Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
A practical guide to the design, implementation, evaluation, and deployment of emerging technologies for intelligent IoT applications With the rapid development in artificially intelligent and hybrid technologies, IoT, edge, fog-driven, and pervasive computing techniques are becoming important parts of our daily lives. This book focuses on recent advances, roles, and benefits of these technologies, describing the latest intelligent systems from a practical point of view. Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications is also valuable for engineers and professionals trying to solve practical, economic, or technical problems. With a uniquely practical approach spanning multiple fields of interest, contributors cover theory, applications, and design methodologies for intelligent systems. These technologies are rapidly transforming engineering, industry, and agriculture by enabling real-time processing of data via computational, resource-oriented metaheuristics and machine learning algorithms. As edge/fog computing and associated technologies are implemented far and wide, we are now able to solve previously intractable problems. With chapters contributed by experts in the field, this book: * Describes Machine Learning frameworks and algorithms for edge, fog, and pervasive computing * Considers probabilistic storage systems and proven optimization techniques for intelligent IoT * Covers 5G edge network slicing and virtual network systems that utilize new networking capacity * Explores resource provisioning and bandwidth allocation for edge, fog, and pervasive mobile applications * Presents emerging applications of intelligent IoT, including smart farming, factory automation, marketing automation, medical diagnosis, and more Researchers, graduate students, and practitioners working in the intelligent systems domain will appreciate this book's practical orientation and comprehensive coverage. Intelligent IoT is revolutionizing every industry and field today, and Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications provides the background, orientation, and inspiration needed to begin.
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Table of Contents
Cover
Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications
Copyright
About the Editors
List of Contributors
Preface
Book Objectives
Target Audience
Organization
Acknowledgments
1 Fog, Edge and Pervasive Computing in Intelligent Internet of Things Driven Applications in Healthcare: Challenges, Limitations and Future Use
1.1 Introduction
1.2 Why Fog, Edge, and Pervasive Computing?
1.3 Technologies Related to Fog and Edge Computing
1.4 Concept of Intelligent IoT Application in Smart (Fog) Computing Era
1.5 The Hierarchical Architecture of Fog/Edge Computing
1.6 Applications of Fog, Edge and Pervasive Computing in IoT‐based Healthcare
1.7 Issues, Challenges, and Opportunity
1.8 Conclusion
Bibliography
Note
2 Future Opportunistic Fog/Edge Computational Models and their Limitations
2.1 Introduction
2.2 What are the Benefits of Edge and Fog Computing for the Mechanical Web of Things (IoT)?
2.3 Disadvantages
2.4 Challenges
2.5 Role in Health Care
2.6 Blockchain and Fog, Edge Computing
2.7 How Blockchain will Illuminate Human Services Issues
2.8 Uses of Blockchain in the Future
2.9 Uses of Blockchain in Health Care
2.10 Edge Computing Segmental Analysis:
2.11 Uses of Fog Computing
2.12 Analytics in Fog Computing
2.13 Conclusion
Bibliography
Note
3 Automating Elicitation Technique Selection using Machine Learning
3.1 Introduction
3.2 Related Work
3.3 Model: Requirement Elicitation Technique Selection Model
3.4 Analysis and Results
3.5 The Error Rate
3.6 Validation
3.7 Conclusion
Bibliography
Chapter 4: Machine Learning Frameworks and Algorithms for Fog and Edge Computing
4.1 Introduction
4.2 Overview of Machine Learning Frameworks for Fog and Edge Computing
Bibliography
Chapter 5: Integrated Cloud Based Library Management in Intelligent IoT driven Applications
5.1 Introduction
5.2 Understanding Library Management
5.3 Integration of Mobile Platform with the Physical Library‐ Brief Concept
5.4 Database (Cloud Based) ‐ A Must have Component for Library Automation
5.5 IoT Driven Mobile Based Library Management ‐ General Concept
5.6 IoT Involved Real Time GUI (Cross Platform) Available to User
5.7 IoT Challenges
5.8 Conclusion
Bibliography
Note
Chapter 6: A Systematic and Structured Review of Intelligent Systems for Diagnosis of Renal Cancer
6.1 Introduction
6.2 Related Works
6.3 Conclusion
Bibliography
Note
Chapter 7: Location Driven Edge Assisted Device and Solutions for Intelligent Transportation
7.1 Introduction to Fog and Edge Computing
7.2 Introduction to Transportation System
7.3 Route Finding Process
7.4 Edge Architecture for Route Finding
7.5 Technique Used
7.6 Algorithms Used for the Location Identification and Route Finding Process
7.7 Results and Discussions
7.8 Conclusion
Bibliography
Note
Chapter 8: Design and Simulation of MEMS for Automobile Condition Monitoring Using COMSOL Multiphysics Simulator
8.1 Introduction
8.2 Related Work
8.3 Vehicle Condition Monitoring through Acoustic Emission
8.4 Piezo‐resistive Micro Electromechanical Sensors for Monitoring the Faults Through AE
8.5 Designing of MEM Sensor
8.6 Experimental Setup:
8.7 Result and Discussions
8.8 Conclusion
Bibliography
Note
Chapter 9: IoT Driven Healthcare Monitoring System
9.1 Introduction
9.2 General Concept for IoT Based Healthcare System
9.3 View of the Overall IoT Healthcare System‐ Tiers Explained
9.4 A Brief Design of the IoT Healthcare Architecture‐individual Block Explanation
9.5 Models/Frameworks for IoT use in Healthcare
9.6 IoT e‐Health System Model
9.7 Process Flow for the Overall Model
9.8 Conclusion
Bibliography
Note
Chapter 10: Fog Computing as Future Perspective in Vehicular Ad hoc Networks
10.1 Introduction
10.2 Future VANET: Primary Issues and Specifications
10.3 Fog Computing
10.4 Related Works in Cloud and Fog Computing
10.5 Fog and Cloud Computing‐based Technology Applications in VANET
10.6 Challenges of Fog Computing in VANET
10.7 Issues of Fog Computing in VANET
10.8 Conclusion
Bibliography
Chapter 11: An Overview to Design an Efficient and Secure Fog‐assisted Data Collection Method in the Internet of Things
11.1 Introduction
11.2 Related Works
11.3 Overview of the Chapter
11.4 Data Collection in the IoT
