A Game-Theoretic Perspective on Resource Management for Large-Scale UAV Communication Networks

As a result of rapid development in electronics and communication technology,large-scale unmanned aerial vehicles(UAVs)are harnessed for various promising applications in a coordinated manner.Although it poses numerous advantages,resource management among various domains in large-scale UAV communication networks is the key challenge to be solved urgently.Specifically,due to the inherent requirements and future development trend,distributed resource management is suitable.In this article,we investigate the resource management problem for large-scale UAV communication networks from game-theoretic perspective which are exactly coincident with the distributed and autonomous manner.By exploring the inherent features,the distinctive challenges are discussed.Then,we explore several gametheoretic models that not only combat the challenges but also have broad application prospects.We provide the basics of each game-theoretic model and discuss the potential applications for resource management in large-scale UAV communication networks.Specifically,mean-field game,graphical game,Stackelberg game,coalition game and potential game are included.After that,we propose two innovative case studies to highlight the feasibility of such novel game-theoretic models.Finally,we give some future research directions to shed light on future opportunities and applications.

Active Queue Management within Large-Scale Wired Networks

Signal transmission control protocol sources with the objective of managing queue utilization and delay is actually a feedback control problem in active queue management (AQM) core routers. This paper extends AQM control design for single network systems to large-scale wired network systems with time delays at each communication channel. A system model consisted of several local networks is first constructed. The stability condition guaranteeing overall stability is subsequently derived using Lyapunov stability theory. The results developed have been successfully verified on a network simulator.

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.

A Large-Scale Group Decision Making Model Based on Trust Relationship and Social Network Updating

With the development of big data and social computing,large-scale group decisionmaking(LGDM)is nowmerging with social networks.Using social network analysis(SNA),this study proposes an LGDM consensus model that considers the trust relationship among decisionmakers(DMs).In the process of consensusmeasurement:the social network is constructed according to the social relationship among DMs,and the Louvain method is introduced to classify social networks to form subgroups.In this study,the weights of each decision maker and each subgroup are computed by comprehensive network weights and trust weights.In the process of consensus improvement:A feedback mechanism with four identification and two direction rules is designed to guide the consensus of the improvement process.Based on the trust relationship among DMs,the preferences are modified,and the corresponding social network is updated to accelerate the consensus.Compared with the previous research,the proposedmodel not only allows the subgroups to be reconstructed and updated during the adjustment process,but also improves the accuracy of the adjustment by the feedbackmechanism.Finally,an example analysis is conducted to verify the effectiveness and flexibility of the proposed method.Moreover,compared with previous studies,the superiority of the proposed method in solving the LGDM problem is highlighted.

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.

Design and Implementation of an Open Network Security Management Platform

In order to manage all kinds of network security devices and software systems efficiently, and make them collaborate with each other, the model for an open network security management platform is presented. The feasibility and key implementing technology of the model are expatiated. A prototype system is implemented to validate it.

Modeling of Intelligent Integrated Network Management System Using Object Oriented Approach *

The concept of intelligent integrated network management (IINM) is briefly introduced. In order to analyze, design and implement IINM successfully, object oriented approach is testified to be an effective and efficient way. In this paper, object oriented technique is applied to the structural model of IINM system, The Domain object class and the MU object class are used to represent the manager and the managed resources. Especially, NM IA is introduced which is a special object class with intelligent behaviors to manage the resources efficiently.

Research and Implementation of Network Intelligent Management Based on CSCW

This paper analyzes progresses and difficulties of subjects on computer network’s management and artificial intelligence, proposes AGIMA, a new model of network intelligent management, which is based on computer supported cooperative work (CSCW) and combining new technologies such as WWW, Java. AGIMA transfers from information distribution centered mode in traditional network management to computing distribution centered mode, providing intelligence capacity for network management by a whole intelligent agent group. The implementation of AGIMA takes much consideration of openess, scalability, proactive adaptability and friendliness of human computer interface. Authors present properties of intelligent agent in details, and conclude that network intelligence should be cooperation between human and computer.

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.

