As satellite network rapidly develops, traditional on-board switching technologies can no longer meet the demands of high-reliability, low-latency, and multi-granularity satellite networks. On-board electronic switching systems encounter the ’electro-optical conversion bottleneck’, limiting their ability to efficiently handle high-bandwidth traffic. The static wavelength routing optical switching architecture struggles to handle multi-granularity traffic requirements between satellites due to its slow switching speed and lack of flexibility, resulting in congestion and packet loss in large-scale constellations. To address these issues, we designed an on-board optical switching prototype based on Ultra-fast Optical Packet Switching (UOPS) technology. This prototype supports 100Gbps transmission per channel and achieves sub-microsecond switching latency. Meanwhile, we found that in timeslot-based optical switching, traditional fixed timeslot configurations face challenges in achieving a reasonable trade-off: longer timeslots can lead to high packet loss rates, while shorter timeslots impose throughput limitations. Therefore, we propose a Dynamic Adaptive Granularity-Aware (DAGA) scheduling algorithm. By precisely sensing the time derivative of queue occupancy to capture the traffic granularity characteristics in dynamic satellite networks, enabling flexible timeslot allocation adjustments. Experimental results demonstrate that the DAGA scheduling algorithm significantly reduces packet loss rates and improves throughput, particularly under high-load and dynamic traffic conditions.

In wireless-powered communication networks (WPCNs) augmented by reconfigurable intelligent surface (RIS), achieving high throughput while managing signaling overhead remains a critical challenge. Conventional approaches rely on instantaneous channel state information (I-CSI) for dynamic RIS beamforming, which leads to prohibitive channel estimation and feedback overhead in large-scale deployments. To address this issue, this paper proposes a novel two-timescale protocol that integrates statistical CSI for long-term RIS beamforming optimization and short-term I-CSI for optimizing resource allocation. In particular, the proposed method designs multiple RIS beamforming patterns using statistical information, while dynamically adjusting time and power allocation within each coherence interval based on effective I-CSI. An alternating optimization (AO) based algorithm is then developed to iteratively refine RIS phase shifts for both downlink energy transfer and uplink information transfer using gradient projection, and derive optimal resource allocation in closed-form expressions via Karush-Kuhn-Tucker (KKT) conditions. Simulation results validate the framework’s efficacy, demonstrating that using only 33% of the total RIS beamforming patterns can achieve 94% of the sum-rate performance of full I-CSI approaches, which provides useful guidelines for reducing the feedback overhead in the considered RIS aided WPCNs.

Optical satellite networks, supported by optical inter-satellite links (OISLs), provide reliable and low-latency optical connectivity. However, periodic and predictable sun outage events significantly compromise OISL availability, leading to frequent OISL interruptions and reduced network reliability. Existing routing algorithms often overlook the regularity of sun outage-induced interrupts and their differentiated impacts on services, resulting in degraded service performance. To address this challenge, this paper proposes a sun outage-enhanced time discretization OISL model and introduces a sun outage link-aware routing (SOLR) algorithm. By incorporating joint awareness of sun outage patterns and service requirements, SOLR employs an adaptive optimization mechanism to dynamically adjust routing decisions within temporal windows. Experimental results demonstrate that SOLR extends stable path durations by 39.9%, reduces interruption rates by 28.5%, and decreases blocking rates by 36.4%, significantly outperforming link-state-based routing algorithms. By effectively mitigating the impact of sun outages, SOLR ensures continuous optical service connections. This interruption-tolerant framework bridges network modeling and service provisioning, offering a robust solution for mission-critical service in optical satellite networks.

Reconfigurable intelligent surfaces (RISs) have been recognized as a revolutionary technology for future wireless networks. However, RIS-assisted communications have to continuously tune phase-shifts relying on accurate channel state information (CSI) that is generally difficult to obtain due to the large number of RIS channels. The joint design of CSI acquisition and subsection RIS phase-shifts remains a significant challenge in dynamic environments. In this paper, we propose a diffusion-enhanced decision Transformer (DEDT) framework consisting of a diffusion model (DM) designed for efficient CSI acquisition and a decision Transformer (DT) utilized for phase-shift optimizations. Specifically, we first propose a novel DM mechanism, i.e., conditional imputation based on denoising diffusion probabilistic model, for rapidly acquiring real-time full CSI by exploiting the spatial correlations inherent in wireless channels. Then, we optimize beamforming schemes based on the DT architecture, which pre-trains on historical environments to establish a robust policy model. Next, we incorporate a fine-tuning mechanism to ensure rapid beamforming adaptation to new environments, eliminating the retraining process that is imperative in conventional reinforcement learning (RL) methods. Simulation results demonstrate that DEDT can enhance efficiency and adaptability of RIS-aided communications with fluctuating channel conditions compared to state-of-the-art RL methods.

Current AI training clusters widely use RoCEv2 to improve the communication efficiency of the interconnect networks across machines. RoCEv2 relies on Priority Flow Control (PFC) to ensure a lossless network. However, PFC brings certain side effects, such as head-of-line blocking, congestion spreading, and deadlock. Numerous studies have been proposed to eliminate the side effects. However, unlike traditional high-performance computing applications, distributed machine learning (DML) is not 100 % loss-intolerant. In light of this observation, this paper proposes a loss-tolerant flow control (LTFC). LTFC does not rely on PFC to ensure a lossless environment, but to control packet loss ratio within the tolerance threshold. Compared to the traditional trigger condition, LTFC reduces the likelihood that the PFC will be triggered. Additionally, we replace RoCEv2's default Go-back-N mechanism with a non-retransmission mechanism to eliminate retransmission latency. We demonstrate the bounded-loss tolerance feature of DML on our testbed and evaluate the performance of LTFC in large-scale simulations. Simulation results show that LTFC reduces the average flow completion time (FCT) by up to 26.9 % and tail FCT by up to 16.9 % compared to existing solutions.

The satellite-to-ground link is susceptible to eavesdropping from various open-space locations. In this paper, we investigate a new eavesdropping scenario in a multiconstellation satellite network, where very low earth orbit (VLEO) satellites eavesdrop on low earth orbit (LEO) constellations from legitimate positions. We model the VLEO eavesdropping risks in a theoretical way and propose a crossinterference scheme called LEO active cross-interference (LEOACI) to protect satellite-to-ground downlinks from eavesdropping attacks. The scheme prevents eavesdropping by scheduling spare satellites to transmit artificial noise (AN) over the protected link. Finally, we conduct simulations to evaluate the performance of the LEO-ACI scheme and it shows that VLEO eavesdroppers can possess high eavesdropping coverage and long duration. Meanwhile, the LEO network can ensure that 98.5 % of downlinks are free from eavesdropping at the cost of 12.3 % of the satellite-to-ground capacity degradation.

Space-air integrated networks as an emerging 6G architecture have recently attracted extensive research interest. This paper investigates the matching of high-capacity, heterogeneous data transmission to the constrained communication time windows between Low Earth Orbit (LEO) satellites and high-altitude platforms (HAPs). By explicitly modeling the heterogeneous data transmission process, based on the matching game, this study examines how to maximize data transmission revenue while maintaining the stable transmission of high-capacity, heterogeneous data. This problem is reformulated as a three-sided matching game with size and cyclic preference lists (TMSC) involving HAP, satellite, and data. Since obtaining stable matching results in the TMSC is NP-complete, we propose a two-tier matching algorithm framework comprising two components: a random path to pairwise-stable (RPPS) matching algorithm for pairing HAPs with satellites, and a Roth-Peranson (RP) algorithm for matching HAP-satellite pairs with data. Efficient matching is achieved by imposing communication time window constraints and integrating the distinct characteristics of HAPs, satellites, and data. Numerical results demonstrate that our proposed method achieves near-optimal data transmission performance while significantly reducing computational complexity.

Smart and mobile devices are essential components of the Internet of Things (IoT) ecosystems, facilitating connectivity and automation across various domains. Due to its flexibility, the Android operating system is widely adopted in these devices. However, their increasing integration into IoT networks has introduced significant security risks, particularly from Android malware. To address these challenges, effective detection methods are needed to enhance IoT security. Given the success of contrastive learning in computer vision, researchers have increasingly explored its potential for Android malware detection. This article presents a static Android malware detection method that integrates deep learning with supervised contrastive learning. Based on the characteristic that contrastive learning enhances the model’s ability to effectively represent input samples, we design a novel contrastive loss based on structural similarity metrics and integrate it with contractive loss and binary cross-entropy loss to construct a hierarchical loss function for guiding model optimization. Furthermore, the method directly analyzes the classes.dex file from Android application package, eliminating the need for feature engineering or domain expertise, thus enhancing its applicability. Experimental results demonstrate that the proposed method achieves an 87.13% F1-score on the AndroZoo dataset, outperforming baseline models while maintaining computational efficiency and practical usability. Ablation studies validate the effectiveness of the hierarchical loss function in improving model performance and ensuring consistent malware representation within the same family.

