电力物联网中5G边缘计算技术的研究

为解决电力物联网中海量设备接入诉求,云服务器集中处理架构逐渐向边缘计算模式演进, 电力物联网演进为云、网、边、端四层模型。本文根据现有电力业务类型和传输需求,分析了电力物联网中边缘计算面临的技术难点,提出采用基于时隙的灵活调度,并结合自时隙结构和灵活时隙配置等方案改进边缘计算网络中的端到端时延; 采用基于5QI配置及ARP优先级方案提升电力物联网中业务保障效果,采用业务安全隔离是保障电力业务安全运行。文章最后给出了基于5G技术的边缘计算网关体系架设和技术特点,指出可以充分利用基于5G的创新技术, 提升电力物联网中边缘计算有效性和安全性,满足电力物联网蓬勃应用的需要。     

物联网环境下移动边缘计算安全问题

物联网时代多类型流量的接入与应用场景的多样性,从计算能力、存储和业务时延等多个方面对当前集中式云计算架构提出新的挑战.移动边缘计算(MEC)作为一种在网络边缘为用户提供服务的解决方案,能够满足物联网多样性的业务需求.针对移动边缘计算在物联网中的安全问题,对移动边缘计算的概念、应用场景和安全进程进行介绍,着重从数据传输安全、存储安全和计算安全3个方面阐述了移动边缘计算在物联网时代所面临的安全挑战.     

基于物联网的智能电网信息化建设研究

随着科学技术的不断发展,对基于物联网的智能电网信息化建设研究也逐渐成为人们关注的焦点。尤其在信息感知、高效信息处理以及可靠传输方面,物联网发挥重要的作用,也因此成为电力系统信息化技术发展的必然趋势。本文主要对电力物联网的基本概述、电力物联网的功能应用以及电力物联网的关键技术进行探析。     

基于边缘计算的电力物联网组网方案研究

根据边缘计算和组网平台技术,设计了一种电力物联网组网系统,能够完成电力物联网的智能自动组网,采用多通道采集技术对电力物联网数据进行信息采集和数据归纳,在边缘计算的基础上融入正交离散多小波变换算法,使系统能够快速判断数据,并进行组网.最后,通过对比验证分析列出三种不同组网方案的数据,从而直观看出本设计的优越性,通过列出的柱状图和描点图发现本研究的电力物联网组网方案延时时间更短,组网效率更高,证明了本设计方案的可行性.     

电力信息系统安全可信的一站式业务服务系统的研究

本文介绍了电力信息系统安全可信的一站式业务服务系统的总体结构,由信任与授权服务、可信Web Service计算、一站式电力业务服务框架等模块组成;然后详细描述了一站式电力业务服务框架核心功能模块的设计;最后介绍了一站式电力业务服务的身份认证、服务请求、服务调度处理的实现流程。     

基于零信任框架的泛在电力物联网安全防护研究

建设泛在电力物联网是充分应用"大云物移智"等现代化信息技术,实现电力系统各个环节的万物互联、人机交互.在这种新场景下,身份认证、数据安全、信息交互等网络安全问题迫切需要解决,本文从零信任框架出发,构建泛在电力物联网安全防护方案,重点从身份认证、动态授权、监测审计三个方面提出防护建议,打破传统以边界防护为基础的网络安全框架,为泛在电力物联网安全防护提供新思路、新方法,以适应泛在电力物联网的业务发展需求.     

泛在电力物联网可信安全接入方案

为全面提高全业务泛在电力物联网安全综合防御能力,解决目前全业务泛在电力物联网安全防护指导和终端认证机制的缺失和不足,本文提出一种泛在电力物联网可信安全接入方案。首先给电力物联网终端层设备确定一个唯一标识的指纹信息;然后结合该指纹信息,采用身份标识密码技术实现终端层设备的接入认证,阻断非法终端的接入;最后设计合法终端的身份信息安全传递机制,根据身份信息对合法终端的异常行为进行溯源。     

物联网无人机应用关键技术研究

物联网中应用无人机可从空中实现服务投递为目的的物联网业务以及包括视频监视、传感数据收集、救灾应急通信、智能交通等在内的物联网增值服务。对于物联网无人机应用,首先对其典型架构及其特点作了详细介绍;详细分析了物联网无人机应用关键技术;最后对其未来的发展趋势进行了展望。     

物联网中可信控制技术研究

在物物通信和物联网技术的基础上综述了国内外物联网的发展现状,提出了物联网中无线传感器网络(WSN)与lnternet的互联融合模型.结合此模型,研究了其可信控制关键技术,包括可信路由技术、信任控制技术,以促进物联网在中国的安全发展.     

