Efficient Digital Twin Placement for Blockchain-Empowered Wireless Computing Power Network

As an open network architecture,Wireless Computing PowerNetworks(WCPN)pose newchallenges for achieving efficient and secure resource management in networks,because of issues such as insecure communication channels and untrusted device terminals.Blockchain,as a shared,immutable distributed ledger,provides a secure resource management solution for WCPN.However,integrating blockchain into WCPN faces challenges like device heterogeneity,monitoring communication states,and dynamic network nature.Whereas Digital Twins(DT)can accurately maintain digital models of physical entities through real-time data updates and self-learning,enabling continuous optimization of WCPN,improving synchronization performance,ensuring real-time accuracy,and supporting smooth operation of WCPN services.In this paper,we propose a DT for blockchain-empowered WCPN architecture that guarantees real-time data transmission between physical entities and digital models.We adopt an enumeration-based optimal placement algorithm(EOPA)and an improved simulated annealing-based near-optimal placement algorithm(ISAPA)to achieve minimum average DT synchronization latency under the constraint of DT error.Numerical results show that the proposed solution in this paper outperforms benchmarks in terms of average synchronization latency.

Game theory in network security for digital twins in industry

To ensure the safe operation of industrial digital twins network and avoid the harm to the system caused by hacker invasion,a series of discussions on network security issues are carried out based on game theory.From the perspective of the life cycle of network vulnerabilities,mining and repairing vulnerabilities are analyzed by applying evolutionary game theory.The evolution process of knowledge sharing among white hats under various conditions is simulated,and a game model of the vulnerability patch cooperative development strategy among manufacturers is constructed.On this basis,the differential evolution is introduced into the update mechanism of the Wolf Colony Algorithm(WCA)to produce better replacement individuals with greater probability from the perspective of both attack and defense.Through the simulation experiment,it is found that the convergence speed of the probability(X)of white Hat 1 choosing the knowledge sharing policy is related to the probability(x0)of white Hat 2 choosing the knowledge sharing policy initially,and the probability(y0)of white hat 2 choosing the knowledge sharing policy initially.When y0?0.9,X converges rapidly in a relatively short time.When y0 is constant and x0 is small,the probability curve of the“cooperative development”strategy converges to 0.It is concluded that the higher the trust among the white hat members in the temporary team,the stronger their willingness to share knowledge,which is conducive to the mining of loopholes in the system.The greater the probability of a hacker attacking the vulnerability before it is fully disclosed,the lower the willingness of manufacturers to choose the"cooperative development"of vulnerability patches.Applying the improved wolf colonyco-evolution algorithm can obtain the equilibrium solution of the"attack and defense game model",and allocate the security protection resources according to the importance of nodes.This study can provide an effective solution to protect the network security for digital twins in the industry.

Analysis of Mobile and Internet Network Coverage: Propagation of Electromagnetic Waves and Concept of Digital Divide in Burundi

Mobile and Internet network coverage plays an important role in digital transformation and the exploitation of new services. The evolution of mobile networks from the first generation (1G) to the 5th generation is still a long process. 2G networks have developed the messaging service, which complements the already operational voice service. 2G technology has rapidly progressed to the third generation (3G), incorporating multimedia data transmission techniques. It then progressed to fourth generation (4G) and LTE (Long Term Evolution), increasing the transmission speed to improve 3G. Currently, developed countries have already moved to 5G. In developing countries, including Burundi, a member of the East African Community (ECA) where more than 80% are connected to 2G technologies, 40% are connected to the 3G network and 25% to the 4G network and are not yet connected to the 5G network and then still a process. The objective of this article is to analyze the coverage of 2G, 3G and 4G networks in Burundi. This analysis will make it possible to identify possible deficits in order to reduce the digital divide between connected urban areas and remote rural areas. Furthermore, this analysis will draw the attention of decision-makers to the need to deploy networks and coverage to allow the population to access mobile and Internet services and thus enable the digitalization of the population. Finally, this article shows the level of coverage, the digital divide and an overview of the deployment of base stations (BTS) throughout the country to promote the transformation and digital inclusion of services.

