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检索条件"任意字段=International Conference on Intelligent and Smart Computing in Data Analytics, ISCDA 2020"

共 3239 条记录，以下是181-190 订阅

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The smart grid system design based on the big data technology

The smart grid system design based on the big data technolog...

引用

1st international conference on Big data analytics for Cyber-Physical System in smart City, BDCPS 2019

作者： Zhang, Hui Yan, Wentao Wang, Wei Su, Qi Zhou, Wei Zhang, Bin Beijing China Power Information Technology Co. Ltd. Beijing100085 China State Grid Shandong Electric Power Company Information Communication Co. Ltd. Ji’nanShandong250000 China

ISBN: (纸本)9789811525674

The design and application of the smart grid system based on the big data technology has achieved some results, but no system has been formed. Therefore, the design, research and application of the smart grid system are still in the exploratory stage. With the continuous maturity of the big data technology application, the application of the big data technology in the smart grid system design will provide a more intelligent and perfect management mechanism for the grid operation, so as to improve management efficiency and achieve the expected goal of the power grid safe operation. © Springer Nature Singapore Pte Ltd. 2020.

关键词： smart power grids

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A Scalable and Composable analytics Platform for Distributed Wide-Area Tracking 20

A Scalable and Composable Analytics Platform for Distributed...

引用

20th international conference on Distributed computing and Networking (ICDCN)

作者： Khochare, Aakash Simmhan, Yogesh Indian Inst Sci Dept Computat & Data Sci Bangalore Karnataka India

ISBN: (纸本)9781450360944

smart cities are a manifestation of the Internet of Things (IoT). The push for smart cities has led to the proliferation of video cameras in public spaces [1]. London is expected to deploy 642,000 cameras by 2020. These cameras will be used primarily for surveillance of the city for urban safety. At the same time, such smart city deployments have also seen edge and fog computing paradigms gain preference over the cloud-only computing model [2]. This is due to the network proximity of the edge and fog devices to the data sources, offering lower latency access to data and lower bandwidth requirements to push high volume data, such as video streams, to the cloud. © 2019 Association for computing Machinery.

关键词： smart city

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Research Directions In The Internet of Every Things(IoET)

Research Directions In The Internet of Every Things(IoET)

引用

IEEE international conference on computing, Communication and Automation (ICCCA)

作者： Deshpande, Prachi Iyer, Brijesh Dr Babasaheb Ambedkar Technol Univ Dept Comp Engn Lonere 402103 Maharashtra India Dr Babasaheb Ambedkar Technol Univ Dept E & TC Engn Lonere 402103 Maharashtra India

ISBN: (纸本)9781509064717

It is believed that technology will take over the day to day life of human begin. No one can remain isolated from the adaptation of technology. Hence it is the need of the hour to introduce a technology which will do the day to day life tasks in human life with smart and intelligent way. Hence, thispaper critically discuss the emergence and progress of Internet of Every Things (IoET) as the next generation communication tool. Further, the various research avenues and open challenges in the area of IoET are discussed in detail.

关键词： data analytics Internet Security Sensor networks smart City

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smart Food Scanner System Based on Mobile Edge computing 8

Smart Food Scanner System Based on Mobile Edge Computing

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8th IEEE international conference on Cloud Engineering (IC2E)

作者： Javadi, Bahman Quoc Lap Trieu Matawie, Kenan M. Calheiros, Rodrigo N. Western Sydney Univ Sch Comp Data & Math Sci Sydney NSW Australia

ISBN: (纸本)9781728110998

smart applications, including Internet of Things (IoT) and Big data analytics, are traditionally hosted by cloud infrastructures, which can result in high latency and cost beyond users expectation. Edge computing has emerged as a paradigm that can alleviate the pressure on clouds by delegating parts of the computation to devices in the edge of the network, at closer proximity to end users and IoT devices. In this paper, we discuss a smart application, built on top of mobile edge computing concept, to enables users to measure and analyse their food intake and support nutritional decision-making. The approach utilizes mobile edge computing to offload application computations and communications to the edge, thus saving battery life, increasing the processing capacity, and improving user comfort. In order to develop this system, we propose a loosely coupled architecture for a smart food scanner and then implement it using various IoT sensors. The performance evaluation results reveal that the implemented system can be used as an interactive appliance by users with minimum dependency and usage of their mobile phones.

关键词： Mobile edge computing

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Construction of smart campus under the background of big data

Construction of smart campus under the background of big dat...

