With the advancement of technologies in Internet of Vehicles (IoV), traditional IoV architectures face challenges in latency and energy consumption due to inefficient resource scheduling, especially as data grows and computational tasks become more complex. Current optimization methods have not addressed the joint optimization of latency and energy consumption oriented to the Integration of Communication, Sensing, and Computation (ICSC) in IoV, which limits practical applications and cannot adapt well to dynamic IoV environments. Therefore, in this paper, we first construct an IoV communication-sensing-computation integration architecture. Then we propose a Deep Reinforcement Learning-based method for the joint optimization of latency and energy consumption for the ICSC in IoV, incorporating weighted decomposition and neighborhood parameter transfer strategy. Simulation results demonstrate that our method has a better ability of convergence and diversity and lower running time than the traditional method.

The demand for precise timing continues to grow with the rapid development of smart cities, where intelligent transportation emerges as a critical application heavily dependent on accurate time distribution. Deterministic communication is anticipated to be a defining feature of nextgeneration mobile networks (6G), enabling end-to-end timecritical applications, and unlocking new possibilities for achieving end-to-end precise time synchronization as well. To cope with the challenge of delivering precise time synchronization across broader areas at a lower cost, this paper proposes a method to achieve end-to-end precise time synchronization by leveraging the deterministic characteristics of communication networks. Specially, the proposed approach adopts the Software-Defined Networking (SDN) paradigm to implement deterministic networking for the seamless integration of Time-Sensitive Networking (TSN) and cellular networks. Furthermore, to mitigate the impact of wireless channel uncertainties on synchronization accuracy, we propose a network delay measurement mechanism based on the principle of time redundancy, designed to reduce end-to-end network delay variation. Experimental results validate the effectiveness of the proposed method, demonstrating its capability to constrain the end-to-end relative time error of wired and wireless converged networks to the microsecond scale, without necessitating modifications to the existing network infrastructure.

Developing trustworthy reinforcement learning (RL) agents for safety-critical control tasks, such as end-to-end autonomous driving, has been a longstanding challenge due to low sample efficiency. Prior works have attempted to address this challenge by performing self-supervised auxiliary tasks like self-reconstruction or predicting long-term future states. However, there still remain unexplored sequential features and causality relationships inherent in sequential state, action, and reward signals in the frequency domain. To fully exploit the temporal and frequential features, we propose a contrastive RL framework called BRain-Inspired causalitY-Aware coNTrastive learning (BRYANT) to achieve efficient representation learning and human-like autonomous driving. Different from existing temporal predictive methods, we transform the sequential latent representations, reward, and action signals into the frequency domain, followed by the symmetric temporal prediction pattern for real and imaginary parts of the frequential signals. To capture the temporal causality for the latent representations, we introduce a brain-inspired network structure called Closed-form Continuous-time (CfC) network to parameterize the derivative of the latent representations and establish the neural dynamic model. Experimental results conducted in the CARLA simulator demonstrate the effectiveness of BRYANT in efficient representation learning, enabling agents to concentrate on potential risks and decrease the collision rate compared to several state-of-the-art RL methods. Furthermore, through the visualization of the latent representation prediction process, we reveal the causal relationships between the critic Q values and the latent representation vectors in the frequency domain, and demonstrate the effectiveness of the frequency domain prediction.

Time-aware shaper (TAS) is key to enabling deterministic guarantees in time-sensitive networks (TSN), but it requires precise configuration for specific traffic scenarios. Dynamic traffic scenarios are increasingly commonplace with the rise of emerging applications, necessitating TAS reconfiguration to adapt to the changes in traffic. However, existing mechanisms primarily reconfigure TAS by generating a new gate control list (GCL) and transitioning to it, which may lead to temporary violations of bounds on delay or jitter, providing no persistently deterministic guarantees. In this paper, we propose a novel TAS reconfiguration mechanism with the virtual GCL (VGCL) to satisfy the demands of dynamic traffic while guaranteeing deterministic transmission. It implements TAS reconfiguration for dynamic traffic by embedding different VGCLs into the GCL, avoiding the need for the GCL transition. Thus, the reconfiguration problem is modeled as an embedding problem by using the VGCL and we develop algorithms to solve it. Experimental results demonstrate that our mechanism can well reconfigure TAS for dynamic traffic without the GCL transition, and increase the reconfiguration success rate in various scenarios compared with the existing approaches.

