Edge Computing-Based Joint Client Selection and Networking Scheme for Federated Learning in Vehicular IoT

In order to support advanced vehicular Internet-of-Things(IoT)applications,information exchanges among different vehicles are required to find efficient solutions for catering to different application requirements in complex and dynamic vehicular environments.Federated learning(FL),which is a type of distributed learning technology,has been attracting great interest in recent years as it performs knowledge exchange among different network entities without a violation of user privacy.However,client selection and networking scheme for enabling FL in dynamic vehicular environments,which determines the communication delay between FL clients and the central server that aggregates the models received from the clients,is still under-explored.In this paper,we propose an edge computing-based joint client selection and networking scheme for vehicular IoT.The proposed scheme assigns some vehicles as edge vehicles by employing a distributed approach,and uses the edge vehicles as FL clients to conduct the training of local models,which learns optimal behaviors based on the interaction with environments.The clients also work as forwarder nodes in information sharing among network entities.The client selection takes into account the vehicle velocity,vehicle distribution,and the wireless link connectivity between vehicles using a fuzzy logic algorithm,resulting in an efficient learning and networking architecture.We use computer simulations to evaluate the proposed scheme in terms of the communication overhead and the information covered in learning.

SEMANTIC NETWORK PRESENTATION OF MECHANICAL MOTION SCHEME AND ITS MECHANISM TYPES SELECTION METHOD

The presentation method of the mechanical motion scheme must support thewhole process of conceptual design. To meet the requirement, a semantic network method is selectedto represent process level, action level, mechanism level and relationships among them. Computeraided motion cycle chart exploration can be realized by the representation and revision of timecoordination of mechanism actions and their effect on the design scheme. The uncertain reasoningtechnology based on semantic network is applied in the mechanism types selection of the needledriving mechanism of industrial sewing mechanism, and the application indicated it is correct,useful and advance.

CONTROL SCHEMES FOR CMAC NEURAL NETWORK-BASED VISUAL SERVOING

In IBVS (image based visual servoing), the error signal in image space should be transformed into the control signal in the input space quickly. To avoid the iterative adjustment and complicated inverse solution of image Jacobian, CMAC (cerebellar model articulation controller) neural network is inserted into visual servo control loop to implement the nonlinear mapping. Two control schemes are used. Simulation results on two schemes are provided, which show a better tracking precision and stability can be achieved using scheme 2.

A Transductive Scheme Based Inference Techniques for Network Forensic Analysis

Network forensics is a security infrastructure,and becomes the research focus of forensic investigation.However many challenges still exist in conducting network forensics:network has produced large amounts of data;the comprehensibility of evidence extracting from collected data;the efficiency of evidence analysis methods,etc.To solve these problems,in this paper we develop a network intrusion forensics system based on transductive scheme that can detect and analyze efficiently computer crime in networked environments,and extract digital evidence automatically.At the end of the paper,we evaluate our method on a series of experiments on KDD Cup 1999 dataset.The results demonstrate that our methods are actually effective for real-time network forensics,and can provide comprehensible aid for a forensic expert.

Neural Network Based Multi-level Fuzzy Evaluation Model for Mechanical Kinematic Scheme

To implement a quantificational evaluation for mechanical kinematic scheme more effectively,a multi-level and multi-objective evaluation model is presented using neural network and fuzzy theory. Firstly,the structure of evaluation model is constructed according to evaluation indicator system. Then evaluation samples are generated and provided to train this model. Thus it can reflect the relation between attributive value and evaluation result,as well as the weight of evaluation indicator. Once evaluation indicators of each candidate are fuzzily quantified and fed into the trained network model,the corresponding evaluation result is outputted and the best alternative can be selected. Under this model,expert knowledge can be effectively acquired and expressed,and the quantificational evaluation can be implemented for kinematic scheme with multi-level evaluation indicator system. Several key problems on this model are discussed and an illustration has demonstrated that this model is feasible and can be regarded as a new idea for solving kinematic scheme evaluation.

