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.

With the continuous addition of an abundant of heterogeneous devices, the limitation of task delay has become an obstacle to the development of the Industrial Internet of Things (IIoT). Task offloading based on edge computing can provide low-latency computing services for these tasks. However, in the actual IIoT scenario, in contrast to cloud computing, edge computing has limited resources and computing capabilities. Resource-constrained edge resources cannot meet the offloading requirements of massive industrial devices. In this article, we propose an optimal joint offloading scheme based on resource occupancy prediction for the problem of computing offloading with limited edge resources. The scheme is divided into two parts, including edge resource occupancy prediction and task offloading. Simultaneously, considering multitask and the limitations of edge resources, gate recurrent unit (GRU) is used to predict the occupancy of edge resources. Formulating an optimal strategy of task offloading by using a reinforcement learning algorithm according to the network state and predicted results. The simulation results show that the scheme can effectively reduce the average delay of tasks, while minimizing the task offloading failure rate.

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) 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.

With the emergence of new services, the complex optical network environment makes it more difficult to predict network anomalies. This paper proposes a multi-index anomaly prediction scheme with hybrid supervised/unsupervised deep learning for elastic optical networks. Aimed at complex optical network indicators, the scheme presents three phases to enhance the abnormal prediction. The scheme first selects the most influential indicators of anomaly label among the mass of network indicators by calculating the Spearman correlation coefficient. Then, considering the timeliness of network data, it predicts time series of different indicators to analyze future network conditions by using long short-term memory neural network. In order to improve the accuracy and efficiency of the anomaly detection model, the scheme further establishes a deep neural network for anomaly classification. We also discuss how to process data without anomaly labels. The feasibility of the proposed scheme is verified on a real network dataset. Experimental results show that the scheme can predict the occurrence of future network anomalies with high accuracy, protect network services from potential abnormalities, and enhance the stability and robustness of the network.

We propose a novel multiple pilot-carriers assisted direct-detection optical orthogonal frequency division multiplexing (DDO-OFDM) superchannel transmission scheme. With the proposed scheme, a 432-Gb/s transmission over 3040-km standard single mode fiber (SSMF) is successfully demonstrated. The nonlinear effect of correlated/uncorrelated sub-band loading is also investigated.