11.5 Fog Computing
11.6 Requirements for Designing a Data Collection Method
11.7 Conclusion
Bibliography
Note
Chapter 12: Role of Fog Computing Platform in Analytics of Internet of Things‐ Issues, Challenges and Opportunities
12.1 Introduction to Fog Computing
12.2 Introduction to Internet of Things
12.3 Conceptual Architecture of Internet of Things
12.4 Relationship between Internet of Things and Fog Computing
12.5 Use of Fog Analytics in Internet of Things
12.6 Conclusion
Bibliography
Note
Chapter 13: A Medical Diagnosis of Urethral Stricture Using Intuitionistic Fuzzy Sets
13.1 Introduction
13.2 Preliminaries
13.3 Max‐Min‐Max Algorithm for Disease Diagnosis
13.4 Case Study
13.5 Intuitionistic Fuzzy Max‐Min Average Algorithm for Disease Diagnosis
13.6 Result
13.7 Code for Calculation
13.8 Conclusion
13.9 Acknowledgement
Bibliography
Chapter 14: Security Attacks in Internet of Things
14.1 Introduction
14.2 Reference Model of Internet of Things (IoT)
14.3 IoT Communication Protocol
14.4 IoT Security
14.5 Security Challenges in IoT
14.6 Conclusion
Bibliography
Note
Chapter 15: Fog Integrated Novel Architecture for Telehealth Services with Swift Medical Delivery
15.1 Introduction
15.2 Associated Work and Dimensions
15.3 Need of Security in Telemedicine Domain and Internet of Things (IoT)
15.4 Fog Integrated Architecture for Telehealth Delivery
15.5 Research Dimensions
15.6 Research Methodology and Implementation on Software Defined Networking
15.7 Conclusion
Bibliography
Chapter 16: Fruit Fly Optimization Algorithm for Intelligent IoT Applications
16.1 An Introduction to the Internet of Things
16.2 Background of the IoT
16.3 Applications of the IoT
16.4 Challenges in the IoT
16.5 Introduction to Optimization
16.6 Classification of Optimization Algorithms
16.7 Network Optimization and IoT
16.8 Network Parameters optimized by Different Optimization Algorithms
16.9 Fruit Fly Optimization Algorithm
16.10 Applicability of FOA in IoT Applications
16.11 Node Deployment Using Fruit Fly Optimization Algorithm
16.12 Conclusion
Bibliography
Chapter 17: Optimization Techniques for Intelligent IoT Applications
17.1 Cuckoo Search
17.2 Glow Worm Algorithm
17.3 Wasp Swarm Optimization
17.4 Real World Applications Area
Summary
Bibliography
Chapter 18: Optimization Techniques for Intelligent IoT Applications in Transport Processes
18.1 Introduction
18.2 Related Works
18.3 TSP Optimization Techniques
18.4 Implementation and Testing of Proposed Solution
18.5 Experimental Results
18.6 Conclusion and Further Works
Bibliography
Chapter 19: Role of Intelligent IOT Applications in Fog paradigm: Issues, Challenges and Future Opportunities
19.1 Fog Computing
19.2 Concept of Intelligent IoT Applications in Smart Computing Era
19.3 Components of Edge and Fog Driven Algorithm
19.4 Working of Edge and Fog Driven Algorithms
19.5 Future Opportunistic Fog/Edge Computational Models
19.6 Challenges of Fog Computing for Intelligent IoT Applications
19.7 Applications of Cloud Based Computing for Smart Devices
Bibliography
Note
Chapter 20: Security and Privacy Issues in Fog/Edge/Pervasive Computing
20.1 Introduction to Data Security and Privacy in Fog Computing
20.2 Data Protection/ Security
20.3 Great Security Practices In Fog Processing Condition
20.4 Developing Patterns in Security and Privacy
20.5 Conclusion
Bibliography
Note
Chapter 21: Fog and Edge Driven Security & Privacy Issues in IoT Devices
21.1 Introduction to Fog Computing
21.2 Introduction to Edge Computing
Bibliography
Index
End User License Agreement
List of Tables
Chapter 3
Table 3.1 Comparison of various Requirement Elicitation Selections Techniques...
Table 3.2 Selection Attributes Dataset Sample.
Table 3.3 Confusing Matrix Report.
Table 3.4 Validation Experiment.
Chapter 6
Table 6.1 Comparison table on existing work done on diagnosis of kidney or re...
Chapter 8
Table 8.1 Description of three sensors designed on COMSOL Multiphysics simula...
Chapter 11
Table 11.1 Some other current methods showing the advantages, disadvantages, ...
Chapter 12
Table 12.1 Comparison of fog computing and cloud computing.
Chapter 13
Table 13.1 Linguistic value and intuitionistic fuzzy values.
Table 13.2 IFS relation between patients and symptoms.
Table 13.3 IFS relation between symptoms and diagnosis.
Table 13.4 IFS relation between patients and diagnosis.
Table 13.5
Table 13.6 A Φ B.
Table 13.7 A
c
Φ B
c
.
Table 13.8
Chapter 14
Table 14.1 Difference between traditional network security and IoT security.
Table 14.2 Summarizing the attacks on different layers of IoT with counter me...
Table 14.3 Security measures on different types of attacks.
Chapter 15
Table 15.1 Benchmark Datasets for Research in Telemedicine.
Chapter 18
Table 18.1 Average results of TSP_GA algorithm evaluation.
Table 18.2 Test results for 50 cities.
Table 18.3 Experimental results for population n=100.
Chapter 19
Table 19.1 Processing on Fog [9].
Chapter 20
Table 20.1 Security issues with possible impact and Solution.
List of Illustrations
Chapter 1
Figure 1.1 Cloud and fog computing architecture.
Figure 1.2 Efficient IoLT approach for collection, storage, and management o...
Figure 1.3 The three pillars of fog computing on the Internet of Living Thin...
Chapter 2
Figure 2.1 Cloud, fog and edge computing.
Chapter 3
Figure 3.1 The systemization model.
Figure 3.2 Mapping the selection Attributes.
Figure 3.3 Error Rate with the
K
value.
Chapter 5
Figure 5.1 Sketch of the three modules of Library management
Figure 5.2 Proposed mobile user interface that will appear after opening the...
Figure 5.3 An E–R diagram for the database for the automation of E‐Library/l...
Figure 5.4 IoT infrastructure for mobile based communication
Figure 5.5 General design for IoT driven cloud computing for any communicati...
Figure 5.6 Pictorial view for IoT driven mobile application (any)
Figure 5.7 The very first GUI for a registered user
Figure 5.8 GUI for Admin module
Figure 5.9 GUI when Admin selects “update database to cloud”
Figure 5.10 GUI for Admin for selecting “control login credentials”
Figure 5.11 GUI for user module
Figure 5.12 GUI when user selects “search for a book” and “button” stands fo...
Figure 5.13 GUI when user selects “download/upload book” and “BUTTON” stands...
Figure 5.14 GUI when user selects “borrow list”
Figure 5.15 GUI when user selects “update profile” option and “BUTTON” is an...
Figure 5.16 GUI for librarian module.
