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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
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.
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Veröffentlichungsjahr: 2020
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Table of Contents
Cover
Title page
Copyright
Preface
1 A Fundamental Overview of Different Algorithms and Performance Optimization for Swarm Intelligence
1.1 Introduction
1.2 Methodology of SI Framework
1.3 Composing With SI
1.4 Algorithms of the SI
1.5 Conclusion
References
2 Introduction to IoT With Swarm Intelligence
2.1 Introduction
2.2 Programming
2.3 Data Generation
2.4 Automation
2.5 Security of the Generated Data
2.6 Swarm Intelligence
2.7 Scope in Educational and Professional Sector
2.8 Conclusion
References
3 Perspectives and Foundations of Swarm Intelligence and its Application
3.1 Introduction
3.2 Behavioral Phenomena of Living Beings and Inspired Algorithms
3.3 Roach Infestation Optimization
3.4 Conclusion
References
4 Implication of IoT Components and Energy Management Monitoring
4.1 Introduction
4.2 IoT Components
4.3 IoT Energy Management
4.4 Implication of Energy Measurement for Monitoring
4.5 Execution of Industrial Energy Monitoring
4.6 Information Collection
4.7 Vitality Profiles Analysis
4.8 IoT-Based Smart Energy Management System
4.9 Smart Energy Management System
4.10 IoT-Based System for Intelligent Energy Management in Buildings
4.11 Smart Home for Energy Management Using IoT
References
5 Distinct Algorithms for Swarm Intelligence in IoT
5.1 Introduction
5.2 Swarm Bird–Based Algorithms for IoT
5.3 Swarm Insect–Based Algorithm for IoT
References
6 Swarm Intelligence for Data Management and Mining Technologies to Manage and Analyze Data in IoT
6.1 Introduction
6.2 Content Management System
6.3 Data Management and Mining
6.4 Introduction to Internet of Things
6.5 Swarm Intelligence Techniques
6.6 Chapter Summary
References
7 Healthcare Data Analytics Using Swarm Intelligence
7.1 Introduction
7.2 Intelligent Agent
7.3 Background and Usage of AI Over Healthcare Domain
7.4 Application of AI Techniques in Healthcare
7.5 Benefits of Artificial Intelligence
7.6 Swarm Intelligence Model
7.7 Swarm Intelligence Capabilities
7.8 How the Swarm AI Technology Works
7.9 Swarm Algorithm
7.10 Ant Colony Optimization Algorithm
7.11 Particle Swarm Optimization
7.12 Concepts for Swarm Intelligence Algorithms
7.13 How Swarm AI is Useful in Healthcare
7.14 Benefits of Swarm AI
7.15 Impact of Swarm-Based Medicine
7.16 SI Limitations
7.17 Future of Swarm AI
7.18 Issues and Challenges
7.19 Conclusion
References
8 Swarm Intelligence for Group Objects in Wireless Sensor Networks
8.1 Introduction
8.2 Algorithm
8.3 Mechanism and Rationale of the Work
8.4 Network Energy Model
8.5 PSO Grouping Issue
8.6 Proposed Method
8.7 Bunch Hub Refreshing Calculation Dependent on an Improved PSO
8.8 Other SI Models
8.9 An Automatic Clustering Algorithm Based on PSO
8.10 Steering Rule Based on Informed Algorithm
8.11 Routing Protocols Based on Meta-Heuristic Algorithm
8.12 Routing Protocols for Avoiding Energy Holes
8.13 System Model
References
9 Swam Intelligence–Based Resources Optimization and Analyses and Managing Data in IoT With Data Mining Technologies
9.1 Introduction
9.2 IoT With Data Mining
9.3 ACO and Data Mining
9.4 Challenges for ACO-Based Data Mining
References
10 Data Management and Mining Technologies to Manage and Analyze Data in IoT
