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

ISBN-13: 9781119681281 / Angielski / Twarda / 2020 / 352 str.

G. R. Kanagachidambaresan

Spis treści

Preface xv1 Smart Health Care Development: Challenges and Solutions 1R. Sujatha, E.P. Ephzibah and S. Sree Dharinya1.1 Introduction 21.2 ICT Explosion 31.2.1 RFID 41.2.2 IoT and Big Data 51.2.3 Wearable Sensors--Head to Toe 71.2.4 Cloud Computing 81.3 Intelligent Healthcare 101.4 Home Healthcare 111.5 Data Analytics 111.6 Technologies--Data Cognitive 131.6.1 Machine Learning 131.6.2 Image Processing 141.6.3 Deep Learning 141.7 Adoption Technologies 151.8 Conclusion 15References 152 Working of Mobile Intelligent Agents on the Web--A Survey 21P.R. Joe Dhanith and B. Surendiran2.1 Introduction 212.2 Mobile Crawler 232.3 Comparative Study of the Mobile Crawlers 472.4 Conclusion 47References 473 Power Management Scheme for Photovoltaic/Battery Hybrid System in Smart Grid 49T. Bharani Prakash and S. Nagakumararaj3.1 Power Management Scheme 503.2 Internal Power Flow Management 503.2.1 PI Controller 513.2.2 State of Charge 533.3 Voltage Source Control 543.3.1 Phase-Locked Loop 553.3.2 Space Vector Pulse Width Modulation 563.3.3 Park Transformation (abc to dq0) 573.4 Simulation Diagram and Results 583.4.1 Simulation Diagram 583.4.2 Simulation Results 63Conclusion 654 Analysis: A Neural Network Equalizer for Channel Equalization by Particle Swarm Optimization for Various Channel Models 67M. Muthumari, D.C. Diana and C. Ambika Bhuvaneswari4.1 Introduction 684.2 Channel Equalization 724.2.1 Channel Models 734.2.1.1 Tapped Delay Line Model 744.2.1.2 Stanford University Interim (SUI) Channel Models 754.2.2 Artificial Neural Network 754.3 Functional Link Artificial Neural Network 764.4 Particle Swarm Optimization 764.5 Result and Discussion 774.5.1 Convergence Analysis 774.5.2 Comparison Between Different Parameters 794.5.3 Comparison Between Different Channel Models 804.6 Conclusion 81References 825 Implementing Hadoop Container Migrations in OpenNebula Private Cloud Environment 85P. Kalyanaraman, K.R. Jothi, P. Balakrishnan, R.G. Navya, A. Shah and V. Pandey5.1 Introduction 865.1.1 Hadoop Architecture 865.1.2 Hadoop and Big Data 885.1.3 Hadoop and Virtualization 885.1.4 What is OpenNebula? 895.2 Literature Survey 905.2.1 Performance Analysis of Hadoop 905.2.2 Evaluating Map Reduce on Virtual Machines 915.2.3 Virtualizing Hadoop Containers 945.2.4 Optimization of Hadoop Cluster Using Cloud Platform 955.2.5 Heterogeneous Clusters in Cloud Computing 965.2.6 Performance Analysis and Optimization in Hadoop 975.2.7 Virtual Technologies 975.2.8 Scheduling 985.2.9 Scheduling of Hadoop VMs 985.3 Discussion 995.4 Conclusion 100References 1016 Transmission Line Inspection Using Unmanned Aerial Vehicle 105A. Mahaboob Subahani, M. Kathiresh and S. Sanjeev6.1 Introduction 1066.1.1 Unmanned Aerial Vehicle 1066.1.2 Quadcopter 1066.2 Literature Survey 1076.3 System Architecture 1086.4 ArduPilot 1096.5 Arduino Mega 1116.6 Brushless DC Motor 1116.7 Battery 1126.8 CMOS Camera 1136.9 Electronic Speed Control 1136.10 Power Module 1156.11 Display Shield 1166.12 Navigational LEDS 1166.13 Role of Sensors in the Proposed System 1186.13.1 Accelerometer and Gyroscope 1186.13.2 Magnetometer 1186.13.3 Barometric Pressure Sensor 1196.13.4 Global Positioning System 1196.14 Wireless Communication 1206.15 Radio Controller 1206.16 Telemetry Radio 1216.17 Camera Transmitter 1216.18 Results and Discussion 1216.19 Conclusion 124References 1257 Smart City