Artificial Intelligence Techniques in IoT Sensor Networks by Elhoseny, Mohamed; Shankar, K; Abdel-Basset, Mohamed;

Rövid leírás:

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.

Hosszú leírás:

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.

Tartalomjegyzék:

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