By decoupling the software function on hardware devices, Network Function Virtualization(NFV) provides a new service architecture named Service Function Chain(SFC), which combines multiple Virtual Network Functions(VNFs) in a specific order. In order to reduce network resources consumption and improve the resource utilization, VNF sharing provides an effective solution for this requirement. However, traditional sharing methods lack a dynamic processing mechanism to select the deployment and shared node location according to the network state dynamically. Moreover, how to further optimize the utilization of network resources is challenging. This paper proposed a VNF sharing evaluation mechanism to evaluate and decide whether to share a VNF, then a node priority calculation mechanism was designed and mapped on node selection probability, which can select appropriate VNF to deploy or share VNF according to network state and resource requirements of SFC, finally, a reinforcement learning approach was utilized to update the selection probability of nodes and complete the VNF sharing process in air-ground network. The experimental results indicate that compared with other five benchmark algorithms, the proposed algorithm can reduce the transmission delay effectively, at the same time, it can improve node and link load resource utilization and acceptance rate of SFC after the VNF sharing.

Time-triggered (TT) flows are usually periodic in time-sensitive networks. However, nondeterministic end systems can generate TT flow frames with significant jitter (i.e., jittery TT flows). Jitter can cause frames to miss the TT windows scheduled for the current period, resulting in excessive access delays, which in turn affect the end-to-end deterministic transmission of the TT flows. In our previous study, we proposed the use of a dynamic multiwindow approach to achieve deterministic access to jittery TT flows; however, its window schedule computation is too slow, and this method is only suitable for small networks with a few TT flows. We therefore propose a group-based, fast scheduling method for accessing and transmitting the windows of jittery TT flows based on multiple windows. A combination of heuristic algorithms and solvers, including the establishment of TT window groups, division of the solution region, and integrated parallel and serial incremental coarse- and fine-grained computations, significantly improves the efficiency of TT window scheduling. For coarse-grained scheduling, by establishing large window clusters and central alignment, the complexity of scheduling is considerably reduced while keeping success rates high. Furthermore, the integer linear programming constraints and objective functions for this method are provided. Compared with the conventional dynamic multiwindow approach, the proposed approach reduces the scheduling time for TT windows by two orders of magnitude for a small star network with a small number of jittery TT flows. Moreover, the reduction in scheduling time becomes more pronounced as the network topology complexity and number of jittery TT flows increase. Finally, the scheduling time performance of the proposed method is verified in commonly used star, tree, and bus networks. Evaluations demonstrate that the access and transmission windows for 500 jittery TT flows can be scheduled in these networks, enabling deterministic access and significantly improving scheduling efficiency.

With the widespread application of unmanned cluster technology, broadband, low latency, high flexibility, and reliable fifth-generation (5G) UAV communication networks are increasingly becoming a key issue. The traditional network technology faces several challenges, including irregular distribution of spectrum resources, real-time changes of network topology, and a variety of unforeseen services. The elastic optical networks (EONs) is integrated into UAV networks to effectively address resource fragmentation and optimize spectrum allocation with its high bandwidth, low latency and dynamic tunability in this article. A heterogeneous UAV-EON system is proposed to realize the collaborative management of network access and resource allocation. To obtain the approximate optimal solution of network access and routing and spectrum allocation (RSA) in UAV-EON system, this article presents a hybrid two-stage optimized algorithm which combines the global search function of whale optimization algorithm (WOA) with the local optimization function of genetic algorithm (GA), ensuring the consistency and effectiveness of the UAV network. Therefore, the algorithm can address the challenges of network selection, routing, and spectrum allocation in UAV-EON. Finally, we test the number of successful assignments and resource utilization under different workloads and network scale. Considering the high dynamic characteristics of UAV nodes and the fading characteristics of atmospheric laser channels, a real network scenario is constructed and a cross-layer optimal algorithm from physical layer to network is proposed. The research results show that compared with the traditional intelligent optimization algorithm, the proposed algorithm can improve by more than 10% in the success rate of task allocation and resource occupation.

Network function virtualization (NFV) offers a flexible and effective means to utilize heterogeneous resources for space-air–ground integrated networks (SAGINs), enabling seamless connectivity for data transmission over large spans. Converging the cloud and edge computing capabilities, SAGINs have the potential to further provision service function chains (SFCs) with various Internet applications. This is driving the need for efficient schemes of the cloud- or edge-based services in SAGINs. In this article, we propose a novel SAGIN architecture based on the cloud-serving and edge-processing collaboration. The cloud-based services are provisioned by the selected ground data centers (DCs), in which the traffic is processed by the virtual network function (VNF) hosted in the edge nodes and DCs. In such an architecture, the edge nodes enable flexible SFC provisioning solutions while DCs offer a variety of cloud-oriented network services. In addition, we apply anycast to further improve the agility of SFC provisioning. From these perspectives, we investigate the cloud-serving and edge-processing SFC provisioning problem leveraging anycast, concerning DC assignment, edge and VNF placement, SFC mapping, and delay constraints simultaneously. The joint problem is formulated by a mixed integer linear program (MILP) model to jointly minimize the communication and computation costs subject to their tradeoff. A decomposition approach is further developed for the sake of scalability. Results from numerical simulations show that the proposed approach can reduce overall costs by up to 32.99%.

The Industrial Internet of Things (IIoT) is a typical future application for the mobile networks. Unmanned aerial vehicle (UAV) has attracted a great deal of interest in relay assisted wireless communication systems due to its benefits of high mobility, rapid deployment and high probability of line-of-sight (LoS) transmissions. This paper proposes the three-dimensional (3-D) geometry-based non-stationary channel models for wireless communication underlaying UAV-based relay assisted industrial internet of things (IIoT) networks in the sub-terahertz (sub-THz) band. In order to accurately describe the propagation characteristics of the UAV-based wireless channels in the sub-THz band, the propagation gain and atmospheric absorption gain in free space, LoS path, single UAV-based relay and double UAVbased relay propagation paths are considered in the proposed channel models. The channel impulse response (CIR) expressions of different propagation paths are derived respectively. The statistical properties of the channel models including path loss, channel capacity, temporal auto-correlation function (T-ACF), and Doppler power spectral density (DPSD) at 140 GHz band are investigated and analysed, with a performance comparison at 60 GHz band.