Time-sensitive networking (TSN) is very appealing to industrial networks due to its support for deterministic transmission based on Ethernet. The implementation of determinism typically demands for precise configuration on each output port of a TSN switch, which is complex and time-consuming. Moreover, many emerging industrial applications bring dynamic scenarios (e.g., in real-time Internet of Things), thus the configurations should change adaptively as application requirements change to provide continued determinism. In this paper, we propose a deep reinforcement learning (DRL) based adaptive configuration scheme in Software-defined time-sensitive networking (SD-TSN). The SD-TSN is a network architecture that integrates the determinism guarantees of TSN and flexible network management of software-defined networking (SDN). Based on the capability of SD-TSN, the proposed configuration scheme exploits DRL to learn from interacting with the environment for adaptive configuration. Experimental results demonstrate the effectiveness of our scheme in dynamic scenarios.

Time-aware shaper (TAS) is key to enabling deterministic transmission guarantees in Time-Sensitive Networks (TSN), but requires precise configuration for a specific traffic scenario. Traffic dynamics change scenarios are gradually increasing with the development of Industry 4.0, necessitating reconfiguring TAS to guarantee persistence determinism. However, previous reconfiguration mechanisms are mostly reconfiguring TAS by modifying the gate control lists (GCLs) to adapt to changes in traffic, which may incur a temporary violation of bounds on delays or jitter with undesirable consequences. In this paper, we propose a novel reconfiguration mechanism with the virtual GCL (VGCL) to satisfy new traffic requirements by embedding different VGCLs into the GCL, such that implements TAS reconfiguration while avoiding the GCL modification. Then we develop an incremental VGCL embedding (IVE) algorithm to determine reconfiguration details. Experimental results show that our approach can reconfigure TAS well for dynamic traffic without modifying the GCL while guaranteeing high schedulability in different scenarios.

The need for precise timing is increasing with the development of the smart city, and intelligent transport is a key application that often relies on the distribution of accurate timing. Existing time synchronization solutions cannot adequately meet the precise timing needs for practical applications in intelligent transportation. Global Navigation Satellite Systems (GNSS) signals, for instance, can be obstructed, weakened, or deflected in urban canyon environments. While certain sectors like telecommunications and smart grids have successfully adopted IEEE 1588 for high-precision time synchronization, its dependence on standalone networks and limited application within local area networks poses challenges when catering to the wide-ranging and mobile demands of intelligent transportation applications. This paper categorizes the time synchronization requirements of intelligent transport and proposes a time synchronization scheme over converged networks, which separates time transmission and precision compensation based on software-defined networking (SDN) principles, and makes it possible to provide precise timing for intelligent transport devices, including on-board units (OBUs) and roadside units (RSUs), over wired and wireless communication networks. Experimental results indicate that the proposed scheme achieves a relative time error of less than 1.2 microseconds over the converged networks.

The performance of the servers directly affects the efficiency of the whole intelligent Internet of Vehicles (lo V) system. A reasonable server deployment strategy can effectively reduce network latency and improve the efficiency of IoV.Therefore, it is necessary to study the problem of server deployment in Io V.In this paper, we formulate a latency-aware server deployment problem based on Multi-Agent Reinforcement Learning (MARL) and utilize an Actor-Critic network based on Long Short-Term Memory (LSTM) encoding network to solve it. Besides, four deployment strategies (DQN, DRQN, Random and No Movement) are used as the baseline methods for performance comparison with our proposed method on two real-world datasets. The simulation results show our solution has excellent performance in reducing network latency under different scenarios.