基于可信执行环境的物联网边缘流处理安全技术综述

万物互联时代,物联网中感知设备持续产生大量的敏感数据。实时且安全的数据流处理是面向物联网关键应用中需要解决的一个挑战。在近年兴起的边缘计算模式下,借助靠近终端的设备执行计算密集型任务与存储大量的终端设备数据,物联网中数据流处理的安全性和实时性可以得到有效的提升。然而,在基于边缘的物联网流处理架构下,数据被暴露在边缘设备易受攻击的软件堆栈中,从而给边缘带来了新的安全威胁。为此,文章对基于可信执行环境的物联网边缘流处理安全技术进行研究。从边缘出发,介绍边缘安全流处理相关背景并探讨边缘安全流处理的具体解决方案,接着分析主流方案的实验结果,最后展望未来研究方向。     

物联网技术及其安全性研究

针对物联网_技术的发展趋势问题,基于物联网的体系结构和关键技术,分析了物联网的安全需求与相关特性,构建了一个以RFID安全和隐私保护为重点的物联网安全框架,提出了应对物联网所面临的安全挑战的解决途径,最后对物联网未来发展趋势作了展望.     

基于区块链的物联网认证机制综述

随着物联网(Internet of Things, IoT)技术的高速发展,各类智能设备数量激增,身份认证成为保障IoT安全的首要需求.区块链作为一种分布式账本技术,提供了去信任的协作环境和安全的数据管理平台,使用区块链技术驱动IoT认证成为学术界和工业界关注的热点.基于云计算和云边协同两种架构分析IoT身份认证机制设计的主要需求,总结区块链技术应用于IoT场景面临的挑战;梳理现有IoT身份认证机制的工作,并将其归结为基于密钥的认证、基于证书的认证和基于身份的认证;分析应用区块链技术的IoT认证工作,并根据认证对象和附加属性对相关文献进行归纳和总结.从形式化和非形式化两个方向总结基于区块链的IoT认证机制的安全性分析方法.最后展望了未来研究方向.     

A secure fog-based platform for SCADA-based IoT critical infrastructure

The rapid proliferation of Internet of things (IoT) devices, such as smart meters and water valves, into industrial critical infrastructures and control systems has put stringent performance and scalability requirements on modern Supervisory Control and Data Acquisition (SCADA) systems. While cloud computing has enabled modern SCADA systems to cope with the increasing amount of data generated by sensors, actuators, and control devices, there has been a growing interest recently to deploy edge data centers in fog architectures to secure low-latency and enhanced security for mission-critical data. However, fog security and privacy for SCADA-based IoT critical infrastructures remains an under-researched area. To address this challenge, this contribution proposes a novel security “toolbox” to reinforce the integrity, security, and privacy of SCADA-based IoT critical infrastructure at the fog layer. The toolbox incorporates a key feature: a cryptographic-based access approach to the cloud services using identity-based cryptography and signature schemes at the fog layer. We present the implementation details of a prototype for our proposed secure fog-based platform and provide performance evaluation results to demonstrate the appropriateness of the proposed platform in a real-world scenario. These results can pave the way toward the development of a more secure and trusted SCADA-based IoT critical infrastructure, which is essential to counter cyber threats against next-generation critical infrastructure and industrial control systems. The results from the experiments demonstrate a superior performance of the secure fog-based platform, which is around 2.8 seconds when adding five virtual machines (VMs), 3.2 seconds when adding 10 VMs, and 112 seconds when adding 1000 VMs, compared to the multilevel user access control platform.     

IoT edge computing-enabled collaborative tracking system for manufacturing resources in industrial park

In manufacturing industry, the movement of manufacturing resources in production logistics often affects the overall efficiency. This research is motivated by a world-leading air-conditioner manufacturer. In order to provide the right manufacturing resources for subsequent production steps, excessive time and human effort has been consumed in locating the manufacturing resources in a huge industrial park. The development of Internet of Things (IoT) has made a profound impact on establish smart manufacturing workshop and tracking applications, however a growing trend of data quantity that generated from massive, heterogeneous and bottomed manufacturing resources objects pose challenge to centralized decision. In this study, the concept of edge-computing deeply integrated in collaborative tracking purpose in virtue of IoT technology. An IoT edge computing enabled collaborative tracking architecture is developed to offload the computation pressure and realize distributed decision making. A supervised learning of genetic tracking method is innovatively presented to ensure tracking accuracy and effectiveness. Finally, the research output is developed and implemented in a real-life industrial park for verification. The results show that the proposed tracking method not only performs constant improving accuracy up to 96.14% after learning compared to other tracking method, but also ensure quick responsiveness and scalability.     