Defending Against Link Failure in Virtual Network Embedding Using a Hybrid Scheme

Nowadays network virtualization is utterly popular.As a result,how to protect the virtual networks from attacking on the link is increasingly important.Existing schemes are mainly backup-based,which suffer from data loss and are helpless to such attacks like data tampering.To offer high security level,in this paper,we first propose a multipath and decision-making(MD) scheme which applies multipath simultaneously delivery and decision-making for protecting the virtual network.Considering different security requirement for virtual link,we devise a hybrid scheme to protect the virtual links.For the critical links,MD scheme is adopted.For the other links,we adopt the Shared Backup Scheme.Our simulation results indicate the proposed scheme can significantly increase the security level of the critical link high in the loss of less acceptance ratio.

Development of Virtual Reference Station in Kinematic Schemes of Geodetic GPS Network Using the Method of Maximum Informative Zone

The factual data on error of positioning in VRS GPS networks have been analyzed, where the mobile receiver is provided with VRS. The method of highly informative zone is suggested for removal of initial vagueness in selection of reference stations for purposes of development of VRS on the basis of minimum GPS network, composed of three reference stations. The recommendations on use of suggested method are given.

Evaluation of Linear Precoding Schemes for Cooperative Multi-Cell MU MIMO in Future Mobile Communication Systems

In Mobile Communication Systems, inter-cell interference becomes one of the challenges that degrade the system’s performance, especially in the region with massive mobile users. The linear precoding schemes were proposed to mitigate interferences between the base stations (inter-cell). These schemes are categorized into linear and non-linear;this study focused on linear precoding schemes, which are grounded into three types, namely Zero Forcing (ZF), Block Diagonalization (BD), and Signal Leakage Noise Ratio (SLNR). The study included the Cooperative Multi-cell Multi Input Multi Output (MIMO) System, whereby each Base Station serves more than one mobile station and all Base Stations on the system are assisted by each other by shared the Channel State Information (CSI). Based on the Multi-Cell Multiuser MIMO system, each Base Station on the cell is intended to maximize the data transmission rate by its mobile users by increasing the Signal Interference to Noise Ratio after the interference has been mitigated due to the usefully of linear precoding schemes on the transmitter. Moreover, these schemes used different approaches to mitigate interference. This study mainly concentrates on evaluating the performance of these schemes through the channel distribution models such as Ray-leigh and Rician included in the presence of noise errors. The results show that the SLNR scheme outperforms ZF and BD schemes overall scenario. This implied that when the value of SNR increased the performance of SLNR increased by 21.4% and 45.7% for ZF and BD respectively.

Performance Analysis of an Enhanced Load Balancing Scheme for Wireless Sensor Networks

Research interest in sensor networks routing largely considers minimization of energy consumption as a major performance criterion to provide maximum sensors network lifetime. When considering energy conservation, routing protocols should also be designed to achieve fault tolerance in communications. Moreover, due to dynamic topology and random deployment, incorporating reliability into protocols for WSNs is very important. Hence, we propose an improved scalable clustering-based load balancing scheme (SCLB) in this paper. In SCLB scheme, scalability is achieved by dividing the network into overlapping multihop clusters each with its own cluster head node. Simulation results show that the proposed scheme achieves longer network lifetime with desirable reliability at the initial state compare with the existing multihop load balancing approach.

Incentive Schemes of Nodes for Ad Hoc and Multi-hop Cellular Networks

Nodes cooperation is a significant prerequisite for the realization of the relaying Ad Hoc.While with the development of multi-hop cellular networks, how to stimulate intermediate nodes to do the packet-forwarding deserves more concerning.At present research,the incentive schemes in pure Ad Hoc and multi-hop cellular networks are analyzed and compared to classify the strengths and drawbacks of each scheme.We explain in particular what the key issues are to implement incentive schemes for cooperation. Finally,an incentive scheme based on integration of reputation and charging systems is proposed not only to block the existence of selfish nodes,but to satisfy the rational requirement of nodes.

市场渗透率约束下的拼车奖励方案优化模型

目前,我国拼车出行交通模式的市场份额相对较低,在缓解交通拥堵、节能减排方面仍有巨大潜力未被充分挖掘。基于奖励的交通需求管理策略可以提高民众拼车出行意愿,但奖励方案的设置与拼车出行的市场渗透率高度相关,不合理的奖励方案容易导致成本增加,甚至项目破产。为进一步激发拼车需求,合理利用交通资源,文中提出一种基于路段的拼车奖励方案优化模型,以拼车为支点,以奖励为杠杆,实现社会出行总成本的降低。其中,上层模型旨在寻找最优拼车奖励方案以最小化社会出行总成本,下层模型为相应奖励方案下的拼车出行车辆和单人驾驶车辆用户均衡流量分配模型。采用嵌套Frank-Wolfe算法的遗传算法求解该模型,并以Sioux Falls路网和Nguyen Dupuis路网为算例对模型的可行性及有效性进行了验证。结果表明:不符合市场渗透率的预算投入会导致社会总成本大幅上涨;在最优拼车奖励方案下,社会出行总成本降低约24.53%,50%的拥堵路段的拥堵得到缓解,出行公平性问题得到缓和。文中提出的模型可为道路管理者设置科学、合理的拼车奖励方案提供理论基础。

