In large-scale Low Earth Orbit (LEO) satellite networks, cache placement and update are facing challenges such as limited cache volume, update capacity, frequently interrupted Inter-Satellite Links (ISLs) and sudden changes in content popularity, leading to the difficulty for obtaining fresh data for existing algorithms by optimizing average user access delay instead of timeliness and adaptability. To address this issue, in this work, we propose an age-driven Multi-Agent Deep Reinforcement Learning (MADRL) based cooperative cache and update mechanism called as Age-Driven Cooperative Cache and Update (ADCCU) algorithm, in which we establish an age-driven cache gain function to evaluate effective value of holding a data item from cache’s perspective. In particular, we formulate the age-driven cooperatively cache scheduling issue as an Integer Programming (IP) problem and solve it by exploiting a Markov Decision Process (MDP). Simulation results demonstrate that, under a cache capacity constraint ofci=3, the ADCCU algorithm significantly outperforms baseline caching strategies: the average cache gain is increased by approximately 1.09 compared to the Deep Deterministic Policy Gradient (DDPG) based cache, 2.88 compared to the Least Frequently Used (LFU), and 4.92 compared to the Least Recently Used (LRU), respectively, and the cache hit ratio is improved by approximately 5.31% over the DDPG-based cache, 56.4% over LFU, and 65.9% over LRU, respectively.

In the upcoming global-coverage 6G networks, high packet loss and long latency in long-distance transmissions exacerbate the trade-off between data timeliness and integrity, particularly in time-sensitive applications involving time-series data with stringent integrity requirements. This challenge exposes the limitations of existing transmission systems, such as source-channel coding and semantic communication, which fail to jointly address both dimensions. In this paper, we propose a deep learning (DL)-enabled generalized deduplication (GD)-based semantic transmission (DLGD-ST) mechanism for time-series data. By leveraging GD to address the impact of semantic ambiguity on data integrity, DLGD-ST exploits the semantic recovery and temporal discreteness of the data to effectively mitigate the conflict between integrity and timeliness. In particular, a well-designed long-short-term memory (LSTM)-based GD algorithm is developed to separate shallow semantic components and supplementary components, ensuring the integrity of semantic transmission. A deep semantic encoding process is then performed using a double-layer progressive dimension reduction (DPDR) and adaptive quantization (AQ) scheme, which capitalizes on the channel robustness of semantics to reduce transmission rounds and improve timeliness. Furthermore, an incremental dimension hybrid automatic repeat request (ID-HARQ) mechanism is introduced to improve semantic reliability by retransmitting high-dimensional semantics, thereby further minimizing end-to-end transmission rounds. To accurately evaluate performance, we introduce the Age of Semantic Information (AoSI), which incorporates integrity constraints into the generalized Age of Information (AoI) to jointly assess integrity and timeliness. Simulation results demonstrate that the proposed DLGD-ST mechanism, enabled by accurate data recovery and reduced transmission rounds, achieves better AoSI performance compared to existing communication systems under both high and low signal-to-noise ratio (SNR) conditions.

In the Large-scale Satellite Network (LSN), inter-beam interference significantly hinders the transmission performance of Low Earth Orbit (LEO) satellite downlinks. This interference exhibits notable time-varying characteristics due to the relatively rapid movements between LEO satellites and ground users, posing a substantial challenge to existing transmission resource allocation techniques. To tackle this issue, we introduce the Hypergraph Neural Network (HGNN)-enabled Resource Allocation (HGNNRA) algorithm for the downlinks of LSN. This algorithm aims to solve a transmission-rate maximization problem, effectively handling the time-varying interference among multiple beams in the downlink through a well-tailored Dynamic Hypergraph Neural Network (DynHGNN). Specifically, considering the coupling complexity of satellite beam coverage and user participation, we have developed a Dynamic Hypergraph-based interference model, along with a customized construction algorithm, to describe their time-varying relationships precisely. Simulation results indicate that our proposed HGNNRA outperforms both Graph Convolutional Network (GCN) [14], HGNN [15], GNN-DDQN [48], and GCNRA in terms of transmission rate and the satisfaction degree of user transmission requirement metrics.

With lunar exploration attracting global attention, there is a growing interest in the design of relay satellite constellations for future lunar communication systems which is challenged by the huge distance between earth and moon. In this paper, we propose a novel hybrid combined Earth-Moon constellation network structure with Earth-Moon Libration 1/2 (EML1/L2) points Halo orbits, ordinary lunar orbits, and Geostationary Earth Orbit (GEO) to minimize both the total number of satellites and the average per-device Age of Information (AoI) as well as maximizing the coverage ratio of specific lunar surface regions to improve information freshness. This is formulated as a multiobjective optimization problem solved by the Nondominated Sorting Genetic Algorithm-II (NSGA-II). By simulating various constellation configurations, we can obtain the optimal configuration parameters for different total numbers of satellites. The simulation results show that our proposed combined constellation significantly outperforms traditional Walker Star and Delta constellations in both AoI and coverage performance.

