Abstract The presence of microparticle viruses significantly impacts the quality of silkworm seeds for domestic sericulture, making their exclusion from detection in silkworm seed production crucial. Traditional methods for detecting microparticle viruses in silkworms, such as manual microscopic observation, molecular biology, and immunological approaches, are cumbersome and unable to achieve intelligent, batch real-time detection. To address this challenge, we employ the YOLOv8 algorithm in this paper. Firstly, NAM attention is introduced in the original algorithm’s Backbone component, allowing the model to extract more generic feature information. Secondly, ODConv replaces Conv in the Head component of the original algorithm, enhancing the model’s ability to identify microparticle viruses. Finally, NWD-LOSS modifies the CIoU loss of the original algorithm to obtain a more accurate prediction box. Experimental results demonstrate that the NN-YOLOv8 model outperforms mainstream detection algorithms in detecting silkworm microparticle diseases. With an average detection time of 22.6 milliseconds per image, the model shows promising prospects for future applications. This model improvement enhances detection efficiency and reduces human resource costs, effectively realizing detection intelligence.

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.

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.

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.

QWERTY is the primary smartphone text input keyboard configuration. However, insertion and substitution errors caused by hand tremors, often experienced by users with Parkinson’s disease, can severely affect typing efficiency and user experience. In this paper, we investigated Parkinson’s users’ typing behavior on smartphones. In particular, we identified and compared the typing characteristics generated by users with and without Parkinson’s symptoms. We then proposed an elastic probabilistic model for input prediction. By incorporating both spatial and temporal features, this model generalized the classical statistical decoding algorithm to correct insertion, substitution and omission errors, while maintaining direct physical interpretation. User study results confirmed that the proposed algorithm outperformed baseline techniques: users reached 22.8 WPM typing speed with a significantly lower error rate and higher user-perceived performance and preference. We concluded that our method could effectively improve the text entry experience on smartphones for users with Parkinson’s disease.

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.

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.

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.

The latest generations of smartphones with built-in AR capabilities enable a new class of mobile apps that merge digital and real-world content depending on a user's task, context, and preference. But even experienced mobile app designers face significant challenges: creating 2D/3D AR content remains difficult and time-consuming, and current mobile prototyping tools do not support AR views. There are separate tools for this; however, they require significant technical skill. This paper presents ProtoAR which supplements rapid physical prototyping using paper and Play-Doh with new mobile cross-device multi-layer authoring and interactive capture tools to generate mobile screens and AR overlays from paper sketches, and quasi-3D content from 360-degree captures of clay models. We describe how ProtoAR evolved over four design jams with students to enable interactive prototypes of mobile AR apps in less than 90 minutes, and discuss the advantages and insights ProtoAR can give designers.

360-degree video is increasingly used to create immersive user experiences; however, it is typically limited to a single user and not interactive. Recent studies have explored the potential of 360 video to support multi-user collaboration in remote settings. These studies identified several challenges with respect to 360 live streams, such as the lack of gaze awareness, out-of-sync views, and missed gestures. To address these challenges, we created 360Anywhere, a framework for 360 video-based multi-user collaboration that, in addition to allowing collaborators to view and annotate a 360 live stream, also supports projection of annotations in the 360 stream back into the real-world environment in real-time. This enables a range of collaborative augmented reality applications not supported with existing tools. We present the 360Anywhere framework and tools that allow users to generate applications tailored to specific collaboration and augmentation needs with support for remote collaboration. In a series of exploratory design sessions with users, we assess 360Anywhere's power and flexibility for three mobile ad-hoc scenarios. Using 360Anywhere, participants were able to set up and use fairly complex remote collaboration systems involving projective augmented reality in less than 10 minutes.

Augmented Reality (AR) developers face a proliferation of new platforms, devices, and frameworks. This often leads to applications being limited to a single platform and makes it hard to support collaborative AR scenarios involving multiple different devices. This paper presents XD-AR, a cross-device AR application development framework designed to unify input and output across hand-held, head-worn, and projective AR displays. XD-AR's design was informed by challenging scenarios for AR applications, a technical review of existing AR platforms, and a survey of 30 AR designers, developers, and users. Based on the results, we developed a taxonomy of AR system components and identified key challenges and opportunities in making them work together. We discuss how our taxonomy can guide the design of future AR platforms and applications and how cross-device interaction challenges could be addressed. We illustrate this when using XD-AR to implement two challenging AR applications from the literature in a device-agnostic way.