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E-grāmata: Swarm Intelligence Optimization: Algorithms and Applications

Edited by (Rajasthan Technical University, India), Edited by (University of Oviedo, Spain), Edited by (Manav Rachna International University Faridabad, India), Edited by (University of Madras, India; Chitkara University, India)
  • Formāts: EPUB+DRM
  • Izdošanas datums: 19-Nov-2020
  • Izdevniecība: Wiley-Scrivener
  • Valoda: eng
  • ISBN-13: 9781119778905
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Swarm Intelligence Optimization: Algorithms and ApplicationsPalielināt
  • Formāts: EPUB+DRM
  • Izdošanas datums: 19-Nov-2020
  • Izdevniecība: Wiley-Scrivener
  • Valoda: eng
  • ISBN-13: 9781119778905
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Resource optimization has always been a thrust area of research, and as the Internet of Things (IoT) is the most talked about topic of the current era of technology, it has become the need of the hour. Therefore, the idea behind this book was to simplify the journey of those who aspire to understand resource optimization in the IoT. To this end, included in this book are various real-time/offline applications and algorithms/case studies in the fields of engineering, computer science, information security, and cloud computing, along with the modern tools and various technologies used in systems, leaving the reader with a high level of understanding of various techniques and algorithms used in resource optimization.

Preface xv
1 A Fundamental Overview of Different Algorithms and Performance Optimization for Swarm Intelligence 1(20)
Manju Payal
Abhishek Kumar
Vicente Garcia Diaz
1.1 Introduction
1(2)
1.2 Methodology of SI Framework
3(4)
1.3 Composing With SI
7(1)
1.4 Algorithms of the SI
7(11)
1.5 Conclusion
18(1)
References
18(3)
2 Introduction to IoT With Swarm Intelligence 21(20)
Anant Mishra
Jafar Tahir
2.1 Introduction
21(1)
2.1.1 Literature Overview
22(1)
2.2 Programming
22(1)
2.2.1 Basic Programming
22(1)
2.2.2 Prototyping
22(1)
2.3 Data Generation
23(3)
2.3.1 From Where the Data Comes?
23(1)
2.3.2 Challenges of Excess Data
24(1)
2.3.3 Where We Store Generated Data?
24(1)
2.3.4 Cloud Computing and Fog Computing
25(1)
2.4 Automation
26(4)
2.4.1 What is Automation?
26(1)
2.4.2 How Automation is Being Used?
26(4)
2.5 Security of the Generated Data
30(3)
2.5.1 Why We Need Security in Our Data?
30(1)
2.5.2 What Types of Data is Being Generated?
31(1)
2.5.3 Protecting Different Sector Working on the Principle of IoT
32(1)
2.6 Swarm Intelligence
33(3)
2.6.1 What is Swarm Intelligence?
33(1)
2.6.2 Classification of Swarm Intelligence
33(1)
2.6.3 Properties of a Swarm Intelligence System
34(2)
2.7 Scope in Educational and Professional Sector
36(1)
2.8 Conclusion
37(1)
References
38(3)
3 Perspectives and Foundations of Swarm Intelligence and its Application 41(8)
Rashmi Agrawal
3.1 Introduction
41(1)
3.2 Behavioral Phenomena of Living Beings and Inspired Algorithms
42(3)
3.2.1 Bee Foraging
42(1)
3.2.2 ABC Algorithm
43(1)
3.2.3 Mating and Marriage
43(1)
3.2.4 MBO Algorithm
44(1)
3.2.5 Coakroach Behavior
44(1)
3.3 Roach Infestation Optimization
45(1)
3.3.1 Lampyridae Bioluminescence
45(1)
3.3.2 GSO Algorithm
46(1)
3.4 Conclusion
46(1)
References
47(2)
