Satellite networks have become vital for Earth observation, environmental monitoring, and disaster warning by interconnecting remote sensing satellites, drones, and ground stations into large-scale distributed monitoring systems. Federated learning (FL) enables intelligent applications on these systems while preserving data privacy. However, satellite networks face challenges such as high latency and jitter, which hinder FL training efficiency and model timeliness. To address these issues, we propose a three-layer asynchronous FL framework that integrates terminals, Low Earth Orbit (LEO), and Medium Earth Orbit (MEO). This architecture employs asynchronous updates, distributed partial aggregation, and hierarchical global aggregation to enhance model convergence under unstable communication conditions. We introduce a dynamic weighted asynchronous update mechanism that mitigates the impact of stale updates based on staleness and similarity metrics. Additionally, a reinforcement learning-based adaptive timeslot request strategy is designed, allowing terminals to optimize global model update timings via a teacher-student model, thus reducing staleness caused by delay jitter. Simulation results on a multilayer satellite network demonstrate that our method significantly improves convergence speed and model accuracy. Compared to baseline methods, dynamic weighting enhances accuracy by 1.12%–1.80%, while adaptive timeslot requests improve accuracy by 3.83%–6.42% and reduce convergence time by 25.79%–59.89%.

Low Earth Orbit (LEO) satellite networks provide global connectivity but are vulnerable to security threats such as link flooding attacks. To defend against such attacks, stateof-the-art approaches employ SDN to acquire a global view of the network, enabling the detection and mitigation of malicious traffic. However, in LEO constellation networks, the distributed nature of satellites across a large spatial scale introduces significant latency in both satellite-to-ground and inter-satellite links, with latency reaching up to tens of milliseconds, while attack traffic dynamically adapts within sub-milliseconds. As a result, existing defense systems face challenges in countering these attacks effectively due to the increased reaction time caused by link latency. In this paper, we leverage programmable switches to build a real-time defense system against link flooding attacks (LFA) in LEO constellation networks. To achieve this, we analyze the practical constraints encountered in the deployment of LFA attacks against state-of-the-art LEO satellite systems. We observe that despite the ability of bots to initiate attack traffic from any location worldwide, an anomalous distribution of flow rate on the affected links can still be detected. We propose SatShield, an in-network defense system that filters out suspicious traffic (heavy flows) in the network and mitigates these threats by leveraging programmable packet scheduling. By using SatShield, we are able to achieve real-time identification and rate-limiting of attacks at line rate on a per-packet basis. We implement SatShield with P4 in a commercial programmable switch and evaluate it with real-world traffic traces. Our evaluation shows that SatShield autonomously identifies LFA attack flows and rapidly mitigates LFA attacks.

Metaverse has emerged as a revolutionary technique for transforming the way people interact with digital content, which relies on a distributed computing and communication infrastructure, encompassing terminal users, edge servers, and cloud servers. However, the rapid evolution of the Metaverse presents challenges that surpass the capabilities of existing communication and network infrastructures, particularly on network bandwidth and latency. Additionally, human experience becomes a critical factor in this domain. Therefore, we introduce a human-aware hierarchical software defined network (SDN) architecture consisting of a Metaverse cloud layer, a mobile edge computing (MEC) server empowered edge layer, and a distributed terminal layer. Each MEC server dynamically controls a multi-antenna base station (BS) and several reconfigurable intelligent surfaces (RISs) according to the terminal immersive experience requirements in real-time. To overcome the bandwidth limitation, we propose a novel smart reconfigurable spatial reuse new radio in unlicensed spectrum (NR-U) framework, which can realize customizable communications through flexibly and coordinately reconfiguring beams among the coordination between BSs and RISs. The objective function is formulated as a Lyapunov optimization based decentralized partially-observable Markov decision process (Dec-POMDP) problem to maximize the spectral efficiency while guaranteeing the latency and reliability requirements in Metaverse, via a joint user selection, phase-shift control, and beam coordination strategy. To solve the above non-convex, strongly coupled, and mixed integer nonlinear programming (MINLP), we propose a novel multi-agent hierarchical deep reinforcement learning (MAHDRL) algorithm that integrates deep Q-network (DQN) to solve discrete problems, deep deterministic policy gradient (DDPG) to solve continuous problems, and mixing network to capture complex interactions between multiple agents. Numerical results demonstrate the effectiveness of the proposed algorithm and verify the performance improvements compared to traditional multi-agent deep reinforcement learning (MADRL) algorithms.

