In this paper, we explore the fine-grained channel state information (CSI) obtained through the sensing function in an reconfigurable intelligent surface (RIS)-assisted integrated sensing and communication (ISAC) system to support the efficient covert communications in the system. We first construct a new fine-grained CSI model for the RIS-assisted ISAC system and propose a novel covert communication scheme based on the new CSI model. We then develop theoretical models for the detection error probability, Cram & eacute;r-Rao Bound and covert rate to depict the covertness, sensing and covert communication performances under the proposed scheme. Based on these theoretical models, we further formulate an optimization problem for covert rate maximization through optimizing the reflection coefficient in RIS and the transmit powers for covert/probing signals. With the help of the homogenization for quadratic constrained quadratic programming, semi-definite relaxation and Dinkelbach transform, an efficient alternating optimization (AO) algorithm is devised to tackle this complex optimization problem. Finally, extensive numerical results are presented to demonstrate the performance enhancement for covert communication in the RIS-assisted ISAC system from exploring the fine-grained CSI and AO-based parameter optimization therein.

Unmanned aerial vehicle (UAV) swarm-assisted integrated sensing and communication (ISAC) networks are a crucial technology for providing communication and sensing services in emergency rescue scenarios without base station support. However, the strong coupling between communication and sensing resources in such networks fundamentally limits the communication and sensing performance of ISAC systems. This paper jointly optimizes spectrum allocation, UAV association and deployment to maximize average system throughput while ensuring localization accuracy in such networks, where sensing is realized through localization. We begin by deriving an analytical expression for localization accuracy, which explicitly captures the joint effects of link quality and anchor geometry under shared communication-localization spectrum resources. We then formulate average system throughput maximization as a mixed-integer nonlinear and non-convex optimization problem with the constraints of localization accuracy, sub-channels, UAV association, UAV deployment and signal-to-interference-plus-noise ratio. We further develop an alternating iterative optimization method to solve this complex optimization problem. Within this method, a particle swarm optimization-based method is developed to jointly optimize spectrum allocation and UAV association, and a dueling double deep Q-network-based method is further employed for UAV deployment optimization. Finally, extensive simulation results are presented to validate the efficiency of our optimization method, and also to illustrate how key parameters influence average system throughput and localization accuracy.

The Airborne Maneuvering Network (AMN) is a novel network architecture that enables flexible deployment across wide areas and provides real-time cross-domain transmission services. Under concurrent and diversified service demands, AMN operating in isolation faces significant challenges in guaranteeing end-to-end transmission reliability. This has prompted the deep integration of AMN with terrestrial and satellite networks to form heterogeneous networks, which has become a crucial trend in improving the continuity and reliability of AMN services. However, network heterogeneity, dynamic resource distribution, and the absence of a unified reliable transmission mechanism impose severe challenges on multi-domain cooperative scheduling and differentiated-service adaptation. This paper serves as a reference for global scholars engaged in thorough research on heterogeneous integrated AMN. It outlines the fundamental characteristics of AMN, reviews recent advances and challenges in communication-sensing-computation coordination, unified control adaptation, and service reliability assurance, and discusses design concepts and future evolution paths for heterogeneous integrated AMN architectures.

The behavior of one-dimensional Hegselmann-Krause (HK) dynamics driven by noise has been extensively studied. Previous research has indicated that within no matter the bounded or the unbounded space of one dimension, the HK dynamics attain quasi-synchronization (synchronization in noisy case) in finite time. However, it remains unclear whether this phenomenon holds in high-dimensional space. This paper investigates the random time for quasi-synchronization of multi-dimensional HK model and reveals that the boundedness and dimensions of the space determine different outcomes. To be specific, if the space is bounded, quasi-synchronization can be attained almost surely for all dimensions within a finite time, whereas in unbounded space, quasi-synchronization can only be achieved in low-dimensional cases (one and two). Furthermore, different integrability of the random time of various cases is proved.

