Sensor Data Analysis and Management E-Book

• Herausgeber: John Wiley & Sons
• Kategorie: Wissenschaft und neue Technologien
• Sprache: Englisch

Seitenzahl: 332

Veröffentlichungsjahr: 2021

Beschreibung

Discover detailed insights into the methods, algorithms, and techniques for deep learning in sensor data analysis Sensor Data Analysis and Management: The Role of Deep Learning delivers an insightful and practical overview of the applications of deep learning techniques to the analysis of sensor data. The book collects cutting-edge resources into a single collection designed to enlighten the reader on topics as varied as recent techniques for fault detection and classification in sensor data, the application of deep learning to Internet of Things sensors, and a case study on high-performance computer gathering and processing of sensor data. The editors have curated a distinguished group of perceptive and concise papers that show the potential of deep learning as a powerful tool for solving complex modelling problems across a broad range of industries, including predictive maintenance, health monitoring, financial portfolio forecasting, and driver assistance. The book contains real-time examples of analyzing sensor data using deep learning algorithms and a step-by-step approach for installing and training deep learning using the Python keras library. Readers will also benefit from the inclusion of: * A thorough introduction to the Internet of Things for human activity recognition, based on wearable sensor data * An exploration of the benefits of neural networks in real-time environmental sensor data analysis * Practical discussions of supervised learning data representation, neural networks for predicting physical activity based on smartphone sensor data, and deep-learning analysis of location sensor data for human activity recognition * An analysis of boosting with XGBoost for sensor data analysis Perfect for industry practitioners and academics involved in deep learning and the analysis of sensor data, Sensor Data Analysis and Management: The Role of Deep Learning will also earn a place in the libraries of undergraduate and graduate students in data science and computer science programs.

Leseprobe

Sensor Data Analysis and Management

The Role of Deep Learning

Edited by

A. Suresh

Department of Computer Networking and Communications, SRM Institute of Science and Technology, Chennai, Tamil Nadu, India

R. Udendhran

Department of Computer Science and Engineering, Sri Sairam Institute of Technology, Chennai, Tamil Nadu, India

M.S. Irfan Ahmed

Department of Computer Science and Information, Taibah University, Al-Ula Campus, Madhina, Saudi Arabia

This edition first published 2021

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Library of Congress Cataloging-in-Publication Data

A catalogue record for this book is available from the Library of CongressHardback ISBN: 9781119682424; ePub ISBN: 9781119682486; ePDF ISBN: 9781119682455; oBook ISBN: 9781119682806.

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Cover design by Wiley

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Contents

Cover

Title page

Copyright

About the Editors

List of Contributors

Preface

1 Efficient Resource Allocation Using Multilayer Neural Network in Cloud Environment

2 Internet of Things for Human-Activity Recognition Based on Wearable Sensor Data

3 Evaluation of Feature Selection Techniques in Intrusion Detection Systems Using Machine Learning Models in Wireless Ad Hoc Networks

4 Neuro-Fuzzy-Based Bidirectional and Biobjective Reactive Routing Schema for Critical Wireless Sensor Networks

5 Feature Detection and Extraction Techniques for Real-Time Student Monitoring in Sensor Data Environments

6 Deep Learning Analysis of Location Sensor Data for Human-Activity Recognition

7 A Quantum-Behaved Particle-Swarm-Optimization-Based KNN Classifier for Improving WSN Lifetime

8 Feature Detection and Extraction Techniques for Sensor Data

9 Object Detection in Satellite Images Using Modified Pyramid Scene Parsing Networks

10 Coronary Illness Prediction Using the AdaBoost Algorithm

11 Geographic Information Systems and Confidence Interval with Deep Learning Techniques for Traffic Management Systems in Smart Cities

Index

End User License Agreement

List of Illustrations

Chapter 1

Figure 1.1 Architecture of resource allocation

Figure 1.2 Structure of the RBNNOM

Figure 1.3 Iteration time for multivariable control theory and RBNNOM.

