Unmanned Aerial Vehicle (UAV) swarms Cooperative Spectrum Sensing (CSS) denotes the technology in which UAVs serve as sensing nodes to detect, monitor, and analyze wireless spectrum resources in target regions through collaborative clustering. Compared with conventional CSS systems, this framework demonstrates advantages in wide-area coverage and flexible deployment, enabling dynamic spectrum sharing in complex electromagnetic environments. However, the on-demand scalability of UAV swarms introduces dynamic variations in signal quality and node availability, which significantly degrade the efficiency and reliability of spectrum sensing. Existing CSS approaches largely neglect these time-varying characteristics, leading to suboptimal sensing performance in dynamic swarm environments. To address this issue, a novel adaptive node screening CSS approach (ANS-CSS) is proposed in this paper, which integrates a node screening strategy based on quality evaluation and a temporal-spatial feature extraction mechanism. Meanwhile, an Adaptive Node Screening Net (ANSNet) is constructed on the basis of ANS-CSS. Specifically, the sensing signals from the UAV nodes are evaluated and screened in real time to form high-quality signal sets. The temporal and spatial features are then extracted from these sets, enabling the network to achieve efficient and reliable spectrum detection in dynamic node scenarios. Simulation results demonstrate that the proposed approach exhibits superior performance in dynamic scenarios of UAV swarms, particularly under low Signal-to-Noise Ratio (SNR) conditions. The detection probability of ANSNet reaches 97.7% when SNR=-16dB, outperforming existing CSS methods.

Space-air-ground integrated networks (SAGINs) augmented with mobile edge computing (MEC) provide a unified yet heterogeneous substrate for latency-sensitive services. Deploying service function chains (SFCs) over satellites, aerial platforms, and ground nodes, however, is difficult due to hierarchical resource heterogeneity, time-varying network states, and stringent end-to-end (E2E) delay requirements. In this paper, we study online SFC embedding in a three-layer SAGIN-MEC architecture under coupled computing and networking constraints. We model the deployment as a two-stage process: (i) placing each virtual network function (VNF) onto feasible nodes subject to computing-capacity constraints, and (ii) mapping inter-VNF traffic onto feasible paths subject to bandwidth and delay constraints. To achieve adaptive decisions under dynamic states, we cast the problem as graph reinforcement learning by jointly encoding the substrate topology and each SFC into a unified graph state, and propose a structure-aware PPO agent that combines graph convolution with domain features and an action-masking mechanism to eliminate infeasible placement/routing actions. Extensive experiments in dynamic large-scale scenarios show that the proposed method consistently outperforms competitive baselines, improving the acceptance rate by 6.991% under high load, reducing the average E2E delay by 13.556%, and increasing the long-term revenue-to-cost ratio by 27.763% on average.

Network testing is critical for evaluating the performance, reliability, and security of modern computer networks. A key challenge is creating an accurate, cost-effective, and high-performance network emulation environment. Network Impairment Emulators (NIEs) emulate real-world network conditions such as bandwidth constraints, latency, and packet loss, but existing CPU- and FPGA-based solutions suffer from limited performance, high costs, and poor flexibility. This paper proposes Software-Defined Network Impairment Emulation (SDNIE), a novel framework that leverages programmable switches for scalable, cost-efficient network impairment emulation. SDNIE introduces three key techniques: (1) intent-driven network impairment configuration, automating impairment modeling; (2) serial-parallel combined execution, optimizing performance; and (3) CPU-Tofino collaborative deployment, offloading complex computations. Experimental results show that SDNIE matches commercial emulators in performance while significantly reducing costs. This work demonstrates the potential of programmable switches in network testing, offering a scalable, cost-effective, and high-performance alternative for next-generation network impairment emulation.

In IoT environments, devices are often constrained by low power consumption and limited resources, making efficient connection establishment with minimal overhead crucial for real-time communication between edge devices and cloud servers. The QUIC protocol shows significant potential, but the encryption and decryption overhead is considerable. Reducing this overhead and improving connection establishment efficiency are critical to enhancing QUIC performance, especially for IoT edge devices that need to handle high-concurrency communication while maintaining low power consumption. This paper proposes CFcoQUIC, a CPU/FPGA co-design architecture that accelerates the handshake process and reduces the initial connection latency by parallelizing multiple encryption/decryption flows in high-concurrency environments. Experimental results demonstrate that the time for RSA encryption and decryption on FPGA is at least 19.1 times faster than on CPU, and the time for AES encryption and decryption is at least 5 times faster on FPGA. These results highlight the effectiveness of the proposed architecture in reducing QUIC handshake overhead in IoT environments with low-power edge devices.

Nonterrestrial networks (NTNs) enabled Internet of Things (IoT) extends connectivity to remote and underserved areas, enhances network reliability and coverage, and supports diverse IoT applications in challenging environments, such as rural, maritime, and disaster-stricken regions. As an emerging and fast-evolving IoT scheme, NTN-enabled IoT requires extensive evaluation to ensure effective deployment in real-world scenarios, such as connectivity, performance, and security evaluation. Since conducting testing in remote and diverse environments is logistically challenging and costly, we propose a generative artificial intelligence (GAI)-based synthetic traffic generation framework that facilitates comprehensive traffic analysis and performance evaluation. The proposed framework employs a GAI model to learn the traffic pattern and generate synthetic traffic from historical data. Our approach includes an embedding-based model for representing network flow attributes and a conditional generative adversarial network (CGAN) for generating traffic flows. Considering both source-destination information and statistical features achieves more comprehensive characterization of traffic flows. Finally, the simulation results demonstrate that the proposed approach can generate high quality traffic that conforms to real data distribution and shows obvious difference between multiple applications.

The Industrial Internet of Things (IIoT) enables communication among automation systems, machinery, and sensors in an industrial setting. To optimize critical industrial operations, a substantial volume of data concerning diverse in-factory activities and automation services is generated by IoT devices and sensors. This data are subsequently transferred to distant processing systems for analysis and decision-making. Nevertheless, a substantial latency in data transmission or any abnormality in the generated data may result in delayed or erroneous decisions, consequently impacting the efficacy of essential industrial systems. To address these challenges, we established an intelligent network architecture utilizing software-defined networking that achieves tactile latencies efficiently while handling industrial data traffic in an energy-efficient manner. To address the initial challenge, the suggested architecture utilizes the self-organized maps approach to distinguish between industrial traffic requiring tactile latencies and nontactile traffic. We utilize a binary tree-based flow table mapping method to enhance flow table matching and decrease lookup times. To address the second challenge, we employ the Support Vector Machine technique to identify anomalies in real-time industrial data traffic. The Hadoop system and Mininet emulator are utilized to evaluate the proposed architecture using the UNSW dataset. The results demonstrate the effectiveness of the suggested solution in providing energy-efficient tactile assurances and identifying anomalies in traffic.

