The Maritime Internet of Things (MIoTs) provides intelligent information services for marine scientific research, emergency response and environmental monitoring by leveraging its wide coverage and ubiquitous connectivity. However, challenging maritime communication conditions and limited sea-based network resources hinder MIoT from meeting the evolving network quality of service requirements of growing maritime activities. This poses a significant challenge to ensuring real-time and reliable transmission of mixed traffic flows. To address issues such as link contention and transmission delays in software-defined MIoT systems, a deterministic scheduling and highly reliable routing mechanism based on cross-layer design is proposed. First, a deterministic scheduling mechanism for mixed traffic flows is introduced, which effectively reduces transmission delays and improves the schedulability of data flows. Second, a high-reliability, low-latency routing mechanism based on Double Deep Q Network (DDQN) is proposed, which is capable of dynamically screening neighbouring nodes based on real-time link and node states, thus facilitating fast and high-quality path selection. Extensive simulation results show that DSMTF improves flow schedulability by 28% compared to traditional algorithms, while HRLDQ increases the network packet delivery rate by 25.8% and reduces the average end-to-end delay by 23.6%.

As an important index of aero-engine, Remaining Useful Life (RUL) is the key content of prediction. Due to the good generation characteristics of Variational Auto-encoder (VAE) and Generation Adversarial Network (GAN) networks, this paper proposes a Health Index (HI) curve generation method based on VAE-GAN. After that, sensor sequence prediction is carried out through Bidirectional Long Short-Term Memory Network (BLSTM). The two networks are parallel, and then RUL prediction is carried out by synthesizing the data of the two networks. As far as the author knows, this is the first use of VAE-GAN in Prognostics Health Management (PHM). It is verified on the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) dataset. Finally, the results show that the VAE-GAN network is effective and superior in RUL prediction. At the same time, the proposed parallel network is superior to other RUL prediction methods by generating HI curves.

The demand for improving productivity in manufacturing systems makes the industrial Internet of things (IIoT) an important research area spawned by the Internet of things (IoT). In IIoT systems, there is an increasing demand for different types of industrial equipment to exchange stream data with different delays. Communications between massive heterogeneous industrial devices and clouds will cause high latency and require high network bandwidth. The introduction of edge computing in the IIoT can address unacceptable processing latency and reduce the heavy link burden. However, the limited resources in edge computing servers are one of the difficulties in formulating communication scheduling and resource allocation strategies. In this article, we use deep reinforcement learning (DRL) to solve the scheduling problem in edge computing to improve the quality of services provided to users in IIoT applications. First, we propose a hierarchical scheduling model considering the central‐edge computing heterogeneous architecture. Then, according to the model characteristics, a deep intelligent scheduling algorithm (DISA) based on a double deep Q network (DDQN) framework is proposed to make scheduling decisions for communication. We compare DISA with other baseline solutions using various performance metrics. Simulation results show that the proposed algorithm is more effective than other baseline algorithms.

With the increasing of number of multimedia network applications, multi-cast services are becoming more and more popular. At the same time, the waveband switching technique is proposed to save ports and to reduce the cost of optical cross-connect. However, the existing multi-cast waveband grooming algorithms are mostly limited to single-domain optical networks. The size of optical backbones keeps enlarging, and the backbones are actually divided into multiple independent domains to provide the needed scalability and confidentiality. To solve these problems, the authors propose three heuristic algorithms: per-domain multi-cast grooming (PDMG), virtual topology multi-cast grooming (VTMG) and hierarchical multi-domain multi-cast grooming (HMMG). The main difference between these algorithms is that the inter-domain routing is performed in a different manner; in other words, the routings in PDMG, VTMG and HMMG are computed based on the domain-by-domain fixed routing table, the high layer in the aggregative virtual topology of multi-domains and the hierarchical integrated multi-cast auxiliary graph, respectively. For the intra-domain routing, the three algorithms employ the same method based on an intra-domain multi-cast integrated auxiliary graph. Simulation results show that compared with PDMG and VTMG, HMMG has the best performance in terms of the number of ports saved and the blocking probability.

Since current optical network is actually divided into multiple domains each of which has its own network provider for independent management, the development of multi-domain networks has become the trend of next-generation intelligent optical networks, and then the survivability has also become an important and challenging issue in fault-tolerant multi-domain optical networks. In this paper, we study protection algorithms in multi-domain optical networks and propose a new heuristic algorithm called multi-domain Hamiltonian cycle protection (MHCP) to tolerate the single-fiber link failure. In MHCP, we present the local Hamiltonian cycle (LHC) method based on the physical topology of each single-domain and the global Hamiltonian cycle (GHC) method based on the abstracted virtual topology of multi-domains to protect the intra-fiber link and inter-fiber link failures, respectively. We also present the link-cost formulas to encourage the load balancing and proper links selection for computing the working path of each connection request. Simulation results show that, compared with previous multi-domain protection algorithm, MHCP can obtain better performances in resource utilization ratio, blocking probability, and computation complexity.