Semantic communication exploits the inherent meaning embedded in data content and has become a key enabler in knowledge-driven image transmission frameworks. However, existing research predominantly focuses on computational methodologies, often overlooking the transmission mechanisms. In particular, quantization noise introduced during semantic compression and transmission severely impacts the fidelity and utility of the received data, which remains an unresolved issue. To address this challenge, we propose an Adversarial Learning-based Quantization Selective Hybrid Automatic Repeat reQuest (ALQS-HARQ) mechanism tailored for semantic image transmission in remote sensing satellite networks. The proposed framework dynamically configures the number of quantization bits at the semantic encoder to enhance robustness against quantization noise. Furthermore, we design a quantized bitmerging retransmission scheme, equipped with an intelligent decision-making module and a standardized packet header. A novel metric is introduced to evaluate the semantic recovery completeness, guiding efficient retransmission. Extensive experiments demonstrate that the proposed ALQS-HARQ mechanism achieves higher task success rates with reduced transmission overhead compared to conventional retransmission schemes, showcasing its superior efficiency and adaptability.

In recent years, self-organizing networks composed of drones have received more attention due to their ability to expand coverage and improve mission efficiency. However, in GPS-denied complex low-altitude environments, typical routing protocols, as the cornerstone of drone communications, are greatly restricted or even in failures by numerous obstacles around, which can cause frequent None Line of Sight (NLOS) links leading to sharp declines in communication performance or even interruptions. Therefore, in this work, we propose a Physical Sensing-aided Intelligent Forwarding (PSIF) mechanism for Low-altitude Drone Network (LDNET), which could enhance the forwarding capability between drones by integrating a Long Short-Term Memory (LSTM) based multi-feature link prediction with a Deep Q-Network (DQN) enabled forwarding decision. Simulation results indicate that PSIF can efficiently facilitate packet forwarding in LDNET, resulting in enhanced system performance with regards to delay, packet loss ratio, throughput, and power consumption.

In the Large-Scale Heterogeneous Satellite Networks (LSHSNs) integrating Low Earth Orbit (LEO) and Medium Earth Orbit (MEO) satellites, data delivery faces complex topology and dynamic connectivity, which poses a significant challenge to current graph-dependable transmission strategies requiring global topological information, incurring huge computational cost and interactive overhead. To address this issue, in this paper, we propose a Local Information-dependable Semantic Delivery Mechanism (LISDM) by exploiting topological features at the semantic level, in which we develop a Task-oriented Semantic-aware Topology Compression Network (TSTCN) to condense the global topology according to specific task demands. Besides, we develop a Deep Q-Network enabled Semantic Coded Routing (DQNSCR) algorithm for the semantic delivery in the LISDM by designing a novel metric called Semantic Delivery Efficiency (SDE). The simulation results indicate that the proposed mechanism performs better in improving the required topology scale and throughput compared with typical data delivery mechanisms such as the conventional Open Shortest Path First (OSPF) routing algorithm, the DQN-based Intelligent Routing (DQN-IR) algorithm, and Real-Time Hop-by-Hop Routing (RTHop) algorithm with Space-Time Graph (STG) model, respectively.

Automated manufacturing is the cornerstone of the Industrial Internet of Things (IIoT) ecosystem, where vibration monitoring technology is a critical tool for maintaining industrial machinery. The prevailing approach mostly employs inertial measurement units (IMUs), lasers, and cameras, each demonstrating deployment constraints. In recent years, millimeter-wave (mmWave) radar has shown high vibration measurement performance, but it faces challenges in accurately localizing vibrating objects and determining observation points. This study introduces a new system called VibCamera, which leverages the mmWave vibration measurement technology with computer vision (CV) algorithms for vibration monitoring. With the positional assistant of CV semantic segmentation, the radar can accurately determine sufficient observation points, thereby achieving precise measurement with high directionality. VibCamera includes two camera modes, RGB-only and RGB+depth, and solves two technical challenges: 1) integrating multimodal information for vibration target localization and 2) extracting high-quality vibration signals in interference environments. VibCamera provides more consistent and precise outcomes without the need for physical contact. The experimental results indicate that the RGB-only mode has amplitude and frequency errors below$27.04 \; \mu \rm m$and 0.22 Hz, respectively, with a 90% probability, and the RGB+depth mode has errors below$23.72 \; \mu \rm m$and 0.21 Hz.

In this paper, a 2-layer rate-splitting multiple access (RSMA) framework with a balanced clustering design is proposed for cell-free massive multiple-input multiple-output (CF-mMIMO) systems. Aiming at enhancing spectral efficiency (SE) and meanwhile ensuring low-complexity and scalability in large-scale systems, this work addresses different types of multi-user interferences by utilizing a two-stage optimization scheme with a designed spatial reduction matrix that encompasses the clustering design and joint RSMA. First, a balanced clustering design is developed for 2-layer RSMA to manage intra-and inter-cluster interference more efficiently than conventional user-centric clustering, simultaneously considering AP-user connectivity in CF-mMIMO systems and the impact of cluster similarity on RSMA. By employing the spectral clustering method to solve the bipartite graph partitioning problem with the min-max cut objective, the number of clusters is determined adaptively. Based on the above clustering design, a joint optimization of inner and outer RSMA is proposed to mitigate intra-and inter-cluster interferences simultaneously. Simulation results verify the SE enhancement, low-complexity, and scalability of the proposed 2-layer RSMA framework, compared with benchmark frameworks.

