With the significant increase of communication network scales, intelligent Network Operation and Maintenance (NOM) becomes essential. Knowledge Graphs (KGs) are a key enabler for intelligent NOM, and Relational Triple Extraction (RTE) plays a critical role in KG construction. However, most existing RTE researches rely on general-domain corpora, with limited exploration into the specialized domain. In this paper, we identify a novel challenge in Chinese NOM corpus —Segmented Entity, which has garnered little attention in prior works. To address it, this paper proposes an Entity-level Autoregressive RTE (EARTE) method, which incorporates an innovative Segmented-BIO (Begin, Inside, Outside) tagging scheme. Furthermore, we construct the CMIM23-NOM1-RA, the first high-quality restricted domain RTE dataset for NOM. Throughout the experimentation, we meticulously reproduce all baselines and provide a comprehensive analysis. The results show that EARTE achieves the best performance on CMIM23-NOM1-RA. EARTE’s F1 scores surpass those of the best-performing baselines by 0.4%, 2.7%, and 0.8% under the strict criterion, the lenient criterion, and the setting focusing only on segmented entities, respectively. Finally, our codes, dataset, and reproduction guidelines are publicly available at: https://github.com/JYzzzzzz/PEAR-RTE.

Network Operation and Maintenance (NOM) is an important part of the IT and telecommunications infrastructure. To improve the efficiency of NOM personnel, predict base station alarms, and classify base stations more precisely, we build a new labeled time series dataset, called BSAlarm, which collects 1000 different time series from processed real-world network alarm log data. Furthermore, we develop a NOM pipeline that employs B-spline interpolation alongside the moving average method to create and utilize this dataset for both long- and short-time series forecasting (LTSF), as well as base station classification tasks. Unlike prior NOM methods, our pipeline is centered on the generation of time series data, thereby enhancing alarm prediction performance and the visualization of changes under base station alarm conditions. Extensive experiments show that the proposed BSAlarm dataset and pipeline achieve remarkable results on time-series-data-based NOM.

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.

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.

In demand-driven personalized production, multi-agent job shop scheduling problem plays a pivotal role. Balancing the private preferences between consumers poses a challenge in achieving efficient resource allocation. A bidding-based deep reinforcement learning approach is proposed to generate a consensus schedule. An intelligent selector and an intelligent bidder (IB) are designed for each consumer to perform operation selection and determine the corresponding bid price, respectively. A job shop agent is established to collect bids and allocate resource through winner determination. To assist the IB to learn the correlation between consumers and the multi-agent job shop scheduling environment without revealing preferences, a graph neural network is adopted to extract observations. A reward function based on critic value guides the negotiation process among IBs. Extensive computational experiments demonstrate that the proposed approach achieves high social welfare outcomes. It outperforms in handling large-scale instances with more than 11 consumers and 20 jobs per consumer.

Root cause analysis for faults is one of the core tasks in the operation and maintenance of communication networks. Although artificial intelligence techniques can be used to assist manual inspections, fault diagnosis is still a tough problem. In the scenario of fault root cause localization, insufficient associated information makes it difficult to accurately determine the root cause. Meanwhile, it is a big challenge to extract as many feature details as possible from limited information and then fully utilize them. Therefore, this paper proposes a Knowledge-Enhanced Transformer-FL method, namely, KETrans-FL, to address the problem of root cause localization, by treating it as a multi-class classification problem. Our method first constructs a knowledge graph for knowledge enhancement, which consists of four types of nodes (base station, alarm, fault and alarm level) and their relationships based on the network operations. This knowledge enhancement technique incorporates real operation data and other external knowledge (e.g., alarm level) to serve as the source of feature information and thus can extract statistical and embedded features. Then, our method designs a Trans-FL (Transformer-Focal Loss) model, which uses the adapted Transformer encoder to learn the correlation information between input features to generate classification probabilities and employs Focal Loss as the loss function to mitigate severe class imbalance in the multi-class classification problem. Experimental results show that our proposed KETrans-FL method achieves a classification accuracy of nearly 91% and an average AUC score of 93%, indicating a significant improvement on fault root cause localization compared to baseline models. In addition, experimental results also validate the remarkable effect of our knowledge enhancement technique on improving the final classification accuracy.

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.

