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Artificial Intelligence Techniques in IoT Sensor Networks is a technical book which can be read by researchers, academicians, students and professionals interested in artificial intelligence (AI), sensor networks and Internet of Things (IoT). This book is intended to develop a shared understanding of applications of AI techniques in the present and near term. The book maps the technical impacts of AI technologies, applications and their implications on the design of solutions for sensor networks. This text introduces researchers and aspiring academicians to the latest developments and…mehr
Artificial Intelligence Techniques in IoT Sensor Networks is a technical book which can be read by researchers, academicians, students and professionals interested in artificial intelligence (AI), sensor networks and Internet of Things (IoT). This book is intended to develop a shared understanding of applications of AI techniques in the present and near term. The book maps the technical impacts of AI technologies, applications and their implications on the design of solutions for sensor networks.
This text introduces researchers and aspiring academicians to the latest developments and trends in AI applications for sensor networks in a clear and well-organized manner. It is mainly useful for research scholars in sensor networks and AI techniques. In addition, professionals and practitioners working on the design of real-time applications for sensor networks may benefit directly from this book. Moreover, graduate and master's students of any departments related to AI, IoT and sensor networks can find this book fascinating for developing expert systems or real-time applications.
This book is written in a simple and easy language, discussing the fundamentals, which relieves the requirement of having early backgrounds in the field. From this expectation and experience, many libraries will be interested in owning copies of this work.
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Dr. Mohamed Elhoseny is an Assistant Professor at the Department of Computer Science, College of Computer & Information Technology, American University in the Emirates (AUE). Dr. Elhoseny is an ACM Distinguished Speaker and IEEE Senior Member. He received his Ph.D. in Computers and Information from Mansoura University/University of North Texas through a joint scientific program. Dr. Elhoseny is the founder and the Editor-in-Chief of IJSSTA journal published by IGI Global. Also, he is an Associate Editor at IEEE Journal of Biomedical and Health Informatics, IEEE Access, Scientific Reports, IEEE Future Directions, Remote Sensing, and International Journal of E-services and Mobile Applications. Moreover, he served as the co-chair, the publication chair, the program chair, and a track chair for several international conferences published by recognized publishers such as IEEE and Springer. Dr. Elhoseny is the Editor-in-Chief of the Studies in Distributed Intelligence Springer Book Series, the Editor-in-Chief of The Sensors Communication for Urban Intelligence CRC Press-Taylor& Francis Book Series, and the Editor-in-Chief of The Distributed Sensing and Intelligent Systems CRC Press-Taylor& Francis Book Series. K. Shankar is currently a Postdoctoral Fellow with Department of Computer Applications, Alagappa University, Karaikudi, India. He has authored/coauthored over 54 ISI Journal articles (with total Impact Factor 150+) and more than 100 Scopus Indexed Articles. He has guest-edited several special issues at many journals published by SAGE, TechScience, Inderscience and MDPI. He has served as Guest Editor and Associate Editor in SCI, Scopus indexed journals like Elsevier, Springer, IGI, Wiley & MDPI. He has served as chair (program, publications, Technical committee and track) on several International conferences. He has delivered several invited and keynote talks, and reviewed the technology leading articles for journals like Scientific Reports - Nature, the IEEE Transactions on Neural Networks and Learning Systems, IEEE Journal of Biomedical and Health Informatics, IEEE Transactions on Reliability, the IEEE Access and the IEEE Internet of Things. He has authored/edited Conference Proceedings, Book Chapters, and 2 books published by Springer. He has been a part of various seminars, paper presentations, research paper reviews, and convener and a session chair of the several conferences. He displayed vast success in continuously acquiring new knowledge and applying innovative pedagogies and has always aimed to be an effective educator and have a global outlook. His current research interests include Healthcare applications, Secret Image Sharing Scheme, Digital Image Security, Cryptography, Internet of Things, and Optimization algorithms. Mohamed Abdel-Basset received the B.Sc., M.Sc., and Ph.D. degrees in information systems and technology from the Faculty of Computers and Informatics, Zagazig University, Egypt. His current research interests are optimization, operations research, data mining, computational intelligence, applied statistics, decision support systems, robust optimization, engineering optimization, multiobjective optimization, swarm intelligence, evolutionary algorithms, and artificial neural networks. He is working on the application of multiobjective and robust meta-heuristic optimization techniques. He is also an/a Editor/reviewer in different international journals and conferences. He has published more than 150 articles in international journals and conference proceedings. He holds the program chair in many conferences in the fields of decision making analysis, big data, optimization, complexity, and the Internet of Things, as well as editorial collaboration in some journals of high impact.