The large-scale low-Earth orbit (LEO) satellite network presents an obvious challenge for user access with obviously dynamical coverage resulting from the fast-changing locations of LEO satellites, i.e., time-varying overlapped range in spatial and temporal coverage by multiple overhead satellites, making existing studies low throughput for supporting various users with fluctuating access demands. In this article, we propose a multiagent deep deterministic policy gradient-based access (MADDPGA) mechanism for the large-scale satellite network, which allows each user to adjust its strategy autonomously by learning the changing network conditions in the long term. By solving a throughput-maximizing optimization problem, we develop a fully decentralized multiagent deep reinforcement learning (MADRL) algorithm by exploiting optimal dormancy probability (DP) and a well-designed weighted allocation strategy. The simulation results show that the proposed method can effectively improve the throughput performance compared with the Random algorithm and fixed DP algorithm.

With the ever-increasing lunar missions, a growing interest develops in designing data relay satellite constellations for cislunar communications, which is challenged by the constrained visibility and huge distance between the earth and moon in pursuit of establishing real-time communication links. In this work, therefore, we propose an age and coverage optimal relay satellite constellation for cislunar communication by considering the self-rotation of the earth as well as the orbital motion of the moon, which consists of hybrid Earth-Moon Libration 1/2 (EML1/L2) points Halo orbits, ordinary lunar orbits, and Geostationary Earth Orbit (GEO) satellites. In particular, by minimizing both the number of satellites and the average per-device Age of Information (AoI) while maximizing the coverage ratio of specific lunar surface regions, a multi-objective optimization problem is formulated and solved by using a well-designed Nondominated Sorting Genetic Algorithm-II (NSGA-II). The simulation results demonstrate that our proposed hybrid constellation significantly outperforms traditional Walker Star and Delta constellations in terms of both AoI and the coverage of communication.

In Large-Scale Heterogeneous Satellite Networks (LSHSNs) integrating Low Earth Orbit (LEO) and Medium Earth Orbit (MEO) satellites, high-timeliness data delivery confronts dynamical connectivity and obvious latency, which heavily challenges existing graph-dependable transmission strategies requiring to obtain global topological information with huge computational cost and signaling overhead. To address this issue, in this paper, we propose an Age-predicting Local Information Dependable Transmission (ALIDT) mechanism for the LSHSN by considering the impact of time-varying topology on the timeliness of data, in which a novel metric of data freshness called Forwarding-aware Age of Information (FAoI) is well-designed to evaluate the timeliness in data forwarding at node. In particular, we develop a satellite Coverage-based Local Information Sharing (CLIS)-assisted Spatial-Temporal Graph Neural Network (STGNN) to extract the topological features in both temporal and spatial dimensions and a Graph Matching Network (GMN)-based topology compression algorithm to improve computation efficiency. The simulation results indicate that the proposed mechanism performs better in improving the storage overhead, throughput and average FAoI compared with the conventional Open Shortest Path First (OSPF) routing algorithm with Time-Varying Graph (TVG) model, GNN-based Multipath Routing (GMR) algorithm, and Gated Recurrent Units (GRU) based metric prediction algorithm in hybrid satellite networks, respectively.

In the Satellite-integrated Internet of Things (SIoT), typical multi-access schemes, i.e., Contention Resolution Diversity Slotted ALOHA scheme (CRDSA), face with the obvious challenge of heavily conflicting packets regarding high channel traffic, which is not well addressed by the methods of Successive Interference Cancellation (SIC) due to the stubborn loop issues. In this work, therefore, we develop a Generalized Deduplication (GD) based Contention Resolution Diversity Slotted ALOHA scheme with a Compulsory Divorce mechanism (CD-CRDSA) by considering the correlative properties among the accessing data from the users. In particular, the proposed mechanism could effectively separate individual packets from conflicting slots to maintain the sustainability of SIC process, thus achieve better throughput. Moreover, we make the theoretical analysis on the throughput performance with the compression gain of the proposed mechanism. The simulation results indicate that compared to the typical CRDSA protocol and the Non-Orthogonal Multiple Access (NOMA) scheme, the proposed CDCRDSA significantly reduces the amounts of un-resolved slots and improves throughput performance, especially in high-load areas.

With its attractive controllable mobility and flexible deployment advantages, the Unmanned Aerial Vehicle (UAV) has emerged as a promising solution to support temporary caching services by pre-fetching popular content. However, it still exists an obvious challenge due to the limited storage and energy of UAVs with stochastic arrival requests, which is not well addressed by present works resulting in low Quality-of-Service (QoS) for users. In this paper, therefore, we propose a queue-aware cooperatively caching mechanism in the multi-UAV assisted system by considering the random user requests, in which a well-designed Connected Dominating Set (CDS) is developed to make collaborative caching schedule. In particular, we formulate the issue as a long-term queue stability constrained energy efficiency maximization problem by a well-tailored Lyapunov optimization framework. As a non-linear mixed-integer optimization with a nonconvex objective function and coupled variables, we solve it by designing a decentralized Cooperative Reinforcement Learning (CRL) algorithm with the developed CDS. The numerical results demonstrate that our proposed joint algorithm outperforms other benchmark algorithms in terms of caching latency, cache hit ratio, and energy efficiency.

Recently, the Satellite-Integrated Internet of Things (S-IoT) has attracted wide interest in remote data-gathering scenarios supporting requirements of user access within a wide area. Nevertheless, considering various priorities of timeliness, huge challenges in the diversified access scenario still exist, which could not be addressed efficiently by the current Irregular Repetition Slotted ALOHA (IRSA) access protocol. Therefore, in this paper, we propose an Value-Optimal Irregular Repetition Slotted ALOHA (V-IRSA) mechanism for the S-IoT via a distributed non-cooperative game theoretic approach, in which a utility function of the Cost of Information Value (CoIV) is developed for capturing the loss of information value during the packet transmission. By deriving the closed-form of the CoIV function, we make optimization on the proposed mechanism parameters during which various priorities of IoT devices could learn autonomously with their respective utilities in a selfish way. Numerical results show that the proposed mechanism could efficiently achieve a higher access probability for more emergent nodes in the system compared with the present algorithm.