Infrastructure Management System Using IoT 127S. Ramamoorthy, M. Kowsigan, P. Balasubramanie and P. John Paul7.1 Introduction 1287.2 Major Challenges in IoT-Based Technology 1297.2.1 Peer to Peer Communication Security 1297.2.2 Objective of Smart Infrastructure 1307.3 Internet of Things (IoT) 1317.3.1 Key Components of Components of IoT 1317.3.1.1 Network Gateway 1327.3.1.2 HTTP (HyperText Transfer Protocol) 1327.3.1.3 LoRaWan (Long Range Wide Area Network) 1337.3.1.4 Bluetooth 1337.3.1.5 ZigBee 1337.3.2 IoT Data Protocols 1337.3.2.1 Message Queue Telemetry Transport (MQTT) 1337.3.2.2 Constrained Application Protocol (CoAP) 1347.3.2.3 Advanced Message Queuing Protocol (AMQP) 1347.3.2.4 Data Analytics 1347.4 Machine Learning-Based Smart Decision-Making Process 1357.5 Cloud Computing 136References 1388 Lightweight Cryptography Algorithms for IoT Resource-Starving Devices 139S. Aruna, G. Usha, P. Madhavan and M.V. Ranjith Kumar8.1 Introduction 1398.1.1 Need of the Cryptography 1408.2 Challenges on Lightweight Cryptography 1418.3 Hashing Techniques on Lightweight Cryptography 1428.4 Applications on Lighweight Cryptography 1528.5 Conclusion 167References 1689 Pre-Learning-Based Semantic Segmentation for LiDAR Point Cloud Data Using Self-Organized Map 171K. Rajathi and P. Sarasu9.1 Introduction 1729.2 Related Work 1739.2.1 Semantic Segmentation for Images 1739.3 Semantic Segmentation for LiDAR Point Cloud 1739.4 Proposed Work 1759.4.1 Data Acquisition 1759.4.2 Our Approach 1759.4.3 Pre-Learning Processing 1799.5 Region of Interest (RoI) 1809.6 Registration of Point Cloud 1819.7 Semantic Segmentation 1819.8 Self-Organized Map (SOM) 1829.9 Experimental Result 1839.10 Conclusion 186References 18710 Smart Load Balancing Algorithms in Cloud Computing--A Review 189K.R. Jothi, S. Anto, M. Kohar, M. Chadha and P. Madhavan10.1 Introduction 18910.2 Research Challenges 19210.2.1 Security & Routing 19210.2.2 Storage/Replication 19210.2.3 Spatial Spread of the Cloud Nodes 19210.2.4 Fault Tolerance 19310.2.5 Algorithm Complexity 19310.3 Literature Survey 19310.4 Survey Table 20110.5 Discussion & Comparison 20210.6 Conclusion 202References 21611 A Low-Cost Wearable Remote Healthcare Monitoring System 219Konguvel Elango and Kannan Muniandi11.1 Introduction 21911.1.1 Problem Statement 22011.1.2 Objective of the Study 22111.2 Related Works 22211.2.1 Remote Healthcare Monitoring Systems 22211.2.2 Pulse Rate Detection 22411.2.3 Temperate Measurement 22511.2.4 Fall Detection 22511.3 Methodology 22611.3.1 NodeMCU 22611.3.2 Pulse Rate Detection System 22711.3.3 Fall Detection System 23011.3.4 Temperature Detection System 23111.3.5 LCD Specification 23411.3.6 ADC Specification 23411.4 Results and Discussions 23611.4.1 System Implementation 23611.4.2 Fall Detection Results 23611.4.3 ThingSpeak 23611.5 Conclusion 23911.6 Future Scope 240References 24112 IoT-Based Secure Smart Infrastructure Data Management 243R. Poorvadevi, M. Kowsigan, P. Balasubramanie and J. Rajeshkumar12.1 Introduction 24412.1.1 List of Security Threats Related to the Smart IoT Network 24412.1.2 Major Application Areas of IoT 24412.1.3 IoT Threats and Security Issues 24512.1.4 Unpatched Vulnerabilities 24512.1.5 Weak Authentication 24512.1.6 Vulnerable API's 24512.2 Types of Threats to Users 24512.3 Internet of Things Security Management 24612.3.1 Managing IoT Devices 24612.3.2 Role of External Devices in IoT Platform 24712.3.3 Threats to Other Computer Networks 24812.4 Significance of IoT Security 24912.4.1 Aspects of