This paper investigates the virtualization of LoRaWAN end nodes through Linux containers (LXCs) to improve scalability, flexibility, and resource management. By leveraging lightweight Docker-based virtualization, we break down the core functions of the LoRaWAN node, comprising the application, LoRaWAN, and LoRa layers, into modular containers. In this work, a fully virtualized end node is demonstrated. The obtainable performance is not only compared against the standard approach that leverages a LoRaWAN-compliant module but also against an emulated solution that mimics the desired functionalities purely in software. A controlled, uniform testbed, exploiting the capability of a virtual machine hypervisor to change the way the underlying hardware is abstracted to guest environments, is considered. Key metrics, including resource utilization and latency, are explicitly defined and evaluated. The results underscore the potential of container technologies to transform the deployment and management of communication solutions targeting Internet-of-Things (IoT) scenarios not only for the infrastructure but also for end devices, with implications for future advancements in wireless network virtualization.

WNV-RA: Wireless Network Virtualization Empowered Resource Allocation in Delay-Sensitivity Airborne Tactical Networks

Abstract Using traditional non‐virtualized Wireless Sensor Networks (WSNs) efficiently is difficult due to the embedded applications, which make the sensor nodes inaccessible to other applications. The proposed study considered both the node‐level and network‐level virtualization of wireless sensor networks to examine dynamic virtual network embedding. WSNs can leverage their shared sensing capabilities through network virtualization. Infrastructure providers earn more revenue by mapping more virtual network embedding (VNE) onto their substrate networks. VNE must therefore improve its acceptance ratio. The proposed RLE‐SVNE is demonstrated to be more efficient than state‐of‐the‐art in respect to acceptance, recovery, failure recovery delay, and revenue cost through simulation results. It compares the RLF‐SVNE method with C‐SVNE and N‐SVNE to demonstrate its superiority.

Cooperative Caching, Rendering, and Beamforming for RIS-Assisted Wireless Virtual Reality Networks

Retracted: Wireless Network Virtualization Resource Sharing Based on Dynamic Resource Allocation Algorithm

Abstract Wireless virtual network uses software defined network and network function virtualisation technologies to create multiple logically isolated virtual networks on a physical wireless network. Wireless virtual network can improve the utilisation of wireless resources to meet the requirements of different services. Delay is an important performance indicator, which has strict requirements for delay‐sensitive services such as video conferencing and online games. In this study, a virtual network embedding method based on node delay perception (VNE‐NDP) is proposed, which considers both the node and link resources as well as the embedding delay requirements of virtual networks. The virtual network embedding method based on node delay perception consists of two phases: virtual node embedding and virtual link embedding. In the virtual node embedding phase, a physical node sorting method based on node delay perception (PNS‐NDP) is proposed. In PNS‐NDP, the node deployment delay is introduced into the node sorting algorithm for the first time. The authors select candidate physical nodes for each virtual node according to their resource availability and delay performance, which can greatly reduce the VN embedding delay without sacrificing too much other performance. In the virtual link embedding phase, a shortest path algorithm with bandwidth and link deployment delay constraints to find feasible physical paths for each virtual link is used. In addition, the VN embedding (VNE) deployment time is set as a new evaluation index. Simulation results show that compared with other VNE methods, VNE‐NDP can achieve higher success rate, revenue‐to‐expenditure ratio, and lower deployment delay.

Analysis of the influencing factors of English short video teaching in wireless network virtualization

In order to improve the efficiency of wireless network virtualization resource processing, this paper combines the dynamic resource allocation algorithm to construct a wireless network virtualization resource sharing model. This paper proposes a task-oriented resource management model and uses the task-oriented TRS model to describe resources and service processes, reducing the complexity of formulating resource allocation strategies. Moreover, this paper comprehensively considers factors such as centralized coordination control cost and limited domain topology visibility to improve the dynamic resource allocation algorithm. Through the abstraction and unified representation of network resources, resource sharing, and efficient reuse, the coexistence and integration of heterogeneous wireless networks can be realized. Wireless network virtualization can decouple complex and diverse network management and control functions from hardware and extract them to the upper layer for unified coordination and management, thereby reducing network management costs and improving network management and control efficiency

