The Resource Internet of things and big data analytics toward next-generation intelligence, Nilanjan Dey, Aboul Ella Hassanien, Chintan Bhatt, Amira S. Ashour, Suresh Chandra Satapathy, editors
Internet of things and big data analytics toward next-generation intelligence, Nilanjan Dey, Aboul Ella Hassanien, Chintan Bhatt, Amira S. Ashour, Suresh Chandra Satapathy, editors
Resource Information
The item Internet of things and big data analytics toward next-generation intelligence, Nilanjan Dey, Aboul Ella Hassanien, Chintan Bhatt, Amira S. Ashour, Suresh Chandra Satapathy, editors represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Sydney Jones Library, University of Liverpool.This item is available to borrow from 1 library branch.
Resource Information
The item Internet of things and big data analytics toward next-generation intelligence, Nilanjan Dey, Aboul Ella Hassanien, Chintan Bhatt, Amira S. Ashour, Suresh Chandra Satapathy, editors represents a specific, individual, material embodiment of a distinct intellectual or artistic creation found in Sydney Jones Library, University of Liverpool.
This item is available to borrow from 1 library branch.
- Summary
- This book highlights state-of-the-art research on big data and the Internet of Things (IoT), along with related areas to ensure efficient and Internet-compatible IoT systems. It not only discusses big data security and privacy challenges, but also energy-efficient approaches to improving virtual machine placement in cloud computing environments.  Big data and the Internet of Things (IoT) are ultimately two sides of the same coin, yet extracting, analyzing and managing IoT data poses a serious challenge. Accordingly, proper analytics infrastructures/platforms should be used to analyze IoT data. Information technology (IT) allows people to upload, retrieve, store and collect information, which ultimately forms big data. The use of big data analytics has grown tremendously in just the past few years. At the same time, the IoT has entered the public consciousness, sparking people’s imaginations as to what a fully connected world can offer.  Further, the book discusses the analysis of real-time big data to derive actionable intelligence in enterprise applications in several domains, such as in industry and agriculture. It explores possible automated solutions in daily life, including structures for smart cities and automated home systems based on IoT technology, as well as health care systems that manage large amounts of data (big data) to improve clinical decisions. The book addresses the security and privacy of the IoT and big data technologies, while also revealing the impact of IoT technologies on several scenarios in smart cities design. Intended as a comprehensive introduction, it offers in-depth analysis and provides scientists, engineers and professionals the latest techniques, frameworks and strategies used in IoT and big data technologies. 
- Language
- eng
- Extent
- 1 online resource.
- Contents
-
- Preface; Contents; Internet of Things Based Sensor Networks; 1 Internet of Things Based Wireless Body Area Network in Healthcare; Abstract; 1 Introduction; 2 IoT Based WBAN for Healthcare Architecture; 2.1 Sensor Nodes; 2.2 Personal Server (Sink Node); 2.3 Medical Server; 2.4 WBAN Communication Architecture; 2.4.1 Intra-BAN Communications; 2.4.2 Inter-BAN Communication; Infrastructure Based Architecture; Ad Hoc Based Architecture; Inter-BAN Communication Technology; 2.4.3 Beyond-BAN Communication; 3 WBAN Topology; 4 Layers of WBAN; 4.1 Physical Layer; 4.2 MAC Layer; 5 WBAN Routing
- 5.1 Challenges of Routing in WBANs6 Security in WBANs; 7 WBAN Requirements in IEEE 802.15.6; 8 Challenges and Open Issues of WBANs; 9 Conclusion; References; 2 Mobile Sensor Networks and Robotics; Abstract; 1 Introduction; 2 Mobile Sensor Networks; 2.1 Coverage; 2.2 Localization; 3 Robotic Sensor Network Applications; 4 IoT Concept and Applications; 5 Coverage for Multi-Robots; 6 Localization for Robot; 7 Wireless Medical Sensor Network; 8 The Challenges in the MSN and Their Limitations; 9 Conclusion; Acknowledgements; References; Big Data Analytics