Geo-distributed machine learning (GDML) can facilitate collaborative learning among geographically-dispersed data centers to meet the demands of distributed and privacy-preserving training for large-scale distributed Internet of Things applications. Unfortunately, the efficiency of distributed training tasks heavily depends on synchronized communication between multiple distributed models over bandwidth-limited wide area networks (WANs). The fine-grained Optical Transport Network (fgOTN), thanks to its adjustable bandwidth connections, represents more flexible transmission and has the ability for accurate synchronization across GDML tasks in WANs. However, flexible bandwidth assignment and complex interdependencies among tasks pose significant challenges to resource allocation for GDML in fgOTN. Specifically, flexible bandwidth assignment exacerbates resource competition among task flows, leading to decreased learning efficiency. This paper provides novel resource allocation solutions for GDML in fgOTN. We first formulate this problem as a linear programming aimed at maximizing the completion ratio of GDML tasks. Subsequently, we propose an innovative resource allocation algorithm based on genetic algorithm (GARA) for GDML in fgOTN. GARA considers both task completion and bandwidth adjustment through population generation based on prior knowledge and adaptive mutation based on completion ratio. Simulation analysis demonstrates that GARA effectively prioritizes resource allocation for high-priority tasks to alleviate resource competition, achieving the highest task completion ratio while avoiding excessive network reconfiguration.

For rapid on-orbit forwarding of high-resolution remote sensing (RS) images, the combination of very low-Earth orbit (VLEO) and optical intersatellite links (OISLs) has recently emerged as a focus of nonterrestrial networks (NTNs). However, at the height of VLEO, not only does the attenuation factor of the optical signal-to-noise ratio grow exponentially, but the service with more than ten times bandwidth expansion, which adds a significant burden to the buffer queue. To address the above reliability and latency challenges, this article proposes a routing framework based on the characteristics of the VLEO scenario. Specifically, the OISL path in VLEO is modelled first. In order to reduce the severe impact of multihop on the routing performance, we perform dynamic group scheduling for OISL. Additionally, taking advantage of the fact that RS missions can be scheduled in advance, we adopt a “route first, then establish links” approach to specifically plan service paths. Considering the coordination of a single service and overall network performance, this problem is solved using a Q-value decomposed multiagent reinforcement learning method. Simulation results demonstrate that our scheme maintains excellent reliability and latency performance under VLEO scenarios with varying heights, network scales, and traffic loads.

The network has become a bottleneck for generative artificial intelligence (GAI) jobs. Accelerating GAI jobs in edge data centers using hybrid electrical/optical switch is considered a promising solution. This architecture optimizes bandwidth utilization by enabling demand-aware topology reconfiguration through flexible configuration of optical circuit switche optical circuit switches (OCS). However, frequent topology reconfiguration may increase latency. Therefore, there is a balanced relationship between latency and bandwidth utilization. In this article, we propose a multigranularity adaptive interleaved algorithm for service scheduling in edge data centers. First, different degrees of time slot shifts are introduced based on the latency sensitivity of jobs, where large bandwidth GAI jobs are transmitted in a single hop by configuring a demand-aware topology. Additionally, when the reconfiguration threshold is met, low-priority ports are prioritized for reconfiguration to ensure latency requirements are met. This approach effectively resolves the tradeoff between bandwidth utilization and latency by decoupling them from each other. Simulation results show that this approach can effectively reduce the latency and improve the network throughput.

The rise of dual carbon goals and Web 3.0 has spurred the rapid development of blockchain-based microgrids (BBMGs). Due to the inherent volatility in power generation within microgrids composed of distributed renewable energy sources, frequent cross-domain interactions between multiple BBMGs are required to ensure stable system operation. As an important stage of cross-domain interaction, trusted authentication is crucial to its development. Currently, cross-domain trusted authentication is generally achieved by setting a unified reputation calculation method and cross-domain validation standard for all nodes in the multimicrogrid system. However, this method identifies malicious nodes through periodic and static reputation calculations but cannot detect or prevent nodes that suddenly become malicious in real time. Moreover, the reputation calculations typically do not consider factors such as node importance and resource conditions, which is not ideal for BBMG systems with limited resources. Additionally, cross-domain authentication in the above approach is usually performed by selecting the most trusted node through polling or random methods for single validation, or by using multiple trusted nodes for consensus-based validation. These methods are inefficient and unsuitable for microgrid systems that require frequent interactions. To address these challenges, we first propose an abnormal behavior real-time detection method based on federated learning to monitor the abnormal behavior of nodes in real time. Then, factors such as abnormal behavior detection and resource conditions are introduced to propose a reputation calculation method that is more suitable for blockchain systems. Based on this method, we design a multifactor trustworthy user access control mechanism to assign roles and permissions of nodes, identifying multiple trusted cross-domain validation nodes (CDVNs) for efficient parallel authentication. To assist the system in quickly selecting and locating the optimal CDVNs and further improve the efficiency of cross-domain trusted authentication, this article proposes an efficient search and location strategy for the optimal CDVNs based on the source node to batch search for the CDVN with the smallest delay for all nodes. Experimental results show that this scheme is feasible in practical applications.

In cell-free massive multiple-input multiple-output (CF-mMIMO) systems, seamless communication coverage is achieved through the dense deployment of numerous access points (APs), significantly enhancing spectral efficiency (SE) for users and overall system capacity. However, the implementation of this approach demands substantial deployment costs and unavoidably necessitates the use of non-ideal hardware. This paper investigates the achievable rate of users in the uplink CF-mMIMO systems that employ nonlinear power amplifiers (PAs) and low-resolution analog-to-digital converters (ADCs) at user equipment (UE) and APs, respectively. In particular, we derive a closed-form expression for the achievable uplink user rate and conduct a comprehensive analysis of various factors, including the number of APs, UE density, number of AP antennas, and ADC resolution. To mitigate the interference among UEs and maximize the sum rate, we propose a graph neural network (GNN) assisted actor-critic algorithm (DMAGNN-AC) for power allocation. The established framework overcomes the representation bottleneck of DRL in high-dimensional unstructured state spaces and provides physically interpretable feature embeddings. In comparison to the full power output, the proposed power allocation scheme is capable of doubling the rate. Furthermore, to address the detrimental impact of low-resolution ADCs on the rate, we develop an enhanced algorithm, multi-agent deep Q-integrated network (MADQIN), which optimizes the allocation strategy of ADC resolutions. Finally, the effectiveness of the proposed schemes is validated by the presented simulation results.

Intelligent computing center (ICC) is a new type of data center constructed with intelligent computing power, such as graphic processing units (GPUs) and artificial intelligence acceleration cards. With billions of parameters, the emergence of large models (e.g., ChatGPT) presents a significant demand of computing power. It may be challenging for a single ICC to provide the required computing power during large model training. Thus, ICC interconnections (ICCI) will become a typical and effective solution to provide intensive computing power. Due to human activities, traditional computing tasks (e.g., transaction processing and online entertainment) exhibit a tidal effect of computing demand, which leads to the tidal variation of remaining computing resources. Moreover, distributed model training (DMT) tasks are likely to cover peaks and valleys of the tidal effect in computing power. In this case, it is easy for DMT tasks to cause an ICC to become a hotspot (i.e., computing load in an ICC exceeds a desired threshold), which significantly degrades the reliability and performance of the ICC. This paper proposes DeepHM, a deep reinforcement learning-based hotspot management strategy through task migration in ICCI networks. To comprehensively consider the bandwidth metrics of the ICCI network, we further propose a dynamic wavelength allocation strategy, i.e., DeepHM-DWA. Simulation results show that the DeepHM and DeepHM-DWA reduce the hotspot compute unit time blocks by 19% and 18% with fewer number of migrated workers while balancing the computing load among multiple ICCs. DeepHM and DeepHM-DWA reduce the average completion time ratio of the DMT tasks by 2% and 5%, respectively.

Satellite optical networks combined with multi-beam technologies can be referred to as multi-beam satellite optical networks (MB-SONs). These networks are expected to play a crucial role in satellite Internet, potentially achieving Gigabit/s inter-satellite (IS) communication in the future. However, the growing demand for the satellite-to-mobile communications bring a challenge for making use of hybrid IS and satellite-to-ground (SG) transmission resources, which is worthy of studying. Given the different characteristics of IS links and SG links in terms of time windows and transmission capacities, existing solutions can hardly provide a suitable option for the optimal utilization of transmission capacity in MB-SONs. In this paper, we focus on the joint scheduling of optical wavelengths in IS and multiple beams in SG for the services sent from one ground station to the other ground station (which are referred to as end-to-end services). In response to the above-mentioned different characteristics of IS and SG links, we find that at least two constraints need to be followed in the joint scheduling. Also, the length of common time window will not exceed the minimum time window of IS and SG links on the path. The second one is that the volume of transmission capacity of a path depends on the length of CTW and the minimum bandwidth of IS and SG links, which should meet the service requirements. Considering these two constraints related to the length of CTW and the volume of transmission capacity, the main contributions of this paper can be concluded: i) defining the joint time slot allocation (JTSA) problem for multiple beams and wavelengths with different transmission capacities, ii) proposing an integer linear programming (ILP) model with object to minimize the number of time slots occupied by services, iii) designing the common time window for time slot assignment (CTW-TSA) algorithm as an option in practical implementations. The proposed ILP and CTW-TSA algorithm are evaluated by comparing the simulation results with the scheme using. The simulation of the CTW-TSA algorithm was compared to separate resource scheduling (SRS) without the store-and-forward function. The results showed a reduction of almost 0.201 in service blocking probability and an increase in average bandwidth utilization of about 0.159 for IS links and 0.164 for SG uplinks/downlinks.