Figure 1.4 Resource scheduling time for multivariable control...

Figure 1.5 Error prediction for multivariable control theory and RBNNOM.

Chapter 2

Figure 2.1 Sequence of sensors and its workflow.

Chapter 3

Figure 3.1 Layers of OSI reference model.

Figure 3.2 Wormhole attack.

Figure 3.3 Relation between the 12 network layer features.

Figure 3.4 Swarm plot representation of the nodes...

Figure 3.5 Swarm plot representation of the nodes ...

Figure 3.6 Feature selections by random forest method.

Figure 3.7 Feature selection by PCA method.

Figure 3.8 Methodology used in intrusion detection systems.

Figure 3.9 Anomaly detection with average difference in neighbor ...

Figure 3.10 Anomaly detection using percentage of delay ...

Figure 3.11 Anomaly detection using percentage of packet sent ...

Figure 3.12 Anomaly detection using percentage of drop ratio ...

Figure 3.13 Anomaly detection using average difference in hop ...

Figure 3.14 Anomaly detection using maximum changes in hop count ...

Figure 3.15 Anomaly detection using number of maximum changes ...

Figure 3.16 Anomaly detection using percentage neighbor count ...

Figure 3.17 Anomaly detection using percentage hop count changed ...

Figure 3.18 Anomaly detection using percentage of route changed ...

Figure 3.19 Anomaly detection using percentage receiver power changed ...

Figure 3.20 Anomaly detection using average difference in sequence ...

Figure 3.21 Elbow point determination.

Figure 3.22 Anomaly detection using K-means cluster algorithm ...

Figure 3.23 K-means clusters for random forest reduced data.

Figure 3.24 Cluster classification with percentage route changed.

Figure 3.25 Cluster classification with percentage-hop-changed feature

Figure 3.26 Cluster classification with receiver-power feature.

Figure 3.27 Cluster classification with packet-drop-ratio feature

Figure 3.28 K-means cluster classification using PCA...

Figure 3.29 Cluster classification of PCA + K-means ...

Figure 3.30 Cluster classification for PCA + K-means IDS ..

Figure 3.31 Anomaly detection in one-class SVM ..

Figure 3.32 IDS metrics of RF + one-class SVM.

Figure 3.33 Performance metrics of PCA + one-class SVM IDS.

Figure 3.34 Comparison of performance metrics of one-class SVM ...

Figure 3.35 Performance metrics of IDS using K-means cluster algorithm ...

Figure 3.36 Performance metrics of K-means cluster classifier...

Figure 3.37 Comparison of IDS with and without feature selection....

Figure 3.38 Comparisons of various IDS techniques in wormhole attack detection.

Figure 3.39 Comparison of proposed IDS.

Chapter 4

Figure 4.1 A requirement of data delivery models for....

Figure 4.2 Sequence diagram of NFBBRR model.81

Figure 4.3 Packet structure of event requisition.

Figure 4.4 Packet structure of event notification.

Figure 4.5 Packet structure of neighbor node’s information.

Figure 4.6 Overall architecture of NFBBRR model.

Figure 4.7 Generic view of dynamic WSN for BSF-based bidirectional routing strategy.

Figure 4.8 Example scenario for depicting BFS-based bidirectional routing strategy.85

Figure 4.9 The architecture of Mamdani-based neuro-fuzzy system.86

Figure 4.10 Membership functions of (a) current energy level of ni, ...

Figure 4.11 Rule inference.89

Chapter 5

Figure 5.1 Comparison of performance parameters

Figure 5.2 Comparison of accuracy parameters

Chapter 6

Figure 6.1 Conventional deep learning with sensor networks.

Figure 6.2 SDAE process with sensor implementation

Figure 6.3 Path loss comparisons.

Figure 6.4 Throughput comparisons.

Figure 6.5 Consumption of energy.

Figure 6.6 Evaluation of total cost ($).

Figure 6.7 Overall network lifetime.

