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Fog, Edge, and Pervasive Computing in Intelligent IoT Driven Applications [Kõva köide]

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  • Formaat: Hardback, 464 pages, kõrgus x laius x paksus: 10x10x10 mm, kaal: 454 g
  • Ilmumisaeg: 12-Mar-2021
  • Kirjastus: Wiley-IEEE Press
  • ISBN-10: 1119670071
  • ISBN-13: 9781119670070
Teised raamatud teemal:
Fog, Edge, and Pervasive Computing in Intelligent IoT Driven ApplicationsSuurem pilt
  • Formaat: Hardback, 464 pages, kõrgus x laius x paksus: 10x10x10 mm, kaal: 454 g
  • Ilmumisaeg: 12-Mar-2021
  • Kirjastus: Wiley-IEEE Press
  • ISBN-10: 1119670071
  • ISBN-13: 9781119670070
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781119670070.html
Märksõnad:

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.

About the Editors xvii
List of Contributors
xix
Preface xxv
Acknowledgments xxxiii
1 Fog, Edge and Pervasive Computing in Intelligent Internet of Things Driven Applications in Healthcare: Challenges, Limitations and Future Use
1(26)
Afroj Alam
Sahar Qazi
Naiyar Iqbal
Khalid Raza
1.1 Introduction
1(2)
1.2 Why Fog, Edge, and Pervasive Computing?
3(3)
1.3 Technologies Related to Fog and Edge Computing
6(3)
1.4 Concept of Intelligent IoT Application in Smart (Fog) Computing Era
9(3)
1.5 The Hierarchical Architecture of Fog/Edge Computing
12(3)
1.6 Applications of Fog, Edge and Pervasive Computing in IoT-based Healthcare
15(2)
1.7 Issues, Challenges, and Opportunity
17(3)
1.7.1 Security and Privacy Issues
18(1)
1.7.2 Resource Management
19(1)
1.7.3 Programming Platform
19(1)
1.8 Conclusion
20(7)
Bibliography
20(7)
2 Future Opportunistic Fog/Edge Computational Models and their Limitations
27(20)
Sonia Singla
Naveen Kumar Bhati
S. Aswath
2.1 Introduction
28(4)
2.2 What are the Benefits of Edge and Fog Computing for the Mechanical Web of Things (IoT)?
32(2)
2.3 Disadvantages
34(1)
2.4 Challenges
34(1)
2.5 Role in Health Care
35(3)
2.6 Blockchain and Fog, Edge Computing
38(2)
2.7 How Blockchain will Illuminate Human Services Issues
40(1)
2.8 Uses of Blockchain in the Future
41(1)
2.9 Uses of Blockchain in Health Care
42(1)
2.10 Edge Computing Segmental Analysis
42(1)
2.11 Uses of Fog Computing
43(1)
2.12 Analytics in Fog Computing
44(1)
2.13 Conclusion
44(3)
Bibliography
44(3)
3 Automating ELicitation Technique Selection using Machine Learning
47(20)
Hatim M. Ethassan Ibrahim Dafallaa
Nazir Ahmad
Mohammed Burhanur Rehman
Iqrar Ahmad
Rizwan Khan
3.1 Introduction
47(1)
3.2 Related Work
48(4)
3.3 Model: Requirement Elicitation Technique Selection Model
52(8)
3.3.1 Determining Key Attributes
54(1)
3.3.2 Selection Attributes
54(1)
3.3.2.1 Analyst Experience
55(1)
3.3.2.2 Number of Stakeholders
55(1)
3.3.2.3 Technique Time
56(1)
3.3.2.4 Level of Information
56(1)
3.3.3 Selection Attributes Dataset
56(1)
3.3.3.1 Mapping the Selection Attributes
57(1)
3.3.4 K-nearest Neighbor Algorithm Application
57(3)
3.4 Analysis and Results
