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Table of Contents
Cover
Title
Copyright
End User License Agreement
Contents
Preface
List of Contributors
Investigating the Features of Physical Layer Structure for Employment of Smart City Models
Rishabh Jain1,*, Srishti Jain2, Muskan Jindal3 and Mahaveer Singh Naruka4
1. INTRODUCTION
2. FUTURE OF DHOLERA SIR SMART CITY
3. DEVELOPMENT OF DHOLERA SIR
4. EMPLOYMENT FRAMEWORK IN DHOLERA
5. FUTURISTIC OPTIONS
6. PROGRAM CODE
CONCLUSION
REFERENCES
Pithy &
Comprehensive Review of Practical and Literal Models
Debajit Mishra1,*, Muskan Jindal2 and Dimitrios A. Karras3
1. INTRODUCTION
2. COMPREHENSIVE ANALYSIS OF PREVIOUS WORKS
3. SMART CITY COMPONENTS
3.1. Smart Agriculture
3.2. Smart City Services
3.3. Smart Energy
3.4. Smart Health
3.5. Smart Home
3.6. Smart Industry
3.7. Smart Infrastructure
3.8. Smart Transportation
4. INTERNET OF THINGS (IOT) FOR SMART CITIES (SCS)
4.1. IoT Architectures for SCs
5. STATE OF THE ART: SMART CITY (SC) MODELS
6. THE CASE OF VIENNA
6.1. Subsystems and Stakeholders in the Vienna Smart City Initiative
6.2. Vienna Smart City Projects and Dimensions
6.3. Global Trends and Urban Challenges for Vienna
6.4. Global Vision and Guidelines
CONCLUSION AND FUTURE SCOPE
REFERENCES
Categorizing Obstacles in the Implementation of Smart Cities with Probable Solution Models
Debajit Mishra1,*, Sumedha Jain2, Muskan Jindal3 and Satya Prakash Yadav4
1. INTRODUCTION
1.1. Cloud Computing
1.2. Fog Computing
1.3. Edge Computing
2. COMPREHENSIVE ANALYSIS OF PREVIOUS WORKS
3. WIDE PURVIEW OF PROBLEMS IN SMART CITY
3.1. Technical Challenges in Smart City Plan
3.2. Financial Challenges in Smart City Plan
3.3. Administrative and Governance Challenges in Smart City Plan
3.4. Location Endemic Purview.
3.5. Miscellaneous Issues
4. CASE STUDIES
4.1. Fujisawa
4.2. Santander
4.3. Vienna
CONCLUSION AND FUTURE SCOPE
REFERENCES
Understanding the Future of Smart Cities from Technological and Commercial Point of View
Arushi Kapoor1,*, Vartika Agarwal2, Muskan Jindal3 and Shashank Awasthi4
1. INTRODUCTION
1.1. Components and Characteristic of Smart Cities
1.2. Internet of Things and its Application in Smart Cities
1.3. The Age of Smart Cities
1.4. Smart Cities Mission of India
1.5. Internet of Things (IoT)
1.6. Framework for ROI
1.6.1. Compare ROI of IoT based Projects
1.6.2. Comparing ROI for Different European Countries
1.7. Copenhagen
1.8. Helsinki
1.9. Brussels
1.10. Vienna
1.11. Contribution of Smart Cities in Urbanisation
1.11.1. Better Public Security
1.11.2. Reducing Travel Time
1.11.3. Better Health Care Facilities
1.11.4. Lower Environmental Impact
1.11.5. Smart Cities Can Create Urban Communities
1.12. Smart Cities as a Way of Improving Commercial and Technological Development
1.12.1. Smart Can Provide Better Employment Opportunities to Its Citizens
1.12.2. Smart Cities Open New Avenues for Partnerships Between Government and Private Entities and also Increase Private Sector Participation
1.12.3. Increased Digital Equity
1.12.4. Better Infrastructure
1.12.5. Increasing Workforce Engagement
REFERENCES
Dynamic Involvement of Deep Learning and Big Data in Smart Cities
Nidhi Shah1,*, Arushi Kapoor2, Namith Gupta3, Vartika Agarwal4 and Muskan Jindal4
INTRODUCTION
INTERNET OF THINGS (IOT)
Deep Learning
Deep Learning Architecture
Deep Learning Models and Algorithms
Applications of Deep Learning
Use of Deep Learning in Smart City Application
Smart Home
Smart Healthcare
Smart Environment
Smart Transportation.