Quantum entanglement enables quantum networks to provide end-to-end sharing of entangled particles, establishing multi-hop path-to-path connections between remote parties. Implementing entanglement distribution plays a vital role in increasing the network scale, and practical entanglement algorithms are required to provide end-to-end multi-hop quantum entanglement. We consider the real-time entanglement distribution (R-TED) and pre-established entanglement distribution (P-EED) to meet this requirement. Based on these two types of entanglement distribution, we propose two algorithms, i.e., R-TED-based routing and entangled pairs allocation (REA) algorithm as well as P-EED-based REA algorithm for end-to-end entanglement establishment, where the practical physical factors (e.g., finite storage capacity and limited storage time) are considered. The R-TED-based REA algorithm can orchestrate the nodes in a route and perform entanglement swapping by adopting real-time entanglement. For the P-EED-based REA algorithm, remote entangled particle sharing can be achieved via pre-shared entanglement distribution and hop-by-hop entanglement swapping. This way, the entanglement routing selection satisfies the storage time constraint and allows two far-apart nodes to share long-distance entangled particles with limited memory cells. We evaluate the performance of the proposed algorithms under different network topologies and sizes, based on which we demonstrate that the network size can significantly affect the efficiency advantage achieved by the P-EED-based approach over the R-TED-based approach.

With the continual growth in user communication needs, classical optical communications are facing developmental bottlenecks. On one hand, the communication capacity of available optical fiber resources is approaching its upper limit. On the other hand, emerging quantum computing technology poses security threats. Quantum key distribution (QKD), as a representative quantum cryptography technology, is being introduced into existing optical infrastructure to mitigate security threats. It also has pioneering application value for next-generation quantum information networks. However, limited optical fiber resources struggle to support the introduction of quantum communication over classical optical networks. There are also incompatible noise factors between the two communication paradigms. This paper proposes transitioning from unilateral adaptive routing and wavelength assignment (ARW A) to bilateral synergistic RW A (SRW A) to facilitate the coexistence of two heterogeneous communication paradigms in optical networks. Simulations have proven SRW A can increase end-to-end key supply rates from bit/s to kbit/s levels. It has an enabling effect on QKD over classical optical networks.

In recent years, research on satellite networks has gained significant attention, with their capability for seamless global coverage and meeting real-time communication demands serving as a key solution to address deficiencies in ground communication network coverage and to improve the real-time transmission of services. Traditional satellite networks, originally employed for singular purposes such as data relay, are gradually transitioning to satellite internet to support various Internet-based services. The integration of satellites with ground networks, known as the Satellite-Terrestrial Integrated Network (STIN), has become an inevitable trend, making the deployment of ground stations (GSs) a critical issue in the STIN construction. Traditional GS deployment strategies are insufficient to meet the real-time demands of emerging services. In this context, a GS deployment strategy based on the data center-user gravity model (GSD-DG) is proposed, where the influence of all data center factors on GS deployment is considered. This approach takes into account constraints such as satellite connectivity, user traffic, and data center gravity. The integration of GS with data centers plays a pivotal role in enhancing the internet service latency performance. Simulation results indicate that the proposed strategy significantly reduces service latency by 24.5% compared to the benchmark, providing a more effective GS deployment solution to further optimize the STIN service latency performance.

The 6G transport network facilitates the Internet of Everything (IoE), carrying numerous services and emphasizing the paramount importance of its reliability. However, within the transport network, the issue of co-route fibers arises. The co-route fibers, encompassing both co-cable and co-trench fibers, presents a significant latent hazard for service disruptions, posing a substantial threat to the seamless connectivity envisioned for the 6G era of pervasive IoE. The segregation of communication and sensing in the transmission network results in mutual interference between communication and sensing signals, rendering it difficult to promptly address sudden fiber interruptions. This article proposes an integrated sensing and communication (ISAC) architecture within transport networks, aiming at the online discernment of co-cable fibers, characterization of fiber optic trenches, and real-time classification of fiber vibration events. In the domain of co-cable fiber identification, our approach has successfully reduced the nuisance alarm rate to an impressive 5.3%, while simultaneously elevating the recognition accuracy to an outstanding 99.7%. As for co-trench fiber identification, our proposed methodology not only facilitates the discernment of co-trench fibers but also achieves an impressive accuracy of 97.7% in classifying fiber trenches. Moreover, in the realm of fiber state prediction, our solution has achieved a remarkable recognition accuracy of 98% across six distinct vibration events. These results underscore the robust performance of the proposed ISAC architecture, which will effectively safeguard the survivability of 6G IoE.

In the realm of emergency disaster relief, it is paramount to attain a thorough comprehension of the semantic information associated with the local disaster scene for strategic rescue path planning and immediate rescue operations for affected individuals. Unmanned aerial vehicle (UAV) networks are widely utilized for rapid data collection in the aftermath of disasters due to their flexibility and maneuverability, assisting in rescue decision-making. However, some disasters, such as seismic events and floods have disrupted the initially structured ground shape information, leading to a disparate distribution of data collected by various UAV groups. This exposes traditional semantic segmentation models susceptible to shortcut bias, posing challenges in adapting to semantic segmentation tasks in disaster scenarios. Thus, this paper proposes a bias-compensation augmentation learning based semantic segmentation framework, which substantially enhances the extraction capability of semantic information. Initially, we exploit an artificial augmentation neural network for bias-awareness to determine the relative bias values of the collected image data. Subsequently, considering the limited computing power resources in UAV networks, we present a bias compensation computation offloading strategy to achieve a relatively balanced distribution of semantic information across UAV nodes, optimizing the trade-off between network scheduling efficiency and model accuracy. We conduct reconstruction validation on the FloodNet dataset, and a plethora of experimental results demonstrate that, compared to traditional methods, this approach greatly improves the accuracy of pixel-level semantic segmentation by over 86.5%. Moreover, the average combined processing time is also reduced by over 50%, enhancing the utilization efficiency of limited computational resources.

Vulnerability detection in source code has been a focal point of research in recent years. Traditional rule-based methods fail to identify complex and unknown vulnerabilities, leading to poor performance. While deep learning (DL)-based methods have improved these shortcomings, there is still room for enhancement. For C/C++ source code, effective vulnerability detection requires considering both the information in code statements and the structural information of the code. Graph-based code representation methods can address this need, but existing approaches often use homogeneous graphs that do not differentiate between various types of code statements or dependencies. Few methods use heterogeneous graphs for C/C++ code representation. This study explores this potential and proposes a new C/C++ vulnerability detection method named HeVulD. HeVulD introduces two node definition approaches and a key-node-based program slicing method, generating heterogeneous graph representations for source code. These representations consist of both heterogeneous nodes and edges, providing a more precise representation of source code. HeVulD achieves an F1-score of 96.4% on the SARD dataset, outperforming nine baseline C/C++ vulnerability detection methods. HeVulD has been tested under adversarial attack scenarios to assess its robustness. Additionally, HeVulD has been tested on ten open-source software projects and the latest CVEs, demonstrating its detection and generalization capabilities in real-world scenarios and its ability to identify unknown vulnerabilities.

Given the spring-up of diverse new applications with different requirements in metro optical networks, network slicing provides a virtual end-to-end resource connection with customized service provision. To improve the quality-of-service (QoS) of slices with long-term operation in networks, it is beneficial to reconfigure the slice adaptively, referring to the future traffic state. Considering the busy-hour Internet traffic with daily human mobility, the tidal pattern of traffic flow occurs in metro optical networks, expressing both temporal and spatial features. To achieve high QoS of slices, this paper proposes a node-oriented slice reconfiguration (NoSR) scheme to reduce the penalty of slices, where a gradient-based priority strategy is designed to reduce the penalties of slices overall penalties in reconfiguration. Besides, given that a precise traffic prediction model is essential for efficient slice reconfiguration with future traffic state, this paper presents the model combining the graph convolutional network (GCN) and gated recurrent unit (GRU) to extract the traffic features in space and time dimensions. Simulation results show that the presented GCN-GRU traffic prediction model achieves a high forecasting accuracy, and the proposed NoSR scheme efficiently reduces the penalty of slices to guarantee a high QoS in metro optical networks.