- 3 Big Data Analytics with Machine Learning ToolsAbstract; 1 Introduction; 1.1 Big Data Analytic Tools; 1.1.1 Big Data Analytic Tools; 1.1.2 Hewlett-Packard Enterprise (HPE) Big Data Platform; 1.1.3 SAP HANA Platform; 1.1.4 Microsoft Azure; 1.1.5 Oracle Big Data; 1.1.6 Other Big Data Platforms; 1.2 Machine Learning Tools for Big Data; 1.2.1 H2O; H2O Architecture and Features; H2O Algorithms; H2O Deployment and Application Example; 1.2.2 MLlib; Architecture and Features; MLlib Algorithms; MLlib Deployment and Application Example; 1.2.3 Comparative Analysis of Machine Learning Algorithms
- 1.3 Conclusions and DiscussionsReferences; 4 Real Time Big Data Analytics to Derive Actionable Intelligence in Enterprise Applications; Abstract; 1 Introduction; 2 Relationship Between the IoT and Big Data; 3 Big Data Analytics Architecture, Framework and Tools; 4 Motivation; 4.1 Data to Insight; 4.2 Shift from Data Management to Value Added Services; 5 Uses of Big Data in Industrial Environment; 5.1 Usecase #1: IoT (Internet of Things) in the Manufacturing Industry; 5.1.1 Connected devices; 5.1.2 Data Acquisition
- 5.2 Usecase #2: Data Analytics for Enterprise Business System in the Manufacturing Industry6 Challenges of Big Data Analytics; 6.1 Technology Shifts; 6.2 Data Visualization; 6.3 Heterogeneous and Imperfect Data; 6.4 Scalability; 6.5 Real Time Execution; 7 Conclusion; References; 5 Revealing Big Data Emerging Technology as Enabler of LMS Technologies Transferability; Abstract; 1 Introduction; 1.1 LMS Aspects; 1.1.1 Synchronous and Asynchronous Communication; 1.1.2 Benefits and Potentials of E-learning to the Students; 2 Literature Review; 3 Managing Change; 3.1 The Benefits
- Isbn
- 9783319604350
- Label
- Internet of things and big data analytics toward next-generation intelligence
- Title
- Internet of things and big data analytics toward next-generation intelligence
- Statement of responsibility
- Nilanjan Dey, Aboul Ella Hassanien, Chintan Bhatt, Amira S. Ashour, Suresh Chandra Satapathy, editors
- Language
- eng
- Summary
- This book highlights state-of-the-art research on big data and the Internet of Things (IoT), along with related areas to ensure efficient and Internet-compatible IoT systems. It not only discusses big data security and privacy challenges, but also energy-efficient approaches to improving virtual machine placement in cloud computing environments.  Big data and the Internet of Things (IoT) are ultimately two sides of the same coin, yet extracting, analyzing and managing IoT data poses a serious challenge. Accordingly, proper analytics infrastructures/platforms should be used to analyze IoT data. Information technology (IT) allows people to upload, retrieve, store and collect information, which ultimately forms big data. The use of big data analytics has grown tremendously in just the past few years. At the same time, the IoT has entered the public consciousness, sparking people’s imaginations as to what a fully connected world can offer.  Further, the book discusses the analysis of real-time big data to derive actionable intelligence in enterprise applications in several domains, such as in industry and agriculture. It explores possible automated solutions in daily life, including structures for smart cities and automated home systems based on IoT technology, as well as health care systems that manage large amounts of data (big data) to improve clinical decisions. The book addresses the security and privacy of the IoT and big data technologies, while also revealing the impact of IoT technologies on several scenarios in smart cities design. Intended as a comprehensive introduction, it offers in-depth analysis and provides scientists, engineers and professionals the latest techniques, frameworks and strategies used in IoT and big data technologies. 