Integrating low-Earth orbit (LEO) satellite constellations with terrestrial mobile networks can achieve global coverage and complement terrestrial networks. The inherent mobility of satellites induces frequent handovers of user equipment (UE), generating massive signaling. Coupled with limited satellite resources, the network functions (NFs) deployed on satellites cannot process these signaling promptly, leading to increased queuing time. Additionally, the movement of onboard NFs increases the distance to UE, extending propagation delay. Extended procedure completion time (PCT) of control plane procedures degrades user plane Quality of Service (QoS). To address the above challenges, we propose a satellite-terrestrial integrated core network architecture to enhance signaling processing performance. First, we redesign the control plane NFs and introduce a satellite-ground synergy method (SGSM), categorizing signaling into time-sensitive and time-tolerant types. The former is processed onboard, while the latter is handled terrestrially, utilizing a designed UE context synchronization mechanism. Furthermore, migration is employed to counteract the movement. We devise a migration procedure to reduce transferred data during migration. Moreover, we model instance migration as a Markov decision process and proposed an online NFs migration algorithm based on deep reinforcement learning to determine migration timing and target satellites. Extensive experiments demonstrate that the proposed methods significantly reduce queuing time and the volume of transferred data, while also exhibiting superior performance in terms of propagation delay and the migration frequency.

The development of automatic driving and assisted driving breeds the problem of proximity detection in road networks, which plays a significant role in ensuring safe driving. Due to the fact that it is a time-sensitive task, the problem of proximity detection requires to judge whether two vehicles are close to each other in a very short time. However, the battery life and computation capacity of vehicles are limited in the actual scenario. Therefore, how to solve this problem with low latency and energy consumption is an important issue. In this paper, we investigate the Joint Optimization of the Latency and Energy consumption problem in the scenario of Proximity Detection, namely, JOLE-PD, which is formulated into a constrained multiobjective optimization problem. The DDPG-CMOA method is proposed to find a tradeoff between latency and energy consumption, achieving the Pareto optimal solutions. Besides, NSGA-II (Non-dominated Sorting Genetic Algorithm-II) and MOEA-D (Multi-objective Evolutionary Algorithm Based on Decomposition), as the typical algorithms to solve multiobjective optimization problem, are used as the baseline methods to compare the performance of DDPG-CMOA method under different parameters. The experimental results show the proposed DDPG-CMOA method requires much lower running time and has strong generalization ability. Moreover, the solutions obtained from the DDPG-CMOA method have a slightly better ability of convergence and diversity.

Integrating terrestrial cellular network with Low Earth Orbit (LEO) satellite constellation has been a popular trend in beyond 5G and 6G eras, called Satellite-Terrestrial Integrated Core Network (STICN). Core Network (CN) is an essential component responsible for authentication, security, mobility, data routing, etc. However, the terrestrial CN is designed for infrastructure-fixed and user-moving scenarios, which would cause the STICN to experience signaling storms and service interruptions when users access by rapid satellites. In this paper, we propose a distributed lightweight stateless satellite CN architecture, which could fit with the dynamic and limited-resource instincts of LEO satellites. And it can cooperate with terrestrial CN to provide seamless services. Firstly, the contexts of Network Functions (NFs) are decoupled from themselves and are managed in a common repository. Moreover, we design a cooperation mechanism between NFs to avoid frequent transmission of context and service interruption. Finally, extensive experiments are carried out on semi-physical simulation environments. The STICN performance could be improved by selecting the optimal number and location of each NF. Our evaluation shows that the proposed scheme could reduce the delay of the handover procedure by 37% and is more resilient compared with terrestrial CN.

Integrating the User Plane Function (UPF), which is responsible for forwarding user data in 5G, with the Low Earth Orbit (LEO) satellite networks can facilitate communication among users and take advantage of satellite edge computing. Satellite UPF (S-UPF) placement strategy is crucial to the integration performance. Static placement, in which the S-UPF drifts away with the satellite, is difficult to adapt to the dynamic satellite networks. The uneven distribution of terrestrial traffic and the resource limitations of satellites cause overload. The fast movement of S-UPF results in an augmented distance between S-UPF and users. This overload and extended distance degrade the Quality of Service (QoS). Dynamic S-UPF placement on satellites is a potential solution, but little attention is paid to it. To fill the gap, we propose a novel approach called Static Assignment Dynamic Placement (SADP), which comprises two key components: static user assignment and dynamic S-UPF placement. Static user assignment is designed to prevent overload, and dynamic S-UPF placement is applied to overcome the QoS degradation due to the extended distance between S-UPF and user. We evaluate the performance of SADP using real satellite constellations, and experimental results demonstrate its effectiveness in reducing latency and energy consumption. Compared to the static deployment, SADP achieves a significant 69.1% latency reduction and lower energy consumption. In contrast to deploying S-UPF on all satellites, SADP significantly reduces energy consumption by 85.2% while maintaining comparable latency performance.