In this paper, we investigate the covertness and secrecy of wireless communications under an active attacker scenario, where attackers perform simultaneous detection/eavesdropping and jamming. To guarantee the covertness and secrecy of transmissions, both detection and eavesdropping attacks are mitigated using physical layer security techniques. To depict the covertness and secrecy performance of the system, we model the covertness outage probability and the secrecy outage probability, and then analytically characterize the optimal covert secrecy rate (CSR) under an artificial noise (AN)-based transmission scheme. Extensive numerical results demonstrate the achievable CSR performance and reveal the impact of active attacks on system security in the context of the AN-based scheme.

For the computation offloading via device-to-device (D2D) terminals and edge servers in a resource-constrained wireless network (RCWN), mobile users can choose to offload their tasks to nearby D2D terminals or edge servers according to quality of service (QoS) requirements (e.g., load balancing at the network edge) by mobile edge computing. To this end, we first formulate computation offloading as a multi-user collaborative resource dynamic management optimization problem that aims to maximize user satisfaction utility function, carefully considering critical issues like the non-uniform distribution of computational resources, user's risk awareness, and the dynamic changes between computing-intensive regions and computing-sparse regions. This is a nonlinear and nonconvex optimization problem, which is generally difficult to be solved. We then construct a resource management scheme for resource allocation of the edge server based on convex optimization. Furthermore, we propose a dynamic offloading update strategy achieving the maximum of user satisfaction utility function based on game theory. The simulation results are presented to show that our proposed method can increase the total system satisfaction utility by nearly 20% and reduce the system energy consumption by nearly 10% compared to the benchmark methods.

Space-Terrestrial Integrated Networks (STINs) serve as the crucial infrastructure for future 6G Networks, while network failures in STINs pose serious threats to the services provided by such networks. This article focuses on the service request recovery in STINs against a satellite failure. For the uplink requests, downlink requests, and relay requests disrupted by a satellite failure, we explore both independent recovery and joint recovery. In independent recovery where each type of requests is recovered independently and sequentially, an Integer Linear Programming (ILP) model and related heuristic are proposed to identify the optimal recovery solution for each type of requests. We further explore the joint recovery where all requests are recovered jointly and simultaneously to achieve a high recovery efficiency. The ILP formulation and time-efficient heuristic are developed as well for the joint recovery. Finally, extensive numerical results are provided to demonstrate the effectiveness of the joint recovery and proposed heuristics in service recovery under a satellite failure.

Available control plane designs for Software Defined LEO Satellite Networks (SDSN) suffer from the problems of high network update latency, large flow setup time, and low network throughput. This paper proposes a new collaborative control plane architecture for SDSN (CC-SDSN), which consists of a Network Control Center (NCC) and multiple Ground Relay Stations (GRSs) in the terrestrial networks, as well as multiple Satellite Group Controllers (SGCs) in the LEO satellite networks. Each SGC maintains the local view of a group of LEO satellites and supports the quick intra-group flow setup. The NCC takes advantage of the powerful computation resources in the terres trial nodes to maintain a global view of the whole LEO satellite network and is responsible for the rapid inter-group flow setup, while the GRSs are used to ensure the real-time communication between the NCC and SGCs and thus to reduce the network update latency. The designs of the protocol for handling data f low and synchronization operations in CC-SDSN, and the pre caching scheme for inter-group flow tables are addressed as well. Finally, extensive experiments based on Satellite Tool Kit (STK) and Mininet are conducted to demonstrate the efficiency of CC SDSN in terms of network update latency, flow setup time, and network throughput.

The integrated sensing and communication system based on the reconfigurable intelligent surface (RIS) technology have found many crucial applications, while the covert communication serves as an attractive approach to implement secure transmission in an RIS-based integrated sensing and communication system. By exploring the RIS functionality, sensing-assisted signal hiding, and two-hop transmission mechanism, this paper proposes a novel covert communication strategy for the considered system. Specifically, we first develop theoretical models to depict covertness, sensing, and transmission performances in terms of detection error probability (DEP), Signal-to-Noise-plus-Interference Ratio (SINR) of echo signals, and transmission outage probability (TOP), respectively. We then formulate an optimization problem to determine the optimal settings of target rate and transmit powers of probing/covert signals for covert throughput maximization, subject to the constraints of DEP, SINR, TOP, and upper bound on transmit powers. By applying the alternating optimization technique to solve this optimization problem, we further derive the analytical formula for the maximum covert throughput. Finally, extensive numerical results illustrate the overall system performance of an RIS-based integrated sensing and communication system as well as demonstrate the effectiveness of the proposed covert communication strategy.

