Search Results (181)

Terahertz (THz) communications and simultaneous wireless information and power transfer (SWIPT) hold the potential to energize battery-less Internet-of-Things (IoT) devices while enabling multi-gigabit data transmission. However, severe path loss, blockages, and rectifier nonlinearity significantly hinder both throughput and harvested energy. Additionally, high-power THz beams pose safety concerns by potentially exceeding specific absorption rate (SAR) limits. We propose a sensing-adaptive power-focusing (APF) framework in which a reconfigurable intelligent surface (RIS) embeds low-rate THz sensors. Real-time backscatter measurements construct a spatial map used for the joint optimisation of (i) RIS phase configurations, (ii) multi-tone SWIPT waveforms, and (iii) nonlinear power-splitting ratios. A weighted MMSE inner loop maximizes the data rate, while an outer alternating optimisation applies semidefinite relaxation to enforce passive-element constraints and SAR compliance. Full-stack simulations at 0.3 THz with 20 GHz bandwidth and up to 256 RIS elements show that APF (i) improves the rate–energy Pareto frontier by 30–75% over recent adaptive baselines; (ii) achieves a 150% gain in harvested energy and a 440 Mbps peak per-user rate; (iii) reduces energy-efficiency variance by half while maintaining a Jain fairness index of 0.999;; and (iv) caps SAR at 1.6 W/kg, which is 20% below the IEEE C95.1 safety threshold. The algorithm converges in seven iterations and executes within <3 ms on a Cortex-A78 processor, ensuring compliance with real-time 6G control budgets. The proposed architecture supports sustainable THz-powered networks for smart factories, digital-twin logistics, wire-free extended reality (XR), and low-maintenance structural health monitors, combining high-capacity communication, safe wireless power transfer, and carbon-aware operation for future 6G cyber–physical systems. Full article

Energy storage plays an essential role in stabilizing fluctuations in renewable energy sources such as wind and solar, enabling surplus electricity retention, and delivering dynamic frequency regulation. However, relying solely on a single form of storage often proves insufficient due to constraints in performance, capacity, and cost-effectiveness. To tackle frequency regulation challenges in remote desert-based renewable energy hubs—where traditional power infrastructure is unavailable—this study introduces a planning framework for an electro-hydrogen energy storage system with grid-forming capabilities, designed to supply both inertia and frequency response. At the system design stage, a direct current (DC) transmission network is modeled, integrating battery and hydrogen storage technologies. Using this configuration, the capacity settings for both grid-forming batteries and hydrogen units are optimized. This study then explores how hydrogen systems—comprising electrolyzers, storage tanks, and fuel cells—and grid-forming batteries contribute to inertial support. Virtual inertia models are established for each technology, enabling precise estimation of the total synthetic inertia provided. At the operational level, this study addresses stability concerns stemming from renewable generation variability by introducing three security indices. A joint optimization is performed for virtual inertia constants, which define the virtual inertia provided by energy storage systems to assist in frequency regulation, and primary frequency response parameters within the proposed storage scheme are optimized in this model. This enhances the frequency modulation potential of both systems and confirms the robustness of the proposed approach. Lastly, a real-world case study involving a 13 GW renewable energy base in Northwest China, connected via a ±10 GW HVDC export corridor, demonstrates the practical effectiveness of the optimization strategy and system configuration. Full article

Currently, quantum steganography schemes utilizing the least significant bit (LSB) approach are primarily optimized for fixed-point data processing, yet they encounter precision limitations when handling extended floating-point data structures owing to quantization error accumulation. To overcome precision constraints in quantum data hiding, the EPlsb-MFQS and MVlsb-MFQS quantum steganography algorithms are constructed based on the LSB approach in this study. The multichannel floating-point quantum representation of digital signals (MFQS) model enhances information hiding by augmenting the number of available channels, thereby increasing the embedding capacity of the LSB approach. Firstly, we analyze the limitations of fixed-point signals steganography schemes and propose the conventional quantum steganography scheme based on the LSB approach for the MFQS model, achieving enhanced embedding capacity. Moreover, the enhanced embedding efficiency of the EPlsb-MFQS algorithm primarily stems from the superposition probability adjustment of the LSB approach. Then, to prevent an unauthorized person easily extracting secret messages, we utilize channel qubits and position qubits as novel carriers during quantum message encoding. The secret message is encoded into the signal’s qubits of the transmission using a particular modulo value rather than through sequential embedding, thereby enhancing the security and reducing the time complexity in the MVlsb-MFQS algorithm. However, this algorithm in the spatial domain has low robustness and security. Therefore, an improved method of transferring the steganographic process to the quantum Fourier transformed domain to further enhance security is also proposed. This scheme establishes the essential building blocks for quantum signal processing, paving the way for advanced quantum algorithms. Compared with available quantum steganography schemes, the proposed steganography schemes achieve significant improvements in embedding efficiency and security. Finally, we theoretically delineate, in detail, the quantum circuit design and operation process. Full article

