Big Data of Home Energy Management in Cloud Computing

A smart grid is the evolved form of the power grid with the integration of sensing,communication,computing,monitoring,and control technologies.These technologies make the power grid reliable,efficient,and economical.However,the smartness boosts the volume of data in the smart grid.To obligate full benefits,big data has attractive techniques to process and analyze smart grid data.This paper presents and simulates a framework to make sure the use of big data computing technique in the smart grid.The offered framework comprises of the following four layers:(i)Data source layer,(ii)Data transmission layer,(iii)Data storage and computing layer,and(iv)Data analysis layer.As a proof of concept,the framework is simulated by taking the dataset of three cities of the Pakistan region and by considering two cloud-based data centers.The results are analyzed by taking into account the following parameters:(i)Heavy load data center,(ii)The impact of peak hour,(iii)High network delay,and(iv)The low network delay.The presented framework may help the power grid to achieve reliability,sustainability,and cost-efficiency for both the users and service providers.

The Roles of 5G Mobile Broadband in the Development of IoT, Big Data, Cloud and SDN

The fast technology development of 5G mobile broadband (5G), Internet of Things (IoT), Big Data Analytics (Big Data), Cloud Computing (Cloud) and Software Defined Networks (SDN) has made those technologies one after another and created strong interdependence among one another. For example, IoT applications that generate small data with large volume and fast velocity will need 5G with characteristics of high data rate and low latency to transmit such data faster and cheaper. On the other hand, those data also need Cloud to process and to store and furthermore, SDN to provide scalable network infrastructure to transport this large volume of data in an optimal way. This article explores the technical relationships among the development of IoT, Big Data, Cloud, and SDN in the coming 5G era and illustrates several ongoing programs and applications at National Chiao Tung University that are based on the converging of those technologies.

Artificial intelligence ecosystem for computational psychiatry:Ideas to practice

Computational psychiatry is an emerging field that not only explores the biological basis of mental illness but also considers the diagnoses and identifies the underlying mechanisms.One of the key strengths of computational psychiatry is that it may identify patterns in large datasets that are not easily identifiable.This may help researchers develop more effective treatments and interventions for mental health problems.This paper is a narrative review that reviews the literature and produces an artificial intelligence ecosystem for computational psychiatry.The artificial intelligence ecosystem for computational psychiatry includes data acquisition,preparation,modeling,application,and evaluation.This approach allows researchers to integrate data from a variety of sources,such as brain imaging,genetics,and behavioral experiments,to obtain a more complete understanding of mental health conditions.Through the process of data preprocessing,training,and testing,the data that are required for model building can be prepared.By using machine learning,neural networks,artificial intelligence,and other methods,researchers have been able to develop diagnostic tools that can accurately identify mental health conditions based on a patient’s symptoms and other factors.Despite the continuous development and breakthrough of computational psychiatry,it has not yet influenced routine clinical practice and still faces many challenges,such as data availability and quality,biological risks,equity,and data protection.As we move progress in this field,it is vital to ensure that computational psychiatry remains accessible and inclusive so that all researchers may contribute to this significant and exciting field.

Similarity Intelligence:Similarity Based Reasoning,Computing,and Analytics

Similarity has been playing an important role in computer science,artificial intelligence(AI)and data science.However,similarity intelligence has been ignored in these disciplines.Similarity intelligence is a process of discovering intelligence through similarity.This article will explore similarity intelligence,similarity-based reasoning,similarity computing and analytics.More specifically,this article looks at the similarity as an intelligence and its impact on a few areas in the real world.It explores similarity intelligence accompanying experience-based intelligence,knowledge-based intelligence,and data-based intelligence to play an important role in computer science,AI,and data science.This article explores similarity-based reasoning(SBR)and proposes three similarity-based inference rules.It then examines similarity computing and analytics,and a multiagent SBR system.The main contributions of this article are:1)Similarity intelligence is discovered from experience-based intelligence consisting of data-based intelligence and knowledge-based intelligence.2)Similarity-based reasoning,computing and analytics can be used to create similarity intelligence.The proposed approach will facilitate research and development of similarity intelligence,similarity computing and analytics,machine learning and case-based reasoning.