In long-span space communication, the current Licklider Transmission Protocol (LTP) confronts apparent challenges such as high packet loss rate and huge latency when carrying the bundles in the Disruption Tolerant Networks (DTN). These challenges incur obviously low freshness of satellite telemetry and instruction data with high timeliness requirements since the typical Automatic Repeat reQuest (ARQ) mechanism is exploited in the LTP for reliable transfer. To address this issue, in this paper, we propose an age-driven bundle delivery mechanism called as Semantic LTP (S-LTP) by considering the semantic correlations in the context-dependent data, which has excellent error-tolerant capability by a well-designed Semantic Supplement Hybrid Automatic Repeat reQuest (SS-HARQ), making it with high timeliness. In particular, a novel metric of semantic freshness of data called Age of Semantic Information (AoSI) is proposed to evaluate the timeliness contribution of information at the semantic level. The simulation results indicate that the proposed SS-HARQ scheme performs better in reducing the average AoSI and AoI by 62.24% and 64.52% respectively compared to the conventional LTP-ARQ with Cyclic Redundancy Check (CRC), 6.39% and 27.09% respectively compared to the Semantic Coding HARQ (SCHARQ) with a similarity detection network called Sim32.

With the proliferation of massive explorations on Lunar Far-side Surface (LFS), deployments of scientific infrastructures have drawn substantial attentions with the aids of relay satellites for the moon, i.e., China’s "Queqiao", which allows real-time reporting for current status information from the landing equipment and space vehicles. Without suitable constellation with well designed scheduling scheme, it would be very difficult to get timely information if depending only on single Halo relay satellite due to large coverage gap and limited energy budget for the communication links. In this paper, we design a hybrid circular-Halo orbital multi-relay constellation system for the LFS communication by minimizing the average per-device Age of Information (AoI) of users in the earth. In particular, we develop an age-optimal scheduling strategy with constellation design for accessing different relay satellite of constellation by solving a Constrained Markov Decision Process (CMDP) optimization problem to significantly reduce the average coverage gap in LFS area. Simulation results show that the average per-device AoI of the proposed scheduling algorithm with the well-designed constellation could achieve 16.20% less in time than that of single Halo satellite relay system compared with typical algorithms.

In large-span space communication, the current bundle delivery mechanism using the Disruption-Tolerant Networks (DTN) technique confronts huge challenges such as high packet loss rate and huge latency. These challenges incur obviously low goodput when delivering scientific and engineering data for the target missions, such as instructions, text, and images. However, the context correlations in these data are not yet excavated to resist the above challenges by current works. To address this issue, therefore, we propose a semantic-aware bundle delivery mechanism for context-dependent data via a cross-layer design on Bundle Protocol (BP) and Licklider Transmission Protocol (LTP), which has the excellent error-tolerance capability by the well-designed semantic-oriented Automatic Repeat reQuest (ARQ) scheme. In particular, the jointed cross-layer design consists of a Semantic Blocking (SB) and Semantic Coding (SC)-based Bundle Updating (BU) mechanism and a dynamic Red/Green-part Allocation method based on Semantic Importance (RGA-SI) for bundles and segments in the two layers. Simulation results show that the proposed mechanism can reduce data latency and improve goodput from about 50% to 70% compared with the current bundle delivery mechanism in DTN with optimal segment size, especially under bad channel conditions.

In large-scale satellite networks, data delivery confronts obvious challenges such as high loss rate and long propagation delay leading to low Packet Delivery Ratio (PDR) and huge delivery latency over intermittent Inter-Satellite Links (ISLs), making the current routing algorithms exploiting typical Automatic Repeat reQuest (ARQ) mechanisms extremely inefficient and even incapable. To address this issue, in this paper, we propose a semantic-aware coding and routing joint mechanism called Semantic Adaptive Coding and Routing (SACR) by considering both the semantic correlations in the context-dependent data and the link status knowledge. In particular, the proposed SACR achieves excellent error-tolerant and routing-agile capabilities by an elaborately interactive design consisting of a customized routing-aware Semantic Adaptive Coding Hybrid ARQ (SAC-HARQ) mechanism and a Semantic Coding-based Routing Mechanism (SCRM). The simulation results indicate that the proposed SACR mechanism performs better in reducing the average delivery latency and improving the effective throughput compared with typical routing mechanisms such as Open Shortest Path First (OSPF) routing, Deep Q-Networks based Intelligent Routing (DQN-IR), and Real-Time Hop-by-hop routing (RTHop), integrating with typical semantic coding methods, i.e., Deep Learning-based Joint Channel-Source Coding (DL-JSCC), Deep learning-based Semantic Communication system (DeepSC), and Semantic Coding HARQ (SCHARQ), respectively.