In contrast to terrestrial networks, the rapid movement of low-earth-orbit (LEO) satellites causes frequent changes in the topology of intersatellite links (ISLs), resulting in dynamic shifts in transmission paths and fluctuations in multi-hop latency. Moreover, limited onboard resources such as buffer capacity and bandwidth competition contribute to the instability of these links. As a result, providing reliable quality of service (QoS) for time-sensitive flows (TSFs) in LEO satellite networks becomes a challenging task. Traditional terrestrial time-sensitive networking methods, which depend on fixed paths and static priority scheduling, are ill-equipped to handle the dynamic nature and resource constraints typical of satellite environments. This often leads to congestion, packet loss, and excessive latency, especially for high-priority TSFs. This study addresses the primary challenges faced by time-sensitive satellite networks and introduces a management framework based on software-defined networking (SDN) tailored for LEO satellites. An advanced queue management and scheduling system, influenced by terrestrial time-sensitive networking approaches, is developed. By incorporating differentiated forwarding strategies and priority-based classification, the proposed method improves the efficiency of transmitting time-sensitive traffic at multiple levels. To assess the scheme’s performance, simulations under various workloads are conducted, and the results reveal that it significantly boosts network throughput, reduces packet loss, and maintains low latency, thus optimizing the performance of time-sensitive traffic in LEO satellite networks. Full article

This study focuses on the integrated scheduling issues of imaging and data transmission for Earth observation satellites, where each target needs to be imaged and transmitted within a feasible time window. The scheduling process also takes into account the constraints of satellite energy and storage capacity. In this paper, a mixed-integer linear programming (MILP) model for the integrated scheduling of imaging data transmission has been proposed. The MILP model was validated through numerical experiments based on simulation data from SuperView-1 series satellites. Additionally, some neighborhood mechanisms are designed based on the characteristics of the problem. Based on the neighborhood mechanisms, the rule-based large neighborhood search algorithm (RLNS) was designed, which constructs initial solutions through various scheduling rules and iteratively optimizes the solutions using multiple destroying and repairing operators. To address the shortcomings of the overly regular mechanism of the destruction and repair operator for large neighborhood search, we design a genetic algorithms (GA) for tuning the heuristic scheduling rules. The calculation results demonstrate the effectiveness of RLNS and GA, highlighting their advantages over CPLEX in solving large-scale problems. Full article

As urban populations grow and energy demands escalate, it is increasingly challenging for existing building electrical infrastructure in densely populated areas to meet contemporary energy requirements. Traditional grid expansion methods often impose prohibitive economic costs and environmental impacts. Photovoltaic-battery (PVB) systems emerge as a sustainable alternative to enhance building energy self-sufficiency while addressing transformer capacity constraints. This study develops a multi-objective optimization methodology for PVB system configuration in retrofit applications, introducing the transmission limit ratio (TLR) metric to quantify grid interaction capacity. Taking a residential building as a case study, the constraints on configuration variables under insufficient transformer capacity are obtained through simulation. Applying the NSGA-II algorithm, optimal configurations are identified for economic and environmental scenarios. In terms of configuration, a PVB system, 0.743 PV penetration, 205 kWh battery is the best optimal configuration for an economic operation scenario, while 1.356 PV penetration and 201 kWh battery is the best for an environmental operation scenario, when the TLR is 0.8. The analysis demonstrates PV penetration’s critical role in scenario transition, while battery capacity primarily ensures system stability across TLR variations. This methodology provides practical insights for engineers in optimizing sustainable energy systems within existing infrastructure constraints, particularly relevant for high-density urban environments. Full article