Chapter 7

Figure 7.1 The general architecture of WSNs.

Figure 7.2 Network model.

Figure 7.3 Simulation model.

Figure 7.4 Number of k-nearest neighbors.

Figure 7.5 Energy consumption.

Figure 7.6 Network lifetime.

Chapter 8

Figure 8.1 Health monitoring system.

Figure 8.2 E-health monitoring architecture.

Figure 8.3 Sensors in human body.

Figure 8.4 Various data processing techniques.

Figure 8.5 Feature extraction from sensor data.

Figure 8.6 System architecture.

Figure 8.7 Proposed architecture.

Chapter 9

Figure 9.1 The spectrum converge of the four ...

Figure 9.2 Proposed pipeline.

Figure 9.3 Preprocessing step.

Figure 9.4 Multispectral U-net architecture.

Figure 9.5 Inverted pyramid.

Figure 9.6 Overview of our proposed PSPNet.

Figure 9.7 Jaccard coefficient.

Figure 9.8 Predicted mask for class 1.

Figure 9.9 Predicted mask for class 7.

Figure 9.10 Predicted output for class 5.

Figure 9.11 Predicted output for class 7.

Figure 9.12 The abovementioned comparison of predicted masks shows ...

Chapter 10

Figure 10.1 Implementation flowchart.

Figure 10.2 Accuracy graph of various algorithms.

Figure 10.3 Specificity graph of various algorithms.

Figure 10.3 Specificity graph of various algorithms.

Figure 10.5 F-measure graph of various algorithms.

Figure 10.6 Precision graph of various algorithms.

Figure 10.7 Error graph of various algorithms

Chapter 11

Figure 11.1 The smart town model.

Figure 11.2 Boyd Cohen’s smart cities wheel.

Figure 11.3 Flowchart of congestion prediction [4].

Figure 11.4 Architecture of intelligent traffic management [5].

Figure 11.5 Percentage of road accidents segregated in terms ...

Figure 11.6 QGIS results for state-wise road accidents for (a)...

Figure 11.7 Road accidents for the different states for the year 2017:...

Figure 11.8 Graph for road accidents for different states...

Figure 11.9 Graph for road accidents for different states for the year 2017.

Figure 11.10 Graph for road accidents on national highways...

Figure 11.11 (a) Car parking lot, (b) car is placed, and (c)...

Figure 11.12 YOLO bounding boxes results for traffic system ...

Figure 11.13 YOLO algorithm for bounding boxes results ...

List of Tables

Chapter 1

Table 1.1 Summary of existing solutions based on reactive method.

Table 1.2 Summary of existing solutions based on proactive method

Table 1.3 Summary of existing solutions for control-theory-based...

Table 1.4 Samples of input data for different software.

Table 1.5 RBNNOM output for some input data.

Chapter 2

Table 2.1 Sequence activities of some case scenes.

Table 2.2 Frequency (number of steps) for each activity and scene.

Chapter 3

Table 3.1 Types of attacks in network layer.

Table 3.2 Intruder detection system for wormhole attacks.

Table 3.3 Simulation parameters.

Table 3.4 Network layer features.

Table 3.5 Performance metrics of one-class SVM without feature selection.

Table 3.6 Performance metrics of the RF + one-class SVM IDS.

Table 3.7 Performance metrics of the PCA + one-class SVM IDS.

Table 3.8 Comparison of one-class SVM with and without feature selection.

Table 3.9 Performance metrics of IDS using K-means ...

Table 3.10 Performance metrics of K-means cluster classifier IDS...

Table 3.11 Comparison of IDSs with and without feature selection...

Table 3.12 Comparison of existing and new IDS in the detection...

Table 3.13 Comparison of proposed IDS.

Chapter 4

Table 4.1 Various types of NFS..

Table 4.2 Rule assortment and outcomes.

Table 4.3 Comparative analysis on the influential...

Table 4.4 Comparative analysis on the influential effect of pause...

