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.

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.

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.

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.

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.

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.

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.

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.

This paper proposes a dual-threaded blockchain-based architecture to detect large-scale intelligent network abnormalities. Anomaly detection is then implemented using an adaptive encoder. The simulation results show that the proposed anomaly detection scheme achieves efficient anomaly detection while reducing latency and improving throughput.

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.

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.

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.

This paper presents an energy-saving wireless power allocation scheme based on convex optimization (PA-CO) in cache-enabled Fiber-Wireless (FiWi) access networks. Experiments indicate that the proposed PA-CO improves the global throughput when confronted with large-scale access terminal sets in 5G IoT scenario, while remaining total power consumption to a low level.

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.

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.

Network slicing is a widely discussed technology for satisfying the diverse requirements in 5G and beyond scenarios. It enables the operators to create end-to-end virtual slices in network infrastructures. The flexible assignation of multi-dimensional slice resources including radio and spectral resources is essential for 5G communication networks. However, there is a lack of effective solutions to reconfigure the spectral and the radio resources in 5G radio access network (RAN) and core network. In this paper, we realize orchestration functionalities by exploiting a novel two-step slice reconfiguration strategy. Firstly, a slice-monitoring model is proposed to map slices to vectors by representation learning. Secondly, a slice reconfiguration algorithm is introduced to realize flexible slice reconfiguration. The slice reconfiguration strategy can decide when to trigger the reconfiguration strategy and how to reconfigure the slice resources. As for network performance, simulation results show our reconfiguration strategy can further improve slice resource utilization rate by 31% as well as reduce the blocking rate by 40% in 5G RAN and core 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.

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.

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.

Applications like cloud computing, video gaming, HD Video streaming, Live Concerts, Remote Medical Surgery and other applications are offered by Data Centers. These data centers are geographically distributed and connected via a network. Many decisions are made in the Application space without any concern of the underlying network. Cross stratum application/network optimization focuses on the challenges and opportunities presented by data center based applications and carriers networks together [1]. Constraint-based path computation is a fundamental building block for traffic engineering systems such as Multiprotocol Label Switching (MPLS) and Generalized Multiprotocol Label Switching (GMPLS) networks. [2] explains the architecture for a Path Computation Element (PCE)-based model to address this problem space. This paper presents Cross Stratum Optimization (CSO) enabled Path Computation Element (PCE) architecture as a building block to achieve Cross Stratum application/network optimization.