Unmanned aerial vehicle (UAV) swarm networks (USNTs) are a crucial component of the emerging low-altitude intelligent networks. For supporting low-altitude economic activities, this paper explores successful task transmission probability (STP) maximization, while maintaining the performance fairness of each UAV swarm for USNTs with resource limitations (e.g., frequency, power). Towards this goal, this paper formulates STP maximization and its fairness as a nonlinear non-convex optimization problem. To solve the complex optimization problem, we first propose an adaptive frequency block sharing algorithm to determine whether different-sized UAV swarms use the same frequency blocks or not, providing an efficient initial solution for inter-swarm resource allocation. Then we develop a double deep Q-network-based algorithm for inter-swarm resource allocation to guarantee fairness among swarms. Based on the inter-swarm allocation results, we propose a genetic algorithm-based method for intra-swarm resource allocation to achieve the STP maximization, which ensures reliable transmission of high-priority tasks. Extensive simulation results are presented to validate the efficiency of our proposed algorithms, and also to illustrate the impact of system parameters on STP and fairness.

As network scale and complexity escalate, fine-grained monitoring has become critical for operation, adminis tration, and maintenance. In-band network telemetry (INT) offers a leading solution for end-to-end visibility by embedding device states directly into packets. However, its widespread deployment faces a fundamental chal lenge, the inherent conflict between comprehensive visibility and limited network resources. To address this, the orchestration of INT is crucial. In this paper, we define in-band network telemetry orchestration (INTO) as a control mechanism that performs the unified modeling, planning, and scheduling of telemetry tasks to determine their triggering modes, coverage, and execution paths under resource constraints. Building on this definition, we provide a comprehensive and structured survey of INTO. With the introduction of a system-level constraint ab straction and a structured taxonomy, we present a thorough analysis of the current INTO research. Specifically, we analyze active INTO from both task-driven and method-based perspectives, and examine passive INTO fo cusing on flow selection and data reduction mechanisms. Furthermore, we conceptualize hybrid INTO as a joint optimization problem that coordinates multi-modal telemetry. Finally, we outline open challenges and future directions to guide the evolution of network telemetry.

Congestion control (CC) is a cornerstone of reliable data transport, but current fixed-rule algorithms struggle with the diverse link characteristics of Internet of Things (IoT) deployments. A more critical challenge is that many resource-constrained IoT devices lack the computational power to run advanced, data-driven methods locally, hindering performance and adaptability. To address this, we introduce KONTROL, a service framework that decouples CC intelligence from end devices by offloading the decision-making logic to a centralized CC server. This enables lightweight clients to leverage sophisticated control strategies without bearing the computational burden. As a critical instance within our framework, we implement deep reinforcement learning (DRL) agents on the server, which learn to optimize window adjustments for each sender based on real-time relayed network statistics via kernel modifications. Our experimental evaluation across challenging simulated environments shows that the proposed scheme achieves consistently high throughput and low latency while maintaining fairness. These results validate the viability of the offloading paradigm, paving the way for more flexible transport protocols for the broader IoT ecosystem.

Efficient harvesting of any directional wave energy by the omnidirectional, anti-overturning and high-output floating water-wave energy harvesting device is the cornerstone of developing passive wireless Marine Internet-of-Things (MIoT) buoy. However, the previous researches on water wave energy collection cannot simultaneously meet the above requirements. Here, we reported a triboelectric-electromagnetic hybrid nanogenerator (OHNG), which can efficiently harvest any directional water-wave energy by the omnidirectional working mode. The performance of OHNG for capturing water-wave power was greatly improved through applying the large-mass coil and magnet of electromagnetic generator (EMG) to fabricate the tumbler and simple pendulum structure, which also endow the excellent anti-overturning performance of OHNG. Furthermore, the OHNG can obtain the floating function without the other auxiliary devices by combining with the light-weight triboelectric nanogenerator (TENG) and the output performance of OHNG was improved by optimizing the structure design of TENG and EMG. Finally, based on the OHNG, a passive wireless MIoT buoy with real-time sensing signal transmission was achieved by combining energy storage device, voltage regulator circuit, sensing and wireless signal transmission modules. Our findings provide important technical support for the efficient development of water-wave energy and the development of smart MIoT buoys.

