Most congestion control mechanisms perform well in specific network environments, but none can consistently deliver good performance across all scenarios. Recently proposed frameworks based on reinforcement learning can flexibly select congestion control algorithms to adapt to dynamic changes in network conditions. However, frequently altering the congestion control mechanisms during relatively stable periods of the network actually leads to instability and unnecessary computational overhead. In this paper, we propose a hierarchical adaptive congestion control algorithm (HACC) to be resilient to the varying network. HACC dynamically selects the appropriate congestion control mechanism only when the current congestion control algorithm is not suitable for the current network state, rather than changing the congestion control scheme every training cycle to ensure network stability. The simulation results show that under different realistic workloads, HACC significantly reduces the computational overhead and improves throughput. Specifically, HACC reduces average overhead by 31% and improves throughput by up to 47%, 35%, 23%, and 15% compared to Cubic, Reno, BBR, and Antelope, respectively.

Given the spring-up of diverse new applications with different requirements in metro optical networks, network slicing provides a virtual end-to-end resource connection with customized service provision. To improve the quality-of-service (QoS) of slices with long-term operation in networks, it is beneficial to reconfigure the slice adaptively, referring to the future traffic state. Considering the busy-hour Internet traffic with daily human mobility, the tidal pattern of traffic flow occurs in metro optical networks, expressing both temporal and spatial features. To achieve high QoS of slices, this paper proposes a node-oriented slice reconfiguration (NoSR) scheme to reduce the penalty of slices, where a gradient-based priority strategy is designed to reduce the penalties of slices overall penalties in reconfiguration. Besides, given that a precise traffic prediction model is essential for efficient slice reconfiguration with future traffic state, this paper presents the model combining the graph convolutional network (GCN) and gated recurrent unit (GRU) to extract the traffic features in space and time dimensions. Simulation results show that the presented GCN-GRU traffic prediction model achieves a high forecasting accuracy, and the proposed NoSR scheme efficiently reduces the penalty of slices to guarantee a high QoS in metro optical networks.

This paper proposes a high-precision deterministic provisioning with federated learning training scheme to provide deterministic delay guarantees for federated learning tasks, and improves the training accuracy of machine learning and reduces computing pressure of edge nodes.

The Industrial Internet of Things (IIoT) is considered to be one of the most promising revolutionary technologies to increase productivity. With the refined development of manufacturing, the entire manufacturing process is split up into several areas of IoT production. Devices from different domains cooperate to perform the same task, which cause security problems in interacted communication among them. Existing authentication methods cause heavy key management overhead or rely on a trusted third party. It is imperative to protect privacy and ensure the credibility of the device during device interaction. This article proposes a federated hierarchical trust interaction scheme (FHTI) for the cross-domain industrial IoT. It builds a low-privacy network platform through blockchain and protects the data privacy of the IIoT. A hierarchical trust mechanism based on federated detection is designed to realize the unified trust evaluation of cross-domain devices. A trusted cross-domain method based on device trust value is designed to ensure the security and trustworthiness of cross-domain devices. The simulation results show that the FHTI scheme can improve the speed of identity authentication and the detection accuracy of malicious devices.

This paper proposes a dual-threaded blockchain-based architecture to detect large-scale intelligent network abnormalities. Anomaly detection is then implemented using an adaptive encoder. The simulation results show that the proposed anomaly detection scheme achieves efficient anomaly detection while reducing latency and improving throughput.

The integration of Internet of Things (IoT) and industry reveals the industrial manufacturing developments, resulting in Industry IoT (IIoT), which is to provide a general interconnect system for the access of various industry devices. However, as the amount and type of terminal increase, the creditability and privacy protection of terminal devices are hard to be guaranteed in IIoT, since the data and digital identity of access devices are nearly transparent for more devices in networks. It is a critical issue for the security of IIoT whether the access and service of device are trustworthy. In this article, we present a novel private blockchain-enabled trusted anonymous access (BlockTrust) architecture for IIoT, where the distributed cloud radio and optical access networks (C-RONs) are considered to provide a risk reduction of privacy leakage. Based on the BlockTrust architecture, a blockchain-enabled tripartite anonymous identification trusted service provisioning (TriTrustServ) scheme is further proposed to guarantee a balanced tradeoff among the credibility, confidentiality, and efficiency in IIoT, including digital identity generation, anonymous access identification, and trusted resource provisioning. Note that for the sake of a high credibility in IIoT networks, a tripartite authentication is presented in this article with the first time among device manufacturer, devices, and network operator for the access process of device in IIoT networks. The feasibility and efficiency of BlockTrust architecture are experimentally verified in the realistic testbed, and the performances of the TriTrustServ scheme are evaluated by comparing with two benchmark schemes in the terms of average mistrust rate, resource utilization, and identification cost.