Chapter 5

Table 5.1 Comparative analysis of the proposed method..

Table 5.2 Comparison of accuracy..

Chapter 6

Table 6.1 Specifications for health monitoring using SDAE.

Chapter 7

Table 7.1 Simulation parameters.

Table 7.2 Comparison of number of k-nearest neighbors for BS.

Table 7.3 Energy consumption comparison.

Table 7.4 Network lifetime comparison.

Chapter 8

Table 8.1 Sensor parameters and their descriptions

Chapter 9

Table 9.1 Three versions of a satellite image.

Table 9.2 Specifications and unique features of different channels.

Table 9.3 Results for each class in terms of pixel accuracy....

Table 9.4 Result comparison of multispectral U-net for different classes...

Chapter 10

Table 10.1 Attribute information.

Table 10.2 Comparison of parameters for different algorithms.

Chapter 11

Table 11.1 Smart town’s characteristics, factors, and measures [2].

Table 11.2 Taxonomy table [5].

Table 11.3 Statistical analysis of driver’s age and gender...

Table 11.4 Road traffic accident mortalities based on gender, age,...

Table 11.5 Environmental factors responsible for road accidents [8].

Table 11.6 The occurrence of RTAs estimation [8].

Table 11.7a State-wise road accident data for the years 2014–2017.

Table 11.7b State-wise road accident data for the years 2014–2017.

Table 11.8 Road accidents for different states for the year 2017

Table 11.9 CI for road accidents in the year 2017.

Table 11.10 CI for road accidents in the year 2016

Table 11.11 CI for road accidents in the year 2015

Table 11.12 CI for road accidents in the year 2014.

Table 11.13 CI for the year 201

Guide

Cover

Title page

Copyright

Table of Contents

About the Editors

List of Contributors

Preface

Begin Reading

Index

End User License Agreement

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About the Editors

Dr A. Suresh, B.E., M.Tech., PhD, works as Associate Professor, Department of Networking and Communications, School of Computing, Faculty of Engineering and Technology, SRM Institute of Science and Technology, SRM Nagar, Kattankulathur, Chengalpattu - 603203, Chennai, Tamil Nadu, India. He has been nearly two decades of experience in teaching and his areas of specializations are Data Mining, Artificial Intelligence, Image Processing, Multimedia and System Software. He has published two patents and 90 papers in international journals. He is author of the book Industrial IoT Application Architectures and Use Cases published by CRC Press and editor of Deep Neural Networks for Multimodal Imaging and Biomedical Application published by IGI Global. He is currently editing Deep Learning and Edge Computing Solutions for High Performance Computing in the series EAI/Springer Innovations in Communications and Computing. He has published more than 40 papers in national and international conference proceedings. He has served as editor/reviewer for Springer, Elsevier, Wiley, IGI Global, IoS Press, and Inderscience journals, among others. He is a member of IEEE (Senior Member), ISTE, MCSI, IACSIT, IAENG, MCSTA and a Global Member of Internet Society (ISOC). He has organized several national workshops, conferences and technical events. He is regularly invited to deliver lectures at various programs for imparting skills in research methodology to students and research scholars. He has published four books with Indian publishers in the fields of Hospital Management, Data Structures & Algorithms, Computer Programming, Problem Solving and Python Programming and Programming in “C”. He has hosted two special sessions for IEEE-sponsored conferences in Osaka, Japan and Thailand.

Mr R. Udendhran, B.Tech, M.Tech. (PhD), works as Assistant Professor Grade III, Department of Computer Science and Engineering at Sri Sairam Institute of Technology, Sairam College Road, Sai Leo Nagar, West Tambaram, Chennai 600044, Tamil Nadu, India. He is a dignified computer science research scholar focusing on Deep Learning. He worked as a data scientist and has presented research work at an international conference held at the University of Cambridge (available in the ACM Digital Library) and has published approx. 5 research papers indexed in the Web of Science and 11 research papers in the Scopus database.