60(1)
3.5 The Error Rate
61(1)
3.6 Validation
61(1)
3.6.1 Discussion of the Results of the Experiment
62(1)
3.7 Conclusion
62(5)
Bibliography
65(2)
4 Machine Learning Frameworks and Algorithms for Fog and Edge Computing
67(18)
Murali Mallikarjuna Rao Perumalla
Sanjay Kumar Singh
Aditya Khamparia
Anjali Goyai
Ashish Mishra
4.1 Introduction
68(1)
4.1.1 Fog Computing and Edge Computing
68(1)
4.1.2 Pervasive Computing
68(1)
4.2 Overview of Machine Learning Frameworks for Fog and Edge Computing
69(16)
4.2.1 Tensor Flow
69(1)
4.2.2 Keras
70(1)
4.2.3 PyTorch
70(1)
4.2.4 Tensor Flow Lite
70(1)
4.2.4.1 Use Pre-train Models
70(1)
4.2.4.2 Convert the Model
70(1)
4.2.4.3 On-device Inference
71(1)
4.2.4.4 Model Optimization
71(1)
4.2.5 Machine Learning and Deep Learning Techniques
71(1)
4.2.5.1 Supervised, Unsupervised and Reinforcement Learning
71(1)
4.2.5.2 Machine Learning, Deep Learning Techniques
72(3)
4.2.5.3 Deep Learning Techniques
75(2)
4.2.5.4 Efficient Deep Learning Algorithms for Inference
77(1)
4.2.6 Pros and Cons of ML Algorithms for Fog and Edge Computing
78(1)
4.2.6.1 Advantages using ML Algorithms
78(1)
4.2.6.2 Disadvantages of using ML Algorithms
79(1)
4.2.7 Hybrid ML Model for Smart IoT Applications
79(1)
4.2.7.1 Multi-Task Learning
79(1)
4.2.7.2 Ensemble Learning
80(1)
4.2.8 Possible Applications in Fog Era using Machine Learning
81(1)
4.2.8.1 Computer Vision
81(1)
4.2.8.2 ML - Assisted Healthcare Monitoring System
81(1)
4.2.8.3 Smart Homes
81(1)
4.2.8.4 Behavior Analyses
82(1)
4.2.8.5 Monitoring in Remote Areas and Industries
82(1)
4.2.8.6 Self-Driving Cars
82(1)
Bibliography
82(3)
5 Integrated Cloud Based Library Management in intelligent IoT driven Applications
85(20)
Md Robiul Alam Robel
Subrato Bharati
Prajoy Podder
M. Rubaiyat Hossain Mondal
5.1 Introduction
86(1)
5.1.1 Execution Plan for the Mobile Application
86(1)
5.1.2 Main Contribution
86(1)
5.2 Understanding Library Management
87(1)
5.3 Integration of Mobile Platform with the Physical Library - Brief Concept
88(1)
5.4 Database (Cloud Based) - A Must have Component for Library Automation
88(1)
5.5 IoT Driven Mobile Based Library Management - General Concept
89(4)
5.6 IoT Involved Real Time GUI (Cross Platform) Available to User
93(5)
5.7 IoT Challenges
98(3)
5.7.1 Infrastructure Challenges
99(1)
5.7.2 Security Challenges
99(1)
5.7.3 Societal Challenges
100(1)
5.1 A Commercial Challenges
101(1)
5.8 Conclusion
102(3)
Bibliography
104(1)
6 A Systematic and Structured Review of Intelligent Systems for Diagnosis of Renal Cancer
105(18)
Nikita
Harsh Sadawarti
Balwinder Kaur
Jimmy Singla
6.1 Introduction
106(1)
6.2 Related Works
107(12)
6.3 Conclusion
119(4)
Bibliography
119(4)
7 Location Driven Edge Assisted Device and Solutions for Intelligent Transportation
123(26)
Saravjeet Singh
Jaiteg Singh
7.1 Introduction to Fog and Edge Computing
124(5)
7.1.1 Need for Fog and Edge Computing
124(1)
7.1.2 Fog Computing
125(1)
7.1.2.1 Application Areas of Fog Computing