Ulnar nerve injuries often lead to muscle atrophy and reduced hand function, necessitating precise monitoring and effective rehabilitation strategies. Current grip strength measurement tools rely on rigid mechanical equipment, which is inconvenient and requires frequent calibration. To address this, a muscle atrophy evaluation and rehabilitation system (MUERS) is presented, featuring a highly sensitive rare earth oxide-enhanced triboelectric sensor (RETS). Utilizing the unique electrochemical properties of rare earth oxides, RETS demonstrates a linear voltage-force response in the range of 8-80 kPa, with a maximum linear error of 1.5%. Integrated with a multi-channel STM32 signal collector, RETS enables real-time grip strength monitoring across all five fingers. Combining sensor output with an SVM algorithm, the system achieves 98.61% accuracy in identifying finger grip strength injuries and classifies damage into three levels with an average accuracy of 96.67%. MUERS evaluates rehabilitation progress by scoring grip strength and providing feedback to clinicians. Over a four-week cycle, it consistently captures improvements in muscle recovery, aiding individualized rehabilitation plans. This system offers fine-grained assessment capabilities for diagnosing and monitoring nerve injury-induced muscle atrophy, paving the way for advanced biomedical sensing and personalized rehabilitation.

The massive number of edge-connected IoT devices currently in SD-AIoT can be weaponized to launch the Link Flooding Attack, a novel Distributed Denial of Service attack. Hence, it is critical to defend against LFAs early. Nevertheless, against such attacks, characterized by high stealth and periodicity, existing defense schemes overly focus on passive detection, neglecting that prevention is the most crucial method to mitigate their impact on the network. Additionally, prevention mechanisms face significant challenges due to the low accuracy of forecast models and the inefficiency of mitigation approaches. To this end, we propose a dynamically driven proactive defense framework based on modeling partially observable Markov decision processes that continuously learn the dynamic behavior of attacks within SD-AloT scenarios. A robust spatiotemporal graph convolutional network traffic attack forecasting model based on honey badger optimized feature selection algorithm is also proposed to capture the composite spatial dependence and nonlinear temporal dependence of periodic data inputs, effectively protect the network element nodes from unexpected spatial noise, and provide the defense framework with a highly accurate responsiveness capable of coping with the ever-changing network environment. The forecasting results and distributed sampling information empower the framework to evolve the optimal choice of multiple defense strategies to achieve early distributed defense against LFAs, thus significantly reducing the loss of benign traffic. A real physical testbed is constructed to evaluate the framework multidimensionally, and model performance experiments are conducted on five public datasets. The results show that the framework is highly effective in defending against LFAs.

This paper investigates the joint relay and transmission mode selection for covert communication in a wireless relay system with amplify-and-forward (AF) forwarding mode, which consists of one source, multiple AF relays, one destination, one friendly jammer and one warden, and each relay can switch between the half-duplex (HD) and full-duplex (FD) transmission modes. We first explore the fundamental covert performance of the system when it works in either the fixed HD or fixed FD mode. Based on this result, we then investigate the covert performance of the system with optimal (resp. random) relay selection and random (resp. optimal) mode selection, so as to reveal the achievable covert performance in the system with solely the relay selection or mode selection. Building upon above results, we further design the optimal joint relay and mode selection scheme, and develop related theoretical models for performance analysis. Finally, we provide extensive numerical results to conduct a comprehensive comparison between the relay selection and mode selection on their achievable covert performance and to illustrate the performance enhancement from adopting the joint relay and mode selection in the relay system.

