In heterogeneous wireless emergency networks, network fault diagnosis plays a critical role in ensuring reliable and secure communication. To improve network transmission quality, the complexity of network equipment—both in hardware and software design—has increased, which inevitably gives rise to equipment failures with complex root causes, significantly elevating the difficulty of fault diagnosis. Current fault diagnosis algorithms are inadequate for addressing the challenges in fault diagnosis of complex emergency access networks, primarily due to their high diagnostic costs and low accuracy. In this study, we first propose a diagnosis framework and a Deterministic Fault Propagation (DFP) model, and a Hierarchical Fault Diagnosis Framework. Second, we develop three algorithms to construct a Fault Cause Relationship Graph, which supports identifying the logical relationships among various fault causes associated with a specific fault. Third, we propose a Fault Diagnosis algorithm based on Relational Graph Inference (FDRGI). Finally, we conduct extensive experiments in real-world wireless access networks. The experimental results demonstrate that our algorithm satisfies the requirements for root cause diagnosis of access failures in emergency networks, and outperforms other comparative algorithms in terms of diagnostic cost and accuracy: it reduces the average diagnostic cost by 13.71%-69.88% and improves the average diagnostic accuracy by 39.06%-1.98-fold.

ABSTRACT As manufacturing faces evolving customer demands, the integration of Industrial Internet of Things (IIoT) networks is crucial for enhancing production flexibility. In this context, the Seru Production System (SPS) has emerged as a highly adaptable production mode and emphasizes the strategic assignment of cross‐trained workers, particularly in hybrid configurations combining divisional and rotating serus. This paper proposes a novel bi‐objective mathematical model incorporating learning effects to minimize makespan and balance workloads among workers. With the development of Artificial Intelligence Generated Content (AIGC) empowered big models, new breakthroughs have emerged in industrial manufacturing decision‐making. These models utilize deep learning for foundational content processing and leverage reinforcement learning to optimize strategies. This process provides robust support for achieving efficient decision optimization. Building on the concepts of AIGC big models training, this study employs reinforcement learning to refine the results of multi‐objective genetic algorithms, thereby improving the solution capability of the bi‐objective model. Experimental results demonstrate that the proposed algorithm effectively provides optimal strategies for tuning crossover and mutation operations. Additionally, numerical experiments offer insights into the formation of hybrid SPS configurations.

Delay-sensitive applications like telemedicine and VR/AR intensify competition for network resources and elevate congestion risks, particularly in mobile networks with highly dynamic link conditions. Traditional end-to-end congestion control methods suffer from prolonged response times, rendering them ineffective for Delay-sensitive applications. To this end, this paper proposes a novel In-Network Congestion Control (INCC) mechanism that accelerates congestion control by enabling network nodes to proactively identify bottlenecks and promptly notify end-hosts. Unlike traditional end-host-centric approaches, INCC facilitates collaborative congestion decision-making between end-hosts and in-network unit. INCC classifies congestion into two phases: “yellow” and “red” based on the local queue length bottleneck awareness and global congestion flow bottleneck statistics. For the “yellow” local congestion phrase, we design an in-network local control algorithm that performs proactive packet dropping and rate adjustment to mitigate emerging congestion. For the “red” global congestion phrase, we design an end-host and network cooperative global congestion control algorithm to make precise sending rate adaptation by proactive bottleneck awareness. We implement INCC via Linux kernel modifications and design three experiments to compare with Cubic, NewReno, and BBR. Experimental results demonstrate INCC has good performance on round-trip time and throughput, achieving 99.03% scheduling fairness in flow contention scenarios. Additionally, INCC has low execution overhead on CPU utilization and realize microsecond computational latency.

At present, multipath transmission realized by multi-interface devices and bandwidth aggregation technology meets user demand for high-bandwidth communication in 6G wireless networks. However, multipath transmission systems face the threat of single-point failure by multi-interface servers themselves. Existing solutions for such failure are not suitable for multipath transmission scenarios, and this seriously limits the ability to bandwidth aggregation and reduces the reliability and invulnerability of the multipath transmission system in 6G wireless networks. In this paper, we propose a novel Intelligent Device Disaster Recovery (Multi-ID2R) mechanism to solve the single-point failure in multipath and aggregated environments for the first time. In particular, we establish the Multi-Dimensional Parameter Joint Analysis model (MDPJA) and propose an algorithm for judging the running state of multi-interface devices. The algorithm takes into account the different network parameters of the paths, including delay, packet loss rate, and throughput. Moreover, an intelligent switching mechanism based on service quality is designed. Multi-ID2R comprehensively considers the characteristics of the business and the current parameters of multipath networks to determine the moment of switching to flexibly adjust switching strategies. Finally, we deploy the mechanism on multi-interface servers in actual networks. Experiments demonstrate that, compared with Virtual Router Redundancy Protocol, Gateway Load Balancing Protocol, and Hot Standby Router Protocol, Multi-ID2R effectively improves the reliability and invulnerability of multi-interface server in 6G wireless networks.

