Quantum-Edge Cloud Computing for IoT: Bridging the Gap between Cloud, Edge, and Quantum Technologies

The rapid expansion of the Internet of Things (IoT) has driven the need for advanced computational frameworks capable of handling the complex data processing and security challenges that modern IoT applications demand. However, traditional cloud computing frameworks face significant latency, scalability, and security issues. Quantum-Edge Cloud Computing (QECC) offers an innovative solution by integrating the computational power of quantum computing with the low-latency advantages of edge computing and the scalability of cloud computing resources. This study is grounded in an extensive literature review, performance improvements, and metrics data from Bangladesh, focusing on smart city infrastructure, healthcare monitoring, and the industrial IoT sector. The discussion covers vital elements, including integrating quantum cryptography to enhance data security, the critical role of edge computing in reducing response times, and cloud computing’s ability to support large-scale IoT networks with its extensive resources. Through case studies such as the application of quantum sensors in autonomous vehicles, the practical impact of QECC is demonstrated. Additionally, the paper outlines future research opportunities, including developing quantum-resistant encryption techniques and optimizing quantum algorithms for edge computing. The convergence of these technologies in QECC has the potential to overcome the current limitations of IoT frameworks, setting a new standard for future IoT applications.

Edge Cloud Selection in Mobile Edge Computing(MEC)-Aided Applications for Industrial Internet of Things(IIoT)Services

In many IIoT architectures,various devices connect to the edge cloud via gateway systems.For data processing,numerous data are delivered to the edge cloud.Delivering data to an appropriate edge cloud is critical to improve IIoT service efficiency.There are two types of costs for this kind of IoT network:a communication cost and a computing cost.For service efficiency,the communication cost of data transmission should be minimized,and the computing cost in the edge cloud should be also minimized.Therefore,in this paper,the communication cost for data transmission is defined as the delay factor,and the computing cost in the edge cloud is defined as the waiting time of the computing intensity.The proposed method selects an edge cloud that minimizes the total cost of the communication and computing costs.That is,a device chooses a routing path to the selected edge cloud based on the costs.The proposed method controls the data flows in a mesh-structured network and appropriately distributes the data processing load.The performance of the proposed method is validated through extensive computer simulation.When the transition probability from good to bad is 0.3 and the transition probability from bad to good is 0.7 in wireless and edge cloud states,the proposed method reduced both the average delay and the service pause counts to about 25%of the existing method.

基于云网吧GPU算力业务技术研究

云网吧GPU边缘算力,以“统一架构、统一运营、统一管理”为原则进行总体技术方案设计,实现资源的多场景复用。在算力应用上,通过算力调度平台,实现多节点算力的一体编排,将算力资源复用给网吧、渲染、云电脑等多类型的用户,资源利用率提高50%;硬件方面采用了CPU+GPU直通的模式,区别于传统的虚拟化方案,对游戏兼容性好,可兼容市面上99%的PC端游戏;此外在串流能力上,通过自适应视频流的编解码方式,网吧场景最高支持2k/165fps的性能,家庭娱乐和图形设计场景最高支持1080P/60fps的性能。同时,基于某省份运营商“3+6+35+X”全光算力底座和全球首个端到端NG-OTN政企专网,实现云网吧端到端全光接入解决方案的试点。

When Edge Computing Meets IoT Systems: Analysis of Case Studies

On a macroscopic level,an edge computing architecture looks like a distributed and decentralized IT(Information Technology)architecture.More in detail,it could be defined as a mesh network of micro data centers capable of processing and storing critical data locally,and to transmit all data received and/or processed to a central data center or a cloud storage repository.This network topology,also taking advantage of the availability on the market of cost-effective small form factor(SFF)electronic components and systems decreasing,brings the essential components of processing,storage,and networking closer to the sources that generate the data.The typical use case is that of Internet of Things(IoT)devices and implementations,which often face latency problems,lack of bandwidth,reliability,which cannot be addressed through the conventional cloud model.In this context,the edge computing architecture can reduce the size of data to be sent to the cloud,processing critical data,sensitive to latency,at the point of origin,via a smart device,or sending it to an intermediate server,located nearby.The aim of this paper is to report some of the main aspects and significant features of edge computing and analyzing several popular case studies.

