An Emulating Environment for Network Management of Space Network

An emulating environment for network management of the space network was presented. The emulating environment was developed with node-link model. The nodes of the satellite entities were controlled by the controlling network, which consist of sets of HLA/RTI based federates. These federates described the different aspect of the attributes of the satellites, such as orbit coordinate, routing table and the chain budget. We described the architecture of the emulation environment and the software design of federates. We measured the response time of the protocol data units and verified the detecting of the network topology in the environment. The environment was open that the on board router and the simulator of satellite link which developed by third party could be integrated.

A Self-Attention Based Dynamic Resource Management for Satellite-Terrestrial Networks

The satellite-terrestrial networks possess the ability to transcend geographical constraints inherent in traditional communication networks,enabling global coverage and offering users ubiquitous computing power support,which is an important development direction of future communications.In this paper,we take into account a multi-scenario network model under the coverage of low earth orbit(LEO)satellite,which can provide computing resources to users in faraway areas to improve task processing efficiency.However,LEO satellites experience limitations in computing and communication resources and the channels are time-varying and complex,which makes the extraction of state information a daunting task.Therefore,we explore the dynamic resource management issue pertaining to joint computing,communication resource allocation and power control for multi-access edge computing(MEC).In order to tackle this formidable issue,we undertake the task of transforming the issue into a Markov decision process(MDP)problem and propose the self-attention based dynamic resource management(SABDRM)algorithm,which effectively extracts state information features to enhance the training process.Simulation results show that the proposed algorithm is capable of effectively reducing the long-term average delay and energy consumption of the tasks.

Security Monitoring and Management for the Network Services in the Orchestration of SDN-NFV Environment Using Machine Learning Techniques

Software Defined Network(SDN)and Network Function Virtualization(NFV)technology promote several benefits to network operators,including reduced maintenance costs,increased network operational performance,simplified network lifecycle,and policies management.Network vulnerabilities try to modify services provided by Network Function Virtualization MANagement and Orchestration(NFV MANO),and malicious attacks in different scenarios disrupt the NFV Orchestrator(NFVO)and Virtualized Infrastructure Manager(VIM)lifecycle management related to network services or individual Virtualized Network Function(VNF).This paper proposes an anomaly detection mechanism that monitors threats in NFV MANO and manages promptly and adaptively to implement and handle security functions in order to enhance the quality of experience for end users.An anomaly detector investigates these identified risks and provides secure network services.It enables virtual network security functions and identifies anomalies in Kubernetes(a cloud-based platform).For training and testing purpose of the proposed approach,an intrusion-containing dataset is used that hold multiple malicious activities like a Smurf,Neptune,Teardrop,Pod,Land,IPsweep,etc.,categorized as Probing(Prob),Denial of Service(DoS),User to Root(U2R),and Remote to User(R2L)attacks.An anomaly detector is anticipated with the capabilities of a Machine Learning(ML)technique,making use of supervised learning techniques like Logistic Regression(LR),Support Vector Machine(SVM),Random Forest(RF),Naïve Bayes(NB),and Extreme Gradient Boosting(XGBoost).The proposed framework has been evaluated by deploying the identified ML algorithm on a Jupyter notebook in Kubeflow to simulate Kubernetes for validation purposes.RF classifier has shown better outcomes(99.90%accuracy)than other classifiers in detecting anomalies/intrusions in the containerized environment.

Performance improvement of licklider transmission protocol in complex deep space networks based on parameter optimization

A reasonable parameter configuration helps improve the data transmission performance of the Licklider Transmission Protocol(LTP).Previous research has focused mainly on parameter optimization for LTP in simplified scenarios with one to two hops or multihop scenarios with a custody mechanism of the Bundle Protocol(BP).However,the research results are not applicable to communications in Complex Deep Space Networks(CDSNs)without the custody mechanism of BP that are more suitable for deep space communications with LTP.In this paper,we propose a model of file delivery time for LTP in CDSNs.Based on the model,we propose a Parameter Optimization Design Algorithm for LTP(LTP-PODA)of configuring reasonable parameters for LTP.The results show that the accuracy of the proposed model is at least 6.47%higher than that of the previously established models based on simple scenarios,and the proposed model is more suitable for CDSNs.Moreover,the LTP parameters are optimized by the LTP-PODA algorithm to obtain an optimization plan.Configuring the optimization plan for LTP improves the protocol transmission performance by at least 18.77%compared with configuring the other parameter configuration plans.