By decoupling substrate resources, network virtualization (NV) is a promising solution for meeting diverse demands and ensuring differentiated Quality of Service (QoS). In particular, virtual network embedding (VNE) is a critical enabling technology that enhances the flexibility and scalability of network deployment by addressing the coupling of Internet processes and services. However, in the existing deep neural networks (DNNs)-based works, the closed-box nature DNNs limits the analysis, development, and improvement of systems. For example, in the Industrial Internet of Things (IIoT), there is a conflict between decision interpretability and the opacity of DNN-based methods. In recent times, interpretable deep learning (DL) represented by deep neuro fuzzy systems (DNFSs) combined with fuzzy inference has shown promising interpretability to further exploit the hidden value in the data. Motivated by this, we propose a DNFS-based VNE algorithm that aims to provide an interpretable NV scheme. Specifically, data-driven convolutional neural networks (CNNs) are used as fuzzy implication operators to compute the embedding probabilities of candidate substrate nodes through entailment operations. And, the identified fuzzy rule patterns are cached into the weights by forward computation and gradient back-propagation (BP). Moreover, the fuzzy rule base is constructed based on Mamdani-type linguistic rules using linguistic labels. In addition, the DNFS-driven five-block structure-based policy network serves as the agent for deep reinforcement learning (DRL), which optimizes VNE decision making through interaction with the environment. Finally, the effectiveness of evaluation indicators and fuzzy rules is verified by simulation experiments.

Drones are sophisticated machines that can hover over extreme locations, conduct aerial surveillance, collect surveillance data, and disseminate it to the distributed edge for processing and analysis. The distributed edge deploys advanced artificial intelligence (AI) models to detect any unwarranted activity or object based on surveillance data. However, these lightweight and low-power unmanned aerial vehicles (UAVs) may experience faults due to unprecedented workload when deployed in extreme surveillance domains. In this article, we have designed an AI framework to detect any safety concerns with drones deployed for aerial surveillance in extreme locations based on real-time drone critical parameters. We also propose a MapReduce-based object recognition and classification module to process large-scale images captured by drones efficiently. However, conventional AI systems behave like black box systems, leading to a lack of trust and transparency. Thus, we convert the traditional framework of AI into an explainable edge AI framework using Shapley additive explanations (SHAPs) that opens Pandora’s black box. The experimental results show the effectiveness of the proposed framework in detecting drone safety concerns through explainable health status tracking alongside ensuring an effective object detection mechanism.

Federated learning (FL) in Industrial IoT (IIoT) facilitates collaborative model training across distributed edge devices, ensuring data privacy and localized insights without centralized data aggregation. However, the networked parameter sharing mechanism in FL renders it vulnerable to exploitation by man-in-the-middle (MITM) attackers, potentially disrupting the model training process. To mitigate this threat, this article presents a novel blockchain-reinforced FL architecture aimed at enabling cooperative intrusion detection. Initially, FL is leveraged to aggregate all learned information from edge servers, thereby disseminating extracted attack characteristics to all participants through gradient sharing. Subsequently, a blockchain-based parameter verification scheme is introduced to safeguard against tampered local parameters affecting the global model. Clients record model parameters in smart contracts deployed on a private chain, and parameter servers verify parameter confidentiality before aggregation, ensuring only valid parameters are considered. Finally, extensive experiments are conducted using an edge IIoT cybersecurity data set comprising 61 features spanning ten protocol layers and five attacks targeting IIoT connectivity protocols. Simulation results demonstrate that the proposed scheme significantly enhances intrusion detection accuracy, achieving a threefold improvement when two-thirds of federated nodes are subjected to MITM attacks.

The efficient and secure management of resources within flying ad-hoc networks (FANETs) poses formidable challenges. FANETs constitute a pivotal element of the space-air–ground-integrated network (SAGIN), employing network virtualization (NV) technology in tandem with service function chain (SFC) to facilitate end-to-end network services, akin to terrestrial networks. Nonetheless, the transient, dynamic nature of FANETs coupled with their susceptibility to network attacks engenders considerable complexity in the placement of SFCs within these networks. To address the rationality and security of resource allocation for SFC placement, this article proposes a reinforcement learning algorithm that sets strict security-level restrictions on the placement process and fully extracts the key features in FANETs. Additionally, a multilayer policy network is devised to dynamically perceive alterations in the FANET environment and compute an optimal SFC placement strategy. The proposed algorithm exhibits real-time adaptability to the dynamic environment, quantifies influential factors during placement, and achieves dynamic SFC placement. To assess the efficacy of the algorithm, three evaluation metrics—namely, SFC placement success rate, long-term average revenue, and long-term revenue cost ratio—are formulated and extensively evaluated through a plethora of experiments. Comparative analysis against alternative algorithms demonstrates enhancements of 20.6%, 15.3%, and 12.1% in the aforementioned metrics, respectively. The experimental findings substantiate both the convergence and efficiency of the proposed algorithm.

Virtual network embedding (VNE) refers to the process of mapping virtual networks onto physical networks, which can improve the utilization and flexibility of network resources. However, due to the complexity of VNE problems and the requirement for network security, how to efficiently complete VNE and ensure network security is important. In this article, we analyze the characteristics of the cloud–edge–terminal collaborative vehicular network and design a resource allocation mechanism based on VNEVNE, abbreviated as DRLS-VNE. First, we establish a multidimensional heterogeneous network model and design a five-layer neural network as the deep reinforcement learning (DRL) agent. The DRL agent can adaptively extract network feature attributes, thereby improving the performance of DRLS-VNE. Second, we introduce a dynamic trust evaluation mechanism, which can evaluate the trustworthiness of each node in the virtual network in real time and set embedding constraints based on the evaluation results. Finally, we conduct experiments to verify the practicality and effectiveness of DRLS-VNE. The experimental results show that our solution can significantly enhance the performance of the VNE solution.

With the increasing penetration of Internet of Vehicles (IoV) in people’s lives, the safety and reliability of its communication has to attract our attention. Frequency hopping orthogonal frequency division multiplexing (FH-OFDM) is applied to vehicle networking communication with its excellent communication performance. But the sub-carrier frequency hopping pattern in the traditional FH-OFDM information transmission technology has the risk of being easily detected, which seriously affects the security, concealment and anti-jamming of its information transmission. However, existing anti-jamming methods cannot solve this problem. This paper presents a data driven frequency hopping OFDM (DDFH-OFDM) information transmission method for IoV. This method combines message-driven frequency-hopping technology with frequency-hopping OFDM system. The selection of subcarriers is controlled by using the encrypted partial transmission data instead of the pseudorandom sequence. At the same time, the frequency transmission block mapping rule is designed to make the distribution of frequency-hopping subcarriers more uniform. Through simulation, under the same bit error rate (BER), compared with the FH-OFDM information transmission method, the proposed method has good anti-noise, anti-broadband interference and anti-narrowband interference performance, which further improves the communication performance of IoV. It provides a reference technology for anti-jamming communication applications in IoV scenes.