Figure 5.17 IoT challenges for mobile based library management system
Chapter 6
Figure 6.1 Flow diagram for extraction of image features [13].
Figure 6.2 Structure of magneto‐optical tweezers [15].
Figure 6.3 Multilayer perceptron is combined with Kattan's and Sorbellini's ...
Figure 6.4 Block diagram of the decision support system.[25].
Chapter 7
Figure 7.1 Computing technology
Figure 7.2 Fog computing
Figure 7.3 Edge computing
Figure 7.4 Navigation system and related components
Figure 7.5 Sample graph
Figure 7.6 Route diagram
Figure 7.7 Architecture used for the edge‐based route‐finding process
Figure 7.8 Input component for edge device
Figure 7.9 Interaction diagram for the complete process involved in edge‐bas...
Figure 7.10 Flow chart of an algorithm for the location identification
Figure 7.11 Sample scenario to find the distance
Figure 7.12 Considered area for the experiment
Figure 7.13 Sample output 1 generated by using the offline OSM data using ro...
Figure 7.14 Output generated by the OSM API using routing process
Figure 7.15 Comparison‐based on the execution time
Figure 7.16 Comparison based on the accuracy ratio
Figure 7.17 Comparison of with and without edge architecture‐based routing p...
Chapter 8
Figure 8.1 Flow chart of the design of MEMS.
Figure 8.2 (a) Tata Manza engine sound frequency plot (b) Amplitude verses f...
Figure 8.3 (a) Comb drive sensor. (b) Four pad structure. (c) Optimized tri‐...
Chapter 9
Figure 9.1 Interpreted features of cloud computing and IoT.
Figure 9.2 IoT based general concept for innovation at the healthcare system...
Figure 9.3 General concept for IoT based health care system
Figure 9.4 Three tiers for the e‐healthcare architecture
Figure 9.5 Patient level architecture of an IoT based e‐health care system
Figure 9.6 Tier 2 for a network communication architecture for e‐healthcare...
Figure 9.7 Basic IoT components and their frameworks in e‐healthcare
Figure 9.8 Topology for IoT driven healthcare framework
Figure 9.9 IoT e‐Health system model illustrated
Figure 9.10 IoT e‐health process model
Chapter 10
Figure 10.1 Fog scenario for the communication of safety messages in VANETs....
Figure 10.2 Application scenario of fog computing
Figure 10.3 Cloud–fog relationship
Figure 10.4 Information delivery through fog computing in VANET
Chapter 11
Figure 11.1 IoT lifestyle.
Figure 11.2 Architecture of fog computing.
Figure 11.3 Features of fog computing.
Figure 11.4 Threats of fog computing.
Figure 11.5 Applications of fog computing.
Chapter 12
Figure 12.1 Hierarchical fog computing architecture.
Figure 12.2 Layered fog computing architecture.
Figure 12.3 Representation of the Internet of Things.
Figure 12.4 Conceptual architecture of IoT.
Chapter 13
Figure 13.1 Urethral stricture.
Figure 13.2 Normal groin
vs
urethral stricture.
Chapter 14
Figure 14.1 Reference Model of the Internet of Things (IoT).
Figure 14.2 Edge nodes.
Figure 14.3 Communication layer.
Figure 14.4 Fog computing layer.
Figure 14.5 Functioning of three layers.
Figure 14.6 Data Accumulation layer.
Figure 14.7 Data Abstraction Layer.
Figure 14.8 Application layer in the IoT.
Figure 14.9 Collaboration and processes layer in the IoT.
Figure 14.10 IoT Protocol suite.
Figure 14.11 Security layer in IoT.
Chapter 15
Figure 15.1 Key Perspectives of Telehealth or Telemedicine.
Figure 15.2 Taxonomy of Telemedical or Telehealth Services.
Figure 15.3 Global Scenario of Telemedicine.
Figure 15.4 Architecture associated with Telemedicine.
Figure 15.5 Fog Based Telehealth Environment.
Figure 15.6 Telemedicine Scenario using Software Defined Networking.
Figure 15.7 Telemedicine Scenario using Virtualization based Environment.
Figure 15.8 SDN based Environment for Telemedicine.
Figure 15.9 Delivery of Services to Patients.
Figure 15.10 Controller Panel for Telemedicine.
Figure 15.11 Fog Integrated Settings with OpenFlow.
Figure 15.12 Wireshark Based Packets Capturing Panel.
Figure 15.13 Virtual Switches for Controller.
Figure 15.14 Medical Services Framework.
Figure 15.15 Delivery of Medical Services using Telemedicine.
Figure 15.16 Showing Execution Time of Simulation in milliseconds.
Chapter 16
Figure 16.1 Internet of Things (IoT) elements [7].
Figure 16.2 Flow chart of the fruit fly optimization algorithm.
Figure 16.3 Flow chart of FOA based sensor nodes deployment in WSN.
Chapter 17
Figure 17.1 Cuckoo bird.
Figure 17.2 A nest.
Figure 17.3 Variants of cuckoo search.
Figure 17.4 Discrete cuckoo search.
Figure 17.5 Binary cuckoo search.
Figure 17.6 Chaotic cuckoo search.
Figure 15.7 Applications of cuckoo search.
Figure 17.8 A glow worm.
Figure 17.9 GSO Optimization.
Figure 17.10 Wasp swarm.
Figure 17.11 Swarm of fish.
Figure 17.12 Dolphin and the prey.
Figure 17.13 Frog leap.
Figure 17.14 Seed in fertile soil with proper care.
Figure 17.15 Seed in infertile soil.
Chapter 18
Figure 18.1 Use case diagram for TSP based on genetic algorithms.
Figure 18.2 Application for TSP.
Figure 18.3 The process of finding the optimal solution of TSP application....
Figure 18.4 Display of the optimized solution and percentage of route improv...
Graph 18.1 Effect of mutation on determining the path length.
Graph 18.2 Relationship between average and minimum number of generations.
Graph 18.3 Effect of mutation on path length determination.
Graph 18.4 Ratio of average and minimum number of generations.
Chapter 19
Figure 19.1 Basic Architecture of Fog network [8].
Figure 19.2 Fog Computing Reference Architecture.
Figure 19.3 Fog Driven Algorithms.
Figure 19.4 Challenges of Fog Computing.
Chapter 20
Figure 20.1 Security & Privacy parameters in Fog Computing.
Figure 20.2 Security issues in Fog Computing.
Figure 20.3 Security practice in Fog Computing.
Figure 20.4 Security Requirement in Fog Computing.
Chapter 21
Figure 21.1 Layered architecture of fog computing.
Figure 21.2 Fog computing is closer to end devices.
Figure 21.3 Fog computing supports various IoT applications.