数据安全防护体系在高校校园网中的应用

在高校网络的基本组成结构中,校园网具有保障高校数据信息得以安全稳定传输的重要作用,校园网目前已成为高校师生赖以实现沟通联系的基础设施支撑。目前在网络技术的快速转型以及发展趋势作用下,高校校园网日益存在更加突出的网络数据安全威胁,进而体现全面防范以及科学预测校园网数据安全隐患的必要性。文章主要探讨高校校园网构建数据安全防护体系的实践价值,合理改进校园网现有的数据传输安全保障机制。

A Dual-Channel Secure Transmission Scheme for Internet-Based Networked Control Systems

Two significant issues in Internet-based networked control systems （ INCSs）, transport performance of different protocols and security breach from Internet side, are investigated. First, for improving the performance of data transmission, user datagram protocol （UDP） is adopted as the main stand for controllers and plants using INCSs. Second, a dual-channel secure transmission scheme （DCSTS）based on data transmission characteristics of INCSs is proposed, in which a raw UDP channel and a secure TCP （transmission control protocol） connection making use of SSL/TLS （secure sockets layer/transport layer security） are included. Further, a networked control protocol （NCP） at application layer for supporting DCSTS between the controllers and plants in INCSs is designed, and it also aims at providing a universal communication mechanism for interoperability of devices among the networked control laboratories in Beijing Institute of Technology of China, Central South University of China and Tokyo University of Technology of Japan. By means of a networked single-degree-of-free- dom robot arm, an INCS under the new protocol and security environment is created. Compared with systems such as IPSec or SSL/TLS, which may cause more than 91% network throughput deduction, the new DCSTS protocol may yield results ten times better, being just 5.67%.

平原河网水动力优化调度与水环境改善的响应关系研究

平原河网河道流动性较差是引起水环境污染严重的主要原因,引调水是改善平原河网地区水环境的重要措施之一。本文以佛山市三山围为例,基于实测资料构建了河网水动力水质耦合模型,采用15天的连续水动力水质监测数据对模型进行了验证,并将NSE和RMSE模型评价指标用于模型评价。同时结合地势、潮汐规律和景观水位等设计了4种闸控模式和7种景观控制水位,共28种模拟工况,模拟分析了不同条件下的水动力水质改善与内外江潮位变化、引排水流量及其空间分布、闸控方式和景观水位等综合响应机理。结果表明:构建的模型合理可靠。研究区域受引排水路径的影响,河道流动性差异较为显著。综合考虑河道流量分布、水流路径、分汊河道分流作用和外江污染物浓度等对河道水质的作用效果并有效结合潮汐河网的动态水环境容量和污染源排放的时空分布对水环境的改善作用将非常显著。与低景观控制水位相比,高景观控制水位的内江动态水环境容量相对较大,污染物浓度较低。景观控制水位从0.2 m上升至0.8 m时,不同引调水路径的引水流量上升28.00%~64.70%,断面氨氮浓度削减了0.85~5.50 mg/L,削减比例达到28.89%~67.23%。本研究为平原潮汐河网水环境优化调度研究提供了新的思路,为相关部门对平原河网的水环境改善提供了重要的参考。

作战方案驱动的可学习兵棋推演智能体研究

为了使智能体能够应对兵棋推演中的复杂作战场景和作战目的,提出作战方案驱动的可学习兵棋推演智能体架构。剖析智能体对兵棋系统的“依附特性”和“松耦合特性”,得到智能体的可学习要求;在智能体框架设计中,使用作战方案压减智能体学习范围。通过有限状态机对应作战方案中的作战阶段知识,依据作战方案框架确定智能体决策空间,设计可学习的深层神经网络实施关键决策空间探索,神经网络采用先验知识模仿学习模式和深度强化学习模式。该架构能迭代探索人类难以充分梳理清楚的多棋子最优部署和协作问题。