4 Implication of IoT Components and Energy Management Monitoring 49(18)
Shweta Sharma
Praveen Kumar Kotturu
Praffid Chandra Narooka
4.1 Introduction
49(4)
4.2 IoT Components
53(3)
4.3 IoT Energy Management
56(1)
4.4 Implication of Energy Measurement for Monitoring
57(1)
4.5 Execution of Industrial Energy Monitoring
58(1)
4.6 Information Collection
59(1)
4.7 Vitality Profiles Analysis
59(2)
4.8 IoT-Based Smart Energy Management System
61(1)
4.9 Smart Energy Management System
61(1)
4.10 IoT-Based System for Intelligent Energy Management in Buildings
62(1)
4.11 Smart Home for Energy Management Using IoT
62(2)
References
64(3)
5 Distinct Algorithms for Swarm Intelligence in IoT 67(16)
Trapty Agarwal
Gurjot Singh
Subham Pradhan
Vikash Verma
5.1 Introduction
67(1)
5.2 Swarm Bird-Based Algorithms for IoT
68(4)
5.2.1 Particle Swarm Optimization (PSO)
68(1)
5.2.1.1 Statistical Analysis
68(1)
5.2.1.2 Algorithm
68(1)
5.2.1.3 Applications
69(1)
5.2.2 Cuckoo Search Algorithm
69(3)
5.2.2.1 Statistical Analysis
69(1)
5.2.2.2 Algorithm
70(1)
5.2.2.3 Applications
70(1)
5.2.3 Bat Algorithm
71(1)
5.2.3.1 Statistical Analysis
71(1)
5.2.3.2 Algorithm
71(1)
5.2.3.3 Applications
72(1)
5.3 Swarm Insect-Based Algorithm for IoT
72(8)
5.3.1 Ant Colony Optimization
72(2)
5.3.1.1 Flowchart
73(1)
5.3.1.2 Applications
73(1)
5.3.2 Artificial Bee Colony
74(1)
5.3.2.1 Flowchart
75(1)
5.3.2.2 Applications
75(1)
5.3.3 Honey-Bee Mating Optimization
75(2)
5.3.3.1 Flowchart
76(1)
5.3.3.2 Application
77(1)
5.3.4 Firefly Algorithm
77(1)
5.3.4.1 Flowchart
78(1)
5.3.4.2 Application
78(1)
5.3.5 Glowworm Swarm Optimization
78(7)
5.3.5.1 Statistical Analysis
79(1)
5.3.5.2 Flowchart
79(1)
5.3.5.3 Application
80(1)
References
80(3)
6 Swarm Intelligence for Data Management and Mining Technologies to Manage and Analyze Data in IoT 83(18)
Kashinath Chandelkar
6.1 Introduction
83(1)
6.2 Content Management System
84(1)
6.3 Data Management and Mining
85(9)
6.3.1 Data Life Cycle
86(1)
6.3.2 Knowledge Discovery in Database
87(1)
6.3.3 Data Mining vs. Data Warehousing
88(1)
6.3.4 Data Mining Techniques
88(4)
6.3.5 Data Mining Technologies
92(1)
6.3.6 Issues in Data Mining
93(1)
6.4 Introduction to Internet of Things
94(1)
6.5 Swarm Intelligence Techniques
94(4)
6.5.1 Ant Colony Optimization
95(1)
6.5.2 Particle Swarm Optimization
95(1)
6.5.3 Differential Evolution
96(1)
6.5.4 Standard Firefly Algorithm
96(1)
6.5.5 Artificial Bee Colony
97(1)
6.6
Chapter Summary
98(1)
References
98(3)
7 Healthcare Data Analytics Using Swarm Intelligence 101(22)
Palvadi Srinivas Kumar
Pooja Dixit
N. Gayathri
7.1 Introduction
101(2)
7.1.1 Definition
103(1)
7.2 Intelligent Agent
103(1)
7.3 Background and Usage of AI Over Healthcare Domain
104(1)
7.4 Application of AI Techniques in Healthcare
105(1)
7.5 Benefits of Artificial Intelligence
106(1)
7.6 Swarm Intelligence Model
107(1)
7.7 Swarm Intelligence Capabilities
108(1)
7.8 How the Swarm AI Technology Works
109(1)
7.9 Swarm Algorithm
110(1)
7.10 Ant Colony Optimization Algorithm
110(2)
7.11 Particle Swarm Optimization
112(1)
7.12 Concepts for Swarm Intelligence Algorithms
113(1)
7.13 How Swarm AI is Useful in Healthcare
114(1)
7.14 Benefits of Swarm AI
115(1)
7.15 Impact of Swarm-Based Medicine
116(1)
7.16 SI Limitations
117(1)
7.17 Future of Swarm AI
