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Mapping Biological Systems to Network Systems 1st ed. 2016 [Kietas viršelis]

  • Formatas: Hardback, 196 pages, aukštis x plotis: 235x155 mm, weight: 553 g, 37 Illustrations, color; 70 Illustrations, black and white; IX, 196 p. 107 illus., 37 illus. in color., 1 Hardback
  • Išleidimo metai: 19-Feb-2016
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3319297805
  • ISBN-13: 9783319297804
Kitos knygos pagal šią temą:
Mapping Biological Systems to Network Systems 1st ed. 2016Didesnis paveikslėlis
  • Formatas: Hardback, 196 pages, aukštis x plotis: 235x155 mm, weight: 553 g, 37 Illustrations, color; 70 Illustrations, black and white; IX, 196 p. 107 illus., 37 illus. in color., 1 Hardback
  • Išleidimo metai: 19-Feb-2016
  • Leidėjas: Springer International Publishing AG
  • ISBN-10: 3319297805
  • ISBN-13: 9783319297804
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9783319297804.html
Raktažodžiai:
Thebook presents the challenges inherent in the paradigm shift of network systemsfrom static to highly dynamic distributed systems - it proposes solutions thatthe symbiotic nature of biological systems can provide into altering networkingsystems to adapt to these changes. The author discuss how biological systems -which have the inherent capabilities of evolving, self-organizing,self-repairing and flourishing with time - are inspiring researchers to takeopportunities from the biology domain and map them with the problems faced innetwork domain. The book revolves around the central idea of bio-inspiredsystems -- it begins by exploring why biology and computer network research aresuch a natural match. This is followed by presenting a broad overview ofbiologically inspired research in network systems -- it is classified by thebiological field that inspired each topic and by the area of networking inwhich that topic lies. Each case elucidates how biological concepts have

been most successfully applied in various domains. Nevertheless, it alsopresents a case study discussing the security aspects of wireless sensornetworks and how biological solution stand out in comparison to optimizedsolutions. Furthermore, it also discusses novel biological solutions forsolving problems in diverse engineering domains such as mechanical, electrical,civil, aerospace, energy and agriculture. The readers will not only get properunderstanding of the bio inspired systems but also better insight fordeveloping novel bio inspired solutions.