Inhaltsangabe
Preface
Chapter 1
Adaptive Regularized Gaussian Kernel FCM for the Segmentation of Medical Images - An Artificial Intelligence Based IoT Implementation for Teleradiology Network
1.1 Introduction
1.2 Proposed Methodology
1.2.1 Fuzzy C Means Clustering
1.3 Results and Discussion
1.4 Conclusion
References
Chapter 2
Artificial Intelligence Based Fuzzy Logic with Modified Particle Swarm Optimization Algorithm for Internet of Things Enabled Logistic Transportation Planning
2.1. Introduction
2.2. Related works
2.3. Proposed Method
2.3.1. Package Partitioning
2.3.2. Planning of delivery path using HFMPSO algorithm
2.3.3. Inserting Pickup Packages
2.4. Experimental Validation
2.4.1. Performance analysis under varying package count
2.4.2. Performance analysis under varying vehicle capacities
2.4.3. Computation Time (CT) analysis
2.5. Conclusion
References
Chapter 3
Butterfly Optimization based Feature Selection with Gradient Boosting Tree for Big Data Analytics in Social Internet of Things
3.1. Introduction
3.2. Related works
3.3. The Proposed Method
3.3.1. Hadoop Ecosystem
3.3.2. BOA based FS process
3.3.3. GBT based Classification
3.4. Experimental Analysis
3.4.1. FS Results analysis
3.4.2. Classification Results Analysis
3.4.3. Energy Consumption Analysis
3.4.4. Throughput Analysis
3.5. Conclusion
References
Chapter 4
An Energy Efficient Fuzzy Logic based Clustering with Data Aggregation Protocol for WSN assisted IoT system
4. 1. Introduction
4. 2. Background Information
4. 2.1. Clustering objective
4. 2. 2. Clustering characteristics
4. 3. Proposed Fuzzy based Clustering and Data Aggregation (FC-DR) protocol
4. 3. 1. Fuzzy based Clustering process
4. 3. 2. Data aggregation process
4. 4. Performance Validation
4. 5. Conclusion
References
Chapter 5
Analysis of Smart Home Recommendation system from Natural Language Processing Services with Clustering Technique
5. 1. Introduction
5. 2. Review of Literatures
5. 3. Smart Home- Cloud Backend Services
5. 3.1 Internet of Things (IoT)
5. 4. Our Proposed Approach
5. 4.1 Natural Language Processing Services (NLPS)
5. 4. 2 Pipeline Structure for NLPS
5. 4. 3 Clustering Model
5. 5. Results and analysis
5. 6. Conclusion
References
Chapter 6
Metaheuristic based Kernel Extreme Learning Machine Model for Disease Diagnosis in Industrial Internet of Things Sensor Networks
6. 1. Introduction
6. 2. Proposed Methodology
6. 2. 1. Deflate based Compression Model
6. 2. 2. SMO-KELM based Diagnosis Model
6. 3. Experimental results and validation
6. 4. Conclusion
References
Chapter 7
Fuzzy Support Vector Machine with SMOTE for Handling Class Imbalanced Data in IoT Based Cloud Environment
7. 1. Introduction
7. 2. The Proposed Model
7. 2.1. SMOTE Model
7. 2.2. FSVM based Classification Model
7. 3. Simulation Results and Discussion
7. 4. Conclusion
References
Chapter 8
Energy Efficient Unequal Clustering Algorithm using Hybridization of Social Spider with Krill Herd in IoT Assisted Wireless Sensor Networks
8. 1. Introduction
8. 2. Research Background
8. 3. Literature survey
8. 4. The proposed SS-KH algorithm
8. 4. 1. SS based TCH selection
8. 4. 2. KH based FCH algorithm
8. 5. Experimental validation
8. 5. 1 Implementation setup
8. 5. 2. Performance analysis
8. 6. Conclusion
References
Chapter 9
IoT Sensor Networks with 5G Enabled Faster RCNN Based Generative Adversarial Network Model for Face Sketch Synthesis
