基于边缘计算的物联网入侵检测技术综述

物联网架构与设备的特殊性,传统集中式入侵检测方案的局限性以及物联网边缘数据的激增,都对物联网的安全性提出了更高的要求.边缘计算的出现为解决这一问题提供了新的思路.本文首先归纳总结了物联网常见攻击方式,并介绍了边缘计算相关概念;其次,本文对基于边缘计算的物联网入侵检测技术的最新研究进展进行了全面调查;最后讨论了基于边缘计...     

基于边缘计算的物联网数据传输优化方法

针对基于边缘计算的物联网架构,研究了一种新的数据传输优化架构,旨在降低数据传输延迟、提高系统效率和稳定性。本研究将边缘缓存和智能路由的概念引入物联网架构,并结合移动终端优化实现数据传输和处理的优化。本研究对数据压缩、路径选择和缓存策略等方面的优化,能够降低数据传输到云计算中心的压力,实现更快速、更高效的数据处理,在提升物联网系统性能的同时,兼顾数据安全和隐私等问题。     

基于物联网的电信运营商机房能耗监控系统

基于物联网技术设计了电信运营商机房的能耗监控管理系统。通过对子节点电信机房主设备及空调用电、机房温度等数据进行采集与分析,采用TCP/IP协议完成了远程数据服务器的采集,实现了对主设备及空调情况的远程监控;在此基础上,设计了能耗分析与管理系统,实现了用电设备不合理报警,并基于能效最大化空调控制算法实现了对远程机房空调用电的有效控制。系统实际运行表明,该系统运行稳定,能有效地降低运营商机房的能耗,机房平均月节能达到10%到20%,对于机房能耗管理有很大的意义。     

基于联合决策模型的物联网边缘计算资源分配

随着应用需求的增加,一些场景要求物联网能够支持密集型计算任务.传统物联网只能提供单机资源,且负载能力有限,无法有效解决时延、资源与任务的配置问题.于是提出基于联合决策模型的物联网边缘计算资源分配方法,利用边缘网络的计算优势来弥补物联网节点本地计算资源的不足,从而提高任务时延与峰值负载的性能.先从时延、能耗、计算资源和带宽资源方面进行分析,并考虑了节点移动、数据传输和卸载等情况带来的问题.根据时间和各类资源模型的分析,建立联合模型来得到资源分配调度的最佳决策,将最小卸载模型推演至最高总效用模型,并通过最速下降法对模型进行分解,在任务卸载率一定时,求解得到资源分配情况.通过动态时变物联网环境下的仿真,得到所提方法能够在较短的执行时间内,达到较高的任务完成率,且保持较低的能耗和资源分配数量.结果表明所提方法能够适应动态时变的物联网应用需求,有效完成任务与资源的卸载决策与调度分配.     

基于边缘计算的工业物联网容器管理引擎设计

针对工业物联网中云端压力大、工业协议标准繁多等问题,设计了一款基于边缘计算的工业物联网容器管理引擎.该引擎具有三个分布式端,分别部署了容器管理引擎KubeEdge、协议转化工具EMQ X Edge和可视化工具OCP、Kuboard,提供资源监控、镜像管理、持续集成、自动伸缩、协议转换等功能.测试显示,集群数据带宽提升339.19％,响应时间减少81.22％.把部分计算任务从云端卸载到边缘后,系统能源消耗减少30％-40％,成功解决带宽不够、云端压力大等问题.     

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.     

油田物联网系统边缘计算研究与实践

随着智能传感器和无线通信技术的发展,油田物联网系统提高了现场生产数据采集的频率和生产过程控制的效率,然而现有物联网系统仍然通过位于远程数据中心的计算资源进行数据处理和控制,网络带宽和通信延迟成为严重的瓶颈。通过对物联网系统的边缘层设备应用边缘计算技术,充分利用边缘网关的计算和存储能力,使用孤立森林算法实现异常数据检测和报警规则学习,同时对温度和阀门开关进行逻辑控制,将之前在云端的处理功能下沉在边缘端实现,降低对网络的要求,满足偏远地区油田生产需要。     

基于边缘计算的多维感知智能物联网系统设计

本设计选取ATmega328 MCU循环调用多个传感器实现多维感知;以ARM架构处理器为核心搭建Web服务器,设计数据处理与控制模块;利用WiFi与有线网实现数据的传输与外部终端访问.实测结果表明,该系统实现了对系统周围多维物理量的感知测量,具有长时间稳定运行能力,物联网数据传输时延低.     