Research on College Network Information Security Protection in the Digital Economy Era

In the era of the digital economy,the informatization degree of various industries is getting deeper and deeper,and network information security has also come into people’s eyes.Colleges and universities are in the position of training applied talents,because of the needs of teaching and education,as well as the requirements of teaching reform,the information construction of colleges and universities has been gradually improved,but the problem of network information security is also worth causing people to ponder.The low security of the network environment will cause college network information security leaks,and even hackers will attack the official website of the university and leak the personal information of teachers and students.To solve such problems,this paper studies the protection of college network information security against the background of the digital economy era.This paper first analyzes the significance of network information security protection,then points out the current and moral problems,and finally puts forward specific countermeasures,hoping to create a safe learning environment for teachers and students for reference.

Optimal decision-making method for equipment maintenance to enhance the resilience of power digital twin system under extreme disaster

Digital twins and the physical assets of electric power systems face the potential risk of data loss and monitoring failures owing to catastrophic events,causing surveillance and energy loss.This study aims to refine maintenance strategies for the monitoring of an electric power digital twin system post disasters.Initially,the research delineates the physical electric power system along with its digital counterpart and post-disaster restoration processes.Subsequently,it delves into communication and data processing mechanisms,specifically focusing on central data processing(CDP),communication routers(CRs),and phasor measurement units(PMUs),to re-establish an equipment recovery model based on these data transmission methodologies.Furthermore,it introduces a mathematical optimization model designed to enhance the digital twin system’s post-disaster monitoring efficacy by employing the branch-and-bound method for its resolution.The efficacy of the proposed model was corroborated by analyzing an IEEE-14 system.The findings suggest that the proposed branch-and-bound algorithm significantly augments the observational capabilities of a power system with limited resources,thereby bolstering its stability and emergency response mechanisms.

Generative Adversarial Networks Based Digital Twin Channel Modeling for Intelligent Communication Networks

Integration of digital twin(DT)and wireless channel provides new solution of channel modeling and simulation,and can assist to design,optimize and evaluate intelligent wireless communication system and networks.With DT channel modeling,the generated channel data can be closer to realistic channel measurements without requiring a prior channel model,and amount of channel data can be significantly increased.Artificial intelligence(AI)based modeling approach shows outstanding performance to solve such problems.In this work,a channel modeling method based on generative adversarial networks is proposed for DT channel,which can generate identical statistical distribution with measured channel.Model validation is conducted by comparing DT channel characteristics with measurements,and results show that DT channel leads to fairly good agreement with measured channel.Finally,a link-layer simulation is implemented based on DT channel.It is found that the proposed DT channel model can be well used to conduct link-layer simulation and its performance is comparable to using measurement data.The observations and results can facilitate the development of DT channel modeling and provide new thoughts for DT channel applications,as well as improving the performance and reliability of intelligent communication networking.

Quality-Aware Massive Content Delivery in Digital Twin-Enabled Edge Networks

Massive content delivery will become one of the most prominent tasks of future B5G/6G communication.However,various multimedia applications possess huge differences in terms of object oriented(i.e.,machine or user)and corresponding quality evaluation metric,which will significantly impact the design of encoding or decoding within content delivery strategy.To get over this dilemma,we firstly integrate the digital twin into the edge networks to accurately and timely capture Quality-of-Decision(QoD)or Quality-of-Experience(QoE)for the guidance of content delivery.Then,in terms of machinecentric communication,a QoD-driven compression mechanism is designed for video analytics via temporally lightweight frame classification and spatially uneven quality assignment,which can achieve a balance among decision-making,delivered content,and encoding latency.Finally,in terms of user-centric communication,by fully leveraging haptic physical properties and semantic correlations of heterogeneous streams,we develop a QoE-driven video enhancement scheme to supply high data fidelity.Numerical results demonstrate the remarkable performance improvement of massive content delivery.