Dr M. S. Irfan Ahmed is working as Associate Professor in the Department of Computer Science and Information, Faculty of Science and Literature at Taibah University, Saudi Arabia. He is a member of ISTE, MCSI, IACSIT, and IAENG.

List of Contributors

Aditya PatelLNCT College, Bhopal, India

Ajmi NaderMicro-Optoelectronic and Nanostructures Laboratory, University of Monastir, Faculty of Sciences of Monastir, Tunisia

Akash SaxenaCITM, Jaipur, India

Akhilesh Vikas KakadeSAP Labs, Bangalore, India

G.R. Anantha RamanProfessor, MRIET,Secunderabad, Telangana, India

M. BalasaraswathiAssociate Professor, ECE,Saveetha School of Engineering, SIMATS, Chennai, India

G. DeivendranNational Engineering College,Tamil Nadu, India

Ganesan SivarajanAssociate Professor, Department of Electrical and Electronics Engineering,Government College of EngineeringSalem, Tamil Nadu, India

Hariprasath ManoharanAssistant Professor, Department of Electronics and Communication Engineering, Audisankara College of Engineering and Technology, Gudur, Andhra Pradesh, India

Helali AbdelhamidMicro-Optoelectronic and Nanostructures Laboratory, University of Monastir, Faculty of Sciences of Monastir, Tunisia

S. Joseph GladwinAssociate Professor, ECE,SSN College of Engineering, Chennai, India

K.M. Karthick RaghunathAssociate Professor, MRIET,Secunderabad, Telangana, India

Mghaieth RidhaMicro-Optoelectronic and Nanostructures Laboratory, University of Monastir, Faculty of Sciences of Monastir, Tunisia

T.J. NagalakshmiAssistant Professor, ECE,Saveetha School of Engineering, SIMATS, Chennai, India

B. ParamasivanNational Engineering College, Tamil Nadu, India

S. Pravin KumarAI Engineer, Smartail Pvt Ltd,Chennai, India

Prisilla JayanthiThe Airports Authority of India Ltd, India

L. PriyaDepartment of IT, Rajalakshmi Engineering College,Chennai, India

Radhika BaskarAssociate Professor, ECE, Saveetha School of Engineering SIMATS, Chennai, India

S. RajkumarVellore Institute of Technology, Vellore, India

L. RamanathanVellore Institute of Technology, Vellore, India

D. RavikumarProfessor, ECE, Vel’s University, Chennai, India

V. SaravananDepartment of Computer Applications (PG), Dr. SNS Rajalakshmi College of Arts and Science, Coimbatore, India

Ms. A. SathyaDepartment of IT, Rajalakshmi Engineering College, Chennai, India

N. Shanmuga PriyaDepartment of Computer Applications (PG),Dr. SNS Rajalakshmi College of Arts and Science, Coimbatore, India

V. SivasankaranSreenivasa Institute of Technology and Management Studies, Chittoor, Andhra Pradesh, India

Subramanian SrikrishnaProfessor, Department of Electrical and Electronics Engineering, Annamalai University, Chidambaram, Tamil Nadu, India

Sudeep Ray GaurLNCT College, Bhopal, India

K. SuganthiVellore Institute of Technology, Vellore, India

A.S. Syed FiazAssistant Professor, CSE, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science & Technology, Chennai, India

S. Thanga RevathiDepartment of IT, Rajalakshmi Engineering College, Chennai, India

Preface

Sensor Data Analysis and Management: The Role of Deep Learning delivers an insightful and practical overview of the applications of deep learning techniques to the analysis of sensor data. The book collects cutting-edge resources into a single collection designed to enlighten the reader on topics as varied as recent techniques for fault detection and classification in sensor data, the application of deep learning to Internet of Things sensors, and a case study on high-performance computer gathering and processing of sensor data.

The editors have curated a distinguished group of perceptive and concise papers that show the potential of deep learning as a powerful tool for solving complex modelling problems across a broad range of industries, including predictive maintenance, health monitoring, financial portfolio forecasting, and driver assistance.

