RDMA-empowered cloud services are gradually deployed across datacenters (DCs) with multiple paths, which exhibit new properties of path asymmetry, delayed congestion signals, and simultaneous flow routing collisions, and further fail existing routing methods.

Future wireless networks, such as 6G, are envisioned to deliver ultra-reliable, high-quality services with ultra-low latency and dynamic connectivity across heterogeneous environments, driving the adoption of edge–cloud collaborative architectures. Within this paradigm, container-based microservices, with their lightweight, modular, and portable characteristics, offer an effective foundation for scalable and adaptive service provisioning in heterogeneous wireless networks. The layered architecture of microservices facilitates efficient resource management through layer scheduling and caching. However, dynamic service requests and diverse container layers pose major challenges for layer-aware service provisioning in future wireless environments. These includetime-exceeded offline service provisioning, tangled microservice orchestration, andlayer cache redundancy. To address these challenges, we propose Tri-Ring, an asynchronous online provisioning framework for future wireless networks, empowered by large AI models (LAMs). The framework optimizes request dispatching, orchestration, and layer updates across three timescales. At the small timescale, we formulate request dispatching as a linear programming (LP) subproblem. At the medium timescale, the estimator-assessor algorithm manages microservice orchestration, where a diffusion-enhanced prediction model serves as the estimator to predict layer caching strategies. Moreover, submodular optimization serves as the assessor to determine deployment and scheduling. At the large timescale, we introduce the age of layer (AoL) to guide the pruning of infrequently accessed cached layers to reduce storage overhead. Comprehensive evaluations on real-world datasets demonstrates that Tri-Ring outperforms existing baselines, increasing utility by 44.78%, reducing microservice startup time by 78.64%, and optimizing storage resources by 36.38%.

In this paper, we propose the energy-tier template insertion shaping (ETTIS) probabilistic shaping algorithm, which performs shaping and deshaping through preset templates and simple bit insertion/deletion operations, thereby significantly reducing the algorithmic complexity. The ETTIS algorithm effectively mitigates the Hamming distance shrinkage problem commonly observed in traditional probabilistic shaping algorithms and exhibits excellent compatibility, allowing seamless integration with standard forward error correction coding and interleaving algorithms. Moreover, the ETTIS framework utilizes predefined symbol components in the shaping templates to implicitly introduce pilot symbols, enabling real-time estimation of channel gain and noise variance without additional bandwidth overhead.. Experimental and simulation results show that, at the same bit rate, the proposed scheme achieves 0.4–0.6 dB performance gains under 16-quadrature amplitude modulation (QAM) and 64-QAM modulation formats, respectively. Compared with other state-ofthe- art algorithms, ETTIS attains comparable performance while maintaining significantly lower complexity. During decoding, the absolute deviation of the estimated noise bit error rate remains below 0.75%. These features make ETTIS a promising solution for high-throughput and cost-sensitive optical interconnect systems.

Ambient LoRa backscatter enables long-range communication due to its long-period symbol. Most of the existing works struggle to balance range and throughput: systems with symbol-level modulation offers long transmission range at the cost of low data rate, while systems with high modulation efficiency suffer from limited transmission distance due to weak signals. We propose SubLoRa, which significantly improves throughput while maintaining long-range communication. SubLoRa achieves the high-rate modulation by the proposed Subchirp Frequency Offset Modulation (SFOM), which divides a chirp into multiple subchirps each being shifted by frequency. We propose a prewaveform sampling strategy that enables SFOM with low power. For decoding, we propose a Frequency-Difference Recombination of Chirp (FDRC) demodulation based on time-domain correlation, which enables reliable decoding of low-power signals in long-range links. We implement SubLoRa and conduct extensive evaluation. The results show SubLoRa can achieve up to 29.66× throughput gain and 7.54× throughput gain compared with the State-Of-The-Art (SOTA) LoRa backscatter system PLoRa and Pacim, respectively.

Recently, Named Data Networking (NDN) has garnered widespread attention in academia as an innovative network architecture, offering solutions to challenges such as the vulnerability of end-to-end connections in IP-based networks. In NDN, nodes utilize a “pull-push” architecture, exchangingInterestandDatapackets for communication. This architecture is particularly well-suited for highly dynamic, topology-varying unmanned aerial vehicle (UAV) swarm networks, known as UAV Named Data Networking (UNDN). However, in UNDN, due to constraints such as the lightweight design and limited energy of UAVs, the UAVs may exhibit selfish behaviors, opting not to share data in order to conserve their own energy consumption. This behavior results in degraded network performance, as the lack of cooperation among UAVs can hinder efficient data sharing and communication. Therefore, an effective incentive mechanism needs to be proposed. In this paper, we formulate the content-sharing process in UNDN as a double auction market for data exchange. To tackle the problem of asymmetric information between content consumers and producers, we propose an Iterative Double Auction algorithm (IDAA). This algorithm introduces a virtual central broker to guide both parties in conducting honest auctions. Furthermore, we develop a diffusion model-based reinforcement learning algorithm (DiffRL-DA) to derive optimal auction policies, with the goal of better capturing market behaviors and overcoming the limitations of the IDAA. Finally, simulation results verify the efficacy of our proposed mechanisms.

Industrial Internet applications require networks to guarantee deterministic end-to-end latency and zero packet loss at both the data link and network layers. Traditional best-effort communication models in consumer networks are insufficient to meet these stringent demands. To meet these stringent demands, the IEEE 802.1 standards introduce Time-Sensitive Networking (TSN) at the data link layer, while the IETF proposes Deterministic Networking (DetNet) for the network layer. However, enabling seamless cross-domain communication between TSN and DetNet remains a significant challenge. This paper proposes a unified cross-domain network architecture and a time-slot alignment strategy that compensates for synchronization errors between the TSN and DetNet layers. We further develop a Joint Routing and Scheduling algorithm for Deterministic Cross-Domain Transmission (JRS-DCT), which simultaneously addresses routing and scheduling under cross-domain constraints. The algorithm leverages Cycle-Specified Queuing and Forwarding (CSQF) in DetNet and Cycle Queuing and Forwarding (CQF) in TSN to ensure bounded latency and deterministic transmission. Extensive simulations demonstrate that the proposed JRS-DCT algorithm significantly improves the scheduling success rate and effectively reduces network resource utilization compared to two baseline algorithms. These results validate the effectiveness and robustness of the proposed framework in supporting time-sensitive communication across heterogeneous network environments.

Remote Direct Memory Access (RDMA) is widely used in distributed services in Data Center Networks (DCNs) due to its high performance. As DCNs expand in scale, RDMA faces scalability issues. The reason is that the high concurrency Queue Pairs (QPs) lead to cache misses on RDMA Network Interface Card (RNIC) and frequent evictions, and the behaviour of fetching the cache via PCIe leads to performance degradation of RDMA. In this paper, we model the behaviour of Work Queue Element (WQE) on RNIC as a producer-consumer model and investigate that the root cause of WQE cache misses is the mismatch between the production rate of the CPU and the consumption rate of the RNIC. We design Weir from the perspective of WQE cache control to avoid cache misses and improve throughput under high concurrent QPs. Weir determines the cache occupancy on the RNIC by monitoring the number of active QPs and the increase/decrease in the life cycle of WQEs, and calculates the production rate and pacing by credit. The implementation of Weir exhibits minimal CPU overhead. Evaluation results show that Weir can maintain 97Gbps throughput without degradation even with up to 16K concurrent QPs, and effectively reduces various observable cache misses by$5\times $to$10\times $compared to commercial RNICs. Additionally, experiments show that Weir has better connection scalability than XRC and DCT.

The rapid development of Artificial Intelligence Generated Content (AIGC) technology has led to a strong demand for elastic computing resources. The crowdsourced edge cloud system builds a flexible resource pool by integrating heterogeneous idle servers and even personal devices to meet the dynamic computing requirements of AIGC services. The system relies on the serverless architecture to realize the dynamic scheduling of resources, which needs to trade off resource benefits and energy consumption costs to improve the overall social welfare, resource efficiency, and environmental sustainability. However, challenges such as unfair resource pricing, dynamic resource availability, and the complexity of strategy optimization remain unresolved for green and efficient resource management. In this paper, we propose a resource management framework named ReFluid. We introduce a game-theoretical pricing model to ensure fair pricing, a fluid model-based analysis for promoting a more sustainable management of computing resources, and a diffusion-based optimization mechanism to enhance model stability and adaptability. The evaluation shows that ReFluid significantly improves average social welfare and reduces energy consumption.

Low Earth Orbit (LEO) satellite networks, characterized by their low latency and extensive coverage, play a pivotal role in the development of future 6 G communication systems. However, due to the dynamic nature of network topology and the challenges associated with real-time perception of link states, the implementation of deterministic routing in LEO satellite networks presents significant difficulties. To address these difficulties, we propose a network calculus-based deterministic routing (NCDR) algorithm. Specifically, we develop a deterministic resource characterization model and design a traffic pre-transmission mechanism that utilizes network calculus theory to calculate the traffic backlog. Additionally, we propose an interruption feedback mechanism to deal with link interruptions. Finally, the NCDR algorithm makes routing decisions aimed at minimizing end-to-end transmission delay and balancing network load. Simulation results demonstrate that the NCDR algorithm significantly outperforms existing algorithms in terms of delay, throughput, and packet loss rate.

Network slicing, which enables multiple services to coexist on the shared physical infrastructure, has been recognized as a key technology in networks. Leveraging the high processing capability of programmable data plane (PDP) devices, network slicing within PDPs can lead to efficient traffic isolation, priority management, and significantly reduced forwarding delays. However, existing inflexible and coarse-grained network slicing orchestration approaches struggle to address the challenges posed by diversified slicing scenarios and the constrained resources of PDP devices. In this paper, we propose AtlasPro, a framework for network slicing orchestration in PDPs, where each network slice is regarded as an independent Service Function Chain (SFC) routing entity. Thus, it enables the allocation of physical resources at a finer granularity. Additionally, we introduce a novel heuristic approach, the Chained Hyper-Generative Algorithm, which jointly optimizes Virtual Network Function (VNF) deployment and routing costs, minimizing total cost while meeting the performance requirements of all network slices. We implement our framework using BMv2 switches in a Mininet environment and evaluated our algorithm. Compared to existing solutions, our framework and algorithm reduce the number of VNF deployments, lower routing delays by up to 34.2 %, and cut overall costs by up to 48.5%.

With the explosive growth in the scale and complexity of large language models (LLMs), there is an urgent need to extend training and inference workloads from within a single data center to across multiple data centers. However, this also introduces new challenges for network transport protocols. To address these issues, we propose SRCC (Sub-RTT Congestion Control), a method designed for inter-datacenter networks. Specifically, SRCC introduces a flowset-based mechanism along with shared node tables, enabling Datacenter Interconnect (DCI) switches to be aware of the path status of each flow. By leveraging information shared among different flows, SRCC can accurately adjust the sending rate at a sub-RTT timescale, thereby significantly improving network performance. Building on this approach, we design detailed mechanisms to address the following challenges: (1) applying INT technology in wide-area networks; (2) acquiring INT information with low overhead; and (3) achieving precise congestion window adjustments under sub-RTT perception.We conducted large-scale simulations using NS3, and the experimental results show that our scheme reduces the average FCT slowdown by 44.17% and 53.86% compared to HPCC and DCTCP, respectively.

The swarm of Unmanned Aerial Vehicles (UAVs) has garnered considerable attention, particularly in scenarios with critical situations or limited communication infrastructure. In such cases, Mission UAVs (MUs) often be deployed in clusters to provide communication services. However, the high mobility of MUs always leads to frequent changes in swarm network topology, posing challenges for the network performance. To tackle this issue, we deploy additional Relay UAVs (RUs) with a two-stage adaptive learning-based approach. In the first stage, we employ a Delaunay triangulation-based algorithm to optimize RUs’ position and construct the initial topology. In the second stage, we implement a centralized learning and decentralized execution (CTDE) reinforcement learning framework to ensure continuous network connectivity and optimize performance throughout the task cycle. To further enhance cooperation among RUs, we introduce a sequential update technique coupled with an entropy regularization term during the policy network updates. Finally, extensive simulation results demonstrate the effectiveness of our proposed algorithms.

In recent years, Satellite-Terrestrial Networks (STNs) have garnered significant attention for extending network coverage to areas beyond the reach of traditional terrestrial networks. With the rapid expansion of STN applications, integrating Service Function Chaining (SFC) technology has become crucial for delivering differentiated services. However, the dynamic and complex structure of STNs presents significant challenges for SFC deployment. To address these, we propose a multimodal reinforcement learning algorithm that uses separate neural networks to process diverse STN data, enabling more effective SFC deployment decisions. Our approach includes a Graph Transformer for processing network states represented as graphs, capturing the relationships between nodes, links, and resource distributions. Additionally, two MLPs are used to handle QoS requests and global network information. Built on these components, the Proximal Policy Optimization (PPO)-based algorithm demonstrates superior performance over conventional AI methods, effectively learning optimal SFC deployment strategies.

Edge devices increasingly require efficient, on-device intelligence for diverse applications in IoT networks. In order to bring the advanced capabilities of large foundation models directly to the point of data generation, there is a growing interest in deploying these models on edge devices. However, due to their inherent resource constraints and the diverse, heterogeneous nature of the data and tasks they encounter, deploying large foundation models directly on these devices remains a significant challenge. To address these challenges, we propose a novel Federated Learning Fine-Tuning (FLFT) framework that leverages adapter-based fine-tuning with a similarity-driven selection mechanism, enabling personalized model adaptation with minimal computational overhead. Furthermore, we introduce the Diffusion-based Soft Actor-Critic (FTFL2DSAC) algorithm, which optimizes real-time resource allocation by balancing energy consumption and latency across heterogeneous edge devices. Our experiments on CIFAR-100 using a pre-trained multimodal model demonstrate that FLFT achieves 82.5% accuracy while reducing model parameters by 14%, outperforming baseline methods with faster convergence and enhanced stability in complex environments.

Large language models (LLMs) have emerged as a transformative advancement in artificial intelligence (AI). To fully leverage their potential, Docker containers, serving as a lightweight, portable, and isolated framework, facilitate the seamless deployment of LLM-based applications. However, the deployment of containerized AI microservices faces challenges such as heavy network loads, delayed image loading, and redundancy. In this paper, we introduce MetaPipe, an innovative incremental deployment approach for containerized AI microservices in edge cloud environments. MetaPipe aims to optimize startup times through a dynamic workflow that incorporates proactive layer pre-fetching and reinforcement layer re-scheduling. The proactive pre-fetching reduces service deployment time through layer caching prediction and pre-scheduling before requests arrive, while the reinforcement re-scheduling addresses inaccuracies by dynamically adjusting layer scheduling strategies after requests arrive. Extensive experiments on realworld datasets show that MetaPipe significantly outperforms traditional methods, achieving 83.58% reduction in initialization startup time and 85.58% reduction in cold startup time. These results highlight its effectiveness in enhancing the performance of AI microservices deployment within edge cloud environments.