Vehicular Ad hoc Network (VANET) is an important component of intelligent transportation systems. In VANET, nodes' high mobility and limited computing resources make it easy for them to be controlled by attackers to become botnets. Link flooding attack (LFA) is a new attack type that uses botnets to send legitimate low-speed traffic to flood critical links to cut off the target area, which poses new security risks for VANET. Therefore, the paper proposes an LFA mitigation scheme based on the programmable network architecture to improve security in VANETs. First, through the designed telemetry and early warning methods, the fine-grained network state can be efficiently obtained in real-time, and the link condition can be evaluated quickly. Afterward, the reroute scheduling policy for traffic is customized with the help of graph neural networks to reduce the pressure on critical links in time. The experiment shows that the scheme can rapidly mitigate LFA.

The convergence of Blockchain and Internet of Things (BIoT) is fully considered as a paradigm for mitigating threats related to the trust, security, and privacy of Internet of Things (IoT) data. However, because the bandwidth across nodes and time varies in practical IoT networks, it is difficult for existing BIoT mechanisms guarantee blockchain consensus performances. The consensus time could become long owing to low-bandwidth nodes taking longer to download blocks than high-bandwidth nodes. Conventional wisdom holds that removing low-bandwidth nodes can decrease the consensus time, but the nodes could have high-bandwidth at another time owing to bandwidth variability; thus, kicking which nodes out of the consensus is a great challenge. In this article, a novel lightweight BIoT convergence (namely, E-Chain) is proposed to overcome bandwidth variability. The E-Chain first decouples the blockchain into on-chain validating and off-chain voting components. In the off-chain voting part, each node incurs a one-bit communication overhead for voting on a block based on a reputation index. This voting component does not need to download the full content of the block, and is therefore not affected by bandwidth variability. The reputation index was formulated using a rating algorithm with multidimensional IoT network metrics. In addition, the voting mechanism is secure and can still reach the correct consensus when suffering from byzantine attacks. By contrast, a block is validated and stored in a dispersed manner in the on-chain validating part. The E-Chain performances were then evaluated and compared with state-of-the-art mechanisms. Experimental results show that the E-Chain mechanism can significantly decrease both the consensus time and memory resources, and incur an acceptable memory overhead for resource-constrained IoT nodes.

Multipath transmission is a critical enabling technology to enhance QoE for edge users. The packet scheduler plays an irreplaceable role in overcoming heterogeneity and dynamicity in multipath transmission. However, current schedulers depend on an inaccurate delay estimation and lack systematic traffic intensity awareness, performing poorly in wireless heterogeneous networks (HetNets). In this paper, we propose a novel traffic-aware two-level packet scheduler (TA2LS) to address the problem and improve aggregated bandwidth while trading off delay. In particular, we design a multipath transmission state machine (MTSM) to perceive link traffic intensity. MTSM replaces network prediction algorithms by identifying the contribution of each link in multipath transmission in a cost-effective way. Further, we propose a scheduling mechanism based on a two-level optimal-path evaluation method (2LOSM) to adjust the packet scheduling policy adaptively. 2LOSM increases the priority of links with low traffic intensity during scheduling, improving aggregated bandwidth performance and reducing end-to-end delay. We have built a real-world 4G/5G/WiFi testbed and deployed 47 dynamic scenarios to evaluate TA2LS and other five schedulers. In 4G/5G/WiFi scenarios, TA2LS improves aggregated bandwidth by 10.32%–48.27% compared to the second-best scheduler and reduces end-to-end delay by 5.04%–39.98% under the premise of fewer or equivalent overheads.