High-efficient energy saving processing of big data of communication under mobile cloud computing

From 21st century,it is hard for traditional storage and algorithm to provide service with high quality because of big data of communication which grows rapidly.Thus,cloud computing technology with relatively low cost of hardware facilities is created.However,to guarantee the quality of service in the situation of the rapid growth of data volume,the energy consumption cost of cloud computing begins to exceed the hardware cost.In order to solve the problems mentioned above,this study briefly introduced the virtual machine and its energy consumption model in the mobile cloud environment,introduced the basic principle of the virtual machine migration strategy based on the artificial bee colony algorithm and then simulated the performance of processing strategy to big data of communication based on artificial bee colony algorithm in mobile cloud computing environment by CloudSim3.0 software,which was compared with the performance of two algorithms,resource management(RM)and genetic algorithm(GA).The results showed that the power consumption of the migration strategy based on the artificial bee colony algorithm was lower than the other two strategies,and there were fewer failed virtual machines under the same number of requests,which meant that the service quality was higher.

Review of Artificial Intelligence with Retailing Sector

This research service provides an original perspective on how artificial intelligence(AI)is making its way into the retail sector.Retail has entered a new era where ECommerce and technology bellwethers like Alibaba,Amazon,Apple,Baidu,Facebook,Google,Microsoft,and Tencent have raised consumers’expectations.AI is enabling automated decision-making with accuracy and speed,based on data analytics,coupled with selflearning abilities.The retail sector has witnessed the dramatic evolution with the rapid digitalization of communication(i.e.Internet)and;smart phones and devices.Customer is no longer the same as they became more empowered by smart devices which has entirely prevailed their expectation,habits,style of shopping and investigating the shops.This article outlines the Significant innovation done in retails which helped them to evolve such as Artificial Intelligence(AI),Big data and Internet of Things(IoT),Chatbots,Robots.This article further also discusses the ideology of various author on how AI become more profitable and a close asset to customers and retailers.

An overview of Hadoop applications in transportation big data

As an open-source cloud computing platform,Hadoop is extensively employed in a variety of sectors because of its high dependability,high scalability,and considerable benefits in processing and analyzing massive amounts of data.Consequently,to derive valuable insights from transportation big data,it is essential to leverage the Hadoop big data platform for analysis and mining.To summarize the latest research progress on the application of Hadoop to transportation big data,we conducted a comprehensive review of 98 relevant articles published from 2012 to the present.Firstly,a bibliometric analysis was performed using VOSviewer software to identify the evolution trend of keywords.Secondly,we introduced the core components of Hadoop.Subsequently,we systematically reviewed the98 articles,identified the latest research progress,and classified the main application scenarios of Hadoop and its optimization framework.Based on our analysis,we identified the research gaps and future work in this area.Our review of the available research highlights that Hadoop has played a significant role in transportation big data research over the past decade.Specifically,the focus has been on transportation infrastructure monitoring,taxi operation management,travel feature analysis,traffic flow prediction,transportation big data analysis platform,traffic event monitoring and status discrimination,license plate recognition,and the shortest path.Additionally,the optimization framework of Hadoop has been studied in two main areas:the optimization of the computational model of Hadoop and the optimization of Hadoop combined with Spark.Several research results have been achieved in the field of transportation big data.However,there is less systematic research on the core technology of Hadoop,and the breadth and depth of the integration development of Hadoop and transportation big data are not sufficient.In the future,it is suggested that Hadoop may be combined with other big data frameworks such as Storm and Flink that process real-time data sources to improve the real-time processing and analysis of transportation big data.Simultaneously,the research on multi-source heterogeneous transportation big data is still a key focus.Improving existing big data technology to enable the analysis and even data compression of transportation big data can lead to new breakthroughs for intelligent transportation.