In the sixth-generation fixed network (F6G), network security becomes an important topic. Encryption is an effective method to prevent network attacks and realize network security. Quantum key distribution (QKD) is a promising technology to effectively address the challenge by providing secret keys due to the laws of quantum physics. New services such as high immersion experience and holographic have the characteristics of time-varying bandwidth and requirements. The introduction of optical service unit (OSU) technology makes it possible to provide the exact bandwidth used by the service. In optical transport networks, a lightpath needs to be established before service transmission, and will be removed after service transmission. Signaling is used for lightpath establishment, removal, and bandwidth adjustment. Data information transmitted in data layer and signaling information transmitted in control layer are highly vulnerable to cyberattacks, such as eavesdropping. The supply of bandwidth and key resources need to be optimized to achieve secure and stable service transmission in optical networks. Hence, how to realize bandwidth and key on demand (BKoD) provisioning for dynamic services is a key problem. To improve the flexibility of bandwidth and key resource allocation and utilization, a QKD-secured OSU-based optical transport network can be deployed. In this paper, a novel QKD-secured OSU-based optical transport network architecture is proposed and a service aware dynamic resource provisioning (SADRP) algorithm is proposed to realize BKoD. The proposed architecture uses the QKD technique to provide keys for both signaling information and data information for the first time. The proposed algorithm supplies resources according to the dynamic demand of bandwidth and key, so as to achieve the balance between dynamic demand and static resource utilization. Simulations results show that compared with the benchmark algorithm, the SADRP algorithm reduces blocking probability by 4.16%, reduces bandwidth resource utilization rate by 4.39%, reduces key resource utilization rate by 3.48%, and improves security rate by 4.17%.

The computing power network (CPN) has emerged as a promising networking paradigm in recent times. Since the characteristics of high bandwidth, low delay and high reliable communication, optical networks have been identified as potential frameworks for establishing the CPN infrastructure across metropolitan areas. In CPN of metropolitan areas, owing to the low delay demands of computing power requests, the traffic of computing power requests is more likely to be burst than others. The burst traffic leads to the exponential increase of the traffic loads instantly, which leads to soft failure in the form of overloading and breaks the tradeoff between resource utilization and load balance, which all decline the survivability severely. To solve the problems above, this article proposes an architecture named metro optical computing power network (MO-CPN) to achieve collaborative scheduling in MO-CPN. And proposed a survivable computing power scheduling scheme during burst traffic. Where a multi visual gate recurrent unit (MV-GRU) neural network based on error feedback is constructed to achieve high-precision of burst traffic prediction. According to the burst traffic prediction, a protection threshold to avoid the overloading of nodes is set. And aiming at multi-objectives of low delay and load balancing, the computing power, spectrum resources, burst traffic and protection threshold are used as constraints in the scheduling scheme. The experimental results reveal that our approach can significantly enhance the survivability during burst traffic and improve the utilization of resources. The proposed scheme can also lower the blocking probability and average processing delay, which has strong robustness and reliability.

In an intelligent reflecting surface (IRS)-assisted wireless communication system, obtaining the real-time channel state information (CSI) through channel estimation (CE) is crucial for achieving the IRS’s passive beamforming gain, which however shortens the effective data transmission time due to the CSI feedback overhead. It is of utmost importance to decide how often to estimate the channels in an IRS-assisted system. In this paper, we propose an integrated CE and beamforming scheme to jointly optimize the adaptive CE interval and passive beamforming strategy, based on the past observation sequences composed of imperfect CSI and data rate feedback. We formulate the two-stage optimization problem as a bilevel partially observable Markov decision process (POMDP), aiming to maximize the expectation of cumulative throughput of the system. We propose two bilevel deep reinforcement learning (DRL) algorithms, namely recurrent neural network (RNN) based proximal policy optimization (PPO) algorithm and Belief-based PPO algorithm, to solve this problem. In these two algorithms, the CSI features from the past observation sequences are implicitly extracted by the RNN network or explicitly inferred by the belief network, which then serve as the inputs for the two-stage policy networks to determine the necessity of CE and the IRS beamforming vector based on the PPO algorithm. Simulation results demonstrate the superiority of the proposed adaptive CE scheme over the periodic counterpart in terms of throughput. Moreover, the results show that it is profitable to estimate the channels less frequently if the channels exhibit a higher correlation across time.

Leading by the development of Starlink, the low earth orbit (LEO) satellite networks are expected to be the customer-grade Internet infrastructure that will co-work with the terrestrial networks for carrying Internet traffic. In this paper, focusing on network usage fluctuations that are caused by human activity variations in work and rest time slots, we introduce the concept of time zones into the global satellite network and develop a time-zone-aware traffic model. Accordingly, we propose a relay-based routing algorithm to balance the workload of the satellites and the inter-satellite links over different time zones. Simulation results show that the proposed model and algorithm can reduce the blocking ratio by up to 34.8% and increase the bandwidth utilization ratio by up to 45.6%, with a limited cost in average connection latency.

This paper proposes a high-precision deterministic provisioning with federated learning training scheme to provide deterministic delay guarantees for federated learning tasks, and improves the training accuracy of machine learning and reduces computing pressure of edge nodes.

We first proposed cross-vendor access technology based on blockchain to provide guarantee for cross-vendor fault traceability of disaggregated optical networks in the future. The feasibility and effectiveness of the proposed architecture are verified on our experimental platform.

This paper proposes a dynamic spectrum sharing scheme in an unmanned aerial vehicle (UAV) assisted cognitive radio network. The UAV serves as a secondary base station to provide communication services to multiple secondary users (SUs) by adaptively utilizing the spatio-temporal spectrum opportunities of multiple device-to-device primary users (PUs), where each PU’s spectrum occupancy follows a two-state Markov process. We jointly optimize the UAV’s trajectory and user association to maximize the expectation of its cumulative energy efficiency subject to the interference constraint of the PUs. We formulate this problem as a partially observable Markov decision process (POMDP), where the UAV can only observe the spectrum occupancy status of the adjacent PUs. Due to the lack of the PUs’ spectrum occupancy statistics, we propose a model-free reinforcement learning algorithm named partially observable double deep Q network (PO-DDQN) to obtain the near-optimal spectrum sharing policy. Simulation results show that our proposed algorithm outperforms the baseline policy gradient (PG) algorithm in terms of convergence speed and the UAV’s energy efficiency. Additionally, the spectrum utilization efficiency can be further enhanced when the UAV has wider observation radius, or if the PUs’ spectrum occupancy exhibits stronger temporal correlation.

The Industrial Internet of Things (IIoT) is considered to be one of the most promising revolutionary technologies to increase productivity. With the refined development of manufacturing, the entire manufacturing process is split up into several areas of IoT production. Devices from different domains cooperate to perform the same task, which cause security problems in interacted communication among them. Existing authentication methods cause heavy key management overhead or rely on a trusted third party. It is imperative to protect privacy and ensure the credibility of the device during device interaction. This article proposes a federated hierarchical trust interaction scheme (FHTI) for the cross-domain industrial IoT. It builds a low-privacy network platform through blockchain and protects the data privacy of the IIoT. A hierarchical trust mechanism based on federated detection is designed to realize the unified trust evaluation of cross-domain devices. A trusted cross-domain method based on device trust value is designed to ensure the security and trustworthiness of cross-domain devices. The simulation results show that the FHTI scheme can improve the speed of identity authentication and the detection accuracy of malicious devices.

Datacenter networks (DCNs) with cloud–edge collaboration are emerging to satisfy the communication, computation, and caching (3C) requirements of future services such as cloud-based IoT services. However, the enroute data over DCNs with cloud–edge collaboration is likely to suffer from cyberattacks such as eavesdropping. A large number of services require not only 3C resources, but also cryptographic resources for encryption to ensure high security. Quantum key distribution (QKD) is a practical approach to provide secret keys for remote users with information-theoretic security against attacks from quantum computing. A QKD-secured DCN (QKD-DCN) with cloud–edge collaboration can be deployed to satisfy the communication, computation, caching, and cryptographic (4C) requirements of services. This article innovatively solves the new 4C resource-allocation (4CRA) problem in the network to minimize the cryptographic resource consumption. It formulates an integer linear programming (ILP) model and proposes a heuristic cryptographic-dependent 4CRA algorithm to find optimal solutions. The proposed algorithm is compared with two baseline 4CRA algorithms which, respectively, consider the minimized service delivery latency and the first-fit resource availability. Analytical simulations show that the proposed algorithm minimizes the key-resource-consumption ratio and the average key-resource consumption under static and dynamic traffic scenarios in different network topologies.

Query compilation and hardware acceleration are important technologies for optimizing the performance of data processing engines. There have been many works on the exploration and adoption of these techniques in recent years. However, a number of engines still refrain from adopting them because of some reasons. One of the common reasons claims that the intricacies of these techniques make engines too complex to maintain. Another major barrier is the lack of widely accepted architectures and libraries of these techniques, which leads to the adoption often starting from scratch with lots of effort. In this paper, we propose Intel Big Data Analytic Toolkit (BDTK), an open-source C++ acceleration toolkit library for analytical data processing engines. BDTK provides lightweight, easy-to-connect, reusable components with interoperable interfaces to support query compilation and hardware accelerators. The query compilation in BDTK leverages vectorized execution and data-centric code generation to achieve high performance. BDTK could be integrated into different engines and helps them to adapt query compilation and hardware accelerators to optimize performance bottlenecks with less engineering effort.

Edge computing provides computing capability at close-user proximity to reduce service latency for end users. To improve the efficiency of edge computing infrastructures, geographically-distributed edge datacenters can co-work with each other and with cloud datacenters, forming a new paradigm referred to as cooperative edge-cloud computing. In this context, applications typically run on a virtual machine (VM) that can be replicated at multiple sites, and thus user traffic can be served at all the sites where corresponding VMs reside. For the performance of many applications, latency is a critical parameter. In this work, taking applications’ latencies as the primary constraint, we model the problem of “VM placement and workload assignment” as a mixed integer linear program and develop heuristic algorithms accordingly. The goal is to minimize the consumption of information technology (IT) infrastructures for placing VMs in cooperative edge-cloud computing, while meeting the heterogeneous latency demands of different applications. Some preliminary results indicate that edge datacenter's resource efficiency can be optimized by proper cross-site VM placement and workload re-direction.

