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E-raamat: Role of Edge Analytics in Sustainable Smart City Development: Challenges and Solutions

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  • Ilmumisaeg: 29-Jul-2020
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  • ISBN-13: 9781119681304
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Role of Edge Analytics in Sustainable Smart City Development: Challenges and SolutionsSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 29-Jul-2020
  • Kirjastus: Wiley-Scrivener
  • Keel: eng
  • ISBN-13: 9781119681304
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Efficient Single Board Computers (SBCs) and advanced VLSI systems have resulted in edge analytics and faster decision making. The QoS parameters like energy, delay, reliability, security, and throughput should be improved on seeking better intelligent expert systems. The resource constraints in the Edge devices, challenges the researchers to meet the required QoS. Since these devices and components work in a remote unattended environment, an optimum methodology to improve its lifetime has become mandatory. Continuous monitoring of events is mandatory to avoid tragic situations; it can only be enabled by providing high QoS. The applications of IoT in digital twin development, health care, traffic analysis, home surveillance, intelligent agriculture monitoring, defense and all common day to day activities have resulted in pioneering embedded devices, which can offer high computational facility without much latency and delay. The book address industrial problems in designing expert system and IoT applications. It provides novel survey and case study report on recent industrial approach towards Smart City development.
Preface xv
1 Smart Health Care Development: Challenges and Solutions
1(20)
R. Sujatha
E.P. Ephzibah
S. Sree Dharinya
1.1 Introduction
2(1)
1.2 ICT Explosion
3(7)
1.2.1 RFID
4(1)
1.2.2 IoT and Big Data
5(2)
1.2.3 Wearable Sensors--Head to Toe
7(1)
1.2.4 Cloud Computing
8(2)
1.3 Intelligent Healthcare
10(1)
1.4 Home Healthcare
11(1)
1.5 Data Analytics
11(2)
1.6 Technologies--Data Cognitive
13(2)
1.6.1 Machine Learning
13(1)
1.6.2 Image Processing
14(1)
1.6.3 Deep Learning
14(1)
1.7 Adoption Technologies
15(1)
1.8 Conclusion
15(6)
References
15(6)
2 Working of Mobile Intelligent Agents on the Web--A Survey
21(28)
P.R. Joe Dhanith
B. Surendiran
2.1 Introduction
21(2)
2.2 Mobile Crawler
23(24)
2.3 Comparative Study of the Mobile Crawlers
47(1)
2.4 Conclusion
47(2)
References
47(2)
3 Power Management Scheme for Photovoltaic/Battery Hybrid System in Smart Grid
49(18)
T. Bharani Prakash
S. Nagakumararaj
3.1 Power Management Scheme
50(1)
3.2 Internal Power Flow Management
50(4)
3.2.1 PI Controller
51(2)
3.2.2 State of Charge
53(1)
3.3 Voltage Source Control
54(4)
3.3.1 Phase-Locked Loop
55(1)
3.3.2 Space Vector Pulse Width Modulation
56(1)
3.3.3 Park Transformation (abc to dqO)
57(1)
3.4 Simulation Diagram and Results
58(9)
3.4.1 Simulation Diagram
58(5)
3.4.2 Simulation Results
63(2)
Conclusion
65(2)
4 Analysis: A Neural Network Equalizer for Channel Equalization by Particle Swarm Optimization for Various Channel Models
67(18)
M. Muthumari
D.C. Diana
C. Ambika Bhuvaneswari
4.1 Introduction
68(4)
4.2 Channel Equalization
72(4)
4.2.1 Channel Models
73(1)
4.2.1.1 Tapped Delay Line Model
74(1)
4.2.1.2 Stanford University Interim (SUI) Channel Models
75(1)
4.2.2 Artificial Neural Network
75(1)
4.3 Functional Link Artificial Neural Network
76(1)
4.4 Particle Swarm Optimization
76(1)
4.5 Result and Discussion
77(4)
4.5.1 Convergence Analysis
77(2)
4.5.2 Comparison Between Different Parameters
79(1)
4.5.3 Comparison Between Different Channel Models
80(1)
4.6 Conclusion
81(4)
References
82(3)
5 Implementing Hadoop Container Migrations in OpenNebula Private Cloud Environment
85(20)
P. Kalyanaraman
K.R. Jothi
P. Balakrishnan
R.G. Navya
A. Shah
V. Pandey
5.1 Introduction
86(4)
5.1.1 Hadoop Architecture
86(2)
5.1.2 Hadoop and Big Data
88(1)
5.1.3 Hadoop and Virtualization
88(1)
