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E-raamat: Bio-Inspired Computing and Networking

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  • Formaat: 552 pages
  • Ilmumisaeg: 19-Apr-2016
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781420080339
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Bio-Inspired Computing and NetworkingSuurem pilt
  • Formaat: 552 pages
  • Ilmumisaeg: 19-Apr-2016
  • Kirjastus: CRC Press Inc
  • Keel: eng
  • ISBN-13: 9781420080339
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Seeking new methods to satisfy increasing communication demands, researchers continue to find inspiration from the complex systems found in nature. From ant-inspired allocation to a swarm algorithm derived from honeybees, Bio-Inspired Computing and Networking explains how the study of biological systems can significantly improve computing, networking, and robotics.

Containing contributions from leading researchers from around the world, the book investigates the fundamental aspects and applications of bio-inspired computing and networking. Presenting the latest advances in bio-inspired communication, computing, networking, clustering, optimization, and robotics, the book considers state-of-the-art approaches, novel technologies, and experimental studies, including bio-inspired:











Optimization of dynamic NP-hard problems Top-down controller design for distributing a robot swarm among multiple tasks Self-organizing data and signals cellular systems Dynamic spectrum access in cognitive radio networks QoS-aware architecture for scalable, adaptive, and survivable network systems Locomotion control of the Hexapod Robot Gregor III

The book explores bio-inspired topology control and reconfiguration methods, as well as bio-inspired localization, synchronization, and mobility approaches. Providing wide-ranging coverage that includes past approaches, current challenges, and emerging concepts such as the evolution and self-healing of network architectures and protocols, this comprehensive reference provides you with the well-rounded understanding you need to continue the advancement of the development, design, and implementation of bio-inspired computing and networking.
Preface ix
Editor xi
Acknowledgment xiii
Contributors xv
PART I ANIMAL BEHAVIORS AND ANIMAL COMMUNICATIONS
1 Animal Models for Computing and Communications: Past Approaches and Future Challenges
3(16)
Karen I. Bales
Carolyn D. Kitzmann
1.1 General Principles of Animal Communication
4(3)
1.2 Current Bio-Inspired Modeling Approaches
7(3)
1.2.1 Insect Models for Communication and Robotics
7(2)
1.2.2 Noninsect, Bio-Inspired Models for Communication and Robotics
9(1)
1.3 Challenges for Future Bio-Inspired Models
10(1)
1.4 Primates and Other Socially Complex Mammals as Biological Models
11(4)
1.4.1 Challenge: Modeling Small vs. Large Groups and Group Heterogeneity
11(1)
1.4.2 Challenge: Incorporating Individual Differences or "Personality"
12(1)
1.4.3 Challenge: Robustness to Damage
13(1)
1.4.4 Challenge: Avoiding Eavesdropping
14(1)
1.5 Conclusions
15(4)
Acknowledgments
15(1)
References
15(4)
2 Social Behaviors of the California Sea Lion, Bottlenose Dolphin, and Orca Whale
19(24)
Neil William Adams
Yang Xiao
2.1 California Sea Lion
21(3)
2.1.1 Introduction to California Sea Lion
21(1)
2.1.2 Classification of the California Sea Lion
21(1)
2.1.3 Social Behaviors of Sea Lions
22(1)
2.1.3.1 Breeding and Perinatal Periods
22(1)
