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Advances in Big data and Cloud computing 1st ed. 2018

引用

丛书名： Advances in intelligent Systems and computing

2018年

作者： Elijah Blessing Rajsingh Jey Veerasamy Amir H. Alavi J. Dinesh Peter

ISBN: (数字)9789811072000

ISBN: (纸本)9789811071997

This book is a compendium of the proceedings of the international conference on Big-data and Cloud computing. It includes recent advances in the areas of big data analytics, cloud computing, the Internet of nano things, cloud security, data analytics in the cloud, smart cities and grids, etc. Primarily focusing on the application of knowledge that promotes ideas for solving the problems of the society through cutting-edge technologies, it provides novel ideas that further world-class research and development. This concise compilation of articles approved by a panel of expert reviewers is an invaluable resource for researchers in the area of advanced engineering sciences.

关键词： Big data

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The Quest for Scalable and intelligent Trajectory data analytics Systems: Status Report and Future Directions

The Quest for Scalable and Intelligent Trajectory Data Analy...

引用

international conference on smart Communications and Networking (smartNets)

作者： Rim Moussa Ahmed Haddad Tarek Bejaoui LaTICE Lab. University of Tunis Tunis Tunisia MEDIATRON Lab. University of Carthage Tunis Tunisia

ISBN: (纸本)9781538692035;9781538692028

A large volume of sensor networks and trajectories of mobile objects are collected. Such data offer us high value knowledge to understand moving objects and locations, fostering a broad range of applications in smart cities, enabling intelligent transportation systems and intelligent urban computing. The next generation of roads needs to be intelligent to accommodate a future transition to fully autonomous vehicles. Consequently, we need to engineer scalable and smart Trajectory data analytics Systems in order to analyze both historical data and real-time data flows, derive insights and convert insights into decisions and *** purpose of this paper is first to identify key functional and non-functional requirements that a Trajectory data Analytical system must provide and second to survey open-source technologies designed for the analysis of general geo-referenced data.

关键词： data mining Trajectory Spatial databases Clustering algorithms Open source software Public transportation data models

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AMI smart Meter Big data analytics for Time Series of Electricity Consumption

AMI Smart Meter Big Data Analytics for Time Series of Electr...

引用

IEEE international conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Mohammad Harun Rashid Pace University New York NY US

AMI smart meters are the advanced meters capable of measuring customer energy consumption at a fine grained time interval, e.g., every 5 minutes, 15 minutes etc. The data are very sizable, and might be from different sources, along with the other social-economic metrics, which make the data management very complex. A smart grid is an intelligent electricity grid that optimizes the generation, distribution and consumption of electricity through the introduction of Information and Communication Technologies on the electricity grid. Deployment of smart grids gives space to an occurrence of new methods of machine learning and data analysis. smart grids can contain a millions of smart meters, which produce a large amount of data of electricity consumption (long time series). Big data technologies offers suitable solutions for utilities. This paper presents a thorough analysis of 5-minutes 100 anonymized commercial buildings meter data sets to explore time series of electricity consumption and on the creation of a simple forecast model, which uses similar day approach. Our big data analytics can help energy companies to improve the management of energy and services, support intelligent grid control, make an accurate forecast or to detect anomalies.

关键词： Time series analysis smart meters Meters Smoothing methods Industries smart grids Predictive models

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Transfer learning and data fusion approach to recognize ac8vi8es of daily life 12

Transfer learning and data fusion approach to recognize ac8v...

引用

12th EAI international conference on Pervasive computing Technologies for Healthcare, PervasiveHealth 2018

作者： Hernandez, Netzahualcoyotl Razzaq, Muhammad Asif Nugent, Chris McChesney, Ian Zhang, Shuai School of Computing Ulster University Newtownabbey Northern IrelandBT37-0QB United Kingdom Ubiquitous Computing Lab Kyung Hee University Global Campus 446-701 Korea Republic of

ISBN: (纸本)9781450364508

Activity recognition is a core domain within intelligent systems that utilizes the sensing devices available in an environment to identify human activity. Conventional solutions rely on machine-learning approaches and the assumption that the target scenario will Rit the algorithm training conditions, which raises the cost and effort of labelling data, as daily living environments are dynamic, unpredictable, and exposed to new activities. Hence, we take advantage of the ubiquitous presence of personal gadgets such as smart-watches combined with data fusion approaches to dynamically transfer learned knowledge across devices in a natural environment while performing daily living activities. In this paper, we focus on recognizing walking as an activity, which might enable carers or medical practitioners to monitor the risk of falling or suffering from a chronic disease whose progression is linked to a reduction in movement and mobility. Preliminary results show a 2% increase in activity recognition accuracy on the wearable approach, and a 10% improvement in accuracy when combining features from both wearable and environmental domains. © 2018 Association for computing Machinery.