Abstract Gamma spectrum denoising can reduce the adverse effects of statistical fluctuations of radioactivity, gamma ray scattering, and electronic noise on the measured gamma spectrum. Traditional denoising methods are intricate and require analytical expertise in gamma spectrum analysis. This paper proposes a segmentation‐enhanced Convolutional Neural Network‐Stacked Denoising Autoencoder (CNN‐SDAE) method based on convolutional feature extraction network and stacked denoising autoencoder to achieve gamma spectrum denoising, which adopts the idea of data segmentation to enhance the learning ability of the neural network. By dividing the complete gamma spectrum into multiple segments and then using the segmentation‐enhanced CNN‐SDAE method for denoising, the method can achieve adaptive denoising without manually setting the threshold. The experimental results show that our method can effectively achieve gamma spectrum denoising while retaining the characteristics of the gamma spectrum. Compared with traditional methods, the denoising speed and effectiveness have been significantly improved, and the proposed method demonstrates an approximately 1.72‐fold enhancement in smoothing performance than the empirical mode decomposition method. Furthermore, in terms of retaining gamma spectrum characteristics, it also achieves a performance improvement of approximately three orders of magnitude than the wavelet method.

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.

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.

With the aid of laboratory typing techniques, infectious disease surveillance networks have the opportunity to obtain powerful information on the emergence, circulation, and evolution of multiple genotypes, serotypes or other subtypes of pathogens, informing understanding of transmission dynamics and strategies for prevention and control. The volume of typing performed on clinical isolates is typically limited by its ability to inform clinical care, cost and logistical constraints, especially in comparison with the capacity to monitor clinical reports of disease occurrence, which remains the most widespread form of public health surveillance. Viewing clinical disease reports as arising from a latent mixture of pathogen subtypes, laboratory typing of a subset of clinical cases can provide inference on the proportion of clinical cases attributable to each subtype (i.e., the mixture components). Optimizing protocols for the selection of isolates for typing by weighting specific subpopulations, locations, time periods, or case characteristics (e.g., disease severity), may improve inference of the frequency and distribution of pathogen subtypes within and between populations. Here, we apply the Disease Surveillance Informatics Optimization and Simulation (DIOS) framework to simulate and optimize hand foot and mouth disease (HFMD) surveillance in a high-burden region of western China. We identify laboratory surveillance designs that significantly outperform the existing network: the optimal network reduced mean absolute error in estimated serotype-specific incidence rates by 14.1%; similarly, the optimal network for monitoring severe cases reduced mean absolute error in serotype-specific incidence rates by 13.3%. In both cases, the optimal network designs achieved improved inference without increasing subtyping effort. We demonstrate how the DIOS framework can be used to optimize surveillance networks by augmenting clinical diagnostic data with limited laboratory typing resources, while adapting to specific, local surveillance objectives and constraints.

A multi-feature broad learning system (MFBLS) is proposed to improve the image classification performance of broad learning system (BLS) and its variants. The model is characterized by two major characteristics: multi-feature extraction method and parallel structure. Multi-feature extraction method is utilized to improve the feature-learning ability of BLS. The method extracts four features of the input image, namely convolutional feature, K-means feature, HOG feature and color feature. Besides, a parallel architecture that is suitable for multi-feature extraction is proposed for MFBLS. There are four feature blocks and one fusion block in this structure. The extracted features are used directly as the feature nodes in the feature block. In addition, a “stacking with ridge regression” strategy is applied to the fusion block to get the final output of MFBLS. Experimental results show that MFBLS achieves the accuracies of 92.25%, 81.03%, and 54.66% on SVHN, CIFAR-10, and CIFAR-100, respectively, which outperforms BLS and its variants. Besides, it is even superior to the deep network, convolutional deep belief network, in both accuracy and training time on CIFAR-10. Code for the paper is available at https://github.com/threedteam/mfbls .

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.

Molecular communication (MC) is a promising technique of using molecules to realize communication between nanomachines for Internet of Bio-Nano Things in the body area nanonetwork. Due to the properties of diffusion and the attenuation of molecular transmission, the diffusion-based MC confronts with challenges in terms of the communication range and the signal detection accuracy. To extend the coverage, the intermediate nanomachine is deployed as relay between transmitter and its intended receiver. In this article, amplify-and-forward (AF) relaying is researched, and the performance under diverse signal detection schemes is analyzed, including mean square error (MSE) detection, maximum a posteriori probability detection, minimum error probability (MEP) detection under stationary fluid environment, and the MEP detection with a drift velocity simulation. The key parameters, such as the number of released molecules, receiving radius, and the relay position, influencing on the AF relaying performance under different detection methods are explored. The simulation results show that the MEP detection can achieve the best performance gain for the AF relay with a drift velocity channel. In particular, when the number of released molecules is 500, the gain is up to 35 dB.