With the continuous addition of an abundant of heterogeneous devices, the limitation of task delay has become an obstacle to the development of the Industrial Internet of Things (IIoT). Task offloading based on edge computing can provide low-latency computing services for these tasks. However, in the actual IIoT scenario, in contrast to cloud computing, edge computing has limited resources and computing capabilities. Resource-constrained edge resources cannot meet the offloading requirements of massive industrial devices. In this article, we propose an optimal joint offloading scheme based on resource occupancy prediction for the problem of computing offloading with limited edge resources. The scheme is divided into two parts, including edge resource occupancy prediction and task offloading. Simultaneously, considering multitask and the limitations of edge resources, gate recurrent unit (GRU) is used to predict the occupancy of edge resources. Formulating an optimal strategy of task offloading by using a reinforcement learning algorithm according to the network state and predicted results. The simulation results show that the scheme can effectively reduce the average delay of tasks, while minimizing the task offloading failure rate.

The integration of Internet of Things (IoT) and industry reveals the industrial manufacturing developments, resulting in Industry IoT (IIoT), which is to provide a general interconnect system for the access of various industry devices. However, as the amount and type of terminal increase, the creditability and privacy protection of terminal devices are hard to be guaranteed in IIoT, since the data and digital identity of access devices are nearly transparent for more devices in networks. It is a critical issue for the security of IIoT whether the access and service of device are trustworthy. In this article, we present a novel private blockchain-enabled trusted anonymous access (BlockTrust) architecture for IIoT, where the distributed cloud radio and optical access networks (C-RONs) are considered to provide a risk reduction of privacy leakage. Based on the BlockTrust architecture, a blockchain-enabled tripartite anonymous identification trusted service provisioning (TriTrustServ) scheme is further proposed to guarantee a balanced tradeoff among the credibility, confidentiality, and efficiency in IIoT, including digital identity generation, anonymous access identification, and trusted resource provisioning. Note that for the sake of a high credibility in IIoT networks, a tripartite authentication is presented in this article with the first time among device manufacturer, devices, and network operator for the access process of device in IIoT networks. The feasibility and efficiency of BlockTrust architecture are experimentally verified in the realistic testbed, and the performances of the TriTrustServ scheme are evaluated by comparing with two benchmark schemes in the terms of average mistrust rate, resource utilization, and identification cost.

The Industrial Internet of Things (IIoT) accommodates a huge number of heterogeneous devices to bring vast services under a distributed computing scenarios. Most productive services in IIoT are closely related to production control and require distributed network support with low delay. However, the resource reservation based on gross traffic prediction ignores the importance of productive services and treats them as ordinary services, so it is difficult to provide stable low delay support for large amounts of productive service requests. For many productions, unexpected communication delays are unacceptable, and the delay may lead to serious production accidents causing great losses, especially when the productive service is security related. In this article, we propose a brain-like productive service provisioning scheme with federated learning (BrainIoT) for IIoT. The BrainIoT scheme is composed of three algorithms, including industrial knowledge graph-based relation mining, federated learning-based service prediction, and globally optimized resource reservation. BrainIoT combines production information into network optimization, and utilizes the interfactory and intrafactory relations to enhance the accuracy of service prediction. The globally optimized resource reservation algorithm suitably reserves resources for predicted services considering various resources. The numerical results show that the BrainIoT scheme utilizes interfactory relation and intrafactory relation to make an accurate service prediction, which achieves 96% accuracy, and improves the quality of service.

Due to the threat of failure and the discrete distribution of data center users, the research of distributed cloud data center provides real-time cloud services with robustness, reliability and security. Faced with data center interconnection, network failures cause mass services delay and interruption, which do a great damage to cloud computing. Many researchers have studied fault location methods in data center interconnection, which are easy to trap in local optimum limited by search capability and reduce the accuracy of location, especially when confronted with large-scale alarm information. In this article, the deep neural evolution network is introduced to extract deep-hidden fault features from massive collected alarm information in cloud data center interconnection. It has the prominent capacity of global search without the constraint of gradient to realize the breakthrough of fault location accuracy. The fault location method based on deep neural evolution network (FL-DNEN) is applied which uses the alarm set and suspicious scope of fault getting from fault propagation model as input and export deterministic faults accurately. The emulations demonstrate that the proposed method dramatically improves the accuracy of fault location to 92 percent with large-scale alarm information, which improves the resilience of cloud data center interconnection dramatically.

To support the fifth-generation bandwidth-hungry applications, such as the Internet of Things, virtual reality, augmented reality, and cloud computing, elastic optical networks have become the most promising infrastructure that allocates bandwidths for services flexibility. Fragmentation caused by dynamic resource allocation deteriorates the availability of resources in networks, increasing the blocking of requests. The fragmentation occurs not only in the used path but also in the neighboring links that are not included in the used path; they are connected to the used path. This paper proposes a service-driven fragmentation-aware (SDFA) resource allocation scheme to enhance resource utilization by avoiding fragmentation with the joint consideration of the used path and neighboring links. A service-driven fragmentation metric (SDFM) is, for the first time, presented to estimate the fragmentation in the used path and neighboring links. The SDFA scheme prefers to assign services at the spectrum slots, which leads to the minimum value of SDFM. Simulation results indicate that SDFA outperforms four conventional fragmentation-aware resource allocation schemes in terms of blocking probability and resource utilization due to a lower fragmentation in the network.

The development of reusable rockets makes it possible to launch a massive number of low earth orbit satellites for Internet access. These satellites are expected to be connected by inter-satellite links (ISLs) to provide global Internet service to end-users on the surface of the earth. From the infrastructure’s perspective, the ISLs can be implemented with lasers, forming the optical satellite networks. The design of terrestrial optical networks is usually traffic-driven, meaning more links/bandwidth are deployed to the traffic-intensive areas. But, the orbiting nature of satellites determines that laser ISLs are also moving at high velocities, making the presence of corresponding bandwidth loosed coupled with a specific area. As a result, the traffic-driven network design methodology will be inapplicable to the optical satellite networks. In this work, we model the optical satellite networks and define the laser ISL planning problem, Accordingly, we propose an ISL removal algorithm to remove parts of the ISLs from the grid-mesh topology to improve the average bandwidth utilization ratio (i.e., bandwidth efficiency) while maintaining networks’ reliability and availability. We conduct a simulation study based on the Starlink constellation to evaluate the grid-mesh topology and to investigate the proposed algorithm’s impact on the network performance. Results show that the bandwidth resource in the grid-mesh topology can hardly be used efficiently and removing certain ISLs properly can improve the bandwidth efficiency of optical satellite networks significantly. We also gain another interesting insight that maintaining too many ISLs will not improve, but degrade the network availability.

Space division multiplexing (SDM), as a potential means of enhancing the capacity of optical transmission systems, has attracted widespread attention. However, the adoption of SDM technology has also additionally increased resource dimensions, introduced complex crosstalk, and made it difficult to integrate multi-dimensional fragments. These factors force the transmission constraints to be more complicated. Especially, some factors have a mutual restraint relationship, and excessive consideration of certain factors will cause the deterioration of other ones. Therefore, how to comprehensively consider the associated factors to achieve trade-offs and improve network performance is a problem worthy of study. This paper exploits the advantages of self-organizing feature mapping (SOFM) model to process multi-dimensional data with relevant features. Firstly, multiple constraints will be input into SOFM as mode vectors from the core level. Then, by judging the similarity between the competition layer neuron and the pattern vector, the position of the winning neuron is located, which determines the transmission level of each core. Finally, a routing, core, and spectrum allocation scheme is proposed by preferentially locating the core with higher transmission quality. Along the selected core, the available slots will be classified twice respectively by the number of adjacent cores and crosstalk direction to quickly find the spectrum blocks with relatively small crosstalk. Results indicate the scheme can reduce blocking probability and the resource fragmentation. Further, it can increase the resource utilization within tested network load.

With the development of new generation information technology, the feasibility of smart city has been significantly improved, and a large number of smart city technologies have been proposed. In this context, a more feasible device access technology is of great significance to ensure the normal operation of smart city network services. Therefore, this paper proposes a secure blockchain device access scheme based on elliptic curve digital signature algorithm and zero knowledge proof algorithm.