Ultra Dense Satellite-Enabled 6G Networks:Resource Optimization and Interference Management

With the evolution of the sixth generation(6G)mobile communication technology,ample attention has gone to the integrated terrestrial-satellite networks.This paper notes that four typical application scenarios of integrated terrestrial-satellite networks are integrated into ultra dense satellite-enabled 6G networks architecture.Then the subchannel and power allocation schemes for the downlink of the ultra dense satellite-enabled 6G heterogeneous networks are introduced.Satellite mobile edge computing(SMEC)with edge caching in three-layer heterogeneous networks serves to reduce the link traffic of networks.Furthermore,a scheme for interference management is presented,involving quality-of-service(QoS)and co-tier/cross-tier interference constraints.The simulation results show that the proposed schemes can significantly increase the total capacity of ultra dense satellite-enabled 6G heterogeneous networks.

Distributionally robust optimization based chance-constrained energy management for hybrid energy powered cellular networks

Energy harvesting has been recognized as a promising technique with which to effectively reduce carbon emis-sions and electricity expenses of base stations.However,renewable energy is inherently stochastic and inter-mittent,imposing formidable challenges on reliably satisfying users'time-varying wireless traffic demands.In addition,the probability distribution of the renewable energy or users’wireless traffic demand is not always fully known in practice.In this paper,we minimize the total energy cost of a hybrid-energy-powered cellular network by jointly optimizing the energy sharing among base stations,the battery charging and discharging rates,and the energy purchased from the grid under the constraint of a limited battery size at each base station.In solving the formulated non-convex chance-constrained stochastic optimization problem,a new ambiguity set is built to characterize the uncertainties in the renewable energy and wireless traffic demands according to interval sets of the mean and covariance.Using this ambiguity set,the original optimization problem is transformed into a more tractable second-order cone programming problem by exploiting the distributionally robust optimization approach.Furthermore,a low-complexity distributionally robust chance-constrained energy management algo-rithm,which requires only interval sets of the mean and covariance of stochastic parameters,is proposed.The results of extensive simulation are presented to demonstrate that the proposed algorithm outperforms existing methods in terms of the computational complexity,energy cost,and reliability.

Tax avoidance and earnings management:a neural network approach for the largest European economies

In this study,we investigate the relationship between tax avoidance and earnings management in the largest five European Union economies by using artificial neural network regressions.This methodology allows us to deal with nonlinearities detected in the data,which is the principal contribution to the previous literature.We ana-lyzed Compustat data for Germany,the United Kingdom,France,Italy,and Spain for the 2006–2015 period,focusing on discretionary accruals.We considered three tax avoidance measures,two based on the effective tax rate(ETR)and one on book-tax differences(BTD).Our results indicate the presence of nonlinear patterns and a posi-tive,statistically significant relationship between discretionary accruals and both ETR indicators implying that when companies resort to earnings management,a larger tax-able income—and thus higher ETR and lesser tax avoidance–would ensue.Hence,as also highlighted by the fact that discretionary accruals do not appear to affect BTD,our evidence does not suggest that companies are exploiting tax manipulation to reduce their tax payments;thus,the gap between accounting and taxation seems largely unaf-fected by earnings management.

The Management of Mental Health, and Service Networks in Italy

Madness has attracted and frightened for centuries,and talking about this means discussing how this diversity was built and managed in different social contexts and historical periods.Not all societies have had,and still have,the same relationship with madness.It is only with the affirmation of the Modern State,and of Capitalism,that the idea of“normality”indispensable to be able to conceive diversity as something dangerously distant and different from the norm takes over.In our post-modern society,people with mental illness in Italy can resort to specialists and social-health services.But the heterogeneous answers given after the approval of law 180 appear to be increasingly diversified.In this research,much attention will be paid to how the social and health services,located in different areas of Italy(Messina,Rome,Trento)face the current growing risk of social,housing and economic isolation of these fragile subjects.The aim of the research is to explore the possibility of a new relationship between the social-health service and the local community.On the one hand,research investigates what the contribution of the services could be.On the other what the spaces of protagonism and participation of the community could be in inclusion process account.In order to better understand the differences between these two dimensions,a qualitative research approach was chosen through the conduct of in-depth interviews.In this way it was possible to investigate:(1)the partial representations characteristic of the single individual,family members,operators and stackholders in general;(2)the services around the topic dealt with is articulated.From the first results of the research it emerges that the territory can no longer be considered as an abstract entity,but becomes the social space within which the construction of a new community welfare can and must take place.