SG-Edge: 电力物联网可信边缘计算框架关键技术

随着国家电网电力物联网的逐步推进,作为其核心支撑技术的边缘计算框架逐渐成为研究热点.首先,总结了物联网和边缘计算框架方面的已有研究工作;其次,通过分析电力物联网在业务场景、边缘计算、信息安全等方面的关键技术难题,提出了一种适应于电力物联网的可信边缘计算框架SG-Edge;随后,结合边缘框架的可信防护关键难题,给出了硬件可信引导、软件行为动态度量等关键技术方法;最后,从业务适应性、安全性以及性能等方面对SG-Edge进行了全面评估,并对未来研究可能面临的挑战进行了展望.     

Processing capacity-based decision mechanism edge computing model for IoT applications

The handling of complex tasks in IoT applications becomes difficult due to the limited availability of resources in most IoT devices. There arises a need to offload the IoT tasks with huge processing and storage to resource enriched edge and cloud. In edge computing, factors such as arrival rate, nature and size of task, network conditions, platform differences and energy consumption of IoT end devices impacts in deciding an optimal offloading mechanism. A model is developed to make a dynamic decision for offloading of tasks to edge and cloud or local execution by computing the expected time, energy consumption and processing capacity. This dynamic decision is proposed as processing capacity-based decision mechanism (PCDM) which takes the offloading decisions on new tasks by scheduling all the available devices based on processing capacity. The target devices are then selected for task execution with respect to energy consumption, task size and network time. PCDM is developed in the EDGECloudSim simulator for four different applications from various categories such as time sensitiveness, smaller in size and less energy consumption. The PCDM offloading methodology is experimented through simulations to compare with multi-criteria decision support mechanism for IoT offloading (MEDICI). Strategies based on task weightage termed as PCDM-AI, PCDM-SI, PCDM-AN, and PCDM-SN are developed and compared against the five baseline existing strategies namely IoT-P, Edge-P, Cloud-P, Random-P, and Probabilistic-P. These nine strategies are again developed using MEDICI with the same parameters of PCDM. Finally, all the approaches using PCDM and MEDICI are compared against each other for four different applications. From the simulation results, it is inferred that every application has unique approach performing better in terms of response time, total task execution, energy consumption of device, and total energy consumption of applications.     

公共机房微机节能方法探索

分析了微机机房管理中不容忽视的能耗问题,探讨了通过加强机房节能技术应用与管理维护等方面的工作,以达到机房节能降耗的目的的相关方法与手段     

Metropolitan intelligent surveillance systems for urban areas by harnessing IoT and edge computing paradigms

Recent technological advances led to the rapid and uncontrolled proliferation of intelligent surveillance systems (ISSs), serving to supervise urban areas. Driven by pressing public safety and security requirements, modern cities are being transformed into tangled cyber‐physical environments, consisting of numerous heterogeneous ISSs under different administrative domains with low or no capabilities for reuse and interaction. This isolated pattern renders itself unsustainable in city‐wide scenarios that typically require to aggregate, manage, and process multiple video streams continuously generated by distributed ISS sources. A coordinated approach is therefore required to enable an interoperable ISS for metropolitan areas, facilitating technological sustainability to prevent network bandwidth saturation. To meet these requirements, this paper combines several approaches and technologies, namely the Internet of Things, cloud computing, edge computing and big data, into a common framework to enable a unified approach to implementing an ISS at an urban scale, thus paving the way for the metropolitan intelligent surveillance system (MISS). The proposed solution aims to push data management and processing tasks as close to data sources as possible, thus increasing performance and security levels that are usually critical to surveillance systems. To demonstrate the feasibility and the effectiveness of this approach, the paper presents a case study based on a distributed ISS scenario in a crowded urban area, implemented on clustered edge devices that are able to off‐load tasks in a “horizontal” manner in the context of the developed MISS framework. As demonstrated by the initial experiments, the MISS prototype is able to obtain face recognition results 8 times faster compared with the traditional off‐loading pattern, where processing tasks are pushed “vertically” to the cloud.     

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。     

移动边缘环境下面向工作流管理的计算迁移方法

针对移动边缘环境下移动设备大量的能源消耗问题，为了优化移动设备的能源消耗，提出一种能耗感知的工作流计算迁移（EOW）方法。首先，基于排队论分析边缘设备中计算任务的平均等待时间，建立了移动设备的时间模型和能耗模型；然后，基于非支配排序算法（NSGA-Ⅲ）提出对应的计算迁移方法，对工作流的计算任务进行合理的分配，将一部分计算任务留在移动设备处理，或者迁移到边缘计算平台和远程云端，实现每个移动设备的节能目标；最后，通过CloudSim仿真平台对提出的计算迁移方法进行仿真和对比实验。实验结果表明，EOW方法能够明显地减少每个移动设备的能源消耗，同时满足每一个工作流的截止时间的要求。     