- Cataloging source
- YDX
- Dewey number
- 004.67/8
- Index
- no index present
- LC call number
- TK5105.8857
- Literary form
- non fiction
- Nature of contents
-
- dictionaries
- bibliography
- http://library.link/vocab/relatedWorkOrContributorDate
-
- 1984-
- 1975-
- 1964-
- http://library.link/vocab/relatedWorkOrContributorName
-
- Dey, Nilanjan
- Hassanien, Aboul Ella
- Bhatt, Chintan
- Ashour, Amira
- Satapathy, Suresh Chandra
- Series statement
- Studies in big data
- Series volume
- Volume 30
- http://library.link/vocab/subjectName
-
- Internet of things
- Big data
- Label
- Internet of things and big data analytics toward next-generation intelligence, Nilanjan Dey, Aboul Ella Hassanien, Chintan Bhatt, Amira S. Ashour, Suresh Chandra Satapathy, editors
- Bibliography note
- Includes bibliographical references
- Carrier category
- online resource
- Carrier category code
-
- cr
- Carrier MARC source
- rdacarrier
- Content category
- text
- Content type code
-
- txt
- Content type MARC source
- rdacontent
- Contents
-
- Preface; Contents; Internet of Things Based Sensor Networks; 1 Internet of Things Based Wireless Body Area Network in Healthcare; Abstract; 1 Introduction; 2 IoT Based WBAN for Healthcare Architecture; 2.1 Sensor Nodes; 2.2 Personal Server (Sink Node); 2.3 Medical Server; 2.4 WBAN Communication Architecture; 2.4.1 Intra-BAN Communications; 2.4.2 Inter-BAN Communication; Infrastructure Based Architecture; Ad Hoc Based Architecture; Inter-BAN Communication Technology; 2.4.3 Beyond-BAN Communication; 3 WBAN Topology; 4 Layers of WBAN; 4.1 Physical Layer; 4.2 MAC Layer; 5 WBAN Routing
- 5.1 Challenges of Routing in WBANs6 Security in WBANs; 7 WBAN Requirements in IEEE 802.15.6; 8 Challenges and Open Issues of WBANs; 9 Conclusion; References; 2 Mobile Sensor Networks and Robotics; Abstract; 1 Introduction; 2 Mobile Sensor Networks; 2.1 Coverage; 2.2 Localization; 3 Robotic Sensor Network Applications; 4 IoT Concept and Applications; 5 Coverage for Multi-Robots; 6 Localization for Robot; 7 Wireless Medical Sensor Network; 8 The Challenges in the MSN and Their Limitations; 9 Conclusion; Acknowledgements; References; Big Data Analytics
- 3 Big Data Analytics with Machine Learning ToolsAbstract; 1 Introduction; 1.1 Big Data Analytic Tools; 1.1.1 Big Data Analytic Tools; 1.1.2 Hewlett-Packard Enterprise (HPE) Big Data Platform; 1.1.3 SAP HANA Platform; 1.1.4 Microsoft Azure; 1.1.5 Oracle Big Data; 1.1.6 Other Big Data Platforms; 1.2 Machine Learning Tools for Big Data; 1.2.1 H2O; H2O Architecture and Features; H2O Algorithms; H2O Deployment and Application Example; 1.2.2 MLlib; Architecture and Features; MLlib Algorithms; MLlib Deployment and Application Example; 1.2.3 Comparative Analysis of Machine Learning Algorithms
- 1.3 Conclusions and DiscussionsReferences; 4 Real Time Big Data Analytics to Derive Actionable Intelligence in Enterprise Applications; Abstract; 1 Introduction; 2 Relationship Between the IoT and Big Data; 3 Big Data Analytics Architecture, Framework and Tools; 4 Motivation; 4.1 Data to Insight; 4.2 Shift from Data Management to Value Added Services; 5 Uses of Big Data in Industrial Environment; 5.1 Usecase #1: IoT (Internet of Things) in the Manufacturing Industry; 5.1.1 Connected devices; 5.1.2 Data Acquisition
- 5.2 Usecase #2: Data Analytics for Enterprise Business System in the Manufacturing Industry6 Challenges of Big Data Analytics; 6.1 Technology Shifts; 6.2 Data Visualization; 6.3 Heterogeneous and Imperfect Data; 6.4 Scalability; 6.5 Real Time Execution; 7 Conclusion; References; 5 Revealing Big Data Emerging Technology as Enabler of LMS Technologies Transferability; Abstract; 1 Introduction; 1.1 LMS Aspects; 1.1.1 Synchronous and Asynchronous Communication; 1.1.2 Benefits and Potentials of E-learning to the Students; 2 Literature Review; 3 Managing Change; 3.1 The Benefits
- Control code
- SPR1001327784
- Dimensions
- unknown
- Extent
- 1 online resource.
