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Integration of Cloud Computing with Internet of Things: Foundations, Analytics and Applications [Kõva köide]

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Püsilink: https://www.kriso.ee/db/9781119768876.html
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"The book aims to integrate the aspects of IoT, Cloud computing and data analytics from diversified perspectives. The book also plans to discuss the recent research trends and advanced topics in the field which will be of interest to academicians and researchers working in this area. Thus, the book intends to help its readers to understand and explore the spectrum of applications of IoT, cloud computing and data analytics. Here, it is also worth mentioning that the book is believed to draw attention on the applications of said technology in various disciplines in order to obtain enhanced understanding of the readers. Also, this book focuses on the researches and challenges in the domain of IoT, Cloud computing and Data analytics from perspectives of various stakeholders."--

The book aims to integrate the aspects of IoT, Cloud computing and data analytics from diversified perspectives. The book also plans to discuss the recent research trends and advanced topics in the field which will be of interest to academicians and researchers working in this area. Thus, the book intends to help its readers to understand and explore the spectrum of applications of IoT, cloud computing and data analytics.

Here, it is also worth mentioning that the book is believed to draw attention on the applications of said technology in various disciplines in order to obtain enhanced understanding of the readers. Also, this book focuses on the researches and challenges in the domain of IoT, Cloud computing and Data analytics from perspectives of various stakeholders.

Preface xv
Acknowledgement xvii
1 Internet of Things: A Key to Unfasten Mundane Repetitive Tasks
1(24)
Hemanta Kumar Palo
Limali Sahoo
1.1 Introduction
1(1)
1.2 The IoT Scenario
2(1)
1.3 The IoT Domains
3(9)
1.3.1 The IoT Policy Domain
3(2)
1.3.2 The IoT Software Domain
5(1)
1.3.2.1 IoT in Cloud Computing (CC)
5(1)
1.3.2.2 IoT in Edge Computing (EC)
6(4)
1.3.2.3 IoT in Fog Computing (FC)
10(1)
1.3.2.4 IoT in Telecommuting
11(1)
1.3.2.5 IoT in Data-Center
12(1)
1.3.2.6 Virtualization-Based IoT (VBIoT)
12(1)
1.4 Green Computing (GC) in IoT Framework
12(1)
1.5 Semantic IoT (SIoT)
13(8)
1.5.1 Standardization Using oneM2M
15(3)
1.5.2 Semantic Interoperability (SI)
18(1)
1.5.3 Semantic Interoperability (SI)
19(1)
1.5.4 Semantic IoT vs Machine Learning
20(1)
1.6 Conclusions
21(4)
References
21(4)
2 Measures for Improving IoT Security
25(16)
Richa Goel
Seema Sahai
Gurinder Singh
Saurav Lall
2.1 Introduction
25(1)
2.2 Perceiving IoT Security
26(1)
2.3 The IoT Safety Term
27(1)
2.4 Objectives
28(2)
2.4.1 Enhancing Personal Data Access in Public Repositories
28(1)
2.4.2 Develop and Sustain Ethicality
28(1)
2.4.3 Maximize the Power of IoT Access
29(1)
2.4.4 Understanding Importance of Firewalls
29(1)
2.5 Research Methodology
30(1)
2.6 Security Challenges
31(2)
2.6.1 Challenge of Data Management
32(1)
2.7 Securing IoT
33(3)
2.7.1 Ensure User Authentication
33(1)
2.7.2 Increase User Autonomy
33(1)
2.7.3 Use of Firewalls
34(1)
2.7.4 Firewall Features
35(1)