118(1)
7.18 Issues and Challenges
119(1)
7.19 Conclusion
120(1)
References
120(3)
8 Swarm Intelligence for Group Objects in Wireless Sensor Networks 123(20)
Kapil Chauhan
Pramod Singh Rathore
8.1 Introduction
123(4)
8.2 Algorithm
127(3)
8.3 Mechanism and Rationale of the Work
130(2)
8.3.1 Related Work
131(1)
8.4 Network Energy Model
132(1)
8.4.1 Network Model
132(1)
8.5 PSO Grouping Issue
132(1)
8.6 Proposed Method
133(1)
8.6.1 Grouping Phase
133(1)
8.6.2 Proposed Validation Record
133(1)
8.6.3 Data Transmission Stage
133(1)
8.7 Bunch Hub Refreshing Calculation Dependent on an Improved PSO
133(1)
8.8 Other SI Models
134(1)
8.9 An Automatic Clustering Algorithm Based on PSO
135(1)
8.10 Steering Rule Based on Informed Algorithm
136(1)
8.11 Routing Protocols Based on Meta-Heuristic Algorithm
137(1)
8.12 Routing Protocols for Avoiding Energy Holes
138(1)
8.13 System Model
138(1)
8.13.1 Network Model
138(1)
8.13.2 Power Model
139(1)
References
139(4)
9 Swam Intelligence-Based Resources Optimization and Analyses and Managing Data in IoT With Data Mining Technologies 143(22)
Pooja Dixit
Palvadi Srinivas Kumar
N. Gayathri
9.1 Introduction
143(10)
9.1.1 Swarm Intelligence
143(10)
9.1.1.1 Swarm Biological Collective Behavior
145(2)
9.1.1.2 Swarm With Artificial Intelligence Model
147(3)
9.1.1.3 Birds in Nature
150(3)
9.1.1.4 Swarm with IoT
153(1)
9.2 IoT With Data Mining
153(8)
9.2.1 Data from IoT
154(3)
9.2.1.1 Data Mining for IoT
154(3)
9.2.2 Data Mining With KDD
157(2)
9.2.3 PSO With Data Mining
159(2)
9.3 ACO and Data Mining
161(1)
9.4 Challenges for ACO-Based Data Mining
162(1)
References
162(3)
10 Data Management and Mining Technologies to Manage and Analyze Data in IoT 165(24)
Shweta Sharma
Satya Murthy Sasubilli
Kunal Bhargava
10.1 Introduction
165(1)
10.2 Data Management
166(1)
10.3 Data Lifecycle of IoT
167(4)
10.4 Procedures to Implement IoT Data Management
171(2)
10.5 Industrial Data Lifecycle
173(1)
10.6 Industrial Data Management Framework of IoT
174(1)
10.6.1 Physical Layer
174(1)
10.6.2 Correspondence Layer
175(1)
10.6.3 Middleware Layer
175(1)
10.7 Data Mining
175(7)
10.7.1 Functionalities of Data Mining
179(1)
10.7.2 Classification
180(2)
10.8 Clustering
182(1)
10.9 Affiliation Analysis
182(1)
10.10 Time Series Analysis
183(2)
References
185(4)
11 Swarm Intelligence for Data Management and Mining Technologies to Manage and Analyze Data in IoT 189(18)
Kapil Chauhan
Vishal Dutt
11.1 Introduction
190(2)
11.2 Information Mining Functionalities
192(1)
11.2.1 Classification
192(1)
11.2.2 Clustering
192(1)
11.3 Data Mining Using Ant Colony Optimization
193(3)
11.3.1 Enormous Information Investigation
194(1)
11.3.2 Data Grouping
195(1)
11.4 Computing With Ant-Based
196(1)
11.4.1 Biological Background
196(1)
11.5 Related Work
197(1)
11.6 Contributions
198(1)
11.7 SI in Enormous Information Examination
198(2)
11.7.1 Handling Enormous Measure of Information
199(1)
11.7.2 Handling Multidimensional Information
199(1)
11.8 Requirements and Characteristics of IoT Data
200(1)
11.8.1 IoT Quick and Gushing Information
200(1)
11.8.2 IoT Big Information
200(1)
11.9 Conclusion
201(1)
References
202(5)
12 Swarm Intelligence-Based Energy-Efficient Clustering Algorithms for WSN: Overview of Algorithms, Analysis, and Applications 207(56)
Devika G.