Network virtualization in wireless sensor networks (WSNs) enables the utilization of their shared sensing capabilities. Efficient assignment of WSN resources to maximize the infrastructure provider’s revenue can be achieved by virtual network embedding (VNE) while considering quality of information (QoI) (as the accuracy of sensing), quality of service (QoS) (as the reliability), and wireless interference handling constraints. Improving the acceptance ratio of VNE is essential because the more the virtual networks can be mapped onto the substrate network, the more revenue they will generate for the infrastructure provider. However, the shared and complex nature of VNE exposes the WSNs to security risks. This article develops a novel offline trust-aware virtual WSN (TA-VWSN) algorithm to maximize the virtual networks acceptance rate while minimizing the cost. This algorithm improves the QoI, QoS, and security by adding required trust level constraints to virtual nodes and links and trust level constraints to the substrate counterparts. Our algorithm embeds virtual nodes and links on substrate nodes and links with the required trust levels. The additional constraints increase the complexity of computation required to achieve an optimal solution. However, our TA-VWSN algorithm achieves a high-quality suboptimal solution in a short duration, enabling us to investigate the tradeoff between solution quality and search time. Our algorithm is also evaluated in large-scale network scenarios to verify all enforced limitations by the WSN substrate. While adding security constraints limits the acceptance ratio, the simulation results demonstrate its superiority in terms of average network throughput, measurement error efficiency, and processing time when the trust attributes are assigned, making the VNE algorithm more practical.

The Internet of Things (IoT) encompasses a wide range of applications and service domains, from smart cities, autonomous vehicles, surveillance, medical devices, to crop control. Virtualization in wireless sensor networks (WSNs) is widely regarded as the most revolutionary technological technique used in these areas. Due to node failure or communication latency and the regular identification of nodes in WSNs, virtualization in WSNs presents additional hurdles. Previous research on virtual WSNs has focused on issues such as resource maximization, node failure, and link-failure-based survivability, but has neglected to account for the impact of communication latency. Communication connection latency in WSNs has an effect on various virtual networks providing IoT services. There is a lack of research in this field at the present time. In this study, we utilize the Evolutionary Multi-Objective Crowding Algorithm (EMOCA) to maximize fault tolerance and minimize communication delay for virtual network embedding in WSN environments for service-oriented applications focusing on heterogeneous virtual networks in the IoT. Unlike the current wireless virtualization approach, which uses the Non-dominated Sorting Genetic Algorithm-II (NSGA-II), EMOCA uses both domination and diversity criteria in the evolving population for optimization problems. The analysis of the results demonstrates that the proposed framework successfully optimizes fault tolerance and communication delay for virtualization in WSNs.

We consider online coordinated precoding design for downlink wireless network virtualization (WNV) in a multi-cell multiple-input multiple-output (MIMO) network with imperfect channel state information (CSI). In our WNV framework, an infrastructure provider (InP) owns each base station that is shared by several service providers (SPs) oblivious of each other. The SPs design their precoders as virtualization demands for user services, while the InP designs the actual precoding solution to meet the service demands from the SPs. Our aim is to minimize the long-term time-averaged expected precoding deviation over MIMO fading channels, subject to both per-cell long-term and short-term transmit power limits. We propose an online coordinated precoding algorithm for virtualization, which provides a fully distributed semi-closed-form precoding solution at each cell, based only on the current imperfect CSI without any CSI exchange across cells. Taking into account the two-fold impact of imperfect CSI on both the InP and the SPs, we show that our proposed algorithm is within an <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$O(\delta)$ </tex-math></inline-formula> gap from the optimum over any time horizon, where <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\delta $ </tex-math></inline-formula> is a CSI inaccuracy indicator. Simulation results validate the performance of our proposed algorithm under two commonly used precoding techniques in a typical urban micro-cell network environment.