基于自组织特征映射模型(SOFM)网络的中国自然资源生态安全区划

自然资源生态安全是国家安全的重要组成部分,自然资源生态安全区划对保障区域可持续发展提供了重要途径。基于自然资源数据、生态环境数据和相关区划资料,从生态敏感性与生态服务重要性角度构建了自然资源生态安全评价指标体系,进而揭示了中国自然资源生态安全的空间格局;通过建立区划的原则和指标,按照一级区主要反映自然资源空间分布格局,二级区主要揭示自然资源生态安全水平的差异,采用SOFM网络制订了中国自然资源生态安全区划方案。结果显示:(1)中国自然资源生态安全水平整体偏低,以中警与重警状态区域为主,安全和较安全状态的区域仅占24.22%,其中低安全等级区多分布于400mm等降水量线以西的干旱、半干旱区,高安全等级区则集中分布于水热资源与生物资源较为丰富的东南部地区;(2)中国自然资源生态安全区划方案包括8个一级区与27个二级区,总结归纳各大区自然资源的特征和威胁生态安全的问题,并针对二级区自然资源生态安全状况提出了对策建议。研究结果可为分区、分类推进全国自然资源可持续利用和国土空间优化提供理论支持与决策依据。

黄河流域水资源配置研究与展望

黄河流域水资源合理利用和优化配置问题受到社会广泛关注。从20世纪80年代开始,相继开展了黄河“八七”分水方案制定和水量统一调度、新径流条件下分水方案调整、支流水量分配方案制定,以及变化环境下分水方案优化等一系列持续性研究,研究成果提升了黄河流域水资源优化调控和节约集约利用水平,实现黄河干流连续25 a不断流。面向黄河流域生态保护和高质量发展重大国家战略的推进,从流域水循环和水资源演变、用水规律与需水预测、健康水平衡、流域地表水-地下水统一配置、多层级水网构建情况下水资源优化配置、应对极端枯水的水资源韧性调配、数字孪生黄河建设等方面,提出了适应环境变化和国家多层级水网构建下的黄河流域水资源研究和突破方向。

新型智算中心组网方案研究

当前算力需求爆发式增长,通用计算也朝着人工智能计算的方向演进,新型智算中心网络作为算力间数据交互的中心,成为影响算力性能发挥的关键。分析了智算中心对网络的需求,对网络协议、架构和运维管理等方面进行了深入研究,并结合业界发展情况,给出了智算中心组网方案的建议。

Cybersecurity and Privacy Protection in Vehicular Networks (VANETs)

As Vehicular ad hoc networks (VANETs) become more sophisticated, the importance of integrating data protection and cybersecurity is increasingly evident. This paper offers a comprehensive investigation into the challenges and solutions associated with the privacy implications within VANETs, rooted in an intricate landscape of cross-jurisdictional data protection regulations. Our examination underscores the unique nature of VANETs, which, unlike other ad-hoc networks, demand heightened security and privacy considerations due to their exposure to sensitive data such as vehicle identifiers, routes, and more. Through a rigorous exploration of pseudonymization schemes, with a notable emphasis on the Density-based Location Privacy (DLP) method, we elucidate the potential to mitigate and sometimes sidestep the heavy compliance burdens associated with data protection laws. Furthermore, this paper illuminates the cybersecurity vulnerabilities inherent to VANETs, proposing robust countermeasures, including secure data transmission protocols. In synthesizing our findings, we advocate for the proactive adoption of protective mechanisms to facilitate the broader acceptance of VANET technology while concurrently addressing regulatory and cybersecurity hurdles.

海上风电与海洋油气融合实现绿色电能替代研究

我国海域蕴含丰富的油气资源和风能资源,海洋油气与海上风电地理分布相同,部分资源存在重叠。随着海上风电的快速发展,如何实现海上风电为海洋油气平台安全稳定绿色供电,促进海洋油气和海上风电互补供电深度融合,提高海洋能源资源综合利用效率,是未来研究的重要方向。以我国渤海海域某油田群为研究对象,通过分析当地风资源、风电出力特性和油气平台用电负荷特性,探讨海上风电接入海洋油气平台组网供电模式,开展海上风电容量优化配置研究,实现平台微电网系统功率平衡,并提出组网供电初步方案。