2.7.5 Mode of Camouflage
35(1)
2.7.6 Protection of Data
35(1)
2.7.7 Integrity in Service
36(1)
2.7.8 Sensing of Infringement
36(1)
2.8 Monitoring of Firewalls and Good Management
36(1)
2.8.1 Surveillance
36(1)
2.8.2 Forensics
37(1)
2.8.3 Secure Firewalls for Private
37(1)
2.8.4 Business Firewalls for Personal
37(1)
2.8.5 IoT Security Weaknesses
37(1)
2.9 Conclusion
37(4)
References
38(3)
3 An Efficient Fog-Based Model for Secured Data Communication
41(16)
V. Lakshman Narayana
R. S. M. Lakshmi Patibandla
3.1 Introduction
41(4)
3.1.1 Fog Computing Model
42(1)
3.1.2 Correspondence in IoT Devices
43(2)
3.2 Attacks in IoT
45(3)
3.2.1 Botnets
45(1)
3.2.2 Man-In-The-Middle Concept
45(1)
3.2.3 Data and Misrepresentation
46(1)
3.2.4 Social Engineering
46(1)
3.2.5 Denial of Service
46(1)
3.2.6 Concerns
47(1)
3.3 Literature Survey
48(1)
3.4 Proposed Model for Attack Identification Using Fog Computing
49(3)
3.5 Performance Analysis
52(2)
3.6 Conclusion
54(3)
References
54(3)
4 An Expert System to Implement Symptom Analysis in Healthcare
57(14)
Subhasish Mohapatra
Kunal Anand
4.1 Introduction
57(2)
4.2 Related Work
59(1)
4.3 Proposed Model Description and Flow Chart
60(2)
4.3.1 Flowchart of the Model
60(1)
4.3.1.1 Value of Symptoms
60(1)
4.3.1.2 User Interaction Web Module
60(1)
4.3.1.3 Knowledge-Base
60(1)
4.3.1.4 Convolution Neural Network
60(1)
4.3.1.5 CNN-Fuzzy Inference Engine
61(1)
4.4 UML Analysis of Expert Model
62(4)
4.4.1 Expert Module Activity Diagram
63(2)
4.4.2 Ontology Class Collaboration Diagram
65(1)
4.5 Ontology Model of Expert Systems
66(1)
4.6 Conclusion and Future Scope
67(4)
References
68(3)
5 An IoT-Based Gadget for Visually Impaired People
71(16)
N. Prakash
E. Udayakumar
N. Kumareshan
K. Srihari
Sachi Nandan Mohanty
5.1 Introduction
71(2)
5.2 Related Work
73(1)
5.3 System Design
74(8)
5.4 Results and Discussion
82(2)
5.5 Conclusion
84(1)
5.6 Future Work
84(3)
References
84(3)
6 IoT Protocol for Inferno Calamity in Public Transport
87(24)
Ravi Babu Devareddi
R. Shiva Shankar
Gadiraju Mahesh
6.1 Introduction
87(2)
6.2 Literature Survey
89(5)
6.3 Methodology
94(9)
6.3.1 IoT Message Exchange With Cloud MQTT Broker Based on MQTT Protocol
98(1)
6.3.2 Hardware Requirement
98(5)
6.4 Implementation
103(3)
6.4.1 Interfacing Diagram
105(1)
6.5 Results
106(2)
6.6 Conclusion and Future Work
108(3)
References
109(2)
7 Traffic Prediction Using Machine Learning and IoT
111(20)
Daksh Pratap Singh
Dolly Sharma
7.1 Introduction
111(1)
7.1.1 Real Time Traffic
111(1)
7.1.2 Traffic Simulation
112(1)
7.2 Literature Review
112(1)
7.3 Methodology
113(3)
7.4 Architecture
116(6)
7.4.1 API Architecture
117(1)
7.4.2 File Structure
117(1)
7.4.3 Simulator Architecture
118(4)
7.4.4 Workflow in Application
122(1)
7.4.5 Workflow of Google APIs in the Application
122(1)
7.5 Results
122(6)
7.5.1 Traffic Scenario
122(2)
7.5.1.1 Low Traffic
124(1)
7.5.1.2 Moderate Traffic
124(1)
7.5.1.3 High Traffic
125(1)
7.5.2 Speed Viewer
125(1)
7.5.3 Traffic Simulator
126(1)
7.5.3.1 1st View
126(2)
7.5.3.2 2nd View
128(1)
7.5.3.3 3rd View
128(1)
7.6 Conclusion and Future Scope
128(3)
References
129(2)
8 Application of Machine Learning in Precision Agriculture
131(22)
Ravi Sharma
Nonita Sharma
8.1 Introduction
131(1)
8.2 Machine Learning
132(2)
8.2.1 Supervised Learning
133(1)
8.2.2 Unsupervised Learning
133(1)
8.2.3 Reinforcement Learning