125(1)
7.1.3 Edge Computing
126(1)
7.1.3.1 Advantages of Edge Computing
127(2)
7.1.3.2 Application Areas of Fog Computing
129(1)
7.2 Introduction to Transportation System
129(2)
7.3 Route Finding Process
131(2)
7.3.1 Challenges Associated with Land Navigation and Routing Process
132(1)
7.4 Edge Architecture for Route Finding
133(2)
7.5 Technique Used
135(2)
7.6 Algorithms Used for the Location Identification and Route Finding Process
137(3)
7.6.1 Location Identification
137(1)
7.6.2 Path Generation Technique
138(2)
7.7 Results and Discussions
140(5)
7.7.1 Output
140(3)
7.7.2 Benefits of Edge-based Routing
143(2)
7.8 Conclusion
145(4)
Bibliography
146(3)
8 Design and Simulation of MEMS for Automobile Condition Monitoring Using COMSOL Multiphysics Simulator
149(12)
Natasha Tiwari
Anil Kumar
Pallavi Asthana
Sumita Mishra
Bramah Hazela
8.1 Introduction
149(2)
8.2 Related Work
151(1)
8.3 Vehicle Condition Monitoring through Acoustic Emission
151(1)
8.4 Piezo-resistive Micro Electromechanical Sensors for Monitoring the Faults Through AE
152(1)
8.5 Designing of MEM Sensor
153(1)
8.6 Experimental Setup
153(4)
8.6.1 FFT Analysis of Automotive Diesel Engine Sound Recording using MATLAB
155(1)
8.6.2 Design of MEMS Sensor using COMSOL Multiphysics
155(1)
8.6.3 Electrostatic Study Steps for the Optimized Tri-plate Comb Structure
156(1)
8.7 Result and Discussions
157(1)
8.8 Conclusion
158(3)
Bibliography
158(3)
9 IoT Driven Healthcare Monitoring System
161(16)
Md Robiut Alam Robel
Subrato Bharati
Prajoy Podder
M. Rubaiyat Hossain Mondal
9.1 Introduction
161(3)
9.1.1 Complementary Aspects of Cloud IoT in Healthcare Applications
162(2)
9.1.2 Main Contribution
164(1)
9.2 General Concept for IoT Based Healthcare System
164(1)
9.3 View of the Overall IoT Healthcare System - Tiers Explained
165(1)
9.4 A Brief Design of the IoT Healthcare Architecture-individual Block Explanation
166(2)
9.5 Models/Frameworks for IoT use in Healthcare
168(3)
9.6 IoT e-Health System Model
171(1)
9.7 Process Flow for the Overall Model
172(1)
9.8 Conclusion
173(4)
Bibliography
175(2)
10 Fog Computing as Future Perspective in Vehicular Ad hoc Networks
177(16)
Harjit Singh
Dr. Vijay Laxmi
Dr. Arun Malik
Dr. Isha
10.1 Introduction
178(2)
10.2 Future VANET: Primary Issues and Specifications
180(1)
10.3 Fog Computing
181(4)
10.3.1 Fog Computing Concept
183(1)
10.3.2 Fog Technology Characterization
183(2)
10.4 Related Works in Cloud and Fog Computing
185(1)
10.5 Fog and Cloud Computing-based Technology Applications in VANET
186(2)
10.6 Challenges of Fog Computing in VANET
188(1)
10.7 Issues of Fog Computing in VANET
189(1)
10.8 Conclusion
190(3)
Bibliography
191(2)
11 An Overview to Design an Efficient and Secure Fog-assisted Data Collection Method in the Internet of Things
193(16)
Sofia
Arun Malik
Isha
Aditya Khamparia
11.1 Introduction
193(1)
11.2 Related Works
194(2)
11.3 Overview of the
Chapter
196(1)
11.4 Data Collection in the IoT
197(1)
11.5 Fog Computing
197(1)
