Cognitive Networked Sensing and Big Data

This book presents the challenges that are unique to this area such as synchronization caused by the high mobility of the nodes. It discusses the integration of software defined radio implementation and testbed development.

Wireless Distributed Computing and Cognitive Sensing defines high-dimensional data processing in the context of wireless distributed computing and cognitive sensing. This book presents the challenges that are unique to this area such as synchronization caused by the high mobility of the nodes. The author will discuss the integration of software defined radio implementation and testbed development. The book will also bridge new research results and contextual reviews. Also the author provides an examination of large cognitive radio network; hardware testbed; distributed sensing; and distributed computing.

Discusses insights and experiences from large network testbeds Examines wireless communications, mobile computing, and networks Covers cognitive radio networks for UAVs Includes supplementary material: sn.pub/extras

Autorentext
Dr. Qiu is a Professor in the Department of Electrical and Computer Engineering, Center for Manufacturing
Research, Tennessee Technological University, Cookeville, Tennessee

Klappentext
Cognitive Networked Sensing and Big Data defines high-dimensional data processing in the context of wireless distributed computing and cognitive sensing. This book presents the challenges that are unique to this area such as synchronization caused by the high mobility of the nodes. The authors discuss the integration of software defined radio implementation and testbed development. The book also bridges new research results and contextual reviews. Additionally, the authors provide an examination of large cognitive radio network; hardware testbed; distributed sensing; and distributed computing.

Inhalt
Fundamentals for Cognitive Radios.- Synchronization.- Network Control.- Concentration of Eigenvalues and their Functionals.- Non-Asymptotic, Local Theory of Random Matrices.- Asymptotic, Global Theory of Random Matrices.- Compressed Sensing and Sparse Recovery.- Matrix Completion and Low-Rank Matrix Recovery.- Covariance Matrix Estimation in High Dimensions.- Detection in High Dimensions.- Database Friendly Data Processing.- From Network to Big Data.