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.

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.

Band division multiplexing technology is a promising medium-term transitional solution to deal with the traffic flood, by leveraging the existing optical fiber transmission infrastructure. Additionally, the deployment of multi-band optical networks requires updating and upgrading key components, such as multi-band amplifiers and multi-band optical cross-connects. From cost view, it would be a promising way for operators to expand transmission capacity on some of the links with high priority, which are used heavily. Therefore, the application scenario of this paper is positioned in the C-C+L hybrid optical networks. In this scenario, the introduction of the L-band brings additional inter-band stimulated Raman scattering (ISRS) which is a non-negligible nonlinear effect, leading new constraint for service provisioning. This paper proposes an ISRS-aware routing, band, modulation, and spectrum assignment (RBMSA) scheme, considering the impact of ISRS generated by new requests on existing requests. By implementing the SNR re-verification, not only the transmission quality of the current request can be realized, but also the service quality of the existing requests will not be seriously degraded to be unacceptable. A two-dimensional model of request bandwidth and duration is established for band selection. Priority is provided for arranging the requests in the L-band when it is available. For each single request, when the least number of slots required by adopting different modulation formats is the same, lower-order modulation is preferred to improve its SNR tolerance of this request under the ISRS impact. Simulation results indicate that the proposed scheme can effectively decrease the blocking probability, and improve the channel SNR for the successfully established requests.

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.

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.