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A Novel Edge-Supported Cost-Efficient Resource Management Approach for smart Grid System

A Novel Edge-Supported Cost-Efficient Resource Management Ap...

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international conference on computing, analytics and Networking (ICCAN)

作者： Mishra, Jyotirmaya Sheetlani, Jitendra Reddy, K. Hemant K. Roy, Diptendu Sinha Gandhi Inst Engn & Technol Dept Comp Sci & Engn Gunupur India Sri Satya Sai Univ Technol & Med Sci Dept Comp Sci & Engn Sehore Madhya Pradesh India Natl Inst Sci & Technol Dept Comp Sci & Engn Berhampur India Natl Inst Technol Dept Comp Sci & Engn Shillong Meghalaya India

ISBN: (纸本)9789811078712;9789811078705

The smart grids, a new-generation power supply system, have the capacity to lowering the cost, can increase service provision tremendously, and make surroundings greener as compared to conventional power supply systems. To interact with the physical world and widen its capabilities, integrated smart grid cyber-physical system (SG-CPS) can be used for computation, communication, and control. To support smart grid (SG), cloud components are employed for storing and processing users' power demand and control flow information generated at different control components like smart meter (SM), home energy management (HEM), phasor measurement units (PMUs), and soon. But storing smart grid data to cloud and processing incurs unacceptable delays. This paper addresses quality-of-service (QoS) requirements of SGs by integrating fog computing along with cloud computing infrastructure for realizing an Edge computing integrated smart Grid (EC-iSG). To that end, this paper presents novel heuristics for resource management of such integrated infrastructure that accounts for parameters such as uplink and downlink communication costs, cost for VM deployment, and cost for communicating among base stations. The results presented demonstrate the efficacy of the proposed methodology.

关键词： smart grid Fog computing Cloud computing Cost optimization HEM PMU

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A SELF-ADAPTED SWARM ARCHITECTURE TO HANDLE BIG data FOR "FACTORIES OF THE FUTURE" 9

A SELF-ADAPTED SWARM ARCHITECTURE TO HANDLE BIG DATA FOR "FA...

引用

9th IFAC/IFIP/IFORS/IISE/INFORMS conference on Manufacturing Modelling, Management and Control (IFAC MIM)

作者： Guerreiro, Guilherme Costa, Ruben Figueiras, Paulo Graca, Diogo Jardim-Goncalves, Ricardo Univ Nova Lisboa FCT Dep Engn Eletrotecn CTSUNINOVA P-2829516 Caparica Portugal Volkswagen Autoeuropa Quinta Marquesa P-2954024 Quinta Do Anjo Portugal

Currently, the manufacturing sector is facing a technological evolution with the so-called Industry 4.0. This poses a paradigm shift, enabling companies to be more competitive by taking advantage of innovative technologies(cloud computing, cyber-physical systems, big data analytics and deep learning), pursuing near-zero fault, near real-time reactivity to any problem, better traceability, more predictability in manufacturing, while working to achieve cheaper product customization. The challenges arise when the dimensionality of the data generated by manufacturing processes grows, affecting the performance of algorithms, decreasing it quickly as the dimension of the search space increases. Handling large datasets with a good performance in a limited time should be the main concern in Big data analytics. This paper focuses on a logistic process of car manufacturing, where batteries are unloaded from trucks to warehouse, and then to the point of fit, where they are assembled into the car. It presents a complete data-driven architecture, using a swarm approach for distributed data processing among all data stages, where processing nodes with different tasks and technologies can work cooperatively to complete a job. The work presented in this paper is funded by the EU project BOOST4.0, focusing on a smart manufacturing scenario for the automotive sector. (C) 2019, IFAC (international Federation of Automatic Control) Hosting by Elsevier Ltd. All rights reserved.

关键词： Swarm Intelligence Industry4.0 Big data Manufacturing data Processing Cyber-Physical Systems

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On Delay-Sensitive Healthcare data analytics at the Network Edge Based on Deep Learning 14

On Delay-Sensitive Healthcare Data Analytics at the Network ...

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14th IEEE international Wireless Communications and Mobile computing conference (IWCMC)

作者： Fadlullah, Zubair Md. Pathan, Al-Sakib Khan Gacanin, Haris Tohoku Univ Grad Sch Informat Sci Sendai Miyagi Japan Southeast Univ Dept CSE Dhaka Bangladesh Nokia Bell Labs Access Lab Antwerp Belgium