For the process of covert transmission of text information, in addition to the need to ensure the quality of the text at the same time, it is also necessary to make the text content match the current context. However, the existing text steganography methods excessively pursue the quality of the text, and lack constraints on the content and emotional expression of the generated steganographic text (stegotext). In order to solve this problem, this paper proposes an emotionally controllable text steganography based on large language model and named entity. The large language model is used for text generation to improve the quality of the generated stegotext. The named entity recognition is used to construct an entity extraction module to obtain the current context-centered text and constrain the text generation content. The sentiment analysis method is used to mine the sentiment tendency so that the stegotext contains rich sentiment information and improves its concealment. Through experimental validation on the generic domain movie reviews dataset IMDB, the results prove that the proposed method has significantly improved hiding capacity, perplexity, and security compared with the existing mainstream methods, and the stegotext has a strong connection with the current context. Full article

In this paper, we investigate a low Earth orbit (LEO) satellite-enabled mobile edge computing (MEC) network, where multiple cache-enabled LEO satellites are deployed to address heterogeneous content requests from ground users. To evaluate the network’s capability in meeting user demands, we adopt the average quality of experience (QoE) of the users as the performance metric, defined based on the effective transmission rate under communication interference. Our analysis reveals that the average QoE is determined by the content caching decisions at the satellites, thereby allowing us to formulate an average QoE maximization problem, subject to practical constraints on the satellite caching capacity. To tackle this NP-hard problem, we design a two-stage content caching algorithm that combines divide-and-conquer and greedy policies for efficient solution. The numerical results validate the effectiveness of the proposed approach. Compared with several benchmark schemes, our algorithm achieves notable improvements in terms of the average QoE while significantly reducing caching costs, particularly under resource-constrained satellite settings. Full article

The increasing penetration of renewable energy sources presents significant challenges for power system stability and operation. Accurately assessing renewable energy absorption capacity is essential to ensuring grid reliability while maximizing renewable integration. This paper proposes a security-constrained sequential production simulation (SPS) framework, which incorporates grid voltage and frequency support constraints to provide a more realistic evaluation of renewable energy absorption capability. Additionally, hierarchical clustering (HC) based on dynamic time warping (DTW) and min-max linkage is employed for temporal aggregation (TA), significantly reducing computational complexity while preserving key system characteristics. A case study on the IEEE 39-bus system, integrating wind and photovoltaic generation alongside high-voltage direct current (HVDC) transmission, demonstrates the effectiveness of the proposed approach. The results show that the security-constrained SPS successfully prevents overvoltage and frequency deviations by bringing additional conventional units online. The study also highlights that increasing grid demand, both locally and through HVDC export, enhances renewable energy absorption, though adequate grid support remains crucial. Full article

To meet the urgent need for high-precision ranging and large-capacity transmission in the current BeiDou-3 inter-satellite link system, this paper proposes a novel two-way measurement method based on Co-frequency Co-time Full Duplex (CCFD) system. This approach effectively addresses the limitations of traditional Time-Division Half-Duplex (TDHD) systems, such as complex link establishment processes, constrained ranging accuracy, and limited transmission efficiency. Based on the spatial configuration of the BeiDou-3 satellite navigation constellation, a dynamic link constraint model is constructed, and a comprehensive link budget analysis is conducted for the entire inter-satellite measurement process. The fundamental principle, system model, and key errors of the two-way measurement in CCFD are derived in detail. Theoretical analysis and experimental simulations demonstrate that the proposed CCFD system is feasible and achieves remarkable ranging accuracy improvements. At a carrier-to-noise ratio of 61.6 dBHz, the system attains 1σ ranging accuracy of 1.9 cm, representing a 51.3% enhancement over the 3.9 cm accuracy of the TDHD system. When operating at 69.3 dBHz, the precision further improves to 0.8 cm, outperforming TDHD’s 2.2 cm by 66.8%. The introduction of CCFD technology can significantly enhance the performance level of the BeiDou-3 satellite navigation system, demonstrating broad application prospects for the future. Full article