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international conference on Application of intelligent Systems in Multi-modal Information analytics, MMIA 2020

作者： Feng, Peilu Hohhot Minzu College Hohhot010051 China

ISBN: (纸本)9783030515553

Since the beginning of the 21st century, the Internet has not only been widely used in various industrial networks, but has gradually extended to every aspect of people’s lives. In recent years, the Internet technology based on big data has been extended to campus, which has put forward higher requirements for the construction of smart campus. Cloud computing and big data technology are typical representatives of information operation efficiency, and new technologies emerge one after another. This paper is based on big data and cloud computing, and carries out research on the construction of smart campus. The purpose of this paper is to analyze the construction strategy of smart campus service system in the context of big data, and to analyze and apply big data technology from a deeper level to provide support for expanding the application space of big data. Based on the comprehensive comparison of existing research results, this paper proposes a smart campus system model based on big data analysis to improve the efficiency of information operation in campus. The experimental results of this paper show that this model has a strong ability to store and process data, and can fully realize the sharing and construction of resources in the smart campus. © Springer Nature Switzerland AG 2021.

关键词： Big data

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Forecasting the potential influence of Covid-19 using data Science and analytics

Forecasting the potential influence of Covid-19 using Data S...

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2021 IEEE international conference on Innovative computing, intelligent Communication and smart Electrical Systems, ICSES 2021

作者： Murale, Monish Devi, N. Gokul, Ar Guru Leela Rani, P. Navishvardanaa, S. Sri Venkateswara College of Engineering Department of Information Technology Sriperumbudur India

ISBN: (纸本)9781665435215

The major purpose of the study topic is to use data science to anticipate the future effect of COVID-19 using existing data. The goal of this research is to use data science and analytics to generate precise forecasts of the number of substantiations and deaths. LSTM, GRUs, and Prophet are the major models created and tested for the solution. An LSTM model is a type of Recurrent Neural Network that is used to forecast datasets with increasingly changing patterns. Gated recurrent units only has two gateways: reboot and update. The prophet is best suited for forecasting assignments involving observation swith at least a year of history. The various models discussed above were used to the covid-19 data set to forecast the number of positive cases, active cases, and deaths associated with covid-19. We trained the model using data from April and May 2021 to demonstrate a comparison between the observed and expected number of positive events. To assume the future happing of COVID-19 by applying models which are in use, so that we will be able to calculate the impact of the disease's potential spread throughout the human being, preparing our selves to make proper planning and idea to prevent further transmission and equip health systems to manage the disease properly and battle the worldwide pandemic. © 2021 IEEE.

关键词： Speech recognition

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Towards Security Monitoring for Cloud Analytic Applications 4

Towards Security Monitoring for Cloud Analytic Applications

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4th IEEE international conference on Big data Security on Cloud (BigdataSecurity) / 4th IEEE international conference on High Performance and smart computing (HPSC) / 3rd IEEE international conference on intelligent data and Security (IDS)

作者： Elsayed, Marwa Zulkernine, Mohammad Queens Univ Sch Comp Kingston ON K7L 2N8 Canada

ISBN: (纸本)9781538643990

Cloud computing is empowering new innovations for big data. At the heart, cloud analytic applications become the most-hyped revolution. Cloud analytic applications have remarkable benefits for big data processing, making it easy, fast, scalable and cost-effective;albeit, they pose many security risks. Security breaches due to malicious, vulnerable, or misconfigured analytic applications are considered the top security risks to big data. The risk is further expanded from the coupling of data analytics with the cloud. Effective security measures, delivered by cloud analytic providers, to detect such malicious and anomalous activities are still missing. This paper presents real-time security monitoring as a service (SMaaS). SMaaS is a novel framework that aims to detect security anomalies in cloud analytical applications running on Hadoop clusters. It aims to detect vulnerable, malicious, and misconfigured applications which violate data integrity and confidentiality. Towards achieving this goal, we are motivated by leveraging big data pipeline that mixes advanced software technologies (Apache NiFi, Hive, and Zeppelin) to automate the collection, management, analysis, and visualization of log data from multiple sources, making it cohesive and comprehensive for security inspection. SMaaS monitors a candidate application by collecting log data on real-time. Then, it leverages log data analysis to model the application's execution in terms of information flow. The information flow model is crucial for profiling processing activities conducted throughout the application's execution. Such model, in turn, enriches the detection of various types of security anomalies. We evaluate the detection effectiveness and performance efficiency of our framework. The experiments are conducted over benchmark applications. The evaluation results demonstrate that our system is a viable solution, yet very efficient. Our system does not make modification in the monitored cluster, nor does it

关键词： Big data security log analysis information flow control anomaly detection

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Distributed-Reasoning for Task Scheduling through Distributed Internet of Things Controller

Distributed-Reasoning for Task Scheduling through Distribute...