The Industrial Internet of Things (IIoT) accommodates a huge number of heterogeneous devices to bring vast services under a distributed computing scenarios. Most productive services in IIoT are closely related to production control and require distributed network support with low delay. However, the resource reservation based on gross traffic prediction ignores the importance of productive services and treats them as ordinary services, so it is difficult to provide stable low delay support for large amounts of productive service requests. For many productions, unexpected communication delays are unacceptable, and the delay may lead to serious production accidents causing great losses, especially when the productive service is security related. In this article, we propose a brain-like productive service provisioning scheme with federated learning (BrainIoT) for IIoT. The BrainIoT scheme is composed of three algorithms, including industrial knowledge graph-based relation mining, federated learning-based service prediction, and globally optimized resource reservation. BrainIoT combines production information into network optimization, and utilizes the interfactory and intrafactory relations to enhance the accuracy of service prediction. The globally optimized resource reservation algorithm suitably reserves resources for predicted services considering various resources. The numerical results show that the BrainIoT scheme utilizes interfactory relation and intrafactory relation to make an accurate service prediction, which achieves 96% accuracy, and improves the quality of service.

To support the fifth-generation bandwidth-hungry applications, such as the Internet of Things, virtual reality, augmented reality, and cloud computing, elastic optical networks have become the most promising infrastructure that allocates bandwidths for services flexibility. Fragmentation caused by dynamic resource allocation deteriorates the availability of resources in networks, increasing the blocking of requests. The fragmentation occurs not only in the used path but also in the neighboring links that are not included in the used path; they are connected to the used path. This paper proposes a service-driven fragmentation-aware (SDFA) resource allocation scheme to enhance resource utilization by avoiding fragmentation with the joint consideration of the used path and neighboring links. A service-driven fragmentation metric (SDFM) is, for the first time, presented to estimate the fragmentation in the used path and neighboring links. The SDFA scheme prefers to assign services at the spectrum slots, which leads to the minimum value of SDFM. Simulation results indicate that SDFA outperforms four conventional fragmentation-aware resource allocation schemes in terms of blocking probability and resource utilization due to a lower fragmentation in the network.

Space division multiplexing (SDM), as a potential means of enhancing the capacity of optical transmission systems, has attracted widespread attention. However, the adoption of SDM technology has also additionally increased resource dimensions, introduced complex crosstalk, and made it difficult to integrate multi-dimensional fragments. These factors force the transmission constraints to be more complicated. Especially, some factors have a mutual restraint relationship, and excessive consideration of certain factors will cause the deterioration of other ones. Therefore, how to comprehensively consider the associated factors to achieve trade-offs and improve network performance is a problem worthy of study. This paper exploits the advantages of self-organizing feature mapping (SOFM) model to process multi-dimensional data with relevant features. Firstly, multiple constraints will be input into SOFM as mode vectors from the core level. Then, by judging the similarity between the competition layer neuron and the pattern vector, the position of the winning neuron is located, which determines the transmission level of each core. Finally, a routing, core, and spectrum allocation scheme is proposed by preferentially locating the core with higher transmission quality. Along the selected core, the available slots will be classified twice respectively by the number of adjacent cores and crosstalk direction to quickly find the spectrum blocks with relatively small crosstalk. Results indicate the scheme can reduce blocking probability and the resource fragmentation. Further, it can increase the resource utilization within tested network load.

With the development of new generation information technology, the feasibility of smart city has been significantly improved, and a large number of smart city technologies have been proposed. In this context, a more feasible device access technology is of great significance to ensure the normal operation of smart city network services. Therefore, this paper proposes a secure blockchain device access scheme based on elliptic curve digital signature algorithm and zero knowledge proof algorithm.