Optical communication is a critical technology for future deep space exploration, offering substantial advantages in transmission capacity and spectrum utilization. This paper establishes a comprehensive theoretical framework for avalanche photodiode (APD)-based deep space optical uplink communication under combined channel impairments, including atmospheric and coronal turbulence induced beam scintillation, pointing errors, angle-of-arrival (AOA) fluctuations, link attenuation, and background noise. A closed-form analytical channel model unifying these effects is derived and validated through Monte Carlo simulations. Webb and Gaussian approximations are employed to characterize APD output statistics, with theoretical symbol error rate (SER) expressions for pulse position modulation (PPM) derived under diverse impairment scenarios. Numerical results demonstrate that the Webb model achieves higher accuracy by capturing APD gain dynamics, while the Gaussian approximation remains viable when APD gain exceeds a channel fading-dependent gain threshold. Key system parameters such as APD gain and field-of-view (FOV) angle are analyzed. The optimal APD gain significantly influences the achievement of optimal SER performance, and angle of FOV design balances AOA fluctuations tolerance against noise suppression. These findings enable hardware optimization under size, weight, power, and cost (SWaP-C) constraints without compromising performance. Our work provides critical guidelines for designing robust APD-based deep space optical uplink communication systems. Full article

We consider the scenario in which the source sends status updates, or packets, to the receiver through an infinite capacity transmitter, where the transmitted packets are subject to potential illegal eavesdropping. Time is discretized into identical time slots. In recent years, the age of information (AoI) metric, which was defined as the time has elapsed since the generation instant of the latest received packet, has been widely applied to characterize the freshness of obtained packets. Due to the presence of eavesdroppers, some packets may be eavesdropped during their transmissions, causing information leakages. To assess an infinite-capacity system’s performance of securely transmitting status updates, in this paper, we define an AoI-related metric called the freshness advantage of the legitimate receiver, F, to be average instantaneous gap between eavesdropper’s and legitimate receiver’s AoI. For arbitrarily distributed packet interarrival times, and assuming that in each time slot with probabilities ${\gamma }_d$, ${\gamma }_E$, the transmitted packet is received by the legitimate receiver and the eavesdropper, we derive the explicit formula of F. The concise expression shows that F is fully determined by the average interarrival time and the ratio of ${\gamma }_d$ to ${\gamma }_E$. For special cases where the interarrival time follows geometric distributions, we first determine the explicit distribution of instantaneous AoI gap. Then, given ${\gamma }_d$ and ${\gamma }_E$, we derive the optimal packet generation rate $p^{\ast }$ that minimizes the combined performance Q, which is constructed as the average AoI minus the freshness advantage F. When imposing timeliness and security constraints at the same time, the feasible regions of p and ${\gamma }_d$ such that both two required performances can be satisfied are depicted and discussed. Finally, we investigate the impacts of different parameters on F and show the tradeoffs between timeliness performance and security performance through numerical simulations. Full article