Quantum key distribution (QKD) has demonstrated a great potential to provide future-proofed security, especially for 5G and beyond communications. As the critical infrastructure for 5G and beyond communications, optical networks can offer a cost-effective solution to QKD deployment utilizing the existing fiber resources. In particular, measurement-device-independent QKD shows its ability to extend the secure distance with the aid of an untrusted relay. Compared to the trusted relay, the untrusted relay has obviously better security, since it does not rely on any assumption on measurement and even allows to be accessed by an eavesdropper. However, it cannot extend QKD to an arbitrary distance like the trusted relay, such that it is expected to be combined with the trusted relay for large-scale QKD deployment. In this work, we study the hybrid trusted/untrusted relay based QKD deployment over optical backbone networks and focus on cost optimization during the deployment phase. A new network architecture of hybrid trusted/untrusted relay based QKD over optical backbone networks is described, where the node structures of the trusted relay and untrusted relay are elaborated. The corresponding network, cost, and security models are formulated. To optimize the deployment cost, an integer linear programming model and a heuristic algorithm are designed. Numerical simulations verify that the cost-optimized design can significantly outperform the benchmark algorithm in terms of deployment cost and security level. Up to 25% cost saving can be achieved by deploying QKD with the hybrid trusted/untrusted relay scheme while keeping much higher security level relative to the conventional point-to-point QKD protocols that are only with the trusted relays.

The combination of space division multiplexing technology with elastic optical networks allows to overcome the possible capacity crunch in backbone networks and also improves network flexibility by jointly managing spectral and spatial resources. However, against this background implemented by multi-core fibers, the interaction between spatial modes will appear as signal crosstalk, thereby affecting the service’s transmission quality. Spectrum resources without crosstalk are always preferred for the services to guarantee quality of service, possibly resulting in the spectrum fragmentation. Conversely, if resources with crosstalk are selected for services to reduce fragments, it may lead to serious crosstalk on the services already carried in the adjacent cores. To achieve a tradeoff between these two factors, this paper firstly exploits the association rule mining method to quantitatively analyze the potential correlation between them. By executing FP-growth mining algorithm, rules not beneficial to service provisioning will be filtered out. Then, an association rules-based core and spectrum assignment algorithm is presented, considering transmission requirements for different levels of services. Simulation results indicate the presented strategy can decrease the proportion of services affected by crosstalk and also reduce the possibility of fragments generation. Additionally, it can effectively make improvement on the blocking and resource utilization.

Hybrid electrical/optical (E/O) switching data center network (DCN) has recently emerged as a promising paradigm for future DCN architectures. However, there exist two major challenges: 1) the traffic is a mixture of both stable and burst components due to the diverse and heterogeneous user demands; 2) current scheduling algorithms are mostly static and not designed for the complex structure of hybrid E/O switching DCN, provoking frequent burst traffic congestion and performance degradation. This article endeavors to overcome the above challenges as follows. We first construct an error feedback-based spiking neural network (SNN) framework with high accuracy burst traffic prediction. We then design a prediction-assisted scheduling algorithm to handle the worst-case burst traffic. On the one hand, the error feedback-based SNN framework can significantly enhance the extraction of burst traffic features by mimicking the biological neuron system. On the other hand, prediction-assisted scheduling arranges the well-predicted traffic using a global evaluation factor and a traffic scaling factor. The simulation results reveal that our approach can efficiently integrate a spiking neural network into the traffic scheduling scheme and achieve satisfying performance with affordable computational complexity.

With the emergence of cloud computing and several ultra-high bitrate data center applications, hybrid E/O switching intra-datacenter network (HS-IDCN) has become an integral architecture of current and future data centers. To meet the diverse and heterogeneous performance requirements of HS-IDCNs, people have considered traffic prediction as a promising solution to ensure effective and flexible traffic scheduling. However, the low accuracy of existing deep learning-based prediction approaches, which cannot fully extract the features of burst traffic, directly restricts the efficiency of traffic scheduling. In view of this, this study considers the spiking neural networks that can predict high burstiness and heterogeneous traffic to further improve the efficiency of traffic scheduling. We first propose a supervised spiking neural network (s-SNN) framework for high accuracy traffic prediction in HS-IDCNs. A traffic prediction-based traffic scheduling (TP-TS) algorithm for HS-IDCNs is then introduced by considering the prediction results of s-SNN. The s-SNN framework can enhance the extraction ability of burst traffic features in a supervised fashion by mimicking the multi-synaptic mechanism of biological neuron system. The efficiency and feasibility of s-SNN are verified on the brain model simulator. The performance of TP-TS is also evaluated in terms of resource utilization and path blocking probability, compared with other scheduling schemes.

In this paper we propose a blockchain-based cross-domain authentication strategy. This strategy uses the cosmos network model to enable mobile devices to reliably access external domain networks when moving across domains. Our test results prove the feasibility of this strategy and have better performance than other cross-domain authentication schemes.

With the development of 5G networks, software and hardware technologies, the feasibility of smart cities has been significantly improved, and a large number of smart city technologies have been proposed. Against this background, a more feasible abnormal behavior monitoring technology is important to ensure the normal operation of smart city network services. Therefore, in this paper, we proposed a Spearman's correlation coefficient abnormal behavior monitoring technology based on recurrent neural network in 5G network for smart city.

This paper proposes a brain-like development based multi-routing combination optimization scheme for high mobility in optical fronthaul. The experimental results show that the scheme can significantly reduce the delay, reduce the communication blocking rate, and improve the communication quality.

We propose an online resource regulation strategy based on resource allocation benefit-state transition in 5G Fronthaul Network (5G-RAB ST). Results show that the presented system can significantly reduce the waste of resources in 5G fronthaul and highly improve the quality of users' service.

With the advent of smart homes, smart cities, and smart everything, the Internet of Things (IoT) has emerged as an area of incredible impact, potential, and growth. Internet of Things date security remain a major challenge, in the current Internet of Things systems, a relatively easy method of data encryption is used to ensure the security of data transmission, which is commonly called lightweight cryptography. However, such method is at the risk of being cracked by quantum computers, which will contribute to many challenges specially related to privacy and security in IoT. As a result, the architecture of IoT needs to be re-designed considering the security challenges brought by quantum computers. On the other hand, Quantum Key Distribution (QKD) allows two users to share unconditionally secure keys. Unlike classical cryptosystems, the security of QKD is based on the fundamentals of quantum mechanics. This security of QKD is independent of computational complexity and will not be affected, no matter how much computing power the adversary has. This paper introduces a quantum key distribution enhanced Internet of Things architecture and proposes a residual-adaptive key provisioning scheme, which is evaluated in terms of key distribution success rate in the simulation.

Free-space long-distance quantum key distribution (QKD) has the characteristics of low attenuation and wide coverage, which can overcome the limitation of transmission distance based on ground optical fiber QKD networks. Long-distance QKD requests can be relayed by quantum satellites to achieve intercontinental QKD. At present, there is only one quantum satellite in space, quantum satellite network with multiple quantum satellites is necessary to be deployed to cover the global. Thus, how to construct the quantum satellite network becomes a new challenge. In this paper, we propose a centralized and distributed collaborative scheme of quantum satellite network. Low-Earth-orbit (LEO) quantum satellites, as important relay satellites, will affect the performance of the global QKD network. Based on the above architecture, we analyze the influence of the structure of LEO quantum satellite constellation on the QKD performance. Simulation results show that the key relay performance of the constellation is related to the form of inter-satellite link. and the more the number of satellites is, the greater the successful probability of the key relay services will be.

For the new fronthaul network structure in 5G, we first apply a capsule network combined with Neural Network (NN) for traffic prediction and introduce a CA-RD strategy to deploy the DU resources. Results show that our strategy improves the prediction accuracy and resources allocation efficiency.

With the rapid development of emerging services in 5G and beyond scenario, ultra-large capacity transmission has become a rigid demand for core optical networks, leaving a general trend to enter P-bit level transmission. New multiplexing technologies that further enhance fiber transmission capacity are development directions worth exploring. Due to the limit of single fiber transmission, space division multiplexing (SDM) is the main technology to increase the optical fiber transmission capacity in the future and has become a research hotspot. However, the introduction of SDM also brings some new problems, such as complex crosstalk assessment and generation of multi-dimensional resource fragments, resulting in more complex and diverse parameters affecting service transmission. If the impact of multiple parameters cannot be comprehensively evaluated, the quality of the service cannot be well guaranteed. Against this background, we propose a routing and resource allocation (RRA) scheme based on self-organizing feature maps (SOM) in core optical networks with multi-core fibers. Multiple parameters affecting service transmission will be uniformly input into the SOM model to obtain a reordered link set which will be used for the RRA process. Simulation results indicate that our presented method can reduce the fragmentation degree, decrease blocking probability, and also improve spectrum utilization.

This paper demonstrates an ultra-high precision time synchronization (U-TS) scheme by reducing link asymmetry for cloud radio over fiber network (C-RoFN) in beyond 5G, The U-TS scheme is supported by a deep reinforcement learning (DRL) based autonomous synchronous signal routing algorithm. Experimental results show that the proposed U-TS scheme achieves <; 100 ns synchronization accuracy by using a large realistic dataset.

We propose a blockchain-based IoT devices anonymous access system using zero-knowledge proof in cloud-based radio over optical fiber networks (C-RoFN) to prevent device sensitive information from being exposed to massive IoT devices.

We first present a blockchain-based cloud radio over fiber network architecture (B-RoFN) with bonus-penalty access control strategy (BPACS) for IoT service. The feasibility and efficiency of the B-RoFN architecture with BPACS are experimentally verified on our testbed.

We demonstrate a novel intent defined optical network (IDON) platform that introduces self-adapted generation and optimization (SAGO) policy, utilizing closed-loop policy generation and closed-loop intent guarantee to achieve zero-touch operation of optical network.