- Form of item
- online
- Isbn
- 9783319604350
- Media category
- computer
- Media MARC source
- rdamedia
- Media type code
-
- c
- Other control number
- 10.1007/978-3-319-60435-0
- Specific material designation
- remote
- System control number
-
- on1001327784
- (OCoLC)1001327784
- Label
- Internet of things and big data analytics toward next-generation intelligence, Nilanjan Dey, Aboul Ella Hassanien, Chintan Bhatt, Amira S. Ashour, Suresh Chandra Satapathy, editors
- Bibliography note
- Includes bibliographical references
- Carrier category
- online resource
- Carrier category code
-
- cr
- Carrier MARC source
- rdacarrier
- Content category
- text
- Content type code
-
- txt
- Content type MARC source
- rdacontent
- Contents
-
- Preface; Contents; Internet of Things Based Sensor Networks; 1 Internet of Things Based Wireless Body Area Network in Healthcare; Abstract; 1 Introduction; 2 IoT Based WBAN for Healthcare Architecture; 2.1 Sensor Nodes; 2.2 Personal Server (Sink Node); 2.3 Medical Server; 2.4 WBAN Communication Architecture; 2.4.1 Intra-BAN Communications; 2.4.2 Inter-BAN Communication; Infrastructure Based Architecture; Ad Hoc Based Architecture; Inter-BAN Communication Technology; 2.4.3 Beyond-BAN Communication; 3 WBAN Topology; 4 Layers of WBAN; 4.1 Physical Layer; 4.2 MAC Layer; 5 WBAN Routing
- 5.1 Challenges of Routing in WBANs6 Security in WBANs; 7 WBAN Requirements in IEEE 802.15.6; 8 Challenges and Open Issues of WBANs; 9 Conclusion; References; 2 Mobile Sensor Networks and Robotics; Abstract; 1 Introduction; 2 Mobile Sensor Networks; 2.1 Coverage; 2.2 Localization; 3 Robotic Sensor Network Applications; 4 IoT Concept and Applications; 5 Coverage for Multi-Robots; 6 Localization for Robot; 7 Wireless Medical Sensor Network; 8 The Challenges in the MSN and Their Limitations; 9 Conclusion; Acknowledgements; References; Big Data Analytics
- 3 Big Data Analytics with Machine Learning ToolsAbstract; 1 Introduction; 1.1 Big Data Analytic Tools; 1.1.1 Big Data Analytic Tools; 1.1.2 Hewlett-Packard Enterprise (HPE) Big Data Platform; 1.1.3 SAP HANA Platform; 1.1.4 Microsoft Azure; 1.1.5 Oracle Big Data; 1.1.6 Other Big Data Platforms; 1.2 Machine Learning Tools for Big Data; 1.2.1 H2O; H2O Architecture and Features; H2O Algorithms; H2O Deployment and Application Example; 1.2.2 MLlib; Architecture and Features; MLlib Algorithms; MLlib Deployment and Application Example; 1.2.3 Comparative Analysis of Machine Learning Algorithms
- 1.3 Conclusions and DiscussionsReferences; 4 Real Time Big Data Analytics to Derive Actionable Intelligence in Enterprise Applications; Abstract; 1 Introduction; 2 Relationship Between the IoT and Big Data; 3 Big Data Analytics Architecture, Framework and Tools; 4 Motivation; 4.1 Data to Insight; 4.2 Shift from Data Management to Value Added Services; 5 Uses of Big Data in Industrial Environment; 5.1 Usecase #1: IoT (Internet of Things) in the Manufacturing Industry; 5.1.1 Connected devices; 5.1.2 Data Acquisition
- 5.2 Usecase #2: Data Analytics for Enterprise Business System in the Manufacturing Industry6 Challenges of Big Data Analytics; 6.1 Technology Shifts; 6.2 Data Visualization; 6.3 Heterogeneous and Imperfect Data; 6.4 Scalability; 6.5 Real Time Execution; 7 Conclusion; References; 5 Revealing Big Data Emerging Technology as Enabler of LMS Technologies Transferability; Abstract; 1 Introduction; 1.1 LMS Aspects; 1.1.1 Synchronous and Asynchronous Communication; 1.1.2 Benefits and Potentials of E-learning to the Students; 2 Literature Review; 3 Managing Change; 3.1 The Benefits
- Control code
- SPR1001327784
- Dimensions
- unknown
- Extent
- 1 online resource.
- Form of item
- online
- Isbn
- 9783319604350
- Media category
- computer
- Media MARC source
- rdamedia
- Media type code
-
- c
- Other control number
- 10.1007/978-3-319-60435-0
- Specific material designation
- remote
- System control number
-
- on1001327784
- (OCoLC)1001327784
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