Multimedia Big Data Computing for IoT Applications - Tanwar, Sudeep; Tyagi, Sudhanshu; Kumar, Neeraj (Springer, 2020)
With an exponential increase in the provisioning of multimedia devices over the Internet of Things (IoT), a significant amount of multimedia big data has been generated from different devices located across the globe. Current proposals in the literature mainly focus on scalar sensor data with less emphasis on the streaming multimedia big data generated from different devices. This textbook examines the unique nature and complexity of MMBD computing for IoT applications and provides unique characteristics and applications divided into different chapters for MMBD over IoT. A number of research challenges are associated with MMBD, such as scalability, accessibility, reliability, heterogeneity, and quality-of-service (QoS) requirements. This textbook is the first-ever “how-to” guide addressing one of the most overlooked practical, methodological, and moral questions in any nations’ journeys to handle the massive amount of multimedia big data being generated from IoT devices’ interactions: For example, how to handle the complexity of facilitating MMBD over IoT? How to organize the unstructured and heterogeneous data? How to deal with cognition and understand complexity associated with MMBD? How to address the real-time and quality-of-service requirements for MMBD applications? How to ensure scalability and computing efficiency. The book is organized into four parts. Part I is focused on technological development, which includes five chapters. Part II discussed the multimedia big data analytics, which has five chapters. Part III illustrates the societal impact of multimedia big data with well-structured four chapters. Finally, Part IV highlights the application environments for multimedia big data analytics with four chapters.
Concepts, Paradigms and Solutions