Wireless edge networks (WENTs) can provide edge services to support various time-critical Internet of Things (IoT) applications, like autonomous vehicles, where cache content updates are significant to maintaining information freshness quantified as Information of Age (AoI). However, frequent content updates result in high energy consumption at the edge nodes. This article investigates the cache content updates in WENTs, aiming to ensure information freshness and low energy consumption. To this end, we propose a rainbow deep reinforcement learning-based cache content update scheme (RB-DRN). In the RB-DRN scheme, we first establish a Markov decision process (MDP) to characterize the process of cache update. By fully taking advantage of R-Learning empowered Rainbow DQN, we then make optimal strategy to obtain the minimum long-term average overhead associated with energy consumption and information freshness. Extensive simulation results are presented to validate our proposed RB-DRN scheme and also to illustrate that our RB-DRN scheme outperforms the benchmark scheme in terms of information freshness and energy consumption.

The large-scale use of ample marine energy will be one of the most important ways for human to achieve sustainable development through carbon neutral development plans. As a burgeoning technological method for electromechanical conversion, triboelectric nanogenerator (TENG) has significant advantages in marine energy for its low weight, cost-effectiveness, and high efficiency in low-frequency range. It can realize the efficient and economical harvesting of low-frequency blue energy by constructing the floating marine energy harvesting TENG. This paper firstly introduces the power transfer process and structural composition of TENG for marine energy harvesting in detail. In addition, the latest research works of TENG on marine energy harvesting in basic research and structural design are systematically reviewed by category. Finally, the advanced research progress in the power take-off types and engineering study of TENG with the marine energy are comprehensively generalized. Importantly, the challenges and problems faced by TENG in marine energy and in situ electrochemical application are summarized and the corresponding prospects and suggestions are proposed for the subsequent development direction and prospects to look forward to promoting the commercialization process of this field.

Owing to their capability of converting low-frequency mechanical motion into electrical energy, triboelectric nanogenerators (TENGs) have become an attractive solution for powering next-generation wearable electronics. Nevertheless, the relatively limited output performance of wearable TENGs still constrains their application in a practical self-powered scene. Herein, we develop a high-performance square-structured TENG (SS-TENG) based on a nylon/chitosan oligosaccharide (COS) composite electrospinning mat (NCEM). Benefiting from the strong polarity of COS, the NCEM exhibits enhanced positive triboelectric properties, leading to a significant boost of about 10-fold higher output compared to pure nylon. The optimized NCEM achieves a maximum improvement to TENG's power density (4.6 W/m2), surpassing most reported biobased triboelectric materials. Furthermore, the SS-TENG demonstrates excellent omnidirectional biomechanical energy-harvesting capability, effectively capturing mechanical inputs from various directions during human motion. It also demonstrated excellent omnidirectional energy-harvesting capability in swing tests ranging from 15° to 45° and from 0.67 to 2 Hz while also featuring good durability and output stability. When suspended on a backpack, the device robustly converts complex, real-world biomechanical motions into usable electrical power to drive small wearable electronics. This work offers an efficient and environmentally sustainable strategy for improving triboelectric performance and facilitating innovation in a next-generation wearable system.

Vehicle-to-everything (V2X) network services are facing new challenges and opportunities in the context of the emerging 6th generation communication networks (6G). In this paper, we first analyze the application requirements in V2X networks under the 6G vision and propose the Vehicle-Road-Cloud intelligent collaboration platform based on our observation. We then optimized the Multi-Access Edge Computing (MEC) reference architecture established by the European Telecommunications Standards Institute (ETSI). Our goal is to empower it with more extensibility to enable intelligent collaboration within vehicle-road-cloud systems, while adhering to the fundamental principles of the V2X architecture. The optimized architecture fully considers user service requirements, vehicle-road cloud collaboration mechanism, infrastructure deployment of V2X, network access methods and edge intelligent computing, which can better support the smart driving service of V2X. After that, we analyze in detail the workflows of typical services such as content caching, computation offloading and service migration in V2X scenarios, revealing their critical role in improving quality of experience (QoE) and quality of service (QoS). Finally, we summarize the advantages and prospects of the application of the MEC-based resource management architecture.