The rapid growth of distributed service infrastructures has promoted container-based deployment as a lightweight and flexible approach for large-scale service delivery. To enhance the trustworthiness of service deployment, blockchain has been incorporated into container networks as a decentralized trust layer. However, most existing blockchain solutions still rely on coarse-grained trust models that neither capture the evolution of node or layer credibility nor incorporate the layered structure of container images into deployment decisions, which may lead to services being deployed on low-reputation nodes and to the propagation of untrusted layers across the network. To address these limitations, this paper proposes ReTainer, a reputation-aware containerized service deployment framework in blockchain networks. We formulate the deployment problem as a joint optimization and decompose it into a linear-programming request routing subproblem and a service orchestration subproblem, which simultaneously covers service deployment, layer precaching, and layer re-scheduling. A hierarchical trust model captures the temporal evolution of node-level and layer-level credibility, and the resulting reputation priorities are used to select the top-k trustworthy layers for pre-caching. We then formulate the service activation and layer re-scheduling as a submodular optimization problem over a p-extendible system. Experimental evaluations demonstrate that ReTainer improves utility by up to 13.56% and reduces startup latency by 43.31% compared with existing deployment schemes.

The rapid advancement of UAV swarm networks has enabled their widespread application across various domains, including disaster relief, environmental monitoring, and intelligent transportation. Collaboration among UAVs within a swarm is vital for efficient resource utilization and optimal performance across these diverse applications. To address diverse service demands, deploying service function chains (SFC) in UAV swarm networks facilitates the real-time implementation of services through efficient resource allocation and UAV cooperation, thereby enhancing network reliability and efficiency. However, traditional SFC deployment strategies struggle to achieve reliability and efficiency due to dynamic topology and limited resources. Additionally, Stochastic Network Calculus (SNC) derives end-to-end latency, guaranteeing quality of service (QoS) in UAV swarm networks. To navigate this issue, we propose a cooperative dynamic SFC deployment algorithm that combines hierarchical proximal policy optimization (HPPO) with an edge-enhanced dynamic graph attention network (EDGAT) for real-time network state extraction. The simulation results validate the effectiveness of our proposed algorithm, showcasing improvements in deployment success rate and long-term average revenue.

The rapid growth of 5G and 6G networks has introduced complex traffic patterns and stringent real-time demands. Traditional SDN architectures struggle to meet the low-latency and dynamic requirements of wireless environments due to high communication overhead and rigid hardwares. Programmable switches, with their ability to dynamically cus-tomize data plane behavior, offer a more flexible solution for real-time traffic management at the network edge. However, most existing solutions rely on offline models with limited real-time detection capabilities, resulting in increased overhead and suboptimal performance. In this paper, we present Gendetect, a generative-AI enabled lightweight traffic detection architec-ture for programmable wireless gateways. Gendetect employs generative knowledge distillation to train decision tree-based models, enabling efficient online training and adaptive updates. By generating synthetic training data in real-time, it reduces the need for frequent control plane interactions, mitigating north-south overhead. Additionally, a feature selection mechanism optimizes resource utilization, balancing table entry consumption and detection accuracy. Extensive simulations demonstrate that Gendetect significantly improves traffic detection performance while reducing match-action table entries, making it well-suited for dynamic and resource-constrained wireless networks.

Recently, unmanned aerial vehicle (UAV) swarm networks have garnered considerable interest from both academia and industry, with applications spanning disaster response and logistics. These environments are complex and demand efficient, stable network performance under highly dynamic conditions. Clustering is a promising solution to manage UAVs by creating a hierarchical structure. We propose a distributed method, Distracted Density Peaks Clustering (DDPC), which uses local information to build a decision graph and identify density centers. Additionally, a dynamic maintenance strategy enhances adaptability, and simulations confirm its effectiveness.

With the advantages of low delay, wide coverage, and high throughput, low-Earth-orbit (LEO) satellite networks hold significant potential for establishing globally interconnected networks. However, the large spatial scale of satellite networks leads to lagging link state perception. Moreover, the perception overhead significantly increases with the growing number of satellites. These characteristics have brought great challenges to the routing design of large-scale LEO satellite networks. To address the above challenges, we propose a multi-region joint routing (MRJR) algorithm based on network calculus (NC) theory to achieve low-delay transmission without relying on traditional state perception. Firstly, a multi-region NC model is introduced to efficiently manage satellite networks and decouple the traffic transmission process. Then, we design the MRJR algorithm, which accurately derives link traffic backlogs to acquire real-time link congestion states, thereby calculating the lowest delay routing path. Additionally, an NC timeslot correction mechanism is proposed to ensure the accuracy of traffic backlog calculations. The simulation results demonstrate that the MRJR algorithm outperforms existing routing algorithms in terms of average delay, throughput, and packet loss.

The rapid evolution of Industrial Cyber-Physical Systems (ICPS) with cloud-fog automation calls for the deployment of Deep Neural Networks (DNNs) on edge devices to enable intelligent and autonomous decision-making. However, the resource constraints, heterogeneity, and dynamic nature of edge devices pose limitations to the efficient deployment of DNNs. Federated Learning-based Neural Architecture Search (FL-NAS) has been proposed to address these limitations, but achieving an effective balance between the generalized global model and personalized local models remains a non-trivial task due tosuboptimal aggregation of homogeneous neural blocks, knowledge waste of heterogeneous neural blocks, and high communication and energy overhead. In this paper, we proposeF²NAS, an energy-friendly federated neural architecture search framework tailored for ICPS. The fine-grained aggregation strategy adapts weights for each device during aggregation, enhancing the global and personalized local models. The bidirectional knowledge transfer mechanism leverages heterogeneous neural blocks, promoting knowledge sharing among local and global models. The adaptive communication strategy optimizes interactions between edge devices and the cloud server based on model performance, reducing energy costs while maintaining effective model collaboration. Extensive experiments demonstrate thatF²NASoutperforms baselines by up to 30.31% in accuracy on edge devices, 38.75% on the cloud server, and achieves a 65.2% reduction in energy consumption. When applied to the surface defect detection task in ICPS,F²NASsurpasses other baselines by up to 13.38% and 302.46% for edge devices and cloud servers, respectively, and reduces energy consumption by 44.8%.

In edge computing, energy-limited distributed edge clients present challenges such as heterogeneity, high energy consumption, and security risks. Traditional blockchain-based federated learning (BFL) struggles to address all three of these challenges simultaneously. This article proposes a Graph-Partitioning Multi-Granularity Federated Learning method on a consortium blockchain, namely GP-MGFL. To reduce the overall communication overhead, we adopt a balanced graph partitioning algorithm while introducing observer and consensus nodes. This method groups clients to minimize high-cost communications and focuses on the guidance effect within each group, thereby ensuring effective guidance with reduced overhead. To fully leverage heterogeneity, we introduce a cross-granularity guidance mechanism. This mechanism involves fine-granularity models guiding coarse-granularity models to enhance the accuracy of the latter models. We also introduce a credit model to adjust the contribution of models to the global model dynamically and to dynamically select leaders responsible for model aggregation. Finally, we implement a prototype system on real physical hardware and compare it with several baselines. Experimental results show that the accuracy of the GP-MGFL algorithm is 5.6% higher than that of ordinary BFL algorithms. In addition, compared to other grouping methods, such as greedy grouping, the accuracy of the proposed method improves by about 1.5%. In scenarios with malicious clients, the maximum accuracy improvement reaches 11.1%. We also analyze and summarize the impact of grouping and the number of clients on the model, as well as the impact of this method on the inherent security of the blockchain itself.

The emerging integrated sensing and communication (ISAC) technologies show great potential for 5G NR, offering a new wireless-sensing infrastructure paradigm. Users can benefit from pervasive sensing applications in various scenarios without communication penalties. Given the diverse demands for sensing resources across different sensing tasks, elastic resource scheduling becomes crucial, particularly when resources are constrained. However, existing approaches often treat users equally, limiting the applicability in dealing with diverse sensing tasks in the real world. In this article, we introduce ElaSe , a pioneering sensing technique that enables elastic and prompt scheduling of sensing resources. At the core of ElaSe is the exploration of the user’s state to precisely determine the sensing resource requirements and schedule resources accordingly. We build the first model for matching sensing resources with sensing demands, and further propose a predictive scheduling scheme to eliminate delays by leveraging the 5G virtualized radio access network (vRAN). ElaSe has been implemented on a CPU-based 5G vRAN and commercial 5G user equipments. We conduct experiments to evaluate the performance of ElaSe under different settings. The results demonstrate that ElaSe outperforms the non-scheduling scheme, with a 34% reduction in trajectory tracking error and a 92% decrease in resource allocation error.

Space-air-ground (SAG) integrated heterogenous networks can provide pervasive intelligence services for various ground users (GUs). The network can help cellular networks release network resources and alleviate congestion pressure. Moreover, one important application of the network is that digital twin (DT) can enable nearly-instant wireless connectivity and highly-reliable data mapping from physical systems to digital world in a real-time fashion. The integration of SAG and DT (SAG-DT) reduces the gap between data analysis and physical status, which can further realize robust edge intelligence services. However, the random computation task arrival, time-varying channel gains, and the lack of mutual trust among ground GUs hinder better quality of service in the promising SAG-DT network. In this paper, we envision a SAG-DT integrated blockchain model to transfer the task data to the aerial network, and then perform the computation offloading, energy harvesting and privacy protection. Moreover, we propose a Lyapunov-aided multi-agent deep federated reinforcement learning (MADFRL) algorithm framework to optimize the CPU cycle frequency, the size of block, the number of DTs, and harvested energy to minimize the execution costs and privacy overhead. Extensive performance analyses indicate that the MADFRL algorithm framework can strengthen the data privacy via blockchain verification mechanism and approaches the optimal performance on the basis of lower computation complexity. Finally, simulation results corroborate that the proposed Lyapunov-aided MADFRL algorithm is superior to advanced benchmarks in terms of execution costs, task processing quantities and privacy overhead.

Satellite-ground twin networks (SGTNs) are regarded as a promising service paradigm, which can provide mega access services and powerful computation offloading capabilities via cloud-fog automation functions. Specifically, cloud-fog automation technologies are collaboratively leveraged to enable dense connectivity, pervasive computing, and intelligent control in terrestrial industrial cyber-physical systems, whose system-level privacy security can be strengthened via blockchain based consensus protocol. Moreover, digital twin (DT) can shorten the gap between physical unities and digital space to enable instant data mapping in SGTNs environments. However, complex multi-modal network environments, such as stochastic task size, dynamic low earth orbit location, and time-varying channel gains, hinder better performance metrics in terms of energy consumption, throughput and privacy overhead. Hence, we establish a SGTN integrated cloud-fog automation model to transfer task data to low earth orbit satellites, and then execute broad communication access, powerful computation offloading, and efficient twin control. Next, we propose a Lyapunov stability theory based multi-modal federated learning (LST-MMFL) method to optimize the battery energy, the size of block, computation frequency, and the number of twin control for minimizing the total energy consumption and privacy overhead. Furthermore, we design a novel blockchain based transaction verification protocol to strengthen privacy security, derive performance upper bounds of SGTN model, and fulfill the long-term average task as well as energy queue constraints. Finally, massive simulation results show that the proposed LST-MMFL algorithm outperforms existing state-of-the-art benchmarks in line with energy consumption, available battery level, networked control and privacy protection overhead.

Currently, beamforming antenna array technologies are of utmost importance in 5G communication systems. These technologies are essential for optimizing the coverage and signal quality of the cellular network. However, the optimization of broadcast beams presents significant challenges due to the complex strategy profile space. Each beam can be configured with different widths and heights, making it difficult for conventional algorithms to handle. To address this issue, we propose a novel approach called Multi-Agent Moth-Flame Reinforcement Learning (MAMF-RL) algorithm for broadcast beam optimization. MAMF-RL combines reinforcement learning and moth-flame optimization algorithms to interactively search for the optimal broadcast beams. By decomposing the problem into multiple single-sector antenna configuration problems, MAMF-RL effectively reduces the algorithm complexity. We conducted experiments utilizing real data in an 18-sector wireless coverage area. To evaluate the performance of our proposed method, we compared it with traditional methods such as the particle swarm algorithm. The results demonstrate that our MAMF-RL model achieves an average coverage rate of 1.82% higher and a 13.74% lower overlapping coverage rate compared to traditional methods.

The rapid advancement of the UAV swarm network has made its widespread application across a multitude of domains. However, the inherently dynamic nature of the network often gives rise to intermittent connectivity issues, leading to a significant reduction in the data transmission capacity. To address this challenge, this study explores the integration of Information-centric Network (ICN) with the delay-tolerant network (DTN). This design aims to enhance message delivery rates by caching content data packets in UAV nodes. Building upon this architecture, we study the congestion control and load balancing problem. We design an on-demand collaborative communication routing algorithm. In our design, we first propose a routing decision model that incorporates multiple routing metrics to capture the dynamic evolution patterns of network nodes, effectively controlling local congestion issues. Subsequently, we employ Lyapunov optimization techniques to achieve a network load balancing. By integrating the Lyapunov drift function, we ensure the stability of a feasible solution space within the model. Additionally, considering the high communication overhead caused by the sparse communication characteristics of DTN, we deploy a Multi-Agent Incentivized Communication (MAIC) algorithm to optimize routing scheduling strategies. Within the MAIC framework, each agent develops unique models for its teammates to generate customized information and minimize network information redundancy. Simulation results demonstrate that this algorithm effectively ensures a congestion control and a load balancing within the UAV swarm network while maintaining communication overhead in routing computations at a minimal level.

Over the past decade, low-Earth-orbit (LEO) satellite networks have emerged as a critical infrastructure in communication systems, providing wide coverage, high reliability, and global connectivity. Recently, the development of 6G technologies has challenged the LEO satellite networks to guarantee deterministic scheduling for time-sensitive services. However, traditional deterministic networking techniques fall short for LEO satellite networks. First, these techniques impose strict time constraints, but in LEO satellite networks, delay and jitter typically range in the tens of milliseconds, which exceed these limits and render them infeasible. Second, the dynamic topologies of LEO satellite networks challenge the inflexible scheduling strategies generated by these techniques, leading to sub-optimal performance and potential strategy failures. To tackle the first problem, we propose a Cycle Specified Queuing and Forwarding (CSQF) based deterministic flows scheduling mechanism. It relaxes strict time constraints by employing cyclic multi-queue scheduling, enabling more flexible and reliable long-distance transmission. For the second problem, we propose a learning-based swarm intelligence method for deterministic flows scheduling in dynamic LEO satellite networks. It includes an algorithm that combines a Dynamic Graph Convolutional Network (DGCN) with an Adaptive Ant Colony Optimization (ACO) algorithm, referred to as the DGCN-ACO algorithm. The DGCN captures the dynamic feature of the network and generates the heuristic information. The Adaptive ACO utilizes the heuristic information and considers each flow's attribute to generate multi-path scheduling strategies for each deterministic flow, as well as updates the DGCN. The experiment results demonstrate the effectiveness of our proposed algorithm.