Ramesh D.
Asha Gowda Karegowda
12.1 Introduction
208(5)
12.1.1 Scope of Work
209(1)
12.1.2 Related Works
209(1)
12.1.3 Challenges in WSNs
210(3)
12.1.4 Major Highlights of the
Chapter
213(1)
12.2 SI-Based Clustering Techniques
213(6)
12.2.1 Growth of SI Algorithms and Characteristics
214(5)
12.2.2 Typical SI-Based Clustering Algorithms
219(1)
12.2.3 Comparison of SI Algorithms and Applications
219(1)
12.3 WSN SI Clustering Applications
219(27)
12.3.1 WSN Services
233(1)
12.3.2 Clustering Objectives for WSN Applications
233(1)
12.3.3 SI Algorithms for WSN: Overview
234(1)
12.3.4 The Commonly Applied SI-Based WSN Clusterings
235(31)
12.3.4.1 ACO-Based WSN Clustering
235(2)
12.3.4.2 PSO-Based WSN Clustering
237(3)
12.3.4.3 ABC-Based WSN Clustering
240(1)
12.3.4.4 CS Cuckoo-Based WSN Clustering
241(1)
12.3.4.5 Other SI Technique-Based WSN Clustering
242(4)
12.4 Challenges and Future Direction
246(1)
12.5 Conclusions
247(6)
References
253(10)
13 Swarm Intelligence for Clustering in Wireless Sensor Networks 263(12)
Preeti Sethi
13.1 Introduction
263(1)
13.2 Clustering in Wireless Sensor Networks
264(2)
13.3 Use of Swarm Intelligence for Clustering in WSN
266(6)
13.3.1 Mobile Agents: Properties and Behavior
266(1)
13.3.2 Benefits of Using Mobile Agents
267(1)
13.3.3 Swarm Intelligence-Based Clustering Approach
268(4)
13.4 Conclusion
272(1)
References
272(3)
14 Swarm Intelligence for Clustering in Wi-Fi Networks 275(16)
Astha Parihar
Ramkishore Kuchana
14.1 Introduction
275(3)
14.1.1 Wi-Fi Networks
275(2)
14.1.2 Wi-Fi Networks Clustering
277(1)
14.2 Power Conscious Fuzzy Clustering Algorithm (PCFCA)
278(4)
14.2.1 Adequate Cluster Head Selection in PCFCA
278(1)
14.2.2 Creation of Clusters
279(3)
14.2.3 Execution Assessment of PCFCA
282(1)
14.3 Vitality Collecting in Remote Sensor Systems
282(2)
14.3.1 Power Utilization
283(1)
14.3.2 Production of Energy
283(1)
14.3.3 Power Cost
284(1)
14.3.4 Performance Representation of EEHC
284(1)
14.4 Adequate Power Circular Clustering Algorithm (APRC)
284(2)
14.4.1 Case-Based Clustering in Wi-Fi Networks
284(1)
14.4.2 Circular Clustering Outlook
284(1)
14.4.3 Performance Representation of APRC
285(1)
14.5 Modifying Scattered Clustering Algorithm (MSCA)
286(2)
14.5.1 Equivalence Estimation in Data Sensing
286(1)
14.5.2 Steps in Modifying Scattered Clustering Algorithm (MSCA)
286(1)
14.5.3 Performance Evaluation of MSCA
287(1)
14.6 Conclusion
288(1)
References
288(3)
15 Support Vector in Healthcare Using SVM/PSO in Various Domains: A Review 291(18)
Vishal Dutt
Pramod Singh Rathore
Kapil Chauhan
15.1 Introduction
291(1)
15.2 The Fundamental PSO
292(1)
15.2.1 Algorithm for PSO
293(1)
15.3 The Support Vector
293(11)
15.3.1 SVM in Regression
299(1)
15.3.2 SVM in Clustering
300(1)
15.3.3 Partition Clustering
301(1)
15.3.4 Hierarchical Clustering
301(1)
15.3.5 Density-Based Clustering
302(1)
15.3.6 PSO in Clustering
303(1)
15.4 Conclusion
304(1)
References
304(5)
16 IoT-Based Healthcare System to Monitor the Sensor's Data of MWBAN 309(16)
Rani Kumari
ParmaNand
16.1 Introduction
310(1)
16.1.1 Combination of AI and IoT in Real Activities
310(1)
16.2 Related Work
311(1)
16.3 Proposed System
312(3)
16.3.1 AI and IoT in Medical Field
312(1)
16.3.2 IoT Features in Healthcare
313(2)
16.3.2.1 Wearable Sensing Devices With Physical Interface for Real World