9. 1. Introduction
9. 2. The Proposed FRCNN-GAN Model
9. 2.1. Data Collection
9. 2.2. Faster R-CNN based Face Recognition
9. 2.3. GAN based Synthesis Process
9. 3. Performance Validation
9. 4. Conclusion
References
Chapter 10
Artificial Intelligence based Textual Cyberbullying Detection for Twitter Data Analysis in Cloud-based Internet of Things
10. 1. Introduction
10. 2. Literature review
10. 3. Proposed Methodology
10. 3.1. Preprocessing
10. 3.2. Feature extraction
10. 3.3. Feature selection using ranking method
10. 3.4. Cyberbully detection
10. 3.5. Dataset Description
10. 4. Result and discussion
10. 4.1. Evaluation Metrics
10. 4.2. Comparative analysis
10. 5. Conclusion
References
Chapter 11
An Energy Efficient Quasi Oppositional Krill Herd Algorithm based Clustering Protocol for Internet of Things Sensor Networks
11. 1. Introduction
11. 2. The Proposed Clustering algorithm
11. 3. Performance Validation
11. 4. Conclusion
References
Chapter 12
An effective Social Internet of Things (SIoT) Model for Malicious node detection in wireless sensor networks
12. 1. Introduction
12. 2. Review of Recent Kinds of literature
12. 3. Network Model: SIoT
12. 3.1 Malicious Attacker Model in SIoT
12. 4. Proposed MN in SIoT System
12. 4.1 Trust based Grouping in SIoT network
12. 4.2 Exponential Kernel Model for MN detection
12. 4.3.1 Example of Proposed Detection System
12. 4.4 Detection Model
12. 5. Results and analysis
12. 6. Conclusion
References
Chapter 13
IoT Based Automated Skin Lesion Detection and Classification using Grey Wolf Optimization with Deep Neural Network
Adaptive Regularized Gaussian Kernel FCM for the Segmentation of Medical Images - An Artificial Intelligence Based IoT Implementation for Teleradiology Network
1.1 Introduction
1.2 Proposed Methodology
1.2.1 Fuzzy C Means Clustering
1.3 Results and Discussion
1.4 Conclusion
References
Chapter 2
Artificial Intelligence Based Fuzzy Logic with Modified Particle Swarm Optimization Algorithm for Internet of Things Enabled Logistic Transportation Planning
2.1. Introduction
2.2. Related works
2.3. Proposed Method
2.3.1. Package Partitioning
2.3.2. Planning of delivery path using HFMPSO algorithm
2.3.3. Inserting Pickup Packages
2.4. Experimental Validation
2.4.1. Performance analysis under varying package count
2.4.2. Performance analysis under varying vehicle capacities
2.4.3. Computation Time (CT) analysis
2.5. Conclusion
References
Chapter 3
Butterfly Optimization based Feature Selection with Gradient Boosting Tree for Big Data Analytics in Social Internet of Things
3.1. Introduction
3.2. Related works
3.3. The Proposed Method
3.3.1. Hadoop Ecosystem
3.3.2. BOA based FS process
3.3.3. GBT based Classification
3.4. Experimental Analysis
3.4.1. FS Results analysis
3.4.2. Classification Results Analysis
3.4.3. Energy Consumption Analysis
3.4.4. Throughput Analysis
3.5. Conclusion
References
Chapter 4
An Energy Efficient Fuzzy Logic based Clustering with Data Aggregation Protocol for WSN assisted IoT system
4. 1. Introduction
4. 2. Background Information
4. 2.1. Clustering objective
4. 2. 2. Clustering characteristics
4. 3. Proposed Fuzzy based Clustering and Data Aggregation (FC-DR) protocol
4. 3. 1. Fuzzy based Clustering process
4. 3. 2. Data aggregation process
4. 4. Performance Validation
4. 5. Conclusion
References
Chapter 5