Multimodal retrieval has received widespread consideration since it can commendably provide massive related data support for the development of computational social systems (CSSs). However, the existing works still face the following challenges: 1) rely on the tedious manual marking process when extended to CSS, which not only introduces subjective errors but also consumes abundant time and labor costs; 2) only using strongly aligned data for training, lacks concern for the adjacency information, which makes the poor robustness and semantic heterogeneity gap difficult to be effectively fit; and 3) mapping features into real-valued forms, which leads to the characteristics of high storage and low retrieval efficiency. To address these issues in turn, we have designed a multimodal retrieval framework based on web-knowledge-driven, calledunsupervised and robust graph convolutional hashing(URGCH). The specific implementations are as follows: first, a “secondary semantic self-fusion” approach is proposed, which mainly extracts semantic-rich features through pretrained neural networks, constructs the joint semantic matrix through semantic fusion, and eliminates the process of manual marking; second, a “adaptive computing” approach is designed to construct enhanced semantic graph features through the knowledge-infused of neighborhoods and uses graph convolutional networks for knowledge fusion coding, which enables URGCH to sufficiently fit the semantic modality gap while obtaining satisfactory robustness features; Third, combined with hash learning, the multimodality data are mapped into the form of binary code, which reduces storage requirements and improves retrieval efficiency. Eventually, we perform plentiful experiments on the web dataset. The results evidence that URGCH exceeds other baselines about$1\%$–$3.7\%$in mean average precisions (MAPs), displays superior performance in all the aspects, and can meaningfully provide multimodal data retrieval services to CSS.

The transformer has become a prominent technique for hyperspectral image (HSI) classification, attributed to its capability to model global dependencies between features. Nevertheless, the predominant transformer-based methods rely on a direct information flow with a fixed number of tokens, causing the sequential transformer encoders to lack crucial interaction. This deficiency results in an inappropriate granularity of discriminative features and the loss of subtle patterns. In response to this limitation, we introduce a novel approach named Multi-level Class Token Transformer with Cross TokenMixer (MCTT) for HSI classification. Specifically, we explore a CNN stem network that incorporates 3D, 2D, and pointwise convolutions to encode local spatial-spectral information. The spectral-spatial features undergo transformation into semantic tokens using a semantic tokenizer. These tokens are then input into the transformer encoder to capture global interactions between different pixels. To create a hierarchical semantic representation, we propose a cross tokenmixer that integrates different levels of class tokens and patch tokens, enabling a multi-grained representation. The cross tokenmixers, with their varied number of tokens, facilitate the learning of distinct discriminative spectral-spatial representations and enable a comprehensive understanding of the HSI through a voting mechanism. Extensive experiments and ablation studies are conducted on three public HSI datasets to evaluate the performance of our proposed method. The results demonstrate the effectiveness and superior performance of our approach in HSI classification.

The Industrial Internet of Things (IIoT) promotes the deep integration of new-generation communication technologies and industrial ecology. However, the popularity of computing and the proliferation of equipment scale make it a meaningful challenge to provide reasonable resource allocation for task offloading. Therefore, this article proposes a novel two-stage coordinated, distributed, and online multidomain virtual network embedding algorithm based on deep reinforcement learning (DRL)-assisted federated learning (FL) for task offloading in the IIoT. We model the IIoT as a dynamic multidomain structure and deploy local DRL servers in each factory domain combined with the distributed paradigm of FL to reduce the local resource fragmentation. Through local and global cooperation, the IIoT environment is controlled in a fine and macroscopic manner. In addition, the mechanisms of FL ensure the privacy of participant data. Finally, a comprehensive evaluation demonstrates the clear superiority of the proposed algorithm, which improves the long-term offloading revenue, resource utilization, and task offloading success rate by average 17.66%, 5.97%, and 4.52% compared to baselines, respectively.

Industry 4.0 integrates industrial Internet of Things (IIoT), artificial intelligence, and cloud computing. The advent of the 5G era has undoubtedly provided a new impetus for the development of many Industry 4.0 applications, but it also presents some key security hurdles. The network scale is becoming larger and larger, the network environment is becoming increasingly complex, and security risks are prominent. Frequent issues, such as malicious attacks, privacy information disclosure, and data transmission security. In order to improve the reliability and security of cyberspace, this article proposes a blockchain-based hierarchical IIoT security solution mechanism. In addition, we propose a random reputation voting mechanism and blockchain (RRV-BC) scheme based on verifiable random function and reputation voting to reduce the communication cost during blockchain consensus communication. Meanwhile, the node credit scoring mechanism is introduced to dynamically evaluate the node credit. The simulation results show that the scheme improves the reliability of data communication and the fault tolerance of consensus mechanism by an average of 5% compared with the traditional practical byzantine fault tolerance (PBFT) protocol method.

With the maturity of 5G and Intelligent Transportation Systems (ITS) technologies and the prospect of Beyond 5G (B5G) and 6G technologies, the limited lifetime and computing of mobile devices pose significant challenges to Quality of Service (QoS). In addition, the problem of inefficient use of computing, storage, communication, and other resources still exists in communication systems. In response to the above issues, Multi-tier Computing Networks (MTCNs) migrate computationally intensive tasks to the cloud, fog, or edge with sufficient resources, thereby realizing energy-efficient collaborative computing and multi-dimensional resource sharing. However, in the MTCN environment with complex heterogeneity, and high-intensity dynamics, how to provide sustainable solutions for resource scheduling strategies is a meaningful issue. Inspired by Virtual Network Embedding (VNE) to decouple physical network configuration, we propose a multi-target-aware dynamic resource scheduling algorithm for MTCN to improve resource flexibility, which is the first attempt in this direction. Specifically, we consider differentiated QoS requirements like computing, storage, bandwidth, delay, etc., and establish multi-target-aware embedded constraints. Additionally, we present a Deep Reinforcement Learning (DRL)-based scheduling network that can interact scientifically and efficiently with the MTCN environment. It extracts environmental information as state input to better focus on dynamic characteristics as well as calculates candidate nodes and links using a three-layer network architecture and related constraints. Furthermore, the learning process is optimized through the combination of the reward mechanism and the gradient descent mechanism. Finally, comparison experiments on three widely used evaluation indicators (long-term average revenue, long-term average revenue-cost ratio, and VNR acceptance rate) verify that the proposed algorithm has made an average improvement of$19.042\%$,$2.563\%$, and$3.932\%$respectively compared with all baselines.

In the collaborative intelligent transportation system, providing precise positioning services is costly. Reducing resource consumption and improving revenue are crucial to the development of positioning services. Therefore, a practical algorithm that combines cloud and edge network environments is necessary to improve the positioning services. Integrating network function virtualization and edge computing can provide users with more flexible and efficient services. Based on the above issues, we use the service function chain (SFC) to improve the positioning services provided in cloud-edge-vehicle collaborative networks (CEVCN). We propose a deep reinforcement learning-assisted SFC embedding algorithm and improve its performance through training. We construct a five-layer policy network to sense the environment of CEVCN and derive the optimal node selection strategy. Finally, we use the breadth-first search algorithm to solve the embedding scheme for virtual links. The simulation results show that our proposed algorithm has excellent performance. The long-term average revenue is improved by 21%, the long-term average revenue-cost ratio is improved by 13%, and the embedding rate is improved by 8%.

