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CRC Press Socio-Technical Networks: Science and Engineering Design

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Description

While there are sporadic journal articles on socio-technical networks, there’s long been a need for an integrated resource that addresses concrete socio-technical network (STN) design issues from algorithmic and engineering perspectives. Filling this need, Socio-Technical Networks: Science and Engineering Design provides a complete introduction to the fundamentals of one of the hottest research areas across the social sciences, networking, and computer science―including its definition, historical background, and models. Covering basic STN architecture from a physical/technological perspective, the book considers the system design process in a typical STN, including inputs, processes/actions, and outputs/products. It covers current applications, including transportation networks, energy systems, tele-healthcare, financial networks, and the World Wide Web. A group of STN expert contributors addresses privacy and security topics in the interdependent context of critical infrastructure, which include risk models, trust models, and privacy preserving schemes. Covers the physical and technological designs in a typical STN Considers STN applications in popular fields, such as healthcare and the virtual community Details a method for mapping and measuring complexity, uncertainty, and interactions among STN components The book examines the most important STN models, including graph theory, inferring agent dynamics, decision theory, and information mining. It also explains structural studies, behavioral studies, and agent/actor system studies and policy studies in different STN contexts. Complete with in-depth case studies, this book supplies the practical insight needed to address contemporary STN design issues. About the Author Dr. Fei Hu is an associate professor in the Department of Electrical and Computer Engineering at the University of Alabama, Tuscaloosa. His research interests are sensor networks, wireless networks, network security, and their applications in biomedicine. His research has been supported by the U.S. National Science Foundation, Cisco, Sprint, and other sources. He obtained his Ph.D. degrees at Tongji University (Shanghai) in the field of signal processing (in 1999), and at Clarkson University (New York) in the field of electrical and computer engineering (in 2002). He obtained his M.S. and B.S. degrees in telecommunication engineering from Shanghai Tiedao University in 1996 and 1993, respectively. He has published over 100 journal/conference papers and book (chapters). Dr. Ali Mostashari is currently the director of the Center for Complex Adaptive Sociotechnological Systems (COMPASS), and an associate professor (Research) at the School of Systems and Enterprises, Stevens Institute of Technology, Hoboken, New Jersey. He obtained his Ph.D. in engineering systems/technology, and management and policy from the Massachusetts Institute of Technology in 2005. He was a Young Global Leader Nominee 2008. He was also listed as Asia 21 Young Leader by the Asia Society (2007). His research focus is complex sociotechnical network design. Dr. Jiang (Linda) Xie received her B.E. degree from Tsinghua University, Beijing, China, in 1997, M.Phil. degree from Hong Kong University of Science and Technology in 1999, and M.S. and Ph.D. degrees from the Georgia Institute of Technology in 2002 and 2004, respectively, all in electrical engineering. She is currently an assistant professor with the Department of Electrical and Computer Engineering at the University of North Carolina at Charlotte. She was a graduate research assistant in the Broadband and Wireless Networking Laboratory (BWNLAB) at the Georgia Institute of Technology from August 1999 to April 2004. She is also a member of the IEEE Communications Society, IEEE Women in Engineering, the Association of Computing Machinery (ACM), and Eta Kappa Nu (ECE Honor Society).

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
30 August 2019
Listed Since
25 November 2018

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No barcode data available

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