Mobile Crowd-Sensing
Challenges, Solutions and Applications
Published on 15. July 2018
Book
Hardback
270 pages
978-1-4987-8202-9 (ISBN)
Description
Crowd-sensing is a recent concept in computer science, promoting by the development of mobile sensing devices such as smartphones. In a crowd-sensing application, individuals equipped with sensing devices can sense and share data, and thus a large-scale phenomenon can be extracted from the aggregated data. This book overviews the research results, describes representative real applications and points out the challenges and opportunities for the future research. This book introduces algorithms, mechanisms and privacy issues, it also analyzes the architectures of these applications, including their hardware design and software design.
More details
Language
English
Place of publication
Portland
United States
Publishing group
Taylor & Francis Inc
Illustrations
150 s/w Abbildungen
150 Illustrations, black and white
Dimensions
Height: 234 mm
Width: 156 mm
ISBN-13
978-1-4987-8202-9 (9781498782029)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Persons
Fan Ye received his Ph.D. in 2004 from the Computer Science Department of UCLA. After about 8 years in IBM T. J. Watson as a Research Staff Member and one year in Peking University, he joined the ECE department of Stony Brook in 2014. He has published over 60 peer-reviewed papers that have received almost 7000 citations according to Google Scholar. He has 21 granted/pending US and international patents/applications. He was the co-chair for the Mobile Computing Professional Interests Community at IBM Watson for two years, received IBM Research Division Award, 5 Invention Achievement Plateau awards, Best Paper Award for International Conference on Parallel Computing 2008.His current research interests include mobile sensing platforms, systems and applications, Internet-of-Things, indoor location sensing, wireless and sensor networks. In the past he also worked on stream processing systems, cloud and wide area messaging.
Content
Introduction. Hardware. Categories. Characteristics. Systemsl. Applications. Task allocation algorithms. Incentive mechanisms. Privacy protection. Data aggregation and mining. Device calibration