5.1.4 What is OpenNebula?
89(1)
5.2 Literature Survey
90(9)
5.2.1 Performance Analysis of Hadoop
90(1)
5.2.2 Evaluating Map Reduce on Virtual Machines
91(3)
5.2.3 Virtualizing Hadoop Containers
94(1)
5.2.4 Optimization of Hadoop Cluster Using Cloud Platform
95(1)
5.2.5 Heterogeneous Clusters in Cloud Computing
96(1)
5.2.6 Performance Analysis and Optimization in Hadoop
97(1)
5.2.7 Virtual Technologies
97(1)
5.2.8 Scheduling
98(1)
5.2.9 Scheduling of Hadoop VMs
98(1)
5.3 Discussion
99(1)
5.4 Conclusion
100(5)
References
101(4)
6 Transmission Line Inspection Using Unmanned Aerial Vehicle
105(22)
A. Mahaboob Subahani
M. Kathiresh
S. Sanjeev
6.1 Introduction
106(1)
6.1.1 Unmanned Aerial Vehicle
106(1)
6.1.2 Quadcopter
106(1)
6.2 Literature Survey
107(1)
6.3 System Architecture
108(1)
6.4 ArduPilot
109(2)
6.5 Arduino Mega
111(1)
6.6 Brushless DC Motor
111(1)
6.7 Battery
112(1)
6.8 CMOS Camera
113(1)
6.9 Electronic Speed Control
113(2)
6.10 Power Module
115(1)
6.11 Display Shield
116(1)
6.12 Navigational LEDS
116(2)
6.13 Role of Sensors in the Proposed System
118(2)
6.13.1 Accelerometer and Gyroscope
118(1)
6.13.2 Magnetometer
118(1)
6.13.3 Barometric Pressure Sensor
119(1)
6.13.4 Global Positioning System
119(1)
6.14 Wireless Communication
120(1)
6.15 Radio Controller
120(1)
6.16 Telemetry Radio
121(1)
6.17 Camera Transmitter
121(1)
6.18 Results and Discussion
121(3)
6.19 Conclusion
124(3)
References
125(2)
7 Smart City Infrastructure Management System Using IoT
127(1)
S. Ramamoorthy
M. Kowsigan
P. Balasubramanie
P. John Paul
7 A Introduction
128(11)
7.2 Major Challenges in IoT-Based Technology
129(2)
7.2.1 Peer to Peer Communication Security
129(1)
7.2.2 Objective of Smart Infrastructure
130(1)
7.3 Internet of Things (IoT)
131(4)
7.3.1 Key Components of Components of IoT
131(1)
7.3.1.1 Network Gateway
132(1)
7.3.1.2 HTTP (HyperText Transfer Protocol)
132(1)
7.3.1.3 LoRaWan (Long Range Wide Area Network)
133(1)
7.3.1.4 Bluetooth
133(1)
7.3.1.5 ZigBee
133(1)
7.3.2 IoT Data Protocols
133(1)
7.3.2.1 Message Queue Telemetry Transport (MQTT)
133(1)
7.3.2.2 Constrained Application Protocol (CoAP)
134(1)
7.3.2.3 Advanced Message Queuing Protocol (AMQP)
134(1)
7.3.2.4 Data Analytics
134(1)
7.4 Machine Learning-Based Smart Decision-Making Process
135(1)
7.5 Cloud Computing
136(3)
References
138(1)
8 Lightweight Cryptography Algorithms for IoT Resource-Starving Devices
139(32)
S. Aruna
G. Usha
P. Madhavan
M. V. Ranjith Kumar
8.1 Introduction
139(2)
8.1.1 Need of the Cryptography
140(1)
8.2 Challenges on Lightweight Cryptography
141(1)
8.3 Hashing Techniques on Lightweight Cryptography
142(10)
8.4 Applications on Lighweight Cryptography
152(15)
8.5 Conclusion
167(4)
References
168(3)
9 Pre-Learning-Based Semantic Segmentation for LiDAR Point Cloud Data Using Self-Organized Map
171(18)
K. Rajathi
P. Sarasu
9.1 Introduction
172(1)
9.2 Related Work
173(1)
9.2.1 Semantic Segmentation for Images
173(1)
9.3 Semantic Segmentation for LiDAR Point Cloud
173(2)
9.4 Proposed Work
175(5)
9.4.1 Data Acquisition
175(1)
9.4.2 Our Approach
175(4)
9.4.3 Pre-Learning Processing
179(1)
9.5 Region of Interest (RoI)
180(1)
9.6 Registration of Point Cloud
181(1)
9.7 Semantic Segmentation
181(1)