2.1.3.2 Inteactions between California Sea Lions and Humans
23(1)
2.1.3.3 Relationship between California Sea Lions and Dolphins
23(1)
2.1.3.4 Methods of Study of Sea Lions and Dolphins
23(1)
2.1.3.5 Results of Study of Sea Lions and Dolphins
24(1)
2.1.3.6 Discussions from Study of Sea Lions and Dolphins
24(1)
2.2 Bottlenose Dolphin
24(8)
2.2.1 Introduction to Bottlenose Dolphin
24(1)
2.2.2 Classification of Bottlenose Dolphin
25(1)
2.2.3 Social Behaviors of the Bottlenose Dolphin
25(2)
2.2.3.1 Bottlenose Dolphins and Behavior Imitation
27(1)
2.2.3.2 Further Study of Interactions between Bottlenose Dolphins and Humans
27(1)
2.2.3.3 Life and Social Analysis of Coastal North Carolina Bottlenose Dolphins
28(1)
2.2.3.4 North Carolina Bottlenose Dolphin Societal Structure
28(1)
2.2.3.5 Bottlenose Dolphins and Birth Factors
29(1)
2.2.3.6 Inter-Birth Intervals for Bottlenose Dolphins
30(1)
2.2.3.7 Female Bottlenose Dolphin Social Patterns
31(1)
2.3 Killer Whale
32(6)
2.3.1 Introduction to the Killer Whale
32(1)
2.3.2 Social Behaviors
33(1)
2.3.2.1 Basic Social Tendencies
33(1)
2.3.2.2 Coordinated Attacks on Seals and Penguins in the Antarctic
33(1)
2.3.2.3 North Pacific Killer Wale Aggregations and Fish Hunting
34(1)
2.3.2.4 Killer Whale Interactions with Other Marine Mammals
35(2)
2.3.2.5 Evidence of Cooperative Attacks by Killer Whales
37(1)
2.3.2.6 Killer Whale Vocalization and Foraging
37(1)
2.3.2.7 Effect of Social Affiliation on Vocal Signatures of Resident Killer Whales
37(1)
2.4 Conclusions
38(5)
Acknowledgments
38(1)
References
38(5)
PART II BIO-INSPIRED COMPUTING AND ROBOTS
3 Social Insect Societies for the Optimization of Dynamic NP-Hard Problems
43(26)
Stephan A. Hartmann
Pedro C. Pinto
Thomas A. Runkler
Joao M.C. Sousa
3.1 Introduction
44(1)
3.2 Optimization of Dynamic NP-Hard Problems
45(2)
3.2.1 Approaches to Problem Optimization
46(1)
3.2.2 Meta-Heuristics Inspired in Social Insects
46(1)
3.3 Ant Colonies Keep Supply Lines
47(4)
3.3.1 Representation of the Problem
50(1)
3.3.2 Pheromone Update
50(1)
3.3.3 Heuristics
51(1)
3.3.4 Probabilistic Rule
51(1)
3.4 Termite Hill-Building
51(4)
3.4.1 Pheromone Table
53(1)
3.4.2 Pheromone Update
53(1)
3.4.3 Routing
54(1)
3.4.4 Route Discovery
55(1)
3.5 Wasp Swarms and Hierarchies
55(4)
3.6 Bees
59(3)
3.7 Conclusions
62(7)
References
63(6)
4 Bio-Inspired Locomotion Control of the Hexapod Robot Gregor III
69(26)
Paolo Arena
Luca Patane
4.1 Introduction
70(1)
4.2 State of the Art
71(2)
4.3 Design Guidelines
73(4)
4.4 Robotic Platform
77(2)
4.4.1 Biological Inspiration
77(1)
4.4.2 Robot Mechanical Design
78(1)
4.5 Locomotion Control
79(1)
4.6 Walknet and Gregor
79(4)
4.6.1 Gregor III
80(1)
4.6.2 Walknet Control on Gregor
80(3)
4.7 Dynamic Simulation
83(1)
4.8 Hardware Architecture and Robot Experiments
84(7)
4.9 Conclusions
91(4)
Acknowledgments
91(1)
References
92(3)
5 Beeclust: A Swarm Algorithm Derived from Honeybees: Derivation of the Algorithm, Analysis by Mathematical Models, and Implementation on a Robot Swarm
95(44)
Thomas Schmickl
Heiko Hamann
5.1 Motivation
96(5)
5.1.1 From Swarm Intelligence to Swarm Robotics
96(3)
5.1.2 From Biological Inspirations to Robotic Algorithms
99(1)
5.1.3 Modeling the Swarm
100(1)
5.2 Our Biological Inspiration
101(5)
5.3 Analyzing Some Basic Results of the Observed Features of the Bee's Behavior
106(2)
5.4 The Robotic Algorithm
108(3)
5.4.1 The Swarm Robot "Jasmine"
108(1)
5.4.2 The Swarm Robot "I-Swarm"
109(1)
5.4.3 Shared and Different Properties of These Two Robots
109(1)
5.4.4 The Algorithm