Hyper-Tuned Convolutional Neural Networks for Authorship Verification in Digital Forensic Investigations

Authorship verification is a crucial task in digital forensic investigations,where it is often necessary to determine whether a specific individual wrote a particular piece of text.Convolutional Neural Networks(CNNs)have shown promise in solving this problem,but their performance highly depends on the choice of hyperparameters.In this paper,we explore the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification.We conduct experiments using a Hyper Tuned CNN model with three popular optimization algorithms:Adaptive Moment Estimation(ADAM),StochasticGradientDescent(SGD),andRoot Mean Squared Propagation(RMSPROP).The model is trained and tested on a dataset of text samples collected from various authors,and the performance is evaluated using accuracy,precision,recall,and F1 score.We compare the performance of the three optimization algorithms and demonstrate the effectiveness of hyperparameter tuning in improving the accuracy of the CNN model.Our results show that the Hyper Tuned CNN model with ADAM Optimizer achieves the highest accuracy of up to 90%.Furthermore,we demonstrate that hyperparameter tuning can help achieve significant performance improvements,even using a relatively simple model architecture like CNNs.Our findings suggest that the choice of the optimization algorithm is a crucial factor in the performance of CNNs for authorship verification and that hyperparameter tuning can be an effective way to optimize this choice.Overall,this paper demonstrates the effectiveness of hyperparameter tuning in improving the performance of CNNs for authorship verification in digital forensic investigations.Our findings have important implications for developing accurate and reliable authorship verification systems,which are crucial for various applications in digital forensics,such as identifying the author of anonymous threatening messages or detecting cases of plagiarism.

Digital Literacy in the Rural Environment of Rivers State, Nigeria

While literacy can generally be defined as the basic ability to read,write,and also count,digital literacy can be described as the ability to perform all these tasks using electronic means.These means would include modern electronic gadgets,such as mobile phones,tablets,computers,kindle books,and the like.To be digitally literate would therefore require the existence of modern technologies such as internet facilities that would make it possible to access online reading and writing.The rural environment,particularly in developing areas,is usually characterised by a seeming lack of modern amenities and even worse,digital internet networks.Yet,those who live in the rural areas of Rivers State belong to the modern digital era and deserve to be digitally literate.This paper examines the prerequisites for digital literacy and explores how these can be achieved for citizens who inhabit the rural areas of an industrially-nascent state like Rivers State.

Regional Economic Development Trend Prediction Method Based on Digital Twins and Time Series Network

At present,the interpretation of regional economic development(RED)has changed from a simple evaluation of economic growth to a focus on economic growth and the optimization of economic structure,the improvement of economic relations,and the change of institutional innovation.This article uses the RED trend as the research object and constructs the RED index to conduct the theoretical analysis.Then this paper uses the attention mechanism based on digital twins and the time series network model to verify the actual data.Finally,the regional economy is predicted according to the theoretical model.The specific research work mainly includes the following aspects:1)This paper introduced the development status of research on time series networks and economic forecasting at home and abroad.2)This paper introduces the basic principles and structures of long and short-term memory(LSTM)and convolutional neural network(CNN),constructs an improved CNN-LSTM model combined with the attention mechanism,and then constructs a regional economic prediction index system.3)The best parameters of the model are selected through experiments,and the trained model is used for simulation experiment prediction.The results show that the CNN-LSTM model based on the attentionmechanism proposed in this paper has high accuracy in predicting regional economies.

