Chaocan Xiang is an Associate Professor at the College of Computer Science, Chongqing University, China. He received his bachelor’s degree and Ph.D. from Nanjing Institute of Communication Engineering, China, in 2009 and 2014, respectively. He subsequently studied at the University of Michigan-Ann Arbor in 2017 (supervised by Prof. Kang G. Shin, IEEE Life Fellow, ACM Fellow). His research interests mainly include UAVs/vehicle-based crowdsensing, urban computing, Internet of Things, Artificial Intelligence, and big data. He has published more than 50 papers, including over 20 in leading venues such as IEEE Transactions on Mobile Computing, IEEE Transactions on Parallel and Distributed Systems, IEEE INFOCOM, and ACM Ubicomp. He has received a best paper award and a best poster award at two international conferences. Panlong Yang is a full Professor at the University of Science and Technology of China. He has been supported by the NSF Jiangsu through a Distinguished Young Scholarship and was honored as a CCF Distinguished Lecturer in 2015. He has published over 150 papers, including 40 in CCF Class A. Since 2012, he has supervised 14 master’s and Ph.D. candidates, including two excellent dissertation winners in Jiangsu Province and the PLA education system. He has been supported by the National Key Development Project and NSFC projects. He has nominated by ACM MobiCom 2009 for the best demo honored mention awards, and won best paper awards at the IEEE MSN and MASS. He has served as general chair of BigCom and TPC chair of IEEE MSN. In addition, he has served as a TPC member of INFOCOM (CCF Class A) and an associate editor of the Journal of Communication of China. He is a Senior Member of the IEEE (2019). Fu Xiao received his Ph.D. in Computer Science and Technology from the Nanjing University of Science and Technology, Nanjing, China, in 2007. He is currently a Professor and Dean of the School of Computer, Nanjing University of Posts and Telecommunications. He has authored more than 60 papers in respected conference proceedings and journals, including IEEE INFOCOM, ACM Mobihoc, IEEE JASC, IEEE/ACM ToN, IEEE TPDS, IEEE TMC, etc. His main research interest is in the Internet of Things. He is a member of the IEEE Computer Society and the Association for Computing Machinery. Xiaochen Fan received his B.S. degree in Computer Science from Beijing Institute of Technology, Beijing, China, in 2013, and his Ph.D. from the University of Technology Sydney, NSW, Australia, in 2021. His research interests include mobile/pervasive computing, deep learning, and Internet of Things (IoT). He has published over 25 peer-reviewed papers in high-quality journals and IEEE/ACM international conference proceedings.

This two-volume set constitutes the refereed proceedings of the 17th CCF Conference on Computer Supported Cooperative Work and Social Computing, ChineseCSCW 2022 held in Datong, China, during September 23–25, 2022. The 60 full papers and 30 short papers included in this two-volume set were carefully reviewed and selected from 211 submissions. They were organized in topical sections as follows: answer set programming; Social Media and Online Communities, Collaborative Mechanisms, Models, Approaches, Algorithms and Systems; Crowd Intelligence and Crowd Cooperative Computing; Cooperative Evolutionary Computation and Human-like Intelligent Collaboration; Domain-Specific Collaborative Applications.

This book constitutes the refereed proceedings of the 9th International Conference on Distributed Computing and Internet Technology, ICDCIT 2013, held in Bhubaneswar, India, in February 2013. The 40 full papers presented together with 5 invited talks in this volume were carefully reviewed and selected from 164 submissions. The papers cover various research aspects in distributed computing, internet technology, computer networks, and machine learning.

This book reports a set of novel research initiatives on ambient intelligence and ubiquitous computing that help researchers and practitioners identify recent advances, as well as the frontiers in these study domains. During the last two decades, both study areas have gained great interest in industry and academia due to the benefits of using smart solutions in various application domains, such as health care, ambient-assisted living, personal security and privacy, citizen participation, provision of urban services, and precision agriculture and farming. The articles included in this book report solutions and provide empirical results on their suitability to address problems and opportunities in these application domains. The articles also include discussions on how the proposals and their evaluation results inform the design of the next generation of ubiquitous and smart systems. Researchers, practitioners, and graduate students take advantage of this knowledge to address innovation and engineering aspects of smart and ubiquitous solutions for the next decade.

This book aims to introduce recent advances in IoT and its applications for smart environments. The state of the art is reviewed with a focus on the technologies, applications, challenges, and opportunities. At this stage, a comprehensive understanding of the formal and practical applications of IoT in the different scenarios of smart environments is necessary to support future research. Therefore, the main contribution of this book is a comprehensive study of the most recent proposals for smart environments. In addition, this book synthesizes existing information and highlights common threads and gaps that lead to new and complex areas of future research. The book covers a range of major research subjects which will foster future implementations. The topics include smart learning environments, crowdsensing applications, participatory citizen sensing, multimodal perception systems and security challenges. This book seeks to provide a valuable framework for future research projects by expounding the topic to academics, engineers, and industry professionals, which is necessary for the design of future IoT architectures for smart environments.

