Distributed Network Structure Estimation Using Consensus Methods
(Sprache: Englisch)
The area of detection and estimation in a distributed wireless sensor network (WSN) has several applications, including military surveillance, sustainability, health monitoring, and Internet of Things (IoT). Compared with a wired centralized sensor network,...
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The area of detection and estimation in a distributed wireless sensor network (WSN) has several applications, including military surveillance, sustainability, health monitoring, and Internet of Things (IoT). Compared with a wired centralized sensor network, a distributed WSN has many advantages including scalability and robustness to sensor node failures. In this book, we address the problem of estimating the structure of distributed WSNs. First, we provide a literature review in: (a) graph theory; (b) network area estimation; and (c) existing consensus algorithms, including average consensus and max consensus. Second, a distributed algorithm for counting the total number of nodes in a wireless sensor network with noisy communication channels is introduced. Then, a distributed network degree distribution estimation (DNDD) algorithm is described. The DNDD algorithm is based on average consensus and in-network empirical mass function estimation. Finally, a fully distributed algorithm forestimating the center and the coverage region of a wireless sensor network is described. The algorithms introduced are appropriate for most connected distributed networks. The performance of the algorithms is analyzed theoretically, and simulations are performed and presented to validate the theoretical results. In this book, we also describe how the introduced algorithms can be used to learn global data information and the global data region.
Inhaltsverzeichnis zu „Distributed Network Structure Estimation Using Consensus Methods “
Preface.- Acknowledgments.- Introduction.- Review of Consensus and Network Structure Estimation.- Distributed Node Counting in WSNs.- Noncentralized Estimation of Degree Distribution.- Network Center and Coverage Region Estimation.- Conclusions.- Bibliography.- Authors' Biographies.
Autoren-Porträt von Sai Zhang, Cihan Tepedelenlioglu, Andreas Spanias, Mahesh Banavar
Sai Zhang received a B.S. degree in electrical and information engineering from Huazhong University of Science and Technology, Wuhan, China, in 2012 and an M.S. degree in electrical engineering from Arizona State University, Tempe, AZ, in 2014. From 2014 to 2017 he was a research assistant at Arizona State University, where he completed his Ph.D. degree in electrical engineering. His research interests include distributed computation in wireless sensor networks, performance analysis of distributed consensus algorithms, and wireless communications.Cihan Tepedelenlioglu was born in Ankara, Turkey in 1973. He received his B.S. degree with highest honors from Florida Institute of Technology in 1995, and his M.S. degree from the University of Virginia in 1998, both in electrical engineering. From January 1999 to May 2001 he was a research assistant at the University of Minnesota, where he completed his Ph.D. degree in Electrical and Computer Engineering. He is currently an associate professor of electrical engineering at Arizona State University. He was awarded the NSF (early) Career grant in 2001, and has served as an associate editor for several IEEE Transactions including IEEE Transactions on Communications, IEEE Signal Processing Letters, IEEE Transactions on Wireless Communications, and IEEE Transactions on Vehicular Technology. His research interests include statistical signal processing, system identification, wireless communications, estimation and equalization algorithms for wireless systems, multi-antenna communications, OFDM, ultra-wideband systems, distributed detection and estimation, and data mining for PV systems.Andreas Spanias is a Professor in the School of Electrical, Computer, and Energy Engineering at Arizona State University. He is also the director of the Sensor Signal and Information Processing (SenSIP) center and the founder of the SenSIP industry consortium (now an NSF I/UCRC site). His research interests are in the areas of adaptive signal
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processing, speech processing, and sensor systems. He and his student team developed the computer simulation software Java-DSP and its award winning iPhone/iPad and Android versions. He is the author of two textbooks: Audio Processing and Coding by Wiley and DSP and An Interactive Approach (2nd ed.). He served as associate editor of the IEEE Transactions on Signal Processing and as General Co-chair of IEEE ICASSP-99. He also served as the IEEE Signal Processing Vice-President for Conferences. Andreas Spanias is co-recipient of the 2002 IEEE Donald G. Fink paper prize award and was elected Fellow of the IEEE in 2003. He served as distinguished lecturer for the IEEE Signal Processing Society in 2004. He is a serieseditor for the Morgan & Claypool lecture series on algorithms and software.Mahesh Banavar is an assistant professor in the Department of Electrical and Computer Engineering at Clarkson University. He received a B.E. degree in telecommunications engineering from Visvesvaraya Technological University, Karnataka, India in 2005, an M.S. degree and a Ph.D. degree, both in electrical engineering, from Arizona State University in 2007 and 2010, respectively. His research area is signal processing and communications, and he is specifically working on wireless communications and sensor networks. He is a member of MENSA and the Eta Kappa Nu honor society.
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Bibliographische Angaben
- Autoren: Sai Zhang , Cihan Tepedelenlioglu , Andreas Spanias , Mahesh Banavar
- 2018, XI, 76 Seiten, Maße: 19,1 x 23,5 cm, Kartoniert (TB), Englisch
- Verlag: Springer, Berlin
- ISBN-10: 3031005562
- ISBN-13: 9783031005565
Sprache:
Englisch
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