The emerging applications in vertical industries generate diverse time-critical traffic flows with bounded delay requirements. Time-Sensitive Networking (TSN) enhances the traditional Ethernet by using Time-Aware Shaper (TAS), providing delay guarantees for traffic flow transmission. However, the fixed scheduling granularity of physical queues in TAS can bring an obstacle for the flow isolation in per-flow scheduling, such as the complex computing and configuration. This paper proposes a method of Virtual Queues-based Time-Aware Traffic Scheduling (VQ-TATS) in Software-Defined Time-Sensitive Networking (SD-TSN). SD-TSN is a networking architecture that integrates the determinism guarantees of TSN and flexible network resource allocation of Software-Defined Networking (SDN). Through the capability of SD-TSN, the physical queues resource is virtualized for VQ-TATS. VQ-TATS includes the VQ clustering and VQ mapping stages. VQ clustering aggerates virtual queues to adapt to the scheduling granularity of TAS. VQ mapping builds the relationship between virtual and physical queues and generates the gate control list of TAS. The clustering and mapping algorithms are also designed to perform VQ-TATS. The evaluation in an industrial control use case shows the effectiveness of the proposed method in schedulability and runtime.

Time synchronization is a key technology in time-sensitive networking (TSN) to support deterministic data transmission with bounded latency, low delay jitter, and zero congestion loss guarantee. TSN uses IEEE 802. IAS protocol to achieve time synchronization. The asymmetry of the propagation link, clock drift, and limited node clock frequency resolution limit the TSN time synchronization performance improvement. In this paper, we propose a scheme for enhancing the time synchronization performance of TSN and design a method to enhance the TSN time synchronization accuracy by precisely measuring the deviation of the start frame delimiter (SFD) identification signal of the frame initiator from the local received clock signal, and taking the measured value as the compensation amount. Furthermore, we introduce a time correction algorithm in the compensation process. The corresponding development implementation is completed in FPGA, and a test environment is constructed for experimental verification. The experimental results obtain a time synchronization accuracy of 7.5ns, and the synchronization accuracy is even better than 5ns after introducing the time correction algorithm.

Applications such as virtual/augmented reality, autonomous systems, and telemedicine require delay-bounded transmission. This paper combines the software-defined network (SDN) paradigm and proposes an integrated solution for topology construction and routing (TCR). First, the solution gives a delaybounded constraint for adding valid links, which can be adapted to various topology construction methods. Then, topology discovery, resource management, flow management, and route selection are performed in the orchestration and configuration plane. TCR enables time-sensitive network management and configuration with flexibility. The performance of the TCR is evaluated in a typical industrial network topology. The experimental results show that the TCR can effectively reduce the average path length (APL), guarantee bounded delay, and is beneficial for load balancing.

This paper aims to solve the trajectory tracking control problem for an autonomous vehicle based on reinforcement learning methods. Existing reinforcement learning approaches have found limited successful applications on safety-critical tasks in the real world mainly due to two challenges: 1) sim-to-real transfer; 2) closed-loop stability and safety concern. In this paper, we propose an actor-critic-style framework SRL-TR2, in which the RL-based TRajectory TRackers are trained under the safety constraints and then deployed to a full-size vehicle as the lateral controller. To improve the generalization ability, we adopt a light-weight adapter State and Action Space Alignment (SASA) to establish mapping relations between the simulation and reality. To address the safety concern, we leverage an expert strategy to take over the control when the safety constraints are not satisfied. Hence, we conduct safe explorations during the training process and improve the stability of the policy. The experiments show that our agents can achieve one-shot transfer across simulation scenarios and unseen realistic scenarios, finishing the field tests with average running time less than 10 ms/step and average lateral error less than 0.1 m under the speed ranging from 12 km/h to 18 km/h. A video of the field tests is available athttps://youtu.be/pjWcN_fV24g.