10.1 Introduction
10.2 Data Management
10.3 Data Lifecycle of IoT
10.4 Procedures to Implement IoT Data Management
10.5 Industrial Data Lifecycle
10.6 Industrial Data Management Framework of IoT
10.7 Data Mining
10.8 Clustering
10.9 Affiliation Analysis
10.10 Time Series Analysis
References
11 Swarm Intelligence for Data Management and Mining Technologies to Manage and Analyze Data in IoT
11.1 Introduction
11.2 Information Mining Functionalities
11.3 Data Mining Using Ant Colony Optimization
11.4 Computing With Ant-Based
11.5 Related Work
11.6 Contributions
11.7 SI in Enormous Information Examination
11.8 Requirements and Characteristics of IoT Data
11.9 Conclusion
References
12 Swarm Intelligence–Based Energy-Efficient Clustering Algorithms for WSN: Overview of Algorithms, Analysis, and Applications
12.1 Introduction
12.2 SI-Based Clustering Techniques
12.3 WSN SI Clustering Applications
12.4 Challenges and Future Direction
12.5 Conclusions
References
13 Swarm Intelligence for Clustering in Wireless Sensor Networks
13.1 Introduction
13.2 Clustering in Wireless Sensor Networks
13.3 Use of Swarm Intelligence for Clustering in WSN
13.4 Conclusion
References
14 Swarm Intelligence for Clustering in Wi-Fi Networks
14.1 Introduction
14.2 Power Conscious Fuzzy Clustering Algorithm (PCFCA)
14.3 Vitality Collecting in Remote Sensor Systems
14.4 Adequate Power Circular Clustering Algorithm (APRC)
14.5 Modifying Scattered Clustering Algorithm (MSCA)
14.6 Conclusion
References
15 Support Vector in Healthcare Using SVM/PSO in Various Domains: A Review
15.1 Introduction
15.2 The Fundamental PSO
15.3 The Support Vector
15.4 Conclusion
References
16 IoT-Based Healthcare System to Monitor the Sensor’s Data of MWBAN
16.1 Introduction
16.2 Related Work
16.3 Proposed System
16.4 System Model
16.5 Challenges of Cyber Security in Healthcare With IoT
16.6 Conclusion
References
17 Effectiveness of Swarm Intelligence for Handling Fault-Tolerant Routing Problem in IoT
17.1 Introduction
17.2 Applications of Swarm Intelligence
17.3 Swarm Intelligence in IoT
17.4 Innovations Based on Swarm Intelligence
17.5 Energy-Based Model
17.6 Conclusion
References
18 Smart Epilepsy Detection System Using Hybrid ANN-PSO Network
18.1 Introduction
18.2 Materials and Methods
18.3 Proposed Epilepsy Detection System
18.4 Experimental Results of ANN-Based System
18.5 MSE Reduction Using Optimization Techniques
18.6 Hybrid ANN-PSO System for Epilepsy Detection
18.7 Conclusion
References
Index
End User License Agreement
List of Illustrations
Chapter 3
Figure 3.1 Bee foraging behavior.
Figure 3.2 ABC Algorithm.
Figure 3.3 RIO.
Figure 3.4 Algorithm: Roach Infestation Optimization (RIO) (source: https://docs...
Figure 3.5 Flowchart of GSO algorithm.
Chapter 4
Figure 4.1 Internet of Things (IoT).
Figure 4.2 Relation with different phases.
Figure 4.3 The Arduino Mega 2560 R3.
Figure 4.4 The ESP8266.
Figure 4.5 RFID wireless component.
Figure 4.6 DHT11 sensor.
Figure 4.7 Gas sensor.
Figure 4.8 Energy management (IoT).
Figure 4.9 Energy monitoring.
Figure 4.10 Power management in IoT.
Figure 4.11 Smart energy management system.
Figure 4.12 Data flow diagram (DFD).
Figure 4.13 Block diagram of smart home energy management systems using IoT.
Chapter 6
Figure 6.1 Content management system.
Figure 6.2 Generic data life cycle.
Figure 6.3 Knowledge data discovery.
Figure 6.4 COVID-19 impact in India until 15 May 2020.
