Managing the Web of Things: Linking the Real World to the Web

ISBN-13: 9780128097649 / Angielski / Miękka / 2017 / 482 str.

Michael Sheng; Yongrui Qin; Lina Yao

Opis

Managing the Web of Things: Linking the Real World to the Web presents a consolidated and holistic coverage of engineering, management, and analytics of the Internet of Things. The web has gone through many transformations, from traditional linking and sharing of computers and documents (i.e., Web of Data), to the current connection of people (i.e., Web of People), and to the emerging connection of billions of physical objects (i.e., Web of Things). With increasing numbers of electronic devices and systems providing different services to people, Web of Things applications present numerous challenges to research institutions, companies, governments, international organizations, and others. This book compiles the newest developments and advances in the area of the Web of Things, ranging from modeling, searching, and data analytics, to software building, applications, and social impact. Its coverage will enable effective exploration, understanding, assessment, comparison, and the selection of WoT models, languages, techniques, platforms, and tools. Readers will gain an up-to-date understanding of the Web of Things systems that accelerates their research.

• Offers a comprehensive and systematic presentation of the methodologies, technologies, and applications that enable efficient and effective management of the Internet of Things
• Provides an in-depth analysis on the state-of-the-art Web of Things modeling and searching technologies, including how to collect, clean, and analyze data generated by the Web of Things
• Covers system design and software building principles, with discussions and explorations of social impact for the Web of Things through real-world applications
• Acts as an ideal reference or recommended text for graduate courses in cloud computing, service computing, and more

Recenzje

"...a collection of 15 standalone papers, assembled into four sections, covering selected aspects of an emerging and fast-moving field generically described as the Internet of Things (IoT), or more recently the Web of Things (WoT)."--Computing Reviews

Spis treści

Part 1: Modeling and Searching 1. Ontologies and context modeling for the Web of Things 2. The Anatomy of An Intent Based Search and Crawler Engine for the Web of Things 3. Modeling RESTful Web of Things Services 4. A Semantic-Rich Approach to IoT Using the Generalized World Entities Paradigm

Part 2: System Building and Practices 6. A WoT Testbed for Research and Course Projects 7. Using Reference Architectures for Design and Evaluation of Web of Things Systems 8. Efficient and Secure Pull Requests for Emergency Cases Using a Mobile Access Framework

Part 3: Data Integration and Analytics 9. Automatic Integration and Querying of Semantic Rich Heterogeneous Data 10. Building Entity Graphs for the Web of Things Management 11. Building Interoperable and Cross-Domain Semantic Web of Things Applications 12. Web of Things Data Storage

Part 4: Applications, Security and Social Impact 14. Security Issues of the Web of Things 15. A Web of Fitness "Things”: An Exploration of Social Impacts & Vulnerable Populations

O autorze

Michael Sheng is a full Professor and Head of Department of Computing at Macquarie University, Sydney, Australia. Before moving to Macquarie University, Michael spent 10 years at School of Computer Science, the University of Adelaide (UoA). Prof. Sheng has more than 400 publications as edited books and proceedings, refereed book chapters, and refereed technical papers in journals and conferences. He is ranked by Microsoft Academic as one of the Top Authors in Services Computing (ranked the 5th of All Time worldwide). He is the recipient of the AMiner Most Influential Scholar Award on IoT (2007-2017), ARC Future Fellowship (2014), Chris Wallace Award for Outstanding Research Contribution (2012), and Microsoft Research Fellowship (2003). Yongrui Qin is currently a Lecturer at School of Computing and Engineering, University of Huddersfield, United Kingdom. His main research interests include the Internet of Things, Graph Data Management, Data Stream Processing, Data Mining, Information Retrieval, Semantic Web, Computer Networks, and Mobile Computing. Dr. Qin has published more than 40 refereed technical papers, including publications in prestigious journals, such as IEEE Trans. on Parallel Distributed Systems, World Wide Web Journal, Journal of Network and Computer Applications, and IEEE Internet Computing, as well as top international conferences, such as SIGIR, EDBT, CIKM, WISE, DASFAA, and SSDBM. Lina Yao is currently a Lecturer at School of Computer Science and Engineering, the University of New South Wales. Her research interest lies in Data Mining, Internet of Things Analytics, Ubiquitous Computing, and Service Computing. Her work has published in a wide range of prestigious journals such as ACM Trans. on Intelligent Systems and Technology (TIST), ACM Trans. on Internet Technology (TOIT), IEEE Transactions on Parallel and Distributed Systems (TPDS), IEEE Trans. on Knowledge Discovery and Engineering (TKDE), IEEE Internet Computing, ACM Trans. on Knowledge Discovery (TKDD) and IEEE Trans. on Services Computing (TSC), as well as top international conferences such as SIGIR, ICDM, UbiComp, CIKM and ICSOC. She is the recipient of Australian Research Council Discovery Early Career Researcher Award (2015) and is recognized as Inaugural Vice Chancellor's Women's Research Excellence Award (2015). Boualem Benatallah is currently a Scientia Professor at the School of Computer Science and Engineering, the University of New South Wales. Prof. Benatallah's research focuses on Web service protocol analysis and management, enterprise services integration, large scale data sharing, cloud computing, and the Internet of Things (IoT). He is the leader of the Service Oriented Computing research group and has more than 220 research papers in leading international journals and conference proceedings. Prof. Benatallah's research has been heavily cited by his international peers. He has more than 14,000 citations with an H-index of 52, according to Google Scholar.