An increasing number of Industry Internet of Things (IIoT) applications put forward the requirements for strict time synchronization and accurate time service from providers. In order to indicate the time service performance of the Internet Time Service Providers (TSPs) on user sides, we build an Internet time service monitoring system which can obtain the real-time data of time service through the Internet. The monitoring system records the time service’s performance from three major categories: NTP pool projects, National Metrology Institutes and Commercial Organizations. After that, we propose three service metrics, in terms of Availability, Stability and Accuracy, to evaluate the performance of time service provided by TSPs. On the other hand, a novel anomaly detection algorithm is proposed to gather the statistics of abnormal data and then remove the abnormal data. Experimental results show that 56 TSPs’ availability are more than 95%. It indicates that a longer transmission path results in a lower availability. The further analyzed results also denote that the link hops have no correlations of Availability, Stability and Accuracy. Moreover, the relationship between Stability and Accuracy is positively correlated.

To satisfy the increasing demand of mobile data traffic and meet the stringent requirements of the emerging Internet-of-Things (IoT) applications such as smart city, healthcare, and augmented/virtual reality (AR/VR), the fifth-generation (5G) enabling technologies are proposed and utilized in networks. As an emerging key technology of 5G and a key enabler of IoT, multiaccess edge computing (MEC), which integrates telecommunication and IT services, offers cloud computing capabilities at the edge of the radio access network (RAN). By providing computational and storage resources at the edge, MEC can reduce latency for end users. Hence, this article investigates MEC for 5G and IoT comprehensively. It analyzes the main features of MEC in the context of 5G and IoT and presents several fundamental key technologies which enable MEC to be applied in 5G and IoT, such as cloud computing, software-defined networking/network function virtualization, information-centric networks, virtual machine (VM) and containers, smart devices, network slicing, and computation offloading. In addition, this article provides an overview of the role of MEC in 5G and IoT, bringing light into the different MEC-enabled 5G and IoT applications as well as the promising future directions of integrating MEC with 5G and IoT. Moreover, this article further elaborates research challenges and open issues of MEC for 5G and IoT. Last but not least, we propose a use case that utilizes MEC to achieve edge intelligence in IoT scenarios.

Edge computing (EC) is an emerging paradigm to push sufficient computation resources towards the network edge, improving application performance significantly by offloading applications to the edge computing node. We investigate continuous application offloading decision in EC, for which it is uncertain how users operate continuous applications and how long continuous applications last before completion. That means some characteristics of continuous applications, e.g., the number of user operations, the uploading and downloading data size for offloading computation of each user operation, and the number of central processing unit (CPU) cycles required to execute computation of each user operation, are unknown when making offloading decision. In this scenario, an energy consumption constrained average response time minimization problem among multiple users for continuous applications under uncertainty is formulated. To tackle this problem, we propose the Response Time-Improved Offloading algorithm with Energy Constraint (RTIOEC) to make offloading decision with fewer characteristics of applications. The evaluation results show that the RTIOEC algorithm achieves comparatively short average response time of continuous applications while satisfying the energy consumption constraint with a predefined upper bound of violation probability. Our results demonstrate the practicality of the RTIOEC algorithm in offloading decision in EC for continuous applications under uncertainty.

The fast-evolving Internet of Things (IoT) requires reliable and flexible networks and low-latency computing to connect billions or even trillions of edge devices and deal with the vast amount of data generated by them at high speed. This situation poised to induce a significant change to the current network architecture and cloud computing. This paper proposes a new design of fiber-wireless (FiWi) node which integrated with computing, storage for IoT, and virtualization is introduced to manage the networking and computing resources. Analytical results demonstrate that its effectiveness on switching reaches the level of hardware switches and can provide low-latency cloud service for IoT applications based on low-cost and low-power computing devices. Additionally, a use case using the FiWi access networks as a sensor is given to show the advantages of the FiWi ECN.