A LoRaWAN application architecture comprises three functional components: (i) nodes, which convert and wirelessly transmit data as LoRaWAN messages; (ii) gateways, which receive and forward these transmissions; and (iii) network servers, which process the received data for application delivery. The nodes convert data into LoRaWAN messages and transmit them wirelessly with the hope that one or more LoRaWAN gateway will receive the messages successfully. Then, the gateways pass on the received messages to a distant network server, where various processing steps occur before the messages are forwarded to the end application. If none of the gateways can receive the messages, then they will be lost. Although this default behaviour is suitable for some applications, there are others where ensuring messages are successfully delivered at a higher rate would be helpful. One such scenario is the application in this paper: monitoring maritime vessels and fishing equipment in offshore environments characterised by intermittent or absent shore connectivity. To address this challenge, the Custodian project was initiated to develop a maritime monitoring solution with enhanced connectivity capabilities. Two additional features are especially welcome in this scenario. The most important feature is the transmission of messages created in offshore areas to end users who are offshore, regardless of the unavailability of the ground network server. An example would be fishermen who are offshore and wish to position their fishing equipment, also offshore, based on location data transmitted from nodes via LoRaWAN, even when both entities are far away from the mainland. The second aspect concerns the potential use of gateway-to-gateway communications, through gateways on various ships, to transmit messages to the coast. This setup enables fishing gear and fishing vessels to be monitored from the coast, even in the absence of a direct connection. The functional constraints of conventional commercial gateways necessitated the conceptualisation and implementation of C-Mesh, a novel relay architecture that extends LoRaWAN functionality beyond standard protocol implementations. The C-Mesh integrates with the Custodian ecosystem, alongside C-Beacon and C-Point devices, while maintaining transparent compatibility with standard LoRaWAN infrastructure components through protocol-compliant gateway emulation. Thus, compatibility with both commercially available nodes and gateways and those already in deployment is guaranteed. We provide a comprehensive description of C-Mesh, describing its hardware architecture (communications, power, and self-monitoring abilities) and data processing ability (filtering duplicate messages, security, and encryption). Sea trials carried out on board a commercial fishing vessel in Sesimbra, Portugal, proved C-Mesh to be effective. Location messages derived from fishing gear left at sea were received by an end user aboard the fishing vessel, independently of the network server on land. Additionally, field tests demonstrated that a single C-Mesh deployment functioning as a signal repeater on a vessel with an antenna elevation of $15m$ above sea level achieved a quantifiable coverage extension of 13 km (representing a 20% increase in effective transmission range), demonstrating the capacity of C-Mesh to increase LoRaWAN’s coverage. Full article

The working of the Internet of Things (IoT) ecosystem indeed depends extensively on the mechanisms of real-time data collection, sharing, and automatic operation. Among these fundamentals, wireless sensor networks (WSNs) are important for maintaining a countenance with their many distributed Sensor Nodes (SNs), which can sense and transmit environmental data wirelessly. Because WSNs possess advantages for remote data collection, they are severely hampered by constraints imposed by the limited energy capacity of SNs; hence, energy-efficient routing is a pertinent challenge. Therefore, in the case of clustering and routing mechanisms, these two play important roles where clustering is performed to reduce energy consumption and prolong the lifetime of the network, while routing refers to the actual paths for transmission of data. Addressing the limitations witnessed in the conventional IoT-based routing of data, this proposal presents an FL-oriented framework that presents a new energy-efficient routing scheme. Such routing is facilitated by the ADDQL model, which creates smart high-speed routing across changing scenarios in WSNs. The proposed ADDQL-IRHO model has been compared to other existing state-of-the-art algorithms according to multiple performance metrics such as energy consumption, communication delay, temporal complexity, data sum rate, message overhead, and scalability, with extensive experimental evaluation reporting superior performance. This also substantiates the applicability and competitiveness of the framework in variable-serviced IoT-oriented WSNs for next-gen intelligent routing solutions. Full article

Efficient data transmission in low Earth orbit (LEO) satellite networks is critical for supporting real-time global communication, Earth observation, and numerous data-intensive space missions. A fundamental challenge in these networks involves solving the maximum flow problem, which determines the optimal data throughput across highly dynamic topologies with limited onboard energy and data processing capability. Traditional algorithms often fall short in these environments due to their high computational costs and inability to adapt to frequent topological changes or fluctuating link capacities. This paper introduces an accelerated maximum flow algorithm specifically designed for dynamic LEO networks, leveraging a prediction-enhanced approach to improve both speed and adaptability. The proposed algorithm integrates a novel energy-time expanded graph (e-TEG) framework, which jointly models satellite-specific constraints including time-varying inter-satellite visibility, limited onboard processing capacities, and dynamic link capacities. In addition, a learning-augmented warm-start strategy is introduced to enhance the Ford–Fulkerson algorithm. It generates near-optimal initial flows based on historical network states, which reduces the number of augmentation steps required and accelerates computation under dynamic conditions. Theoretical analyses confirm the correctness and time efficiency of the proposed approach. Evaluation results validate that the prediction-enhanced approach achieves up to a 32.2% reduction in computation time compared to conventional methods, particularly under varying storage capacity and network topologies. These results demonstrate the algorithm’s potential to support high-throughput, efficient data transmission in future satellite communication systems. Full article