This book constitutes the proceedings of the 4th International Conference on Internet of Vehicles, IOV 2017, held in Kanazawa, Japan, in November 2017. The 19 papers presented in this volume were carefully reviewed and selected from 40 submissions. They deal with advances in the state of the art and practice of the IoV architectures, protocols, services and applications, as well as identifying emerging research topics and define the future directions of Internet of Vehicles.

This book offers the latest research results in recent development on the principles, techniques and applications in mobile crowdsourcing. It presents state-of-the-art content and provides an in-depth overview of the basic background in this related field. Crowdsourcing involves a large crowd of participants working together to contribute or produce goods and services for the society. The early 21st century applications of crowdsourcing can be called crowdsourcing 1.0, which includes businesses using crowdsourcing to accomplish various tasks, such as the ability to offload peak demand, access cheap labor, generate better results in a timely matter, and reach a wider array of talent outside the organization. Mobile crowdsensing can be described as an extension of crowdsourcing to the mobile network to combine the idea of crowdsourcing with the sensing capacity of mobile devices. As a promising paradigm for completing complex sensing and computation tasks, mobile crowdsensing serves the vital purpose of exploiting the ubiquitous smart devices carried by mobile users to make conscious or unconscious collaboration through mobile networks. Considering that we are in the era of mobile internet, mobile crowdsensing is developing rapidly and has great advantages in deployment and maintenance, sensing range and granularity, reusability, and other aspects. Due to the benefits of using mobile crowdsensing, many emergent applications are now available for individuals, business enterprises, and governments. In addition, many new techniques have been developed and are being adopted. This book will be of value to researchers and students targeting this topic as a reference book. Practitioners, government officials, business organizations and even customers -- working, participating or those interested in fields related to crowdsourcing will also want to purchase this book.

This book provides a comprehensive introduction to applying compressive sensing to improve data quality in the context of mobile crowdsensing. It addresses the following main topics: recovering missing data, efficiently collecting data, preserving user privacy, and detecting false data. Mobile crowdsensing, as an emerging sensing paradigm, enables the masses to take part in data collection tasks with the aid of powerful mobile devices. However, mobile crowdsensing platforms have yet to be widely adopted in practice, the major concern being the quality of the data collected. There are numerous causes: some locations may generate redundant data, while others may not be covered at all, since the participants are rarely systematically coordinated; privacy is a concern for some people, who don’t wish to share their real-time locations, and therefore some key information may be missing; further, some participants may upload fake data in order to fraudulently gain rewards. To address these problematic aspects, compressive sensing, which works by accurately recovering a sparse signal using very few samples, has proven to offer an effective solution.

An authoritative treatment of urban computing, offering an overview of the field, fundamental techniques, advanced models, and novel applications. Urban computing brings powerful computational techniques to bear on such urban challenges as pollution, energy consumption, and traffic congestion. Using today's large-scale computing infrastructure and data gathered from sensing technologies, urban computing combines computer science with urban planning, transportation, environmental science, sociology, and other areas of urban studies, tackling specific problems with concrete methodologies in a data-centric computing framework. This authoritative treatment of urban computing offers an overview of the field, fundamental techniques, advanced models, and novel applications. Each chapter acts as a tutorial that introduces readers to an important aspect of urban computing, with references to relevant research. The book outlines key concepts, sources of data, and typical applications; describes four paradigms of urban sensing in sensor-centric and human-centric categories; introduces data management for spatial and spatio-temporal data, from basic indexing and retrieval algorithms to cloud computing platforms; and covers beginning and advanced topics in mining knowledge from urban big data, beginning with fundamental data mining algorithms and progressing to advanced machine learning techniques. Urban Computing provides students, researchers, and application developers with an essential handbook to an evolving interdisciplinary field.

This book presents the latest findings in the areas of data management and smart computing, big data management, artificial intelligence and data analytics, along with advances in network technologies. The volume is a collection of peer reviewed research papers presented at Seventh International Conference on Data Management, Analytics and Innovation (ICDMAI 2023), held during 20 – 22 January, 2023 in Pune, India. It addresses state-of-the-art topics and discusses challenges and solutions for future development. Gathering original, unpublished contributions by scientists from around the globe, the book is mainly intended for a professional audience of researchers and practitioners in academia and industry.