基于无服务器边缘计算下的服务负载调度算法

随着边缘计算的不断发展,其在资源管理配置方面逐渐出现相关问题,无服务器计算作为一种新的方式可以有效解决边缘计算的相关问题。然而,无服务器计算不具备在分布式边缘场景中高效处理请求所需服务负载调度的能力,针对这一问题,提出了一种基于无服务器边缘计算的服务负载调度算法(service load scheduling algorithm, SLSA)。SLSA的核心是通过隐式建模充分考虑了动态变化的节点状态、负载调度器放置等影响因素来优化整体时延,然后通过改进的平滑加权轮询调度(smooth weighted round robin, SWRR)算法进行服务调度。经仿真实验分析,SLSA在资源消耗上有着明显下降,同时在单城市场景与多城市场景下均有良好的性能表现,其中在单城市场景中相对于集中式轮询调度(round robin centralized, RRC)算法提升了43.01%,在多城市场景中提升了53.81%。实验结果表明,SLSA可以有效降低资源消耗率并提升性能。     

面向IoT的两级多接入边缘计算节能卸载策略

多接入边缘计算（multi-access edge computing,MEC）技术将计算和存储资源下沉到网络边缘,可大幅提高物联网（Internet of things,IoT）系统的计算能力和实时性。然而,MEC往往面临计算需求增长和能量受限的约束,高效的计算卸载及能耗优化机制是MEC技术中重要的研究领域。为保证计算效率的同时最大程度提升计算过程中的能效,提出了两级边缘节点（edge nodes,ENs）中继网络模型,并设计了一种计算资源及信道资源联合优化的最优能耗卸载策略算法（optimal energy consumption algorithm,OECA）。将MEC中的能效建模为0-1背包问题;以最小化系统总体能耗为目标,系统自适应地选择计算模式和分配无线信道资源;在Python环境下仿真验证了算法性能。仿真结果表明,相比于基于有向无环图的卸载策略算法（directed acyclic graph algorithm,DAGA）,OECA可将网络容量提升18.3%,能耗缩减13.1%。     

基于边缘计算的公共交通工具疫情监测系统

现有监测系统无法很好地应对疫情环境下存在的交叉传染以及追溯困难等问题,因此提出了一套基于边缘计算的公共交通检测系统的设计方案。首先,建立图数据库来储存乘车人员与乘车信息,同时使用双数据库模型防止建立索引带来的阻塞,从而完成插入效率与搜索效率的均衡;其次,在车辆人像信息提取中,采用HSV色彩空间对图片进行预处理,并建立人脸三维空间模型来提升神经网络的识别准确率,在目标佩戴口罩时,通过较明显的鼻尖特征点、下颌特征点与未遮挡的鼻梁部特征点回归出其口鼻等特征点信息;最后,通过k度搜索快速找出密切接触乘客。在特征对比测试中,该方案在BioID数据集和PubFig数据集上分别达到了99.44%和99.23%的正确率,且在两数据集上的假阴性率均小于0.01%;在图搜索效率测试中,在浅层次搜索的时候,图数据库与关系型数据库并无较大差异,当搜索层次变深时,图数据库效率更高;在验证理论可行性之后,模拟了公交车与公交站的实际环境,经测试所提系统在其中的识别准确率为99.98%,识别时间平均约为21 ms,符合疫情监测的要求。所提系统设计可以满足疫情时期公共安全的特殊需求,能够实现人员甄别、路径记录、潜在接触者搜索等功能,从而有效地保证公共交通安全。     

基于MQTT协议扩展的IoT设备完整性监控

随着物联网飞速发展,设备数量呈指数级增长,随之而来的IoT安全问题也受到了越来越多的关注.通常IoT设备完整性认证采用软件证明方法实现设备完整性校验,以便及时检测出设备中恶意软件执行所导致的系统完整性篡改.但现有IoT软件证明存在海量设备同步证明性能低、通用IoT通信协议难以扩展等问题.针对这些问题,本文提供一种轻量级的异步完整性监控方案,在通用MQTT协议上扩展软件证明安全认证消息,异步推送设备完整性信息,在保障IoT系统高安全性的同时,提高了设备完整性证明验证效率.我们的方案实现了以下3方面安全功能:以内核模块方式实现设备完整性度量功能,基于MQTT的设备身份和完整性轻量级认证扩展,基于MQTT扩展协议的异步完整性监控.本方案能够抵抗常见的软件证明和MQTT协议攻击,具有轻量级异步软件证明、通用MQTT安全扩展等特点.最后在基于MQTT的IoT认证原型系统的实验结果表明, IoT节点的完整性度量、MQTT协议连接认证、PUBLISH报文消息认证性能较高,都能满足海量IoT设备完整性监控的应用需求.     

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.