11.5.1 Why fog Computing for Data Collection in IoT?
197(3)
11.5.2 Architecture of Fog Computing
200(1)
11.5.3 Features of Fog Computing
200(2)
11.5.4 Threats of Fog Computing
202(1)
11.5.5 Applications of Fog Computing with the IoT
203(1)
11.6 Requirements for Designing a Data Collection Method
204(2)
11.7 Conclusion
206(3)
Bibliography
206(3)
12 Role of Fog Computing Platform in Analytics of Internet of Things - Issues, Challenges and Opportunities
209(12)
Mamoon Rashid
Umer Iqbal Wani
12.1 Introduction to Fog Computing
209(5)
12.1.1 Hierarchical Fog Computing Architecture
210(2)
12.1.2 Layered Fog Computing Architecture
212(1)
12.1.3 Comparison of Fog and Cloud Computing
213(1)
12.2 Introduction to Internet of Things
214(2)
12.2.1 Overview of Internet of Things
214(2)
12.3 Conceptual Architecture of Internet of Things
216(1)
12.4 Relationship between Internet of Things and Fog Computing
217(1)
12.5 Use of Fog Analytics in Internet of Things
218(1)
12.6 Conclusion
218(3)
Bibliography
218(3)
13 A Medical Diagnosis of Urethral Stricture Using Intuitionistic Fuzzy Sets
221(16)
Prabjot Kaur
Maria Jamal
13.1 Introduction
221(2)
13.2 Preliminaries
223(2)
13.2.1 Introduction
223(1)
13.2.2 Fuzzy Sets
223(1)
13.2.3 Intuitionistic Fuzzy Sets
224(1)
13.2.4 Intuitionistic Fuzzy Relation
224(1)
13.2.5 Max-Min-Max Composition
224(1)
13.2.6 Linguistic Variable
224(1)
13.2.7 Distance Measure In Intuitionistic Fuzzy Sets
224(1)
13.2.7.1 The Hamming Distance
224(1)
13.2.7.2 Normalized Hamming Distance
224(1)
13.2.7.3 Compliment of an Intuitionistic Fuzzy Set Matrix
225(1)
13.2.7.4 Revised Max-Min Average Composition of A and B(AOB)
225(1)
13.3 Max-Min-Max Algorithm for Disease Diagnosis
225(1)
13.4 Case Study
226(1)
13.5 Intuitionistic Fuzzy Max-Min Average Algorithm for Disease Diagnosis
227(1)
13.6 Result
228(1)
13.7 Code for Calculation
229(4)
13.8 Conclusion
233(1)
13.9 Acknowledgement
234(3)
Bibliography
234(3)
14 Security Attacks in Internet of Things
237(26)
Rajit Nair
Preeti Sharma
Dileep Kumar Singh
14.1 Introduction
238(1)
14.2 Reference Model of Internet of Things (IoT)
238(8)
14.3 IoT Communication Protocol
246(1)
14.4 IoT Security
247(9)
14.4.1 Physical Attack
248(4)
14.4.2 Network Attack
252(2)
14.4.3 Software Attack
254(1)
14.4.4 Encryption Attack
255(1)
14.5 Security Challenges in IoT
256(1)
14.5.1 Cryptographic Strategies
256(1)
14.5.2 Key Administration
256(1)
14.5.3 Denial of Service
256(1)
14.5.4 Authentication and Access Control
257(1)
14.6 Conclusion
257(6)
Bibliography
257(6)
15 Fog Integrated Novel Architecture for Telehealth Services with Swift Medical Delivery
263(24)
Inderpreet Kaur
Kamaljit Singh Saini
Jaiteg Singh Khaira
15.1 Introduction
264(2)
15.2 Associated Work and Dimensions
266(1)
15.3 Need of Security in Telemedicine Domain and Internet of Things (IoT)
267(1)
15.3.1 Analytics Reports
268(1)
15.4 Fog Integrated Architecture for Telehealth Delivery
268(1)
15.5 Research Dimensions
269(1)
15.5.1 Benchmark Datasets
269(1)
15.6 Research Methodology and Implementation on Software Defined Networking
270(12)
15.6.1 Key Tools and Frameworks for IoT, Fog Computing and Edge Computing