The Internet of Things (IoT) is gradually becoming mature and has already entered our daily life, which interconnects more machines and makes communication more convenient and more intelligent. Massive IoT devices produce innumerable data which need to be analyzed in joint cloud computation (JointCloud) with diversified services. However, due to the weak security of IoT devices, the existing JointCloud architecture hardly provides a secure trusted trade environment for users, which affects severely the application in the IoT network. In this article, we propose a hierarchical trust networking architecture based on permissioned blockchain to implement JointCloud (HTJC). The proposed Hyperledger fabric-based architecture has a better performance than those based on Ethereum in latency. By introducing the credit bonus-penalty strategy (CBPS), HTJC can solve the trust problem and provide users with a secure trusted trade environment. The availability of the proposed architecture is evaluated and compared to the existing models. The numerical results show that the HTJC can defend distributed denial-of-service (DDoS) attacks and provide users with a trusted and effective trade platform.

Mobile edge computing (MEC) sinks computing power to the edge of networks and integrates mobile access networks and Internet services in 5G and beyond. With the continuous development of services, privacy protection is extremely important in a heterogeneous MEC system for multiserver collaboration. However, most of the existing schemes only consider the privacy of users or services other than the privacy of network topology. For the purpose of topology privacy protection, this article employs blockchain to construct heterogeneous MEC systems and adopts accommodative bloom filter as a carrier for multidomain collaborative routing consensus without exposing topology privacy. Blockchain is used to implement multiplex mutual trust networking and collaborative routing verification through the membership service and consensus mechanism. Experiments are conducted to evaluate the feasibility and performances of our scheme. The results indicate that the proposed scheme can highly improve the credibility and efficiency of MEC collaboration.

Quantum key distribution (QKD) networks are potential to be widely deployed in the immediate future to provide long-term security for data communications. Given the high price and complexity, multi-tenancy has become a cost-effective pattern for QKD network operations. In this work, we concentrate on addressing the online multi-tenant provisioning (On-MTP) problem for QKD networks, where multiple tenant requests (TRs) arrive dynamically. On-MTP involves scheduling multiple TRs and assigning non-reusable secret keys derived from a QKD network to multiple TRs, where each TR can be regarded as a high-security-demand organization with the dedicated secret-key demand. The quantum key pools (QKPs) are constructed over QKD network infrastructure to improve management efficiency for secret keys. We model the secret-key resources for QKPs and the secret-key demands of TRs using distinct images. To realize efficient On-MTP, we perform a comparative study of heuristics and reinforcement learning (RL) based On-MTP solutions, where three heuristics (i.e., random, fit, and best-fit based On-MTP algorithms) are presented and a RL framework is introduced to realize automatic training of an On-MTP algorithm. The comparative results indicate that with sufficient training iterations the RL-based On-MTP algorithm significantly outperforms the presented heuristics in terms of tenant-request blocking probability and secret-key resource utilization.

Traffic flows are often processed by a chain of Service Functions (SFs) (known as Service Function Chaining (SFC)) to satisfy service requirements. The deployed path for a SFC is called Service Function Path (SFP). SFs can be virtualized and migrated to datacenters, thanks to the evolution of Software Defined Network (SDN) and Network Function Virtualization (NFV). In such a scenario, provisioning of paths (i.e., SFPs) between virtualized network functions is an important problem. SFP provisioning becomes more complex in a multi-domain network topology. `Topology aggregation' helps to create a single-domain view of such a network by abstracting multi-domain networks. However, traditional `topology aggregation' methods are unable to abstract SF resources properly, which is required for SFP provisioning. In this paper, we propose an SFC-Oriented Topology Aggregation (SOTA) method to enable abstraction for SFs in multi-domain optical networks. This study explores the node and the link aggregation degree to evaluate information compression during the `Topology aggregation' process. Additionally, we also propose a new data structure named wheel matrix and related operations to store routing information in the aggregated topology. Based on SOTA, we propose two cross-domain SFP provisioning algorithms named Ordered Anchor Selection (OAS) and ${k}$ -paths OAS (K-OAS), and a benchmark named Global OAS (GOAS). Simulation results show that SOTA could aggregate large-scale multi-domain optical networks into a small network that contains only 6.9% of the nodes and 10.1% of the links. Both OAS and K-OAS can calculate SFPs efficiently and reduce blocking probability up to 52.10% compared to the benchmark.

With the rapid evolution of smart city all over the world, the appealing services of IoT and big data analytics have prompted the design of more reliable assurance mechanism for network quality. It has been a crucial issue of network operation that once multiple links fail simultaneously, the transmission of real-time services cannot be guaranteed. Therefore, rapid locating of faults is the premise for network to recover quickly. However, current faults location methods can't satisfy the requirement due to the expansion scale of wireless and optical networks and the growing demands of customers. In this paper, we propose an efficient multi-link faults location algorithm based on Hopfield Neural Network (HNN). We make full use of the information of network topology and the services transmitted to model the relationship between fault set and alarm set. HNN is used as an optimization method to analyze the uncertainty of faults and alarms and to find where the faults most likely occur by constructing a proper energy function. It has been proved by experiments that this method can achieve real-time faults location while ensuring positioning accuracy, which provides a good solution for smart city service assurance.

Space-division multiplexing elastic optical networks (SDM-EONs) will play an important role in addressing the increasing Internet traffic, thanks to their spectrum utilization flexibility and superior capacity. However, besides traditional physical layer impairments (PLIs), newly introduced crosstalk (XT) coupled with the unpredictability of future services makes transmission quality assurance more challenging. Therefore, it is urgent to design more intelligent and effective resource assignment (RA) algorithms in SDM-EONs. The rise of artificial intelligence provides a clear solution to such problems. This paper proposes a novel RA scheme based on dynamic unsupervised fuzzy clustering considering both XT and PLIs. All resource combinations meeting services' transmission needs will be found to form an available resources set. If the sample scale is relatively large, we will exploit fuzzy C-means clustering for higher accuracy. To reduce the costs and complexity of clustering and also obtain better clustering results, a direct clustering method will be used for a small sample scale. The resource combination most suitable for the services' transmission needs will be assigned to different levels of services. Simulation results disclose that the cluster centers present a regional distribution which is consistent with resource occupation, and it can also effectively reduce blocking probability by an average of 59.68% while greatly improving resource utilization by 12.5% on average in the measured network load range.

We propose a multi-path fragmentation-aware routing, modulation and spectrum assignment algorithm (RMSA) for advance reservation (AR) and immediate reservation (IR) requests in elastic optical networks. To decrease fragmentation, we propose splitting requests into different parts and transferring each of these parts along a single-path or multi-paths utilizing sliceable bandwidth variable transponders. We first introduce a model to solve the problem and propose a two-dimensional fragmentation occurrence measurement in spectrum and time domains. Then we propose a multi-path fragmentation-aware RMSA algorithm (MPFA). Simulation results show that MPFA can achieve better performance than existing algorithms in terms of blocking probability and spectrum utilization.

In network virtualization, when a disaster hits a physical network infrastructure, it is likely to break multiple virtual network connections. So, after a disaster occurs, the network operator has to schedule multiple teams of repairmen to fix the failed components, by considering that these elements may be geographically dispersed. An effective schedule is very important as different schedules may result in very different amounts of time needed to restore a failure. In this study, we introduce the multiple traveling repairmen problem (MTRP) for post-disaster resilience, i.e., to reduce the impact of a disaster. Re-provisioning of failed virtual links is also considered. We first formally state the problem, where our objective is to find an optimal schedule for multiple teams of repairmen to restore the failed components in physical network, maximizing the traffic in restored virtual network and with minimum damage cost. Then, we propose a greedy (GR) and a simulated annealing (SA) algorithm, and we measure the damage caused by a disaster in terms of disconnected virtual networks (DVN), failed virtual links (FVL), and failed physical links (FPL). Numerical result shows that both proposed algorithms can make good schedules for multiple repairmen teams, and SA leads to significantly lower damage in terms of DVN, FVL, and FPL than GR.

Cloud radio access network (C-RAN) has become a promising scenario to accommodate high-performance services with ubiquitous user coverage and real-time cloud computing using cloud BBUs. In this paper, we propose a novel multistratum resources optimization (MSRO) architecture for cloud-based radio over optical fiber networks with software defined networking. Additionally, a global evaluation strategy (GES) is introduced in the proposed architecture. The MSRO can enhance the responsiveness to end-to-end user demands and globally optimize radio frequency, optical spectrum and BBU processing resources effectively to maximize radio coverage. The overall feasibility and efficiency of the proposed architecture with GES strategy are experimentally verified on OpenFlow-enabled testbed in terms of resource occupation rate and path provisioning latency.