As a diagnostic method of equipment operational status, vibration monitoring plays a significant role in industrial systems. It is necessary to monitor multiple equipment components simultaneously, due to their different vibration modes. Previous solutions either work in an invasive manner or face challenges in object localization and results correspondence. Therefore, we propose VibCamera, a vibration monitoring system that combines mmWave radar and computer vision technology. We propose an expand-shrink method to optimize object detection results of computer vision and combine camera localization results to extract mmWave signals. Additionally, we employ mmWave data recombination and respective fitting methods to calculate the vibration characteristics for each point accurately. The experiment shows that after fusing visual information, the target detection accuracy is improved to 94.8%, and the cluster point efficiency is improved by 23.3%. Furthermore, amplitude and frequency measurement errors are reduced to 29.1μm and 0.08Hz, respectively.

Low orbit satellite constellation as a relay network provides a possible solution for remote real-time data gathering applications, in which freshness information updates will be forwarded via dynamical intersatellite links (ISLs). Modeling by a time-varying network, this article studies minimizing Age of Information (AoI) of delivering the data through a multihop path, in particular, focusing on the effect of frequent interruptions of ISLs. Subjected to two constraints of path and effective arrival rate, the minimizing AoI problem is formulated to find a pair of optimal transmission delay and arrival rate. In particular, the$\mathcal {H}$-approximate optimal algorithm, called a latest update routing (LUR) algorithm, is proposed with a well-designed continuous time-varying graph. Using LUR, a set of paths can be obtained with degraded transmission delay that satisfies a given arrival rate. By screening all the arrival rates satisfying the effective constraint, the maximum rate and a corresponding path set that minimizes age can be found. The simulation results verified that a degraded average AoI can be obtained by the proposed path selection mechanism compared with the typical shortest delay path (SDP) strategy, minimum spanning tree (MST) strategy, and MAoIG. In particular, the numerical findings show that the proposed LUR reduces average AoI by a maximum of 12.66% compared with SDP, 75.28% compared with MST, and 69.3% compared with MAoIG, respectively, under different scenarios.

The Internet of Vehicles (IoV) can offer safe and comfortable driving experience, by the enhanced advantages of space–air–ground-integrated networks (SAGINs), i.e., global seamless access, wide-area coverage, and flexible traffic scheduling. However, due to the huge popular traffic volume, limited cache/power resources, and the heterogeneous network infrastructures, the burden of backhaul link will be seriously enlarged, degrading the energy efficiency of IoV in SAGIN. In this article, to implement the popular content severing multiple vehicle users (VUs), we consider a cache-enabled satellite-UAV-vehicle-integrated network (CSUVIN), where the geosynchronous Earth orbit (GEO) satellite is regard as a cloud server, and unmanned aerial vehicles are deployed as edge caching servers. Then, we propose an energy-aware coded caching strategy employed in our system model to provide more multicast opportunities, and to reduce the backhaul transmission volume, considering the effects of file popularity, cache size, request frequency, and mobility in different road sections (RSs). Furthermore, we derive the closed-form expressions of total energy consumption both in single-RS and multi-RSs scenarios with asynchronous and synchronous services schemes, respectively. An optimization problem is formulated to minimize the total energy consumption, and the optimal content placement matrix, power allocation vector, and coverage deployment vector are obtained by well-designed algorithms. We finally show, numerically, our coded caching strategy can greatly improve energy efficient performance in CSUVINs, compared with other benchmarked caching schemes under the heterogeneous network conditions.

In this article, we consider the data updating problem in the Satellite-integrated Internet of Things network for the time-critical scenarios, i.e., animal tracking and environmental monitoring. Due to the limited channel rate during contact of transmission, however, constantly updating data with huge volume over the uplink will incur obvious waiting and transmission delay bringing stale information to the satellite node. To address this issue, we propose a novel metric, spatially temporally correlative mutual information (STI), to characterize the information timeliness from perspective of information entropy by considering the correlations between the last update message and the status of the information source. By maximizing the averaged STI, we find the optimal allocation policy of channel slots with a fixed updating period by formulating the problem as a Markov decision process (MDP) with possibly infinite state space. Furthermore, we derive the optimal amounts of allocated time slots in a unit frame by solving a constrained range integer optimization problem with respect to the average STI. The simulation results show that the proposed periodically updating policy can significantly improve the information freshness compared with the original slot allocation strategy and current commonly used scheduled access strategies, i.e., slotted ALOHA and Threshold-ALOHA.

In a typical Satellite-integrated Internet of Things (SIoT), the limited transmission rate of a sensor causes a stale in data freshness due to unavoidable time waiting for transmission. Moreover, due to the high bit error rate (BER) of the satellite-to-ground link, data freshness will be further exacerbated by frequent retransmissions. Generally, this issue is partially solved using a powerful compression scheme that can reduce the data volume. However, conventional compression schemes will necessitate a significant amount of time to accumulate constant data to a certain quantity, posing a difficult challenge. Therefore, this study proposes an age-optimal hybrid temporal-spatial generalized deduplication and automatic repeat request (HARQ-GD) protocol for the high-sampling data collection in SIoT, considering data compression, and transmission collaboratively. A novel Age of Information (AoI) metric is developed for timeliness evaluation over a two-hop end-to-end link of SIoT, which is optimized to design the proposed HARQ-GD protocol by considering the temporal and spatial correlations of sampled data with specific encoding/decoding algorithms and packet formats. The simulation results indicate that the proposed HARQ-GD protocol performs better performance than typical generalized deduplication (GD) and hybrid automatic repeat request with chase combing (HARQ-CC) schemes in reducing AoI, because of its fewer transmission times and higher compression rate.

