Social Edge Computing - Zhang, Daniel 'Yue'

. .

Résumé :
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. ...

Biographie:
Dong Wang: Dong Wang is a professor in School of Information Sciences and Siebel School of Computing and Data Science (affiliated) at the University of Illinois at Urbana Champaign (UIUC). His research interests lie in social sensing and intelligence, human-centered AI, and big data analytics. Dong Wang has published over 200 technical papers in peer reviewed conferences and journals. His work has been applied in a wide range of real-world applications such as data reliability, social network analysis, disaster response, AI for science, and AI for social good. His research on social sensing and intelligence resulted in software tools that found applications in academia, industry, and government research labs. He also authored three books: Social Intelligence to be published by Springer in 2025, Social Edge Computing published by Springer in 2023, and Social Sensing published by Elsevier in 2015. He is the recipient of NSF CAREER Award, Google Faculty Research Award, ARO Young Investigator Program (YIP), 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) and the Best Paper Honorable Mention of 2025 ACM CHI and 8th IEEE SmartComp. He serves as an associate editor of IEEE Transactions on Big Data, Frontiers in Big Data, and Social Network and Analysis Journal (SNAM). He is also an IEEE Senior Member and ACM and AAAI Member. Lanyu Shang: Lanyu Shang is an assistant professor in Computer Science at the Loyola Marymount University. She earned her Ph.D. in information sciences from the University of Illinois Urbana-Champaign. Prior to this, she received an M.S. in Data Science from New York University and a B.S. in Applied Mathematics from the University of California, Los Angeles. Her research interest lies in human-centric AI, human-AI collaboration, social media analysis, AI for social good, and applied AI. Her work has been published in top venues in data mining and machine learning/AI, such as The WebConf, ICWSM, AAAI, IJCAI, and IEEE Big Data. She is also the recipient of the Best Paper Award at ACM/IEEE ASONAM 2022, the Best Paper Honorable Mention at IEEE SmartComp 2022, the Outstanding Graduate Student Teaching Award from the University of Notre Dame, and the N2Women Young Researcher Fellowship. Yang Zhang: Yang Zhang is an assistant professor in Computer Science and Software Engineering at the Miami University. Previously, he was a Postdoctoral Research Associate at UIUC and a W. J. Cody Research Associate at Argonne National Laboratory. Yang earned his Ph.D. in Computer Science & Engineering from the University of Notre Dame, an M.S. in Data Science from Indiana University Bloomington, and a B.S. in Software Engineering from Wuhan University. His research focuses on human-centered AI, human-AI collaboration, deep learning, and generative AI. He has authored over 80 peer-reviewed conference and journal papers published in top venues such as ACM CSCW, ACM Web Conference, AAAI, IJCAI, and IEEE BigData. His work has been recognized with prestigious honors, including the Outstanding Graduate Research Award from the University of Notre Dame and the W. J. Cody Research Associateship at Argonne National Laboratory....

Sommaire:
A New Human-centric Computing Age at Edge.- Social Edge Trends and Applications.- Rational Social Edge Computing.- Taming Heterogeneity in Social Edge Computing.- Real-time AI in Social Edge.- Human-AI Interaction.- Privacy in Social Edge.- Further Readings.- Conclusion and Remaining Challenges.