In-band network telemetry (INT) is an emerging network measurement technique that offers real-time and fine-grained visualization capabilities for networks. However, the utilization of INT for network measurement introduces additional overheads to the network. The process of data collection consumes extra bandwidth resources, and adjustments to the data collection scheme can impact network stability. Additionally, the INT orchestration scheme requires adaptation to dynamics in the network to improve measurement accuracy. Therefore, striking a balance between accuracy and stability becomes a critical problem. In this paper, our focus lies in the trade-off between measurement accuracy and network stability. We consider the long-term orchestration of multiple telemetry tasks, rationally deploying distinct telemetry tasks to different application flows. To address the challenge, we propose a self-adaptive Dynamic INT Orchestration scheme, D-INTO. Specifically, we formulate a stochastic optimization problem for dynamic INT orchestration. Then we employ Lyapunov optimization to decouple the stochastic optimization problem and use surrogate Lagrangian relaxation to construct a polynomial-time approximation algorithm. Theoretical analysis and experimental results demonstrate that our proposed D-INTO outperforms existing schemes in terms of adaptability to the network dynamics.

In recent years, there has been significant attention in UAV Named Data Networking (UNDN) from both industry and academia. This network paradigm adopts a “request-reply” communication model that allows UAVs to access desired content without the need for specific information regarding the geographical location or IP address of the content producer. This IP-independent design is well-suited for dynamic UAV swarms, but it presents challenges in establishing matching policies between content consumers and producers. This is because that during the distributed decision-making process in content sharing, consumers cannot possess private information regarding producers, and producers may lack the motivation to distribute content. As a result, a revelation and incentive mechanism is needed to be formulated in the system. In this paper, a resource-efficient content-sharing mechanism is proposed to address the aforementioned challenges. First, we propose a contract-based mechanism to incentivize content producers to share content and reveal their private information at the same time. The problem of obtaining the optimal contract is discussed in both cases of information asymmetry and complete information. Then, the Gale-Shapley (GS) algorithm is adopted to make a stable many-to-one matching between content consumers and content producers. The simulation results verify the feasibility, effectiveness and energy efficiency of the proposed mechanism.

In recent years, the integration of mobile edge computing (MEC) and non-orthogonal multiple access (NOMA) has gained significant attention for its potential to reduce energy consumption and offloading latency in future wireless networks. While NOMA can enhance system capacity, accommodating multiple users on the same channel may lead to decoding inaccuracies and reduced offloading accuracy. To tackle these problems, this paper proposes a multi-user offloading model that combines NOMA and orthogonal multiple access (NOMA-OMA) to optimize resource allocation. Users are divided into groups based on their geographical locations, with each group further divided into subgroups. OMA is used within each subgroup, while NOMA is employed between different subgroups to achieve joint multi-user offloading. We divide the optimization problem into two sub-problems, namely power and time allocation between different subgroups and delay allocation within the same subgroup. Closed-form expressions for the two sub-problems are derived. The proposed method achieves optimal system energy consumption while increasing the number of users and maintaining low system complexity. Simulation results demonstrate the effectiveness of the proposed method.

The Digital Twin (DT) represents a rapidly advancing technological innovation within the Industrial Internet of Things (IIoT) domain. DT leverages the power of simulation, machine learning, and data mining to facilitate optimal decision-making for physical objects. However, the creation of a dynamic and living digital counterpart comes at a considerable cost. It requires continuous massive data updating and processing every time the physical object changes. As most data collected by IIoT devices are in their original form, such as images and videos, transmitting such data to remote cloud computing will result in large delays. Furthermore, data processing is often a computationally intensive operation, such as image recognition and video coding, making it impractical to perform processing tasks directly in IIoT devices. To overcome this problem, we introduced the Multi-access/mobile Edge Computing (MEC) architecture to enhance capabilities of DT-enabled IIoT devices. IIoT devices can leverage the extra computing resources in MEC to process raw data, transmitting only the calculation results to update the digital counterpart. To efficiently allocate resources between IIoT devices and MEC, we propose a double auction-based resource allocation scheme. The IIoT devices can purchase computing power from MEC, and an iterative double auction scheme is applied to achieve system efficiency within this market. Furthermore, we propose the Win or Learn Fast Algorithm Policy Hill Climbing (Wolf-PHC) algorithm, which enables agents to improve their strategies continuously through participation in auctions. Simulation results demonstrate that this algorithm accelerates the process of market equilibrium convergence.

The escalating demands for Artificial Intelligence-generated content (AIGC) services greatly require computing resources. The edge-cloud market offers an effective solution for AIGC services by integrating, managing, and trading distributed computing resources. Within this novel service market, participants contribute idle resources to support AIGC services to earn income, creating a more flexible market environment. Meanwhile, the generation quality and computing resource requirements of AIGC services are related to input prompts. Therefore, this relationship introduces new challenges, such asthe information uncertainty in input prompts, the inability to model resource continuity, and high-dimensional complexity for optimization.In this paper, we propose a resource fluid equilibrium management strategy for supporting AIGC services within edge-cloud market, termedFluE. To address the challenge of information uncertainty in user prompts, we measure the content value of AIGC prompts by information entropy and introduce a redundancy reduction approach to focus on meaningful information in prompts. To tackle the challenge of the inability to model the continuity provision of computing resources, we utilize the fluid model to ensure seamless resource provision and facilitate a more balanced management of computing resources. To address the challenge of high-dimensional complexity of strategy optimization, we develop a diffusion-based algorithm namedReDiffto reconstruct the target strategy distribution and generate precise and effective optimization decisions. We evaluate our proposed scheme under a dynamic resource provisioning environment. Based on the DiffusionDB dataset, the publicly available real trace of AIGC service prompt, ourReDiffalgorithm achieves up to 69.8% and 77.4% improvements in average social welfare compared to LySAC and CD-PPO, respectively.

The global rise of the green economy has positioned green financing as a critical method for enterprises to secure external funding. However, due to the high costs and imperfect regulations associated with environmental information disclosure, some enterprises establish the green image through greenwashing to attract external investment and minimize production inefficiencies, caused by abnormalities such as material shortages. The greenwashing behavior of enterprises is essentially a deception intended for investors, making it challenging for them to make informed decisions. To protect investors' interests, and maintain the market and social order, this paper conducts a study on corporate greenwashing based on the environmental module of ESG reports issued by Chinese Ashare listed companies. First, this paper utilizes MacBERT-LDA to extract topic distribution features (explicit features) and perform topic clustering. It then uses Bi-LSTM to extract topic sentiment features (implicit features) based on the clustering results. The two mutually independent feature extraction networks form a dual-channel structure, fully capturing the explicit and implicit information of the text data. Second, a Transformer model is utilized to fuse the dual-channel feature extraction results, producing richer and more comprehensive semantic features. Finally, these fused features are input into IT2F-BLS to achieve an accurate prediction of the degree of corporate greenwashing, which provides a powerful reference for investors to make decisions. We refer to this ensembled model as the IT2F-BLS greenwashing degree prediction model based on explicit and implicit textual features. Experimental results demonstrate that the model effectively captures both types of information and predicts corporate greenwashing with higher accuracy.

With the ongoing advancement of information and communication technologies, the communication technologies for UAV swarm networks have undergone rapid development, especially in the context of large-scale UAV network deployments. In recent years, UAVs have found wide-ranging applications in both military and civilian domains. However, the inherent complexity and high dynamic nature of UAV swarm activities present substantial challenges to traditional routing algorithms, prompting the need for the design and implementation of efficient and sustainable routing solutions. To address these challenges, this paper introduces a UAV swarm routing algorithm based on evolutionary game theory, with a particular focus on energy efficiency and resource optimization. We leverage evolutionary game theory to enhance cooperation among nodes and adopt a strategy update rule that imitates the best-performing agents. In the proposed algorithm, nodes engage in continuous packet forwarding and participate in game interactions with neighboring nodes, adjusting their strategies based on accumulated gains. This strategy not only significantly enhances network lifetime and improves the packet delivery rate but also optimizes energy consumption and resource utilization, aligning with sustainable computing principles. To validate the effectiveness of the proposed routing method, we conduct extensive simulation experiments within a designed and implemented system model under different environmental contexts. The analysis confirmed the accuracy and effectiveness of the proposed routing method, highlighting its exceptional performance in terms of the network survival time, the number of successfully transmitted packets, and the adaptability in dynamic scenarios.

Satellite-Ground Integrated Networks (SGINs) are promising network architectures that can help reduce the load on terrestrial networks, provide mega-access capabilities and intensive task offloading functions. However, traditional resource management methods are difficult to apply directly into SGINs due to their multi-layered, heterogeneous and dynamic three-dimensional characteristics. In addition, massive multi-modal and multi-task information hinders better service performance in SGINs. Therefore, we design a multi-task integrated computation offloading model to process complex multi-modal network information, such as time-varying channel gains and dynamic Low Earth Orbit (LEO) locations, which can efficiently improve data transmission rate and privacy level. Furthermore, we propose three multi-modal based learning methods, such as centralized actor-critic (C-AC) algorithm, distributed multi-agent deep deterministic policy gradient (D-MADDPG) algorithm, and quantization-based federated learning (Q-FL) algorithm for computation-intensive, latency-critical and privacy-preserving tasks, which can further optimize the local execution or LEO offloading ratio, CPU cycle frequency and transmission power. Meanwhile, we demonstrate the quantization error upper bound between the optimal solution and the quantization scheme through massive mathematical derivations. Finally, extensive simulation results show that the proposed multi-modal based learning methods have better performance gains in terms of model convergence performance, quantization metrics, data transmission rate and number of bits processed.

To overcome the challenges of complex time-varying satellite channels and severe inter-user interference in non-orthogonal multiple access (NOMA), rational power allocation and accurate multi-user joint detection methods are essential. In this paper, a sparrow search algorithm-based resource allocation and deep learning-based joint detection scheme (SSA-DeepJD) in the satellite-terrestrial NOMA system is proposed. First, the NOMA-orthogonal frequency division multiplexing (OFDM) system model is constructed. Next, a convolutional neural network-based image super-resolution recovery network is proposed for offline training and online channel estimation, which incorporates densely connected convolutional layers and residual learning to model for handling complex non-linear channel fitting. Then, a multi-user signal detection based on an iterative deep neural network is proposed, which is iteratively retrained to improve the detection accuracy. Finally, due to the significant impact of the power allocation on the system error performance, the optimal power allocation is found within the power allocation factor threshold based on SSA. Simulation results show that the proposed SSA-DeepJD algorithm is well-suited for multi-user superposed NOMA systems and complex non-linear channel environments. Compared to the baseline algorithms, the SSA-DeepJD algorithm degrades the Bit Error Rate (BER) by 21.5 dB and 11.9 dB in the 2-user and 3-user NOMA systems, respectively.

The presence of Artificial Intelligence Generated Content (AIGC) has garnered widespread interest. AIGC enables content creation by analyzing big data, leveraging the capabilities of extensive AI models, and substantial AI computing. Computing power networks (CPNs) represent an excellent approach for offering pervasive AI computing resources to AIGC. However, these characteristics have posed unprecedented challenges to the CPNs helped AIGC, including the uncertainty of prompts’ information value, the inability to model the continuity of computing resources, and the incapacity to represent complex multi-dimensional spaces. In this paper, we propose a computing resources equilibrium strategy based on the fluid model for AIGC helped by CPNs, namely FluE. This mechanism obtains information entropy by constructing an AIGC prompt tree to measure the information value of AIGC prompts. In addition, we model the continuity of computing resources by the fluid model. A fluid-stopping equilibrium strategy is formulated to obtain the average fluid level of computing resources based on the Laplace-Stieltjes transform. To solve the equilibrium strategy, we develop a diffusion-based algorithm for FluE to adjust the fluid policy dynamically to maximize resource rewards. Finally, the evaluations demonstrate improvements in average social welfare.

Driven by technological advances and new business models, we have seen a renewed interest in LEO satellite constellations. The deployment of large-scale LEO satellite networks is becoming a reality. The network topology changes periodically, as satellites orbit the Earth. This imposes a great challenge to network monitoring. Meanwhile, as a new network monitoring method, In-band Network Telemetry (INT) can provide per-hop granular telemetry metadata, needed to tackle the mobile nature of LEO satellite constellations. Given this, we apply INT to LEO satellite networks for real-time fine-grained monitoring. We propose a path planning solution to identify the paths for network-wide telemetry and the paths for disseminating the telemetry data to the ground facilities. By taking advantage of the predictable satellite trajectories and topology variations, the path planning solution is designed to achieve network-wide coverage and minimize telemetry overhead. We take the LEO48 constellation as an example to visually show the detailed paths of the monitoring scheme. We conduct experiments on different sizes of networks to evaluate the original path planning algorithm and the improved balanced algorithm in this paper, demonstrating the timeliness and balance of the telemetry solution.

Over the past decade, the Unmanned Aerial Vehicle (UAV) swarm has emerged as a disruptive force reshaping our lives and work. Benefiting from its fast and flexible deployment capabilities, UAV swarms have been widely applied to emergency communications. In the event of damaged ground communication base stations, UAV swarms can quickly reconstruct an emer-gency communication network. However, considering the limited coverage power of a single UAV node, it underscores the need for effective coordination among swarm units as well as diligent planning of a coverage trajectory. In this paper, we propose a cooperative intelligence-based UAV swarm approach for establishing emergency communications. We model a multi-UAV base station-assisted emergency communication scenario as a team Markov game model. To achieve cooperative collaboration among multiple UAVs, we propose a Q-function mixing network based coverage trajectory planning algorithm. Our experimental results demonstrate the superior convergence speed and throughput of the proposed algorithm.

The rapid growth of edge computing calls for fine-tuned deep neural network (DNN) deployment that emphasizes energy-efficient implementation, due to the resource constraints of edge devices. Traditional Federated Learning-based Neural Architecture Search (FL-based NAS) has been instrumental in the complexities of this deployment, particularly in addressing constraints posed by device heterogeneity, limited resources, and privacy preservation. However, it is hindered by issues such as suboptimal aggregation of homogeneous neural blocks, significant knowledge waste in disregarding heterogeneous neural blocks, and excessive communication energy consumption. This paper introduces F2NAS in green edge computing, a novel energy-efficient approach that addresses these limitations by ensuring flexible and energy-efficient model design and training for edge devices. Firstly, F2NAS introduces an innovative aggregation strategy that enhances the integration of homogeneous neural blocks by using inter-block distances to optimize weight allocation. Further, it employs a unique parameter extraction technique that recaptures valuable insights from previously overlooked heterogeneous neural blocks. Finally, F2NAS meticulously calibrates communication energy consumption by balancing loss function and model interaction, setting and refining an upper limit for model communication. Experimental results reveal F2NAS enhances model accuracy by 2.8% to 4.7%, simultaneously reducing the energy consumption by nearly 50% through optimizing the communication cost.

In order to ensure reliable transmission of important traffic in industrial networks, time-sensitive network (TSN) technology and fifth-generation mobile communication technology (5G) are introduced into the industrial network. However, there are still challenges in integrating TSN networks with 5G networks, especially in terms of end-to-end scheduling in hybrid systems. Considering the diverse range of traffic types and their end-to-end transmission requirements within the industrial Internet, we propose a differentiated traffic scheduling model and develop a population generation algorithm, termed Genetic Algorithm (GA) based two-stage population generation algorithm (PTPG). Notably, the algorithm utilizes a non-target training approach to generate the initial population and integrate Proximal Policy Optimization (PPO) to improve algorithm convergence and facilitate the inheritance of advantages across generations. The simulation results demonstrate notable enhancements in end-to-end delay, the number of occupied queues, and algorithm convergence status compared to other algorithms.