Figure 21.4 Challenges of fog computing.
Figure 21.5 Open research challenges in fog security and privacy issues.
Figure 21.6 Applications and use cases of edge computing.
Figure 21.7 Characteristics of edge Computing.
Guide
Cover Page
IEEE Press
Title Page
Copyright
About the Editors
List of Contributors
Preface
Acknowledgments
Table of Contents
Begin Reading
Index
WILEY END USER LICENSE AGREEMENT
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Jeffrey Reed
Diomidis Spinellis
Sarah Spurgeon
Ahmet Murat Tekalp
Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications
Edited by
Deepak Gupta
Aditya Khamparia
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Published by John Wiley & Sons, Inc., Hoboken, New Jersey.
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Names: Gupta, Deepak, editor. | Khamparia, Aditya, 1988‐ editor.
Title: Fog, edge, and pervasive computing in intelligent IoT driven
applications / edited by Deepak Gupta, Aditya Khamparia.
Description: Hoboken, New Jersey : Wiley‐IEEE Press, 2020. | Includes
bibliographical references and index. | Description based on print version
record and CIP data provided by publisher; resource not viewed.
Identifiers: LCCN 2020025436 (print) | LCCN 2020025437 (ebook) | ISBN
9781119670100 (epub) | ISBN 9781119670094 (adobe pdf) | ISBN 9781119670070
(hardback) | ISBN 9781119670070q(hardback) | ISBN 9781119670094q(adobe
pdf) | ISBN 9781119670100q(epub)
Subjects: LCSH: Internet of things. | Cloud computing. | Distributed
databases. | UbIquitous computing.
Classification: LCC TK5105.8857 (ebook) | LCC TK5105.8857 .F64 2020 (print) |
DDC 004.67/8–dc23
LC record available at https://lccn.loc.gov/2020025436 LC record available at https://lccn.loc.gov/2020025437
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About the Editors
Deepak Gupta is an eminent academician; he has various roles and responsibilities juggling his time between lectures, research, publications, consultancy, community service, PhD and post‐doctorate supervision etc. With 12 years of rich expertise in teaching and two years in industry; he focuses on rational and practical learning. He has contributed substantial literature in the fields of Human–Computer Interaction, Intelligent Data Analysis, Nature‐Inspired Computing, Machine Learning and Soft Computing. He is working as an Assistant Professor at Maharaja Agrasen Institute of Technology (GGSIPU), Delhi, India. He has served as Editor‐in‐Chief, Guest Editor, Associate Editor in SCI and various other reputed journals (Elsevier, Springer, Wiley & MDPI). He has actively been involved in the organization of various well reputed International
conferences. He is not only backed with a strong reputation but his innovative ideas, end‐results of his research and implementation of technology in the medical field is significantly contributing to society. He is currently a Post‐Doc researcher at University of Valladolid, Spain. He has completed his Post‐Doc from Inatel, Brazil, and Ph.D. from Dr. APJ Abdul Kalam Technical University. He has authored/edited 33 books published internationally (Elsevier, Springer, Wiley, Katson). He has published 103 scientific research works in reputed International Journals and Conferences including 50 SCI Indexed Journals of the IEEE, Elsevier, Springer, Wiley and many more. He has also published one patent. He is Editor‐in‐Chief of the OA journal Computers and Quantum Computing and Applications (QCAA), Associate Editor of Expert Systems (Wiley), Intelligent Decision Technologies (IOS Press), Journal of Computational and Theoretical Nenoscience, Honorary Editor of ICSES Transactions on Image Processing and Pattern Recognition. He is also a series editor of Intelligent Biomedical Data Analysis De Gruyter (Germany), series editor of Smart Sensor Technologies for Biomedical Engineering with Elsevier. He is also associated with various professional bodies such as ISTE, IAENG, IACSIT, SCIEI, ICSES, UACEE, Internet Society, SMEI, IAOP, and IAOIP. Invited as a Faculty Resource Person/Session Chair/Reviewer/TPC member in different FDP, conferences and journals. He is the convener of the ‘ICICC’ and ‘ICDAM’ springer conference series.
Aditya Khamparia is an eminent academician; he has various roles and responsibilities including lectures, research, publications, consultancy, community service and PhD supervision etc. With seven years of rich expertise in teaching and two years in industry; he focuses on individual centric and practical learning. Currently, he is working as Associate Professor of Computer Science and Engineering at Lovely Professional University, Punjab, India. His research areas are Machine Learning, Soft Computing, Educational Technologies, IoT, Semantic Web and Ontologies. He has published more than 50 scientific research publications in reputed International/National Journals and Conferences, which are indexed in various international databases. Invited as a Faculty Resource Person/Session Chair/Reviewer/TPC member in different FDP, conferences and journals. Dr. Aditya received research excellence award in 2016, 2017, 2018 and 2019 at Lovely Professional University for his research contribution during the academic year. He is member of CSI, IET, ISTE, IAENG, ACM and IACSIT. He is also acting as reviewer and member of various renowned national and international conferences/journals. Invited as a Faculty Resource Person/Session Chair/Reviewer/TPC member in different FDP, conferences and journals.