Time‐varying connectivity is one of main challenges faced by controlling a team of unmanned aerial vehicles (UAVs) in the multipath fading channel, incurring low accuracy and significant convergence time of formation control law. To address this issue, in this study, a topology optimised based decentralised consensus is developed for controlling a multi‐UAV system in a multipath fading channel, in which a formation structure reconfiguration scheme is proposed as well as a transmission power allocating algorithm to guarantee the control accuracy in a limited convergence time. In particular, the objective function for topology optimisation is well‐designed by considering the second eigenvalue of Laplacian matrix of topology as a feasible index of connectivity degree. To improve the efficiency of information transmission, a specified consensus protocol is proposed with well‐tailored packet format and signalling procedure for control messages. Through the comparative simulation results, the proposed consensus can achieve high convergence accuracy and less convergence time in a multipath fading channel, indicating high resilience of the proposed protocol under a multipath fading channel.

Airport aircraft detection is a research hotspot in the field of automatic target detection in optical remote sensing images. The existing detection methods generally have low efficiency and poor detection accuracy due to the uncertainty of aircraft scale and orientation and to the complexity of airport remote sensing images. To address these problems, this paper presents an effective aircraft detection framework called Local Context DPM (LC-DPM). Our method is conducted in two main stages. (1) During aircraft candidate region extraction, we propose a non-flat region extraction method and a regular region elimination method to extract aircraft candidate regions in airport. (2) We propose LC-DPM during identification of the aircraft candidate region, we achieve accurate identification by constructing the local context histogram of oriented gradients (HOG) feature pyramids, which combine the local context information and HOG features of the aircraft targets. In addition, in order to further improve the efficiency of the method, we use the circle- frequency filter to predict the orientation of the suspected aircraft target, before the identification at various orientations based on LC-DPM. We test the proposed method on complex airport area sets with varying types of aircraft. The experimental results show that the proposed method is both highly accurate and computationally efficient.

Space‐terrestrial integrated networks consisting of low earth orbit (LEO) satellites andterrestrial users are widely developed for potentially diversified requirementsof content distribution. With an obviously time‐varying topology, however, designing a distribution strategy faces several explicit challenges, such asprolonged content access latency and significant transmission overheads, due tolack of contact opportunities and limited on‐board storage space. In this study, therefore, a novel back‐tracing partition directed on‐path caching distributionmechanism (BPDM) is proposed for the file distribution in the hybrid LEOconstellation and terrestrial network. In the proposed strategy, a group offeasible on‐path cache nodes is iteratively selected by utilising awell‐designed cross‐timeslot graph, as well as a collaborative cached contentplacement strategy, called as multiple regions cooperative cache algorithm, bycarefully considering diversified popularity of target files. As a result, theproposed BPDM could efficiently reduce redundant transmissions of content accessfor different users by fetching objective file mainly from limited quantities ofintermediate caching nodes. Through the simulation results, the proposed methodcan obviously decrease the holistic overheads and access delay compared with theminimum spanning tree algorithm and Network Central Location (NCL) nodeselection metric.

Recently, a hybrid remote sensing network constituted by satellites in constellation and Unmanned Aerial Vehicles (UAVs) in formation attracts a lot of interests, benefiting from the flexible architecture and excellent rapid responsiveness. Considering frequently intermittent connectivity and limited resource onboard, Disruption‐Tolerant Networking (DTN) develops a feasible solution for the remote sensing scenarios. However, the intrinsic motion models of multifarious nodes lead to deterministic or semi‐deterministic contacts, which makes finding a reliable end‐to‐end routing path for timely data delivery difficult, with typical routing strategies such as Contact Graph Routing (CGR). To cope with such routing challenge in the hybrid network, a Probabilistic Contact Graph (PCG) is designed, taking the diverse node properties into consideration. In particular, a probability prediction model for semi‐deterministic contacts between the UAV nodes is proposed, with a semi‐Markov motion model for the UAV nodes. Besides, a Markov Decision Process based Routing (MDPR) algorithm is designed to search for a feasible data transmission path with a series of hybrid deterministic and semi‐deterministic contacts. Through the numerical and experimental simulations with Interplanetary Overlay Network (ION), the proposed MDPR algorithm shows excellent routing performance concerning delivery delay and delivery ratio, compared with the typical CGR strategy.

In a satellite disruption/delay‐tolerant network, bundle delivery is obviously affected by time‐varying parameters, i.e. bit error rate and propagation latency, due to constantly changing distance and connectivity between paired orbital nodes. The authors proposed a Markov decision‐based optimisation approach for bundle size, which could efficiently improve the expected time of delivery over a dynamic two‐hop inter‐satellite link. In particular, a group of optimal bundle sizes are adaptively selected according to current distance‐dependent channel parameters, leading to a full utilisation on intermediate node's memory. The simulation results verified the proposed method under different conditions with comparison.

The study of Restricted Boltzmann Machine(RBM) attracts considerable attentions in recent years. RBM training algorithm is an unsupervised learning method with many applications, moreover, it is the basic module in deep learning. Maximizing the log-likelihood by gradient ascent method, RBM training algorithm can approximate the probability distribution underlying the observing data. For a simple RBM system, we give the closed-form representation for the solutions of the training algorithm, which forms a manifold. Based on the result, it is understood that the solution of the maximization of the log-likelihood function calculated by the gradient ascent method give only one point on the manifold. To illustrate the phenomenon more clearly, a family of new parameters are introduced to express the solution manifold.

