensing (MCS), which is a grassroots sensing paradigm that utilizes the idea of crowdsourcing, has attracted the attention of academics. More and more researchers have devoted themselves to adopting MCS in space–air–ground–sea integrated networks (SAGSINs). Given the dynamics of the environmental conditions in SAGSINs and the uncertainty of the sensing capabilities of mobile people, the quality and coverage of the sensed data change periodically. To address this issue, we propose a novel UAV-assisted cluster-based task allocation (UCTA) algorithm for MCS in SAGSINs in a two-stage process. We first introduce the edge nodes and establish a three-layer hierarchical system with UAV-assistance, called “Platform–Edge Cluster–Participants”. Moreover, an edge-aided attribute-based cluster algorithm is designed, aiming at organizing tasks into clusters, which significantly diminishes both the communication overhead and computational complexity while enhancing the efficiency of task allocation. Subsequently, a greedy selection algorithm is proposed to select the final combination that performs the sensing task in each cluster. Extensive simulations are conducted comparing the developed algorithm with the other three benchmark algorithms, and the experimental results unequivocally endorse the superiority of our proposed UCTA algorithm. Full article

Regarding the new demands and challenges of sixth-generation (6G) mobile communications, wireless networks are undergoing a significant shift from traditional terrestrial networks to space-air-ground-sea-integrated networks. Unmanned aerial vehicle (UAV) communications in complicated mountainous scenarios are typical applications and have practical implications, especially in emergency communications. In this paper, the ray-tracing (RT) method was applied to reconstruct the propagation scenario and then acquire the wireless channel data. Channel measurements are also conducted in real mountainous scenarios for verification. By setting different flight positions, trajectories, and altitudes, channel data in the millimeter wave (mmWave) band was obtained. Important statistical properties, such as the power delay profile (PDP), Rician K-factor, path loss (PL), root mean square (RMS) delay spread (DS), RMS angular spreads (ASs), and channel capacity were compared and analyzed. The effects of different frequency bands on channel characteristics at 3.5 GHz, 4.9 GHz, 28 GHz, and 38 GHz bands in mountainous scenarios were considered. Furthermore, the effects of extreme weather, especially different precipitation, on the channel characteristics were analyzed. The related results can provide fundamental support for the design and performance evaluation of future 6G UAV-assisted sensor networks in complicated mountainous scenarios. Full article

In recent years, major natural disasters and public safety accidents have frequently occurred worldwide. In order to deal with various disasters and accidents using rapidly deployable, reliable, efficient, and stable emergency communication networks, all countries in the world are strengthening and improving emergency communication network construction and related technology research. Motivated by these situations, in this paper, we provide a state-of-the-art survey of the current situation and development of emergency communication networks. In this detailed investigation, our primary focus is the extensive discussion of emergency communication network technology, including satellite networks, ad hoc networks, cellular networks, and wireless private networks. Then, we explore and analyze the networks currently applied in emergency rescue, such as the 370M narrowband private network, broadband cluster network, and 5G constellation plan. We propose a broadband-narrowband integrated emergency communication network to provide an effective solution for visual dispatch of emergency rescue services. The main findings derived from the comprehensive survey on the emergency communication network are then summarized, and possible research challenges are noted. Lastly, we complete this survey by shedding new light on future directions for the emergency communication network. In the future, the emergency network will develop in the direction of intelligence, integration, popularization, and lower cost, and space-air-ground-sea integrated networks. This survey provides a reference basis for the construction of networks to mitigate major natural disasters and public safety accidents. Full article