134(1)
8.3 Agriculture
134(1)
8.4 ML Techniques Used in Agriculture
135(13)
8.4.1 Soil Mapping
135(5)
8.4.2 Seed Selection
140(1)
8.4.3 Irrigation/Water Management
141(2)
8.4.4 Crop Quality
143(1)
8.4.5 Disease Detection
144(1)
8.4.6 Weed Detection
145(2)
8.4.7 Yield Prediction
147(1)
8.5 Conclusion
148(5)
References
149(4)
9 An IoT-Based Multi Access Control and Surveillance for Home Security
153(12)
K. Yogeshwaran
C. Ramesh
E. Udayakumar
K. Srihari
Sachi Nandan Mohanty
9.1 Introduction
153(2)
9.2 Related Work
155(1)
9.3 Hardware Description
156(5)
9.3.1 Float Sensor
158(1)
9.3.2 Map Matching
158(1)
9.3.3 USART Cable
159(2)
9.4 Software Design
161(1)
9.5 Conclusion
162(3)
References
162(3)
10 Application of IoT in Industry 4.0 for Predictive Analytics
165(18)
Ahin Banerjee
Debanshee Datta
Sanjay K. Gupta
10.1 Introduction
165(3)
10.2 Past Literary Works
168(8)
10.2.1 Maintenance-Based Monitoring
168(1)
10.2.2 Data Driven Approach to RUL Finding in Industry
169(4)
10.2.3 Philosophy of Industrial-IoT Systems and its Advantages in Different Domain
173(3)
10.3 Methodology and Results
176(3)
10.4 Conclusion
179(4)
References
180(3)
11 IoT and Its Role in Performance Enhancement in Business Organizations
183(14)
Seema Sahai
Richa Goel
Parul Bajaj
Gurinder Singh
11.1 Introduction
183(7)
11.1.1 Scientific Issues in IoT
184(1)
11.1.2 IoT in Organizations
185(2)
11.1.3 Technology and Business
187(1)
11.1.4 Rewards of Technology in Business
187(1)
11.1.5 Shortcomings of Technology in Business
188(1)
11.1.6 Effect of IoT on Work and Organization
188(2)
11.2 Technology and Productivity
190(3)
11.3 Technology and Future of Human Work
193(1)
11.4 Technology and Employment
194(1)
11.5 Conclusion
195(2)
References
195(2)
12 An Analysis of Cloud Computing Based on Internet of Things
197(14)
Farhana Ajaz
Mohd Naseem
Ghulfam Ahamad
Sparsh Sharma
Ehtesham Abbasi
12.1 Introduction
197(5)
12.1.1 Generic Architecture
199(3)
12.2 Challenges in IoT
202(1)
12.3 Technologies Used in IoT
203(1)
12.4 Cloud Computing
203(2)
12.4.1 Service Models of Cloud Computing
204(1)
12.5 Cloud Computing Characteristics
205(1)
12.6 Applications of Cloud Computing
206(1)
12.7 Cloud IoT
207(1)
12.8 Necessity for Fusing IoT and Cloud Computing
207(1)
12.9 Cloud-Based IoT Architecture
208(1)
12.10 Applications of Cloud-Based IoT
208(1)
12.11 Conclusion
209(2)
References
209(2)
13 Importance of Fog Computing in Emerging Technologies-IoT
211(22)
Aarti Sahitya
13.1 Introduction
211(1)
13.2 IoT Core
212(15)
13.3 Need of Fog Computing
227(6)
References
230(3)
14 Convergence of Big Data and Cloud Computing Environment
233(18)
Ranjan Ganguli
14.1 Introduction
233(1)
14.2 Big Data: Historical View
234(3)
14.2.1 Big Data: Definition
235(1)
14.2.2 Big Data Classification
236(1)
14.2.3 Big Data Analytics
236(1)
14.3 Big Data Challenges
237(1)
14.4 The Architecture
238(3)
14.4.1 Storage or Collection System
240(1)
14.4.2 Data Care
240(1)
14.4.3 Analysis
240(1)
14.5 Cloud Computing: History in a Nutshell
241(1)
14.5.1 View on Cloud Computing and Big Data
241(1)
14.6 Insight of Big Data and Cloud Computing
241(4)
14.6.1 Cloud-Based Services
242(2)
14.6.2 At a Glance: Cloud Services
244(1)
14.7 Cloud Framework
245(3)
14.7.1 Hadoop
245(1)
14.7.2 Cassandra
246(1)
14.7.2.1 Features of Cassandra
246(1)
14.7.3 Voldemort
247(1)