Fault Diagnosis of 5G Networks Based on Digital Twin Model

Fault diagnosis of 5G networks faces the challenges of heavy reliance on human experience and insufficient fault samples and relevant monitoring data.The digital twin technology can realize the interaction between virtual space and physical space through the fusion of model and data,providing a new paradigm for fault diagnosis.In this paper,we first propose a network digital twin model and apply it to 5G network diagnosis.We then use an improved Average Wasserstein GAN with Gradient Penalty(AWGAN-GP)method to discover and predict failures in the twin network.Finally,we use XGBoost algorithm to locate the faults in physical network in real time.Extensive simulation results show that the proposed approach can significantly increase fault prediction and diagnosis accuracy in the case of a small number of labeled failure samples in 5G networks.

Digital watermarking algorithm based on neural network in multiwavelet domain

A novel blind digital watermarking algorithm based on neural networks and multiwavelet transform is presented. The host image is decomposed through multiwavelet transform. There are four subblocks in the LL- level of the multiwavelet domain and these subblocks have many similarities. Watermark bits are added to low- frequency coefficients. Because of the learning and adaptive capabilities of neural networks, the trained neural networks almost exactly recover the watermark from the watermarked image. Experimental results demonstrate that the new algorithm is robust against a variety of attacks, especially, the watermark extraction does not require the original image.

Wireless location algorithm using digital broadcasting signals based on neural network

In order to enhance the accuracy and reliability of wireless location under non-line-of-sight （NLOS） environments,a novel neural network （NN） location approach using the digital broadcasting signals is presented. By the learning ability of the NN and the closely approximate unknown function to any degree of desired accuracy,the input-output mapping relationship between coordinates and the measurement data of time of arrival （TOA） and time difference of arrival （TDOA） is established. A real-time learning algorithm based on the extended Kalman filter （EKF） is used to train the multilayer perceptron （MLP） network by treating the linkweights of a network as the states of the nonlinear dynamic system. Since the EKF-based learning algorithm approximately gives the minimum variance estimate of the linkweights,the convergence is improved in comparison with the backwards error propagation （BP） algorithm. Numerical results illustrate thatthe proposedalgorithmcanachieve enhanced accuracy,and the performance ofthe algorithmis betterthanthat of the BP-based NN algorithm and the least squares （LS） algorithm in the NLOS environments. Moreover,this location method does not depend on a particular distribution of the NLOS error and does not need line-of-sight （ LOS ） or NLOS identification.

Construction of Human Digital Twin Model Based on Multimodal Data and Its Application in Locomotion Mode Identifcation

With the increasing attention to the state and role of people in intelligent manufacturing, there is a strong demand for human-cyber-physical systems (HCPS) that focus on human-robot interaction. The existing intelligent manufacturing system cannot satisfy efcient human-robot collaborative work. However, unlike machines equipped with sensors, human characteristic information is difcult to be perceived and digitized instantly. In view of the high complexity and uncertainty of the human body, this paper proposes a framework for building a human digital twin (HDT) model based on multimodal data and expounds on the key technologies. Data acquisition system is built to dynamically acquire and update the body state data and physiological data of the human body and realize the digital expression of multi-source heterogeneous human body information. A bidirectional long short-term memory and convolutional neural network (BiLSTM-CNN) based network is devised to fuse multimodal human data and extract the spatiotemporal features, and the human locomotion mode identifcation is taken as an application case. A series of optimization experiments are carried out to improve the performance of the proposed BiLSTM-CNN-based network model. The proposed model is compared with traditional locomotion mode identifcation models. The experimental results proved the superiority of the HDT framework for human locomotion mode identifcation.

Digital Watermarking Algorithm Based on Wavelet Transform and Neural Network

An effective blind digital watermarking algorithm based on neural networks in the wavelet domain is presented. Firstly, the host image is decomposed through wavelet transform. The significant coefficients of wavelet are selected according to the human visual system （HVS） characteristics. Watermark bits are added to them. And then effectively cooperates neural networks to learn the characteristics of the embedded watermark related to them. Because of the learning and adaptive capabilities of neural networks, the trained neural networks almost exactly recover the watermark from the watermarked image. Experimental results and comparisons with other techniques prove the effectiveness of the new algorithm.