313(1)
16.3.2.2 Input Through Organized Information to the Sensors
313(1)
16.3.2.3 Small Sensor Devices for Input and Output
314(1)
16.3.2.4 Interaction With Human Associated Devices
314(1)
16.3.2.5 To Control Physical Activity and Decision
314(1)
16.3.3 Approach for Sensor's Status of Patient
315(1)
16.4 System Model
315(5)
16.4.1 Solution Based on Heuristic Iterative Method
317(3)
16.5 Challenges of Cyber Security in Healthcare With IoT
320(1)
16.6 Conclusion
321(1)
References
321(4)
17 Effectiveness of Swarm Intelligence for Handling Fault-Tolerant Routing Problem in IoT 325(18)
Arpit Kumar Sharma
Kishan Kanhaiya
Jaisika Talwar
17.1 Introduction
325(3)
17.1.1 Meaning of Swarm and Swarm Intelligence
326(1)
17.1.2 Stability
327(1)
17.1.3 Technologies of Swarm
328(1)
17.2 Applications of Swarm Intelligence
328(2)
17.2.1 Flight of Birds Elaborations
329(1)
17.2.2 Honey Bees Elaborations
329(1)
17.3 Swarm Intelligence in IoT
330(3)
17.3.1 Applications
331(1)
17.3.2 Human Beings vs. Swarm
332(1)
17.3.3 Use of Swarms in Engineering
332(1)
17.4 Innovations Based on Swarm Intelligence
333(2)
17.4.1 Fault Tolerance in IoT
334(1)
17.5 Energy-Based Model
335(5)
17.5.1 Basic Approach of Fault Tolerance With Its Network Architecture
335(2)
17.5.2 Problem of Fault Tolerance Using Different Algorithms
337(3)
17.6 Conclusion
340(1)
References
340(3)
18 Smart Epilepsy Detection System Using Hybrid ANN-PSO Network 343(16)
Jagriti Saini
Maitreyee Dutta
18.1 Introduction
343(2)
18.2 Materials and Methods
345(4)
18.2.1 Experimental Data
345(1)
18.2.2 Data Pre-Processing
345(1)
18.2.3 Feature Extraction
346(1)
18.2.4 Relevance of Extracted Features
346(3)
18.3 Proposed Epilepsy Detection System
349(1)
18.4 Experimental Results of ANN-Based System
350(1)
18.5 MSE Reduction Using Optimization Techniques
351(2)
18.6 Hybrid ANN-PSO System for Epilepsy Detection
353(2)
18.7 Conclusion
355(1)
References
356(3)
Index 359
Abhishek Kumar gained his PhD in computer science from the University of Madras, India in 2019. He is assistant professor at Chitkara University and has more than 80 publications in peer-reviewed international and national journals, books & conferences His research interests include artificial intelligence, image processing, computer vision, data mining and machine learning.

Pramod Singh Rathore has a MTech in Computer Science & Engineering from the Government Engineering College Ajmer, Rajasthan Technical University, Kota India, where he is now an assistant professor. He has more than 60 papers, chapters, and a book to his credit and his research interests are in networking cloud and IoT.

Vicente Garcķa Dķaz obtained his PhD in Computer Science in 2011 at the University of Oviedo, Spain where he is now an associate professor in the School of Computer Science. He has published more than 100 publications and his research interests include domain-specific languages, e-learning, decision support systems.

Rashmi Agrawal obtained her PhD in Computer Applications in 2016 from Manav Rachna International University Faridabad, India, where she is now a professor in the Department of Computer Applications. Her research area includes data mining and artificial intelligence and she has published more than 65 publications to her credit.
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