In space disruption‐tolerant networks (DTNs), a cessation resulting from a sudden link failure may occur during bundle transfer, which could introduce additional delay and probably lead to inefficient exploiting of precious successive contacts with current version of typical bundle protocol. Although the DTN architecture provides a preliminary idea – bundle reactive fragmentation to alleviate these problems, both it and the relevant works involve little on the modification to packet format, which supports fragmentation and reassembly. Moreover, most studies in the context of reactive fragmentation bases on a premise that routing header is received before connection breakdown, which would lack generality. In this study, without the premise, the author propose an expedited mechanism with specific packet format's modification to handle an unexpected disruption, through which extra waiting‐time for recovery of the interrupted transfer could be efficiently avoided and thus achieving fast forwarding at intermediate node. The simulation results confirm that the proposed scheme can effectively reduce bundle delivery time, without introducing significant extra overheads with the respect of the well‐defined metric of wasted transmission effort.

To improve the reliability of file transfer and shorten file transfer time in space communication, this study aims to provide an improved strategy for deferred negative acknowledgement (NAK) in Consultative Committee for Space Data Systems File Delivery Protocol (CFDP). Based on a theoretical analysis of the recommended deferred NAK, the authors propose a double retransmission deferred NAK strategy instead to guarantee the reliability of file transfer; the file transfer time is reduced significantly using fewer retransmission spurts. They make the performance comparisons of the recommended deferred NAK in CFDP with the authors’ proposed strategy under several typical scenarios. Numerical and simulation results show the effectiveness of the proposed strategy.

This paper is concerned with the spatial modulation (SM), a multiple-input multiple-output (MIMO) transmission technique, that maps information bits not only into the conventional two-dimensional signal points but also into the indices of active transmit antennas. We present a two-layer coded SM scheme, in which the spatial bits carried by the antenna indices and the signal bits carried by the conventional signals are protected separately by two error correction codes. For the ease of decoding process, the code rates are allocated according to the chain rule of the mutual information. We choose block Markov superposition transmission (BMST) codes for each layer, since they are easily designed for any given code rate with a predictable performance lower bound. An iterative sliding-window decoding algorithm is also presented by exchanging messages iteratively between the two BMST decoders and the soft-in soft-out (SISO) demapper of the SM. To reduce the computational complexity, we propose to implement the SISO demapping algorithm by employing only partial soft inputs. Numerical results show that the BMST-SM system performs well over uncorrelated Rayleigh fading channels.

In this paper, we initiate a theoretical study on N-expert fusion (N ≥ 2) in the context of biometric authentication (BA). Optimal fusion weights, which depend on performances and variances of, and correlations among individual base-experts have been found, and we also give and prove some new theorems that serve as the basis for analyzing the performance of the overall system. Our conclusion is that provided that optimal weights are used as fusion coefficients, linear fusion will definitely lead to a better performance than the best individual expert. This contradicts many existing conclusions, which assert that fusion is not always beneficial and that performance improvement due to fusion is guaranteed only when some conditions as to baseexperts' performances, variances, and correlations are satisfied. Besides, for the first time the definition of correlation in the context of BA is clearly and explicitly given to avoid the longstanding ambiguity and vagueness concerning this term, and we make an initial attempt to propose and investigate three types of correlation coefficients. Furthermore, the connection between our proposed optimal fusion method and Fisher's discriminant is discussed. Extensive experiments have been conducted to confirm our theoretical results and construct counter-examples for the existing conclusions.

Disruption-Tolerant Networks provides store-and-forward enabled routing strategies for the satellite networks with frequently intermittent links. However, current dynamic route selection algorithm (DRSA), including Contact Graph Routing (CGR) algorithm, could not find an end-to-end route over a serial of link segments with time-disjointed contacts. In this paper, we proposed a novel routing algorithm as expanding range route selection (ERRS), which could find the EDT-optimal route by searching at each snapshot of time-varying topology. The proposed algorithm is compared with DRSA on the Linux-based experimental platform with built-in ION (Interplanetary Overlay Networks) software. The results show our algorithm has less delivery time and the obviously improved throughput of the network.

This paper introduces a domain Spatio-Temporal Consistency (STeC) language for the application domain of space-air-ground integrated networks. The STeC language is taken as the foundation of modeling our systems, bacause it works well on specifying real-time systems concerning not only the time characteristic but also the location characteristic and the consistency between them. In this paper, a domain-STeC language is devised and applied to model satellite observation processes. We use two tools to check and verify this STeC model, the syntax and spatio-temporal consistency were checked in STeC tool, and after being transformed into timed automata model, some further verification can be finished in UPPAAL tool. It shows that the instantiated STeC approach is suitable for modeling and verifying space-air-ground integrated networks on the requirement level. This work helps to ensure the spatial-temporal consistency in that application domain, and increases the reliability and dependability of our system.

In this paper, a novel online signature verification technique based on discrete cosine transform (DCT) and sparse representation is proposed. We find a new property of DCT, which can be used to obtain a compact representation of an online signature using a fixed number of coefficients, leading to simple matching procedures and providing an effective alternative to deal with time series of different lengths. The property is also used to extract energy features. Furthermore, a new attempt to apply sparse representation to online signature verification is made, and a novel task-specific method for building overcomplete dictionaries is proposed, then sparsity features are extracted. Finally, energy features and sparsity features are concatenated to form a feature vector. Experiments are conducted on the Sabancı University's Signature Database (SUSIG)-Visual and SVC2004 databases, and the results show that our proposed method authenticates persons very reliably with a verification performance which is better than those of state-of-the-art methods on the same databases.