CifNet Network Multi-Well Data Management System

The CifNet network multi-well data management system is developed for 100MB or 1000MB local network environments which are used in Chinese oil industry. The kernel techniques of CifNet system include: 1, establishing a high efficient and low cost network multi-well data management architecture based on the General Logging Curve Theory and the Cif data format; 2, implementing efficient visit and transmission of multi-well data in C/S local network based on TCP/IP protocol; 3,ensuring the safety of multi-well data in store, visit and application based on Unix operating system security. By using CifNet system, the researcher in office or at home can visit curves of any borehole in any working area of any oilfield. The application foreground of CifNet system is also commented.

Long Short-Term Memory Recurrent Neural Network-Based Acoustic Model Using Connectionist Temporal Classification on a Large-Scale Training Corpus

A Long Short-Term Memory(LSTM) Recurrent Neural Network(RNN) has driven tremendous improvements on an acoustic model based on Gaussian Mixture Model(GMM). However, these models based on a hybrid method require a forced aligned Hidden Markov Model(HMM) state sequence obtained from the GMM-based acoustic model. Therefore, it requires a long computation time for training both the GMM-based acoustic model and a deep learning-based acoustic model. In order to solve this problem, an acoustic model using CTC algorithm is proposed. CTC algorithm does not require the GMM-based acoustic model because it does not use the forced aligned HMM state sequence. However, previous works on a LSTM RNN-based acoustic model using CTC used a small-scale training corpus. In this paper, the LSTM RNN-based acoustic model using CTC is trained on a large-scale training corpus and its performance is evaluated. The implemented acoustic model has a performance of 6.18% and 15.01% in terms of Word Error Rate(WER) for clean speech and noisy speech, respectively. This is similar to a performance of the acoustic model based on the hybrid method.

Aviation-oriented Mobility Management Method in IP/LEO Satellite Networks

Taking into chief consideration the features of aviation nodes in satellite networks, such as high moving speed, long communication distance, and high connection frequency, this article proposes an aviation-oriented mobility management method for IP/low earth orbit （LEO） satellite networks. By introducing the concept of ground station real-time coverage area, the proposed method uses ground-station-based IP addressing method and cell paging scheme to decrease the frequency of IP binding update requests as well as the paging cost. In comparison with the paging mobile IP （P-MIP） method and the handover-independent IP mobility management method, as is verified by the mathematical analysis and simulation, the proposed method could decrease the management cost. It also possesses better ability to support the aviation nodes because it is subjected to fewer influences from increased node speeds and newly coming connection rates.

Self-Organized Public-Key Management for Mobile Ad Hoc Networks Based on a Bidirectional Trust Model

In traditional networks , the authentication is performed by certificate authoritys（CA）,which can＇t be built in distributed mobile Ad Hoc Networks however. In this pa per, we propose a fully self-organized public key management based on bidirectional trust model without any centralized authority that allows users to generate their public-private key pairs, to issue certificates, and the trust relation spreads rationally according to the truly human relations. In contrast with the traditional self-organized public-key management, the average certificates paths get more short, the authentication passing rate gets more high and the most important is that the bidirectional trust based model satisfys the trust re quirement of hosts better.

Machine Learning Empowered Beam Management for Intelligent Reflecting Surface Assisted MmWave Networks

Recently,intelligent reflecting surface(IRS)assisted mmWave networks are emerging,which bear the potential to address the blockage issue of the millimeter wave(mmWave)communication in a more cost-effective way.In particular,IRS is built by passive and programmable electromagnetic elements that can manipulate the mmWave propagation channel into a more favorable condition that is free of blockage via judicious joint base station(BS)-IRS transmission design.However,the coexistence of IRSs and mmWave BSs complicates the network architecture,and thus poses great challenges for efficient beam management(BM)that is one critical prerequisite for high performance mmWave networks.In this paper,we systematically evaluate the key issues and challenges of BM for IRS-assisted mmWave networks to bring insights into the future network design.Specifically,we carefully classify and discuss the extensibility and limitations of the existing BM of conventional mmWave towards the IRS-assisted new paradigm.Moreover,we propose a novel machine learning empowered BM framework for IRS-assisted networks with representative showcases,which processes environmental and mobility awareness to achieve highly efficient BM with significantly reduced system overhead.Finally,some interesting future directions are also suggested to inspire further researches.