The space-air-ground-ocean integrated network (SAGOI-Net) has become the focus of research in recent years, which has the characteristics of wide coverage and strong adaptability. However, due to the influence of multiple heterogeneous network segments, this network is unable to provide excellent quality of service (QoS). Based on the software-defined network and virtual network architecture, we abstract SAGOI-Net as a three-layer heterogeneous physical network resource, and propose a multi-domain virtual network embedding solution to optimize QoS. Specifically, before virtual network embedding, we collected SAGOI-Net's resource information through software-defined network and modeled it. In the virtual network embedding process, we first classify the virtual network request through K-means, and dynamically adjust the reward function to use reinforcement learning to solve the optimal virtual network embedding strategy. Finally, simulation experiments verify the effectiveness of the scheme.

As electric vehicle (EV) ownership becomes more commonplace, partly due to government incentives, there is a need also to design solutions such as energy allocation strategies to more effectively support sustainable vehicle-to-grid (V2G) applications. Therefore, this work proposes an energy allocation strategy, designed to minimize the electricity cost while improving the operating revenue. Specifically, V2G is abstracted as a three-domain network architecture to facilitate flexible, intelligent, and scalable energy allocation decision-making. Furthermore, this work combines virtual network embedding (VNE) and deep reinforcement learning (DRL) algorithms, where a DRL-based agent model is proposed, to adaptively perceives environmental features and extracts the feature matrix as input. In particular, the agent consists of a four-layer architecture for node and link embedding, and jointly optimizes the decision-making through a reward mechanism and gradient back-propagation. Finally, the effectiveness of the proposed strategy is demonstrated through simulation case studies. Specifically, compared to the used benchmarks, it improves the VNR acceptance ratio, Long-term average revenue, and Long-term average revenue-cost ratio indicators by an average of 3.17%, 191.36, and 2.04%, respectively. To the best of our knowledge, this is one of the first attempts combining VNE and DRL to provide an energy allocation strategy for V2G.

Space-aerial-ground integrated networks (SAGIN) is gaining eye-catching attention in 6G research. Comparing with terrestrial networks, SAGIN guarantees to provide three-dimensional (3D), seamless connectivity, global coverage and high resource usage efficiency. In addition, network softwarization (NetSoft) is recognized as the crucial attribute of 6G networks. With softwarization, traditional dedicated hardware will be decoupled into software blocks and general-purpose hardware. Tailored service requests can be implemented in the forms of chained software blocks (also called as slices) and coexist on top of these general-purpose hardware. The softwarization scheme can enhance the resource utilization and service diversity. Though SAGIN and NetSoft are separately studied well, their joint research is still in its infancy. In this paper, we focus on the research of softwarized SAGIN and propose one novel resource allocation and orchestration framework, labeled as Stice-Soft-SAGIN. The goal of our Stice-Soft-SAGIN framework is to provide reliable and efficient slicing service in quasi-static state. When receiving one slicing service request, our Slice-Soft-SAGIN will conduct the first procedure of available resource checking. After successfully doing the resource checking, our Stice-Soft-SAGIN will turn to conducting the slicing resource allocation and orchestration from three ordered parts (terrestrial part, aerial part, and satellite part). Take note that resources considered in Stice-Soft-SAGIN belong to wireless (spectrum) and wired (computing and storage) types. In order to validate the Stice-Soft-SAGIN, we conduct the evaluation in the simulation form. Evaluation results are illustrated and analyzed.

The use of satellite technology for communication and data transmission has greatly increased in recent years, and low earth orbit (LEO) satellites have become an important part of this infrastructure. LEO satellites orbit the earth at an altitude of around 500 to 2,000 kilometers, which allows them to provide coverage to a larger area compared to higher altitude satellites. However, routing data through LEO satellites presents unique challenges due to their low altitude and the need to continuously adjust their communication links as they move. To address these challenges, various routing algorithms have been developed to optimize the transmission of data through LEO satellite networks. In this review, we will examine the different types of LEO satellite routing algorithms and their key features, as well as the challenges and opportunities they present. We will also discuss the performance and trade-offs of these algorithms and their potential applications in various scenarios.

Financial Technology have revolutionized the delivery and usage of the autonomous operations and processes to improve the financial services. However, the massive amount of data (often called as big data) generated seamlessly across different geographic locations can end up as a bottleneck for the underlying network infrastructure. To mitigate this challenge, software-defined network (SDN) has been leveraged in the proposed approach to provide scalability and resilience in multicontroller environment. However, in case if one of these controllers fail or cannot work as per desired requirements, then either the network load of that controller has to be migrated to another suitable controller or it has to be divided or balanced among other available controllers. For this purpose, the proposed approach provides an adaptive recovery mechanism in a multicontroller SDN setup using support vector machine-based classification approach. The proposed work defines a recovery pool based on the three vital parameters, reliability, energy, and latency. A utility matrix is then computed based on these parameters, on the basis of which the recovery controllers are selected. The results obtained prove that it is able to perform well in terms of considered evaluation parameters.

The proliferation of users and data traffic poses substantial pressure on resource management in the Internet of Things (IoT). In addition to beneficially allocating scarce network resources, it also needs to meet differentiated users’ Quality-of-Service (QoS) requirements, such as low delay, high security, etc. The distributed management architecture of blockchain and its inherent security features bring inspiration to resource management in the IoT. In this article, we propose a blockchain-enabled resource orchestration scheme for IoT by deep reinforcement learning (DRL), where the IoT edge server and the end user can reach a consensus on the allocation of network resources based on blockchain theory. Moreover, relying on the policy network, the intelligent agent can be trained by these resource attributes to fully perceive the change of the network’s state and hence make dynamic resource allocation decisions. Finally, simulation results show that the proposed resource orchestration scheme has good performance in comparison to other security resource allocation algorithms. The average revenue, the user request acceptance rate, and the profitability are increased by an average of 8.5%, 1.8%, and 11.9%, respectively, compared with other algorithms.