List of Contributors
Iqrar Ahmad
Department of information system Community College
King Khalid University Muhayel
Kingdom of Saudi Arabia
Nazir Ahmad
Department of information system Community College
King Khalid University Muhayel
Kingdom of Saudi Arabia
Afroj Alam
Department of Computer Science
Integral University
Lucknow
India
Pallavi Asthana
Amity University
Uttar Pradesh
India
S. Aswath
Department of Computer Science
PES University
Bangalore, 560008
India
Subrato Bharati
Department of EEE
Ranada Prasad Shaha University
Narayanganj-1400
Bangladesh
Naveen Kumar Bhati
Sunder Deep College of Engineering and Technology
Uttar Pradesh Technical University
India
Hatim M. Elhassan Ibrahim Dafallaa
Department of information system Community College
King Khalid University Muhayel
Kingdom of Saudi Arabia
Charu Gandhi
Department of Computer Science
JIIT
Noida
India
Aarti Goel
Department of Information Technology
Netaji Subhas University of Technology
New Delhi
India
Sonia Goel
Punjabi University Patiala
Anjali Goyal
Department of Computer Applications
GNIMT
Ludhiana
Punjab
India
Sonia Goyal
Department of Electronics and Communication Engineering
Punjabi University Patiala
Adnan Hasanović
University of Novi Pazar
36300 Novi Pazar
Dimitrija Tucovica bb
Serbia
Bramah Hazela
Amity University
Uttar Pradesh
India
Naiyar Iqbal
Department of Computer Science and IT
Maulana Azad National Urdu University
Hyderabad
Isha
Associate Professor
Lovely Professional University
Punjab
India
Maria Jamal
Department of Mathematics
Birla Institute of Technology
India
Balwinder Kaur
School of Computer Science and Engineering
Lovely Professional University
Phagwara
India
Inderpreet Kaur
Reseach Scholar
CU Gharaun
Dept. of Computer Applications
CGC landran
Prabjot Kaur
Department of Mathematics
Birla Institute of Technology
India
Ranjit Kaur
Department of Electronics and Communication Engineering
Punjabi University Patiala
Shweta Kaushik
Department of Computer Science Engineering
ABES Institute of Technology
New Delhi
India
Jaiteg Singh Khaira
Dept. of Computer Applications Chitkara University
Punjab Campus
Aditya Khamparia
School of Computer Science and Engineering
Lovely Professional University
Phagwara
Punjab
and
School of Computer Science and Engineering
Lovely Professional University
Punjab
India
Rizwan khan
Al-Barkaat College of Graduate Studies
Aligarh
India
Anil Kumar
Amity University
Uttar Pradesh
India
Priyanka Rajan Kumar
Punjabi University Patiala
Vijay Laxmi
Professor
Guru Kashi University
Punjab
India
Zoran Lončarević
ITS - Studies for Information Technologies
11000 Belgrade
Savski nasip 7
Serbia
Arun Malik
Associate Professor
Lovely Professional University
Punjab
India
Pragun Mangla
Department of Electronics and Communication
Netaji Subhas Institute of Technology
New Delhi
India
Ashish Mishra
Gyan Ganga Institute of Technology
Jabalpur
Madhya Pradesh
India
Bhabani Shankar Prasad Mishra
School of Computer Engineering
KIIT University
Bhubaneswar
Odisha
Subhashree Mishra
School of Electronics Engineering
KIIT University
Bhubaneswar
Odisha
Sumita Mishra
Amity University
Uttar Pradesh
India
Satinder Singh Mohar
Department of Electronics and Communication Engineering
Punjabi University Patiala
M. Rubaiyat Hossain Mondal
Institute of ICT
Bangladesh University of Engineering and Technology
Dhaka
Bangladesh
Rajit Nair
Jagran Lakecity University
Nikita
School of Engineering & Technology
CT University
Ludhiana
India
Priyanka Pattnaik
School of Computer Engineering
KIIT University
Bhubaneswar
Odisha
Murali Mallikarjuna Rao Perumalla
School of Computer Science and Engineering
Lovely Professional University
Phagwara
Punjab
Prajoy Podder
Institute of ICT
Bangladesh University of Engineering and Technology
Dhaka
Bangladesh
Sahar Qazi
Department of Computer Science
Jamia Millia Islamia
New Delhi
Mamoon Rashid
Assistant Professor
School of Computer Science and Engineering
Lovely Professional University
Jalandhar
India
Khalid Raza
Department of Computer Science
Jamia Millia Islamia
New Delhi
Mohammed Burhanur Rehman
Department of information system Community College
King Khalid University Muhayel
Kingdom of Saudi Arabia
Md Robiul Alam Robel
Department of CSE
Cumilla University
Cumilla
Bangladesh
Harsh Sadawarti
School of Engineering & Technology
CT University
Ludhiana
India
Kamaljit Singh Saini
Dept. of Computer Applications Chandigarh University
Gharuan
Muzafer Saračević
University of Novi Pazar
36300 Novi Pazar
Dimitrija Tucovica bb
Serbia
Deepak Kumar Sharma
Department of Information Technology
Netaji Subhas University of Technology
New Delhi
India
Preeti Sharma
Bansal College of Engineering
Dileep Kumar Singh
Jagran Lakecity University
Harjit Singh
Research Scholar
Guru Kashi University
Punjab
India
Jaiteg Singh
Chitkara University Institute of Engineering and Technology
Chitkara University
140401 Punjab
Rajpura
India
Sanjay Kumar Singh
School of Computer Science and Engineering
Lovely Professional University
Phagwara
Punjab
Saravjeet Singh
Chitkara University Institute of Engineering and Technology
Chitkara University
140401 Punjab
Rajpura
India
Jimmy Singla
School of Computer Science and Engineering
Lovely Professional University
Phagwara
India
Sonia Singla
University of Leicester
U.K.
Sofia
Research Scholar
Lovely Professional University
Punjab
India
Natasha Tiwari
University of Oxford
UK
Umer Iqbal Wani
Assistant Professor
School of Computer Science and Engineering
Lovely Professional University
Jalandhar
India
Preface
This book focuses on recent advances, roles and benefits of fog, edge, and pervasive computing for intelligent and smart Internet of Things (IoT) enabled applications, aimed at narrowing the increasing gap. This book aims to describe the different techniques of intelligent systems from a practical point of view: solving common life problems. But this book also brings a valuable point of view to engineers and businessmen, trying to solve practical, economical, or technical problems in the field of their company activities or expertise. The purely practical approach helps to transmit the idea and the aim of the author is to communicate the way to approach and to cope with problems that would be intractable in any other way. This book solicits contributions which include theory, applications, and design methods of intelligent systems, Ubiquitous techniques, trends of fog, edge, and cloud applications as embedded in the fields of engineering, computer science, mathematics, and life sciences, as well as the methodologies behind them.
Book Objectives
With the rapid growth and emerging development in artificial technology, novel hybrid and intelligent IoT, edge, fog driven, and pervasive computing techniques are an important part of our daily lives. These technologies are utilized in various engineering, industrial, smart farming, video security surveillance, VANETs and vision augmented driven applications. These applications required real time processing of associated data and work on the principle of computational resource oriented meta heuristic and machine learning algorithms. Due to physical size limitations, small computing IoT and mobile devices are having resource limited constraints with low computing power and are unable to manage good quality of service and related parameters for distinguished applications. To overcome the limitations of such mobile devices edge/fog and pervasive computing have been proposed as a promising research area to carry out high end infrastructure usage and provide computation, storage and task execution effectively for end device users. As edge/fog computing is implemented at network edges, it promises low latency as well as agile computation augmenting services for device users. To successfully support intelligent IoT applications, therefore, there is a significant need for (1) exploring the efficient deployment of edge/fog/pervasive computing services at the network nodes level, (2) identifying the novel algorithm related to fog/edge/pervasive computing for resource allocation with low constraint and power usage, and (3) designing collaborative and distributed architectures specialized for edge/fog/pervasive computing.
Target Audience
The target audience of book is professionals and practitioners in the field of intelligent system, edge computing and cloud enabled applications and ubiquitous computing science paradigm may benefit directly from others' experiences. Graduate and master students of final projects and particular courses in intelligent system, edge and fog based real‐life applications or medical domain can take advantage, making the book interesting for engineering and medical university teaching purposes. The research community of intelligent systems, sensor applications and intelligent sensor‐based applications, consisting of many conferences, workshops, journals and other books, will take this as a reference book.