Lens-Oppositional Wild Geese Optimization Based Clustering Scheme for Wireless Sensor Networks Assists Real Time Disaster Management

Recently,wireless sensor networks(WSNs)find their applicability in several real-time applications such as disaster management,military,surveillance,healthcare,etc.The utilization of WSNs in the disaster monitoring process has gained significant attention among research communities and governments.Real-time monitoring of disaster areas using WSN is a challenging process due to the energy-limited sensor nodes.Therefore,the clustering process can be utilized to improve the energy utilization of the nodes and thereby improve the overall functioning of the network.In this aspect,this study proposes a novel Lens-Oppositional Wild Goose Optimization based Energy Aware Clustering(LOWGO-EAC)scheme for WSN-assisted real-time disaster management.The major intention of the LOWGO-EAC scheme is to perform effective data collection and transmission processes in disaster regions.To achieve this,the LOWGOEAC technique derives a novel LOWGO algorithm by the integration of the lens oppositional-based learning(LOBL)concept with the traditional WGO algorithm to improve the convergence rate.In addition,the LOWGO-EAC technique derives a fitness function involving three input parameters like residual energy(RE),distance to the base station(BS)(DBS),and node degree(ND).The proposed LOWGO-EAC technique can accomplish improved energy efficiency and lifetime of WSNs in real-time disaster management scenarios.The experimental validation of the LOWGO-EAC model is carried out and the comparative study reported the enhanced performance of the LOWGO-EAC model over the recent approaches.

Multiscale Characteristics and Connection Mechanisms of Attraction Networks:A Trajectory Data Mining Approach Leveraging Geotagged Data

Urban tourism is considered a complex system,and multiscale exploration of the organizational patterns of attraction networks has become a topical issue in urban tourism,so exploring the multiscale characteristics and connection mechanisms of attraction networks is important for understanding the linkages between attractions and even the future destination planning.This paper uses geotagging data to compare the links between attractions in Beijing,China during four different periods:the pre-Olympic period(2004–2007),the Olympic Games and subsequent‘heat period’(2008–2013),the post-Olympic period(2014–2019),and the COVID-19(Corona Virus Disease 2019)pandemic period(2020–2021).The aim is to better understand the evolution and patterns of attraction networks at different scales in Beijing and to provide insights for tourism planning in the destination.The results show that the macro,meso-,and microscales network characteristics of attraction networks have inherent logical relationships that can explain the commonalities and differences in the development process of tourism networks.The macroscale attraction network degree Matthew effect is significant in the four different periods and exhibits a morphological monocentric structure,suggesting that new entrants are more likely to be associated with attractions that already have high value.The mesoscale links attractions according to the common purpose of tourists,and the results of the community segmentation of the attraction networks in the four different periods suggest that the functional polycentric structure describes their clustering effect,and the weak links between clusters result from attractions bound by incomplete information and distance,and the functional polycentric structure with a generally more efficient network of clusters.The pattern structure at the microscale reveals the topological transformation relationship of the regional collaboration pattern,and the attraction network structure in the four different periods has a very similar importance profile structure suggesting that the attraction network has the same construction rules and evolution mechanism,which aids in understanding the attraction network pattern at both macro and micro scales.Important approaches and practical implications for planners and managers are presented.

Threshold-Based Software-Defined Networking(SDN)Solution for Healthcare Systems against Intrusion Attacks

The healthcare sector holds valuable and sensitive data.The amount of this data and the need to handle,exchange,and protect it,has been increasing at a fast pace.Due to their nature,software-defined networks(SDNs)are widely used in healthcare systems,as they ensure effective resource utilization,safety,great network management,and monitoring.In this sector,due to the value of thedata,SDNs faceamajor challengeposed byawide range of attacks,such as distributed denial of service(DDoS)and probe attacks.These attacks reduce network performance,causing the degradation of different key performance indicators(KPIs)or,in the worst cases,a network failure which can threaten human lives.This can be significant,especially with the current expansion of portable healthcare that supports mobile and wireless devices for what is called mobile health,or m-health.In this study,we examine the effectiveness of using SDNs for defense against DDoS,as well as their effects on different network KPIs under various scenarios.We propose a threshold-based DDoS classifier(TBDC)technique to classify DDoS attacks in healthcare SDNs,aiming to block traffic considered a hazard in the form of a DDoS attack.We then evaluate the accuracy and performance of the proposed TBDC approach.Our technique shows outstanding performance,increasing the mean throughput by 190.3%,reducing the mean delay by 95%,and reducing packet loss by 99.7%relative to normal,with DDoS attack traffic.