With the increasing number of Internet of Things (IoT) devices being deployed and used in daily life, the load on computational devices has grown exponentially. This situation is more prevalent in smart cities where such devices are used for autonomous control and monitoring. Smart cities have different kinds of applications that are aided through IoT devices that collect data, send it to computational processing and storage devices, and get back decisions or actuate the actions based on the input data. There has been a stringent requirement to reduce the end-to-end delay in this process owing to the remote deployment of cloud data centres. This eventually led to the revolution of edge computing, wherein nano–micro-processing devices can be deployed closer to the premises of the smart application and process the data generated with a lower turnaround time. However, due to the limited computational power and storage, controlling the workload diverted to the edge devices has been challenging. The workload scheduling policies and task allocation schemes often fail to consider the run time health of the edge devices due to a lack of proper monitoring infrastructure. Thus, in this article, we proposed a health monitoring and diagnosis framework for geo-distributed edge clusters processing big data generated by smart city applications. This framework is built over the Map-Reduce approach for distributed processing of big data on edge clusters deployed across the smart city. Within this framework, SmartMonit (a monitoring agent) is deployed that collects the health statistics of edge devices and predicts the potential failures using an artificial neural network-based self-organising maps approach. The proposed framework is deployed over different clusters to test the efficacy concerning failure detection.

In the 5G era, the problem of data islands in various industries restricts the development of artificial intelligence technology, so data sharing is proposed. High-quality data sharing directly affects the effectiveness of machine learning models, but data leakage and abuse will inevitably occur in the process. As a consequence, in order to solve this problem, federated learning is proposed. This method uses the personalized data of multiple edge devices to train the model. The central server collects the training results of the edge devices and updates the global model, and then iteratively tests and updates the model through the edge devices. However, edge devices may have problems, such as unbalanced load and exit from the training process, which makes the training time of the model long and the effect is poor. Therefore, in the process of federated learning, the selection of reliable and high-quality edge devices becomes crucial. On this basis, in this article, we introduce reinforcement learning (RL) to preselect edge devices and obtain a set of candidate devices and then determine reliable edge devices through social attribute perception. The simulation experiment data analysis demonstrates that this scheme can improve the reliability of federated learning and complete the training process in a shorter time, the efficiency of federated learning increased by approximately 10.3%.

Traditional terrestrial wireless communication networks cannot support the requirements for high-quality services for artificial intelligence applications such as smart cities. The space–air–ground-integrated network (SAGIN) could provide a solution to address this challenge. However, SAGIN is heterogeneous, time-varying, and multidimensional information sources, making it difficult for traditional network architectures to support resource allocation in large-scale complex network environments. This article proposes a service provision method based on service function chaining (SFC) to solve this problem. Network function virtualization (NFV) is essential for efficient resource allocation in SAGIN to meet the resource requirements of user service requests. We propose a federated learning (FL)-based algorithm to solve the embedding problem of SFCs in SAGIN. The algorithm considers different characteristics of nodes and resource load to balance resource consumption. Then, an SFC scheduling mechanism is proposed that allows SFC reconfiguration to reduce the service blocking rate. Simulation results show that our proposed FL-VNFE algorithm is more advantageous compared to other algorithms, with 12.9%, 2.52%, and 10.5% improvement in long-term average revenue, acceptance rate, and long-term average revenue–cost ratio, respectively.

Due to geographical factors and resource constraints, the traditional Internet architecture cannot meet the needs of the space–air–ground-integrated network (SAGIN) resource layout in the Industrial Internet of Things (IIoT) service. How to arrange network resources in SAGIN quickly and efficiently to meet the quality of service requirements of users has become a hot research topic in the industry. Based on the characteristics of SAGIN with multiple network segments, we convert the resource scheduling problem of SAGIN into a multidomain virtual network embedding (VNE) problem. This article proposes a latency-sensitive VNE algorithm based on deep reinforcement learning (DDRL-VNE) in the SAGIN environment. Unlike traditional latency optimization algorithms, we consider the effect of traffic size and hop count on latency when evaluating latency. We constructed a learning agent composed of a five-layer policy network and extracted a feature matrix as its training environment based on the network attributes of SAGIN. The node embedding is completed according to the probability that each node is embedded in the training, and then the breadth-first search strategy is used to complete the link embedding. The experimental results effectively illustrate the effectiveness of the algorithm in the SAGIN resource allocation problem.

Microwave Power Transfer (MPT) is a promising technology to charge sensor devices (SDs) wirelessly in wireless sensor networks, and Cloud Computing (CC) can significantly promote task processing capacity of SDs. However, the propagation loss can dramatically influence the harvested energy and computation performance. So, for wireless sensor networks, we study an unmanned aerial vehicle-assisted cloud wireless charging system with the cooperation of the cloud server and the unmanned aerial vehicle (UAV). First, the UAV acts as the energy transmitter, and we design a quantitative charging scheme according to the energy-aware of SDs’ battery capacity. Second, the cloud server processes the tasks uploaded by SDs with the cooperation of the UAV, and we consider the communication connection between the cloud server and the UAV. Third, we propose the joint resource-trajectory optimization to reduce the energy consumption of UAVs. We put forward the Chaotically Adaptive Beetle Swarm Optimization Based on Cauchy Mutation (CABSOC) assisted block coordinate descent algorithm for addressing this non-convex problem. Numerical results indicate that the proposed solution can significantly improve the energy performance of the UAV. And the energy consumption is reduced by 11% compared with the solution with network function virtualization (NFV).

With the development of deep neural networks, hyperpsectral image (HSI) classification systems have achieved a significant improvement. These systems require numerous and accurate labeled hyperspectral data to be adequately trained. However, noisy labels are inherent in real-world hyperspectral systems, resulting in unreliable decisions. To handle noisy labels in hyperpsectral classification, an end-to-end attentive-adaptive network (AAN) is proposed for robust HSI classification training. The goal is to build a classifier with strong generalization capabilities that can be applied to both clean and noisy training sets without explicit noise label pre-treatment. Specifically, a spectral stem network with non-adjacent shortcut is exploited initially to re-distribute the sensitive layers for noisy labels to achieve robust spectral representation. Then, a group-shuffle attention module is proposed to capture the discriminative and robust spatial-spectral features in the presence of noisy labels. Finally, an adaptive noise-robust loss function is developed to fight against noisy labels by learning a parameter to balance the normalized cross entropy (NCE) and reverse cross entropy (RCE). Experimental results on three HSI benchmark datasets with simulated noisy labels demonstrate the effectiveness of AAN on HSI classification.

Network Virtualization (NV) is an emerging network dynamic planning technique to overcome network rigidity. As its necessary challenge, Virtual Network Embedding (VNE) enhances the scalability and flexibility of the network by decoupling the resources and services of the underlying physical network. For future multi-domain physical network modeling with the characteristics of dynamics, heterogeneity, privacy, and real-time, the existing related works perform unsatisfactorily. Federated learning (FL) jointly optimizes the network by sharing parameters among multiple parties and is widely used to address data privacy and data silos. Aiming at the NV challenge of multi-domain physical networks, this work is the first to propose using FL to model VNE, and presents a VNE architecture based on Horizontal Federated Learning (HFL) (HFL-VNE). Specifically, combined with the distributed training paradigm of FL, we deploy local servers in each physical domain, which can effectively focus on local features and reduce resource fragmentation. A global server is deployed to aggregate and share training parameters, which enhances local data privacy and significantly improves learning efficiency. Furthermore, we deploy the Deep Reinforcement Learning (DRL) model in each server to dynamically adjust and optimize the resource allocation of the multi-domain physical network. In DRL-assisted FL, HFL-VNE jointly optimizes decision-making through specific local and federated reward mechanisms and loss functions. Finally, the superiority of HFL-VNE is proved by combining simulation experiments and comparing it with related works.