Integrated Sensing and Communication (ISAC) has been witnessed to be a new paradigm of wireless sensing in 5G networks. Users can benefit from pervasive sensing applications in various scenarios with no communication penalty. Given the diverse demands for sensing resources across different sensing tasks, elastic resource scheduling becomes crucial, particularly when resources are constrained. However, existing approaches often treat users equally, limiting their applicability in dealing with diverse sensing tasks in the real world. In this paper, we introduce ElaSe, a pioneering sensing technique that enables real-time elastic scheduling of sensing resources. At the core of ElaSa is the exploration of the user's state to precisely determine the sensing resource requirements and schedule resources accordingly. We build the first model for matching sensing resources with sensing demands, and further propose a predictive scheduling scheme to eliminate delays by leveraging the 5G virtualized radio access network (vRAN). We conduct experiments to evaluate the performance of ElaSe under different settings. The results demonstrate that ElaSe outperforms the non-scheduling scheme, with a 34% reduction in trajectory tracking error and a 92% decrease in resource allocation error.

Giant models have transformed vision-language tasks by mastering consistent representations across text and images, highlighting the critical role of deploying such models in the expanding domain of edge scenarios, such as monitoring and segmentation. However, the deployment is challenged by device heterogeneity, limited computational resources, and privacy concerns. Federated learning (FL) presents itself as a viable solution, facilitating decentralized training on devices and preserving data confidentiality. Despite its potential, FL faces obstacles with fine-tuning efficiency, including complex granularity data, static personalized prompt generation, and high energy consumption. This paper introduces a cross-granular and personalized prompt-based green federated tuning (CP2GFed) approach, aiming to address these issues by enabling giant model deployment on devices. CP2GFed introduces a cross-granularity knowledge transfer mechanism to leverage semantic relationships across varying data granularities. Meanwhile, it pioneers in generating dynamic personalized prompts based on inter-device affinities to improve model performance. In addition, CP2GFed meticulously optimizes energy consumption, including model local learning and interaction, by setting local computing steps and selecting communication devices. Empirical results indicate that CP2GFed elevates accuracy by up to 6.64% on diverse datasets and reduces energy consumption by nearly 60% per unit of accuracy, achieving a superior tradeoff between model performance and energy consumption compared to baselines.

The integration of Software-Defined Networking (SDN) and Artificial Intelligence (AI) presents promising opportunities for managing and optimizing LEO satellite network routing. However, as the scale and coverage of satellite networks continue to expand, challenges are posed to both centralized and distributed architectures in terms of managing network information and coping with routing complexity. To overcome these challenges, leveraging distributed SDN technology, a stigmergy multi-agent hierarchical deep reinforcement learning routing algorithm is proposed in multi-domain collaborative satellite networks. A pheromone-based mechanism is incorporated to facilitate collaboration during independent training, and hierarchical control is employed to decouple the complexity of cross-domain routing decisions. Simulation results demonstrate that our proposed algorithm exhibits good scalability and performance in large-scale satellite networks.

Web 3.0 pursues the establishment of decentralized ecosystems through blockchain technologies, driving digital transformation in commerce and governance. With consensus algorithms and smart contracts grounded in cryptographic technologies, Web 3.0 enables secure and transparent digital services, such as digital identity, asset management, decentralized autonomous organizations (DAOs), and decentralized finance (DeFi), fostering integration between digital and physical economies. As quantum devices rapidly advance, Web 3.0 is being developed in parallel with the deployment of quantum cloud computing and quantum Internet. In this regard, quantum computing first disrupts the original cryptographic systems that protect data security while reshaping modern cryptography with enhanced quantum computing and communication capabilities. This article provides a comprehensive overview of blockchain-based Web 3.0, examining its quantum and postquantum advancements from two key perspectives. On the one hand, postquantum migration methods and quantum-resistant signatures offer robust solutions to safeguard blockchain against quantum threats. On the other hand, quantum and postquantum encryption and verification algorithms boost blockchain performance, creating a decentralized, secure, and value-driven system. Additionally, we outline potential applications of quantum blockchain and offer guidance for implementation within the Web 3.0 ecosystem. Finally, we discuss future directions for developing a provably secure and decentralized digital ecosystem.

Due to the scarcity of spectrum resources in Non-orthogonal Multiple Access (NOMA) systems and insufficient satellite-ground integration in satellite Internet of Things (IoT), this paper investigates its issue in spectrum resource management. We propose a resource allocation method based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG) for NOMA enabled satellite IoT. We formulate the spectrum allocation problem of the satellite-ground integrated network as a distributed optimization problem. Then we decouple the problem into two sub-problems. Firstly, a user grouping method based on matching coefficients is defined, and a Linear Programming (LP) method is utilized for obtaining solution. Secondly, the power allocation problem is transformed into a multi-agent problem, where MADDPG is employed to allocate the power. Through this approach, the system is capable of real-time user association and spectrum resource allocation optimization, achieving optimal user grouping while maximizing system transmission rate. Based on the simulation results, the MADDPG-based method demonstrates fast convergence within 100 training iterations. The proposed MADDPG-based resource management method also achieves increased system transmission rate with more effective matching outcomes over Deep Deterministic Policy Gradient (DDPG), Orthogonal Multiple Access (OMA), and random allocation baselines.

Satellite-ground integrated heterogeneous networks can relieve network congestion, release network resources and provide ubiquitous intelligence services for terrestrial users. Furthermore, digital twin technology can enable nearly-instant data mapping from the physical world to digital systems. The integration between satellite-ground integrated heterogeneous networks and digital twin alleviates the gap between data analyses and physical unities. However, the current challenges, such as the pricing policy, the stochastic task arrivals, the time-varying satellite locations, mutual channel interference, and resource scheduling mechanisms between the users and cloud servers, severely affect the improvement of quality of service. Hence, we establish a blockchain-aided Stackelberg game model for maximizing the pricing profits and network throughput in terms of minimizing privacy overhead, which is able to perform computation offloading, decrease channel interference, and improve privacy protection. Due to the long-term task queue in Stackelberg model, we propose a Lyapunov stability theory-based model-agnostic meta-learning aided multi-agent deep federated reinforcement learning framework to transfer the long-term task queue into the single time slot, and then optimize the central processing unit frequency, channel selection, task-offloading decision, block size, and cloud server price, which facilitate the integration of communication, computation, and block resources. Subsequently, several performance analyses show that the proposed learning framework can strengthen the privacy protection, approach the optimal time average function, and fulfill the long-term average queue size via lower computational complexity. Finally, our simulation results indicate that the proposed learning framework is superior to the existing baseline methods in terms of network throughput, channel interference, cloud server profits, and privacy overhead.

Mobile edge computing (MEC) and millimeter-wave (mmWave) communication are promising techniques for future cellular networks. MEC enables latency-critical tasks offloading at the network edge, while mmWave provides an abundant spectrum for gigabit-per-second data transmission. Dense deployment of remote radio units (RRUs) is necessary due to high mmWave signal path loss, and hence limiting the deployment cost becomes a prime network design factor. Our work considers that RRUs are deployed to provide mmWave access and to offload computation requests to edge servers (ESs) via fronthaul links. We propose an edge network (EN) deployment problem by jointly optimizing the mmWave access and fronthaul networks. Converged fiber and in-band mmWave techniques are utilized for flexible fronthaul links deployment and cost reduction. The deployed EN is expected to fulfill coverage, reliability and latency requirements of ultra-reliable low-latency (uRLLC) services. We formulate the optimization problem as an integer linear program (ILP) and propose a multi-objective evolutionary algorithm to solve the problem. The numerical results demonstrate that our proposed algorithm can achieve close-to-optimal solutions compared with the ILP formulation. We also comparatively evaluate the deployment costs under different EN settings and show that our algorithm provides up to 20.3% cost savings compared to non-converged solutions.

We demonstrate a probabilistic shaping (PS) four-dimensional (4D) modulation in self-homodyne coherent transmission system. The 4D modulation is based on inter-symbol amplitude translation (AT) to perform set partitioning. The distribution of constellation points after AT is optimized by de-DC. The parity bits produced by the AT are transmitted with the pilot tone by remapping. In addition, a soft decision for this 4D-PS signal is proposed. An experiment of self-homodyne coherent ultra-high order 4D signal transmission based on two cores of a 7-core fiber is demonstrated with a spectral efficiency of 16.37 bit/s/Hz. The 4D signals with soft decision can provide up to 0.78 bit/symbol and 1.85 bit/symbol gain compared to normal polarization division multiplexing signals and hard-decision 4D signals.

Unmanned aerial vehicle (UAV) swarms have found extensive applications owing to their flexibility, mobility, cost-effectiveness, and capacity for collaborative and autonomous service delivery. Empowered by intelligent algorithms, UAV swarm can exhibit cohesive behaviors and autonomously coordinate to achieve collective objectives. Nonetheless, in real-world scenarios with uncertainty and stochasticity, its performance suffers from the unstable information exchange among UAVs and inefficient data sampling. In this paper, we introduce a metaverse-based UAV swarm system, where monitoring, observation, analysis, and simulation can be realized collaboratively and virtually. Within the metaverse, virtual service providers (VSPs) utilize digital twin (DT) to generate and render virtual sub-worlds, while providing diverse virtual services. In particular, the VSP trains the learning model using high-fidelity data from the physical world, formulates optimal decisions for diverse tasks, and returns these decisions to the UAV swarm for the execution of the corresponding tasks. Since synchronization between two worlds needs frequent data exchange, we employ the semantic communication technique in our system which could reduce communication latency by transmitting only the semantic information. In such design, UAVs as workers are employed to collect data and provide extracted semantic information to the VSPs. Moreover, we propose a hierarchical framework to investigate the reliability and sustainability of the metaverse-based UAV swarm system. In the lower layer, we design a worker selection scheme to determine reliable UAVs for data synchronization. In the upper layer, we consider deep learning (DL)-based auction as the incentive mechanism for resource allocation in semantic information trading between UAV swarm and VSPs.

In recent years, the rapid growth of Internet of Things (IoT) devices and applications has posed significant challenges for existing Mobile Edge Computing (MEC) architectures. The inherent latency uncertainties in MEC architectures make it difficult to support latency-sensitive applications such as autonomous vehicles. Additionally, the increasing number of connected devices has led to substantial challenges in terms of limited throughput for MEC servers. With the recent advancements in programmable network hardware, such as SmartNICs and programmable switches, the Network-based Computing (NBC) paradigm has gained widespread attention. Leveraging line-rate processing capabilities, NBC offers a promising solution for high throughput and low latency processing. This paper aims to explore the potential benefits and challenges of incorporating NBC into existing MEC architectures. The feasibility of our proposed architecture is evaluated using two use cases, Linear Quadratic Regulator (LQR) control and Complex Event Processing (CEP), demonstrating significant improvements in latency performance.

The flying ad hoc networks (FANETs) have attracted a large amount of attention from both academia and industry. Benefiting from the flexibility, the FANETs have been widely deployed in various scenarios, ranging from agricultural production to emergency rescue. However, in FANETs, the mobility of unmanned aerial vehicles (UAVs) has led to critical challenges for the stability of communications. Especially, the routing flooding mechanism extremely limits the scalability of FANET. To overcome these technical challenges, constructing a hierarchy and clustering structure in FANETs is considered a promising solution. In this paper, we propose the fission spectral clustering (FSC) strategy for UAV swarm networks. We model the UAV clustering problem as a graph cut problem. The time-sequential attributes weight of nodes and edges will be input to the FSC algorithm. Then, it will construct the Laplace matrix and calculate the first k-th eigenvectors of it. We apply the K-Means algorithm into this feature space to cut the graph by clustering the eigenvectors. Each cluster will constantly fission with this strategy until it satisfies the size and structure constraints in the UAV clusters. Some simulations are implemented to evaluate our proposed algorithm in comparison to the other state-of-the-art solutions.

With the continuous growth of network traffic, optical wireless communication (OWC) technology based on orbital angular momentum (OAM) can meet the needs of large-capacity modern communication and is an effective way to substantially increase wireless information transmission capacity. However, the OAM beam distortion caused by atmospheric turbulence in the actual link and the limitations caused by phase singularities are major challenges faced by the OAM-OWC system. In this paper, we address these issues and propose a low-complexity nonprobe adaptive optics (AO) compensation technique based on Y-net which can achieve high-accuracy distortion compensation and OAM mode demodulation simultaneously. In this approach, only one CCD is required for the Y-net-aided AO (Y-net AO) technique without a traditional probe path while satisfyingly balancing OAM-based optical transmission system complexity and transmission performance. Extensive simulations show that the proposed Y-net-aided AO technique can indeed decontaminate distorted OAM beams in both single- and multiplexed-channel OAM links. Furthermore, a noise model is established to analyze the robustness of the Y-net AO technique. The Y-net AO technique exhibits less system complexity and better anti-noise performance than the ordinary convolutional neural network (CNN)-based AO scheme. In summary, Y-net AO technology for OAM-OWC systems with high correction accuracy and low structural complexity is considered for effectively improving the transmission performance in this paper. Key AO technologies for the high-quality and innovative development of large-capacity communications are also expected to be formed.

Recently, diverse emerging scenarios have precipitated a substantial surge in the variety of devices and applications, which has consequently imposed more stringent demands on Quality of Service (QoS) prerequisites. As a burgeoning emerging network measurement method, In-band network telemetry (INT), can provide detailed metrics for QoS by obtaining fine-grained network status information. However, INT only outlines device-level operations, which fails to provide an entire network view for monitoring. To address this, INT orchestration based on network topology and application requirements to achieve network-level monitoring is necessary. In this paper, we propose an INT orchestration model that efficiently measures the entire network while minimizing measuring overhead. The model outputs the probe path and collects requirements for the devices it passes through. Our method effectively reduces network bandwidth consumption caused by INT process and ensures telemetry items remain fresh. Experiment results support the effectiveness of our approach.

The satellite-ground (SG) integrated networks are regarded as a promising network structure, which can provide ubiquitous intelligence and pervasive services for multiple ground users. Moreover, digital twin (DT) can drive real-time data mapping and wireless access from usual physical utilities to digital units. Therefore, the fusion of SG and DT can decrease the gap between real-time data analysis and physical system states, which can help boost SG-DT edge intelligence paradigms. Nevertheless, the unexpected task arrivals, time-varying channel gains, and distrust among ground devices cause the network service performance degradation. Hence, in this paper, we propose a privacy-assisted blockchain computation offloading model to shine upon original tasks to the corresponding aerial platforms, and then orchestrate the task scheduling, resource allocation, and privacy protection. Additionally, we envision a Lyapunov stability theory-based multi-agent federated reinforcement learning (LST-MAFRL) algorithm to further resolve the CPU cycle frequency, the size of each blockchain, the number of DTs, and related harvested solar energy to minimize the execution energy consumption and privacy time overhead. Finally, extensive simulation results indicate that the proposed LST-MAFRL algorithm framework outperforms some state-of-the-art benchmarks for the sake of execution energy efficiency, processed bit quantities, and privacy time overhead.