A Novel Digital Image Watermarking Scheme on Copyright Protection Based on Network Manufacture Environment

In this paper,we propose a novel wavelet-domain digital image watermarking scheme on copyright protection based on network manufacture environment.It codes the watermarking with error correcting coding and encrypts the watermarking with chaotic encryption.It embeds the watermarking into the coefficients which have large absolute values in the middle-frequency parts got by Discrete Wavelet Transform (DWT) repeatedly.The extraction doesn’t need the original image.Experiment results show that the proposed scheme is easy to implement,and has good robustness to some attacks,such as JPEG compression,average filtering,median filtering,wiener filtering,pepper (?) salt noise,especially to cropping and scaling.In order to solve the prob- lem of the copyright protection of the network manufacture production,the problems of digital image production such as tamper preventing and watermarking attacks preventing and so on are discussed.It solves the problems of manufacture information such as secure exchange and transmissions and production copyright protection and so on.

Optimal design study of high order FIR digital filters based on neural network algorithm

An optimal design approach of high order FIR digital filter is developed based on the algorithm of neural networks with cosine basis function . The main idea is to minimize the sum of the square errors between the amplitude response of the desired FIR filter and that of the designed by training the weights of neural networks, then obtains the impulse response of FIR digital filter . The convergence theorem of the neural networks algorithm is presented and proved, and the optimal design method is introduced by designing four kinds of FIR digital filters , i.e., low-pass, high-pass, bandpass , and band-stop FIR digital filter. The results of the amplitude responses show that attenuation in stop-bands is more than 60 dB with no ripple and pulse existing in pass-bands, and cutoff frequency of passband and stop-band is easily controlled precisely .The presented optimal design approach of high order FIR digital filter is significantly effective.

Machine learning-based stiffness optimization of digital composite metamaterials with desired positive or negative Poisson's ratio

Mechanical metamaterials such as auxetic materials have attracted great interest due to their unusual properties that are dictated by their architectures.However,these architected materials usually have low stiffness because of the bending or rotation deformation mechanisms in the microstructures.In this work,a convolutional neural network(CNN)based self-learning multi-objective optimization is performed to design digital composite materials.The CNN models have undergone rigorous training using randomly generated two-phase digital composite materials,along with their corresponding Poisson's ratios and stiffness values.Then the CNN models are used for designing composite material structures with the minimum Poisson's ratio at a given volume fraction constraint.Furthermore,we have designed composite materials with optimized stiffness while exhibiting a desired Poisson's ratio(negative,zero,or positive).The optimized designs have been successfully and efficiently obtained,and their validity has been confirmed through finite element analysis results.This self-learning multi-objective optimization model offers a promising approach for achieving comprehensive multi-objective optimization.

Design of twodimensional digital filters using neural networks

A new approach for the design of two-dimensional （2-D） linear phase FIR digital filters based on a new neural networks algorithm （NNA） is provided. A compact expression for the transfer function of a 2-D linear phase FIR filter is derived based on its frequency response characteristic, and the NNA, based on minimizing the square-error in the frequency-domain, is established according to the compact expression. To illustrate the stability of the NNA, the convergence theorem is presented and proved. Design examples are also given, and the results show that the ripple is considerably small in passband and stopband, and the NNA-based method is of powerful stability and requires quite little amount of computations.

Home Network and Digital Rights Management Technology

The home network is a major concern for the growth of digital and information society. Yet, how to guarantee the security of its digital content and protect the legal benefits for each section of the value chain becomes a crucial "bottleneck" in the home network development. The Digital Rights Management (DRM) technology provides total solution for usage, storage, transfer, and tracing the digital contents and rights. Its basic features are systematic and controllability. Considering the growth of the new media and services and the requirements of the Intellectual Property Rights (IPR) protection in a home network, it's necessary to solve consistency problems in usage, storage, and transfer of contents and rights. In addition, it is inevitable to conduct researches of key techniques such as end-to-end secure transmission, conditional access and play, and right description.