Industrial Internet of Things (IIoT) and fifth generation (5G) network have fueled the development of Industry 4.0 by providing an unparalleled connectivity and intelligence to ensure timely (or real time) and optimal decision-making. Under this umbrella, the edge intelligence is ready to propel another ripple in the industrial growth by ensuring the next generation of connectivity and performance. With the recent proliferation of blockchain, edge intelligence enters a new era, where each edge trains the local learning model, then interconnecting the whole learning models in a distributed blockchain manner, known as blockchain-assisted federated learning. However, it is quiet challenging task to provide secure edge intelligence in 5G-enabled IIoT environment alongside ensuring latency and throughput. In this article, we propose a proof-of-learning consensus protocol that considers the reputation opinion for edge blockchain to ensure secure and trustworthy edge intelligence in IIoT. This protocol fetches each edge’s reputation opinion by executing a smart contract, and partly adopts the winner’s learning model according to its reputation opinion. By quantitative performance analysis and simulation experiments, the proposed scheme demonstrates the superior performance in contrast to the traditional counterparts.

Although modern Maritime Transportation Systems (MTS) have been extensively benefited from Internet of Things (IoT) technology, but still the risks and challenges in safety and reliability have increased substantially. The involvement of different maritime parties in the marine transportation flow scheduling and management further escalates these challenges. Thus, we need an IoT-based collaborative processing system that unifies the modular structure and integrates multiple modules involved in MTS. Moreover, the need for a shared and controlled access mechanism that cannot be manipulated or tampered by unauthorized parties is also essential requirement in MTS. Blockchain, as an emerging technology, has become a key tool in data security protection because of its non-tampering and non-forgery characteristics. Keeping in view of this aspect, in this paper, an IoT-based collaborative processing system based on blockchain is proposed for marine transportation flow scheduling and management. In addition, we propose a novel consensus mechanism based on Verifiable Random Function (VRF) and reputation voting to reduce the communication cost in blockchain consensus communication process. The proposed scheme has been validated in a simulated environment and the results illustrate that the scheme has obvious effect in resisting replay attack and camouflage attack. Furthermore, the optimized consensus mechanism improves the security by 8% and the transaction processing speed by 6% on the premise that the communication cost is basically unchanged.

To realize the Interconnection of Everything (IoE) in the 6G vision, the space-based, air-based, and ground-based networks have shown a trend of integration. Compared with the traditional communications system, Space-Air-Ground Integrated Networks (SAGINs) can provide a seamless global network connection, while making full use of different network characteristics for synergy and complementarity. However, the increasing global coverage of the Internet, the growing number and variety of smart terminals, and the emergence of various high-bandwidth services have led to an explosion in communication data transmission. Despite the continuous development of communication technologies such as airborne processing and forwarding and high-throughput satellites, the quality of service (QoS) and quality of experience (QoE) for different users still cannot be guaranteed due to the power limitations of satellites and the scarcity of spectrum resources. In this work, drawing on wireless edge caching, considering that the relay of SAGIN has edge caching capability, the hot task is cached in the network nodes in advance. More, this process is optimized using distributed Deep Reinforcement Learning (DRL), thereby reducing transmission delay and relieving the pressure of task offloading on space-based networks. Compared with advanced related works, the long-term node utilization, link utilization, long-term average revenue-to-cost ratio and acceptance ratio of the proposed algorithm are increased by about 4.22%, 31.36%, 11.75% and 7.14%, respectively.

With the advent of the Internet-of-Things (IoT) era, more and more devices are connected to the IoT. Under the traditional cloud-thing centralized management mode, the transmission of massive data is facing many difficulties, and the reliability of data is difficult to be guaranteed. As emerging technologies, blockchain technology and edge computing (EC) technology have attracted the attention of academia in improving the reliability, privacy, and invariability of IoT technology. In this article, we combine the characteristics of the EC and blockchain to ensure the reliability of data transmission in the IoT. First, we propose a data transmission mechanism based on blockchain, which uses the distributed architecture of blockchain to ensure that the data is not tampered with; second, we introduce the three-tier structure in the architecture in turn; and finally, we introduce the four working steps of the mechanism, which are similar to the working mechanism of blockchain. In the end, the simulation results show that the proposed scheme can ensure the reliability of data transmission in the IoT to a great extent.

Regular safety inspection is critical to reduce safety risk in industry. Applying the consortium blockchain technology to safety inspection can ensure the effectiveness of the inspection process and tracing of problems. However, there are two major issues when using conventional consortium blockchain. It is challenging to guarantee the authenticity of the retrieved data source, and meanwhile, achieving a balance between performance and security is not easy. Hence, this article proposes a blockchain-based performance-security balanced safety inspection framework (PSB-SIF), in which a safety inspection box is designed to ensure the authenticity of the inspector’s identity while inspection logic is executed automatically via smart contracts. In addition, this article also proposes a novel credit scoring-based Byzantine fault-tolerant (BFT) consensus algorithm, named safety inspection BFT consensus algorithm (SIBFT), which is used to balance the performance and security of consensus network in a safety inspection. We evaluate the proposed approach by comparing with the solutions using RAFT, Practical BFT (PBFT), and SIBFT consensus algorithms in terms of throughput, transaction latency, scalability, and security of PSB-SIF. The evaluation results show that PSB-SIF is efficient for all these quality metrics.

The development of intelligent cyber–physical systems (ICPSs) in the virtual network environment is facing severe challenges. On the one hand, the Internet of Things (IoT) based on ICPSs construction needs a large amount of reasonable network resources support. On the other hand, ICPSs are facing severe network security problems. The integration of ICPSs and network virtualization (NV) can provide more efficient network resource support and security guarantees for IoT users. Based on the above two problems faced by ICPSs, we propose a virtual network embedded (VNE) algorithm with computing, storage resources, and security constraints to ensure the rationality and security of resource allocation in ICPSs. In particular, we use the reinforcement learning (RL) method as a means to improve algorithm performance. We extract the important attribute characteristics of the underlying network as the training environment of the RL agent. The agent can derive the optimal node embedding strategy through training, so as to meet the requirements of ICPSs for resource management and security. The embedding of virtual links is based on the breadth first search (BFS) strategy. Therefore, this is a comprehensive two-stage RL-VNE algorithm considering the constraints of computing, storage, and security 3-D resources. Finally, we design a large number of simulation experiments from the perspective of typical indicators of VNE algorithms. The experimental results effectively illustrate the effectiveness of the algorithm in the application of ICPSs.