Challenges of Deep Learning in Smart Cities
Future Trends in Smart Cities using Deep Learning
CONCLUSION
REFERENCES
IoT Enabled Energy Optimization Through an Intelligent Home Automation
N. Chitra Kiran1,*, J. Viswanatha Rao2, Sagaya Aurelia3, M. G. Skanda4 and M. Lakshminarayana5
1. INTRODUCTION
2. BACKGROUND AND MOTIVATION
3. LITERATURE REVIEW
4. PROPOSED INTELLIGENT AUTOMATION SYSTEM
5. SIMULATION OF AN INTELLIGENT AUTOMATION SYSTEM USING CISCO PACKET TRACER
5.1. Description of Software
Algorithm
6. HARDWARE IMPLEMENTATION
6.1. Wi-Fi Module Interface Circuit
7. NOVELTY OF THE PROPOSED METHOD
CONCLUSION
REFERENCES
Garbage Management and Monitoring System Using IOT Applications
A. Kumaraswamy1,*, Chandra Sekhar Kolli2, Sagaya Aurelia3, P. Vasantha Kumar4 and M. Lakshminarayana5
1. INTRODUCTION
2. LITERATURE SURVEY
3. PROPOSED SYSTEM
3.1. Proposed Module-1
3.2. Proposed Module-2
3.2.1. Sensible Dumpsters
3.2.2. Sensor Usage
3.2.3. Wi-Fi Module
3.2.4. Arduino-Uno Controller
3.2.5. Management and Control System
3.2.6. Transport System
3.2.7. Webpage of Garbage Management System
4. NOVELTY OF THIS PROPOSED WORK
CONCLUSION
REFERENCES
Power Generation Prediction in Solar PV system by Machine Learning Approach
Rajesh Kumar Patnaik1,*, Chandra Sekhar Kolli2, N. Mohan3, S. Kirubakaran4 and Ranjan Walia5
1. INTRODUCTION
2. RELATED WORKS
3. ISSUES IN ARTIFICIAL NEURAL TRAINING
3.1. Weights Initial Value
3.2. Rate of Learning
3.3. Oversampling or Overfitting
3.4. Scaling of the Input
4. IMPORTANT ELEMENTS IN ARTIFICIAL NEURAL NETWORK (ANN) FOR PV
4.1. Feed-forward ANN Network
4.2. Feed-backward ANN Network
The Following Algorithm 1 Depicts the Simplified Operation of an ANN.
Algorithm 2: Proposed Functionality of PV Prediction System
Machine Learning Algorithm 3
5. PROPOSED METHODOLOGY
5.1. Current Sensing Unit
5.2. Voltage Sensing Unit
5.3. The PV Generation Prediction Process and Implementation with Sensor Outputs
6. VARIABILITY IN DATA
6.1. Data Processing
7. RESULTS
7.1. Temperature Values
7.2. Power Generated Values
8. THE OUTCOME OF THIS METHODOLOGY
CONCLUSION
REFERENCES
An Efficient Framework and Implementation of a Weather Prediction System
Smitha Shekar1,*, G. Harish1, K. N. Asha1 and K. P. Asha Rani1
1. INTRODUCTION
2. RELATED WORKS
3. PROPOSED SYSTEM
3.1. System Architecture
3.1.1. Node-MCU
3.1.2. DHT22 Humidity Sensors
3.1.3. BMP Sensors
3.1.4. Rain Intrusion Sensors (FC37)
3.2. Artificial Neural Network (ANN) and Components
3.2.1. Variations in ANN
3.2.2. ANN Methodology and its Background
3.2.3. Analytical Eorking of ANN
3.2.4. Types of Manipulation in Intermediate Layers
4. DATA PROCUREMENT AND ANALYSIS
Algorithm 2: Proposed Functionality of Smart Weather Prediction System
5. HARDWARE IMPLEMENTATION AND SENSOR OUTPUTS
6. NOVELTY OF THIS RESEARCH WORK
CONCLUSION
REFERENCES
Hybrid Machine Learning Techniques for Secure IoT Applications
Udayabalan Balasingam1,*, S. B. Prathibha2, K. R. Swetha3, C. Muruganandam4 and Urmila R. Pol5
1. INTRODUCTION
2. ML ALGORITHM
2.1. Supervised and Unsupervised Learning
3. INTRODUCTION TO IOT
4. METHODS OF ML FOR THE LEARNING
4.1. Improved Understanding Techniques
4.2. Acquiring Implicit Data
4.3. ML and Repetition
5. MODELS AND METHODS FOR HYBRID ML
6. HYBRID ALGORITHMS IN IOT APPLICATIONS
6.1. Energy
6.2. Routing
6.3. In Living
6.4. Industry
7. ML TECHNIQUES IN IOT SECURITY
8. DISCUSSION
CONCLUSION
REFERENCES.
Subject Index.