Optimized Energy Efficient Strategy for Data Reduction Between Edge Devices in Cloud-IoT

Numerous Internet of Things(IoT)systems produce massive volumes of information that must be handled and answered in a quite short period.The growing energy usage related to the migration of data into the cloud is one of the biggest problems.Edge computation helps users unload the workload again from cloud near the source of the information that must be handled to save time,increase security,and reduce the congestion of networks.Therefore,in this paper,Optimized Energy Efficient Strategy(OEES)has been proposed for extracting,distributing,evaluating the data on the edge devices.In the initial stage of OEES,before the transmission state,the data gathered from edge devices are supported by a fast error like reduction that is regarded as the largest energy user of an IoT system.The initial stage is followed by the reconstructing and the processing state.The processed data is transmitted to the nodes through controlled deep learning techniques.The entire stage of data collection,transmission and data reduction between edge devices uses less energy.The experimental results indicate that the volume of data transferred decreases and does not impact the professional data performance and predictive accuracy.Energy consumption of 7.38 KJ and energy conservation of 55.57 kJ was found in the proposed OEES scheme.Predictive accuracy is 97.5 percent,data performance rate was 97.65 percent,and execution time is 14.49 ms.

Edge of Things Inspired Robust Intrusion Detection Framework for Scalable and Decentralized Applications

Ubiquitous data monitoring and processing with minimal latency is one of the crucial challenges in real-time and scalable applications.Internet of Things(IoT),fog computing,edge computing,cloud computing,and the edge of things are the spine of all real-time and scalable applications.Conspicuously,this study proposed a novel framework for a real-time and scalable application that changes dynamically with time.In this study,IoT deployment is recommended for data acquisition.The Pre-Processing of data with local edge and fog nodes is implemented in this study.The thresholdoriented data classification method is deployed to improve the intrusion detection mechanism’s performance.The employment of machine learningempowered intelligent algorithms in a distributed manner is implemented to enhance the overall response rate of the layered framework.The placement of respondent nodes near the framework’s IoT layer minimizes the network’s latency.For economic evaluation of the proposed framework with minimal efforts,EdgeCloudSim and FogNetSim++simulation environments are deployed in this study.The experimental results confirm the robustness of the proposed system by its improvised threshold-oriented data classification and intrusion detection approach,improved response rate,and prediction mechanism.Moreover,the proposed layered framework provides a robust solution for real-time and scalable applications that changes dynamically with time.

面向多智能体与双层卸载的车联网卸载算法

在车联网(IoV)边缘计算环境中,针对如何高效地进行任务卸载和资源分配来缓解移动车辆存储和计算能力有限的问题,提出多智能体与双层卸载的IoV卸载算法。首先,提出移动边缘计算(MEC)服务器与车辆以及空闲车辆(MEC-V-NTVC)互联的3层网络模型,建立了任务模型、判断模型和计算模型;其次,将任务车辆的计算卸载以及资源分配抽象成部分可观测马尔可夫决策过程(POMDP),并提出双层卸载机制以达到最小化系统总成本的目的。基于空闲车辆云以及单调值函数分解QMIX,提出一种基于双层卸载机制的深度强化学习卸载算法DLSQMIX。该算法协调任务车辆、空闲车辆以及环境信息,在考虑车辆任务时间约束的情况下,充分利用MEC服务器以及空闲车辆的计算能力,求得系统最优卸载决策。从边缘服务器、空闲车辆的计算能力、任务车辆、空闲车辆的数量以及平均任务量等方面对系统开销和时延进行对比。仿真实验结果表明,DLSQMIX算法能够有效求解任务卸载问题,与遗传算法(GA)、粒子群优化(PSO)算法以及QMIX算法相比,所提算法的系统开销减小2.52%~3.91%,时延降低3.50%~6.59%。