Random Linear Network Coding holds great potential for enhancing the performance of mega-constellation networks. However, its implementation brings several challenges such as complexity in matrix operations, bandwidth limitations, and increased latency. Although some CPU- and GPU-based solutions have achieved sufficient coding throughput, the gateway stations of satellites require processing rates greater than 10 Gbps while maintaining sub-millisecond delays, which is a challenge with current solutions. In this study, we present and assess efficient RLNC encoding strategies for programmable hardware pipelines, such as the widely adopted Tofino chip with multiple programmable packet parsers and match-action stages. Our approach differs from existing RLNC implementations by offloading the demanding matrix operations from the CPU to the programmable network switch hardware pipeline. Additionally, we optimize logical table ID and other resource utilization by scheduling matrix multiplications across multiple stages. We performed a preliminary evaluation of our design on the Tofino switch and observed a significant improvement in RLNC encoding latency, with a reduction to sub-millisecond levels. Moreover, our design outperforms state-of-the-art solution in terms of throughput when the generation size is greater than 100.

With the rapid growth of Internet of Things (IoT), smart home develops rapidly in these years, which could assist people who need family medical support. It could integrate health care with ambient assisted living (AAL) technologies and provide activities of daily life (ADLs) to the people who need care. This paper proposes a smart home and cross-cloud-and-edge computing based nursing system (NS). In general, a good NS requires low latency, high stability, and the real-time analysis and response, where the conventional centralized cloud computing based approaches cannot meet those requirements very well. To this end, we introduce a novel distributed joint edge-cloud structure to better satisfy these requirements. Moreover, to deal with the security and privacy issues, we introduce the blockchain to verify the identity of data exchanging and differential-privacy (DP) in the NS to protect the healthcare takers’ data privacy. In a word, we propose a privacy-preserving context-aware multi-armed bandit based online learning approach for edge-cloud-enabled NS via blockchain in IoTs. Additionally, our system with a novel top-down expanding tree based structure can support dynamically increasing health care datasets. Extensive experimental and numerical results demonstrate our solution can achieve accurate recommendation results with sublinear regret performance.

Vehicular ad hoc network (VANET) has become an accessible technology for improving road safety and driving experience, the problems of heterogeneity and lack of resources it faces have also attracted widespread attention. With the development of software-defined networking (SDN) and multiaccess edge computing (MEC), a variety of resource allocation strategies in MEC-enabled software-defined networking-based VANET (SDVN) have been proposed to solve these problems. However, we note that few of these work involves the situation where SDVN is under Distributed Denial of Service (DDoS) attacks. Actually, Internet of Things (IoT) devices are extremely easy to be compromised by malicious users, and compromised IoT devices may be used to launch edge DDoS attacks against the MEC servers in MEC-enabled SDVN at any time. In this article, we propose a graph neural network (GNN)-based collaborative deep reinforcement learning (GCDRL) model to generate the resource provisioning and mitigating strategy. The model evaluates the trust value of the vehicles, formulates mitigation of edge DDoS attacks and resource provisioning strategies to ensure that the MEC servers can work normally under edge DDoS attacks. In addition, GNN is adopted in the DRL model to extract the structure feature of the graph composed of MEC servers, and help transfer computing tasks between MEC servers to alleviate the problem of resources imbalance between them. Experimental results show that the method of estimating the vehicular trust value is effective, and our method can make the average throughput of edge nodes more stable and lower down the average delay and the average energy consumption under the edge DDoS attack. Also, a real-world case study is conducted to verify our conclusion.