Big data analytics with applications

In this paper,recent developments on the Internet of Things(IoT)and its applications are surveyed,and the impact of newly developed Big Data(BD)on manufacturing information systems is especially discussed.Big Data analytics(BDA)has been identified as a critical technology to support data acquisition,storage,and analytics in data management systems in modern manufacturing.The purpose of the presented work is to clarify the requirements of predictive systems,and to identify research challenges and opportunities on BDA to support cloudbased information systems.

An Experimental Analysis of the Applications of Datamining Methods on Bigdata

Data mining is a procedure of separating covered up,obscure,however possibly valuable data from gigantic data.Huge Data impactsly affects logical disclosures and worth creation.Data mining(DM)with Big Data has been broadly utilized in the lifecycle of electronic items that range from the structure and generation stages to the administration organize.A far reaching examination of DM with Big Data and a survey of its application in the phases of its lifecycle won't just profit scientists to create solid research.As of late huge data have turned into a trendy expression,which constrained the analysts to extend the current data mining methods to adapt to the advanced idea of data and to grow new scientific procedures.In this paper,we build up an exact assessment technique dependent on the standard of Design of Experiment.We apply this technique to assess data mining instruments and AI calculations towards structure huge data examination for media transmission checking data.Two contextual investigations are directed to give bits of knowledge of relations between the necessities of data examination and the decision of an instrument or calculation with regards to data investigation work processes.

Time to forge ahead:The Internet of Things for healthcare

Situated at the intersection of technology and medicine,the Internet of Things(IoT)holds the promise of addressing some of healthcare's most pressing challenges,from medical error,to chronic drug shortages,to overburdened hospital systems,to dealing with the COVID-19 pandemic.However,despite considerable recent technological advances,the pace of successful implementation of promising IoT healthcare initiatives has been slow.To inspire more productive collaboration,we present here a simple—but surprisingly underrated—problemoriented approach to developing healthcare technologies.To further assist in this effort,we reviewed the various commercial,regulatory,social/cultural,and technological factors in the development of the IoT.We propose that fog computing—a technological paradigm wherein the burden of computing is shifted from a centralized cloud server closer to the data source—offers the greatest promise for building a robust and scalable healthcare IoT ecosystem.To this end,we explore the key enabling technologies that underpin the fog architecture,from the sensing layer all the way up to the cloud.It is our hope that ongoing advances in sensing,communications,cryptography,storage,machine learning,and artificial intelligence will be leveraged in meaningful ways to generate unprecedented medical intelligence and thus drive improvements in the health of many people.

On the design of an AI-driven secure communication scheme for internet of medical things environment

The Internet of Medical Things(IoMT)is a collection of smart healthcare devices,hardware infrastructure,and related software applications,that facilitate the connection of healthcare information technology system via the Internet.It is also called IoT in healthcare,facilitating secure communication of remote healthcare devices over the Internet for quick and flexible analysis of healthcare data.In other words,IoMT is an amalgam of medical devices and applications,which improves overall healthcare outcomes.However,this system is prone to securityand privacy-related attacks on healthcare data.Therefore,providing a robust security mechanism to prevent the attacks and vulnerability of IoMT is essential.To mitigate this,we proposed a new Artificial-Intelligence envisioned secure communication scheme for IoMT.The discussed network and threat models provide details of the associated network arrangement of the IoMT devices and attacks relevant to IoMT.Furthermore,we provide the security analysis of the proposed scheme to show its security against different possible attacks.Moreover,a comparative study of the proposed scheme with other similar schemes is presented.Our results show that the proposed scheme outperforms other similar schemes in terms of communication and computation costs,and security and functionality attributes.Finally,we provide a pragmatic study of the proposed scheme to observe its impact on various network performance parameters.

