Social Edge Computing (PDF)

Name: Social Edge Computing
Brand: Springer International Publishing
SKU: 149156853
Price: 160.49 EUR
Availability: OnlineOnly

Empowering Human-Centric Edge Computing, Learning and Intelligence (Sprache: Englisch)

Autoren: Dong Wang , Daniel 'Yue' Zhang

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Social Edge Computing, Dong Wang, Daniel 'Yue' Zhang

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The rise of the Internet of Things (IoT) and Artificial Intelligence (AI) leads to the emergence of edge computing systems that push the training and deployment of AI models to the edge of networks for reduced bandwidth cost, improved responsiveness, and better privacy protection, allowing for the ubiquitous AI that can happen anywhere and anytime. Motivated by the above trend, this book introduces a new computing paradigm, the Social Edge Computing (SEC), that empowers human-centric edge intelligent applications by revolutionizing the computing, intelligence, and the training of the AI models at the edge. The SEC paradigm introduces a set of critical human-centric challenges such as the rational nature of edge device owners, pronounced heterogeneity of the edge devices, real-time AI at the edge, human and AI interaction, and the privacy of the edge users. The book addresses these challenges by presenting a series of principled models and systems that enable the confluence of the computing capabilities of devices and the domain knowledge of the people, while explicitly addressing the unique concerns and constraints from humans.

Compared to existing books in the field of edge computing, the vision of this book is unique: we focus on the social edge computing (SEC), an emerging paradigm at the intersection of edge computing, AI, and social computing. This book discusses the unique vision, challenges and applications in SEC. To our knowledge, keeping humans in the loop of edge intelligence has not been systematically reviewed and studied in an existing book. The SEC vision generalizes the current machine-to-machine interactions in edge computing (e.g., mobile edge computing literature), and machine-to-AI interactions (e.g., edge intelligence literature) into a holistic human-machine-AI ecosystem.

Autoren-Porträt von Dong Wang, Daniel 'Yue' Zhang

Dong Wang is currently an Associate Professor in the School of Information Sciences at the University of Illinois Urbana-Champaign (UIUC). Before joining UIUC, he previously served as an associate professor and assistant professor in the Computer Science and Engineering Department at the University of Notre Dame. Dong Wang received his Ph.D. in Computer Science from UIUC in 2012. He has published more than 160 referred publications in networked sensing, edge computing/IoT, distributed systems, and social computing, with emphasis on human-centric challenges. He authored a monograph "Social Sensing: Building Reliable Systems on Unreliable Data" published by Elsevier 2015. His research interests lie in the area of social sensing, intelligence and computing, human-centered AI, human cyber-physical systems, and smart city applications. He received the NSF CAREER Award, Google Faculty Research Award, Young Investigator Program (YIP) Award from Army Research Office, NSF CRII Award, Wing Kai Cheng Fellowship from University of Illinois, the Best Paper Award of 2022 ACM/IEEE International Conference on Advances in Social Networks Analysis and Mining (ASONAM), the Best Paper Award of 16th IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS), the Best Paper Award Candidate from 8th IEEE International Conference on Smart Computing (SMARTCOMP). He also served as the Steering Committee Member of SocialSens workshop 2015-2022 and TPC co-Chair of IEEE DCoSS 2022.

Daniel 'Yue' Zhang is currently a research scientist at Amazon Alexa AI, USA. Daniel obtained his Ph.D. from the department of Computer Science and Engineering at University of Notre Dame, IN, USA in 2020. He received his M.S. degree from Purdue University, West Lafayette, IN, USA, in 2012 and a B.S. degree from Shanghai Jiao Tong University, Shanghai, China, in 2008. His research interests include human-centric learning and computing, social sensing, edge computing, and natural language

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understanding. Daniel has published over 70 peer reviewed articles and served as TPC and reviewers in multiple top conferences and journals in related fields.

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