The integration of unmanned aerial vehicles (UAVs) with 5G networks presents a promising opportunity to revolutionize wireless communication and provide high-speed internet access to remote areas. Nevertheless, the vast quantity of data generated by UAVs requires the implementation of efficient distributed learning techniques. In this study, we present a novel hybrid approach that merges Federated Learning (FL) and Split Learning (SL) to optimize the performance of UAV swarms in 5G networks. While FL is capable of reducing communication overhead and preserving privacy, SL can enhance the accuracy of the model through the utilization of the local computational resources of each device. To realize the hybrid approach, we first locally train the model on each UAV using split learning. Subsequently, the encrypted model parameters are transmitted to a central server for federated averaging. Finally, the updated model is dispatched back to each UAV for local fine-tuning, and this cycle is repeated until convergence is achieved. The hybrid approach capitalizes on the strengths of both FL and SL to minimize communication overhead and increase accuracy. To tackle the challenge of selecting the most suitable UAVs for participation in the learning process, we propose a multiagent algorithm that considers factors such as communication latency and training time. Our experimental results indicate that the proposed approach leads to substantial improvements in communication overhead and accuracy compared to conventional methods.

As an important complement to the terrestrial network and an essential component of the future 6G, Low Earth Orbit (LEO) satellite network is expected to provide higher-quality communication services in combination with terrestrial network and attracts widespread research interest. Since the unbalanced distribution of terrestrial services may lead to inter-satellite link (ISL) congestion, balancing the network load has become one of the key issues for LEO satellite networks. In order to prevent ISL congestion in LEO networks, we propose a multi-region division routing algorithm based on fuzzy-shortest-path-first (FSPF-MDR) for LEO satellite networks. We divide the satellite network into multiple small regions and share link information within the regions to achieve congestion avoidance. Simulation results show that, using the proposed algorithm, the computing complexity is reduced significantly with a slightly cost of increasing average hop count. Furthermore, it is able to achieve congestion prevention while using fewer resources. With different scales of LEO networks, the computing complexity can be reduced by 45% to 70%.

Elastic cognitive optical network(ECON) embeds artificial intelligence technology into network management to enable resource self-optimization ability, which has aroused the wide interest of researchers. However, realizing precise traffic prediction (TP) in optical networks has been a challenging problem due to channels’ complex variable bandwidth conditions. We provide an ECON architecture and propose a graph-convolutional-network-transformer (GCN-transformer) TP algorithm. The proposed algorithm has been evaluated and compared with the traditional schemes. We build a testbed for the proposed algorithm by OMNET++. The results show a prediction accuracy of 99.74%, which reduces the inaccuracy by 2.03% compared with other typical algorithms.

The combination of digital twin (DT) and Internet of Vehicles (IoV) has gained significant attention from both academia and industry in recent times. DT can establish a high fidelity virtual representation of IoV based on the real-time sensor data, and feedback the decision policy, therefore generating possible improvements. Especially, as the advance of Mobile Edge Computing (MEC) technique, it has the potential to facilitate digital twin¡¯s computationally intensive tasks. However, how to schedule the MEC computing resource is the key to efficient operation of the whole system. Therefore, this study aims to investigate pricing considerations and resource management that exist between the vehicle and MEC server in order to mitigate this issue. Specifically, we model the interaction between the MEC server and vehicles as a Stackelberg game, where the leader (i.e., the MEC service provider) sets prices, and then the vehicles act as followers. By leveraging information about social interactions from other vehicles, utility functions are formulated by the vehicles. Additionally, the study analyzes the existence and uniqueness of the Stackelberg equilibrium, and proposes a dynamic iterative algorithm to find the appropriate Nash equilibrium for the proposed Stackelberg game. Experimental results demonstrate that the proposed scheme effectively formulates suitable prices and meets computational requirements.

Cyclic queuing and forwarding (CQF), proposed in IEEE 802.1 Qch, is a practical mechanism for guaranteeing deterministic transmission for time-sensitive networks (TSNs). However, only the queue model and the workflow for terrestrial networks are defined in IEEE 802.1 Qch. To make TSNs practical for future 6G applications, a general scheduling model that maps time-sensitive flows (TSFs) to the underlying resources of low-Earth-orbit satellite-terrestrial integration networks (LEOSTINs) is urgently needed. The networking conditions of STINs are quite different from those of terrestrial networks due to the large-scale spatial coverage of STINs. Hence, in order to determine the feasibility of deploying TSNs in LEO-STINs, we evaluate the CQF performance for LEO-STINs in this paper. Then, a software-defined-network-based LEO-STIN architecture for the entire lifecycle of TSFs is designed. To address the drawbacks of the LEO-STIN scenario, we propose a cyclic priority and forwarding (CPF) mechanism to improve the performance of time-sensitive services. CPF removes the bandwidth limitation of CQF for TSFs, which makes TSNs practical for LEO-STINs. We perform a simulation of a Walker constellation to test the proposed algorithm and existing TSN techniques using OMNET ++. The results show that the proposed algorithm reduces the packet loss ratio by an order of magnitude and the service time-out ratio by 70% compared to existing mechanisms.

The past few years have witnessed an exponential growth of diverse Internet of Things (IoT) devices as well as compelling applications ranging from industrial production to medical care. Dramatic advances in IoT technology not only brought enormous economic opportunities but also challenges (e.g., privacy and security vulnerabilities). Recently, with the appearance of blockchain technology, the integration of IoT and blockchain (BCoT) is considered a promising solution to address these issues. Blockchain provides a secure and scalable data management framework for IoT devices. However, the huge computation and energy cost of the consensus process in blockchain prevents it from being directly applied as a generic platform. To overcome this challenge, in this article, we propose a cloud mining pool-aided BCoT architecture, where the IoT devices can rent the computing resources from the cloud mining pools to offload the mining process. Based on this architecture, we study the mining pool selection problem and analyze the colony behaviors of IoT devices with different pooling strategies. We propose a centralized evolutionary game-based pool selection algorithm for the sake of maximizing the system utility. Considering the non-cooperative relationship among multiple miners, we also propose a lightweight distributed reinforcement learning algorithm, named the ‘WoLF-PHC’ algorithm.

Driven by the burgeoning growth of the Internet of Everything and the substantial breakthroughs in deep learning (DL) algorithms, a booming of artificial intelligence (AI) applications keep emerging. Meanwhile, the advance in existing computing paradigms, i.e., cloud computing and edge computing, provide assorted computing solutions to satisfy the increasingly high requirements for ubiquitous AI services. Nevertheless, there are some non-trivial issues in the computing frameworks, including the underutilization of computing power, the self-interest of computing-power trading mechanism, and the inefficiency of AI services management. To tackle the above issues, we propose a computing-power trading framework based on blockchain, also named AI-Bazaar. In AI-Bazaar, the AI consumers play multiple roles and feel free to contribute the computing power rented from the computing-power provider (CPP) for blockchain mining and AI services. Accordingly, we formulate the computing trading problem as a Stackelberg game. Based on the win or learn fast principle (WoLF), we design a profit-balanced multi-agent reinforcement learning (PB-MARL) algorithm to search the AI-Bazaar equilibrium, while finding the balanced profits for AI consumers and CPP. Numerical simulations are carried out to demonstrate the satisfactory performance and effectiveness of the proposed framework.

With the rapid development of the Industrial Internet of Things (IIoT), massive IIoT devices connect to industrial networks via wired and wireless. Furthermore, industrial networks pose new requirements on communications, such as strict latency boundaries, ultra-reliable transmission, and so on. To this end, time-sensitive networking (TSN) embedded fifth-generation (5G) wireless communication technology (i.e., TSN-5G networks), is considered the most promising solution to address these challenges. TSN can provide deterministic end-to-end latency and reliability for real-time applications in wired networks. 5G supports ultra-reliable and low-latency communications (uRLLC), providing increased flexibility and inherent mobility support in the wireless network. Thus, the integration of TSN and 5G provides numerous benefits, including increased flexibility, lower commissioning costs, and seamless interoperability of various devices, regardless of whether they use a wired or wireless interface. Nonetheless, the potential barriers between the TSN and 5G systems, such as clock synchronization and end-to-end traffic scheduling, are inevitable. Time synchronization has been studied in many works, so this paper focuses on the end-to-end traffic scheduling problem in TSN-5G networks. We propose a novel integrated TSN and 5G industrial network architecture, where the 5G system acts as a logical TSN-capable bridge. Based on this network architecture, we design a Double Q-learning based hierarchical particle swarm optimization algorithm (DQHPSO) to search for the optimal scheduling solution. The DQHPSO algorithm adopts a level-based population structure and introduces Double Q-learning to adjust the number of levels in the population, which evades the local optimum to further improve the search efficiency. Extensive simulations demonstrate that the DQHPSO algorithm can increase the scheduling success ratio of time-triggered flows compared to other algorithms.

LEO satellite networks have become a necessary supplement to terrestrial networks aiming to provide worldwide, ubiquitous connectivity, especially in complicated areas (e.g., mountains, oceans, and disaster areas) where terrestrial network infrastructures are typically sparingly distributed or unavailable. However, the increasing computation-intensive Internet-of-Things (IoT) applications (e.g., real-time remote monitoring, intelligent transportation) require not only efficient and reliable communication but also massive computing capabilities. Constrained by the battery and computing resources, the computing tasks and data of applications have to be transmitted to remote cloud servers. This bandwidth limitation and high transmission delay in LEO networks will reduce the quality-of-service (QoS) of IoT applications. Recently, the combination of LEO networks and edge computing (i.e., Satellite Mobile Edge Computing, SMEC) offers significant opportunities to address these problems. The IoT devices can directly get the computing resources directly from satellites rather than remote servers, thus avoiding long-distance transmission. Considering the resource constraints on satellites, offloading policy plays a crucial role in whole system performance. In this paper, we design a hybrid offloading architecture, which applies a centralized training and distributed execution framework. Also, we propose a multi-agent actor-critic reinforcement learning algorithm, where a centralized “critic” is augmented with the global network state to ease the training procedure of distributed user equipments (UE) by evaluating the benefits of their decisions, while the UEs can adjust their policies according to the critic’s evaluation and choose their own decisions relying on their observations.

The space-air-ground (SAG) integrated networks will play a major role in the sixth generation (6G) mobile networks, which will provide global coverage, full connection and pervasive intelligence services for multiple ground Internet of Things (IoT) devices. Moreover, massive computing tasks can be either performed by local devices, or offloaded to edge servers, such as low orbit satellites, high altitude platforms (HAPs) and remote base stations. Nevertheless, the joint computation and communication resource allocation solutions are becoming challenging due to the large-scale state space, time-varying network scenarios, and limited battery capacity. In this paper, we propose a SAG-integrated three-layer heterogenous network model to maximize the sum-rate of ground IoT devices, which further enhances the deep integration of communication and computation resources. Additionally, we develop a Lyapunov-assisted multi-agent proximal policy optimization algorithm to process the task scheduling, HAP selection, battery harvesting, and CPU cycle frequency optimization. Extensive simulation results corroborate that the proposed method has superior performance gains in terms of the remaining battery capacity, energy consumption, and maximum average sum-rate compared with the state-of-the-art baselines.

The Unmanned Aerial Vehicle (UAV) swarm has attracted widespread attention from both academia and industry. It has been widely adopted in disaster recovery, military communication, agricultural production, and industrial automation. In critical situations or places where communication infrastructure is lacking, deploying a UAV swarm network is a cost-effective solution. However, considering the high speed of UAV devices, designing an effective routing mechanism has been a challenging problem. In this paper, enlightened by the recent success of multi-agent reinforcement learning, we propose a multi-agent policy gradients-based UAV routing algorithm. We adopt a centralized training and decentralized executing framework, where a centralized training platform is implemented to guide the policy updating of each UAV node. Moreover, we introduce a counterfactual baseline scheme in our algorithm to improve the convergence speed. Extensive simulation results validate the effectiveness of the proposed algorithms compared to the state-of-the-art schemes.

With the introduction of the concept of 6G ubiquitous intelligence, a network infrastructure that can provide ubiquitous intelligence services has been expected widely. Among all the promising technologies, satellite-terrestrial Internet of Things (IoT) networks are one of the key enablers of the implementation of 6G IoT, offering multiple services for remote IoT applications, such as disaster rescue and remote area monitoring in global coverage. As the satellite-terrestrial link is vulnerable to eavesdropping which can greatly damage users' privacy, implementing information security protection for reliable communication is extremely necessary. Therefore, we introduce the security service provider to offer encryption services for remote IoT users. However, higher security configuration can lead to higher overhead for the service provider and higher prices for users. Thus, to find the optimal service price and encryption security configuration, this paper models the interaction between IoT users and the service provider as a Stackelberg game. To achieve the Nash equilibrium, we formulate the decision-making process as a Markov Decision Process. Then, we apply the ‘Wolf-PUC’ multi-agent reinforcement learning algorithm to learn the optimal security configuration and pricing strategies. Finally, the feasibility and performance of the algorithm are demonstrated with our simulation results.

The fast and intelligent reconfigurability of recon-figurable add-drop multiplexers (ROADMs) in metropolitan area networks (MANs) has gained much attention recently due to technical advancements in artificial intelligence and fast optical switching. However, it is challenging to realize submillisecond-level automatic reconfiguration for MANs under fast time-varying traffic pattern, because of the latency of the wavelength scheduling and traffic cognition lag. On the one hand, the latency for wavelength scheduling takes tens of millisecond for the most-used ROADMs; On the other hand, the lag involved in the traffic cognition weakens the advantage of fast wavelength scheduling. To view of these problems, this article proposes a fast-reconfigurable MAN architecture with closed control plane targeted to the submillisecond-level reconfiguration. The proposed architecture reduces the reconfigurable latency for both the data plane and the control plane. Furthermore, we design a latency estimator based on graph neural network (GNN) for congestion awareness, and develop a fast-reconfigurable ROADM based on semiconductor optical amplifier. We evaluate the estimator and proposed architecture under various scenarios. The results show that the GNN-based estimator can achieve high precision in the latency estimation.

Emerging latency-sensitive applications (e.g., industrial control, in-vehicle networks) require that the networks guaranteed data delivery with low, bounded latency. To meet this requirement, the IEEE 802.1 Working Group developed the time-sensitive networks (TSN) standard to enable deterministic communication on standard Ethernet. TSN technology is developed to enable deterministic communication using traffic scheduling and shaping technology. However, while the TSN standards define the mechanisms to handle scheduled traffic, it does not specify algorithms to compute fine-grained traffic scheduling policy. Current TSN flow scheduling schemes largely rely on a manual process, requiring knowledge of the traffic pattern and network topology features. Inspired by recent successes in applying reinforcement learning in online control, we propose a deep reinforcement learning aided no-waiting flow scheduling algorithm in TSN. Extensive simulations are performed to verify that our algorithm can find the optimal solution in an acceptable time.