The problem of proximity detection is often encountered in autonomous driving and traffic safety related applications, which require low-latency proximity detection with relatively low communication cost. However, (i) most existing proximity detection solutions focus on the Euclidean space which cannot be used in road network space, and (ii) the solutions for road networks focus on static road networks and thus cannot be applied in time-aware road networks. Motivated by these, we first design a low-latency proximity detection architecture based on Mobile Edge Computing (MEC) to achieve low communication latency, and then propose a proximity detection method including a client-side algorithm and a server-side algorithm, aiming at reducing the communication cost. Experimental results show that our MEC based proximity detection architecture and our proximity detection method can reduce the communication latency and the communication cost effectively.

Edge Computing (EC) has been proposed as a promising approach to fulfil the requirements of high bandwidth and ultra‐latency of mobile applications. However, existing researches on resource allocation only consider the computing resource of mobile devices and EC servers, while ignored the constraint of the networking resources once spreading applications among multiple EC servers via wireless and wired networks. Fibre‐Wireless access networks (FiWi) combine the huge bandwidth of optical fibre networks and flexible access of wireless networks to address the above issue and bridge the coexistence of multiple EC servers. They propose a Virtualisation‐based Architecture converging EC over FiWi (VAECFW) to centralise control and allocate networking and computing resources for serving requested services. In addition, they study the problem of resource allocation of EC over FiWi and propose two algorithms Revenue‐based Virtual Network Embedding (R‐VNE) and Balanced Central Processing Unit Resource Allocation with Virtual Network Embedding (BCRA‐VNE). Simulation experiments show that the services acceptance ratio is increased about 35% under their proposed architecture, and the average service requests bandwidth utilisation of R‐VNE is increasing from 50 to 66%, and the BCRA‐VNE is from 37 to 48%. The two algorithms not only achieve higher revenue but also get better profit rate.

With the enhanced development of networks, management system has to meet different kinds of security and invulnerability challenges, such as unexpected changes of topology caused by targeted attack or failure, which disrupts existing traffic. In order to reduce the negative impact of topological changes on traffic, the topology needs to be adjusted in a short period of time before the recovery of the attacked node or the failure node. With the substantial flexibility offered by virtual network to implement new topology, how to calculate the new topology efficiently has become a hot topic of research. To achieve the goal, we present a novel algorithm based on the closeness centrality (CC) of nodes to reconfigure the topology in response to unexpected topological changes. The Efficient Topology Reconfiguration algorithm (ETR) reconfigures the topology by adding a fraction of links to the currently existing topology to satisfy the network requirements set before. We demonstrate the performance of the ETR algorithm and compare it with other algorithms. The experimental results show that we reconfigure the topology with the ETR algorithm more efficiently under targeted attacks and failures while ensuring the performance of reconfiguration.

Software Defined Network (SDN) mixed with Virtual Network (VN) is considered as a future network architecture-Virtual Software Defined Network (VSDN) for enhancing the network planning and the resource usage of networks. However, how to assign VSDN resources to virtual links and virtual network topologies efficiently and on- demand is one of the most challenging problems of any VSDN solution. This paper first proposes a dynamic bandwidth management architecture based on VSDN, and then presents an overall virtual network request scheduling model and finally proposes an optimization algorithm for dynamic bandwidth allocation, namely, K- Shortest Path based on Historical Path Data (HP-KSP). By taking advantage of historical path data and the real-time bandwidth of the physical network links, our HP-KSP algorithm adds a path allocation mechanism to the bandwidth management. Independent of the number of physical nodes, simulation results show that our proposed approach outperforms existing K-Shortest Path (KSP) and K-Shortest Path based on Disjoint Paths (DPKSP) algorithms since our HP-KSP satisfies more virtual requests which are mapped onto the same substrate and has higher QoA (Quality of Algorithms).