ISBN: (纸本)9781538620700

As the age of the Internet of Things (IoT) continues to flourish, the concept of smart healthcare has taken an unprecedented turn due to interdisciplinary thrusts. To carry the big healthcare data emanating from the plethora of bio-sensors and machines in the IoT sensing plane to the central cloud, next generation high-speed delivery networks are essential. On the other hand, once the IoT data are delivered to the cloud, the massive IoT healthcare data are processed and analyzed employing the state-of-the-art analytics tools such as deep machine learning and so forth. However, given the explosion of big data (from various sources in addition to the healthcare data), the delivery network as well the cloud may experience network and computational congestion, respectively. This may impact the real-time analytics of the healthcare data, e.g., critical for in-house patients and senior citizens aging at home. To address this issue, the emerging IoT edge analytics concept can be regarded as a promising solution to process the big healthcare data close to the source. For large-scale IoT deployments, this functionality is critical because of the sheer volumes of data being generated. In this paper, we propose a deep learning based IoT edge analytics approach to support intelligent healthcare for residential users. The performance of the proposal is validated using computer-based simulation for online training of a real dataset. The reported results of our proposal exhibit encouraging performance in terms of low loss rate, high accuracy, and low execution time to support near real-time actionable decision making on the healthcare data.

关键词： Medical services Machine learning Delays Cloud computing Senior citizens Sensors Aging"

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The Future of smart Clothing: A Kit Designed to Detect Vital Health Parameters. 2

The Future of Smart Clothing: A Kit Designed to Detect Vital...

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2nd international conference on intelligent computing and Control Systems (ICICCS)

作者： Kansara, Rashi Chauhan, Jigar Univ Coll Engn Mumbai Maharashtra India

ISBN: (纸本)9781538628423

The Future of smart Clothing: A Kit Designed to Detect Vital Health Parameters is a prototype that can be embedded in any type of fabric anytime, anywhere to find health related information and people can view their previously collected data on mobile application through their authentication, thereby measuring few parameters such as heart rate (BPM) using Pulse Sensor. The system counts the number of times emotion change detected by using Galvanic Skin Response (GSR) Sensor and real time body temperature is also calculated using an accurate Temperature Sensor and lastly 3- Axis Accelerometer Sensor shows the step count of an individual. All these above sensed parameters will be sent to a central processor for processing and then transferring it to a mobile web application through wireless communication. The data can be viewed by the user on smart phone application. Real time data analytics can be done for diagnosing various symptoms for detecting diseases.

关键词： Pulse sensor Beats Per Minute (BPM) Galvanic Skin Response (GSR) 3-Axis Accelerometer MLX90614 Digital Infrared Temperature Sensor NodeMCU Android Application WiFi

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Machine learning based smart Surveillance System

Machine learning based Smart Surveillance System

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international conference on I-SMAC (IoT in Social, Mobile, analytics and Cloud) (I-SMAC)

作者： Vanita Babanne Nikita Sandeep Mahajan Renu Lalchand Sharma Pratiksha Pradip Gargate Computer science RMDSSOE (of Affiliation) Pune India Computer Science RMDSSOE Pune India

ISBN: (数字)9781728143651

ISBN: (纸本)9781728143668

In modern cities video surveillance becomes an important part for protection and security. In modern cities for security and protection smart cameras equipped with intelligent analysis of video can monitor events and give alert system by capturing abnormal activities and events. Now a day's there are many cameras have been equipped for surveillance in all over world. So we are going to implementation focus on video surveillance by giving video contents containing early fire events detection, abnormal activities and smart parking system and crowd estimation. We are going to overcome existing drawbacks of post investigation techniques of video surveillance systems by providing alert generation system. Our work is based on machine learning techniques for video analysis with better performance and event detection with advantages of alert generation.

关键词： Video surveillance Machine learning Big data Cloud computing conferences

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Internet of Things for smart Healthcare: A Hardware Perspective

Internet of Things for Smart Healthcare: A Hardware Perspect...

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First international conference of intelligent computing and Engineering (ICOICE)

作者： Mohammad Tabrez Quasim Mohammad Ayoub Khan Monir Abdullah Mohammad Meraj S P Singh P. Johri College of Computing & Information Technology University of Bisha Bisha Saudi Arabia NIMS University Jaipur Rajasthan India School of Computing Sciences and Engineering Galgotia University Gr Noida India

ISBN: (数字)9781728144870

ISBN: (纸本)9781728144887

With the fast of ultra-fast 5G/6G mobile wireless, Artificial Intelligence (AI), and Big data analytics, the Internet of Things (IoT) is getting great attention in healthcare industry. The combing of these powerful technologies with the Internet of Things will likely revolutionize the healthcare industry in next few years. The growth of IoT in healthcare industry using these latest technologies will transform the way patients are monitored and treated remotely to improve the productivity of the healthcare industry workers. This paper presents the state-of-the-art research relating to IoT and Health care with focus on hardware requirements, complexity and challenges.

关键词： Medical services Internet of Things Monitoring Biomedical monitoring Sensors Hardware Medical diagnostic imaging

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intelligent Network Services enabling Industrial IoT Systems for Flexible smart Manufacturing 14

Intelligent Network Services enabling Industrial IoT Systems...