110(1)
5.5 Swarm Experiments Using a Multi-Agent' Simulation of the Robots
111(2)
5.5.1 Simulating the Swarm Robot "Jasmine"
112(1)
5.5.2 Simulating the Swarm Robot "I-Swarm"
112(1)
5.6 Preliminary Robotic Experiments
113(3)
5.7 Macroscopic Model of the Robots' Collective Behavior
116(3)
5.8 The Compartment Model
119(4)
5.9 Macroscopic Model---Step 3
123(3)
5.9.1 Macroscopic, Space-Continuous Models for Robot Swarms
123(1)
5.9.2 Modeling the Collision-Based Adaptive Swarm Aggregation in Continuous Space
124(2)
5.10 Results of Our Two Different Modeling Approaches
126(2)
5.11 Discussion
128(6)
5.12 Conclusion
134(5)
Acknowledgments
134(1)
References
135(4)
6 Self-Organizing Data and Signal Cellular Systems
139(28)
Andre Stauffer
Gianluca Tempesti
6.1 Bio-Inspired Properties
140(2)
6.1.1 Cellular Architecture
140(1)
6.1.2 Cloning
141(1)
6.1.3 Cicatrization
141(1)
6.1.4 Regeneration
142(1)
6.2 Functional Design
142(5)
6.2.1 Structural Configuration Mechanism
142(2)
6.2.2 Functional Configuration Mechanism
144(1)
6.2.3 Cloning Mechanism
144(1)
6.2.4 Cicatrization Mechanism
145(2)
6.2.5 Regeneration Mechanism
147(1)
6.3 Hardware Design
147(12)
6.3.1 Data and Signals
147(4)
6.3.2 Configuration Level
151(1)
6.3.3 Self-Repair Level
152(3)
6.3.4 Application Level
155(1)
6.3.5 Transmission Level
155(3)
6.3.6 Output Level
158(1)
6.4 Hardware Simulation
159(5)
6.4.1 Functional Application
159(3)
6.4.2 Multicellular Organism
162(1)
6.4.3 Population of Organisms
162(2)
6.5 Hardware Implementation
164(3)
6.5.1 Confetti Platform
164(1)
6.5.2 SOS Application
165(1)
References
166(1)
7 Bio-Inspired Process Control
167(42)
Konrad Wojdan
Konrad Swirski
Michal Warchol
Grzegorz Jarmoszewicz
Tomasz Chomiak
7.1 Nature of Industrial Process Control
168(2)
7.2 Bio-Inspired Algorithms in Base Control Layer
170(1)
7.3 Advanced Control Layer
171(8)
7.3.1 Artificial Neural Networks in MPC Controllers
174(1)
7.3.2 Hybrid Process Model
175(1)
7.3.2.1 Step One
175(1)
7.3.2.2 Step Two
176(2)
7.3.2.3 Step Three
178(1)
7.3.2.4 Step Four
178(1)
7.4 SILO---Immune-Inspired Control System
179(21)
7.4.1 Immune Structure of SILO
182(1)
7.4.1.1 Pathogen
182(1)
7.4.1.2 B Cell
183(3)
7.4.1.3 Antibody
186(2)
7.4.2 Basic Concept of SILO Operation
188(2)
7.4.3 Optimization Module
190(3)
7.4.3.1 Mixed Model--Based Optimization Layer
193(4)
7.4.3.2 Global Model--Based Optimization Layer
197(1)
7.4.3.3 Stochastic Optimization Layer
197(1)
7.4.3.4 Layers Switching Algorithm
198(2)
7.5 Application of Bio-Inspired Methods in Industrial Process Control
200(9)
7.5.1 SILO Results
201(2)
7.5.2 IVY Results
203(1)
7.5.3 Summary
204(1)
References
204(5)
8 Multirobot Search Using Bio-Inspired Cooperation and Communication Paradigms
209(16)
Briana Wellman
Quinton Alexander
Monica Anderson
8.1 Introduction
210(2)
8.1.1 Successes in Nature
211(1)
8.1.2 Challenges and Problems
211(1)
8.2 Study of Primates
212(4)
8.2.1 Introduction to Primates
213(1)
8.2.2 Role Selection
214(1)
8.2.3 Communication and Navigation
215(1)
8.2.4 Impact of Environment on Cooperation and Communication
216(1)
8.3 Bio-Inspired Multi-Robot Systems
216(4)
8.3.1 Decentralized, Asynchronous Decision Making
217(1)
8.3.2 Limited Communications Modalities
218(2)
8.3.3 Transient Role Selection
220(1)
8.4 Toward Bio-Inspired Coverage: A Case Study in Decentralized Action Selection
220(2)
8.5 Summary and Conclusions
222(3)
References
223(2)
9 Abstractions for Planning and Control of Robotic Swarms
225(18)
Calin Belta
9.1 Specification-Induced Hierarchical Abstractions
