Vor Ort
Merkzettel Warenkorb
Produktbild: Mobile Crowdsourcing

Mobile Crowdsourcing From Theory to Practice

Aus der Reihe Wireless Networks

195,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

04.08.2024

Herausgeber

Jie Wu + weitere

Verlag

Springer

Seitenzahl

457

Maße (L/B/H)

23,5/15,5/2,6 cm

Gewicht

709 g

Sprache

Englisch

ISBN

978-3-031-32399-7

Beschreibung

Portrait

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.

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

04.08.2024

Herausgeber

Verlag

Springer

Seitenzahl

457

Maße (L/B/H)

23,5/15,5/2,6 cm

Gewicht

709 g

Sprache

Englisch

ISBN

978-3-031-32399-7

Herstelleradresse

Springer-Verlag KG
Sachsenplatz 4-6
1201 Wien
AT

Email: GPSR Kontakt

Noch keine Bewertungen vorhanden

Verfassen Sie die erste Bewertung zu diesem Artikel

Helfen Sie anderen Kundinnen und Kunden durch Ihre Meinung.

Kundinnen und Kunden meinen

Bewertungen (0)
  • Produktbild: Mobile Crowdsourcing
  • 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.- CrowdsourcingThrough 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.