Figure 6.5 Results using statistical methods.
Figure 6.6 Association rules.
Figure 6.7 Stream graph for deaths and predicted deaths.
Figure 6.9 Data mining issues.
Figure 6.10 Ant colony optimization to find food.
Chapter 7
Figure 7.1 AI in healthcare startups. From CB Insights (2016).
Figure 7.2 Concept of an intelligent agent.
Figure 7.3 Medical diagnostic-therapeutic cycle.
Figure 7.4 Key capabilities of Swarm Intelligence.
Figure 7.5 Navigation of ants through ants colony algorithm.
Figure 7.6 Architecture of feature selection for classification using ACO.
Figure 7.7 Ant colony optimization.
Figure 7.8 The probability of the swarm diagnosing pneumonia in a patient. Credi...
Figure 7.9 AI benefits in health.
Chapter 8
Figure 8.1 The clustering phenomena [2].
Figure 8.2 Centralized clustering algorithm [6].
Figure 8.3 Schematic diagram of clustering mechanism [12].
Figure 8.4 Architecture of WSN [20].
Figure 8.5 Process of automatic clustering [23].
Chapter 9
Figure 9.1 Process of designing a swarm intelligence model and the corresponding...
Figure 9.2 The main principle of collective behavior.
Figure 9.3 Models of collective behavior of Swarm Intelligence.
Figure 9.4 The simple flow scheme of a swarm.
Figure 9.5 Food finding procedure followed by ants.
Figure 9.6 Ants stigmergic conduct in finding the smallest route among food and ...
Figure 9.7 Basic structure of PSO.
Figure 9.8 Roadmap of Data Mining with IoT.
Figure 9.9 Big Data Mining system for IoT.
Figure 9.10 Classification process.
Figure 9.11 Clustering process.
Figure 9.12 Frequent pattern mining process.
Figure 9.13 Different combination of mining technologies for the IoT.
Figure 9.14 Steps of the knowledge discovery process.
Chapter 10
Figure 10.1 Data management IoT.
Figure 10.2 IoT data life cycle.
Figure 10.3 Data management challenges.
Figure 10.4 Industrial data management.
Figure 10.5 A industrial data management system framework of an IoT.
Figure 10.6 Data mining integrated IoT architecture.
Figure 10.7 Data mining process.
Figure 10.8 The research structure of classification.
Figure 10.9 Structure of clustering.
Figure 10.10 Affiliation analysis research structure.
Figure 10.11 Structure of time series analysis.
Chapter 11
Figure 11.1 Data management and principles of analyze data.
Figure 11.2 The research structure of classification.
Chapter 12
Figure 12.1 Relative comparison of SI-based algorithms during 2000–2019.
Figure 12.2 Swarm intelligence algorithms (see Table 12.3 for abbreviations).
Figure 12.3 Conceptual distribution of SI techniques from 2000 to 2018.
Figure 12.4 Key characteristics of WSN.
Figure 12.5 Typical nodes cluster formation.
Figure 12.6 Key services of WSN.
Figure 12.7 Key issues of WSN.
Chapter 13
Figure 13.1 Architecture of WSN.
Figure 13.2 Types of Wireless Sensor Network.
Figure 13.3 Architecture of MC3F2.
Figure 13.4 Working algorithm of ABC.
Figure 13.5 Flowchart of ABC.
Figure 13.6 Randomly deployed clusters with elected cluster heads.
Figure 13.7 Clustered ISUs.
Chapter 14
Figure 14.1 Clustering in wireless network.
Figure 14.2 A multi-hop clustered Wi-Fi network.
Figure 14.3 Circular clustering in Wi-Fi networks.
Chapter 15
Figure 15.1 The support vector concept.
Chapter 16
Figure 16.1 Network model.
Chapter 17
Figure 17.1 Swarm behavior of ants [2].
Figure 17.2 Benefits of Swarm Intelligence [1].
Figure 17.3 Flock of birds [20].