9.8 Self-Organized Map (SOM)
182(1)
9.9 Experimental Result
183(3)
9.10 Conclusion
186(3)
References
187(2)
10 Smart Load Balancing Algorithms in Cloud Computing--A Review
189(30)
K.R. Jothi
S. Anto
M. Kohar
M. Chadha
P. Madhavan
10.1 Introduction
189(3)
10.2 Research Challenges
192(1)
10.2.1 Security 8c Routing
192(1)
10.2.2 Storage/Replication
192(1)
10.2.3 Spatial Spread of the Cloud Nodes
192(1)
10.2.4 Fault Tolerance
193(1)
10.2.5 Algorithm Complexity
193(1)
10.3 Literature Survey
193(8)
10.4 Survey Table
201(1)
10.5 Discussion & Comparison
202(1)
10.6 Conclusion
202(17)
References
216(3)
11 A Low-Cost Wearable Remote Healthcare Monitoring System
219(24)
Konguvel Elango
Kannan Muniandi
11.1 Introduction
219(3)
11.1.1 Problem Statement
220(1)
11.1.2 Objective of the Study
221(1)
11.2 Related Works
222(4)
11.2.1 Remote Healthcare Monitoring Systems
222(2)
11.2.2 Pulse Rate Detection
224(1)
11.2.3 Temperate Measurement
225(1)
11.2.4 Fall Detection
225(1)
11.3 Methodology
226(10)
11.3.1 NodeMCU
226(1)
11.3.2 Pulse Rate Detection System
227(3)
11.3.3 Fall Detection System
230(1)
11.3.4 Temperature Detection System
231(3)
11.3.5 LCD Specification
234(1)
11.3.6 ADC Specification
234(2)
11.4 Results and Discussions
236(3)
11.4.1 System Implementation
236(1)
11.4.2 Fall Detection Results
236(1)
11.4.3 ThingSpeak
236(3)
11.5 Conclusion
239(1)
11.6 Future Scope
240(3)
References
241(2)
12 IoT-Based Secure Smart Infrastructure Data Management
243(14)
R. Poorvadevi
M. Kowsigan
P. Balasubramanie
J. Rajeshkumar
12.1 Introduction
244(1)
12.1.1 List of Security Threats Related to the Smart IoT Network
244(1)
12.1.2 Major Application Areas of IoT
244(1)
12.1.3 IoT Threats and Security Issues
245(1)
12.1.4 Unpatched Vulnerabilities
245(1)
12.1.5 Weak Authentication
245(1)
12.1.6 Vulnerable API's
245(1)
12.2 Types of Threats to Users
245(1)
12.3 Internet of Things Security Management
246(3)
12.3.1 Managing IoT Devices
246(1)
12.3.2 Role of External Devices in IoT Platform
247(1)
12.3.3 Threats to Other Computer Networks
248(1)
12.4 Significance of IoT Security
249(1)
12.4.1 Aspects of Workplace Security
249(1)
12.4.2 Important IoT Security Breaches and IoT Attacks
250(1)
12.5 IoT Security Tools and Legislation
250(1)
12.6 Protection of IoT Systems and Devices
251(2)
12.6.1 IoT Issues and Security Challenges
251(1)
12.6.2 Providing Secured Connections
252(1)
12.7 Five Ways to Secure IoT Devices
253(2)
12.8 Conclusion
255(2)
References
255(2)
13 A Study of Addiction Behavior for Smart Psychological Health Care System
257(16)
V. Sabapathi
K.P. Vijayakumar
13.1 Introduction
258(1)
13.2 Basic Criteria of Addiction
258(1)
13.3 Influencing Factors of Addiction Behavior
259(3)
13.3.1 Peers Influence
259(1)
13.3.2 Environment Influence
260(2)
13.3.3 Media Influence
262(1)
13.3.4 Family Group and Society
262(1)
13.4 Types of Addiction and Their Effects
262(7)
13.4.1 Gaming Addiction
263(1)
13.4.2 Pornography Addiction
264(1)
13.4.3 Smart Phone Addiction
265(2)
13.4.4 Gambling Addiction
267(1)
13.4.5 Food Addiction
267(1)
13.4.6 Sexual Addiction
268(1)
13.4.7 Cigarette and Alcohol Addiction
268(1)
13.4.8 Status Expressive Addiction
269(1)
13.4.9 Workaholic Addiction
269(1)
13.5 Conclusion
269(4)