The book contains real-time examples of analyzing sensor data using deep learning algorithms and a step-by-step approach for installing and training deep learning using the Python Keras library. Readers will also benefit from the inclusion of:

A thorough introduction to the Internet of Things for human activity recognition, based on wearable sensor data

An exploration of the benefits of neural networks in real-time environmental sensor data analysis

Practical discussions of supervised learning data representation, neural networks for predicting physical activity based on smartphone sensor data, and deep-learning analysis of location sensor data for human activity recognition

An analysis of boosting with XGBoost for sensor data analysis

Perfect for industry practitioners and academics involved in deep learning and the analysis of sensor data, Sensor Data Analysis and Management: The Role of Deep Learning will also earn a place in the libraries of undergraduate and graduate students in data science and computer science programs.

Chapter 1 presents an effective better resource allocation method by using a multilayer neural network named RBNNOM. Additionally, the novel method of RBNNOM considers priority-based resource allocation in terms of qualities and quantities.

Chapter 2 demonstrates recognition strategy that can identify body sensor data irrespective of the cell phone’s position. An area for potential research would be to find novel methods of movement recognition. Future research could also concentrate on recognizing new exercise routines trying to gather information from more users of different ages; and removing highlights that could fine-tune the segregation of various exercises.

Chapter 3 emphasizes the need for secure cyber security systems for sensor data. In recent years, the growth of network-based services has been incredible. Therefore, reliable network security becomes of primary importance in the cyber world.

Chapter 4 presents the challenges and risks of routing sensed data or newly generated events in a critical environment, which is always of major interest for researchers. A wireless sensor network deployed in critical infrastructure/environs mostly implies the difficulties of processing the sensed data, which may raise problems for compatibility factors in network deployment – for example, jeopardizing scenarios and monitoring emergencies such as fire eruption and explosion and environment-oriented and hazardous pollution.

Chapter 5 focuses on the potential of student motion behavior analysis. This study is conducted for the learning of repeated motion behavior with respect to the students and thereafter to show that it is possible to detect unusual behavior using the knowledge of frequent behavior. The best example for this scenario is taking a wrong route and getting lost. An important objective of pervasive computing is to give accurate information about human behavior. It has a wide range of applications such as in medicine, security solutions, and student monitoring in educational campuses.

Chapter 6 considers improving the working efficiency of sensors and for testing them under different conditions, where a predictive algorithm is essential. This is possible only when deep learning methods are used, whereby different strategies are followed when problems occur on the network. If sensors are installed, then the network depends on main node for the purpose of storing and accessing the data. For sensing the information and sending it to applications, such as those for health monitoring, there should be less delay. This robust prediction of health is necessary because it can save lives.

Chapter 7 proposes a hybrid algorithm based on KNN and QPSO for improving WSN lifetime by updating the location of the BS to reduce long-distance communication between the BS and sensor nodes. QPSO is applied to optimize KNN. Furthermore, the fitness function is also designed considering two parameters – energy consumption and distances

Chapter 8 discusses the EHR and the various sensors used in healthcare systems. The use of sensor data and its parameters is elaborated. The various feature extraction techniques are discussed, and case studies are provided for better understanding feature extraction through sensor data.

Chapter 9 proposes an approach that successfully handles object detection problems, which significantly improves objection detection in satellite images using modified pyramid scene parsing networks. This work incorporates several steps such as the adaptation of fully convolutional networks to multispectral satellite images and the evaluation of several data fusion strategies on semantic segmentation tasks of satellite images with a combined training objective.

Chapter 10 focuses on improving heart disease prediction accuracy by using different machine learning algorithms. Neural networks have proven to be more efficient in prediction and can be used for classification. Feature selection techniques can be created to obtain a more extensive view of the critical highlights to build the presentation of coronary illness forecast.

Dr. A. Suresh

Mr. R. Udendhran

Dr M. S. Irfan Ahmed