226(3)
9.2 Continuous Abstractions: Extracting the Essential Features of a Swarm
229(5)
9.2.1 Examples of Continuous Abstractions
231(3)
9.3 Discrete Abstractions: Accommodating Rich Specifications
234(2)
9.4 Hierarchical Abstractions: Automatic Deployment of Swarms from Human-Like Specifications
236(2)
9.5 Limitations of the Approach and Directions for Future Work
238(2)
9.6 Conclusion
240(3)
Acknowledgment
240(1)
References
240(3)
10 Ant-Inspired Allocation: Top-Down Controller Design for Distributing a Robot Swarm among Multiple Tasks
243(32)
Spring Berman
Adam Halasz
M. Ani Hsieh
10.1 Introduction
244(2)
10.2 Background
246(2)
10.2.1 Related Work
246(1)
10.2.2 Ant House-Hunting
247(1)
10.3 Problem Statement
248(4)
10.3.1 Definitions
249(1)
10.3.2 Linear Model
250(1)
10.3.3 Time-Delayed Model
250(1)
10.3.4 Quorum Model
251(1)
10.4 Analysis
252(5)
10.4.1 Linear Model
252(1)
10.4.2 Time-Delayed Model
253(2)
10.4.3 Quorum Model
255(2)
10.5 Design of Transition Rate Matrix K
257(2)
10.5.1 Linear Model
257(1)
10.5.2 Time-Delayed Model
258(1)
10.6 Simulation Methodology
259(1)
10.7 Results
260(9)
10.7.1 Linear Model vs. Quorum Model
261(2)
10.7.2 Linear Model vs. Time-Delayed Model
263(6)
10.8 Discussion
269(2)
10.9 Conclusions
271(4)
Acknowledgments
271(1)
References
272(3)
11 Human Peripheral Nervous System Controlling Robots
275(30)
Panagiotis K. Artemiadis
Kostas J. Kyriakopoulos
11.1 Introduction
276(2)
11.2 EMG-Based Control
278(11)
11.2.1 System Overview
278(1)
11.2.2 Background and Problem Definition
279(1)
11.2.3 Recording Arm Motion
279(2)
11.2.4 Recording Muscles Activation
281(1)
11.2.5 Dimensionality Reduction
281(5)
11.2.6 Motion Decoding Model
286(2)
11.2.7 Robot Control
288(1)
11.3 Experimental Results
289(7)
11.3.1 Hardware and Experiment Design
289(1)
11.3.2 Method Assessment
290(4)
11.3.3 EMG-Based Control vs. Motion-Tracking Systems
294(2)
11.4 Conclusions
296(9)
Acknowledgments
296(1)
Appendix 11.A
297(1)
11.A.1 Arm Kinematics
297(3)
References
300(5)
PART III BIO-INSPIRED COMMUNICATIONS AND NETWORKS
12 Adaptive Social Hierarchies: From Nature to Networks
305(46)
Andrew Markham
12.1 Introduction
306(2)
12.2 Social Hierarchies in Nature
308(3)
12.2.1 Formation and Maintenance of Hierarchies
308(2)
12.2.2 Purpose of Social Hierarchies
310(1)
12.3 Using Adaptive Social Hierarchies in Wireless Networks
311(4)
12.3.1 Constructing an Adaptive Social Hierarchy
313(2)
12.4 Pairwise ASH
315(10)
12.4.1 Pairwise ASH with Reinforcement
321(4)
12.5 One-Way ASH(1-ASH)
325(6)
12.5.1 Domination ASH
326(1)
12.5.2 Domination Ratio with Switching
327(4)
12.6 Dealing with Mixed Mobility: An Agent-Based Approach
331(7)
12.6.1 Agent Rules
332(2)
12.6.2 Realistic Meetings
334(4)
12.7 Suitable Attributes to Rank
338(2)
12.7.1 Energy/Lifetime
338(1)
12.7.2 Connectivity
338(1)
12.7.3 Buffer Space
339(1)
12.7.4 Functions of Attributes
339(1)
12.7.5 Levels and Loops
339(1)
12.8 Example Scenarios of ASH
340(4)
12.8.1 Enhancing Spray and Focus
340(1)
12.8.2 Enhanced Context-Aware Routing
341(1)
12.8.3 A Simple Cross-Layer Protocol
342(1)
12.8.3.1 Medium Access
342(1)
12.8.3.2 Routing and Replication
343(1)
12.8.3.3 Application
344(1)
12.9 Related Work
344(1)
12.10 Conclusions and Future Work
345(6)
12.10.1 Future Directions
345(1)
12.10.2 Conclusion
346(1)
References
347(4)
13 Chemical Relaying Protocols
351(18)
Daniele Miorandi
Iacopo Carreras
Francesco De Pellegrini
Imrich Chlamtac
Vilmos Simon
Endre Varga
13.1 Introduction
351(2)
13.2 Related Work