Figure 17.4 Colony of bees [6].
Figure 17.5 Uses of IoT [19].
Figure 17.6 Number of devices use in near future representation by graph [12].
Figure 17.7 Type of fault tolerance and its characteristics [15].
Figure 17.8 Network architecture of fault tolerance [15].
Figure 17.9 Flowchart representation of IEIFTA network [7].
Figure 17.10 Flowchart representation of fault tolerance routing [22].
Chapter 18
Figure 18.1 Sample EEG signals from dataset under consideration.
Figure 18.2 Proposed ANN model for epilepsy detection.
Figure 18.3 ANN structure obtained using feedforward net training.
Figure 18.4 Confusion matrix obtained after ANN training on 300 datasets.
Figure 18.5 Confusion matrix of hybrid ANN + PSO epilepsy detection system.
List of Tables
Chapter 6
Table 6.1 Clustering using K Means.
Chapter 9
Table 9.1 Combination of algorithm.
Chapter 12
Table 12.1 Summarization of survey works on routing protocols in WSN.
Table 12.2 Comparison of typical SI algorithms frequently applied for clustering...
Table 12.3 SI techniques applied to WSN and other domains.
Table 12.4 Appraisal of swarm intelligence based metaheuristic WSN clustering.
Chapter 16
Table 16.1 Time complexity of Heuristic Iterative Algorithm.
Chapter 18
Table 18.1 Statistical features obtained from raw data.
Table 18.2 Parameters calculated from the confusion matrix.
Table 18.3 Effect of PSO, GA, and SA on MSE of ANN.
Guide
Cover
Table of Contents
Title Page
Copyright
Preface
Begin Reading
Index
End User License Agreement
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Scrivener Publishing
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Publishers at Scrivener
Martin Scrivener ([email protected])
Phillip Carmical ([email protected])
Swarm Intelligence Optimization
Algorithms and Applications
Edited by
Abhishek Kumar, Pramod Singh Rathore, Vicente Garcia Diaz
and
Rashmi Agrawal
This edition first published 2021 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA © 2021 Scrivener Publishing LLC For more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Preface
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.
Nearly all aspects of resource optimization using the IoT are covered in the 18 chapters of this book. Chapter 1 basically describes what the IoT is, and how electronic devices are connected to the Internet in order to start thinking about and generating the data which can be very beneficial for mankind. The objective of Chapter 2 on the perspectives and foundations of swarm intelligence (SI) is to discuss some biomimicry algorithms and their applications. Along with the basics of SI, ant colony optimization (ACO), bee-inspired algorithms, particle swarm optimization (PSO), bacterial foraging optimization, firefly algorithms, fish swarm optimization, and many more SI algorithms are presented.
Industrial IoT (IIoT)–enhanced energy management systems are created to help advance changes in undertakings. Among the topics covered in Chapter 3 on energy management in IoT are the ability of systems to increment the straightforwardness of vitality utilization insights, improve the workforce consciousness of vitality misfortunes, and give prescient investigation instruments for determining potential modern mishaps and future vitality requests. The main focus of Chapter 4 on healthcare data analytics using SI is on some foundation principles that help to find solutions in optimistic form. This chapter also discusses SI techniques like PSO, ACO, and the use of swarm AI in healthcare; along with the issues and challenges of SI healthcare systems.
Chapter 5 discusses SI for group objects in wireless sensor networks (WSNs), which are utilized in different places as alert finders and sensors. Quantities of grouping calculations have been created to improve the vitality parity of the WSNs on the grounds that vitality is the fundamental part of the WSN during information transmission. These calculations are chiefly utilized for expanding the lifetime of these sensor systems. The support vector machine (SVM) using PSO in various healthcare domains is reviewed in Chapter 6. The PSO is motivated by the social conduct of winged animal rushing and fish tutoring. It is a stochastic optimization algorithm in which each key is regarded as a “particle” and each particle has a fitness value calculated by a function called the “objective function.”
