This book focuses on the control and state estimation problems for dynamical network systems with complex samplings subject to various network-induced phenomena. It includes a series of control and state estimation problems tackled under the passive sampling fashion. Further, it explains the effects from the active sampling fashion, i.e., event-based sampling is examined on the control/estimation performance, and novel design technologies are proposed for controllers/estimators. Simulation results are provided for better understanding of the proposed control/filtering methods. By drawing on a variety of theories and methodologies such as Lyapunov function, linear matrix inequalities, and Kalman theory, sucient conditions are derived for guaranteeing the existence of the desired controllers and estimators, which are parameterized according to certain matrix inequalities or recursive matrix equations.
Covers recent advances of control and state estimation for dynamical network systems with complex samplings from the engineering perspective Systematically introduces the complex sampling concept, methods, and application for the control and state estimation Presents unified framework for control and state estimation problems of dynamical network systems with complex samplings Exploits a set of the latest techniques such as linear matrix inequality approach, Vandermonde matrix approach, and trace derivation approach Explains event-triggered multi-rate fusion estimator, resilient distributed sampled-data estimator with predetermined specifications
This book is aimed at researchers, professionals, and graduate students in control engineering and signal processing.
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xi | |
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xiii | |
| Preface |
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xv | |
| Author Biographies |
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xvii | |
| Acknowledgements |
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xix | |
| Symbols |
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xxi | |
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xxiii | |
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1 | (16) |
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1 | (3) |
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4 | (8) |
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1.2.1 Nonuniform Sampling |
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4 | (2) |
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1.2.2 Stochastic Sampling |
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6 | (1) |
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1.2.3 Event-Triggered Sampling |
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7 | (2) |
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1.2.4 Dynamic Event-Triggered Sampling |
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9 | (3) |
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12 | (5) |
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2 Stabilization and Control under Noisy Sampling Intervals |
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17 | (22) |
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2.1 Stabilization with Single Input |
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17 | (6) |
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2.1.1 Problem Formulation |
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17 | (2) |
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19 | (4) |
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2.2 Quantized/Saturated Control with Multiple Inputs |
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23 | (8) |
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2.2.1 Problem Formulation |
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23 | (2) |
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25 | (6) |
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2.3 Illustrative Examples |
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31 | (6) |
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31 | (1) |
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32 | (5) |
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37 | (2) |
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3 Distributed State Estimation with Nonuniform Samplings |
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39 | (16) |
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39 | (3) |
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42 | (8) |
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3.3 An Illustrative Example |
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50 | (4) |
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54 | (1) |
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4 Event-Triggered Control for Switched Systems |
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55 | (42) |
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4.1 Event-Triggered Control: The Input-to-State Stability |
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55 | (20) |
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4.1.1 Problem Formulation |
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56 | (4) |
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60 | (15) |
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4.2 Event-Triggered Pinning Synchronization Control |
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75 | (13) |
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4.2.1 Problem Formulation |
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75 | (3) |
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78 | (10) |
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4.3 Illustrative Examples |
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88 | (8) |
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88 | (3) |
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91 | (5) |
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96 | (1) |
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5 Event-Triggered State Estimation for State-Saturated Systems |
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97 | (30) |
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5.1 Distributed Event-Triggered Hoo State Estimation in Sensor Networks |
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97 | (11) |
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5.1.1 Problem Formulation |
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97 | (4) |
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101 | (7) |
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5.2 Event-Triggered State Estimation in Complex Networks |
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108 | (10) |
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5.2.1 Problem Formulation |
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108 | (4) |
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112 | (6) |
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5.3 Illustrative Examples |
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118 | (7) |
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118 | (4) |
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122 | (3) |
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125 | (2) |
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6 Event-Triggered State Estimation for Discrete-Time Neural Networks |
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127 | (36) |
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6.1 Event-Triggered State Estimation with Stochastic Parameters |
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127 | (17) |
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6.1.1 Problem Formulation |
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128 | (4) |
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132 | (12) |
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6.2 Event-Triggered H∞ State Estimation in Genetic Regulatory Networks |
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144 | (9) |
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6.2.1 Problem Formulation |
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144 | (3) |
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147 | (6) |
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6.3 Illustrative Examples |
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153 | (8) |
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153 | (1) |
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154 | (7) |
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161 | (2) |
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7 Event-Triggered Fusion Estimation for Multi-Rate Systems |
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163 | (32) |
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7.1 Event-Triggered Fusion Estimation with Coloured Measurement Noises |
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163 | (11) |
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7.1.1 Problem Formulation |