Design and Implementation of Cloud Platform for Intelligent Logistics in the Trend of Intellectualization

Intellectualization has become a new trend for telecom industry, driven by intelligent technology including cloud computing, big data, and Internet of things. In order to satisfy the service demand of intelligent logistics, this paper designed an intelligent logistics platform containing the main applications such as e-commerce, self-service transceiver, big data analysis, path location and distribution optimization. The intelligent logistics service platform has been built based on cloud computing to collect, store and handling multi-source heterogeneous mass data from sensors, RFID electronic tag, vehicle terminals and APP, so that the open-access cloud services including distribution, positioning, navigation, scheduling and other data services can be provided for the logistics distribution applications. And then the architecture of intelligent logistics cloud platform containing software layer(SaaS), platform layer(PaaS) and infrastructure(IaaS) has been constructed accordance with the core technology relative high concurrent processing technique, heterogeneous terminal data access, encapsulation and data mining. Therefore, intelligent logistics cloud platform can be carried out by the service mode for implementation to accelerate the construction of the symbiotic win-winlogistics ecological system and the benign development of the ICT industry in the trend of intellectualization in China.

A Trusted NUMFabric Algorithm for Congestion Price Calculation at the Internet-of-Things Datacenter

The important issues of network TCP congestion control are how to compute the link price according to the link status and regulate the data sending rate based on link congestion pricing feedback information.However,it is difficult to predict the congestion state of the link-end accurately at the source.In this paper,we presented an improved NUMFabric algorithm for calculating the overall congestion price.In the proposed scheme,the whole network structure had been obtained by the central control server in the Software Defined Network,and a kind of dual-hierarchy algorithm for calculating overall network congestion price had been demonstrated.In this scheme,the first hierarchy algorithm was set up in a central control server like Opendaylight and the guiding parameter B is obtained based on the intelligent data of global link state information.Based on the historical data,the congestion state of the network and the guiding parameter B is accurately predicted by the machine learning algorithm.The second hierarchy algorithm was installed in the Openflow link and the link price was calculated based on guiding parameter B given by the first algorithm.We evaluate this evolved NUMFabric algorithm in NS3,which demonstrated that the proposed NUMFabric algorithm could efficiently increase the link bandwidth utilization of cloud computing IoT datacenters.

Internet of robotic things for mobile robots:Concepts,technologies,challenges,applications,and future directions

Nowadays,Multi Robotic System(MRS)consisting of different robot shapes,sizes and capabilities has received significant attention from researchers and are being deployed in a variety of real-world applications.From sensors and actuators improved by communication technologies to powerful computing systems utilizing advanced Artificial Intelligence(AI)algorithms have rapidly driven the development of MRS,so the Internet of Things(IoT)in MRS has become a new topic,namely the Internet of Robotic Things(IoRT).This paper summarizes a comprehensive survey of state-of-the-art technologies for mobile robots,including general architecture,benefits,challenges,practical applications,and future research directions.In addition,remarkable research of i)multirobot navigation,ii)network architecture,routing protocols and communications,and iii)coordination among robots as well as data analysis via external computing(cloud,fog,edge,edge-cloud)are merged with the IoRT architecture according to their applicability.Moreover,security is a long-term challenge for IoRT because of various attack vectors,security flaws,and vulnerabilities.Security threats,attacks,and existing solutions based on IoRT architectures are also under scrutiny.Moreover,the identification of environmental situations that are crucial for all types of IoRT applications,such as the detection of objects,human,and obstacles,is also critically reviewed.Finally,future research directions are given by analyzing the challenges of IoRT in mobile robots.

Smart and collaborative industrial IoT: A federated learning and data space approach

Industry 4.0 has become a reality by fusing the Industrial Internet of Things(IIoT)and Artificial Intelligence(AI),providing huge opportunities in the way manufacturing companies operate.However,the adoption of this paradigm shift,particularly in the field of smart factories and production,is still in its infancy,suffering from various issues,such as the lack of high-quality data,data with high-class imbalance,or poor diversity leading to inaccurate AI models.However,data is severely fragmented across different silos owned by several parties for a range of reasons,such as compliance and legal concerns,preventing discovery and insight-driven IIoT innovation.Notably,valuable and even vital information often remains unutilized as the rise and adoption of AI and IoT in parallel with the concerns and challenges associated with privacy and security.This adversely influences interand intra-organization collaborative use of IIoT data.To tackle these challenges,this article leverages emerging multi-party technologies,privacy-enhancing techniques(e.g.,Federated Learning),and AI approaches to present a holistic,decentralized architecture to form a foundation and cradle for a cross-company collaboration platform and a federated data space to tackle the creeping fragmented data landscape.Moreover,to evaluate the efficiency of the proposed reference model,a collaborative predictive diagnostics and maintenance case study is mapped to an edge-enabled IIoT architecture.Experimental results show the potential advantages of using the proposed approach for multi-party applications accelerating sovereign data sharing through Findable,Accessible,Interoperable,and Reusable(FAIR)principles.