353(2)
13.2.1 Relaying in Intermittently Connected Witeless Networks
353(1)
13.2.2 Chemical Computing
354(1)
13.3 System Model and Framework
355(5)
13.4 Fraglets Implementation
360(2)
13.5 Performance Evaluation
362(4)
13.6 Conclusions
366(3)
References
367(2)
14 Attractor Selection as Self-Adaptive Control Mechanism for Communication Networks
369(22)
Kenji Leibnitz
Masayuki Murata
Tetsuya Yomo
14.1 Introduction
370(1)
14.2 Noise and Fluctuations in Dynamical Systems
371(5)
14.2.1 Dynamic Systems under the Influence of Noise
374(1)
14.2.2 Relationship between Fluctuation and Its Response
375(1)
14.3 Mathematical Models of Attractor Selection
376(6)
14.3.1 Mutually Inhibitory Operon Regulatory Network
376(2)
14.3.2 Sigmoid Gene Activation Model
378(3)
14.3.3 Gaussian Mixture Attractor Model
381(1)
14.4 Application to Self-Adaptive Network Control
382(5)
14.4.1 Differences between Biological Networks and Communication Networks
382(1)
14.4.1.1 Mapping of Growth Rate
382(1)
14.4.1.2 Fluctuations: Ambient or Controllable?
383(1)
14.4.1.3 Centralized vs. Distributed Control
383(1)
14.4.2 Applications to Self-Adaptive Network Control
383(1)
14.4.2.1 Self-Adaptive Overlay Path Selection
384(2)
14.4.2.2 Next Hop Selection in Ad Hoc Network Routing
386(1)
14.5 Conclusion
387(4)
Acknowledgments
387(1)
References
388(3)
15 Topological Robustness of Biological Systems for Information Networks---Modularity
391(18)
S. Eum
S. Arakawa
Masayuki Murata
15.1 Introduction
392(1)
15.2 Topological Robustness of Biological Systems
393(3)
15.2.1 Overview of Biological Systems
393(1)
15.2.2 Resemblance between Biological Systems and Information Networks
394(1)
15.2.3 Topological Characteristic of Biological Networks
394(1)
15.2.3.1 Scale-Free Structure
394(1)
15.2.3.2 Bow-Tie Structure
395(1)
15.2.3.3 Hierarchical Structure
395(1)
15.2.3.4 Modularity Structure
396(1)
15.3 Modularity and Robustness of Systems
396(9)
15.3.1 Attack Vulnerability
397(1)
15.3.2 Isolation and Localization
398(2)
15.3.3 Bottleneck
400(2)
15.3.4 Adaptability
402(3)
15.4 Summary
405(4)
References
405(4)
16 Biologically Inspired Dynamic Spectrum Access in Cognitive Radio Networks
409(18)
Baris Atakan
Ozgur B. Akan
16.1 Introduction
410(2)
16.2 Immune System and Cognitive Radio Networks
412(3)
16.2.1 Biological Immune System
412(2)
16.2.2 Immune System--Inspired Cognitive Radio Networks
414(1)
16.3 Immune System--Inspired Spectrum Sensing and Management in Cognitive Radio Networks
415(4)
16.3.1 Immune System--Inspired Spectrum-Sensing Model for Cognitive Radio Networks
415(1)
16.3.2 Immune System--Inspired Spectrum Management Model for Cognitive Radio Networks
416(3)
16.4 Biological Task Allocation and Spectrum Sharing in Cognitive Radio Networks
419(3)
16.4.1 Biological Task Allocation Model
419(1)
16.4.2 Biological Task Allocation--Inspired Spectrum-Sharing Model for Cognitive Radio Nerworks
420(2)
16.5 Biological Switching--Inspired Spectrum Mobility Management in Cognitive Radio Nerworks
422(3)
16.6 Conclusion
425(2)
References
425(2)
17 Weakly Connected Oscillatory Networks for Information Processing
427(30)
Michele Bonnin
Fernando Corinto
Marco Gilli
17.1 Introduction
428(1)
17.2 Networks of Structurally Stable Oscillators
429(5)
17.3 Networks of Oscillators Close to Bifurcations
434(9)
17.4 Pattern Recognition by Means of Weakly Connected Oscillatory Networks
443(9)
17.4.1 WCON-Based Associative Memories
446(2)
17.4.2 WCON-Based Dynamic Memories
448(4)
17.5 Conclusions
452(5)
Acknowledgments