Adoption of network and plan-do-check-action in the international classification of disease 10 coding

BACKGROUND with the widespread application of computer network systems in the medical field,the plan-do-check-action(PDCA)and the international classification of diseases tenth edition(ICD-10)coding system have also achieved favorable results in clinical medical record management.However,research on their combined application is relatively lacking.Objective:it was to explore the impact of network systems and PDCA management mode on ICD-10 encoding.Material and Method:a retrospective collection of 768 discharged medical records from the Medical Record Management Department of Meishan People’s Hospital was conducted.They were divided into a control group(n=232)and an observation group(n=536)based on whether the PDCA management mode was implemented.The two sets of coding accuracy,time spent,case completion rate,satisfaction,and other indicators were compared.AIM To study the adoption of network and PDCA in the ICD-10.METHODS A retrospective collection of 768 discharged medical records from the Medical Record Management Department of Meishan People’s Hospital was conducted.They were divided into a control group(n=232)and an observation group(n=536)based on whether the PDCA management mode was implemented.The two sets of coding accuracy,time spent,case completion rate,satisfaction,and other indicators were compared.RESULTS In the 3,6,12,18,and 24 months of PDCA cycle management mode,the coding accuracy and medical record completion rate were higher,and the coding time was lower in the observation group as against the controls(P<0.05).The satisfaction of coders(80.22%vs 53.45%)and patients(84.89%vs 51.72%)in the observation group was markedly higher as against the controls(P<0.05).CONCLUSION The combination of computer networks and PDCA can improve the accuracy,efficiency,completion rate,and satisfaction of ICD-10 coding.

An Efficient Key Management Protocol for Wireless Sensor Networks

Key management is a fundamental security service in wireless sensor networks. The communication security problems for these networks are exacerbated by the limited power and energy of the sensor devices. In this paper, we describe the design and implementation of an efficient key management scheme based on low energy adaptive clustering hierarchy（LEACH） for wireless sensor networks. The design of the protocol is motivated by the observation that many sensor nodes in the network play different roles. The paper presents different keys are set to the sensors for meeting different transmitting messages and variable security requirements. Simulation results show that our key management protocol based-on LEACH can achieve better performance. The energy consumption overhead introduced is remarkably low compared with the original Kerberos schemes.

Trust management-based and energy efficient hierarchical routing protocol in wireless sensor networks

The single planar routing protocol has a slow convergence rate in the large-scale Wireless Sensor Network(WSN).Although the hierarchical routing protocol can effectively cope with large-scale application scenarios,how to elect a secure cluster head and balance the network load becomes an enormous challenge.In this paper,a Trust Management-based and Low Energy Adaptive Clustering Hierarchy protocol(LEACH-TM)is proposed.In LEACH-TM,by using the number of dynamic decision cluster head nodes,residual energy and density of neighbor nodes,the size of the cluster can be better constrained to improve energy efficiency,and avoid excessive energy consumption of a node.Simultaneously,the trust management scheme is introduced into LEACH-TM to defend against internal attacks.The simulation results show that,compared with LEACH-SWDN protocol and LEACH protocol,LEACH-TM outperforms in prolonging the network lifetime and balancing the energy consumption,and can effectively mitigate the influence of malicious nodes on cluster head selection,which can greatiy guarantee the security of the overall network.

Research on Risk Prevention and Resolution Strategies of College Network Ideology in the Context of the Internet

With the rapid development of the Internet,the network ideology of colleges and universities is facing severe challenges.This paper deeply analyzes the root of the risk of network ideology and makes a specific investigation of the status quo of network public opinion in colleges and universities.On this basis,the study explores and puts forward a series of targeted risk prevention and resolution strategies,aiming at providing a systematic solution for the network ideology security of colleges and universities.In this paper,with the combination of theory and practice as the path,we verify the effectiveness and applicability of the proposed strategy through the analysis of the implementation effect of the strategy.This study also provides theoretical support and practical guidance for the prevention and control of ideological risks and public opinion guidance in universities under the network environment,which has important practical significance.With the continuous progress of network technology,the threats to the network ideology of colleges and universities are increasing.For example,the spread of false information has become a serious problem affecting the security of college network ideology.