This study proposes a novel adaptive network‐coded rateless coding scheme for an erasure multiple‐access relay system with two distributed sources and an asymmetric network topology. To increase transmission efficiency, a two‐dimensional degree distribution, as part of network‐coded relay protocol, is designed based on the AND–OR tree analysis technique. The degree distributions of rateless coding at the sources and network coding at the relay are optimised by the linear programming approach under asymmetric channel conditions. Simulation results demonstrate that the proposed scheme outperforms existing classical relay protocols under time‐varying channel conditions, and achieves a significantly better performance.

The Feher quadrature phase shift keying (FQPSK) modulation is significantly susceptible to frequency and phase offsets under low signal‐to‐noise ratios. In this study, the authors proposed a serially concatenated signalling scheme with FQPSK modulation and low‐density parity‐check coding, which could efficiently resist residual frequency offset by employing an intended compensation algorithm. The designed maximum‐likelihood estimation‐enabled compensation algorithm is incorporated into the iterative concatenated demodulation‐decoding process by using soft‐input–soft‐output‐based maximum‐a‐posteriori‐probability criterion. On the other side, the codeword sequence to be transmitted at the sender is re‐arranged in a pre‐configured order different from original codeword, in order to help the compensation algorithm diminish the impacts of frequency offsets. Simulation results show that the bit error rate of the proposed scheme can be improved efficiently up to three orders of magnitude with the frequency offsets from 100 to 700 ppm.

Delay/Disruption Tolerant Networking (DTN) architecture is expected to play a promising role in future deep space missions. Scientific data interactions over space DTN involve several hops inevitable, since simultaneous and direct connectivity among all intermediate nodes are becoming more difficult in space scenarios. Therefore, the characteristics and capabilities of the node storage are vital factors for the quality of data delivery over space DTN. This paper proposes an analytical framework based on multi-dimension Markov chain to evaluate the dynamic on storage of intermediate nodes in space DTN. According to the proposed framework, we develop a delay model and consequently a success probability model for bundles delivery over space DTN, both of which are dependent closely on the sojourn time in node storages. The numerical results show that: a) dividing source-file data into bigger bundles can bring longer high-storage-occupancy time on intermediary nodes; b) the shorter storage occupation time of node is more susceptible to the bundle sizes than to LTP segment sizes. c) the delivery success probability of the bundles is more dependent on smaller DTN bundles than on LTP segment sizes given the constrains on Time-to-live of bundles in space missions.

The Feher quadrature phase shift keying (FQPSK) modulation based Physical-layer Network Coding (PNC) is investigated in this paper, by which the nonlinear distortion effects resulted from the high power amplifier (HPA) in the system can be avoided. In our presented framework, a novel remapping rule for the FQPSK modulation in the PNC system is proposed to make a better bit error rate (BER) performance. Moreover, a joint demapping-and-demodulation scheme based on Low Density Parity Check (LDPC) is employed to recover the data bits with a low computational burden. Numerical results demonstrate the efficiency of the proposed method.

In this paper, we propose a blind image watermarking algorithm based on the multiband wavelet transformation and the empirical mode decomposition. Unlike the watermark algorithms based on the traditional two-band wavelet transform, where the watermark bits are embedded directly on the wavelet coefficients, in the proposed scheme, we embed the watermark bits in the mean trend of some middle-frequency subimages in the wavelet domain. We further select appropriate dilation factor and filters in the multiband wavelet transform to achieve better performance in terms of perceptually invisibility and the robustness of the watermark. The experimental results show that the proposed blind watermarking scheme is robust against JPEG compression, Gaussian noise, salt and pepper noise, median filtering, and ConvFilter attacks. The comparison analysis demonstrate that our scheme has better performance than the watermarking schemes reported recently.

In this paper, we present a steganalytic method to attack JPEG2000 lazy-mode steganography proposed by Su et al. The key element of the method is the Hilbert-Huang transform based analysis of the code-block noise variance sequences of stego images and non-stego noisy images. The Hilbert transform based characteristic vectors are constructed via empirical mode decomposition of the sequences and the support vector machine classifier is used in classification. Experimental results have demonstrated effectiveness of the proposed steganalytic method. According to our best knowledge, this method is the first successful attack of JPEG2000 lazy-mode steganography. And furthermore, the proposed method takes first step towards the application of Hilbert-Huang transform in steganalysis and proves its great advantage.

Chinese fonts are recognized by a new method based on empirical mode decomposition. Five basic strokes have been selected to characterize the features of Chinese fonts. Based on them, stroke feature sequences of a given text block are calculated. Once decomposed by EMD, the first two intrinsic mode functions corresponding to each stroke feature sequence are used to calculate the stroke energy of all the five basic strokes. These energies are combined with the five averages of the residues to produce a ten-dimensional feature vector. Finally, the minimum distance classifier is used to recognize the fonts. Experiments show encouraging recognition rates.

An approach to analyze the period of a signal based on Hilbert-Huang transform is presented in this paper. For an approximately periodic signal which contains plenty of high frequency components, the relation between its period and its main frequency is established. Our main result is that, for an approximately periodic signal which contains plenty of high frequency components, its period can be estimated accurately according to its main-frequency distribution. By applying the technique on texture analysis, a novel method to extract the periodicity features of a texture image is developed, which can be used in texture classification, segmentation, recognition and other applications.