A key challenge related to Radio Access Network (RAN) slicing is deciding how to efficiently map radio resources from the physical radio to realise RAN slices, known as the virtual wireless network embedding problem. To the best of our knowledge, this is the first paper to model and derive an analytical solution for embedding heterogeneous RAN slices with different waveforms, numerologies and Radio Access Technologies (RATs) with resources isolated down to the Physical (PHY) layer, ultimately enabling secure technology-agnostic RAN as a Service (RANaaS). First, we assess how current virtual wireless network embedding solutions model the allocation of radio resources to realise RAN slices. Then, we propose a graph-based model for embedding heterogeneous RAN slices that considers the guard bands required to ensure isolation in the frequency domain. This approach is transparent to the type and granularity of radio resources of each RAN slice, and can be extended to support RAN slices with new waveforms, numerologies and RATs. Next, we introduce a resource management optimisation problem solved at the Network Provider (NP) to determine the optimal embedding of RAN slices that maximises the total useful bandwidth occupied by tenants; and we propose three different heuristic algorithms to obtain solutions in near real-time. We compare their performance against the analytical solution using different metrics, and our results show that the best heuristic depends on the NP’s business model, e.g., using the Greedy Algorithm (GA) to increase resource utilisation or the Nearest Neighbour Algorithm (NNA) to increase the number of allocated RAN slices.

With the advancement of wireless network technology to the next generation, network function virtualization (NFV) brings the advantages of centralized scheduling of virtual wireless resources and the ability to orchestrate virtual network functions. This makes it possible to dynamically deploy and manage service function chains (SFCs) in virtualized wireless networks according to changes in network load. Through the network function virtualization technology, the DU/CU separation architecture under 5G-NG-RAN is considered. Aiming at the resource orchestration problem of the 5G access network virtual protocol stack function and service function chain after deployment, an energy-aware virtualized network function instance (VNFI) orchestration algorithm is proposed. By modeling the underlying physical network and node energy consumption, the node load, resource type, tolerable delay and migration loss are included in the decision, and the state of the general server node is divided into two types: running state and sleep state. The algorithm decouples the decision-making process of VNF migration into two specific modules, namely "the VNF selection module" and "the migration destination node selection module". It is verified by simulation that compared with other energy-saving strategies, the algorithm in this paper has better performance in reducing energy consumption and reducing migration loss, and it can take into account both the reduction of the number of open server nodes and the improvement of resource utilization.

In order to improve the efficiency of wireless network virtualization resource processing, this paper combines the dynamic resource allocation algorithm to construct a wireless network virtualization resource sharing model. This paper proposes a task-oriented resource management model and uses the task-oriented TRS model to describe resources and service processes, reducing the complexity of formulating resource allocation strategies. Moreover, this paper comprehensively considers factors such as centralized coordination control cost and limited domain topology visibility to improve the dynamic resource allocation algorithm. Through comparative research, it can be seen that the wireless network virtualization resource sharing method proposed in this paper considering the dynamic resource allocation algorithm can effectively improve the processing efficiency of wireless network virtualization resources.

Wireless network virtualization is widely used to solve the ossification problem of networks, such as 5G and the Internet of Things. The most crucial method of wireless network virtualization is virtual network embedding, which allows virtual networks to share the substrate network resources. However, in wireless networks, link interference is an inherent problem while mapping virtual networks because of the characteristics of wireless channels. To distribute resources efficiently and address the problem of interference, a dynamic embedding algorithm with deep reinforcement learning is proposed. During the training stage, we take resource use and interference from substrate networks as observations to train the agent, and then the agent generates a resource allocation strategy. Aiming at realizing load balance, we reshape the reward function considering the execution ratio and residual ratio of substrate network resources as well as the cost consumed by current virtual network request. Numerical tests show that our embedding approach increases the acceptance ratio and maintains better robustness. Moreover, the results also illustrate that our algorithm maintains a high acceptance ratio while producing less interference and lower cost.