274(2)
15.6.2 Simulation Analysis
276(6)
15.7 Conclusion
282(5)
Bibliography
282(5)
16 Fruit Fly Optimization Algorithm for Intelligent IoT Applications
287(24)
Satinder Singh Mohar
Sonia Goyal
Ranjit Kaur
16.1 An Introduction to the Internet of Things
287(1)
16.2 Background of the IoT
288(1)
16.2.1 Evolution of the IoT
288(1)
16.2.2 Elements Involved in IoT Communication
288(1)
16.3 Applications of the IoT
289(2)
16.3.1 Industrial
290(1)
16.3.2 Smart Parking
290(1)
16.3.3 Health Care
290(1)
16.3.4 Smart Offices and Homes
290(1)
16.3.5 Augment Maps
291(1)
16.3.6 Environment Monitoring
291(1)
16.3.7 Agriculture
291(1)
16.4 Challenges in the IoT
291(2)
16.4.1 Addressing Schemes
291(1)
16.4.2 Energy Consumption
292(1)
16.4.3 Transmission Media
292(1)
16.4.4 Security
292(1)
16.4.5 Quality of Service (QoS)
292(1)
16.5 Introduction to Optimization
293(1)
16.6 Classification of Optimization Algorithms
293(2)
16.6.1 Particle Swarm Optimization (PSO) Algorithm
293(1)
16.6.2 Genetic Algorithms
294(1)
16.6.3 Heuristic Algorithms
294(1)
16.6.4 Bio-inspired Algorithms
294(1)
16.6.5 Evolutionary Algorithms (EA)
294(1)
16.7 Network Optimization and IoT
295(1)
16.8 Network Parameters optimized by Different Optimization Algorithms
295(2)
16.8.1 Load Balancing
295(1)
16.8.2 Maximizing Network Lifetime
295(1)
16.8.3 Link Failure Management
296(1)
16.8.4 Quality of the Link
296(1)
16.8.5 Energy Efficiency
296(1)
16.8.6 Node Deployment
296(1)
16.9 Fruit Fly Optimization Algorithm
297(3)
16.9.1 Steps Involved in FOA
297(1)
16.9.2 Flow Chart of Fruit Fly Optimization Algorithm
298(2)
16.10 Applicability of FOA in IoT Applications
300(2)
16.10.1 Cloud Service Distribution in Fog Computing
300(1)
16.10.2 Cluster Head Selection in IoT
300(1)
16.10.3 Load Balancing in IoT
300(1)
16.10.4 Quality of Service in Web Services
300(1)
16.10.5 Electronics Health Records in Cloud Computing
301(1)
16.10.6 Intrusion Detection System in Network
301(1)
16.10.7 Node Capture Attack in WSN
301(1)
16.10.8 Node Deployment in WSN
302(1)
16.11 Node Deployment Using Fruit Fly Optimization Algorithm
302(2)
16.12 Conclusion
304(7)
Bibliography
304(7)
17 Optimization Techniques for Intelligent IoT Applications
311(22)
Priyanka Pattnaik
Subhashree Mishra
Bhabani Shankar Prasad Mishra
17.1 Cuckoo Search
312(5)
17.1.1 Introduction to Cuckoo
312(1)
17.1.2 Natural Cuckoo
312(1)
17.1.3 Artificial Cuckoo Search
313(1)
17.1.4 Cuckoo Search Algorithm
313(1)
17.1.5 Cuckoo Search Variants
314(1)
17.1.6 Discrete Cuckoo Search
314(1)
17.1.7 Binary Cuckoo Search
314(2)
17.1.8 Chaotic Cuckoo Search
316(1)
17.1.9 Parallel Cuckoo Search
317(1)
17.1.10 Application of Cuckoo Search
317(1)
17.2 Glow Worm Algorithm
317(4)
17.2.1 Introduction to Glow Worm
317(1)
17.2.2 Glow Worm Swarm Optimization Algorithm (GSO)
317(4)
17.3 Wasp Swarm Optimization
321(7)
17.3.1 Introduction to Wasp Swarm and Wasp Swarm Algorithm (WSO)
321(1)
17.3.2 Fish Swarm Optimization (FSO)
322(1)
17.3.3 Fruit Fly Optimization (FLO)
322(2)
17.3.4 Cockroach Swarm Optimization
324(1)
17.3.5 Bumblebee Algorithm
324(1)
17.3.6 Dolphin Echolocation
325(1)