The combination of space division multiplexing technology with elastic optical networks allows to overcome the possible capacity crunch in backbone networks and also improves network flexibility by jointly managing spectral and spatial resources. However, against this background implemented by multi-core fibers, the interaction between spatial modes will appear as signal crosstalk, thereby affecting the service’s transmission quality. Spectrum resources without crosstalk are always preferred for the services to guarantee quality of service, possibly resulting in the spectrum fragmentation. Conversely, if resources with crosstalk are selected for services to reduce fragments, it may lead to serious crosstalk on the services already carried in the adjacent cores. To achieve a tradeoff between these two factors, this paper firstly exploits the association rule mining method to quantitatively analyze the potential correlation between them. By executing FP-growth mining algorithm, rules not beneficial to service provisioning will be filtered out. Then, an association rules-based core and spectrum assignment algorithm is presented, considering transmission requirements for different levels of services. Simulation results indicate the presented strategy can decrease the proportion of services affected by crosstalk and also reduce the possibility of fragments generation. Additionally, it can effectively make improvement on the blocking and resource utilization.

With the emergence of cloud computing and several ultra-high bitrate data center applications, hybrid E/O switching intra-datacenter network (HS-IDCN) has become an integral architecture of current and future data centers. To meet the diverse and heterogeneous performance requirements of HS-IDCNs, people have considered traffic prediction as a promising solution to ensure effective and flexible traffic scheduling. However, the low accuracy of existing deep learning-based prediction approaches, which cannot fully extract the features of burst traffic, directly restricts the efficiency of traffic scheduling. In view of this, this study considers the spiking neural networks that can predict high burstiness and heterogeneous traffic to further improve the efficiency of traffic scheduling. We first propose a supervised spiking neural network (s-SNN) framework for high accuracy traffic prediction in HS-IDCNs. A traffic prediction-based traffic scheduling (TP-TS) algorithm for HS-IDCNs is then introduced by considering the prediction results of s-SNN. The s-SNN framework can enhance the extraction ability of burst traffic features in a supervised fashion by mimicking the multi-synaptic mechanism of biological neuron system. The efficiency and feasibility of s-SNN are verified on the brain model simulator. The performance of TP-TS is also evaluated in terms of resource utilization and path blocking probability, compared with other scheduling schemes.

This paper presents an energy-saving wireless power allocation scheme based on convex optimization (PA-CO) in cache-enabled Fiber-Wireless (FiWi) access networks. Experiments indicate that the proposed PA-CO improves the global throughput when confronted with large-scale access terminal sets in 5G IoT scenario, while remaining total power consumption to a low level.

With the development of 5G networks, software and hardware technologies, the feasibility of smart cities has been significantly improved, and a large number of smart city technologies have been proposed. Against this background, a more feasible abnormal behavior monitoring technology is important to ensure the normal operation of smart city network services. Therefore, in this paper, we proposed a Spearman's correlation coefficient abnormal behavior monitoring technology based on recurrent neural network in 5G network for smart city.

This paper proposes a brain-like development based multi-routing combination optimization scheme for high mobility in optical fronthaul. The experimental results show that the scheme can significantly reduce the delay, reduce the communication blocking rate, and improve the communication quality.

We propose an online resource regulation strategy based on resource allocation benefit-state transition in 5G Fronthaul Network (5G-RAB ST). Results show that the presented system can significantly reduce the waste of resources in 5G fronthaul and highly improve the quality of users' service.

For the new fronthaul network structure in 5G, we first apply a capsule network combined with Neural Network (NN) for traffic prediction and introduce a CA-RD strategy to deploy the DU resources. Results show that our strategy improves the prediction accuracy and resources allocation efficiency.

With the rapid development of emerging services in 5G and beyond scenario, ultra-large capacity transmission has become a rigid demand for core optical networks, leaving a general trend to enter P-bit level transmission. New multiplexing technologies that further enhance fiber transmission capacity are development directions worth exploring. Due to the limit of single fiber transmission, space division multiplexing (SDM) is the main technology to increase the optical fiber transmission capacity in the future and has become a research hotspot. However, the introduction of SDM also brings some new problems, such as complex crosstalk assessment and generation of multi-dimensional resource fragments, resulting in more complex and diverse parameters affecting service transmission. If the impact of multiple parameters cannot be comprehensively evaluated, the quality of the service cannot be well guaranteed. Against this background, we propose a routing and resource allocation (RRA) scheme based on self-organizing feature maps (SOM) in core optical networks with multi-core fibers. Multiple parameters affecting service transmission will be uniformly input into the SOM model to obtain a reordered link set which will be used for the RRA process. Simulation results indicate that our presented method can reduce the fragmentation degree, decrease blocking probability, and also improve spectrum utilization.