Introduction to the special section on “Advancement in Communication Networks: From Wireless Mesh Networks, Wireless Sensor Networks, to Cellular Networks”

TY - JOUR

T1 - Introduction to the special section on “Advancement in Communication Networks

T2 - From Wireless Mesh Networks, Wireless Sensor Networks, to Cellular Networks”

AU - Shadaram, Mehdi

AU - Rehmani, Mubashir Husain

AU - Sai, Vyasa

N1 - Funding Information: Mehdi Shadaram a [email protected] Mubashir Husain Rehmani b [email protected] Vyasa Sai c [email protected] a The University of Texas at San Antonio, TX, USA The University of Texas at San Antonio TX USA b Waterford Institute of Technology, Ireland Waterford Institute of Technology Ireland c Intel, CA, USA Intel CA USA Wireless communication systems are evolving due to the requirements of increasing data volume. These communication systems have paved its way into every aspect of our daily lives. For instance, when we are at home, we rely on Wi-Fi to connect to the Internet. When we are on the move from home to office; we rely on vehicular communication networks or cellular networks in general. We have been using data generated from different sensor networks deployed around us. For instance, we use data generated by environmental monitoring sensor nodes. When we go to a coffee shop or to a conference, we share our data using wireless mesh networks. To summarize, humans nowadays heavily rely on wireless communication systems and every electronic device is going to be connected with the global communication network i.e., Internet [1] . Wireless radio spectrum is a scarce resource and it is witnessed that a few chunks of licensed frequency auctions for multi-billion US dollar prices. Thus, network operators try to find ways to minimize this cost by looking into the methods to improve the utilization of licensed frequency bands. Cognitive radio networks is one such a paradigm in which cognitive radio nodes perform spectrum sensing to identify the under-utilized frequency band and then select the best available channel for its use without causing harmful interference to the licensed frequency band owners. This capability by the cognitive radio nodes is achieved by interacting with the radio environment and tuning its transmission parameters. Considering these advantages, cognitive radio nodes have been applied to various other networks such as machine-to-machine networks (M2M), cognitive radio based smart grid [2] , and cognitive radio sensor networks. To summarize, in this special section, we focus on diverse wireless communication systems such as wireless sensor networks (WSNs), wireless mesh networks (WMNs), and cellular networks. Out of 134 papers in this special section, 29 submissions that have been accepted after a rigorous peer review process in response to the open call from all around the world. Among these articles, one article is related to WMNs, fifteen articles are related to WSNs, two articles are related to MANETs, two from CRNs, and nine articles are related to cellular networks. We hope you enjoy reading this section and find the articles useful. We would like to thank the authors of the papers whose keen efforts and dedications made the publication of this section possible. We are indebted to the promptness and devotion of the reviewers whose valuable evaluations improved the quality of the papers significantly. In addition, we would like to thank the Elsevier's publication staff members for their continuous support and dedication. We particularly appreciate the relentless support and encouragements granted to us by the Editor-in-Chief. Mehdi Shadaram is the Briscoe Distinguished Professor in the Department of Electrical and Computer Engineering and the founding Director of the Center for Excellence in Engineering Research and Education at The University of Texas at San Antonio. His main area of research activity is in the broadband analog and digital fiber optic and wireless communication systems. He has published more than 120 articles in refereed journals and conference proceedings. He has been the General Chair, Session Chair, TPC Chair, and Panelist in several conferences. He has also served in numerous review panels. He is senior member of IEEE. He received his PhD in electrical engineering in 1984 from The University of Oklahoma. Mubashir Husain Rehmani [M’14, SM’15] received his Ph.D. degree from the University Pierre and Marie Curie, Paris, in 2011. He is currently associated with Telecommunications Software and Systems Group (TSSG), Waterford Institute of Technology, Ireland. He served as an Assistant Professor in COMSATS for five years. He is an editor of IEEE Communications Surveys & Tutorials, IEEE Communications Magazine, and lead guest editor of IEEE Transactions on Industrial Informatics. He received Best Paper Award from IEEE Communications Society Technical Committee on Communications Systems Integration and Modeling (CSIM) in the IEEE ICC 2017, Paris, France. Vyasa Sai currently works for the graphics hardware division at Intel, CA, USA. He received his PhD from the Department of Electrical and Computer Engineering at the University of Pittsburgh, Pittsburgh, PA, USA in 2013. He is a Senior IEEE member. He has authored a book, book chapter, several US patents and numerous refereed journal publications in the fields of Internet of Things, RFID, Low Power Electronics, and Security. He is an associate editor @ IEEE Access, associate technical editor @ IEEE Communications Magazine, associate editor @ Elsevier CAEE, IEEE Technical committee member @ Communications CAS, Editorial Board member @ Intl. Journal of RFID Technology & Applications, etc. He also serves on various reviewer boards for many international journals and conferences. He is the recipient of the 2018 AIM Global Williams award, 2018 IETE Manorama Rathore Memorial Award, etc.

PY - 2019/1

Y1 - 2019/1

UR - http://www.scopus.com/inward/record.url?scp=85060592652&partnerID=8YFLogxK

U2 - 10.1016/j.compeleceng.2018.11.019

DO - 10.1016/j.compeleceng.2018.11.019

M3 - Editorial

AN - SCOPUS:85060592652

SN - 0045-7906

VL - 73

SP - 376

EP - 377

JO - Computers and Electrical Engineering

JF - Computers and Electrical Engineering

ER -