Light-Weight Simple Network Management Protocol in Wireless Sensor Networks

A Light-Weight Simple Network Management Protocol (LW-SNMP) for the wireless sensor network is proposed, which is a kind of hierarchical network management system including a sink manager, cluster proxies, and node agents. Considering the resource limitations on the sensor nodes, we design new management messages, new data types and new management information base completely. The management messages between the cluster proxy and node agents are delivered as normal data packets. The experiment results show that LW-SNMP can meet the management demands in the resource-limited wireless sensor networks and has a good performance in stability, effectiveness of memory, extensibility than the traditional Simple Network Management Protocol (SNMP).

A Parking Management System Based on Wireless Sensor Network

This paper introduces a parking management system based on a wireless sensor network developed by our group. The system consists of a large amount of parking space monitoring nodes, a few parking guiding nodes, a sink node and a management station. All the nodes exchange information with each other through wireless communication. The prototype of the parking management system has been implemented and the preliminary test results show that the performance of the system can satisfy the requirements of the application.

Cognitive Network Management in Internet of Things

The wide variety of smart embedded computing devices and their increasing number of applications in our daily life have created new op- portunities to acquire knowledge from the physical world anytime and anywhere, which is envisioned as the＂Internet of Things＂ （IoT）. Since a huge number of heterogeneous resources are brought in- to IoT, one of the main challenges is how to effi- ciently manage the increasing complexity of IoT in a scalable, flexNle, and autonomic way. Further- more, the emerging IoT applications will require collaborations among loosely coupled devices, which may reside in various locations of the Inter- net. In this paper, we propose a new IoT network management architecture based on cognitive net- work management technology and Service-Orien- ted Architecture to provide effective and efficient network management of loT.

Application Research and Implementation of the CORBA-Based Web Distributed Network Management System

The distributed management has become an important tendency of development for the NMS (Network Management System) with the development of Internet. Based on the analysis of CORBA (Conmon Object Request Broker Architecture) technique, we mainly discuss about the applicability of the approach by which CORBA combined with Java has been applied to the system model and Web architecture: and address the applied frame and the interface definitions that are the, key technologies for implementing the Distributed Object Computing (DOC). In addition, we also conduct the research on its advantages and disadvantages and further expected improvements. Key words distributed Web network management - CORBA - Java CLC number TP 393.07 Foundation item: Supported by the QTNG (Integrated Network Management System) Project Foundation and QT-NMS (SDH NMS) Project Foundation of Wuhan Qingtian Information Industry Co., LTD of Hubei of China (SDH.001)Biography: WANG Feng (1979-), male Master candidate, research direction: administration of network and software engineering.

Quantum Communication Networks and Trust Management: A Survey

This paper summarizes the state of art in quantum communication networks and trust management in recent years.As in the classical networks,trust management is the premise and foundation of quantum secure communication and cannot simply be attributed to security issues,therefore the basic and importance of trust management in quantum communication networks should be taken more seriously.Compared with other theories and techniques in quantum communication,the trust of quantum communication and trust management model in quantum communication network environment is still in its initial stage.In this paper,the core technologies of establishing secure and reliable quantum communication networks are categorized and summarized,and the trends of each direction in trust management of quantum communication network are discussed in depth.

A Neural Network-Based Trust Management System for Edge Devices in Peer-to-Peer Networks

Edge devices in Internet of Things(IoT)applications can form peers to communicate in peer-to-peer(P2P)networks over P2P protocols.Using P2P networks ensures scalability and removes the need for centralized management.However,due to the open nature of P2P networks,they often suffer from the existence of malicious peers,especially malicious peers that unite in groups to raise each other’s ratings.This compromises users’safety and makes them lose their confidence about the files or services they are receiving.To address these challenges,we propose a neural networkbased algorithm,which uses the advantages of a machine learning algorithm to identify whether or not a peer is malicious.In this paper,a neural network(NN)was chosen as the machine learning algorithm due to its efficiency in classification.The experiments showed that the NNTrust algorithm is more effective and has a higher potential of reducing the number of invalid files and increasing success rates than other well-known trust management systems.