Wang authors book on social edge computing

Dong Wang
Dong Wang, Associate Professor

Associate Professor Dong Wang is the lead author of a book that introduces a new paradigm integrating edge computing, humans, and artificial intelligence (AI). Social Edge Computing: Empowering Human-Centric Edge Computing, Learning and Intelligence (Springer) is coauthored by Daniel "Yue" Zhang, research scientist at Amazon Alexa AI. 

With the rise in smart devices and advances in AI, data is increasingly being captured, processed, and analyzed near where it is created. This practice, edge computing, is different from cloud computing, where data is processed in a data center or public cloud. Advantages of computing at the edge include reduced bandwidth cost, improved responsiveness, and better privacy protection. In their book, Wang and Zhang define a new paradigm, social edge computing (SEC), that generalizes the current machine-to-machine interactions in edge computing and machine-to-AI interactions into a "holistic human-machine-AI ecosystem."

The SEC paradigm introduces a set of critical research challenges such as the rational nature of device owners, pronounced heterogeneity of the edge devices, real-time AI at the edge, human and AI interaction, and the privacy concern of the human users. This book addresses these challenges by presenting a series of principled models and system designs that enable the confluence of the computing capabilities of devices and the intelligence of the people, while explicitly addressing the unique concerns of humans.

"SEC enables 'social interactions' between machines and humans at the edge by allowing the devices to obtain the unique domain knowledge and expertise from humans to improve the performance and transparency of the application," said Wang. "It also motivates novel AI for social good applications such as privacy-aware health monitoring, disaster damage assessment, crowd abnormal event detection, and vehicle-based criminal tracking."

According to the authors, the techniques introduced in Social Edge Computing can help fully harness the power of devices, algorithms, and humans in the next generation of computing, intelligence, and learning applications at the edge.

Wang's research interests lie in the areas of human-centered AI, social sensing and intelligence, big data analytics, misinformation detection, and human cyber-physical systems. He holds a PhD in computer science from the University of Illinois Urbana-Champaign.

Updated on
Backto the news archive

Related News

Trainor receives the Karen Wold Level the Learning Field Award

Senior Lecturer Kevin Trainor has been selected by the Division of Disability Resources and Educational Services (DRES) to receive the 2024 Karen Wold Level the Learning Field Award. This award honors exemplary members of faculty and staff for advocating and/or implementing instructional strategies, technologies, and disability-related accommodations that afford students with disabilities equal access to academic resources and curricula. 

Kevin Trainor

Seo coauthors chapter on data science and accessibility

Assistant Professor JooYoung Seo and Mine Dogucu, professor of statistics in the Donald Bren School of Information and Computer Sciences at the University of California Irvine, have coauthored a chapter in the new book Teaching Accessible Computing. The goal of the book, which is edited by Alannah Oleson, Amy J. Ko and Richard Ladner, is to help educators feel confident in introducing topics related to disability and accessible computing and integrating accessibility into their courses.

JooYoung Seo

iSchool instructors ranked as excellent

Fifty-five iSchool instructors were named in the University's List of Teachers Ranked as Excellent for Fall 2023. The rankings are released every semester, and results are based on the Instructor and Course Evaluation System (ICES) questionnaire forms maintained by Measurement and Evaluation in the Center for Innovation in Teaching and Learning. 

iSchool Building

ConnectED: Tech for All podcast launched by Community Data Clinic

The Community Data Clinic (CDC), a mixed methods data studies and interdisciplinary community research lab led by Associate Professor Anita Say Chan, has released the first episode of its new podcast, ConnectED: Tech for All. Community partners on the podcast include the Housing Authority of Champaign County, Champaign-Urbana Public Health District, Project Success of Vermilion County, and Cunningham Township Supervisor’s Office.

Community Data Clinic podcast logo

New study shows LLMs respond differently based on user’s motivation

A new study conducted by PhD student Michelle Bak and Assistant Professor Jessie Chin, which was recently published in the Journal of the American Medical Informatics Association (JAMIA), reveals how large language models (LLMs) respond to different motivational states. In their evaluation of three LLM-based generative conversational agents (GAs)—ChatGPT, Google Bard, and Llama 2—the researchers found that while GAs are able to identify users' motivation states and provide relevant information when individuals have established goals, they are less likely to provide guidance when the users are hesitant or ambivalent about changing their behavior.