物联网安全测评技术综述

近年来,物联网大规模应用于智能制造、智能家居、智慧医疗等产业,物联网的安全问题日益突出,给物联网的发展带来了前所未有的挑战。安全测评技术是保障物联网安全的重要手段,在物联网应用的整个开发生命周期都需要进行安全测评工作,以保证物联网服务的安全性和健壮性。物联网节点面临计算能力、体积和功耗受限等挑战,智慧城市等应用场景提出了大规模泛在异构连接和复杂跨域的需求。本文首先总结了目前物联网中常用的安全测评方法和风险管理技术;然后从绿色、智能和开放三个方面分析物联网安全技术的发展现状和存在的安全问题,并总结了物联网安全测评面临的挑战以及未来的研究方向。     

Detecting Vulnerability on IoT Device Firmware: A Survey

Internet of things (IoT) devices make up 30% of all network-connected endpoints, introducing vulnerabilities and novel attacks that make many companies as primary targets for cybercriminals. To address this increasing threat surface, every organization deploying IoT devices needs to consider security risks to ensure those devices are secure and trusted. Among all the solutions for security risks, firmware security analysis is essential to fix software bugs, patch vulnerabilities, or add new security features to protect users of those vulnerable devices. However, firmware security analysis has never been an easy job due to the diversity of the execution environment and the close source of firmware. These two distinct features complicate the operations to unpack firmware samples for detailed analysis. They also make it difficult to create visual environments to emulate the running of device firmware. Although researchers have developed many novel methods to overcome various challenges in the past decade, critical barriers impede firmware security analysis in practice. Therefore, this survey is motivated to systematically review and analyze the research challenges and their solutions, considering both breadth and depth. Specifically, based on the analysis perspectives, various methods that perform security analysis on IoT devices are introduced and classified into four categories. The challenges in each category are discussed in detail, and potential solutions are proposed subsequently. We then discuss the flaws of these solutions and provide future directions for this research field. This survey can be utilized by a broad range of readers, including software developers, cyber security researchers, and software security engineers, to better understand firmware security analysis.      

Deep learning models for cloud,edge, fog,and IoT computing paradigms: Survey,recent advances,and future directions

In recent times, the machine learning (ML) community has recognized the deep learning (DL) computing model as the Gold Standard. DL has gradually become the most widely used computational approach in the field of machine learning, achieving remarkable results in various complex cognitive tasks that are comparable to, or even surpassing human performance. One of the key benefits of DL is its ability to learn from vast amounts of data. In recent years, the DL field has witnessed rapid expansion and has found successful applications in various conventional areas. Significantly, DL has outperformed established ML techniques in multiple domains, such as cloud computing, robotics, cybersecurity, and several others. Nowadays, cloud computing has become crucial owing to the constant growth of the IoT network. It remains the finest approach for putting sophisticated computational applications into use, stressing the huge data processing. Nevertheless, the cloud falls short because of the crucial limitations of cutting-edge IoT applications that produce enormous amounts of data and necessitate a quick reaction time with increased privacy. The latest trend is to adopt a decentralized distributed architecture and transfer processing and storage resources to the network edge. This eliminates the bottleneck of cloud computing as it places data processing and analytics closer to the consumer. Machine learning (ML) is being increasingly utilized at the network edge to strengthen computer programs, specifically by reducing latency and energy consumption while enhancing resource management and security. To achieve optimal outcomes in terms of efficiency, space, reliability, and safety with minimal power usage, intensive research is needed to develop and apply machine learning algorithms. This comprehensive examination of prevalent computing paradigms underscores recent advancements resulting from the integration of machine learning and emerging computing models, while also addressing the underlying open research issues along with potential future directions. Because it is thought to open up new opportunities for both interdisciplinary research and commercial applications, we present a thorough assessment of the most recent works involving the convergence of deep learning with various computing paradigms, including cloud, fog, edge, and IoT, in this contribution. We also draw attention to the main issues and possible future lines of research. We hope this survey will spur additional study and contributions in this exciting area.     

基于区块链与边缘计算的物联网访问控制模型

边缘计算的出现扩展了物联网（IoT）云-终端架构的范畴,在减少终端设备海量数据的传输和处理时延的同时也带来了新的安全问题。针对IoT边缘节点与海量异构设备间的数据安全和管理问题,并考虑到目前区块链技术广泛应用于分布式系统中数据的安全管理,提出基于区块链与边缘计算的IoT访问控制模型SC-ABAC。首先,提出集成边缘计算的IoT访问控制架构,并结合智能合约和基于属性的访问控制（ABAC）提出并设计了SC-ABAC;然后,给出工作量证明（PoW）共识算法的优化和SC-ABAC的访问控制管理流程。实验结果表明,所提模型对区块连续访问下的耗时随次数呈线性增长,连续访问过程中央处理器（CPU）的利用率稳定,安全性良好。本模型下仅查询过程存在调用合约的耗时随次数呈线性增长,策略添加和判断过程的耗时均为常数级,且优化的共识机制较PoW每100块区块共识耗时降低约18.37个百分点。可见,该模型可在IoT环境中提供去中心化、细颗粒度和动态的访问控制管理,并可在分布式系统中更快达成共识以确保数据一致性。