Organization
Chapter 1 describes the Internet of Things, fog, edge and pervasive computing are emerging technologies, having several promising applications including healthcare. These technologies are witnessing a paradigm shift in the healthcare sector moving out from traditional ways of visiting hospitals. It connects the doctors, patients, and nurses through smart intelligent sensor devices at low cost with high bandwidth network. In this chapter, authors discussed new computing paradigms precisely and present their applications in ubiquitous healthcare. This chapter also covers various problems and challenges that have been faced by the practitioners in the last few years in the field of cloud computing and IoT that has been solved by fog, edge and pervasive computing.
Chapter 2 discusses difficulties and future headings to investigate the role of fog, edge and pervasive computing. Studies have revealed that fog/edge computing (FEC) based organizations can expect an essential activity in expanding the cloud by means of finishing go‐between organizations at the edge of the framework. Dimness/edge computing‐based IoT's (FECIoT) appropriated configuration overhauls organization provisioning along the cloud‐to‐things continuum, thus making it sensible for key applications. Edge and fog registering are firmly related – both allude to the capacity to process information closer to the requester/buyer to lessen idleness cost and increment client experience. Both can channel information before it "hits" a major information lake for further utilization, lessening the measure of information that should be handled.
Chapter 3 addresses the technique selection issue encountered during the requirements elicitation stage, through a proposed machine learning model to transfer the experts' knowledge of elicitation technique selection to the less experienced. Based on the system analysts, stakeholders automate various techniques to provide the best optimization technique nomination.
Chapter 4 covers the advantages and disadvantages of using machine learning in edge/fog/pervasive computing. The various studies carried out by researchers is also covered. Every field has numerous applications, and in this chapter we discuss a few possible applications in this fog era using machine learning techniques. By the end of the chapter you should know about ML frameworks and the various machine learning algorithms used for fog/edge computing.
Chapter 5 provides a description of the software which has three modules: student, librarian and admin. These modules have unique features for searching for library books with the title, author's name, subject, ISBN/ISSN, etc. Within the chapter the interfaces of the software are shown as images which is an abstraction that may be developed on available mobile operating system like iOS, Android, etc. The interfaces are designed bearing in mind that it will be used on cross platform environments fulfilling minimum requirements using the IoT available in the market. Furthermore, overall information is preserved with the help of cloud storage while keeping parallel options for physical storage on the destination master computer. The cloud‐based system has given library management a new dimension while giving a new feature referred as "management on the go" as a web or abstract GUI.
Chapter 6 describes a systematic review that was conducted to determine work done by various publishers on kidney cancer and to spot the research gaps between the studies so far. The outcome of this study permitted the effective diagnose of kidney cancer or renal cancer carried out using an adaptive neuro fuzzy method with 94% accuracy. Although, many data mining techniques were applied by researchers, the accuracy of these methods was less than the adaptive neuro fuzzy method. This method is worthwhile to identify the diagnosis of renal cancer better and more rigorously.
Chapter 7 explains a proposed approach to use edge computing in a transportation and route‐finding process in order to handle performance issues. Huge demand for centralized cloud computing poses severe challenges such as degraded spectral efficiency, high latency, poor connection, and security issues. To handle these issues, fog computing and edge computing has come into existence. One application of cloud computing is location based services (LBS). Intelligent transport systems being the important application of LBS rely on GPS, sensors, and spatial databases for convenient transport facilities. These location‐based applications are highly dependent on external systems like GPS devices and map API's (cloud support) for the spatial data and location information. These applications acquire spatial data using API's from different proprietary service providers. The dependency on the API's and GPS devices, create challenges for effective fleet management and routing process in dead zones. Dead zones are areas where no cellular coverage exists.
Chapter 8 describes the simulation and design of an optimized low‐cost comb drive based acoustic MEMS sensor. These sensors would be useful for condition monitoring of automobiles on the basis of changes in sound waves emerging from malfunctioning or defective parts of automobiles. These sensors can be developed from silicon substrates. Simulation is done using COMSOL Multiphysics simulation software based on finite element analysis. This optimized sensor is sensitive for the frequency range of 30–300 Hz. This frequency range was obtained after the FFT analysis of various signals received from engines using MATLAB software.
Chapter 9 offers an outline of developing the Internet of Things (IoT) technology in the area of healthcare as a flourishing research and experimental trend at the present time. The main advantages and benefits are considered in this chapter. In recent times, several studies in the healthcare information system proposed that the disintegration of health information is one of the most significant challenges in the arrangement of patient medical records. As a result, in this chapter, we provide an detailed design and overview of IoT healthcare systems along with its architecture.
Chapter 10 presents the combination of VANET and fog computing offering a range of options for cloud computing applications and facilities. Fog computing deals with high‐virtualized VANET software and communication systems, where dynamic‐speed vehicles travel. Mobile Adhoc Netoworks may also require low‐latency fog computing in VANET and local connections within short distances. The modern state of the work and upcoming viewpoints of VANET fog computing are explored in this chapter. In addition, this chapter outlines the features of fog computing and fog‐based services for VANETs. In addition to this, fog and cloud computing‐based technology applications in VANET are discussed. Some possibilities for challenges and issues associated connected with fog computing are also discussed in this chapter.
Chapter 11 outlines an idea to design an efficient data collection method in the IoT network. IoT technology deals with smart devices to collect data as well as to provide useful and accurate data to users. Many data collection methods already exist, but they still have some drawbacks and need more enhancements. This chapter outlines detailed information about the design of a novel data collection method using fog computing in the IoT network. The main reason for using fog computing over the cloud computing is to provide security to data which is completely lacking in cloud computing. Now‐a‐days, security of data is one of the most important requirements.
Chapter 12 provides an overview of using a fog computing platform for analyzing data generated by IoT devices. A fog computing platform will be compared with state‐of‐the‐art to differentiate its impact in terms of analytics. Lots of data is being generated in IoT based devices used in smart homes, traffic sensors, smart cities, and various connected appliances. Fog computing is one area which is quite popular in processing this huge amount of IoT data. However, there are challenges in these models for performing real time analytics in such data for quick analytics and insights. A fog analytics pipeline is one such area which could be a possible solution to address these challenges.
Chapter 13 proposes a method of diagnosis based on the relationship between patients and symptoms, and between symptoms and diagnosis using linguistic variables by intuitionistic fuzzy sets. It then describes the state of some patients after knowing the results of their medical tests by degree of membership and degree of non‐membership based on the relationship between patients and symptoms, and symptoms and diagnosis. Later a max‐min‐max
composition and formula is applied to calculate the Hamming distance to identify the disease with the least Hamming distance for various patients. A revised max‐min average composition is applied to identify the disease with the maximum score. Finally, it shows how urethral stricture in various patients is mathematically diagnosed.