Introduction: Bio-inspired Systems.- Computer Networks.- InceptiveFinding.- Swarm Intelligence and Social Insects.- Immunology and Immune System.-Information Epidemics and Social Networking.- Artificial Neural Networks.- GeneticAlgorithms.- Bio-inspired Software Defined Networking.- Case Study: ProvidingTrust in Wireless Sensor Networks.- Bio-inspired Approaches in VariousEngineering Domain.
1 Introduction: Bio-inspired Systems
1(10)
1.1 Biological Systems
1(1)
1.2 Network Systems
2(1)
1.3 Mapping Biological Systems to Network Systems
3(1)
1.4 Motivation
4(3)
1.5 Organization of Book
7(4)
References
9(2)
2 Computer Networks
11(16)
2.1 Introduction
11(1)
2.2 Network Topologies, Types, and Design Strategies
12(6)
2.2.1 Network Topologies
12(2)
2.2.2 Network Types
14(2)
2.2.3 Design Strategies for Communications
16(2)
2.3 Wireless Networking
18(2)
2.4 Usage of Networking
20(1)
2.5 Challenges and Issues of Networking
21(3)
2.5.1 Quality of Service
21(1)
2.5.2 Connectivity, Manageability, and Scalability
22(1)
2.5.3 Network Security
23(1)
2.5.4 Network Congestion
23(1)
2.6 Future of Networking
24(1)
2.7 Summary
24(3)
References
25(2)
3 Inceptive Findings
27(10)
3.1 Introduction
27(1)
3.2 Structural Composition
28(2)
3.3 Highly Optimized Tolerance (HOT) Model
30(1)
3.4 Biological Evolution
31(1)
3.5 Natural Computing
32(1)
3.6 Feedback Loops for Kidney-Blood Pressure
33(1)
3.7 Summary
34(3)
References
35(2)
4 Swarm Intelligence and Social Insects
37(14)
4.1 Ant Colony Optimization
37(2)
4.2 Bird Colony Optimization
39(2)
4.3 Bee Colony Optimization
41(1)
4.4 Firefly Synchronization
42(1)
4.5 Bacterial Foraging Optimization
43(4)
4.6 Cuckoo Search
47(1)
4.7 Other Inspirations
48(1)
4.8 Summary
49(2)
References
49(2)
5 Immunology and Immune System
51(16)
5.1 Human Immune System
51(4)
5.2 Primary Versus Secondary Response
55(1)
5.3 Artificial Immune Systems
56(7)
5.3.1 Negative Selection
56(2)
5.3.2 Clonal Selection
58(1)
5.3.3 Artificial Immune Systems
59(2)
5.3.4 Pattern Recognition
61(2)
5.4 Summary
63(4)
References
63(4)
6 Information Epidemics and Social Networking
67(12)
6.1 Epidemic Spreading
67(5)
6.2 A System for Building Immunity in Social Networks
72(2)
6.3 Bio-inspired Solutions for Social Networks
74(2)
6.4 Summary
76(3)
References
77(2)
7 Artificial Neural Network
79(18)
7.1 Introduction
79(1)
7.2 Biological Neural Network
80(1)
7.3 Artificial Neural Network
81(12)
7.3.1 Learning Rules in ANN
83(3)
7.3.2 Types of ANN
86(7)
7.4 Applications of Artificial Neural Networks
93(1)
7.4.1 Pattern Classification
93(1)
7.4.2 Clustering
93(1)
7.4.3 Optimization
93(1)
7.4.4 Prediction and Forecasting
94(1)
7.5 Summary
94(3)
References
94(3)
8 Genetic Algorithms
97(10)
8.1 Introduction
97(2)
8.2 Genetic Algorithms
99(2)
8.3 Encoding
101(1)
8.4 Genetic Algorithm Operators
101(3)
8.4.1 Selection
102(1)
8.4.2 Crossover
103(1)
8.4.3 Mutation
104(1)
8.5 Applications of Genetic Algorithms
104(2)
8.6 Summary
106(1)
References
106(1)
9 Bio-inspired Software-Defined Networking
107(10)
9.1 Software-Defined Networking
107(2)
9.2 Wireless Software-Defined Networking
109(1)
9.3 Security in Software-Defined Networking
110(1)
9.4 Bio-inspired Solutions for Software-Defined Networking
111(3)
9.4.1 Self-organization and Stability in Software-Defined Networks
111(1)
9.4.2 Fault Management in Software-Defined Networking
112(1)
9.4.3 Cognition: A Tool for Reinforcing Security in Software-Defined Network
112(1)
9.4.4 Control Loops for Autonomic Systems
113(1)
9.4.5 Self-governance and Self-organization in Autonomic Networks
113(1)
9.5 Summary
114(3)
References
115(2)
10 Case Study: A Review of Security Challenges, Attacks and Trust and Reputation Models in Wireless Sensor Networks
117(60)
10.1 Introduction
117(1)
10.2 Constraints on Wireless Sensor Networks
118(1)
10.3 Threat Model
118(1)
10.4 Security Requirements for Wireless Sensor Networks
119(1)
10.5 Focus and Contents
119(1)
10.6 Attacks in Wireless Sensor Networks
120(6)
10.6.1 Attacks on Data
120(3)
10.6.2 Attacks on Infrastructure
123(3)
10.7 Key Management in Wireless Sensor Networks
126(2)
10.8 Trust and Reputation Model
128(16)
10.8.1 Methods to Calculate Trust
129(1)
10.8.2 Methodologies to Model Trust
130(12)
10.8.3 Comparative Analysis
142(2)
10.9 Bio-inspired Security Model for Wireless Sensor Networks
144(18)
10.9.1 Machine Learning Model
145(6)
10.9.2 Immune Model
151(5)
10.9.3 Experiments and Results
156(6)
10.10 Intelligent Water Drops
162(9)
10.10.1 IWD in Wireless Sensor Networks
162(2)
10.10.2 Algorithm
164(7)
10.11 Summary
171(6)
References
172(5)
11 Bio-inspired Approaches in Various Engineering Domain
177(18)
11.1 Bio-inspired Energy Systems
177(3)
11.1.1 Bio-inspired Solar Energy Program at CIFAR (Canadian Institute for Advance Research)
177(1)
11.1.2 Bio-inspired Optimization of Sustainable Energy Systems
178(1)
11.1.3 Biomimicry Innovations for Energy Sustainability
178(1)
11.1.4 Bio-inspired Artificial Light-Harvesting Antennas for Enhancement of Solar Energy
179(1)
11.2 Bio-inspired Agriculture Systems
180(2)
11.2.1 CO--CH Project
180(1)
11.2.2 Bio-inspired Sensing for Agriculture Robots
181(1)
11.2.3 Bio-inspired Special Wettability
181(1)
11.2.4 Biorefinery: A Bio-inspired Process to Bulk Chemicals
182(1)
11.3 Bio-inspired Aerospace Systems
182(3)
11.3.1 Biomorphic Explorers
183(1)
11.3.2 Bio-inspired Design of Aerospace Composite Joints for Improved Damage Tolerance
184(1)
11.3.3 Pneumatic Artificial Muscles
184(1)
11.4 Bio-inspired Electrical Systems
185(2)
11.4.1 Biologically Inspired Electrically Small Antenna Arrays with Enhanced Directional Sensitivity
185(1)
11.4.2 Biologically Inspired At-scale Robotic Insect
185(1)
11.4.3 Biomimetic and Bio-inspired Robotics in Electric Fish Research
186(1)
11.4.4 Future Power Grid Inspired from Brain
186(1)
11.5 Bio-inspired Mechatronics Systems
187(2)
11.5.1 Bio-inspired Mechatronics
187(1)
11.5.2 Bio-inspired Actuation
187(1)
11.5.3 Bio-inspired Control
188(1)
11.5.4 Future of Bio-inspired Mechatronics
188(1)
11.6 Bio-inspired Civil Engineering
189(2)
11.6.1 Corrosion Protection via Application of Bacteria and Bio-polymers
189(1)
11.6.2 Nondestructive Evaluation of Civil Infrastructures
190(1)
11.6.3 Designing Model Systems for Enhanced Adhesion
190(1)
11.6.4 Artificial Neural Networks in Urban Runoff Forecast
190(1)
11.7 Future: Bio-inspired Computation
191(4)
References
192(3)
Index 195