In Chapter 7, different bird and insect swarm–based algorithms are studied. Complex problems with incomplete information and dynamic properties are used to resolve different
SI algorithms. In Chapter 8, a design based on an artificial neural network is proposed for automatic detection of epileptic signals from an electroencephalogram dataset obtained from the University of Bonn, Germany, which contains observations from healthy and epileptic brains. Electroencephalogram signals are nonstationary and nonlinear in nature, so it becomes quite difficult for medical doctors to interpret details about the significant data. Therefore, it is important to design a smart system by combining the IoT-based network with artificial intelligence to sense the disease conditions with more accuracy.
The main purpose of Chapter 9 is to present some biological motivations and basic SI concepts using two models: ACO and PSO. These are probabilistic techniques which help to solve computational problems by finding optimistic solutions. Chapter 10 on data management and mining technologies to manage and analyze data in IOT presents a layered reference model for IoT information on the dashboard. IoT has become a functioning zone of research, since it guarantees, among other things, the improvement of the nature and security of smart cities, making assets flexible, and ensuring executives are progressively effective. Also reviewed are advanced traffic management systems applications, including grouping, bunching, affiliation investigation, and time arrangement examination, along with the most recent application cases.
In Chapter 11, the authors supply an orderly technique to audit the mining of facts in order to know the device view and alertness, including characterization, bunching, affiliation examination, time association research, and exception investigation. Furthermore, the most current software instances are likewise overviewed. As an ever-increasing number of gadgets are associated with IoT, a huge amount of records must be dissected and the maximum recent calculations have to be altered to use facts. Authors evaluate these calculations and highlight open research problems associated with them. Finally, an endorsed significant records mining framework is proposed. Chapter 12 answers the frequently asked questions of what, why, how, and where SI can be applied so as to optimize network energy utilization. The chapter covers almost 60+ SI algorithm applications in brief. Furthermore, various issues of WSN clustering and WSN services are presented for the sake of completeness. A major contribution of this chapter is the survey of various SI techniques applied for WSN, in particular for cluster formation and CH selection.
Chapter 13 discusses SI for clustering in WSNs. The term “swarm” refers to a group of flying objects/insects which cooperatively work to achieve a common goal. The concept of “swarm intelligence” means “collective intelligence” inhibited by the group of units involved in a given network. SI owes its roots to the life of social insects (i.e., wasps, ants, bees, and termites), which are known for their organization and for having an efficient communication and warning system, maintaining an army and dividing labor.
System lifetime, discussed in Chapter 14, is a standout among the most critical measurements in wireless body area networks (WBANs). The authors propose a healthcare monitoring system based on IoT which monitors the sensor’s data/information to analyze the patient’s condition in a mobile WBAN (MWBAN). For this, the implementation of a transfer for determining harmful acts is proposed under the topology which defines a heuristic approach to enhance the network lifetime. Chapter 15 reviews the effectiveness of SI for handling fault-tolerant routing problems in the IoT. The SI algorithms are most effective for handling routing problems in the IoT. Swarm is an optimization algorithm so this chapter presents an in-depth discussion of how the drawbacks present in IoT can be compensated for using SI. The places where IoT is used, its benefits, as well as the use of swarm in different fields are elaborated.
Cluster nodes play a precious role in preserving energy. Clustering perspective targets resolves the collision of data, resulting in useful information being broadcast. In Chapter 16, the authors define some modern adequate clustering approaches for power system control to enhance the life of sensing networks.
Data mining faces challenges in the case of dynamic nodes. These dynamic nodes are a part of smart systems called the IoT and work together with certain techniques to create intelligence. Since SI is one such area that helps to manage data when the nodes are moving and sharing data in a distributed network, Chapter 17 discusses SI models inspired by nature, examines them, and finds implementable models using this technology. Finally, Chapter 18 presents a fundamental overview of different algorithms and performance optimization for SI.
In conclusion, we would like to thank all those who contributed to this book and hope that readers will not only enjoy reading it but also benefit from its contents.
Editorial TeamAugust 2020