The multipath transmission structure decouples network services from a single transmission carrier, which has great potential for shaping a more secure and efficient 6G network. Existing multipath transmission schemes face challenges such as network heterogeneity, perception lag, and additional scheduling delay, which limits their ability to improve bandwidth aggregation capacity and information security. To address these issues, we propose the Sequential Reinforcement Evolution (SRE) scheme, which utilizes deep reinforcement learning to predict the value of future scheduling actions based on past network states. The SRE scheme regards improving bandwidth aggregation capacity and anti-eavesdropping ability as optimization goals, and designs a semi-symmetric attention recurrent neural network (SARNN) to better mine the sequential nature of the scheduling process. The SRE scheme utilizes approximately 500 million real network data points to pre-train the SARNN model, and performs cycle optimization during the actual deployment process. Experimental results show that SRE significantly outperforms state-of-the-art scheduling schemes with a 32% increase in bandwidth aggregation and a 117% increase in traffic security dispersion with minimal impact on latency.

Efficient transmission control is a challenging issue in vehicular networks due to the highly dynamic network environment. In this paper, we propose a Deep reinforcement learning based adaptive Transmission Scheduling Mechanism (DTSM), which is able to adaptively select different transmission control policies based on the current network status and the history data learning. In particular, we first introduce the adaptive transmission scheduling units (ATSU) in both Software-Defined Vehicular Networking (SDVN) controllers and the corresponding base stations. Based on this architecture, we formulate a mathematical model for optimal decision-making in SDVN controllers. Besides, in ATSUs, we proposed a deep Q-learning based transmission control method to dynamically adapt to the time-varying vehicular network scenarios. Simulation results verify that the proposed DTSM solution outperforms the single transmission control method of four existing benchmarks (e.g., TcpVegas, TcpBic, TcpWestwood, TcpVeno) in terms of average throughput and round-trip time.

It is well known that concurrent multipath transfer (CMT) can improve the transmission rate. However, due to multiple heterogeneous paths from users to the access network, a large number of out-of-order packets significantly degrade the overall transmission reliability. Cybertwin provides a potential solution to alleviate the packet out-of-order problem by accurately detecting and perceiving the path state. In this article, we investigate the data scheduling problem and propose a learning-based cybertwin-driven CMT algorithm to obtain the optimal data scheduling policy. In particular, we first formulate the data scheduling problem as an integer linear programming by taking the QoS metrics into account. To cope with the packet out-of-order problem in CMT, we propose a reliable cybertwin-CMT with deep reinforcement learning (CMT-DRL) algorithm to determine the data scheduling decisions. The proposed algorithm takes multipath throughput, end-to-end delay, and packet loss rate into account. Besides, CMT-DRL adopts an asynchronous learning framework to efficiently execute data collection, packet scheduling, and neural network training in sequence by decoupling model training and execution. We conduct extensive experiments in a P4-based programmable network platform. Experimental results indicate that the CMT-DRL outperforms the existing benchmarks in terms of the number of out-of-order packets, round-trip time, and throughput.

Recent advances in the Internet of Things (IoT) technologies have enabled ubiquitous smart devices to sense and process various kinds of data. However, these innovations also raise the concern of efficient data transmission. Tackling the above issue is nontrivial since the resource constraints and environmental randomness in IoT require a lightweight transmission scheme while guaranteeing system stability. In this paper, we formulate the transmission scheduling problem of multi‐interface IoT devices as a concave optimization, aimed at accommodating the randomness of the IoT environment within the network capacity. By applying the Lyapunov optimization technique, we divide the stochastic problem into a series of low‐complex subproblems, which can be individually solved per time slot, and develop a dynamical control algorithm that does not require a priori knowledge such as link states. Theoretical analysis shows that our algorithms nicely bound the average queue length and are asymptotically optimal. Finally, extensive simulation results verify the theoretical conclusions and validate the effectiveness of the proposed algorithm.

Due to the severe environment along the High-Speed Railway (HSR), it is essential to research an efficient HSR communication system. In our previous work, we collected and analyzed an amount of the first hand dataset of signal intensity in HSR networks. We first observed that the link status variation presented an obvious Two-Time-Scale characteristics. However, that work did not analyze the cause of the Two-Time-Scale characteristics clearly. In this work, we focus on the fundamental cause of the periodic Two-Time-Scale characteristics, and make a lot of in-depth studies on this interesting phenomenon. Furthermore, we rebuild Two-Time-Scale characteristics by leveraging the relationship between the link state variation and the geographical position along HSR lines. In particular, considering the distribution of urban areas and rural ones along the HSR, a periodic distance based small time-scale model and a path-loss based large time-scale model are proposed respectively. Simulation results show the proposed models can perfectly explain the Two-Time-Scale characteristics and predict HSR link quality.