COCM: Class Based Optimized Congestion Management Protocol for Healthcare Wireless Sensor Networks

Wireless Sensor Networks (WSNs) consist of numerous sensor nodes which can be used in many new emerging applications like healthcare. One of the major challenges in healthcare environments is to manage congestion, because in applications, such as medical emergencies or patients remote monitoring, transmitted data is important and critical. So it is essential in the first place to avoid congestion as much as possible and in cases when congestion avoidance is not possible, to control the congestion. In this paper, a class based congestion management protocol has been proposed for healthcare applications. We distinguish between sensitive, non-sensitive and control traffics, and service the input traffics based on their priority and quality of service requirements (QoS). The proposed protocol which is called COCM avoids congestion in the first step using multipath routing. The proposed AQM algorithm uses separate virtual queue's condition on a single physical queue to accept or drop the incoming packets. In cases where input traffic rate increases and congestion cannot be avoided, it mitigates congestion by using an optimized congestion control algorithm. This paper deals with parameters like end to end delay, packet loss, energy consumption, lifetime and fairness which are important in healthcare applications. The performance of COCM was evaluated using the OPNET simulator. Simulation results indicated that COCM achieves its goals.

A FUNCTION NODE-BASED MULTIPLE PAIRWISE KEYS MANAGEMENT PROTOCOL FOR WIRELESS SENSOR NETWORKS AND ENERGY CONSUMPTION ANALYSIS

In this letter, a Function node-based Multiple Pairwise Keys Management (MPKMF) protocol for Wireless Sensor Networks (WSNs) is firstly designed, in which ordinary nodes and cluster head nodes are responsible for data collection and transmission, and function nodes are responsible for key management. There are more than one function nodes in the cluster consulting the key generation and other security decision-making. The function nodes are the second-class security center because of the characteristics of the distributed WSNs. Secondly, It is also described that the formation of function nodes and cluster heads under the control of the former, and five kinds of keys, i.e., individual key, pairwise keys, cluster key, management key, and group key. Finally, performance analysis and experiments show that, the protocol is superior in communication and energy consumption. The delay of establishing the cluster key meets the requirements, and a multiple pairwise key which adopts the coordinated security authentication scheme is provided.

Research and Implementation of Network Intelligent Management Based on CSCW

本文分析了计算机网络管理同人工智能领域的现状和困难，提出了基于计算机支持协同工作ＣＳＣＷ的原理，结合ＷＷＷ及Ｊａｖａ等新技术的新型网络智能管理模型ＡＧＩＭＡ．ＡＧＩＭＡ将传统网管以信息分布为中心的模式转变到以计算分布为中心的模式，由智能代理群整体向网络管理员提供智能能力．ＡＧＩＭＡ的实现充分体现了开放性，可扩展性，主动适应性和人机界面友好性．作者详细分析了智能代理的特性，对比人类社会模型，得出了网络智能必须是人机协同的结论．

Discussion on data-security of space-earth integrated network and analysis ofspace communications protocol standards

The architecture and protocols of Internet can't work well in space environments. To form a reliable and safe space network, characteristics of space communication network one discusse, brief synthesis is performed for consultative committee for space data system (CCSDS) space communications protocol standards (SCPS), and a model accounting for data security problem of space-earth integrated network is provided.

What to Cache：Differentiated Caching Resource Allocation and Management in Information-Centric Networking

Information-centric networking(ICN) aims to improve the efficiency of content delivery and reduce the redundancy of data transmission by caching contents in network nodes. An important issue is to design caching methods with better cache hit rate and achieve allocating on-demand. Therefore, an in-network caching scheduling scheme for ICN was designed, distinguishing different kinds of contents and dynamically allocating the cache size on-demand. First discussing what was appropriated to be cached in nodes, and then a classification about the contents could be cached was proposed. Furthermore, we used AHP to weight different contents classes through analyzing users' behavior. And a distributed control process was built, to achieve differentiated caching resource allocation and management. The designed scheme not only avoids the waste of caching resource, but also further enhances the cache availability. Finally, the simulation results are illustrated to show that our method has the superior performance in the aspects of server hit rate and convergence.