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.

This book investigates the cloud-based techniques of content distribution mainly for mobile Internet. It starts with hot topics such as cellular traffic optimization and video content delivery. By integrating the cloud scheme, it further tackles issues of traffic-saving, energy-efficient, high-speed, and delay-tolerant content delivery with regard to mobile Internet. It covers both theoretical algorithms and their real-world system implementations. In particular, various well-known cloud platforms such as Baidu Traffic Guard, Tencent QQXuanfeng, Google Drive, Microsoft OneDrive, and Dropbox are elaborated respectively in the book. Lastly, it includes an educational and experimental cloud computing platform allowing public access, which benefits researchers, practitioners, and developers in the field of cloud computing/storage and mobile Internet. Throughout the book there are helpful and practical tips on setting up cloud systems that readers can easily follow.

Content distribution, i.e., distributing digital content from one node to another node or multiple nodes, is the most fundamental function of the Internet. Since Amazon's launch of EC2 in 2006 and Apple's release of the iPhone in 2007, Internet content distribution has shown a strong trend toward polarization. On the one hand, considerable investments have been made in creating heavyweight, integrated data centers ("heavy-cloud") all over the world, in order to achieve economies of scale and high flexibility/efficiency of content distribution. On the other hand, end-user devices ("light-end") have become increasingly lightweight, mobile and heterogeneous, creating new demands concerning traffic usage, energy consumption, bandwidth, latency, reliability, and/or the security of content distribution. Based on comprehensive real-world measurements at scale, we observe that existing content distribution techniques often perform poorly under the abovementioned new circumstances. Motivated by the trend of "heavy-cloud vs. light-end," this book is dedicated to uncovering the root causes of today's mobile networking problems and designing innovative cloud-based solutions to practically address such problems. Our work has produced not only academic papers published in prestigious conference proceedings like SIGCOMM, NSDI, MobiCom and MobiSys, but also concrete effects on industrial systems such as Xiaomi Mobile, MIUI OS, Tencent App Store, Baidu PhoneGuard, and WiFi.com. A series of practical takeaways and easy-to-follow testimonials are provided to researchers and practitioners working in mobile networking and cloud computing. In addition, we have released as much code and data used in our research as possible to benefit the community.

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.