Chapter 14 discusses the types of attacks involved in the IoT network with their counter measures, also covering the different layers, protocols, and the security challenges related to the IoT. It is the capability of the device that makes the IoT brilliant and this has been achieved by placing the intelligence into the devices. The intelligence in the sensors is developed by adding sensors and actuators which can collect information and pass it to the cloud through Wi‐Fi, Bluetooth, ZigBee, and so on. But these IoT network are vulnerable to different types of attack: physical, network, software, and encryption, and these attacks actually stop the IoT devices performing their normal operations. So, it required that we must overcome these attacks.
Chapter 15 discusses the domain of IoT integrated telehealth or telemedical services with various segments where there is a need to work on advanced technologies to achieve a higher degree of accuracy and performance. As shown by the research reports and analytics from Allied Market Research, the global value and market of the IoT in telehealth and medical services will exceed 13 billion dollars. From the extracts of Statista, the Statistical Research Portal the huge usage of IoT based deployments is quite prominent and is increasing very frequently because of the usage patterns in various domains. In another research report, the usage patterns of tablets in various locations from 2014 to 2019 shows that the figures are growing. Because of these data, it is necessary to enforce the security mechanisms for the IoT and wireless based environment.
Chapter 16 explores optimization in the IoT which is used to improve the performance of network by enhancing the efficiency of the network, reducing the overheads and energy consumption, and increasing the rate of deployment of various devices in the IoT. The applications of IoT are smart cities, augmented maps, IoT in health care etc. and various issues in IoT such as security, addressing schemes etc. are discussed. Various optimization techniques such as heuristic and bio‐inspired algorithms, evolutionary algorithms, and their applicability in IoT are described. Further the fruit fly optimization algorithm (FOA) and flow chart of FOA is explored in detail. Finally the applications of FOA in IoT and node deployment using FOA are explained. On the basis of observation FOA can be used to increase the coverage rate of sensor nodes.
Chapter 17 chapter presents an overall and in‐depth study of different optimization algorithms inspired from nature's behaviour. Today optimization is a powerful tool for the engineer in virtually every discipline. It provides a rigorous, systematic method for rapidly zeroing in on the most innovative, cost‐effective solutions to some of today's most challenging engineering design problems. The IoT is the concept of connecting everyday devices to the internet allowing the devices to send and receive data. With the IoT, devices can constantly report their status to a receiving computer that uses information to optimize decision making. IoT network optimization many benefits for improving traffic management, operating efficiency, energy conservation, reduction in latency, higher throughput, and faster rates in scaling up or deploying IoT services and devices in the network.
Chapter 18 outlines the optimization techniques for intelligent IoT applications in transport processes. The travelling salesman problem (TSP) has an important role in operational research and in this case, it was implemented in the design of the IoT application. The chapter describes some specific methods of solving, analysing and implementing a possible solution with an emphasis on a technique based on genetic algorithms. In this chapter we connect the TSP optimization problem in transport and traffic with IoT‐enabled applications for a smart city. In the experimental part of the chapter we present specific development and implementation of the application for TSP with testing and experimental results.
Chapter 19 describes the impact of the Internet of everything solutions which are connecting every object. This has generated a large amount of data. This amount of data cannot be processed by a centralized cloud environment. There are applications where data needs real time response and low latency. The data being sent to the cloud for processing and then coming back to the application generating data can seriously impact the performance. This delay can cause delay in decision making and this is not acceptable in real time applications. To handle such scenarios, fog computing has emerged as a solution. Fog computing extends the cloud near to the edge of the network to decrease latency as well as bandwidth requirements. It acts as an intermediate layer between the cloud and devices generating data.
Chapter 20 is concerned with security and privacy handling issues occurred in pervasive and edge boundary system for recognizing voice, sound using intelligent IoT mining techniques. Fog computing is a promising registering worldview that extends distributed computing to the edge of systems. Like distributed computing yet with unique qualities, fog computing faces new security challenges other than those acquired from distributed computing. This chapter studies existing writing on fog figuring applications to recognize basic security holes. Comparable innovations like edge figuring, cloudlets, and micro‐server farms have additionally been incorporated to give an all‐encompassing survey process.
Chapter 21 focuses on fog computing and the second section deals with edge computing. In this the author first introduces the basics of fog and edge computing, its architecture, working, advantages and use cases, and then primarily focuses on their security and privacy issues separately. In the end solutions and research opportunities in both fields are discussed.
Closing Remarks
In conclusion, we would like to sum up here with few lines. This book is a small step towards the enhancement of academic research through motivating the research community and research organizations to think about the impact of fog, edge and IoT computing frameworks, networking principles and its applications for augmenting the academic research. This book is giving insight on the various aspects of academic computing research and the need for knowledge sharing and prediction of relationships through several links and their usages. This includes research studies, experiments, and literature reviews about pervasive, fog computational activities and to disseminate cutting‐edge research results, highlight research challenges and open issues, and promote further research interest and activities in identifying missing links in cloud computing. We hope that research scholars, educationalists and students alike will find significance in this book and continue to use it to expand their perspectives in the field of edge, fog and pervasive computing and its future challenges.
Deepak Gupta
Maharaja Agrasen Institute of Technology, India
Aditya Khamparia
Lovely Professional University, India
Acknowledgments
We would like to thank the many people; those who contributed, supported and guided us through this book by different means. This book would not have been possible without their guidance and help.
First and foremost, we want to express heartfelt gratitude to our Guru for spiritual empathy and incessant blessings, to all teachers and friends for their continued guidance and inspiration throughout the period of our studies and career.
We would like to thank Wiley‐IEEE Press publisher who gave us an opportunity to publish with them. We would like to express our appreciation to all contributors including the accepted chapters' authors, and many other contributors who submitted their chapters that cannot be included in the book. Special thanks to Mary Hatcher, Victoria Bradshaw, Teresa Netzler and Louis Vasanth Manoharan from Wiley‐IEEE Press for their kind support and great efforts in bringing the book to completion. The encouragement of the Editorial Advisory Board (EAB) cannot be overstated. These are renowned experts who took time from their busy schedules to review chapters, provide constructive feedback, and improve the overall quality of the chapters.
We would like to thank our dear friends and colleagues for their continuous support and countless efforts throughout the process of publication of this book.
We express our personal and special thanks to our family members for supporting us throughout our careers, for love, the tremendous support and inspiration which they gave throughout the years.