电力物联网边缘智能:概念、架构、技术及应用

近年来,随着传感器、采集装置、感知终端的规模化部署,以及人工智能、5G、北斗等新技术的融合应用,智能巡检、在线监测、需求响应等电力物联网应用产生海量感知数据,数据上传至云端服务器会占用大量通信带宽,为网络通道和云端资源带来巨大压力,处理分析的实时性与时效性也不满足应用要求。为解决上述问题,考虑将边缘计算和人工智能赋予电力物联网,电力物联网边缘智能技术应运而生。电力物联网边缘智能通过在边缘侧嵌入人工智能算法,在靠近数据产生源处对数据进行预处理、本地计算、推理研判,从而减少上传到云端的通信带宽需求,降低传输时延和传输功耗,为上述问题的解决提供一种有效技术路径。首先,阐释电力物联网边缘智能的概念与演进,提出电力物联网边缘智能体系架构;其次,从边缘侧芯片、边缘计算操作系统、边缘计算框架3个层次分析电力物联网边缘智能软硬件基础,同时从云边协同、模型压缩、模型加速、群体智能、联邦学习5个方面讨论电力物联网边缘智能关键技术;然后,从“发输变配用”5个环节探讨电力物联网边缘智能应用场景;最后,分析电力物联网边缘智能应用的机遇和挑战。

电力物联网下基于云边协同的计算任务放置算法

电力无线网具有高可靠、安全性优势,但存在频段资源有限、输变电场景基站取能较为困难等诸多不利因素,基于云边协同的计算任务放置算法进行电力无线网的优化研究具有重要意义。云计算作为一种集中式的解决方案可以提供充足的计算资源,但是电力物联网设备与云服务器通信时存在低带宽和高时延的问题。由此,研究人员提出了边缘计算的概念,综合云计算和边缘计算的优点,云边协同逐渐以互补运作的模式得到广泛应用。文章提出一种云边协同场景下计算任务放置的改进优化算法,即基于文化基因(memetic algorithm,MA)的计算任务放置算法,以最小化电力物联网设备的能耗以及电力物联网应用程序的执行时间。基于MA的计算任务放置算法分3个阶段:预调度阶段、并行应用程序的计算任务放置阶段和故障恢复阶段。通过仿真结果验证,与现有算法对比,文章所提算法的性能包括带宽、最大迭代数、决策时间等方面都得到显著提高。

配电物联网云边协同系统计算资源双目标优化配置方法

云边协同系统能够支持计算资源弹性扩展,适应配电物联网技术的需求。针对云边协同系统的计算资源配置问题,刻画了云边协同系统的计算资源,建立了计算业务模型,提出了批量计算业务的业务超时系数以对系统效果进行定量评价。基于上述模型,以最小化云边协同系统开销和最小化业务超时系数为目标,建立了考虑云边互动的计算资源优化配置双目标规划模型,并采用改进差分进化算法得到帕累托前沿。基于仿真算例讨论了不同双目标处理方法、通信质量、并发业务规模等对所提计算资源配置方法的影响。

基于云计算和物联网技术的控制系统探索

为解决冶金行业废渣无害化处理项目中生产效率低、质量不稳定和能耗高等问题,本文提出了基于云计算和物联网的“云物冶控平台系统”。该系统融合了远程云计算中心和就地数据中心,通过物联网层实时采集设备运行数据,控制层执行优化决策和控制功能,用户端提供可视化监控和指令输入。系统设计遵循智能化、安全性、高效性和易用性的原则,包括工业物联网、云边协同控制、大数据分析和机器学习等关键技术。相比传统系统,该平台在数据处理、远程维护、成本效益等方面具有显著优势。结合案例分析,该系统在技术可行性、经济效益、环保贡献等方面表现出色,为冶金行业带来全新的智能管控模式,同时具备良好的扩展性和可持续性。

基于云边协同的电力物联终端数据轻量化处理方法

电力物联网在大规模化高频数据传输应用中,由于现场存在海量实时数据传输,因而会造成对现场数据无法进行有效压缩,进而导致云边信息传输效率低以及传输流量费用高等问题。该文提出一种基于云边协同的电力物联数据轻量化处理方法(LPCE)。该方法基于一种物模型结构,通过提取云边交互数据形成压缩字典,并将压缩字典同步至边缘设备来完成海量数据压缩。针对传统常见的压缩方法,该文在数据有效率、压缩率、压缩时间以及压缩速度和解压缩速度等方面分别做了对比分析实验。实验结果表明,在面对高频、实时传输的电力物联网系统中以JSON(JavaScript Object Notation)格式报文数据为代表的交互数据的压缩,提出的LPCE方法具有明显效果和优势。该方法实现了电力物联网云边数据无损压缩,可减少电力物联网云边之间的冗余数据传输,提升了云边数据传输效率,降低了云边之间数据传输成本。