17.3.7 Shuffled Frog-leaping Algorithm
326(1)
17.3.8 Paddy Field Algorithm
327(1)
17.4 Real World Applications Area
328(5)
Summary
329(1)
Bibliography
329(4)
18 Optimization Techniques for Intelligent IoT Applications in Transport Processes
333(18)
Muzafer Saracevic
Zoran Lonc Arevic
Adnan Hasanovic
18.1 Introduction
333(2)
18.2 Related Works
335(1)
18.3 TSP Optimization Techniques
336(2)
18.4 Implementation and Testing of Proposed Solution
338(4)
18.5 Experimental Results
342(4)
18.5.1 Example Test with 50 Cities
343(1)
18.5.2 Example Test with 100 Cities
344(2)
18.6 Conclusion and Further Works
346(5)
Bibliography
347(4)
19 Role of Intelligent IOT Applications in Fog paradigm: Issues, Challenges and Future Opportunities
351(6)
Priyanka Rajan Kumar
Sonia Goel
19.1 Fog Computing
352(3)
19.1.1 Need of Fog computing
352(1)
19.1.2 Architecture of Fog Computing
353(1)
19.1.3 Fog Computing Reference Architecture
354(1)
19.1 A Processing on Fog
355(1)
19.2 Concept of Intelligent IoT Applications in Smart Computing Era
355(1)
19.3 Components of Edge and Fog Driven Algorithm
356(1)
19 A Working of Edge and Fog Driven Algorithms
357(12)
19.5 Future Opportunistic Fog/Edge Computational Models
360(1)
19.5.1 Future Opportunistic Techniques
361(1)
19.6 Challenges of Fog Computing for Intelligent IoT Applications
361(2)
19.7 Applications of Cloud Based Computing for Smart Devices
363(6)
Bibliography
364(5)
20 Security and Privacy Issues in Fog/Edge/Pervasive Computing
369(20)
Shweta Kaushik
Charu Gandhi
20.1 Introduction to Data Security and Privacy in Fog Computing
370(5)
20.2 Data Protection / Security
375(2)
20.3 Great Security Practices In Fog Processing Condition
377(4)
20.4 Developing Patterns in Security and Privacy
381(4)
20.5 Conclusion
385(4)
Bibliography
385(4)
21 Fog and Edge Driven Security & Privacy Issues in IoT Devices
389(20)
Deepak Kumar Sharma
Aarti Goel
Pragun Mangla
21.1 Introduction to Fog Computing
390(4)
21.1.1 Architecture of Fog
390(2)
21.1.2 Benefits of Fog Computing
392(1)
21.1.3 Applications of Fog with IoT
393(1)
21.1 A Major Challenges for Fog with IoT
394(5)
21.1.5 Security and Privacy Issues in Fog Computing
395(4)
21.2 Introduction to Edge Computing
399(5)
21.2.1 Architecture and Working
400(1)
21.2.2 Applications and use Cases
400(3)
21.2.3 Characteristics of Edge Computing
403(1)
21.2 A Challenges of Edge Computing
404(5)
21.2.5 How to Protect Devices "On the Edge"?
405(1)
21.2.6 Comparison with Fog Computing
405(1)
Bibliography
406(3)
Index 409
Deepak Gupta, PhD, is an Assistant Professor in the Department of Computer Science and Engineering at the Maharaja Agrasen Institute of Technology, Delhi, India. He has published 158 papers and 3 patents. He is associated with numerous professional bodies, including IEEE, ISTE, IAENG, and IACSIT. He is the convener and organizer of the ICICC, ICDAM Springer Conference Series.

Aditya Khamparia, PhD, is Associate Professor of Computer Science at Lovely Professional University, Punjab, India. He has published more than 45 scientific research publications and is a member of CSI, IET, ISTE, IAENG, ACM and IACSIT.