关键词： Machine learning

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Performance evaluation of RPL routing protocol for IoT based power distribution network 3rd

Performance evaluation of RPL routing protocol for IoT based...

引用

3rd international conference on Next Generation computing Technologies, NGCT 2017

作者： Tom, Rijo Jackson Sankaranarayanan, Suresh SRM University Chennai603203 India

ISBN: (纸本)9789811086595

Power distribution automation plays an important role in the smart grid world. With the development of Internet of Things (IoT) technology and fog computing capability, real time streaming analytics and efficient monitoring of the grid is possible. So towards this an IoT based Power Distribution system integrated with Fog been proposed. 6LoWPAN which is IPv6 over Low Power Personal Area network been integrated into smart meters, pole transformer intelligent Electronic Devices (IEDs) and distribution line sensors of the IoT based system for transmitting data wirelessly using 6LoWPAN communication network to fog which acts as a 6LoWPAN gateway. Now for efficient transmission of data from 6LoWPAN devices to fog for analysis, there is a need to look into efficient routing protocol for efficient power distribution. Routing Protocol for Low power Lossy Networks (RPL) is the most standard routing protocol been used in 6LoWPAN network. So now for efficient distribution of power using IoT Technologies employing 6LoWPAN network, we here have evaluated the RPL routing protocol for a small area in a town with fog router as the 6LoWPAN gateway in real time. The system been evaluated for a real time situation of power distribution network for varying transmission range and number of nodes. Also the system been evaluated by changing the position of fog router to a center point of the area where maximum network efficiency is achieved. These been simulated using Cooja Network simulator running on Contiki OS. © Springer Nature Singapore Pte Ltd. 2018.

关键词： Fog computing

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Patient-Centered Innovative Monitoring System and smart Personalized Health Care Services Using ICT 31

Patient-Centered Innovative Monitoring System and Smart Pers...

引用

31st international-Business-Information-Management-Association conference

作者： Ianculescu, Marilena Alexandru, Adriana Radulescu, Constanta Zoie Natl Inst R&D Informat Bucharest Romania

ISBN: (纸本)9780999855102

The demographic shift of the worldwide population has already a heavy impact. An ageing population implies increasing necessary resources for health and social systems due to the degenerative diseases, co-morbidities and continuous loss of independence that leads to their institutionalization. In order to avoid as much as possible a dramatic change in seniors' way of living, providing in the same time all the means, supports and facilities to help them to have an active, independent and healthy ageing, multi-disciplinary, age-friendly tools must be developed. The emerging digital technology, the empowerment of the patients regarding the management of their own health, the huge openness and access to health information have already an important social impact. Health informatics, smart devices and IoT-based technologies are increasingly involved in the healthcare of seniors providing thema large range of health supporting services like patient identification, real-time monitoring of vital signals, tracking, location, or personal security. This paper highlights the main approaches for further deploy a health informatics system in order to provide a better support to seniors to live at home and to have real-time access to healthcare services. The proposed software application for the management of senior patients is an update with new components of the PROlonging ACTIVE life for an independent and healthy AGEING (ProActive Ageing) integrated platform for online services. The resulting system will be an on-line monitoring and intelligent decision support system with real-time locating facilities. The system is implemented in collaboration with Romanian health specialists.

关键词： IoT patient monitoring intelligent Decision Support Systems Big data analytics

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Transfer Learning and data Fusion Approach to Recognize Activities of Daily Life 18

Transfer Learning and Data Fusion Approach to Recognize Acti...

引用

12th EAI international conference on Pervasive computing Technologies for Healthcare (PervasiveHealth)

作者： Hernandez, Netzahualcoyotl Razzaq, Muhammad Asif Nugent, Chris McChesney, Ian Zhang, Shuai Ulster Univ Sch Comp Newtownabbey BT37 0QB North Ireland Kyung Hee Univ Ubiquitous Comp Lab Global Campus Seoul 446701 South Korea

ISBN: (纸本)9781450364508

Activity recognition is a core domain within intelligent systems that utilizes the sensing devices available in an environment to identify human activity. Conventional solutions rely on machine-learning approaches and the assumption that the target scenario will fit the algorithm training conditions, which raises the cost and effort of labelling data, as daily living environments are dynamic, unpredictable, and exposed to new activities. Hence, we take advantage of the ubiquitous presence of personal gadgets such as smart-watches combined with data fusion approaches to dynamically transfer learned knowledge across devices in a natural environment while performing daily living activities. In this paper, we focus on recognizing walking as an activity, which might enable carers or medical practitioners to monitor the risk of falling or suffering from a chronic disease whose progression is linked to a reduction in movement and mobility. Preliminary results show a 2% increase in activity recognition accuracy on the wearable approach, and a 10% improvement in accuracy when combining features from both wearable and environmental domains.