453(1)
References
453(4)
18 Modeling the Dynamics of Cellular Signaling for Communication Networks
457(24)
Jian-Qin Liu
Kenji Leibnitz
18.1 Introduction
458(2)
18.2 The Dynamics of Cellular Signaling
460(5)
18.2.1 Spatial Dynamics
460(2)
18.2.2 Temporal Dynamics
462(3)
18.2.3 Concluding Remarks
465(1)
18.3 Dynamical Graph Representation of Cellular Signaling
465(5)
18.3.1 Formulating the Structural Variations of Pathway Networks by Graph Rewriting
466(1)
18.3.2 Representation of Cellular Signaling by Graph Automata
466(3)
18.3.3 Methodologies of Pathway Networking
469(1)
18.4 Graph Rewiring Operations for Self-Configuration of Dynamical Networks
470(4)
18.4.1 Self-Configuration in Communication Networks
470(1)
18.4.2 Graph Rewiring Algorithm for Self-Configuring Dynamical Networks
471(1)
18.4.3 Information Theoretic Measures for Cellular Signaling Coding
472(2)
18.4.4 Concluding Discussion
474(1)
18.5 Robustness of Pathway Networks
474(3)
18.5.1 The Heat Shock Response Pathway
475(1)
18.5.2 The MAPK Pathway for Ultrasensitivity
476(1)
18.6 Conclusion
477(4)
Acknowledgments
478(1)
References
478(3)
19 A Biologically Inspired QoS-Aware Architecture for Scalable, Adaptive, and Survivable Network Systems
481(40)
Paskorn Champrasert
Junichi Suzuki
19.1 Introduction
482(2)
19.2 Design Principles in SymbioticSphere
484(3)
19.3 SymbioticSphere
487(8)
19.3.1 Agents
487(1)
19.3.2 Platforms
487(2)
19.3.3 Behavior Policies
489(1)
19.3.3.1 Agent Behavior Policies
489(1)
19.3.3.2 Platform Behavior Policies
490(1)
19.3.4 Energy Exchange
491(1)
19.3.5 Constraint-Aware Evolution
492(3)
19.4 Evaluation
495(22)
19.4.1 Simulation Configurations
496(4)
19.4.2 Evaluation of Energy Exchange
500(1)
19.4.3 Evaluation of Adaptability
500(9)
19.4.4 Evaluation of Scalability
509(5)
19.4.5 Evaluation of Survivability
514(3)
19.5 Related Work
517(1)
19.6 Concluding Remarks
518(3)
References
519(2)
Index 521
Dr. Yang Xiao worked in the industry as a medium access control (MAC) architect and was involved in the IEEE 802.11 standard enhancement work before joining the Department of Computer Science at the University of Memphis in 2002. He is currently with the Department of Computer Science (with tenure) at the University of Alabama.

Dr. Xiao was a voting member of the IEEE 802.11 Working Group from 2001 to 2004. He is also a senior member of the IEEE. Dr. Xiao serves as a panelist for the U.S. National Science Foundation (NSF), the Canada Foundation for Innovation (CFI)s Telecommunications Expert Committee, and the American Institute of Biological Sciences (AIBS). He also serves as a referee/reviewer for many national and international funding agencies. His research interests include security, communications/networks, robotics, and telemedicine. He has published more than 160 refereed journal papers and over 200 refereed conference papers and book chapters related to these areas. His research has been supported by the U.S. National Science Foundation (NSF), U.S. Army Research the Global Environment for Network Innovations (GENI), Fleet Industrial Supply CenterSan Diego (FISCSD), FIATECH, and the University of Alabamas Research Grants Committee. He currently serves as editor-in-chief for the International Journal of Security and Networks (IJSN) and the International Journal of Sensor Networks (IJSNet). He was also the founding editor-in-chief for the International Journal of Telemedicine and Applications (IJTA) (20072009).
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