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 computationtasks, 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.
Tabela de Conteúdo
Crowdsourcing as a Future Collaborative Computing Paradigm.- Urban Mobility-Driven Crowdsensing: Recent Advances in Machine Learning Designs and Ubiquitous Applications.- Unknown User Recruitment in Mobile Crowdsourcing.- Quality-Aware Incentive Mechanism for Mobile Crowdsourcing.- Incentive mechanism design for mobile crowdsourcing without verification.- Stable Worker-Task Assignment in Mobile Crowdsensing Applications.- Spatio temporal Task Allocation in Mobile Crowdsensing.- Joint Data Collection and Truth Inference in Spatial Crowdsourcing.- Cost-quality Aware Compressive Mobile Crowdsensing.- Information Integrity in Participatory Crowd-Sensing via Robust Trust Models.- AI-Driven Attack Modeling and Defence Strategies in Mobile Crowdsensing: A special Case Study on Fake Tasks.- Traceable and Secure Data Sharing in Mobile Crowdsensing.- User Privacy Protection in MCS: Threats, Solutions and Open Issues.- Crowdsourcing Through Tiny ML as a Way to Engage End-users in Io T Solutions.- Health Crowd Sensing and Computing: From Crowdsourced Digital Health Footprints to Population Health Intelligence.- Crowdsourcing Applications and Techniques in Computer Vision.- Mobile Crowdsourcing Task Offloading on Social Collaboration Networks: An Empirical Study.
Sobre o autor
Jie Wu: He is the Laura H. Carnell Professor at Temple University and the Director of the Center for Networked Computing. He is a Fellow of the AAAS and the IEEE. His research interests focus on Mobile Computing and Wireless Networks, Cloud Computing, and Applied Machine Learning.
En Wang: He is a Full Professor in the Department of Computer Science and Technology at Jilin University. His current research focuses on Mobile Computing, Crowd Intelligence, and Data Mining.
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