This book provides the first comprehensive treatment of the marginal and joint distributions of Age of Information (AoI), which is a key performance metric for Internet of Things (IoT)-enabled real-time applications. The authors apply their general distributional results of AoI to derive marginal or joint higher-order statistics of age processes, such as the variance of each age process and the correlation coefficients between all possible pairwise combinations of age processes. The book includes examples under a variety of queuing disciplines and status updating systems settings, including those powered by energy harvesting. The authors also highlight promising directions for future research in the area. This book is an essential resource for researchers working on AoI, as well as advanced graduate students.

Mohamed A. Abd-Elmagid, Ph.D., is a Research Assistant Professor of Electrical and Computer Engineering at Virginia Tech. He received a B.Sc. in Electronics and Electrical Communications Engineering from Cairo University, an M.S. in Wireless Communications from Nile University, and a Ph.D. in Electrical Engineering from Virginia Tech. His research interests include wireless networks, Age of Information (AoI), semantic communications, and machine learning.

Harpreet S. Dhillon, Ph.D., is the W. Martin Johnson Professor of Engineering and the Associate Dean for Research and Innovation at Virginia Tech.  He received a B.Tech. in Electronics and Communication Engineering from IIT Guwahati, an M.S. in Electrical Engineering from Virginia Tech, and a Ph.D in Electrical Engineering from The University of Texas at Austin. His research interests include communication theory, wireless networks, geolocation, and stochastic geometry.