Linghe Kong; Bowen Wang; Guihai Chen When Compressive Sensing Meets Mobile Crowdsensing 9789811377754

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Автор: Linghe Kong; Bowen Wang; Guihai Chen
Название: When Compressive Sensing Meets Mobile Crowdsensing
ISBN: 9789811377754
Издательство: Springer
Классификация:

Финансы

Сетевое оборудование

Алгоритмы и структуры данных

Программирование мобильных и портативных устройств / Программирование приложений

Компьютерные сети и коммуникации

ISBN-10: 9811377758
Обложка/Формат: Hardcover
Страницы: 127
Вес: 0.38 кг.
Дата издания: 2019
Язык: English
Издание: 1st ed. 2019
Иллюстрации: 35 illustrations, color; 4 illustrations, black and white; xii, 127 p. 39 illus., 35 illus. in color.
Размер: 234 x 156 x 10
Читательская аудитория: Professional & vocational
Основная тема: Computer Science
Ссылка на Издательство: Link
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Поставляется из: Германии
Описание: 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.

Дополнительное описание: Introduction.- Mathematical Theory of Compressive Sensing.- Basic Compressive Sensing for Data Reconstruction.- Bayesian Compressive Sensing for Task Allocation.- Adaptive Compressive Sensing for Incentive Mechanism.- Encoded Compressive Sensing for Priva

When Compressive Sensing Meets Mobile Crowdsensing, Linghe Kong; Bowen Wang; Guihai Chen