We consider online wireless network virtualization (WNV) in a multi-cell multiple-input multiple output (MIMO) system with delayed feedback of channel state information (CSI). Multiple service providers (SPs) simultaneously share the base station resources of an infrastructure provider (InP). We aim at minimizing the accumulated precoding deviation of the InP's actual precoder from the SPs' virtualization demands via managing both inter-SP and inter-cell interference, subject to both long-term and short-term per-cell transmit power constraints. We develop an online coordinated precoding solution and show that it provides provable performance bounds. Our precoding solution is fully distributed at each cell, based only on delayed local CSI. Furthermore, it has a closed-form expression with low computational complexity. Finally, simulation results demonstrate the substantial performance gain of our precoding solution over the current best alternative.

In recent years, with the continuous development of IT industry, network virtualization technology has gradually developed into a hotspot of research and application in the field of communication because IT allows the construction of customized virtual network (VN) on shared physical infrastructure. In particular, the proposal of wireless network virtualization for the development of the enhanced Internet. Because these technologies require effective algorithms named virtual network mapping (VNE) to instantiate virtual networks on the SN, on the basis of satisfying the isolation between wireless network slices, a resource allocation mechanism based on bilateral auction is proposed. At present, many researchers have proposed that network virtualization technology is a feasible way to solve the rigid Internet architecture. In the field of existing research focuses on the cable network virtualization, mainly related to the data center network and backbone, then, the side of the wireless access network virtualization the key technology research is still in its infancy, in order to accelerate the process of wireless skill for innovation and meet the demand of the difference of the emerging business, wireless network virtualization becomes the research hotspot and focus of academia and industry. However, no one has applied the dynamic resource allocation algorithm to wireless network virtualization resource sharing field. Here, this paper analyzes and summarizes the existing virtual network resource allocation schemes at home and abroad. The wireless virtual network resource allocation model and cross-domain virtual network resource allocation model are established, and the network centrality theory in social network and complex network is introduced to analyze the topology properties of virtual network and physical network, and two efficient virtual network resource allocation methods are proposed.

With the booming and proliferation of 5G wireless network services in the future, a large number of wireless virtual network resources will emerge, and the densification and heterogeneity of the wireless communication networks that provide services for them will become the trend of development. To this end, a wireless virtual network resource scheduling model based on user satisfaction is constructed, and a reinforcement learning-based wireless virtual network resource scheduling mechanism, IRSUP, is proposed. IRSUP is designed with an intelligent optimization module for user service preferences to address the personalized needs of user service customization, and a reinforcement learning-based intelligent scheduling module is designed to address the challenge of joint optimization of multistar resources. Simulation results show that IRSUP can effectively improve resource scheduling rationality and link resource utilization and user satisfaction, among which service capacity is improved by 30% to 60% and user satisfaction is more than doubled.

Interference management in NOMA-enabled virtualized wireless networks

Wireless network virtualization (WNV) is an evolving technology that supports networking for future wireless networks and agile IoT applications that optimize wireless bandwidth, provide network protection, and increase network performance in general. However, the exponential growth and increased demand for IoT have posed considerable scalability problems, interoperability, and heterogeneity across diverse fields from intelligent cities to healthcare, urban computing, and tactile fast internet growth. In this paper, IoT-based WNV (IoT-WNV) technology has been used. The leading technologies that create a scalable and stable network, consistent with many IoT products, for effective wireless virtualization in next-generation agile IoT systems are the networking function virtualization (NFV), mobile edge computing, and software-defined networks (SDNs). The network function virtualization (NFV) feature improves network stability, while the SDN operates, optimizes, and configures network services dynamically, enabling dynamically-based network functionality and interconnectivity. This special problem involves virtualizing IoT system wireless sensor networks to speed up network resource management by analyzing data obtained from devices deployed in various applications. This technological combination will satisfy the demands of modern applications. IoT-WNV aims to improve wireless virtualization for the next-generation network by implementing human-smart technology for load balance, network management, and network security.