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14th international conference on Wireless and Mobile computing, Networking and Communications (WiMob)

作者： Behnke, Daniel Mueller, Marcel Boek, Patrick-Benjamin Bonnet, Jose Weidmuller Grp Detmold Germany Altice Labs Aveiro Portugal

ISBN: (纸本)9781538668764

In recent years developments of Industrial IoT components and data analysis paved the way for optimization of manufacturing processes. Additional sensors provide manifold data of machine parks and individual machines. Due to the huge amount of data, communication and storage systems get into focus of research. In this work, we are introducing a concept for the usage of Network Services in smart manufacturing, creating the missing link between the closed machine architecture and flexible SG services.

关键词： Firewalls (computing) data analysis 5G mobile communication Wireless communication smart manufacturing Sensors Internet

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High-performance computing based big data analytics for smart manufacturing 13

High-performance computing based big data analytics for smar...

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ASME 2018 13th international Manufacturing Science and Engineering conference, MSEC 2018

作者： Yang, Yuhang Dora Cai, Y. Lu, Qiyue Zhang, Yifang Koric, Seid Shao, Chenhui Department of Mechanical Science and Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States National Center for Supercomputing Applications University of Illinois at Urbana-Champaign UrbanaIL61801 United States

ISBN: (纸本)9780791851371

With the rapid development of sensing, communication, and computing technologies and infrastructure, today's manufacturing industry is marching towards a big data era and a new generation of digitalization and intelligence. The availability of big data provides us with a golden opportunity to promote smart manufacturing. Nevertheless, the deployment and popularization of big data analytics in manufacturing is still at its nascent stage. One critical challenge results from the lack of highperformance computing (HPC) capability, which is crucial for responsive and intelligent decision-making in the modern manufacturing industry. To address this challenge, this paper proposes a framework and some general guidelines for implementing big data analytics in an HPC environment. The details of the whole workflow, from the prototype to the final application, are highlighted. A case study for intelligent 3D sensing with real-world manufacturing data is presented to demonstrate the effectiveness of the proposed framework. Copyright © 2018 ASME.

关键词： Genetic algorithms

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A Review on Machine Learning Trends, Application and Challenges in Internet of Things 7

A Review on Machine Learning Trends, Application and Challen...

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7th international conference on computing, Communications and Informatics (ICACCI)

作者： Reddy, R. Ravinder Mamatha, Ch Reddy, R. Govardhan CBIT Hyderabad India JBIET Hyderabad Telangana India

ISBN: (纸本)9781538653142

The rapid growth of the internet and its connecting devices makes the things connected to the entire world. In these days, the world is running on the internet of things (IoT), with the increased communication capability and most effective way of communication and transmission lines many of things are connected to the internet. The advent and revolution in smart sensor technology attracts many of the users and most of the devices are connected to the internet. Internet connected sensors and devices generate exponential data. Knowingly or unknowingly IoT is generating lots of data. This data is significant in decision making system, but the problem is how to segregate this data for the future analysis purposes. The Internet of Things (IoT) offers engineering teams an innovative way to collect data and observe the status of their products, services and equipment in the field. Machine learning techniques are used to learn from these data to make the device or thing intelligent. For example, using the machine learning identifying the abnormalities from our wearable and taking necessary actions like calling doctor and ambulance automatically when it necessary.

关键词： machine learning data analytics Internet of things smart city

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Towards IoT analytics. A Vf-OS Approach 9

Towards IoT Analytics. A Vf-OS Approach

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9th international conference on intelligent Systems (IS)

作者： Anaya, Victor Fraile, Francisco Aguayo, Armando Garcia, Oscar Ortiz, Angel Univ Politecn Valencia CIGIP Valencia Spain ICE Informat Catalyst Manchester Lancs England ICE Informat Catalyst Valencia Spain

ISBN: (纸本)9781538670972

New challenges faced by the current Industrial Internet of Things (IIoT) are stringent latency, capacity constraints, uninterrupted devices with intermittent connectivity cannot be solved with centralized cloud computing architecture [1]. Sending all the data to the cloud will require prohibitively high network bandwith [2]. vf-OS - Virtual Factory Open Operating System - is a project funded by the 112020 Framework Programme of the European Commission under Grant Agreement 723710, with the purpose to provide an Open Operating System for Virtual Factories composed of a kernel, application programming interface, and middleware specifically designed for the factory of the future. This paper presents the Virtual Open Operating System (vf-OS) approach to IoT analytics, and how vf-OS 10 (Input/Output) components and of-OS analytics can be used to capture data from sensors and Program Logic Controllers (PLCs) to generate and run machine learning models to do analytics in the cloud and in the edge. The main contribution of the paper is the proposal of new architectural solutions and providing alternative scenarios for developers developing applications for the manufacturing sector.

关键词： driver IoT Edge computing analytics IIoT

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