The satellite-assisted Internet of Things (IoT) communication is considered a key component of the 6G network, and low Earth orbit (LEO) satellite is the leading choice of IoT-related satellites due to its minimum delay. Hybrid requests, including immediate reservation (IR) and advanced reservation (AR) services in LEO satellite netoworks cause the occurrence of resource fragments (RFrags), which adversely affect the network performance. To alleviate the degradation of network performance caused by resource fragmentation, a routing, wavelength, and time-slots assignment algorithm, which is named elastic resource allocation algorithm based on the dispersion degree (ERA-DD), is proposed in this article. Possible resource fragmentation types are analyzed and a fragmentation description named dispersion degree (DD) is designed. In the DD, the number of free resource blocks is accurately described by the state jumps (SJs) of adjacent resource slots, and the numerical relationship between SJs and free resource blocks is proposed and proved. Besides, restrictions on the selection priority of candidate schemes in the ERA-DD algorithm are analyzed. Finally, the traffic blocking rate, the wavelength utilization, the average communication delay, and the average initial delay are evaluated by simulation. The results demonstrate that RFrags are more fully utilized compared with the maximum total link spectrum consecutiveness (MTLSC) algorithm. The traffic blocking rate can be reduced by 32.5% and wavelength utilization can be increased by 1.6%.

In this article, a distributed optical fiber sensing system for large infrastructure temperature monitoring is proposed. To meet the requirements of monitoring networks in terms of measurement accuracy, spatial resolution, and real-time or quasireal-time performance, a quaternion wavelet transform (QWT) image denoising algorithm is proposed to address the original edge node data for the structural monitoring networks of large infrastructures. A distributed Brillouin optical time-domain analysis (BOTDA) sensing system with a 40-km sensing fiber is established. The raw Brillouin gain spectrum (BGS) image is decomposed into one magnitude image and three phase images by QWT. The phase images of the OWT are distributed randomly and disorderly with respect to the noise, while the magnitude image of the quaternion wavelet is greatly affected by the noise. The useful message energy of the magnitude image is concentrated on a small number of coefficients with large amplitude, while the noise mainly corresponds to the coefficients with smaller amplitude. Then, the Bayes shrink threshold method is introduced to filter out noise in the magnitude image. The results indicate that the signal-to-noise ratio (SNR) and the frequency uncertainty have been improved significantly. The accuracy of the retrieved Brillouin frequency shift from denoised BGS images reaches 0.2 MHz, which corresponds to a temperature error of ±0.1 °C. Less than 4 s are required to process a BGS image with 50$\times $40 000 pixels by the QWT denoising technique. The uploaded data obtained from 40 M bytes of raw data are reduced to 0.08 M bytes for each measurement. We hope that with technological progress and algorithm optimization, the distributed optical fiber sensing system based on the QWT image denoising algorithm will have an important role in the real-time application of large-scale infrastructure structural health monitoring for the Internet of Things.

Orbital angular momentum (OAM) has the potential to dramatically enhance the amount of information in the Internet of Underwater Things (IoUT) system. Nevertheless, underwater-turbulence-induced scintillation will destroy the orthogonality of OAM modes, hence degrading the performance of the system. In this article, a random-amplitude-mask-based adaptive optics (AOs) technique is proposed for the sake of mitigating the turbulence effects in the OAM-based underwater wireless optical communication (UWOC) system. Combined with phase retrieval algorithms, the magnitudes of linear measurements obtained from the distorted OAM beams modulated with a series of random amplitude masks and focused by a lens are employed for the phase estimation. Furthermore, we present a comprehensive performance comparison against state-of-the-art phaseless wave-front sensing techniques. Moreover, the mixture exponential-generalized gamma (EGG) distribution is applied for characterizing the probability density function (PDF) of reference-channel irradiance of OAM beams coupled into a single-mode fiber (SMF). In the end, the performance metrics, such as the outage probability, the average bit-error-rate (BER), and the ergodic capacity are analyzed with the aid of PDF for both single-input-single-output (SISO) and multiinput-multioutput (MIMO) systems. In a nutshell, this article provides new insights for the applications of AO in the OAM-based UWOC system, which can serve as a candidate for supporting IoUT devices.

Nowadays, the compelling applications of the Internet of Things (IoT) bring unexpected economic benefits to our daily lives. But at the same time, it also poses huge challenges to service providers. Diverse proprietary hardware (i.e., firewall and code conversion) have to be deployed in networks for meeting different applications’ requirements. Recently, network functions virtualization (NFV) is considered a promising technique. In the NFV-enabled architecture, network services can be implemented via a set of orderly virtual network functions (VNFs) on standardized compute nodes, which is termed service function chains (SFCs). However, with the explosion of IoT applications, embedding multiple SFCs in a shared NFV-enabled infrastructure becomes a challenging problem. Centralized schemes suffer from the scalability and private issue, while distributed schemes suffer from the nonconvergence problem. In this article, we propose a hybrid intelligent control architecture, which adopts the centralized training and distributed execution paradigm. A centralized critic is introduced to ease the training process of the distributed network nodes. Besides, considering the competitive behavior of users, we formulate the resource allocation problem as a multiuser competition game model. Based on this, we proposed a multiagent reinforcement learning-based SFCs deployment algorithm.

Network traffic classification has become an important part of network management, which is beneficial for achieving intelligent network operation and maintenance, enhancing the network quality of service (QoS), and for network security. Given the rapid development of various applications and protocols, more and more encrypted traffic has emerged in networks. Traditional traffic classification methods exhibited the unsatisfied performance since the encrypted traffic is no longer in plain text. In this work, we modeled the time-series network traffic by the recurrent neural network (RNN). Moreover, the attention mechanism was introduced for assisting network traffic classification in the form of the following two models, the attention aided long short term memory (LSTM) as well as the hierarchical attention network (HAN). Finally, relying on the ISCX VPN-NonVPN dataset, extensive experiments were conducted, showing that the proposed methods achieved 91.2 percent in accuracy while the highest accuracy of other methods was 89.8 percent relying on the same dataset.

Recently, the term ‘Internet of Things’ (IoT) has garnered great attention. As a trusted, dependable, and decentralized approach, blockchain has already been used in IoT. However, the existing blockchain has a number of drawbacks that prevent it from being used as a generic platform for IoT. The nodes in IoT are heavily resource-limited, especially computing and networking resources. Unfortunately, they are necessary for the blockchain to solve complicated puzzles and propagate blocks. In this paper, we propose agent mining and cloud mining approaches to solve the above problem in the blockchain-enabled IoT. To be specific, miners act as mining agents for nodes in IoT, offload mining tasks to cloud computing servers, and use networking resources dynamically. Furthermore, in order to enhance the performance, the access selection of users, computing resources allocation, and networking resources allocation are formulated as a joint optimization problem. We then propose a dueling deep reinforcement learning approach to address this problem. Numerical results justify the effectiveness of our proposed scheme.

With the growth of the number of Internet of Things (IoT) devices and the emergence of new applications, satisfying distinct QoS in the same physical network becomes more challenging. Recently, with the advance of network functions virtualization and software-defined networking (SDN) technologies, the network slicing technique has emerged as a promising solution. It can divide a physical network into multiple virtual networks, therefore providing different network services. In this article, to meet distinct QoS in industrial IoT, we design a network slicing architecture over the SDN-based long-range wide area network. The SDN controller can dynamically split the network into multiple virtual networks according to different business requirements. On this basis, we proposed a deep deterministic policy gradient (DDPG) based slice optimization algorithm. It enables LoRa gateways to intelligently configure slice parameters (e.g., transmission power and spreading factor) to improve the slice performance in terms of QoS, energy efficiency, and reliability. In addition, to accelerate the training process across multiple LoRa gateways, we leverage the transfer learning framework and design a transfer learning-based multiagent DDPG algorithm.

Relying on offloading computation tasks to the network edge, ultra dense networks (UDNs) are capable of providing delay-aware service to nearby users. Meanwhile, software defined networking (SDN) is deemed as an effective technology to ease the management of infrastructure plane and control plane in UDNs, which is termed as SDN-based ultra dense networks. Specifically, the centralized SDN controller is capable of managing the whole network globally. With the increasing demands for various applications as well as the limitation of computation, storage and communication resource, how to allocate spectrum resource appropriately is imperative. In this article, we mainly show solicitude for spectrum sharing and edge computation offloading problems in SDN-based ultra dense networks, constituted of various macro base stations (MBSs), small-cell base stations (SBSs) and user equipments (UEs). To address this issue, we propose a second-price auction scheme for ensuring the fair bidding for spectrum rent, which enables the MBS edge cloud and SBS edge cloud to occupy the channel in cooperative and competitive modes. Moreover, the MBS edge cloud is termed as the buyer, and the SBS edge clouds are the sellers who sell the offloading resource to the MBS edge cloud. To be specific, the spectrum sharing and computation offloading scheme is executed in the SDN controller, and the controller is responsible for distributing spectrum allocation instructions to the infrastructure plane. Finally, experimental results validate the effectiveness of our proposed scheme in SDN-based ultra dense networks.

Benefiting from dense connections in data center networks (DCNs), load balancing algorithms are capable of steering traffic into multiple paths for the sake of preventing traffic congestion. However, given each path’s time-varying and asymmetrical traffic state, this may also lead to worse congestion when some paths are overutilised. Especially in the two-tier hybrid optical/electrical DCNs (Hoe-DCNs), the port contentions and large-grained optical packets of the fast optical switch (FOS) require the top-of-rack (TOR) switch to have microsecond-level load balancing capability for microburst traffic. This paper establishes a leaf-spine Hoe-DCN model to illustrate the principal characteristic of dynamic load balancing in TOR switches for the first time. Moreover, we propose the dynamic distributed multi-path (DDMP) load balancing algorithm that relies on dynamic hashing computing for network flow distribution in DCNs, which dynamically adjusts traffic flow distribution at microsecond level according to the inverse ratio of the buffer occupancy. The simulation results show that our proposed algorithm reduces the TOR-to-TOR latency by 15.88% and decreases the packet loss by 22.06% compared to conventional algorithms under regular load conditions, which effectively improves the overall performance of the Hoe-DCNs. Moreover, our proposed algorithm prevents more than 90% packet loss under low load conditions.

Adaptive optics (AO) can efficiently compensate for turbulence-induced distortion in orbital angular momentum (OAM)-based optical wireless communication (OWC) systems. In this paper, we design a modified phase diversity algorithm (MPDA)-based wavefront sensor to enhance the reconstruction accuracy of distorted OAM wavefront information. Aiming to further strike a compelling trade-off between AO system complexity and compensation accuracy, we first construct a novel AO system that applies a quickly and electronically controlled focus-tunable lens (FTL). It decontaminates distorted OAM signaling beams while having a low systemic complexity and superior convergence performance. Furthermore, we propose the 3-modified phase diversity algorithm (3-MPDA) AO scheme relying upon a Fourier intensity and two defocused intensities as the prior information, which beneficially balances the compensation effect and the number of defocused intensities and exhibits good noise robustness against charge-coupled device (CCD) detectors. In summary, this paper provides new insight for designing AO schemes with high compensation performance in communication links.

In microgrid, peer-to-peer (P2P) electricity trading has quickly ascended to the spotlight and gained enormous popularity. However, there are inevitable credit problems and system security problems. Besides, the current model in the electricity trading system cannot balance the utilities of multiple trading entities. In this paper, we propose a blockchain-based distributed P2P electricity trading system. We define elecoins as currency in circulation within our trading system. In order to jointly optimize the utilities of both parties in the elecoins trading, we formulate the elecoins purchasing problem as a hierarchical Stackelberg game. Then, we design a distributed multi-agent utility-balanced reinforcement learning (DMA-UBRL) algorithm to search the Nash equilibrium. Finally, we factually build a blockchain system with a blockchain explorer and deploy an electricity trading smart contract (ETSC) on Ethereum, with a website interface for operating. The numerical results and the implemented realistic system show the advantages of our work.

The sixth generation wireless communication networks (6G) are anticipated to bring a disruptive innovation on multiple scenarios, where deep edge networks (DENs) turn into a vital network structure on vertical industrial paradigms, including the combination of communication, computing and caching (3C). In this paper, we present the DENs scene to facilitate the deep convergence of computing and communication resources. More specifically, we formulate the optimization problem in terms of energy consumption and latency in order to minimize the total agents overhead. At the same time, for the sake of executing tasks and alleviating interference among different edge networks and high-dynamic network environments, we propose a CPU cycle frequency aided multi-agent deep deterministic policy gradient (C-MADDPG) algorithm framework to optimize the task scheduling, transmission power, CPU cycle frequency and mutual interference from multiple channels to obtain the optimal overhead. Finally, extensive simulation and experimental results demonstrate that our proposed C-MADDPG algorithm has better performance gain in term of execution overhead for different network parameters.

Resource allocation is a difficult online decision-making problem in Software Defined Networks (SDN). Traditional algorithms usually formulated the network resource topologies as simple matrices or edge lists (e.g., adjacency matrices). However, the rapid growth of the network scale and data volume brings new challenges to the scalability and efficiency of these models. Recently, network representation learning (NRL) has been widely adopted in network modeling, which can embed network nodes and links into low-dimensional vectors. Specifically, as one of NRL techniques, knowledge graph (KG) integrates both pieces of knowledge and their relations, and therefore capturing relational information in the network. Therefore, in this paper, we adopt KG for SDN representation and propose a novel resource allocation scheme based on the relational information learned by KG embedding. We first extract relational information between network nodes, links and requests, and then embed them into low-dimensional vectors. Based on these vectors, we calculate resource allocation schemes by consecutively selecting relay nodes and links in consideration of both available resources and their relational vectors. The extensive simulations are conducted to evaluate our proposed algorithm in comparison to state-of-the-art schemes.

In recent years, satellite communications have played an increasingly important role in daily life. With the explosive growth of new businesses, the expectations for the performance and reliability of satellite communications are greater than ever. However, due to the unique characteristics of satellite node (e.g., fast transmission speed, saturation of resources), it brings unprecedented challenges for load balance in multiple satellite paths. In this paper, to overcome this issue, we proposed a multi-agent reinforcement learning aided load balance architecture. We formulate the load balance in satellites communications as a partially observable Markov decision process (POMDP). Besides, we adopt a multi-agent reinforcement algorithm named Collaborate Q-learning (CollaQ) in our architecture. In addition, some stimulation are performed to evaluate the correctness of our architecture and algorithm.

The past few years have witnessed the compelling applications of the Internet of Things (IoT) in our daily life. The explosive growth of the number of IoT devices also presents a great challenge in network security, especially the DDoS attack. Current DDoS defense mechanisms adopted out-of-band architecture, which is accomplished by a process that receives monitoring data from routers and switches, then analyzes that flow data to detect attacks. However, facing IoT devices growing rapidly, this out-of-band architecture confronted with limited processing capacity, bandwidth resources, and service assurance problems. Recently, with the development of the programming switch, it opens up new possibilities for in-network DDoS detection, where the detection algorithms could be directly implemented inside the routers and switches. Benefit from switch processing performance, the in-network mechanism could achieve high scalability and line speed performance. Therefore, in this article, we design a machine learning-based in-network DDoS detection framework. We implement the lightweight variational Bayes algorithm in each switch to detect the anomaly traffic. Besides, considering the shortage of training data in each switch, a centralized platform is introduced to synchronize parameters among distributed switches to realize collaborative learning. Extensive simulations are conducted to evaluate our proposed algorithm in comparison to some state-of-the-art schemes.