Analysis of Smart Home Recommendation system from Natural Language Processing Services with Clustering Technique
5. 1. Introduction
5. 2. Review of Literatures
5. 3. Smart Home- Cloud Backend Services
5. 3.1 Internet of Things (IoT)
5. 4. Our Proposed Approach
5. 4.1 Natural Language Processing Services (NLPS)
5. 4. 2 Pipeline Structure for NLPS
5. 4. 3 Clustering Model
5. 5. Results and analysis
5. 6. Conclusion
References
Chapter 6
Metaheuristic based Kernel Extreme Learning Machine Model for Disease Diagnosis in Industrial Internet of Things Sensor Networks
6. 1. Introduction
6. 2. Proposed Methodology
6. 2. 1. Deflate based Compression Model
6. 2. 2. SMO-KELM based Diagnosis Model
6. 3. Experimental results and validation
6. 4. Conclusion
References
Chapter 7
Fuzzy Support Vector Machine with SMOTE for Handling Class Imbalanced Data in IoT Based Cloud Environment
7. 1. Introduction
7. 2. The Proposed Model
7. 2.1. SMOTE Model
7. 2.2. FSVM based Classification Model
7. 3. Simulation Results and Discussion
7. 4. Conclusion
References
Chapter 8
Energy Efficient Unequal Clustering Algorithm using Hybridization of Social Spider with Krill Herd in IoT Assisted Wireless Sensor Networks
8. 1. Introduction
8. 2. Research Background
8. 3. Literature survey
8. 4. The proposed SS-KH algorithm
8. 4. 1. SS based TCH selection
8. 4. 2. KH based FCH algorithm
8. 5. Experimental validation
8. 5. 1 Implementation setup
8. 5. 2. Performance analysis
8. 6. Conclusion
References
Chapter 9
IoT Sensor Networks with 5G Enabled Faster RCNN Based Generative Adversarial Network Model for Face Sketch Synthesis
9. 1. Introduction
9. 2. The Proposed FRCNN-GAN Model
9. 2.1. Data Collection
9. 2.2. Faster R-CNN based Face Recognition
9. 2.3. GAN based Synthesis Process
9. 3. Performance Validation
9. 4. Conclusion
References
Chapter 10
Artificial Intelligence based Textual Cyberbullying Detection for Twitter Data Analysis in Cloud-based Internet of Things
10. 1. Introduction
10. 2. Literature review
10. 3. Proposed Methodology
10. 3.1. Preprocessing
10. 3.2. Feature extraction
10. 3.3. Feature selection using ranking method
10. 3.4. Cyberbully detection
10. 3.5. Dataset Description
10. 4. Result and discussion
10. 4.1. Evaluation Metrics
10. 4.2. Comparative analysis
10. 5. Conclusion
References
Chapter 11
An Energy Efficient Quasi Oppositional Krill Herd Algorithm based Clustering Protocol for Internet of Things Sensor Networks
11. 1. Introduction
11. 2. The Proposed Clustering algorithm
11. 3. Performance Validation
11. 4. Conclusion
References
Chapter 12
An effective Social Internet of Things (SIoT) Model for Malicious node detection in wireless sensor networks
12. 1. Introduction
12. 2. Review of Recent Kinds of literature
12. 3. Network Model: SIoT
12. 3.1 Malicious Attacker Model in SIoT
12. 4. Proposed MN in SIoT System
12. 4.1 Trust based Grouping in SIoT network
12. 4.2 Exponential Kernel Model for MN detection
12. 4.3.1 Example of Proposed Detection System
12. 4.4 Detection Model
12. 5. Results and analysis
12. 6. Conclusion
References
Chapter 13
IoT Based Automated Skin Lesion Detection and Classification using Grey Wolf Optimization with Deep Neural Network
13. 1. Introduction
13. 2. The Proposed GWO-DNN Model
13. 2.1. Feature Extraction
13. 2.2. DNN based classification
13. 3. Experimental Validation
13. 4. Conclusion
References
Index
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