With the development of data plane programmable Software-Defined Networking (SDN), Distributed Denial of Service (DDoS) attacks on the data plane increasingly become fatal. Currently, traditional attack detection methods are mainly used to detect whether a DDoS attack occurs and it is difficult to find the path that the attack flow traverses the network, which makes it difficult to accurately mitigate DDoS attacks. In this article, we propose a detection method based on Spatial-Temporal Graph Convolutional Network (ST-GCN) over the data plane programmable SDN, which maps the network into a graph. It senses the state of switches through In-band Network Telemetry (INT) with sampling, inputs the network state into the spatial-temporal graph convolutional network detection model, and finally finds out the switches through which DDoS attack flows pass. Based on this, we propose a defense method combined with an enhanced whitelist and a precise dropping strategy, which can effectively mitigate DDoS attacks and minimize the impact on legitimate network traffic. The evaluation results show that our detection method can accurately detect the path that the DDoS attack flows pass through, and can effectively mitigate the DDoS attack. Compared to classic methods, our method improves the detection accuracy by nearly 10%. At the same time, the southbound interface load and CPU overhead brought by our detection and defense process are much lower than the classic methods.

Protecting enterprise information security is a main task of Internet of Things system. The interaction between employees in same enterprise is based on level structures. So it is important to discover levels of employees for urban developers to protect enterprise information security. In this article, we propose a level discovery method for employees (LDME) from the records of employees using mobile phones named LDME. The call behavior between employees are expressed as several weighted directed complex networks, LDME represent edges in these weighted directed complex networks as vectors to exact both direction and weight information of the edges. Combined with supervised learning method, LDME prune these weighted directed networks into directed acyclic networks, which accurately reflect levels information between employees. At the same time, LDME mines the maximal frequent directed acyclic substructure from the above directed acyclic networks with efficient way, which indicate the stable levels information. We use real data to verify the performance of our method. The experimental result shows that the level of employees mined with our method is accurate and stable.

Purchasing behavior analysis plays a crucial role in pricing, recommendation, and market strategy designing. One of the fundamental questions that arises in purchasing behavior analysis is to understand, characterize and estimate purchasing capability. In this article, we investigate the patterns of purchasing capability from the perspective of network usage of smartphone consumers based on a large-scale usage detail records (UDRs). First, the purchasing capability of smartphone consumers are divided into three (high/middle/low) levels according to corresponding device retail price around observation period. Then we pairwise integrating interest and temporality information, and conduct a clustering analysis for each purchasing capability level users. Finally, the profile of each community is extracted by a visualization process. The learned patterns can not only illustrate how users behave on interest, but also describe their network usage preference on temporality, providing fine-granularity to understand the dynamics behind each purchasing capability group. Moreover, inspired by learned patterns, we find network usage distribution on temporality, spatiality and interest can serve as good indicators for estimating users purchasing capability. Our work has broad applicability in fields such as pricing, recommendation, and market strategy designing.

A integrated propagation model for VANET in densely built-up urban scenario, based on analyzing the special factors of the measurements, is proposed in the paper. The proposed propagation model can be used as the physical module in OMNet++ to evaluate the packet level performance for VANET in a densely built-up urban scenario. The effectiveness of the proposed method is demonstrated by using experiments under various radio environments in Wuhan, China. The simulation results of the statistical packet level performance are approximately consistent with that in measurements, and the error between measurements and simulation is acceptable.

Vehicular ad hoc network (VANET), a subclass of mobile ad hoc networks (MANETs), is a promising approach for future intelligent transportation system (ITS). Understanding and modeling packet error in the VANET is especially relevant to the design and analysis of higher layer wireless communication protocols. In this paper, the analysis of the packet error characteristics based on measurements in live VANET in different scenarios is shown form which Traces-4 are selected for further analysis. Based on the statistical dependence between burstlengths and gaplengths, we present a packet-level Markov (PLM) model which is capable of describing the measured statistics properly. The parameters of the model are obtained from the packet error statistical characteristics. Finally, it is demonstrated that both the accuracy and the efficiency of the proposed PLM model outperform other popular Markov models.

Selective forwarding is one of the routing methods that if more than one downstream node are available at either the source or an intermediate node, the packet is forwarded along only one downstream link based on local conditions. In the paper, the selective forwarding probability based on the node degree and link loss is presented to increase the reliability of selective forwarding. The performance is analyzed through regular square network topology qualitatively and the simulation results show the effects of the parameters on our methods