Network virtualization (NV) is a technology with broad application prospects. Virtual network embedding (VNE) is the core orientation of VN, which aims to provide more flexible underlying physical resource allocation for user function requests. The classical VNE problem is usually solved by the heuristic method, but this method often limits the flexibility of the algorithm and ignores the time limit. In addition, the partition autonomy of physical domain and the dynamic characteristics of virtual network request (VNR) also increase the difficulty of VNE. This article proposed a new type of VNE algorithm, which applied reinforcement learning (RL) and graph neural network (GNN) theory to the algorithm, especially the combination of graph convolutional neural network (GCNN) and RL algorithm. Based on a self-defined fitness matrix and fitness value, we set up the objective function of the algorithm implementation, realized an efficient dynamic VNE algorithm, and effectively reduced the degree of resource fragmentation. Finally, we used comparison algorithms to evaluate the proposed method. Simulation experiments verified that the dynamic VNE algorithm based on RL and GCNN has good basic VNE characteristics. By changing the resource attributes of physical network and virtual network, it can be proved that the algorithm has good flexibility.

Summary Unmanned aerial vehicle (UAV) has a broad application prospect in the future, especially in the Industry 4.0. The development of Internet of Drones (IoD) makes UAV operation more autonomous. Network virtualization technology is a promising technology to support IoD, so the allocation of virtual resources becomes a crucial issue in IoD. How to rationally allocate potential material resources has become an urgent problem to be solved. The main work of this paper is presented as follows: (a) In order to improve the optimization performance and reduce the computation time, we propose a multidomain virtual network embedding algorithm (MP‐VNE) adopting the centralized hierarchical multidomain architecture. The proposed algorithm can avoid the local optimum through incorporating the genetic variation factor into the traditional particle swarm optimization process. (b) In order to simplify the multiobjective optimization problem, we transform the multiobjective problem into a single‐objective problem through weighted summation method. The results prove that the proposed algorithm can rapidly converge to the optimal solution. (c) In order to reduce the mapping cost, we propose an algorithm for selecting candidate nodes based on the estimated mapping cost. Each physical domain calculates the estimated mapping cost of all nodes according to the formula of the estimated mapping cost, and chooses the node with the lowest estimated mapping cost as the candidate node. The simulation results show that the proposed MP‐VNE algorithm has better performance than MC‐VNM, LID‐VNE, and other algorithms in terms of delay, cost and comprehensive indicators.

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.

The Internet of things (IoT) has certainly become one of the hottest technology frameworks of the year. It is deep in many industries, affecting people’s lives in all directions. The rapid development of the IoT technology accelerates the process of the era of “Internet of everything” but also changes the role of terminal equipment at the edge of the network. It has changed from a single data user to a dual role of both producing and using data. And collaborative edge computing (CEC) has been born in time. CEC itself can not only solve the problem of computing and storage but also combines with the deep learning (DL) model to make full use of edge computing ability. However, as the core of DL, the robustness of neural network is often not high. In addition, edge devices of CEC are facing a highly dynamic environment, which can easily cause the edge network to be attacked by malicious devices. Therefore, user privacy protection and security issues for CEC deserve more attention. To avoid privacy leakage and security crisis of CEC in social IoT systems, a data protection method based on data disturbance method and adversarial training viewpoint is introduced in this article. Besides, a new adversarial sample generation method based on the firefly algorithm (FA) is proposed. This method reduces the time complexity of traditional by an order for magnitude compared with traditional generative adversarial network (GAN) generation. Since sentences, information on CEC in the IoT system is characterized by a large amount of data, strict confidentiality, and high-security requirements, and they are usually high-risk information on privacy leakage. The proposed method is conducted to the sentence similarity analysis model based on a convolutional neural network (CNN) in the CEC scene to test the feasibility of the method. Compared with the original CNN, the accuracy of the model using the confrontation training method is improved by 4.8%. At the same time, the security value of our model is 2.1% higher than that of the simple CNN model, and it has the best security performance among the four comparison models. Further experiments have demonstrated that the model performs better in its capacity of resisting disturbance and can effectively help multiple organizations to implement data usage and sentence information on the requirements of user privacy protection, data security, and government regulations.

Under the times of the Industrial Internet of Things, the traditional centralized machine learning management method cannot deal with such huge data streams, and the problem of data privacy has aroused widespread concern. In view of these difficulties, in this article, we use the advantages of edge computing and federated learning, combined with the outstanding characteristics of the blockchain, to propose a secure data transmission method. First, we separate the local model updating process from the mobile device independent process; second, we add an edge server so that most of the computation is carried out on the server, which improves the learning efficiency; and finally, we use a distributed architecture of the blockchain to protect data security and privacy. Extensive simulation experiments show that the accuracy of our model can reach 98$\%$. In addition, BC-EdgeFLs interception rate of illegal information can reach 0.8, which has good defensive capabilities. Therefore, the security of data transmission can be strongly guaranteed.

Traditional ground wireless communication networks cannot provide high-quality services for artificial intelligence (AI) applications such as intelligent transportation systems (ITS) due to deployment, coverage and capacity issues. The space-air-ground integrated network (SAGIN) has become a research focus in the industry. Compared with traditional wireless communication networks, SAGIN is more flexible and reliable, and it has wider coverage and higher quality of seamless connection. However, due to its inherent heterogeneity, time-varying and self-organizing characteristics, the deployment and use of SAGIN still faces huge challenges, among which the orchestration of heterogeneous resources is a key issue. Based on virtual network architecture and deep reinforcement learning (DRL), we model SAGIN’s heterogeneous resource orchestration as a multi-domain virtual network embedding (VNE) problem, and propose a SAGIN cross-domain VNE algorithm. We model the different network segments of SAGIN, and set the network attributes according to the actual situation of SAGIN and user needs. In DRL, the agent is acted by a five-layer policy network. We build a feature matrix based on network attributes extracted from SAGIN and use it as the agent training environment. Through training, the probability of each underlying node being embedded can be derived. In test phase, we complete the embedding process of virtual nodes and links in turn based on this probability. Finally, we verify the effectiveness of the algorithm from both training and testing.

Cognizant of the ease with which softwarized functions can be dynamically scaled according to real time resource requirements, and the fact that multiple services can have common VNFs in their chaining, this paper tackles the problem of cost effective deployment of online services from the perspective of sharing their VNF instances. First, we formally formulate the deployment problem under VNFs sharing. Secondly, given the NP-hard nature of the above problem, we propose a reinforcement learning (RL) algorithm capable of making intelligent placement decisions while considering multiple conflicting costs. Costs of transmission, VNF instantiation or energy consumption, among others. Thanks to the intelligence of the RL algorithm, simulation results show that the performance of the proposed algorithm is within a 14% margin and similar to an optimal solution in terms of request provisioning cost and acceptance ratio, respectively. Moreover, the algorithm results in more than a 20% and a 70% improvement in terms of request deployment cost and time compared to a state-of-the-art algorithm, and up to more than a 40% improvement in terms of cost compared to an algorithm that greedily minimizes the transmission or VNF activation costs.

