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Mobile crowdsourcing = from theory t...
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Wu, Jie.
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Mobile crowdsourcing = from theory to practice /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Mobile crowdsourcing/ edited by Jie Wu, En Wang.
其他題名:
from theory to practice /
其他作者:
Wu, Jie.
出版者:
Cham :Springer International Publishing : : 2023.,
面頁冊數:
xii, 457 p. :ill., digital ;24 cm.
內容註:
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 TinyML as aWay to Engage End-users in IoT 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.
Contained By:
Springer Nature eBook
標題:
Crowdsourcing. -
電子資源:
https://doi.org/10.1007/978-3-031-32397-3
ISBN:
9783031323973
Mobile crowdsourcing = from theory to practice /
Mobile crowdsourcing
from theory to practice /[electronic resource] :edited by Jie Wu, En Wang. - Cham :Springer International Publishing :2023. - xii, 457 p. :ill., digital ;24 cm. - Wireless networks,2366-1445. - Wireless networks..
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 TinyML as aWay to Engage End-users in IoT 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.
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.
ISBN: 9783031323973
Standard No.: 10.1007/978-3-031-32397-3doiSubjects--Topical Terms:
3377825
Crowdsourcing.
LC Class. No.: QA76.9.H84
Dewey Class. No.: 004.678
Mobile crowdsourcing = from theory to practice /
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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 TinyML as aWay to Engage End-users in IoT 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.
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