Recently, the term “Internet of Things” (IoT) has elicited escalating attention. The flexibility, agility, and ubiquitous accessibility have encouraged the integration between machine learning (ML) with IoT. However, there are many challenges that present the key inhibitors in moving ML to the public solution, such as centralized training, poor training efficiency, and heavy computing capabilities requirements. Therefore, bringing learning intelligence to edge IoT nodes has been spotlighted for some researches. Meanwhile, how to govern the use of learning results efficiently, reliably, scalably, and safely is hampered by the heterogeneity and nonconfidence among IoT nodes. In this article, we propose a blockchain-based collective Q-learning (CQL) approach to address the above issues, where lightweight IoT nodes are used to train parts of learning layers, then employing blockchain to share learning results in a verifiable and permanent manner. We further improve the traditional Proof of Work (PoW). Instead of solving a meaningless puzzle, we regard the learning process in the IoT node as a piece of work. Accordingly, the winner is the IoT node with the minimum reduced percentage of the learning loss function, referred to as the Proof-of-Learning (PoL) consensus protocol. Specifically, in order to show how the CQL approach works, we use it to address a networking integrated cloud-edge-end resource allocation in IoT. The experimental results reveal the superior performance of the proposed scheme.

With the rapid development of Internet of Things (IoT), more and more applications focus on the detection of unmanned areas. With the assistance of unmanned aerial vehicle (UAV), IoT devices are able to access the network via aerial base stations. These UAV-assisted IoT applications still face security and energy challenges. The open environment of IoT applications makes the application easy to encounter external invasion. Limited energy of UAV results in the limited lifetime of network access. To address these challenges, researches on IoT security and energy efficiency are becoming hotspots. Nevertheless, in the UAV continuous coverage scenario, there is still an enormous potential to improve the security and efficiency of data collection in IoT applications. In this article, blockchain is introduced into the scene of UAV-assisted IoT, and a data collection system considering security and energy efficiency is proposed. In this system, UAV, as an edge data collection node, provides a long-term network access for IoT devices through regular cruises with recharging. By forwarding data and recording transactions, UAVs get charging coins as rewards. UAVs use charging coins to exchange charging time. UAV swarm builds distributed ledgers based on blockchain to resist the invasion of malicious UAV. In order to reduce energy consumption, this article designs an adaptive linear prediction algorithm. Through this algorithm, IoT devices upload prediction model instead of original data to greatly reduce in-network transmissions. Simulation results show that the proposed system can effectively improve the security and efficiency of data collection.

Load balancing is a difficult online decision-making problem in the current network. Recently, with the development of the programmable data-plane, it is feasible to perform flexibly load balance directly inside the network. This in-network load balance scheme can quickly adapt to the volatility of network traffic. However, previous in-network solutions are largely relying on the manual process. Inspired by recent successes in applying machine learning in online control, automating the in-network load balance process is thus appealing. But as a distributed control system, it behooves us to ask the critical question: “Can the distributed switches learn globally optimal scheduling policy and still be deployed in a distributed fashion to allow rapid reaction in real-time?” To tackle this question, we adopt a centralized learning and distributed execution framework and propose a multi-agent actor-critic reinforcement learning algorithm in this paper. The centralized “critic” is reinforced with the global network state and joint actions of all agents to ease the training process whilst distributed switches can take actions relaying on their local observations. In addition, a baseline scheme is introduced to solve the credit assignment problem in the multi-agent system. The extensive simulations are conducted to evaluate our proposed algorithm in comparison to state-of-the-art schemes.

The revolutions of computing and communication have opened up demands for the high quality of service (QoS), such as high data transmission, high reliability, and low latency. These new opportunities have spawned numerous studies on edge computing and artificial intelligence (AI), even the cooperation between them, referred to as edge intelligence. However, there are a number of handicaps that prevent edge intelligence from being used as a generic platform. The most intractable one is the heterogeneity and un-credibility among edges, hindering the way of sharing the learning results reliably, flexibly, and efficiently. In this paper, we propose a blockchain-assisted edge intelligence (B-EI) approach to solve the problem. The edge learning nodes train their local intelligence, followed by the improved blockchain to share the local intelligence, constructing edge intelligence among the heterogeneous and uncredible edges. Specifically, the improved blockchain employs a novel learning-measured consensus protocol, named Proof of Learning. The edges, also acted as the blockchain nodes, compete to have more superior local intelligence, instead of solving a hashed result. The superior local intelligence is then shared and distributed with other edges. It is not only beneficial to achieve edge intelligence, but also efficient to employ the computation resource, by replacing the hashing as the intelligence training. In order to show the potential benefits, we then use the proposed B-EI approach to solve a joint resource assignment problem. Simulation results show that our scheme outperforms the other state-of-art solutions, in terms of training episodes, and resource utility.

In the past few years, satellites have been widely influenced in our daily life, from Global Positioning System(GPS) to Military investigation. Due to the limitation of computation power and insufficient energy, the satellite has to offload their data to the ground station for further processing. However, long-range transmission has a great impact on many real-time services, such as hotspot tracing. As a remedy, in this paper, we introduce space stations to offload computation tasks of Low Earth Orbit(LEO) satellites to reduce the transmission delay. We formulate the problem of computing resource allocation between LEO satellites and space stations based on double auction mechanisms. Then, we describe an algorithm for searching Nash equilibrium based on Experience-Weighted Attraction(EWA) which is executed after each participant. Auction participants can obtain information from other adversaries and accumulate experience and reflection. So they can complete transactions with other participants in a fuzzy environment and maximize the overall benefit. Simulation results show the convergence and effectiveness of the algorithm.

Past few years have witnessed the compelling applications of the satellite communications and networking in our daily life. Due to the extremely high moving speeds and limited networking resources of LEO satellites, how to optimize inter-satellite traffic has received amount of attention from both academia and industry. In this paper, we proposed a hybrid satellites network traffic control paradigm. In our architecture, the centralized platform collect the global state and the joint action from each agent during the training phase to ease the training, and during execution, the each agent can return the action to the local state through the trained policy. Besides, we adopt a multiagent actor-critic algorithms named Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments(MADDPG) to our architecture. In addition, some simulation results are presented to evaluate the correctness of our architecture and algorithm.

In recent years, we have witnessed the compelling application of the Internet of Things (IoT) in our daily life, ranging from daily living to industrial production. On account of the computation and power constraints, the IoT devices have to offload their tasks to the remote cloud services. However, the long-distance transmission poses significant challenges for latency-sensitive businesses, such as autonomous driving and industrial control. As a remedy, mobile edge computing (MEC) is deployed at the edge of the network to reduce the transmission delay. With the MEC joining in, how to allocate the limited computing resource of MEC is a critical problem to guarantee efficient working of the whole IoT system. In this article, we formulate the resource management among MEC and IoT devices as a double auction game. Also, for searching the Nash equilibrium, we introduce the experience-weighted attraction (EWA) algorithm performing behind each participant. With this AI method, auction participants acquire and accumulate experience by observing others' behavior and doing introspection, which accelerates the trading policy's learning process of each agent in such an opaque environment. Some simulation results are presented to evaluate the convergence and correctness of our architecture and algorithm.

Unmanned aerial vehicles (UAVs) have been widely used to provide enhanced information coverage as well as relay services for ground Internet-of-Things (IoT) networks. Considering the substantially limited processing capability, the IoT devices may not be able to tackle with heavy computing tasks. In this article, a multi-UAV-aided mobile-edge computing (MEC) system is constructed, where multiple UAVs act as MEC nodes in order to provide computing offloading services for ground IoT nodes which have limited local computing capabilities. For the sake of balancing the load for UAVs, the differential evolution (DE)-based multi-UAV deployment mechanism is proposed, where we model the access problem as a generalized assignment problem (GAP), which is then solved by a near-optimal solution algorithm. Based on this, we are capable of achieving the load balance of these drones while guaranteeing the coverage constraint and satisfying the quality of service (QoS) of IoT nodes. Furthermore, a deep reinforcement learning (DRL) algorithm is conceived for the task scheduling in a certain UAV, which improves the efficiency of the task execution in each UAV. Finally, sufficient simulation results show the feasibility and superiority of our proposed load-balance-oriented UAV deployment scheme as well as the task scheduling algorithm.

The Internet of Things (IoT) is gradually becoming mature and has already entered our daily life, which interconnects more machines and makes communication more convenient and more intelligent. Massive IoT devices produce innumerable data which need to be analyzed in joint cloud computation (JointCloud) with diversified services. However, due to the weak security of IoT devices, the existing JointCloud architecture hardly provides a secure trusted trade environment for users, which affects severely the application in the IoT network. In this article, we propose a hierarchical trust networking architecture based on permissioned blockchain to implement JointCloud (HTJC). The proposed Hyperledger fabric-based architecture has a better performance than those based on Ethereum in latency. By introducing the credit bonus-penalty strategy (CBPS), HTJC can solve the trust problem and provide users with a secure trusted trade environment. The availability of the proposed architecture is evaluated and compared to the existing models. The numerical results show that the HTJC can defend distributed denial-of-service (DDoS) attacks and provide users with a trusted and effective trade platform.

Relying on the computation offloading technology, edge computing has shown potential in countless tasks processing in the industrial Internet of Things (IIoT), which is composed of multiple edge clouds and multiple IIoT devices. Nevertheless, with increasing demands for computation service, how to design reliable transmission mechanism and allocate proper computation resource has become bottlenecks. In this article, we propose a computation offloading mechanism based on two-stage Stackelberg game to analyze the interaction between multiple edge clouds and multiple IIoT devices. To be specific, the edge clouds are denoted as leaders who set the appropriate price for their computation resource. Besides considering the payment cost, the IIoT devices which are termed as the followers formulate their utility function by considering the social interaction information from the potential IIoT devices. The existence and uniqueness of the Stackelberg equilibrium are analyzed considering two possible cases, i.e., complete information and incomplete information. Moreover, two dynamic iterative algorithms are invoked for solving both problem models, respectively. Finally, experimental results show that our proposed scheme is conducive to seeking the appropriate price and computation requirement. Besides, social interaction information plays an important role in achieving a reasonable computation requirement for IIoT devices.

Network Virtualization (NV) techniques allow multiple virtual network requests to beneficially share resources on the same substrate network, such as node computational resources and link bandwidth. As the most famous family member of NV techniques, virtual network embedding is capable of efficiently allocating the limited network resources to the users on the same substrate network. However, traditional heuristic virtual network embedding algorithms generally follow a static operating mechanism, which cannot adapt well to the dynamic network structures and environments, resulting in inferior nodes ranking and embedding strategies. Some reinforcement learning aided embedding algorithms have been conceived to dynamically update the decision-making strategies, while the node embedding of the same request is discretized and its continuity is ignored. To address this problem, a Continuous-Decision virtual network embedding scheme relying on Reinforcement Learning (CDRL) is proposed in our paper, which regards the node embedding of the same request as a time-series problem formulated by the classic seq2seq model. Moreover, two traditional heuristic embedding algorithms as well as the classic reinforcement learning aided embedding algorithm are used for benchmarking our prpposed CDRL algorithm. Finally, simulation results show that our proposed algorithm is superior to the other three algorithms in terms of long-term average revenue, revenue to cost and acceptance ratio.

Recently, the emergence of blockchain has stirred great interests in the field of Internet of Things (IoT). However, numerous non-trivial problems in the current blockchain system prevent it from being used as a generic platform for large-scale services and applications in IoT. One notable drawback is the scalability problem. Lots of projects and researches have been done to solve this problem. Nevertheless, they do not consider different users' conditions, only using a single consensus protocol as the best fit one, as well as the IoT system is heavily constrained by computing and networking resources. In this article, we study a permissioned blockchain-based IoT architecture. In order to improve the scalability of the blockchain system and meet the needs of different users, we propose a service-oriented permissioned blockchain, where different consensus protocols are launched according to users' quality of service (QoS) requirements. Specially, we quantify a few popular consensus protocols. Additionally, we select block producers, which need a great number of computation resources, as well as dynamically allocate network bandwidth to the blockchain system. We formulate consensus protocols selection, block producers selection, and network bandwidth allocation as a joint optimization problem. We then use a dueling deep reinforcement learning approach to solve the problem. Simulation results demonstrate the effectiveness of our proposed scheme.

Network Function Virtualization (NFV) and Machine Learning (ML) are envisioned as possible techniques for the realization of a flexible and adaptive 5G network. ML will provide the network with experiential intelligence to forecast, adapt and recover from temporal network fluctuations. On the other hand, NFV will enable the deployment of slice instances meeting specific service requirements. Moreover, a single slice instance may require to be deployed across multiple substrate networks; however, existing works on multi-substrate Virtual Network Embedding fall short on addressing the realistic slice constraints such as delay, location, etc., hence they are not suited for applications transcending multiple domains. In this paper, we address the multi-substrate slicing problem in a coordinated manner, and we propose a Reinforcement Learning (RL) algorithm for partitioning the slice request to the different candidate substrate networks. Moreover, we consider realistic slice constraints such as delay, location, etc. Simulation results show that the RL approach results into a performance comparable to the combinatorial solution, with more than 99% of time saving for the processing of each request.

In ultra-dense networks, the increasing demand for wireless services has led to severe energy consumption problem. In this paper, we mainly focus on green communication and computation offloading in ultra-dense networks, constituted of different macro base stations and small-cell base stations. This paper jointly considers edge energy consumption and delay under the limited network resource for multiple users.To address this issue, we propose an efficient computation offloading scheme in multi- user multi-task scenario, and a cuckoo search algorithm is invoked for solving the computation offloading problem. To be specific, the global convergence analysis presents the validity of this computation offloading scheme. Finally, experimental results validate that our proposed scheme is conducive to improving the efficiency of entire system in ultra-dense networks.

Due to the easy deployment, low cost and high maneuverability, unmanned aerial vehicles (UAVs) serving as aerial base stations can be efficiently deployed according to realtime situations for providing high-quality coverage, which can improve the communication efficiency and meet the requirements of green communications. However, due to the finite flight energy, a single UAV has limited capability of providing seamless long-term service to ground users. Therefore, the cooperation of multiple drones relying on sophisticated recharging and reshuffling schemes is necessary. In this paper, we investigate an energy- efficient cooperation strategy of multi-UAVs for providing seamless long-term coverage, where the positioning and the flight strategy are jointly considered. We first introduce a novel UAV power model, based on which we derive the cyclic UAV recharging and reshuffling constraint in order to satisfy the seamless long-term coverage requirement. For maximizing the energy-efficiency, we introduce a two-stage joint optimization algorithm for solving both the optimal UAV deployment as well as the cyclic UAV recharging and reshuffling strategy (CRRS). Finally, the efficiency of our proposed algorithm is shown by the simulation results.

Nowadays, more and more researchers are paying their attention to green routing. In this paper, we consider power consumption as a kind of QoS (quality of service) and apply a new learning-based approach for energy efficient transportation and QoS routing. Compared with traditional rule-based methods, the proposed method can learn additional information from the networks to improve routing performance, and have the flexibility to meet different QoS requirements. First, we propose a new identification of network nodes, namely node vectors, and a basic routing algorithm using node vectors is designed accordingly. Then, energy efficient transportation and QoS routing are proposed by adding QoS constraints into the routing decision. Link attributes such as power consumption, bandwidth and delay can be learned from these node vectors with neural networks. The learned link attributes together with the estimated distance can be used for routing decisions with QoS constraints. Simulation results show that the proposed method is reliable in routing tasks, and can achieve a remarkable performance when compared with the state-of-the-art work on the delay constrained least cost path (DCLC) problem.