家电行业工业互联网平台的云边协同资源优化调度研究

针对家电行业在数智化转型中的痛点,海尔集团建立了基于云边协同的家电行业工业互联网平台,通过平台内置模块能够实现云边资源的智能分配和任务优化调度。该模块包含任务响应接收与处理子模块、任务部署子模块和任务调度算法库子模块三部分,能够从大规模任务统一调度、异构计算协同部署与边缘任务实时重构三个层面对云边计算资源进行高效管控,满足异构计算、任务部署、协同优化的实时需求。目前该平台已经在多家工厂落地应用,为家电企业的数字化、智能化转型提供了良好的启示借鉴。

Recent advances in Industrial Internet:insights and challenges

The Industrial Internet is a promising technology combining industrial systems with Internet connectivity to significantly improve the product efficiency and reduce production cost by cooperating with intelligent devices,in which the advanced computing,big data analysis and intelligent perception techniques have been involved.This paper comprehensively surveys the recent advances of the Industrial Internet,including reference architectures,key technologies,relative applications and future challenges.Reference architectures which have been proposed for different application scenarios and their corresponding characteristics are summarized.Key technologies,such as cloud computing,mobile edge computing,fog computing,which are classified according to different layers in the architecture,are presented to support a variety of applications in the Industrial Internet.Meanwhile,future challenges and research trends are discussed as well to promote further research of the Industrial Internet.

Computing Paradigms in Emerging Vehicular Environments:A Review

Determining how to structure vehicular network environments can be done in various ways.Here,we highlight vehicle networks’evolution from vehicular ad-hoc networks(VANET)to the internet of vehicles(Io Vs),listing their benefits and limitations.We also highlight the reasons in adopting wireless technologies,in particular,IEEE 802.11 p and 5 G vehicle-toeverything,as well as the use of paradigms able to store and analyze a vast amount of data to produce intelligence and their applications in vehicular environments.We also correlate the use of each of these paradigms with the desire to meet existing intelligent transportation systems’requirements.The presentation of each paradigm is given from a historical and logical standpoint.In particular,vehicular fog computing improves on the deficiences of vehicular cloud computing,so both are not exclusive from the application point of view.We also emphasize some security issues that are linked to the characteristics of these paradigms and vehicular networks,showing that they complement each other and share problems and limitations.As these networks still have many opportunities to grow in both concept and application,we finally discuss concepts and technologies that we believe are beneficial.Throughout this work,we emphasize the crucial role of these concepts for the well-being of humanity.

An Overview of Privacy Preserving Schemes for Industrial Internet of Things

The concept of Internet of Everything is like a revolutionary storm,bringing the whole society closer together.Internet of Things(IoT)has played a vital role in the process.With the rise of the concept of Industry 4.0,intelligent transformation is taking place in the industrial field.As a new concept,an industrial IoT system has also attracted the attention of industry and academia.In an actual industrial scenario,a large number of devices will generate numerous industrial datasets.The computing efficiency of an industrial IoT system is greatly improved with the help of using either cloud computing or edge computing.However,privacy issues may seriously harmed interests of users.In this article,we summarize privacy issues in a cloud-or an edge-based industrial IoT system.The privacy analysis includes data privacy,location privacy,query and identity privacy.In addition,we also review privacy solutions when applying software defined network and blockchain under the above two systems.Next,we analyze the computational complexity and privacy protection performance of these solutions.Finally,we discuss open issues to facilitate further studies.