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12th international conference on Ambient Systems, Networks and Technologies (ANT) / 4th international conference on Emerging data and Industry 4.0 (EDI40)

作者： Firouzi, Ramin Rahmani, Rahim Kanter, Theo Stockholm Univ Dept Comp & Syst Sci S-16407 Stockholm Sweden

The adoption of distributed reasoning through ubiquitous instrumentation in the distributed Internet of Things (IoT) leads to outstanding improvements in real-time monitoring, optimization, fault tolerance, traffic, healthcare, etc. Using a ubiquitous controller to interconnect devices in the IoT is still in the embryonic stage. However, it has the potential to create distributed-intelligent IoT solutions that are more efficient and secure than centric intelligence. It is essential to take a new direction to design a distributed intelligent controller for task scheduling that can firstly dynamically interact with a smart environment in efficient real-time data processing and secondly respond to flexible changes. To address these issues, we outline a two-level intelligence scheme that leverages edge computing to improve distributed IoT. The edge scheme shifts the capability of streaming processing from the cloud to edge devices to alleviate latency, support better reliable streaming analytics, and improve smart IoT applications' performance. In this work, to enable better, reliable, and flexible streaming analytics and overcome the data uncertainties, we proposed an IoT gateway controller that provides low-level intelligence by using a fuzzy abductive reasoner. Numerical simulations support the feasibility of our proposed approaches. (C) 2021 The Authors. Published by Elsevier B.V.

关键词： Internet of Things (IoT) edge computing reasoning fuzzy controller abductive reasoning

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IoT/CPS Ecosystem for Efficient Electricity Consumption Invited Paper 10

IoT/CPS Ecosystem for Efficient Electricity Consumption Invi...

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10th international Green and Sustainable computing conference (IGSC)

作者： Alharthi, Saad Johnson, Princy Alharthi, Mohammad Jose, Chacko Liverpool John Moores Uni Elect & Elect Engn Liverpool Merseyside England Saudi Elect Co Maintenance Dept Mecca Saudi Arabia

ISBN: (纸本)9781728154169

Modern society relies on smart systems like internet of things (IoT) and cyber physical systems (CPS) to monitor and control physical processes. The widespread deployment of IoT and CPSs result in fast growth of sensor data as physical processes are constantly monitored by billions of IP-enabled sensors (44 zettabytes by 2020). Hence, fog nodes are deployed to make network edge rich in computing resources to enable real-time data analytics using artificial intelligence/machine learning (AI/ML) for Big data generated from IoT and CPSs. This paper proposes IoT/CPS ecosystem for smart grid (SG) utilizing industry 4.0 concept to manage and control the loads using an intelligent predictive controller based on artificial neural network (ANN). The ANN is trained to predict the loads in certain districts based on previous smart meter readings installed at consumers and substations. This is a novel approach which integrates IoT/CPSs ecosystem into electric power system to deliver energy to consumers with high efficiency, reduce the cost, optimize the energy consumption, improve the reliability and enable real-time monitoring of power consumption.

关键词： CPS IoT fog nodes AI/ML SG fog computing industry 4.0. AGC LFC

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A Novel Continuous Learning and Collaborative Decision Making Mechanism for Real-time Cooperation of Humanoid Service Robots 15

A Novel Continuous Learning and Collaborative Decision Makin...

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IEEE international conference on Computer and Information

作者： Jiang, Ming Zhang, Li Northumbria Univ Dept Comp Sci & Digital Technol Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9781509001545

This paper introduces and proposes a novel Continuous Learning and Collaborative Decision Making (CLCDM) mechanism to support the real-time cooperation of affective humanoid service robots in smart home/campus environment, in which many highly complicated and intelligence demanding applications, such as homecare and children education are either currently partly assisted or expected to be fully provided in the future by the collaborations of intelligent and affective humanoid robots. The core of the CLCDM approach is a streaming data analytics framework, which incorporates Big data analytics facilities and decision making under uncertainty techniques to facilitate the provision of CLCDM capability for affective humanoid service robots to succeed in serving human users needs. An experimental case study is conducted to validate a prototype implementation of the CLCDM approach and the preliminary result demonstrates the feasibility and effectiveness of the promising approach.

关键词： continuous learning collaborative decision making real-time cooperation streaming data big data analytics service robots humanoid

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