大数据与计算模型

当前,人工智能持续升温,大语言模型吸引了众多人士的关注,并在全球范围内掀起了一股热潮。人工智能的成功本质上不是大算力“出奇迹”,而是改变了计算模型。首先,肯定了数据对于人工智能的基础性作用,指出合成数据将是未来数据的主要来源。然后,回顾了计算模型的发展历程,重点介绍了神经网络模型与图灵模型的历史性竞争;指出了大模型的重要标志是机器涌现智能,强调大模型的本质是“压缩”;分析了大模型产生“幻觉”的原因。最后,呼吁科技界在智能化科研中要重视大科学模型。

大庆油田CIFLog测井数智云平台建设应用实践

针对大庆油田生产中测井数据量大、类型多和数据来源复杂等问题,以中国石油天然气集团有限公司大型测井处理解释软件CIFLog为基础,以业务需求为主导,采用微服务架构和测井分布式云计算技术体系,研发测井大数据存储管理、中间服务层和云端测井处理解释应用等新功能,形成了大庆油田测井数智云应用平台。目前,平台已全面安装部署到大庆油田相关单位,应用效果显著。特别在大庆油田智能决策中心,平台直接用于重点水平井随钻地质导向的现场决策,大幅提升了Ⅰ类储层的钻遇率。未来平台将重点围绕新功能研发、油田数智化应用场景建设和标准化技术体系构建等开展工作,并将取得的成果及时推广复制到西南油田、塔里木油田等油气田。CIFLog云平台作为中国油气工业软件数智化建设应用的先行典范,必将发挥越来越重要的示范引领作用。

数字经济视角下我国国际贸易转型升级的路径研究

数字经济以大数据技术为背景,通过不断创新应用新型技术带动自身发展从而实现经济价值提升。信息时代,经济发展需要具备物联网及人工智能等先进技术的支持,而数字经济结合国际贸易的发展在未来也会成为经济模式转型的重要手段。当前经济的创新融合中仍然存在未全面转型或不具备核心竞争力等弱点。要促进数字经济与国际贸易的协同发展,则需深入探索数字技术并科学运用其带动经济治理,以此为国际贸易完善经济链实现真正的转型升级。基于此,本文对数字经济影响国际贸易改革的具体因素进行分析,研究当前以数字经济为基础的国际贸易改革面临的关键问题,并针对性地提出通过数字经济促进国际贸易改革发展的应对方案,以供参考。

数字化重症快速反应体系建设探索

目的:建设数字化重症快速反应体系,实现急危重症患者早期识别和干预,保障患者医疗安全。方法:利用物联网、大数据和人工智能等技术,建设重症快速反应信息平台,完善快速反应管理体系。结果:构建急危重症大数据中心、智能预警及快速反应系统、组织管理体系三位一体的重症快速反应体系,为患者提供智慧、同质、高效的医疗服务。结论:重症快速反应体系建设在我国尚处于探索阶段,通过全流程的信息化、数字化管理,切实提高患者医疗安全保障,不断推动构建适合我国医疗高质量发展的重症快速反应体系。

数字文旅视域下乡村美食旅游开发模式创新研究——以四川省为例

数字文旅是新时代乡村美食旅游高质量发展的重要引擎,对乡村美食旅游的供给侧、需求侧和资源配置有变革性影响。将四川省乡村美食旅游开发置于数字文旅视域下,针对开发现状和开发困境,提出应从大数据文旅、云计算文旅、物联网文旅三个视角出发,创新构建“大云物”乡村美食旅游开发模式,助力乡村美食旅游数字化转型升级发展。