Artificial Intelligence Techniques in IoT Sensor Networks
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 intends to develop a shared understanding of ap...
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| Main Authors | , , |
|---|---|
| Format | eBook |
| Language | English |
| Published |
Milton
CRC Press
2021
CRC Press LLC |
| Edition | 1 |
| Series | Chapman & Hall/CRC Distributed Sensing and Intelligent Systems Series |
| Subjects | |
| Online Access | Get full text |
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| Abstract | 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 intends 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 book introduces the researchers and aspiring academicians the subject of 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 be benefited directly from this book. Moreover, graduate and master students of any departments related to AI, IoT and sensor networks can find this book fascinating for developing expert systems or real time applications.
The book is written in a simple and easy language, discusses the concepts from fundamentals which relieve the requirement of earlier background of the field finds it readable. From this expectation and experience, we believe that every library will be interested to collect copies of this book. |
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| AbstractList | This book explores the frontiers and challenges of applying Artificial Intelligence (AI) techniques to Sensor Networks. It covers how sensor networks are widely used to collect environmental parameters in homes, buildings, vehicles, etc., and how they are used as a source of information to aid decision-making processes. 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. 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 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 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, o 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 intends 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 book introduces the researchers and aspiring academicians the subject of 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 be benefited directly from this book. Moreover, graduate and master students of any departments related to AI, IoT and sensor networks can find this book fascinating for developing expert systems or real time applications. The book is written in a simple and easy language, discusses the concepts from fundamentals which relieve the requirement of earlier background of the field finds it readable. From this expectation and experience, we believe that every library will be interested to collect copies of this book. |
| Author | Mohamed Elhoseny K Shankar Mohamed Abdel-Basset |
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| Copyright | 2021 selection and editorial matter, Mohamed Elhoseny, K. Shankar, Mohamed Abdel-Basset; individual chapters, the contributors |
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| Keywords | SVM Approach SVM Model GWO Algorithm MLP Model Network Lifetime CH Selection Sensor Node Local Search Phase KH GWO IoT Device MN SSIM Modified Particle Swarm Optimization IoT System Intercluster Communication Wireless Sensor Networks Energy Efficiency Unequal Clustering CHs CT Brain Image Minimum Logistic Cost Proposed Clustering Algorithm Maximized Network Lifetime IoT Network |
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| Snippet | Artificial Intelligence Techniques in IoT Sensor Networks
is a technical book which can be read by researchers, academicians, students and professionals... Artificial Intelligence Techniques in IoT Sensor Networks is a technical book which can be read by researchers, academicians, students and professionals... This book explores the frontiers and challenges of applying Artificial Intelligence (AI) techniques to Sensor Networks. It covers how sensor networks are... |
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| SubjectTerms | AI techniques Artificial Intelligence Computer Engineering Computer Science (General) COMPUTERSCIENCEnetBASE ElectricalEngineeringnetBASE ENGnetBASE INFORMATIONSCIENCEnetBASE Instrumentation, Measurement & Testing Internet of things Machine Learning Networks SCI-TECHnetBASE Sensor Networks STMnetBASE Systems & Computer Engineering |
| TableOfContents | Chapter 13: IoT-Based Automated Skin Lesion Detection and Classification Using Gray Wolf Optimization with Deep Neural Network -- Index Cover -- Half Title -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- About the Editors -- Chapter 1: Adaptive Regularized Gaussian Kernel FCM for the Segmentation of Medical Images: An Artificial Intelligence-Based IoT Implementation for Teleradiology Network -- Chapter 2: Artificial Intelligence-Based Fuzzy Logic with Modified Particle Swarm Optimization Algorithm for Internet of Things-Enabled Logistic Transportation Planning -- Chapter 3: Butterfly Optimization-Based Feature Selection with Gradient Boosting Tree for Big Data Analytics in Social Internet of Things -- Chapter 4: An Energy-Efficient Fuzzy Logic-Based Clustering with Data Aggregation Protocol for WSN-Assisted IoT System -- Chapter 5: Analysis of Smart Home Recommendation System from Natural Language Processing Services with Clustering Technique -- Chapter 6: Metaheuristic-Based Kernel Extreme Learning Machine Model for Disease Diagnosis in Industrial Internet of Things Sensor Networks -- Chapter 7: Fuzzy Support Vector Machine with SMOTE for Handling Class Imbalanced Data in IoT-Based Cloud Environment -- Chapter 8: Energy-Efficient Unequal Clustering Algorithm Using Hybridization of Social Spider with Krill Herd in IoT-Assisted Wireless Sensor Networks -- Chapter 9: IoT Sensor Networks with 5G-Enabled Faster RCNN-Based Generative Adversarial Network Model for Face Sketch Synthesis -- Chapter 10: Artificial Intelligence-Based Textual Cyberbullying Detection for Twitter Data Analysis in Cloud-Based Internet of Things -- Chapter 11: An Energy-Efficient Quasi-Oppositional Krill Herd Algorithm-Based Clustering Protocol for Internet of Things Sensor Networks -- Chapter 12: An Effective Social Internet of Things (SIoT) Model for Malicious Node Detection in Wireless Sensor Networks |
| Title | Artificial Intelligence Techniques in IoT Sensor Networks |
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