14.7.3.1 A Comparison With Relational Databases and Benefits
247(1)
14.8 Conclusions
248(1)
14.9 Future Perspective
248(3)
References
248(3)
15 Data Analytics Framework Based on Cloud Environment
251(26)
K. Kanagaraj
S. Geetha
15.1 Introduction
251(1)
15.2 Focus Areas of the
Chapter
252(1)
15.3 Cloud Computing
252(11)
15.3.1 Cloud Service Models
253(1)
15.3.1.1 Software as a Service (SaaS)
253(1)
15.3.1.2 Platform as a Service (PaaS)
254(1)
15.3.1.3 Infrastructure as a Service (IaaS)
255(1)
15.3.1.4 Desktop as a Service (DaaS)
256(1)
15.3.1.5 Analytics as a Service (AaaS)
257(1)
15.3.1.6 Artificial Intelligence as a Service (AIaaS)
258(1)
15.3.2 Cloud Deployment Models
259(1)
15.3.3 Virtualization of Resources
260(1)
15.3.4 Cloud Data Centers
261(2)
15.4 Data Analytics
263(3)
15.4.1 Data Analytics Types
263(1)
15.4.1.1 Descriptive Analytics
263(1)
15.4.1.2 Diagnostic Analytics
264(1)
15.4.1.3 Predictive Analytics
265(1)
15.4.1.4 Prescriptive Analytics
265(1)
15.4.1.5 Big Data Analytics
265(1)
15.4.1.6 Augmented Analytics
266(1)
15.4.1.7 Cloud Analytics
266(1)
15.4.1.8 Streaming Analytics
266(1)
15.4.2 Data Analytics Tools
266(1)
15.5 Real-Time Data Analytics Support in Cloud
266(2)
15.6 Framework for Data Analytics in Cloud
268(1)
15.6.1 Data Analysis Software as a Service (DASaaS)
268(1)
15.6.2 Data Analysis Platform as a Service (DAPaaS)
268(1)
15.6.3 Data Analysis Infrastructure as a Service (DAIaaS)
269(1)
15.7 Data Analytics Work-Flow
269(1)
15.8 Cloud-Based Data Analytics Tools
270(2)
15.8.1 Amazon Kinesis Services
271(1)
15.8.2 Amazon Kinesis Data Firehose
271(1)
15.8.3 Amazon Kinesis Data Streams
271(1)
15.8.4 Amazon Textract
271(1)
15.8.5 Azure Stream Analytics
271(1)
15.9 Experiment Results
272(1)
15.10 Conclusion
272(5)
References
274(3)
16 Neural Networks for Big Data Analytics
277(22)
Bithika Bishesh
16.1 Introduction
277(1)
16.2 Neural Networks---An Overview
278(1)
16.3 Why Study Neural Networks?
279(1)
16.4 Working of Artificial Neural Networks
279(9)
16.4.1 Single-Layer Perceptron
279(1)
16.4.2 Multi-Layer Perceptron
280(1)
16.4.3 Training a Neural Network
281(1)
16.4.4 Gradient Descent Algorithm
282(2)
16.4.5 Activation Functions
284(4)
16.5 Innovations in Neural Networks
288(4)
16.5.1 Convolutional Neural Network (ConvNet)
288(1)
16.5.2 Recurrent Neural Network
289(2)
16.5.3 LSTM
291(1)
16.6 Applications of Deep Learning Neural Networks
292(1)
16.7 Practical Application of Neural Networks Using Computer Codes
293(1)
16.8 Opportunities and Challenges of Using Neural Networks
293(3)
16.9 Conclusion
296(3)
References
296(3)
17 Meta-Heuristic Algorithms for Best IoT Cloud Service Platform Selection
299(20)
Sudhansu Shekhar Patra
Sudarson Jena
G.B. Mund
Mahendra Kumar Gourisaria
Jugal Kishor Gupta
17.1 Introduction
299(2)
17.2 Selection of a Cloud Provider in Federated Cloud
301(6)
17.3 Algorithmic Solution
307(7)
17.3.1 TLBO Algorithm (Teaching-Learning-Based Optimization Algorithm)
307(1)
17.3.1.1 Teacher Phase: Generation of a New Solution
308(1)
17.3.1.2 Learner Phase: Generation of New Solution
309(1)
17.3.1.3 Representation of the Solution
309(1)
17.3.2 JAYA Algorithm
309(2)
17.3.2.1 Representation of the Solution
311(1)
17.3.3 Bird Swarm Algorithm
311(2)
17.3.3.1 Forging Behavior
313(1)
17.3.3.2 Vigilance Behavior
313(1)
17.3.3.3 Flight Behavior
313(1)
17.3.3.4 Representation of the Solution
313(1)