Non-orthogonal multiple access (NOMA) has been considered as a promising communication technology to enhance the spectral efficiency and support massive connections in fog radio access networks (F-RANs). In this paper, with the aim of maximizing the weighted sum rate while taking co-channel interference into consideration, a joint resource block (RB) and power allocation problem is formulated. To solve this problem, we first propose the optimal resource allocation scheme. Specifically, the monotonic optimization is applied and an outer polyblock approximation algorithm is proposed to get the global optimal solution. In order to reduce the computational complexity, we then propose the suboptimal resource allocation scheme. In particular, the original problem is decomposed into separated RB and power allocation problems. The RB allocation problem is modeled as a many-to-one matching game and a modified swap-enabled matching algorithm is proposed. The power allocation problem is converted into a convex form through some approximations and solved by a successive convex approximation algorithm. Simulation results demonstrate that the suboptimal scheme can achieve almost the same performance as the optimal scheme, while requiring much less computational complexity. In addition, the superiority of NOMA-enabled F-RANs over the conventional OMA-enabled F-RANs is verified.

The emerging demands of the immersive virtual reality (VR) experience require current and future wireless networks to provide ultra-low end-to-end latency. Against this backdrop, fog radio access networks (F- RANs), which take full advantages of both fog computing and caching technologies, are anticipated as a promising solution for meeting the stringent latency requirement of mobile VR delivery. In this paper, we propose a mobile VR delivery framework, in which certain VR videos and computing tasks are cached at and offloaded to the edge of F-RANs, respectively. In the considered framework, we jointly optimize the bandwidth, caching, and computing resource allocation in order to minimize the average latency. To this end, we first derive the closed-form expression of the average latency. Based on which, we then analytically examine the optimal resource allocation decision. Moreover, through the numerical results, we reveal the non-trivial trade-offs among communication, caching, and computation, showing how the bandwidth, caching, and computing capabilities can have significant impacts on the average latency.

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.

Molecular communication (MC) is a new communication engineering paradigm where molecules are employed as information carriers. MC via diffusion is the most promising approach for the communication between nanomachines. Intersymbol interference (ISI) caused by the Brownian motion of diffusion molecules will seriously affected the reliability of communication. Meanwhile, with the increase of communication distance, the signal attenuation is serious, which leads to the decline of communication quality. In this paper, a decode-and-forward (DF) relay in diffusion-based molecular communication systems is proposed to improve communication quality, in which the depleted molecule shift keying (D-MoSK) coding is used to reduce ISI. The channel performance including the BER and capacity is analyzed. Meanwhile, the relationship among BER, capacity, and the key parameters, including the number of the released molecules, receiving radius, and relay position, is investigated. The simulation experiments show that the proposal D-MoSK coding can improve the communication reliability significantly, in which the performance gain can be maximized through optimizing the position of relay and the receiving radius.

Non-orthogonal multiple access (NOMA) has been considered as a promising communication technology to enhance spectral efïciency (SE) and support massive connections in fog radio access networks (F-RANs). In this paper, to maximize weighted sum rate while taking co-channel interference into consideration, a joint resource block (RB) and power allocation problem is formulated. Based on the suboptimal scheme with low computational complexity proposed in last work, the optimal algorithm is proposed to provide an upper bound of the system performance. In particular, monotonic optimization is applied and an outer polyblock approximation algorithm is proposed to get the globally optimal solution. Simulation results demonstrate the performance of proposed algorithms and verify the superiority of NOMA-enabled F-RANs over OMA scenarios.

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.

The edge cache is an effective way to reduce the heavy traffic load and the end-to-end latency in radio access networks (RANs) for supporting a number of critical Internet of Things (IoT) services and applications. It has been verified to provide high spectral efficiency (SE), high energy efficiency (EE), and low latency. Along with several key techniques that have been applied, such as device-to-device communication and predictive caching, the edge cache techniques in RANs for IoT are becoming diversified. This paper comprehensively surveys the recent advances of the edge cache in RANs, including the key techniques and the corresponding performances. In particular, the key techniques are presented from the viewpoints of the deployment location of edge caches, content placement strategy, and coded caching. An advanced hierarchical edge cache structure is presented, and the main impacts on SE, EE, and latency of the key techniques are mainly summarized. Several open issues and challenges are identified as well to spur future investigations, in which the joint optimization of radio and cache resources, the edge cache with mobile edge computing and network intelligence, privacy, and security are discussed.