The past few years have witnessed a wide deployment of low earth orbit (LEO) satellites communications and networking. With the explosive growth of new businesses, satellite network is expected to provide global coverage and high bandwidth availability service. Toward this end, Multipath TCP(MPTCP) is a promising transport protocol to use in LEO satellites networks. MPTCP can not only achieve seamless handover, but also enhance throughput by using multiple paths transmission mechanism. However, following the improvement of the performance and scalability, it also brings unprecedented challenges for congestion control of multiple sub-flows. Especially, currently works on the congestion control largely relies on a manual process which presents a poor performance in the high-dynamic complexity network environment. Inspired by the recent success of applying machine learning in many challenging control decision domains, such as video game, self-driving, we employ deep deterministic policy gradient for learning the optimal congestion control strategies by interacting with the underlying network environment. Some simulation results demonstrated the effectiveness and feasibility of our architecture and algorithms.

Past few years have witnessed the compelling applications of the remote sensing satellites in our daily life, ranging from the weather forecast to military surveillance. Due to the computation and power constraints, the LEO satellites have to download the remote sensing data to the ground stations for further processing. However, the long-distance transmission and the ionospheric interference is problematic for supporting the latency-sensitive remote sensing services, such as hotspot detection, hotspot tracing. As a remedy, in this paper, the space stations are introduced to offload the computation task of the remote sensing satellites to reduce the transmission delay. With the space station joining in, a three-tier intelligent remote sensing satellites operation system is constructed. In order to perform well, we study the computation resource allocation strategies in this three-tier system. We model the resource management and pricing problems among three players as a Stackelberg game, where the space stations act as the leaders, the ground stations as the followers, and the LEO satellites as the sub-followers. For searching the Nash equilibrium of this game, we apply ’WoLF-PHC’ algorithm for learning the optimal resource management strategies. In addition, some simulation results are presented to demonstrate the feasibility and performance of our architecture and algorithm.

With the development of Next-generation Wireless Networks (NWNs), delay-sensitive traffic triggered by mobile applications (such as video stream and online games) will become an important part of the NWNs. With the increasing demand for massive video content transmission and good quality of users' experience, NWNs have to face up to some serious challenges. As a remedy, efficient routing schemes are capable of achieving load balance. In this article, we propose a load balance routing based on machine learning. First, a dimension-reduced vector matrix can be obtained from the original adjacency matrix of the network topology by Principal Component Analysis (PCA). Then, a neural network is used for the prediction of the network queue status, which can be used as a metric for making intelligent routing decisions. Finally, a load balance routing algorithm considering Queue Utilization (QU) is designed accordingly. Simulation results show the performance of our proposed machine learning-based routing scheme compared to the shortest path algorithm (Bellman-Ford (BF)) and its variant (QUBF) in terms of the packet loss ratio, the throughput and the delay.

Physical (PHY)-layer security has received considerable interest as a way to safeguard data confidentiality and achieve security and privacy in wireless networks. Authentication between two devices is a challenging problem. In this paper, a machine learning algorithm is proposed to detect and identify rogue transmitters relying on a low-dimensional channel feature vector that is obtained by the Karhunen-Loeve transform (KLT). Specifically, a Linde-Buzo-Gray algorithm is designed for improving the reliability and robustness of the proposed scheme, where a Gaussian Mixture Model (GMM) is employed to learn and track the changes of physical layer properties. Simulation results demonstrate that the proposed authentication scheme achieves a higher spoofing detection rate compared to other existing methods.

Unmanned aerial vehicle (UAV) communication is a promising technology for Internet of Things (IoT) systems. In this paper, we combine UAV communication and nonorthogonal multiple access (NOMA) for constructing high capacity IoT uplink transmission systems, where UAVs are used as aerial base stations for collecting data from IoT nodes while NOMA is invoked for uplink transmission. We aim to maximize the system capacity by jointly optimize the subchannel assignment, the uplink transmit power of IoT nodes, and the flying heights of UAVs. We commence by proposing an efficient subchannel assignment algorithm relying on the classic K-means clustering method and matching theory. Then, we determine both the distributed uplink transmit power of IoT nodes and flying heights of UAVs based on successive optimization approach. An alternative optimization algorithm is also proposed for finding the near-optimal solutions. Finally, the numerical results demonstrate the superiority of our proposed scheme.

Due to their high flexibility, high maneuverability, and line-of-sight (LOS) predominant channel, unmanned aerial vehicles (UAVs) serving as flying base stations have received a lot of interest in emerging Internet of Things (IoT) networks. This article studies the energy-efficient cooperative strategy of rechargeable multi-UAVs for providing seamless coverage and long-term information services for IoT nodes. Considering the limited cruising duration of the UAV, multiple rechargeable UAVs are capable of constructing a closed chain for the sake of alternately supporting IoT nodes. Moreover, a joint IoT node assignment and UAV configuration optimization problem is proposed in order to maximize the energy efficiency of the system. Since the proposed problem is a mixed-integer nonconvex problem, we divide it into three subproblems, namely, node assignment scheduling, UAV trajectory planning, and transmit power control. By exploiting sequential convex optimization techniques, we reformulate the nonconvex subproblems into three convex optimization problems which can be solved within the polynomial time. A block coordinate descent-based iterative algorithm is proposed for solving these energy-efficiency oriented subproblems. Finally, the simulation results corroborate the effectiveness of our proposed method.

With the developments of communication technologies and smart manufacturing, Industrial Internet of Things (IIoT) has emerged. Software-defined networking (SDN), a promising paradigm shift, has provided a viable way to manage IIoT dynamically, called software-defined IIoT (SDIIoT). In SDIIoT, lots of data and flows are generated by industrial devices, where a physically distributed but logically centralized control plane is necessary. However, one of the most intractable problems is how to reach consensus among multiple controllers under complex industrial environments. In this paper, we propose a blockchain (BC)-based consensus protocol in SDIIoT, along with detailed consensus steps and theoretical analysis, where BC works as a trusted third party to collect and synchronize network-wide views between different SDN controllers. Specially, it is a permissioned BC. In order to improve the throughput of this BC-based SDIIoT, we jointly consider the trust features of BC nodes and controllers, as well as the computational capability of the BC system. Accordingly, we formulate view change, access selection, and computational resources allocation as a joint optimization problem. We describe this problem as a Markov decision process by defining state space, action space, and reward function. Due to the fact that it is difficult to solve this joint problem by traditional methods, we propose a novel dueling deep Q-learning approach. Simulation results are presented to show the effectiveness of our proposed scheme.

Intrusion detection technology has received increasing attention in recent years. Many researchers have proposed various intrusion detection systems using machine learning (ML) methods. However, there are two noteworthy factors affecting the robustness of the model. One is the severe imbalance of network traffic in different categories and the other is the nonidentical distribution between training set and test set in feature space. This paper presents a multilevel intrusion detection model framework named multilevel semi-supervised ML (MSML) to address these issues. The MSML framework includes four modules: 1) pure cluster extraction; 2) pattern discovery; 3) fine-grained classification (FC); and 4) model updating. In the pure cluster module, we introduce an concept of “pure cluster” and propose a hierarchical semi-supervised k-means algorithm with an aim to find out all the pure clusters. In the pattern discovery module, we define the “unknown pattern” and apply cluster-based method aiming to find those unknown patterns. Then a test sample is sentenced to labeled known pattern or unlabeled unknown pattern. The FC module can achieves FC for those unknown pattern samples. The model updating module provides a mechanism for retraining. KDDCUP99 dataset is applied to evaluate MSML. Experimental results show that MSML is superior to other existing intrusion detection models in terms of overall accuracy, F1-score, and unknown pattern recognition capability.

With rapid development of compelling application scenarios of the Internet of Things (IoT), such as smart cities, it becomes substantially important to strengthen the management of data traffic in IoT networks. Traffic classification is beneficial in terms of both ensuring network security and improving quality of service. Traditional IoT traffic classification methods separate the classification algorithm and the design of feature engineering, which includes feature extraction and feature selection. Then, traffic identification or classification is performed by combining both. This paper proposes an end-to-end IoT traffic classification method relying on a deep learning aided capsule network for the sake of forming an efficient classification mechanism that integrates feature extraction, feature selection, and classification model. Our proposed traffic classification method beneficially eliminates the process of manually selecting traffic features, and is particularly applicable to smart city scenarios. To the best of our knowledge, this is the first time that capsule networks have been used in the context of traffic classification. Experimental results show the feasibility and effectiveness of our proposed traffic classification mechanism, which yields high classification accuracy.

Past few years have witnessed the compelling applications of the blockchain technique in our daily life ranging from the financial market to health care. Considering the integration of the blockchain technique and the industrial Internet of Things (IoT), blockchain may act as a distributed ledger for beneficially establishing a decentralized autonomous trading platform for industrial IoT (IIoT) networks. However, the power and computation constraints prevent IoT devices from directly participating in this proof-of-work process. As a remedy, in this treatise, the cloud computing service is introduced into the blockchain platform for the sake of assisting to offload computational task from the IIoT network itself. In addition, we study the resource management and pricing problem between the cloud provider and miners. More explicitly, we model the interaction between the cloud provider and miners as a Stackelberg game, where the leader, i.e., cloud provider, makes the price first, and then miners act as the followers. Moreover, in order to find the Nash equilibrium of the proposed Stackelberg game, a multiagent reinforcement learning algorithm is conceived for searching the near-optimal policy. Finally, extensive simulations are conducted to evaluate our proposed algorithm in comparison to some state-of-the-art schemes.

With the explosion in the number of connected devices and Internet of Things (IoT) services in smart city, the challenges to meet the demands from both data traffic delivery and information processing are increasingly prominent. Meanwhile, the connected vehicle networks have become an essential part in smart city, bringing massive data traffic as well as significant networking, caching and computing resources. In this paper, we propose a novel vehicle network architecture, mitigating the network congestion with the joint optimization of networking, caching and computing. Cloud computing at the data centers as well as mobile edge computing (MEC) at the evolved node Bs (eNodeBs) and on-board units (OBUs) are taken as the paradigms to provide caching and computing resources. The programmable control principle originated from software-defined networking (SDN) paradigm has been introduced to facilitate the system architecture and resource integration. With the careful modeling of the services, the vehicle mobility and the system state, a joint resource management scheme is proposed and formulated as a partially observable Markov decision process (POMDP) to minimize system cost, which consists of both network overhead and execution time of computing tasks. Extensive simulation results with different system parameters reveal that the proposed scheme could significantly improve the system performance compared to the existing schemes.

Summary In this paper, we propose an effective model for the similarity metrics of English sentences. In the model, we first make use of word embedding and convolutional neural network (CNN) to produce a sentence vector and then leverage the information of the sentence vector pair to calculate the score of sentence similarity. Considering the case of long‐range semantic dependencies between words, we propose a novel method transforming word embeddings to construct the three‐dimensional sentence feature tensor. In addition, we incorporate the k‐max pooling into the convolutional neural network to adapt to variable lengths of input sentences. The proposed model requires no external resource such as WordNet and parse tree. Meanwhile, it consumes very little time for training. Finally, we carried out extensive simulations to evaluate the performance of our model compared with other state‐of‐the‐art works. Experimental results on SemEval 2014 task (SICK test corpus) indicated that our model can achieve a good performance in the terms of Pearson correlation coefficient, Spearman correlation coefficient, and mean squared errors. Furthermore, experimental results on Microsoft research paraphrase identification (MSRP) indicated that our model can achieve an excellent performance in the terms of F1 and Accuracy.

The past few years have witnessed a wide deployment of software defined networks facilitating a separation of the control plane from the forwarding plane. However, the work on the control plane largely relies on a manual process in configuring forwarding strategies. To address this issue, this paper presents NetworkAI, an intelligent architecture for self-learning control strategies in software defined networking networks. NetworkAI employs deep reinforcement learning and incorporates network monitoring technologies, such as the in-band network telemetry to dynamically generate control policies and produces a near optimal decision. Simulation results demonstrated the effectiveness of NetworkAI.

Network virtualization can offer more flexibility and better maintainability for the current Internet through allowing multiple heterogeneous virtual networks (VNs) to share the network resource of a common infrastructure provider. The main challenge in this respect is the efficient embedding the virtual nodes and virtual links from the VN requests onto the limited substrate network resources. The notion of storage resource can exchange bandwidth resource to some extent gives us a hint that the efficient utilization of storage resource can relieve the bandwidth resource consumption. The existing VN embedding model does not consider the storage resource constraints on substrate nodes and virtual nodes, and does not keep up with the need of actual situation. In this paper, we propose a novel VN embedding model based on 3-D resource constraints including computing, network and storage, and devise two heuristic algorithms as the baseline algorithms to deal with the VN embedding problem. To our best of our knowledge, this is the first time to propose VN embedding problem based on 3-D resources including computing, network, and storage.

As an important part of the Internet-of-Things (IoT), machine-to-machine (M2M) communications have attracted great attention. In this paper, we introduce mobile edge computing (MEC) into virtualized cellular networks with M2M communications, to decrease the energy consumption and optimize the computing resource allocation as well as improve computing capability. Moreover, based on different functions and quality of service (QoS) requirements, the physical network can be virtualized into several virtual networks, and then each MTCD selects the corresponding virtual network to access. Meanwhile, the random access process of MTCDs is formulated as a partially observable Markov decision process (POMDP) to minimize the system cost, which consists of both the energy consumption and execution time of computing tasks. Furthermore, to facilitate the network architecture integration, software-defined networking (SDN) is introduced to deal with the diverse protocols and standards in the networks. Extensive simulation results with different system parameters reveal that the proposed scheme could significantly improve the system performance compared to the existing schemes.

The recently proposed concept of beamspace MIMO can significantly reduce the number of power- hungry radio frequency (RF) chains in millimeter- wave (mmWave) massive MIMO systems. However, most existing studies ignore the power leakage problem in beamspace MIMO systems, which results in an obvious loss in the achievable sum rate. In this paper, a phase shifter network (PSN)-based precoding structure is proposed to solve this problem. Its key idea is to employ multiple phase shifters from each RF chain to select multiple instead of only one beam to collect most of the leaked power. Based on the proposed structure, a rotation-based precoding algorithm is further designed to maximize the signal-to-noise-ratio (SNR) of each user by rotating the channel gains of the selected beams to the same direction. Simulation results show that the proposed PSN- based precoding can effectively collect the leaked power to achieve the near-optimal sum rate, and enjoys a higher energy efficiency than the conventional precoding solutions.

Summary In order to suppress the interference in local area networks, this paper presents a Wireless Local Area Networks (WLAN) interference self‐optimization method based on a Self‐Organizing Feature Map (SOM) neural network model. This method trains the model by using original data sets as the initial vector set and using the whole Signal to Interference plus Noise Ratio (SINR) vector generated by the change of one Wireless Access Point (AP) channel as the basic feature. After the training, the SOM neural network can quickly locate the fault AP and optimize the network according to the changes of the network environment. Simulation results reveal that the proposed scheme can efficiently locate the AP where interference happens and optimize the interference with an improved user experience. Copyright © 2016 John Wiley & Sons, Ltd.