Workplace Security 24912.4.2 Important IoT Security Breaches and IoT Attacks 25012.5 IoT Security Tools and Legislation 25012.6 Protection of IoT Systems and Devices 25112.6.1 IoT Issues and Security Challenges 25112.6.2 Providing Secured Connections 25212.7 Five Ways to Secure IoT Devices 25312.8 Conclusion 255References 25513 A Study of Addiction Behavior for Smart Psychological Health Care System 257V. Sabapathi and K.P. Vijayakumar13.1 Introduction 25813.2 Basic Criteria of Addiction 25813.3 Influencing Factors of Addiction Behavior 25913.3.1 Peers Influence 25913.3.2 Environment Influence 26013.3.3 Media Influence 26213.3.4 Family Group and Society 26213.4 Types of Addiction and Their Effects 26213.4.1 Gaming Addiction 26313.4.2 Pornography Addiction 26413.4.3 Smart Phone Addiction 26513.4.4 Gambling Addiction 26713.4.5 Food Addiction 26713.4.6 Sexual Addiction 26813.4.7 Cigarette and Alcohol Addiction 26813.4.8 Status Expressive Addiction 26913.4.9 Workaholic Addiction 26913.5 Conclusion 269References 27014 A Custom Cluster Design With Raspberry Pi for Parallel Programming and Deployment of Private Cloud 273Sukesh, B., Venkatesh, K. and Srinivas, L.N.B.14.1 Introduction 27414.2 Cluster Design with Raspberry Pi 27614.2.1 Assembling Materials for Implementing Cluster 27614.2.1.1 Raspberry Pi4 27714.2.1.2 RPi 4 Model B Specifications 27714.2.2 Setting Up Cluster 27814.2.2.1 Installing Raspbian and Configuring Master Node 27914.2.2.2 Installing MPICH and MPI4PY 27914.2.2.3 Cloning the Slave Nodes 27914.3 Parallel Computing and MPI on Raspberry Pi Cluster 27914.4 Deployment of Private Cloud on Raspberry Pi Cluster 28114.4.1 NextCloud Software 28114.5 Implementation 28114.5.1 NextCloud on RPi Cluster 28114.5.2 Parallel Computing on RPi Cluster 28214.6 Results and Discussions 28614.7 Conclusion 287References 28715 Energy Efficient Load Balancing Technique for Distributed Data Transmission Using Edge Computing 289Karthikeyan, K. and Madhavan, P.15.1 Introduction 29015.2 Energy Efficiency Offloading Data Transmission 29015.2.1 Web-Based Offloading 29115.3 Energy Harvesting 29115.3.1 LODCO Algorithm 29215.4 User-Level Online Offloading Framework (ULOOF) 29315.5 Frequency Scaling 29415.6 Computation Offloading and Resource Allocation 29515.7 Communication Technology 29615.8 Ultra-Dense Network 29715.9 Conclusion 299References 29916 Blockchain-Based SDR Signature Scheme With Time-Stamp 303Swathi Singh, Divya Satish and Sree Rathna Lakshmi16.1 Introduction 30316.2 Literature Study 30416.2.1 Signatures With Hashes 30416.2.2 Signature Scheme With Server Support 30516.2.3 Signatures Scheme Based on Interaction 30516.3 Methodology 30616.3.1 Preliminaries 30616.3.1.1 Hash Trees 30616.3.1.2 Chains of Hashes 30616.3.2 Interactive Hash-Based Signature Scheme 30716.3.3 Significant Properties of Hash-Based Signature Scheme 30916.3.4 Proposed SDR Scheme Structure 31016.3.4.1 One-Time Keys 31016.3.4.2 Server Behavior Authentication 31016.3.4.3 Pre-Authentication by Repository 31116.4 SDR Signature Scheme 31116.4.1 Pre-Requisites 31116.4.2 Key Generation Algorithm 31216.4.2.1 Server 31316.4.3 Sign Algorithm 31316.4.3.1 Signer 31316.4.3.2 Server 31316.4.3.3 Repository 31416.4.4 Verification Algorithm 31416.5 Supportive Theory 31516.5.1 Signing Algorithm Supported by Server 31516.5.2 Repository Deployment 31616.5.3 SDR Signature Scheme Setup 31616.5.4 Results and Observation 31616.6 Conclusion 317References 317Index 321

O autorze

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.