Non-orthogonal multiple access (NOMA) is promising to further improve spectral efficiency (SE) and decrease transmit latency in fog radio access networks (F-RANs) through serving multi-users in the same frequency-time resource block simultaneously, while the complexity of user association is challenging to exploit the corresponding performance gains. In this paper, a performance analysis framework for the user association in NOMA based F-RANs is proposed and the closed-form analytical results are developed by using stochastic geometry tool. In particular, we propose two user association algorithms based on evolutionary game and reinforcement learning, respectively. The performance model jointly considering quality of service, delay cost, and power consumption is formulated as a payoff function, and the corresponding performance expressions are derived for these two user association algorithms. Numerical and simulation results demonstrate that the derived expressions are accurate, and the NOMA based F-RAN can provide over 50% performance gains on SE compared to the orthogonal multiple access scheme. Furthermore, these two proposed user association algorithms work well with high convergence, which can effectively enhance the overall performance and the fairness of users.

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.

Recently, points of interest (POIs) recommendation has evolved into a hot research topic with real-world applications. In this paper, we propose a novel semantics-enhanced density-based clustering algorithm SEM-DTBJ-Cluster, to extract semantic POIs from GPS trajectories. We then take into account three different factors (popularity, temporal and geographical features) that can influence the recommendation score of a POI. We characterize the impacts caused by popularity, temporal and geographical information, by using different scoring functions based on three developed recommendation models. Finally, we combine the three scoring functions together and obtain a unified framework PTG-Recommend for recommending candidate POIs for a mobile user. To the best of our knowledge, this work is the first that considers popularity, temporal and geographical information together. Experimental results on two real-world data sets strongly demonstrate that our framework is robust and effective, and outperforms the baseline recommendation methods in terms of precision and recall.

Given a set of moving clients as well as their friend relationships, a road network, and a distance threshold per friend pair, the proximity detection problem in road networks is to find each pair of friends such that the road network distance between them is within the given threshold. The problem of proximity detection is often encountered in friend-locator applications and massively multiplayer online games. Because of the limited battery power and bandwidth, it is better to develop a solution which incurs less communication cost. Hence, the main objective of this problem is to reduce the total communication cost. However, most of the existing proximity detection solutions focus on the Euclidean space but cannot be used in road network space; the solutions for road networks incur substantial communication costs. Motivated by this, we propose two types of solutions to solve the proximity detection problem in road networks. In the first type of solution, each mobile client is assigned with a mobile region of a fixed size. We design algorithms with a fixed radius for the client and server respectively, with the purpose of reducing unnecessary probing messages and update messages. Second, we present a self-tuning policy to adjust the radius of the mobile region automatically to minimize the communication cost. Experiments show that our second type of solution works efficiently and robust with a much lower communication cost with respect to various parameters. In addition, we present our server-side computational cost optimization techniques to reduce the total computational cost.

SUMMARY The anonymity and dynamic character of a Peer‐to‐Peer (P2P) network makes it an ideal medium for selfish and vicious action. In order to solve this problem, P2P reputation systems are proposed to evaluate the trustworthiness of peers and to prevent the selfish, dishonest, and malicious peers' behaviors, which collects local reputation scores and aggregates them into the global reputation. In this paper we propose a DHT (Distributed Hash Table) trust overlay network (DHTON) to model the network structure and the storage of reputation information. We also design a robust and distributing reputation system, DHTrust, which takes full advantage of the DHT to distribute local reputation to trade‐off the damage of fake reputation information by genuine reputation information. By using the trust evaluation towards two reputation scores, we can also distinguish and evaluate the fundamental behaviors of peers in the P2P network, i.e. providing service and issuing reputation scores. To adapt to the dynamic P2P networks, we take dynamic node mechanism into account. Our scheme can assure convergence effectiveness and robustness, when nodes enter or leave the system. We conduct extensive simulations to evaluate the performance of DHTrust. The results show that our system makes significant improvement in convergence speed and aggregation accuracy. Moreover, it is robust to malicious peers. Copyright © 2011 John Wiley & Sons, Ltd.

The anonymity and dynamic characters of Peer-to-Peer (P2P) system makes it an ideal medium for selfish and vicious action. In order to solve this problem, P2P reputation systems are proposed to evaluate the trustworthiness of peers and to prevent the selfish, dishonest, and malicious peers' behaviors, which collects local reputation scores and aggregates them into the global reputation. In this paper we propose a DHT trust overlay network (DHTON) to model the network structure and the storage of reputation information. We also design a robust and distributing reputation system, DHTrust, which takes full advantage of the Distributed Hash Table (DHT) to distribute local reputation to trade off the damage of fake reputation information by genuine reputation information. By using the trust evaluation towards two reputation scores, we can also distinguish and evaluate the fundamental behaviors of peers in P2P network, i.e., providing service and issuing reputation scores. Through the simulation experiments, we find our system makes significant performance gains in convergence speed and aggregation accuracy, and the most important, is robust to malicious peers.