Optimal Data Placement and Replication Approach for SIoT with Edge

Social networks(SNs)are sources with extreme number of users around the world who are all sharing data like images,audio,and video to their friends using IoT devices.This concept is the so-called Social Internet of Things(SIot).The evolving nature of edge-cloud computing has enabled storage of a large volume of data from various sources,and this task demands an efficient storage procedure.For this kind of large volume of data storage,the usage of data replication using edge with geo-distributed cloud service area is suited to fulfill the user’s expectations with low latency.The major issue is the way to store the data and replicate these large data items optimally and allocate the request from the data center efficiently.For efficient storage of these data,we use edge server,which is part of the cloud server,in this study.Thus,the data are distributed and stored with quick access,which will reduce the latency with response.The proposed data placement approach learns with machine learning(ML)algorithm called radial basis kernel function assisted with support vector machine(RBF-SVM)to classify the data center for storing the user and friend’s data from the SIoT devices.These learning algorithms will be used to predict the workload of the data stored in the data center as either edge or cloud depending on the existing time slots.The data placement with dynamic nature is also optimized using the proposed dynamic graph partitioning(GP)method to meet the individual user’s demand of low latency with minimum costs.This way will keep the SIoT data placement efficient and effective over time.Accordingly,this proposed data placement and replication approach introduces three kinds of innovations compared with the existing data placement approach.(i)Rather than storing the user data in a single cloud,this study uses the edge server closest to the SIoT devices for faster access with reduced response time.(ii)The classification algorithm called RBF-SVM is used to find storage for user for reducing data replication.(iii)Dynamic GP is introduced for data placement with reduced latency and minimum cost to fulfil the dynamic nature of the SN.The simulation result of this approach obtains reduced latency of 130 ms and minimum cost compared with those of the existing data placement approaches.Therefore,our proposed data placement with ML-based learning on edge provides promising results in terms of efficiency,effectiveness,and performance with reduced latency and minimum cost.

面向区域能源互联网的边云协同架构及其优化策略研究

现有区域能源互联网(regional energy internet,REI)呈现出多维度、多主体的运行特征,若采用传统集中式云端服务架构,系统易引发高延迟、云爆炸等问题。为此,提出一种边云协同架构及其优化策略。首先,建立REI的数学模型;其次,以REI为分布式边缘控制单元,建立含云服务层、边缘服务层、设备层的REI边云协同架构,以实际工作量分配计算任务;然后,综合考虑系统多方利益主体博弈关系,设计云服务层、边缘服务层优化策略;最后,在此基础上,为提升紧急情况下系统恢复运行的速度,提出一种应急处理方法。通过两种多REI算例测试,结果表明:与传统云端服务架构相比,所提架构在计算速度、应急处理速度方面具有显著优势;所提优化策略能够有效提升REI的运行收益。

智能配电网边缘计算研究现状与展望

随着人工智能和物联网技术的快速发展,配电网正逐步走向智能化。但是海量数据使云计算面临时延变长、网络拥堵、隐私泄露等问题。边缘计算作为一种新的计算范式,可以通过网络边缘节点,有效解决上述问题,在智能配电网中的应用日益广泛。本文针对近年来智能配电网边缘计算技术进行了综述。首先概述智能配电网的特征以及在该应用场景中边缘计算的定义和架构;其次从不同维度分析了应用现状,包括故障诊断与检测、数据分析、优化调度和数据安全与保护,最后对智能配电网场景下的研究挑战进行了总结,提出数据精细化管理、资源模块化共享、边缘安全性维护3个方面的研究展望。

基于边缘计算的软件定义云制造和柔性资源调度研究

本研究的重点是在云制造环境中实现快速重构、实现灵活的资源调度、开发资源潜力以应对各种变化。因此,本文首先提出了一种新的基于云和软件定义网络(SDN)的制造模型——软件定义云制造(SDCM),该模型将控制逻辑从自动化硬件转移到软件上。这种转变意义重大,因为软件可以充当制造系统的“大脑”,并且可以轻松更改或更新以支持快速系统重新配置、运营和演进。随后,边缘计算被引入,以接近终端的计算和存储能力来补充云。另一个关键问题是管理由不同服务质量(QoS)要求的大量物联网(IoT)数据传输而导致的严重网络拥塞。基于SDCM的虚拟化和灵活的网络能力,本研究形式化了面向复杂制造任务集的时间敏感性数据流量控制问题,并考虑了子任务分配和数据路由路径选择。为了解决这一优化问题,提出了一种将遗传算法(GA)、Dijkstra最短路径算法和排队算法相结合的方法。实验结果表明,该方法能有效地防止网络拥塞,减少SDCM中的总通信延迟。