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163 | (3) |
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7.1.2 Design of Local Filters |
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166 | (7) |
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173 | (1) |
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7.2 Event-Triggered Fusion Estimation with Sensor Degradations |
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174 | (10) |
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7.2.1 Problem Formulation |
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174 | (3) |
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7.2.2 Design of Local Filters |
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177 | (5) |
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182 | (2) |
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7.3 Illustrative Examples |
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184 | (8) |
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184 | (4) |
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188 | (4) |
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192 | (3) |
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8 Synchronization Control under Dynamic Event-Triggered Mechanisms |
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195 | (16) |
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195 | (2) |
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197 | (8) |
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8.3 Illustrative Examples |
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205 | (4) |
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8.3.1 Demonstrations of Results |
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205 | (1) |
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8.3.2 Comparisons of Results |
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206 | (3) |
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209 | (2) |
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9 Filtering or Estimation under Dynamic Event-Triggered Mechanisms |
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211 | (46) |
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9.1 Dynamic Event-Triggered Robust Filtering with Censored Measurements |
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212 | (11) |
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9.1.1 Problem Formulation |
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212 | (2) |
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214 | (9) |
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9.2 Dynamic Event-Triggered Distributed Filtering on GE Channels |
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223 | (9) |
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9.2.1 Problem Formulation |
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223 | (3) |
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226 | (6) |
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9.3 Dynamic Event-Triggered Resilient Hoc State Estimation |
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232 | (14) |
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9.3.1 Problem Formulation |
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232 | (3) |
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235 | (11) |
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9.4 Illustrative Examples |
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246 | (10) |
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246 | (4) |
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250 | (2) |
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252 | (4) |
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256 | (1) |
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10 Conclusions and Future Work |
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257 | (4) |
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257 | (3) |
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260 | (1) |
| Bibliography |
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261 | (20) |
| Index |
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281 | |
Bo Shen received the B.Sc. degree in mathematics from Northwestern Polytechnical University, Xian, China, in 2003, and the Ph.D. degree in control theory and control engineering from Donghua University, Shanghai, China, in 2011. From 2009 to 2010, he was a Research Assistant with the Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong. From 2010 to 2011, he was a Visiting Ph.D. Student with the Department of Information Systems and Computing, Brunel University London, London, U.K. From 2011 to 2013, he was a Research Fellow (Scientific Co-Worker) with the Institute for Automatic Control and Complex Systems, University of Duisburg-Essen, Duisburg, Germany. He is currently a Professor with the College of Information Science and Technology, Donghua University. He has published around 80 articles in refereed international journals. His research interests include nonlinear control and filtering, stochastic control and filtering, as well as complex networks and neural networks. Professor Shen is a program committee member for many international conferences. He serves (or has served) as an Associate Editor or Editorial Board Member for eight international journals, including Systems Science and Control Engineering, Journal of The Franklin Institute, Asian Journal of Control, Circuits, Systems, and Signal Processing, Neurocomputing, Assembly Automation, Neural Processing Letters, and Mathematical Problems in Engineering.
Zidong Wang is currently Professor of Dynamical Systems and Computing at Brunel University London in the United Kingdom. From January 1997 to December 1998, he was an Alexander von Humboldt research fellow with the Control Engineering Laboratory, Ruhr-University Bochum, Germany. From January 1999 to February 2001, he was a Lecturer with the Department of Mathematics, University of Kaiserslautern, Germany. From March 2001 to July 2002, he was a University Senior Research Fellow with the School of Mathematical and Information Sciences, Coventry University, U.K. In August 2002, he joined the Department of Information Systems and Computing, Brunel University, U.K., as a Lecturer, and was then promoted to a Reader in September 2003 and to a Chair Professor in July 2007. Professor Wang's research interests include dynamical systems, signal processing, bioinformatics, control theory and applications. He has published more than 200 papers in refereed international journals. According to the Web of Science, his publications have received more than 8000 citations (excluding self-citations) with h-index 48. He was awarded the Humboldt research fellowship in 1996 from Alexander von Humboldt Foundation, the JSPS Research Fellowship in 1998 from Japan Society for the Promotion of Science, and the William Mong Visiting Research Fellowship in 2002 from the University of Hong Kong. Professor Wang is an IEEE Fellow for his contributions to networked control and complex networks. He has served or is serving as an Associate Editor for IEEE Transactions on Automatic Control, IEEE Transactions on Neural Networks, IEEE Transactions on Signal Processing, IEEE Transactions on Systems, Man, and Cybernetics - Part C, IEEE Transactions on Control Systems Technology, Circuits, Systems & Signal Processing, Asian Journal of Control, an Action Editor for Neural Networks, an Editorial Board Member for IET Control Theory and Applications, International Journal of Systems Science, Neurocomputing, International Journal of Computer Mathematics, International Journal of General Systems, and an Associate Editor on the Conference Editorial Board for the IEEE Control Systems Society. He is a Senior Member of the IEEE, a Fellow of the Royal Statistical Society, a member of program committee for many international conferences, and a very active reviewer for many international journals. He was nominated an appreciated reviewer for IEEE Transactions on Signal Processing in 2006-2008 and 2011, an appreciated reviewer for IEEE Transactions on Intelligent Transportation Systems in 2008; an outstanding reviewer for IEEE Transactions on Automatic Control in 2004 and for the journal Automatica in 2000.
Qi Li received her B.Eng. degree in electrical engineering and automation from Jiangsu University of Technology, Changzhou, China, in 2013 and the Ph.D. degree in control science and engineering from Donghua University, Shanghai, China, in 2018. She is currently a lecturer with the School of Information Science and Engineering, Hangzhou Normal University, Hangzhou, China. From June 2016 to July 2016, she was a Research Assistant in the Department of Mathematics, Texas A&M University at Qatar, Qatar. From November 2016 to November 2017, she was a Visiting Ph.D. Student in the Department of Computer Science, Brunel University London, U.K. Her current research interests include network communication, complex networks and sensor networks. She is a very active reviewer for many international journals.