References
270(3)
14 A Custom Cluster Design With Raspberry Pi for Parallel Programming and Deployment of Private Cloud
273(16)
B. Sukesh
K. Venkatesh
L.N.B. Srinivas
14.1 Introduction
274(2)
14.2 Cluster Design with Raspberry Pi
276(3)
14.2.1 Assembling Materials for Implementing Cluster
276(1)
14.2.1.1 Raspberry Pi4
277(1)
14.2.1.2 RPi 4 Model B Specifications
277(1)
14.2.2 Setting Up Cluster
278(1)
14.2.2.1 Installing Raspbian and Configuring Master Node
279(1)
14.2.2.2 Installing MPICH and MPI4PY
279(1)
14.2.2.3 Cloning the Slave Nodes
279(1)
14.3 Parallel Computing and MPI on Raspberry Pi Cluster
279(2)
14.4 Deployment of Private Cloud on Raspberry Pi Cluster
281(1)
14.4.1 NextCloud Software
281(1)
14.5 Implementation
281(5)
14.5.1 NextCloud on RPi Cluster
281(1)
14.5.2 Parallel Computing on RPi Cluster
282(4)
14.6 Results and Discussions
286(1)
14.7 Conclusion
287(2)
References
287(2)
15 Energy Efficient Load Balancing Technique for Distributed Data Transmission Using Edge Computing
289(14)
K. Karthikeyan
P. Madhavan
15.1 Introduction
290(1)
15.2 Energy Efficiency Offloading Data Transmission
290(1)
15.2.1 Web-Based Offloading
291(1)
15.3 Energy Harvesting
291(2)
15.3.1 LODCO Algorithm
292(1)
15.4 User-Level Online Offloading Framework (ULOOF)
293(1)
15.5 Frequency Scaling
294(1)
15.6 Computation Offloading and Resource Allocation
295(1)
15.7 Communication Technology
296(1)
15.8 Ultra-Dense Network
297(2)
15.9 Conclusion
299(4)
References
299(4)
16 Blockchain-Based SDR Signature Scheme With Time-Stamp
303(14)
Swathi Singh
Divya Satish
Sree Rathna Lakshmi
16.1 Introduction
303(1)
16.2 Literature Study
304(2)
16.2.1 Signatures With Hashes
304(1)
16.2.2 Signature Scheme With Server Support
305(1)
16.2.3 Signatures Scheme Based on Interaction
305(1)
16.3 Methodology
306(5)
16.3.1 Preliminaries
306(1)
16.3.1.1 Hash Trees
306(1)
16.3.1.2 Chains of Hashes
306(1)
16.3.2 Interactive Hash-Based Signature Scheme
307(2)
16.3.3 Significant Properties of Hash-Based Signature Scheme
309(1)
16.3.4 Proposed SDR Scheme Structure
310(1)
16.3.4.1 One-TimeKeys
310(1)
16.3.4.2 Server Behavior Authentication
310(1)
16.3.4.3 Pre-Authentication by Repository
311(1)
16.4 SDR Signature Scheme
311(4)
16.4.1 Pre-Requisites
311(1)
16.4.2 Key Generation Algorithm
312(1)
16.4.2.1 Server
313(1)
16.4.3 Sign Algorithm
313(1)
16.4.3.1 Signer
313(1)
16.4.3.2 Server
313(1)
16.4.3.3 Repository
314(1)
16.4.4 Verification Algorithm
314(1)
16.5 Supportive Theory
315(2)
16.5.1 Signing Algorithm Supported by Server
315(1)
16.5.2 Repository Deployment
316(1)
16.5.3 SDR Signature Scheme Setup
316(1)
16.5.4 Results and Observation
316(1)
16.6 Conclusion
317(1)
References 317(4)
Index 321
G. R. Kanagachidambaresan received his PhD from Anna University Chennai in 2017. He is currently an associate professor in the Department of Computer Science Engineering, Veltech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, India. His main research interests include Industry 4.0, smart city projects, Body Sensor Network and Fault Tolerant Wireless Sensor Network. He has published several articles in SCI journals and is an associate editor of Wireless Networks.
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