Last but not least: we request forgiveness of all those who have been with us over the course of the years and whose names we have failed to mention.
Dr. Deepak Gupta
Maharaja Agrasen Institute of Technology, India
Dr. Aditya Khamparia
Lovely Professional University, India
1Fog, Edge and Pervasive Computing in Intelligent Internet of Things Driven Applications in Healthcare: Challenges, Limitations and Future Use
Afroj Alam1, Sahar Qazi2, Naiyar Iqbal3, and Khalid Raza2*
1Department of Computer Science, Integral University, Lucknow, India
2Department of Computer Science, Jamia Millia Islamia, New Delhi
3Department of Computer Science and IT, Maulana Azad National Urdu University, Hyderabad
Abstract
The Internet of Things (IoT), fog, edge and pervasive computing are all emerging technologies, which have several promising applications including healthcare. This technology is witnessing a paradigm shift in the healthcare sector, moving out from traditional ways of visiting hospitals. It connects the doctors, patients, and nurses through smart intelligent sensor devices at low cost with high bandwidth networks. In this chapter, we discuss these new computing paradigms precisely and present their applications in ubiquitous healthcare. This chapter also covers various problems and challenges that have been faced by practitioners in the last few years in the field of cloud computing and the IoT that have been solved by fog, edge and pervasive computing.
Keywords:Fog Computing; Edge Computing; Pervasive Computing; IoT; Healthcare;
1.1 Introduction
Today, the Internet of Things (IoT), fog, edge and pervasive computing are buzzwords, which have pivotal applications in different fields of studies including healthcare, engineering, and other intelligent applications. Cloud computing and the IoT have emerged as a new paradigm in the field of information and communication technology (ICT) as a revolution of the 21st century. It was a long‐awaited dream of to use computing as a utility. Traditional computing extends the model to a cloud computing paradigm which has the capability to renovate a huge portion of the information technology industry, making the software even more interesting as a service that customers can access on‐demand. The IoT acts as an interconnection between various gadgets and the Internet, including mobile phones, vehicles, farms, factories, home automation systems, and wearable devices from the viewpoint of the enhancement of the competence of real‐life computing usage. This new technology, especially in the healthcare sector, is a change from the conventional approach of visiting clinics or hospitals. It links doctors, patients, and nurses by means of intelligent, affordable sensor gadgets with the support of cloud computing (Qi et al., 2019). Unfortunately, a number of IoT based intelligent sensor gadgets are developing at a rapid rate. On the basis of evaluation, if the pace of extension proceeds constantly from 2020, the number of wearable gadgets on the planet will reach to around 26 billion (Imran and Qadeer, 2019). The volume of data generated using these IoT gadgets is very large. The capability of the present cloud model is not adequate to deal with the requirements of the IoT, i.e., the current cloud has issues regarding volume, latency, and bandwidth. The current cloud cannot fulfill every one of the prerequisites of QoS (Quality of Service) in the IoT, therefore the goal is that another framework, fog computing, is introduced that will solve the issues of volume, latency and bandwidth (Shi et al., 2015).
Fog computing has appeared with a new computation model which is placed between the cloud and intelligence sensor‐based IoT devices through which an assortment of heterogeneous gadgets are pervasively associated as the terminal of a network which provides communication facilities to ease the execution of relevant IoT services (Chang et al., 2019). Fog computing covers the cloud computing approach in the direction of the edge of the network, which has many advantages over cloud computing. Fog computing is appropriate for the applications by which real‐time, high response time, and less latency are important issues, specifically in healthcare utilization (Mutlag et al., 2019). It is enabling new or mutated applications and facilities with a productive transaction between cloud and fog, especially with the issues of volume, latency, and bandwidth regarding data management (JoSEP et al., 2010).
In this chapter, we propose to explain new trends of computing models to understand the evolving IoT applications, exclusively fog and edge computing, their background, features, model architecture and current challenges. This chapter also covers various problems and challenges that have been faced by the practitioners in previous years in the field of cloud computing associated with the IoT that has been solved by fog, edge and pervasive computing (De Donno et al., 2019). Further, because the Cybercrime Report 2016 suggests that cybercrime damages will be around $6 trillion every year by 2021, up from $3 trillion in 2015 it will cover how to secure the privacy of IoT based sensor devices and private data in the cloud using machine learning. Further, we will demonstrate in this chapter that fog computing definitely reduces latency as opposed to cloud computing. The low latency is significant for the medical IoT framework because of real‐time requirements. Although the Cloud‐based IoT (CIoT) structure is a typical way to deal with executing IoT frameworks, it is, however, confronting developing difficulties in the IoT. Specifically, CIoT deals with current challenges such as data transmission rate, latency rate, interruption, limitation of resource and secure system. The developing difficulties of CIoT have brought up an issue – what is needed to conquer the barrier of current cloud‐driven architecture? Fog computing architecture is a visionary model that includes all probabilities to encompass the cloud to the edge network of CIoT, from the distant central cloud datacenter, the interim system hubs to the far edge where the front‐end IoT gadgets are situated.
1.2 Why Fog, Edge, and Pervasive Computing?
Fog Computing
Fog computing is a distributed paradigm that provides computation, storage and network facilities between client gadgets and cloud datacenters mostly but not specifically situated on edge networks (Inbaraj, 2020). In such a way a cloud‐based facility can be enlarged nearer to the IoT gadgets/centers. In this scenario, fog acts as a middle layer between IoT based sensor machines and cloud datacenters (Bangui et al., 2018). The idea of fog computing was first created by Cisco in 2012 to report the difficulties of the IoT applications in traditional cloud computing. The challenges of fog computing are the facilitation and enhancement of mobility, real‐time interaction, privacy, security, low latency, low energy consumption and network bandwidth for real‐life applications where we need a quick response from the cloud, especially in the healthcare sector.
One of the benefits of fog computing is that, in place of transferring the entire data of IoT devices to the cloud, the fog will filter the data and then send a summary of the data. Another benefit is that fog computing processes the data before transferring to the cloud and will lead to reducing the communication period rate along with reducing the requirement of storage of massive data at the cloud. The key role of fog computing is data gathering from IoT sensors gadgets, data processing, data filtering and then finally sending a summary of the data to the cloud (Mehdipour et al., 2019).
The Need for Fog Computing
Over the past decade, we have seen that the trend of storage, computing, controlling, and network management function over the data has been shifted from traditional computing to the cloud computing paradigm. On the basis of evaluation, if the pace of extension proceeds constantly from 2020, the number of wearable gadgets on the planet will reach around 26 billion (Imran and Qadeer, 2019). The volume of data generated using these IoT gadgets is very large. The capability of the present cloud model is insufficient to deal with the necessities of IoT, i.e