With the rapid development of networks, a great number of applications supported by fifth generation (5G), such as the Tactile Internet, ought to be provided with low delay. Hence, decreasing the delay of networks has attracted plenty of attention from academia. In this paper, we focus on constructing scale-free topologies toward low delay for the Core Network (CN) of 5G. The small average path length (APL), a notable property of the small-world, can be reflected in the Barabási-Albert model (BA model). The small APL indicates that packets can be routed from the source to the destination through fewer switches, decreasing the nodal processing delay. We modify the BA model to construct scale-free networks with smaller APL to reduce the nodal processing delay caused by switches. Furthermore, we generalize the BA model with the delay of propagation. In addition, we compare our proposed model with the BA model in terms of the performance of delay and we analyze the effect of the parameters on delay. Experiments validate that the improvement of the model is affected by the total number of nodes in the network and the index parameter. Experimental results also show the delay of scale-free networks constructed by our proposed model is lower than the original BA model.

Multipath algorithms except Equal-Cost Multi-Path(ECMP) which has been widely used in networks are difficult to apply, because multipath provisioning is more complex at cross layers and multipath routing need to get all nodes' information. To address the dilemma, this paper proposes a multipath network virtualization implementation scheme with Software Defined Networking (SDN) and Network Function Virtualization (NFV). In this scheme, SDN schedules network resources in a global view for selecting multiple paths and computing weight of each path, and NFV provides computing and storage resources to split flow, add tag, recover flow, to name a few. This paper also proposes a multipath algorithm for elephant flow with network virtualization. Besides, we build an experimental platform based on OPNFV and SDN, and conduct experiments under this experimental platform. The results show that our proposed algorithm applied on multipath network virtualization experimental platform has superior performance than ECMP applied in networks without virtualization.

As a combination of fiber networks' huge available bandwidth and wireless networks' ubiquity and mobility, the fiber-wireless (FiWi) access network is considered as a promising network architecture for future networks. The wireless subnetwork of FiWi networks is responsible for users' Internet access and connected to back-end fiber subnetwork. Inheriting from pure wireless networks, the wireless subnetwork is also vulnerable to the factors in surrounding environment such as noise, mutual interference of different users and etc. Although fiber links are quite good, they are not perfect. When a fiber link is long enough, the signal fading can not be ignored which may make receiver receive a false packet. The interference in wireless subnetworks and the signal fading in fiber subnetworks severely affect the quality of service in FiWi networks. In this paper, a code-based packet recovery mechanism in FiWi access networks is proposed. Based on the XOR coding, a packet which contains recovery message is generated by sender after sending several normal packets. The recovery packet follows these normal packets. Our packet recovery mechanism makes the receiver be able to cope with the situation of one packet loss, rather than request the sender to retransmit. The recovery packet generating algorithm and the packet recovery algorithm are also presented. Through extensive numerical simulation, we discuss the cost of packet recovery mechanism and compare its performance with the alternative without packet recovery mechanism.

Fiber-wireless (FiWi) access networks, which are a combination of fiber networks and wireless networks, have the advantages of both networks, such as high bandwidth, high security, low cost, and flexible access. However, with the increasing need for bandwidth and types of service from users, FiWi networks are still relatively incapable and ossified. To alleviate bandwidth tension and facilitate new service deployment, we attempt to apply network virtualization in FiWi networks, in which the network’s control plane and data plane are separated from each other. Based on a previously proposed hierarchical model and service model for FiWi network virtualization, the process of service implementation is described. The performances of the FiWi access networks applying network virtualization are analyzed in detail, including bandwidth for links, throughput for nodes, and multipath flow transmission. Simulation results show that the FiWi network with virtualization is superior to that without.

Wireless mesh networks (WMNs) are thought by many as one of the most potential networking technologies for next generation wireless networks. However, WMNs' performance is vulnerable to the interference in the environment. In order to get higher bandwidth and data transmission rate, fiber links are used to assist the communication in WMNs. The new network is called fiber aided wireless mesh networks (FAWMNs). There are two kinds of nodes in FAWMNs: those which only support wireless link and those which simultaneously support wireless link and fiber link. Correspondingly, there are two kinds of manners for packets transmission in FAWMNs: (i) several single wireless links manner; (ii) the wireless-fiber- wireless link manner. These make the packet transmission have multiple path choices. In this paper, a throughput model based on prior link probability for FAWMNs is proposed. The results demonstrate that the introduction of fiber links can bring some positive changes on the distribution of links and a noticeable promotion for the throughput of the node in FAWMNs.