Due to the accelerated evolution of application services, optical network virtualization simplifies optical-layer resource management and provides flexibility in spectrum resource allocation. However, the energy consumption is one of the great challenges in the virtual optical networks (VONs). This paper focuses on the energy efficiency problem in survivable VONs with the sliceable multi-flow transponders and the elastic regenerators. For each VON, all virtual links provide the dedicated-path protection in the flexible bandwidth optical networks. An integer linear program (ILP) and a minimum unit-energy submatrix (MinEnSub) VON mapping approach are developed to improve the energy efficiency, minimize the power consumption, and reduce the spectrum usage under different line rates. For comparison, a baseline VON mapping approach is introduced. Simulation results show that the ILP model and the proposed MinEnSub VON mapping approach can save power consumption, improve the energy efficiency, and reduce the spectrum usage compared with the baseline VON mapping approach in a 6-node network. As expected, in a 14-node network, simulation results also validate that our proposed MinEnSub VON mapping approach can achieve better performance in terms of power consumption, energy efficiency, number of frequencies, and the number of regenerators.

In this paper, we propose a planning mechanism based on telecom user behavior to choose locations of telecom operator's service halls. Telecom service hall network consists of service requirements nodes (RNs) and telecom service hall sites (TSs). Telecom service hall location selection problem mainly focuses on choosing locations of TSs from RNs. With analysis of base station data, we formulate a method based on telecom user distribution model to group users and to find RNs. Then, we propose a theoretical model to obtain telecom operator's greatest economic income with constraints of service satisfaction perceived by telecom users. Finally, a mechanism combined with improved genetic algorithm is put forward to solve it. Our results, supported by extensive experiments using MATLAB, confirm the feasibility and flexibility of our proposed planning mechanism.

This paper addresses the minimum network cost problem for survivable virtual optical network mapping in flexible bandwidth optical networks. We develop an ILP model and the LBSD (the largest bandwidth requirement (LB) of virtual links versus the shortest distance (SD)) mapping approach to minimize the network cost for a given set of VONs, and we introduce two baseline mapping approaches, named LCLC (the largest computing resources' requirement versus the largest computing resources' provisioning (LC)) and LCSD (the largest computing resources' requirement versus shortest distance) mapping approaches, for comparison. Simulation results show that LBSD can achieve network cost near the ILP solutions in a 6-node network. Also, LBSD greatly reduces the cost, the spectrum usage, and the number of regenerators compared to LCLC and LCSD in the 6-node and NSFNET networks.

This paper addresses the problem of minimizing spectrum usage for shared-path protection with joint failure probability constraint in flexible bandwidth optical networks. To achieve this goal, we propose an integer linear programming (ILP) model for shared-path protection and a heuristic spectrum-aware shared protection (SASP) algorithm that considers joint failure probability. Simulation results show that the ILP model minimizes the total usage of frequency slots and average hops, but leads to high average joint failure probability. Moreover, the SASP algorithm achieves better trade-off between total usage of frequency slots and average joint failure probability compared to the ILP model and a conventional shared-path protection (CSPP) algorithm. As expected, in a 14-nodes network, the SASP algorithm performs better with respect to total spectrum usage and average hops, but results in much larger average joint failure probability compared to the CSPP algorithm.

The key current challenges for the industrial application of all optical switching networks are energy consumption, transmission rate, spectrum efficiency, and switching throughput. The energy consumption problem is mainly researched in this paper. From the perspective of components and modules, node equipment, and network levels, different enabling technologies are proposed to overcome this problem, which are also evaluated through different experimental demonstrations. First, high-sampling-rate digital-to-analog converters (DACs) and WSS-based ROADM modules are demonstrated as components and modules for energy-efficient all optical switching networks. Then, an all optical transport network test-bed consisting of 10 Pbit/s level all optical switching nodes based on multi-level and multi-planar switching architecture is experimentally demonstrated for the first time, which can reduce power consumption by 43%. A control architecture for energy-efficient all optical switching networks is built with OpenFlow based software defined networking (SDN), and experimental results are given to verify the performance of this control architecture. Finally, we describe an All Optical Networks Innovation (AONI) project in China, which aims to explore transmission, switching, and networking technologies in all optical switching networks, and then two application scenarios are forecast based on the technical breakthroughs of this project.

By utilizing Layer-1 Virtual Private Networks (L1VPN), a single physical network, e.g., optical backbone networks, can support multiple virtual networks, which is the basic infrastructure for cloud computing and other enterprise networks. The L1VPN hose model is an elegant and flexible way to specify the customers' bandwidth requirements, by defining the total incoming and outgoing demand for each endpoint. Furthermore, multi-domain physical infrastructures are common in L1VPNs, since these are usually deployed on a global scale. Thus, high-performance Routing for Multi-domain VPN Provisioning (RMVP) for the hose model is an important problem to efficiently support a global virtual infrastructure. In this paper, we formulate the RMVP problem as a Mixed Integer Linear Program (MILP). Also, we propose a Top-Down Routing (TDR) strategy to compute the optimal routing for the hose-model L1VPN in multi-domain backbone networks. Results indicate that TDR approaches the minimum routing cost when compared to ideal case of single-domain routing.

With the emergence of various broadband services, a lot of bandwidth fragments will be generated during the operation of services being mapped into optical channels, which will waste too many bandwidth resources and decrease the transmission performance of optical networks. Traffic grooming strategy in dynamic optical networks can optimise the utilisation of bandwidth resources and reduce the blocking probability. However, in the distributed control plane of automated switched optical networks, all the traffic grooming strategies are implemented in each control node, and resource collision will still occur because the same link resource may be used by two path computation requests or traffic engineering information flooded by open shortest path first-traffic engineering protocol may be not synchronous at each control node or signalling delay time is too long. To reduce the collision of resource, a unified control plane is designed based on path computation element (PCE) for Internet protocol over wavelength division multiplexing networks, and a novel PCE-based traffic grooming strategy is proposed in the framework of unified control plane. Based on this strategy, four PCE-based traffic grooming algorithms are proposed and compared with the distributed traffic grooming strategy without PCE on a simulation platform implemented using disperse event simulation tool OMNET++.

Pre-configured protection cycles (p-cycles) have drawn many attentions due to the fully pre-connected cyclic structures, ring-like speed, mesh-like spare capacity efficiencies and fast switching characteristics. In this paper, we propose a novel removal of loop back (RLB) approach with standard protocol extension (the flooding based distributed cycle pre-configuration (F-DCPC)) to solve the resource inefficiency issue due to long restored paths for symmetric dynamic networks. Meanwhile, distributed cycle pre-configuration (DCPC) is a representative technique for automatic p-cycle configuration. But for the mesh network topology under asymmetric service distribution, it works especially with more network convergence time and larger amount of extra controlling overhead. So in our RLB approach, F-DCPC scheme using generalized multi-protocol label switching (GMPLS) protocol to solve the problems of configurations and reconfigurations with asymmetric digraph in high-speed optical mesh networks is presented. Additionally, to describe the basic idea of traffic splitting strategy in the unidirectional resource distribution, we present the mathematic model with a heuristic algorithm to accelerate resource configuration. We evaluate the performance of F-DCPC utilizing proposed RLB approach with various classical p-cycles enumeration algorithms and allocation strategies by experimental simulations implemented in OPNET modeler, and the simulation results show the effectiveness of the proposed scheme.

In transparent WSON (wavelength switched optical networks), signals are switched optically and propagate thousands of kilometers without electrical regeneration. Over such distances, physical impairments, such as crosstalk, ASE noise and so on, can accumulate along the path and lead to signal quality degradation. If the admission of a lightpath will either cause its BER to be too high, or sufficiently degrade the performance of the already established lightpaths, it must be blocked. Most of recent research only consider the first case, but ignore the second case, which will lead to service interruption. In this paper, a new noise-aware wavelength assignment scheme called NAWA has been proposed, which use IIES (Impairments impact Evaluation Scheme) to solve both problems mentioned above in a distributed way. Simulations have been conducted and numerical results show that: Compared with normal impairment-aware solutions, NAWA can eliminate the occurrence of service interruption, and achieve better performance in total blocking.

Multi-granularity optical cross-connect (MG-OXC)-based optical network is a promising optical network architecture as it is capable of flexible switching at different granularity levels. In MG-OXC-based optical networks, wavelength conversion (WC) capability and the number of usable add/drop ports of the nodes are two key factors affecting its performance. Two analytical models of blocking probability for MG-OXC-based optical networks both without WC capability and with sparse WC capability are proposed, exploiting Erlang's loss formula and birth–death process. Based on the models and simulation, the impact of WC capability and the number of add/drop ports on the blocking probability are investigated. Three kinds of granularities (i.e. fibre, waveband and wavelength) are considered in MG-OXC nodes to reduce the complexity and size of switch fabric. Both the analytical and simulation results are given on two network topologies under dynamic traffic patterns. Simulation results show that the proposed models are accurate and effective for the analysis of blocking probability in MG-OXC-based optical networks.

Translucent WDM networks use a set of sparsely but strategically placed 2R or/and 3R regenerators to overcome physical impairments and wavelength collision introduced by fully transparent networks. In this paper, we concentrate on the study of control architectures and management approaches for connection establishment in translucent networks. A hybrid OCP (optical control plane) has been proposed, which needs the extensions of both routing and signaling protocol. In hybrid OCP, we combine the best features of routing based information updating and signaling based data collection and path evaluation, in order to achieve better performance. Simulations are conducted to compare hybrid OCP with two existing control architectures: signaling based OCP and routing based OCP. Numerical results show that hybrid OCP keeps a lower blocking probability than the other approaches, and also minimize the stability and scalability problems under various traffic conditions.