关键词： Transfer learning activity recognition data fusion wearable devices

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The Case of Big data Platform Services for Semiconductor Wafer Fabrication Foundries 9

The Case of Big Data Platform Services for Semiconductor Waf...

引用

9th international conference on Information and Communication Technology Convergence, ICTC 2018

作者： Chang, Andy Rk Chen, Yu-Ling Chou, Po-Yu Huang, Yen-Zhou Hsiao, Hung-Chang Hsieh, Tsung-Ting Hsu, Michael Lee, Chia-Chee Lee, Hsin-Yin Shih, Yun-Chi Shih, Wei-An Tang, Chien-Hsiang Tsai, Chia-Ping Tseng, Kuan-Po Information Technology Division United Microelectronics Corporation Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Tainan70101 Taiwan Taiwan

ISBN: (纸本)9781538650400

We present in this paper two novel infrastructural services based on Hadoop for big data storage and computing in a Taiwan's semiconductor wafer fabrication foundry. The two services include Hadoop data service (HDS) and distributed R language computing service (DRS), which have been built and operated in production systems for 3.5 years. They evolve over time by incrementally accommodating users' requirements. HDS is a web- based distributed big data storage facility. Users simply rely on HDS to access data objects stored in Hadoop with the HTTP protocol. In addition, HDS is scalable and reliable. Moreover, HDS is efficient and effective by intelligently selecting either Hadoop distributed file system (HDFS) or database (HBase) for publishing data objects. Specifically, HDS is transparent to existing analytics and data inquiry applications, such as Spark and Hive. While HDS is a unified storage for supporting sequential and random data accesses for big data in the wafer fabrication foundry, DRS is a distributed computing framework for typical R language users. R users employ DRS to enjoy data-parallel computations, effortlessly and seamlessly. Similar to HDS, DRS can be horizontally scaled up. It guarantees the completion of computational jobs even with failures. In particular, it adaptively reallocates computational resources on the fly, minimizing job execution time and maximizing utilization of allocated resources. This paper1 discusses the design and implementation features for HDS and DRS. © 2018 IEEE.

关键词： File organization

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computing Platforms for Big data analytics in Electric Vehicle Infrastructures

Computing Platforms for Big Data Analytics in Electric Vehic...

引用

international conference on Big data computing and Communications (BIGCOM)

作者： Md Muzakkir Hussain M.M. Sufyan Beg Mohammad Saad Alam Mahesh Krishnamurthy Qazi Mazhar Ali Department of Mechanical and Mechatronic Engineering Stellenbosch University Stellenbosch South Africa

With the emergence of ever-growing smart vehicular applications and ubiquitous deployment of IoT devices across different architectural layers of intelligent Transportation System (ITS), data-intensive analysis emerges to be a major challenge. Without powerful communication and computational support, various vehicular applications and services will still stay in the concept phase and cannot be put into practice in the daily life. In this paper, we consider the case of Electric Vehicle (EV) to smart Grid (SG) integration. The EVs are key players for Transport Oriented smart Cities (TOSC) as they help cities to become greener by reducing emissions and carbon footprint. We analyze different use-cases in EV to SG integration to show how Big data analytics (BDA) platforms can play a vital role towards successful EV rollout. We then present two computing platforms namely, distributed cloud computing and edge/fog computing. We highlighted the distinguishing features of each towards supporting BDA activities in EV integration. Finally, we provide a detailed overview of opportunities, trends, and challenges of both these computing techniques.

关键词： Cloud computing Big data Batteries Electric vehicles Roads Internet of Things

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Wireless Sensor Sequence data Model for smart Home and IoT data analytics 1st

Wireless Sensor Sequence Data Model for Smart Home and IoT D...

引用

1st international conference on Computational Intelligence and Informatics (ICCII)

作者： Suryadevara, Nagender Kumar Geethanjali Coll Engn & Technol Hyderabad Telangana India

ISBN: (纸本)9789811024719;9789811024702

In this paper, Wireless sensor sequence data mining model is demonstrated for the smart home and Internet of Things data analytics. Exploration of the sensor data patterns by correlating with the multi stream sensor data that are fused from the wireless sensor network is presented. The effective realization of the sensor data patterns from heterogeneous sensing systems for various applications of IoT can be known from the proposed conceptual data model. The conceptual data model includes the discovering of frequent pattern item sets using various computational archetypal. Results of the explicit patterns augmented for data analytics are encouraging as the prototype was tested through real-time data rather than test bed scenario data or synthetic data.

关键词： Wireless sensor networks Activities of daily living Wellness smart home Internet of Things data analytics

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