17.4 Analyzing the Algorithms
314(2)
17.5 Conclusion
316(3)
References
316(3)
18 Legal Entanglements of Cloud Computing In India
319(24)
Sambhabi Patnaik
Lipsa Dash
18.1 Cloud Computing Technology
319(3)
18.2 Cyber Security in Cloud Computing
322(1)
18.3 Security Threats in Cloud Computing
323(2)
18.3.1 Data Breaches
323(1)
18.3.2 Denial of Service (DoS)
323(1)
18.3.3 Botnets
323(1)
18.3.4 Crypto Jacking
324(1)
18.3.5 Insider Threats
324(1)
18.3.6 Hijacking Accounts
324(1)
18.3.7 Insecure Applications
324(1)
18.3.8 Inadequate Training
325(1)
18.3.9 General Vulnerabilities
325(1)
18.4 Cloud Security Probable Solutions
325(2)
18.4.1 Appropriate Cloud Model for Business
325(1)
18.4.2 Dedicated Security Policies Plan
325(1)
18.4.3 Multifactor Authentication
325(1)
18.4.4 Data Accessibility
326(1)
18.4.5 Secure Data Destruction
326(1)
18.4.6 Encryption of Backups
326(1)
18.4.7 Regulatory Compliance
326(1)
18.4.8 External Third-Party Contracts and Agreements
327(1)
18.5 Cloud Security Standards
327(1)
18.6 Cyber Security Legal Framework in India
327(2)
18.7 Privacy in Cloud Computing---Data Protection Standards
329(1)
18.8 Recognition of Right to Privacy
330(2)
18.9 Government Surveillance Power vs Privacy of Individuals
332(1)
18.10 Data Ownership and Intellectual Property Rights
333(2)
18.11 Cloud Service Provider as an Intermediary
335(2)
18.12 Challenges in Cloud Computing
337(2)
18.12.1 Classification of Data
337(1)
18.12.2 Jurisdictional Issues
337(1)
18.12.3 Interoperability of the Cloud
338(1)
18.12.4 Vendor Agreements
339(1)
18.13 Conclusion
339(4)
References
341(2)
19 Securing the Pharma Supply Chain Using Blockchain
343(15)
Pulkit Arora
Chetna Sachdeva
Dolly Sharma
19.1 Introduction
343(2)
19.2 Literature Review
345(4)
19.2.1 Current Scenario
346(1)
19.2.2 Proposal
347(2)
19.3 Methodology
349(5)
19.4 Results
354(4)
19.5 Conclusion and Future Scope
358(1)
References 358(3)
Index 361
Monika Mangla PhD is an Assistant Professor in the Department of Computer Engineering at Lokmanya Tilak College of Engineering (LTCoE), Mumbai, India. Her research areas include IoT, cloud computing, algorithms and optimization, location modelling and machine learning.

Suneeta Satpathy PhD is an Associate Professor in the Department of Computer Science & Engineering at College of Engineering Bhubaneswar (CoEB), Bhubaneswar. Her research interests include computer forensics, cybersecurity, data fusion, data mining, big data analysis, and decision mining.

Bhagirathi Nayak has 25 years of experience in the areas of computer science and engineering and database designing. Prof. Nayak earned his PhD in Computer Science from IIT Kharagpur. He is currently associated with Sri Sri University, Cuttack as head of the Department of Information & Communication Technology. He has obtained five patents in the area of computer science and engineering and his areas of interest are data mining, big data analytics, artificial intelligence and machine learning.

Sachi Nandan Mohanty obtained his PhD from IIT Kharagpur in 2015 and is now an Associate Professor in the Department of Computer Science & Engineering at ICFAI Foundation for Higher Education Hyderabad. Dr. Mohantys research areas include data mining, big data analysis, cognitive science, fuzzy decision making, brain-computer interface, and computational intelligence.