Space-air-ground integration to support seamless coverage of ground, satellite, airborne, and marine communications, is likely to be a key trend in the 6G era. One of several key challenges in such space-air-ground integration networks (SAGINs) is to design efficient scheduling approaches for multi-dimension network resources. Due to the inherent heterogeneity characteristics, we demonstrate how can transform the network resource allocation problem in SAGINs into a multi-domain virtual network resource allocation problem, as well as proposing a reinforcement learning assisted bandwidth aware virtual network resource allocation algorithm (RL-BA-VNA). Specifically, RL-BA-VNA leverages reinforcement learning and uses a policy network as an agent to perform the node embedding. In order to support users’ exacting bandwidth requirements, we prefer to select virtual network requests with large bandwidth for embedding. Experiment findings show that the proposed algorithm RL-BA-VNA outperforms respectively the other three conventional virtual network resource allocation algorithms RL, DRL and BASELINE by an average of 2.06%, 4.93%, 11.07% in terms of long-term average reward, acceptance rate, and long term reward/cost.

The unbalanced traffic distribution is a severe problem in cellular networks, which leads to congestion and reduces spectrum efficiency. To tackle this problem, we propose an unmanned aerial vehicle (UAV)-assisted wireless network architecture in which UAV acts as relay to divert the traffic from the overloaded cell to its neighbor underloaded cell. Considering that UAV communications are easily eavesdropped, we use the secrecy capacity to evaluate the performance of the network. To fully exploit the advantages of the proposed architecture, we formulate a joint UAV position optimization, user association, and time allocation problem to maximize the sum-log-rate of all users in two adjacent cells. To tackle the complicated joint optimization problem, we first design a genetic-based algorithm to optimize the UAV position, and then use the branch-and-bound method to devise a low-complexity algorithm to get the optimal user association and time allocation schemes. The simulation results indicate that the proposed UAV-assisted wireless network architecture is superior to the terrestrial network, and the proposed algorithms can further improve the network performance in comparison with the other schemes.

The development of smart technology and smart cities has solved the problem of data islands, but it has also brought about information security problems. Federated learning provides solutions to information security problems, which is a new machine learning method that effectively protects the local privacy of edge devices by distributing models to edge devices for training. However, due to malicious attacks from malicious edge devices, the accuracy and efficiency of federated learning are greatly compromised. Therefore, to solve the above problems, this paper proposes a reputation-aware method based on reinforcement learning (RA-RL) to select edge devices to ensure that the federated learning process is not attacked. Specifically, we introduce a reputation measurement scheme to evaluate the reputation of edge devices and use it as one of the features of edge devices. Then extract the feature matrix of candidate edge devices as the RL training environment to calculate the probability of each edge device is selected, and finally use the greedy algorithm to determine the devices that will eventually participate in the federated learning. Simulation experiments show that the RA-RL algorithm can effectively solve the training data security problem in federated learning, and is superior to other algorithms in terms of load balance, efficiency and accuracy.

To a large extent, the deployment of edge computing (EC) can reduce the burden of the explosive growth of the Internet of Things. As a powerful hub between the Internet of Things and cloud servers, edge devices make the transmission of cloud to things no longer complicated. However, edge nodes are faced with a series of problems, such as a large number, a wide range of distribution, and complex environment, the security of EC should not be underestimated. Based on this, we propose a tactic to improve the safety of EC by virtualizing edge nodes. In detail, first of all, we propose a strategy of edge node partition, virtualize the edge nodes dealing with different types of things into various virtual networks, which are deployed between the edge nodes and the cloud server. Second, considering that different information transmission has different security requirement, we propose a security tactic based on security level measurement. Finally, through simulation experiments, we compare with the existing advanced algorithms which are committed to virtual network security, and prove that the model proposed in this article has definite progressiveness in enhancing the security of edge computing.

Sentence similarity analysis has been applied in many fields, such as machine translation, the question answering system, and voice customer service. As a basic task of natural language processing, sentence similarity analysis plays an important role in many fields. The task of sentence similarity analysis is to establish a sentence similarity scoring model through multi-features. In previous work, researchers proposed a variety of models to deal with the calculation of sentence similarity. But these models do not consider the association information of sentence pairs, but only input sentence pairs into the model. In this article, we propose a sentence feature extraction model based on multi-feature attention. In addition, with the development of deep learning and the application of nature-inspired algorithms, researchers have proposed various hybrid algorithms that combine nature-inspired algorithms with neural networks. The hybrid algorithms not only solve the problem of decision-making based on multiple features but also improve the performance of the model. In the model, we use the attention mechanism to extract sentence features and assign weight. Then, the convolutional neural network is used to reduce the dimension of the matrix. In the training process, we integrate the firefly algorithm in the neural networks. The experimental results show that the accuracy of our model is 74.21%.

The continuous expanded scale of the industrial Internet of Things (IIoT) leads to IIoT equipments generating massive amounts of user data every moment. According to the different requirement of end users, these data usually have high heterogeneity and privacy, while most of users are reluctant to expose them to the public view. How to manage these time series data in an efficient and safe way in the field of IIoT is still an open issue, such that it has attracted extensive attention from academia and industry. As a new machine learning paradigm, federated learning (FL) has great advantages in training heterogeneous and private data. This article studies the FL technology applications to manage IIoT equipment data in wireless network environments. In order to increase the model aggregation rate and reduce communication costs, we apply deep reinforcement learning (DRL) to IIoT equipment selection process, specifically to select those IIoT equipment nodes with accurate models. Therefore, we propose a FL algorithm assisted by DRL, which can take into account the privacy and efficiency of data training of IIoT equipment. By analyzing the data characteristics of IIoT equipments, we use MNIST, fashion MNIST, and CIFAR-10 datasets to represent the data generated by IIoT. During the experiment, we employ the deep neural network model to train the data, and experimental results show that the accuracy can reach more than 97%, which corroborates the effectiveness of the proposed algorithm.

At present, the traditional heuristic method to solve the problem of virtual network embedding (VNE) is still the mainstream. In the environment of network virtualization (NV), a more efficient VNE algorithm is needed to serve the construction of smart city. Using heuristic algorithm to solve the problem of VNE does not meet its development requirements. In this paper, a VNE algorithm based on node probability is proposed by using reinforcement learning (RL) algorithm. The algorithm extracts three attributes of each substrate node to form a feature matrix, which is used as the input of the policy network to train the agent. The purpose is to deduce the mapping probability of each node and rank the base nodes according to this probability, then embed the virtual nodes in this order. Finally, the breadth first search (BFS) strategy is used to map the links. Simulation results show that our algorithm is superior to a representative algorithm based on node ranking in terms of the acceptance rate of virtual network requests (VNR), long-term revenue consumption ratio and long-term average revenue.

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.

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.

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.

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.

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.

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.