Title
Copyright
End User License Agreement
Contents
Preface
List of Contributors
Investigating the Features of Physical Layer Structure for Employment of Smart City Models
Rishabh Jain1,*, Srishti Jain2, Muskan Jindal3 and Mahaveer Singh Naruka4
1. INTRODUCTION
2. FUTURE OF DHOLERA SIR SMART CITY
3. DEVELOPMENT OF DHOLERA SIR
4. EMPLOYMENT FRAMEWORK IN DHOLERA
5. FUTURISTIC OPTIONS
6. PROGRAM CODE
CONCLUSION
REFERENCES
Pithy &
Comprehensive Review of Practical and Literal Models
Debajit Mishra1,*, Muskan Jindal2 and Dimitrios A. Karras3
1. INTRODUCTION
2. COMPREHENSIVE ANALYSIS OF PREVIOUS WORKS
3. SMART CITY COMPONENTS
3.1. Smart Agriculture
3.2. Smart City Services
3.3. Smart Energy
3.4. Smart Health
3.5. Smart Home
3.6. Smart Industry
3.7. Smart Infrastructure
3.8. Smart Transportation
4. INTERNET OF THINGS (IOT) FOR SMART CITIES (SCS)
4.1. IoT Architectures for SCs
5. STATE OF THE ART: SMART CITY (SC) MODELS
6. THE CASE OF VIENNA
6.1. Subsystems and Stakeholders in the Vienna Smart City Initiative
6.2. Vienna Smart City Projects and Dimensions
6.3. Global Trends and Urban Challenges for Vienna
6.4. Global Vision and Guidelines
CONCLUSION AND FUTURE SCOPE
REFERENCES
Categorizing Obstacles in the Implementation of Smart Cities with Probable Solution Models
Debajit Mishra1,*, Sumedha Jain2, Muskan Jindal3 and Satya Prakash Yadav4
1. INTRODUCTION
1.1. Cloud Computing
1.2. Fog Computing
1.3. Edge Computing
2. COMPREHENSIVE ANALYSIS OF PREVIOUS WORKS
3. WIDE PURVIEW OF PROBLEMS IN SMART CITY
3.1. Technical Challenges in Smart City Plan
3.2. Financial Challenges in Smart City Plan
3.3. Administrative and Governance Challenges in Smart City Plan
3.4. Location Endemic Purview.
3.5. Miscellaneous Issues
4. CASE STUDIES
4.1. Fujisawa
4.2. Santander
4.3. Vienna
CONCLUSION AND FUTURE SCOPE
REFERENCES
Understanding the Future of Smart Cities from Technological and Commercial Point of View
Arushi Kapoor1,*, Vartika Agarwal2, Muskan Jindal3 and Shashank Awasthi4
1. INTRODUCTION
1.1. Components and Characteristic of Smart Cities
1.2. Internet of Things and its Application in Smart Cities
1.3. The Age of Smart Cities
1.4. Smart Cities Mission of India
1.5. Internet of Things (IoT)
1.6. Framework for ROI
1.6.1. Compare ROI of IoT based Projects
1.6.2. Comparing ROI for Different European Countries
1.7. Copenhagen
1.8. Helsinki
1.9. Brussels
1.10. Vienna
1.11. Contribution of Smart Cities in Urbanisation
1.11.1. Better Public Security
1.11.2. Reducing Travel Time
1.11.3. Better Health Care Facilities
1.11.4. Lower Environmental Impact
1.11.5. Smart Cities Can Create Urban Communities
1.12. Smart Cities as a Way of Improving Commercial and Technological Development
1.12.1. Smart Can Provide Better Employment Opportunities to Its Citizens
1.12.2. Smart Cities Open New Avenues for Partnerships Between Government and Private Entities and also Increase Private Sector Participation
1.12.3. Increased Digital Equity
1.12.4. Better Infrastructure
1.12.5. Increasing Workforce Engagement
REFERENCES
Dynamic Involvement of Deep Learning and Big Data in Smart Cities
Nidhi Shah1,*, Arushi Kapoor2, Namith Gupta3, Vartika Agarwal4 and Muskan Jindal4
INTRODUCTION
INTERNET OF THINGS (IOT)
Deep Learning
Deep Learning Architecture
Deep Learning Models and Algorithms
Applications of Deep Learning
Use of Deep Learning in Smart City Application
Smart Home
Smart Healthcare
Smart Environment
Smart Transportation.
Challenges of Deep Learning in Smart Cities
Future Trends in Smart Cities using Deep Learning
CONCLUSION
REFERENCES
IoT Enabled Energy Optimization Through an Intelligent Home Automation
N. Chitra Kiran1,*, J. Viswanatha Rao2, Sagaya Aurelia3, M. G. Skanda4 and M. Lakshminarayana5
1. INTRODUCTION
2. BACKGROUND AND MOTIVATION
3. LITERATURE REVIEW
4. PROPOSED INTELLIGENT AUTOMATION SYSTEM
5. SIMULATION OF AN INTELLIGENT AUTOMATION SYSTEM USING CISCO PACKET TRACER
5.1. Description of Software
Algorithm
6. HARDWARE IMPLEMENTATION
6.1. Wi-Fi Module Interface Circuit
7. NOVELTY OF THE PROPOSED METHOD
CONCLUSION
REFERENCES
Garbage Management and Monitoring System Using IOT Applications
A. Kumaraswamy1,*, Chandra Sekhar Kolli2, Sagaya Aurelia3, P. Vasantha Kumar4 and M. Lakshminarayana5
1. INTRODUCTION
2. LITERATURE SURVEY
3. PROPOSED SYSTEM
3.1. Proposed Module-1
3.2. Proposed Module-2
3.2.1. Sensible Dumpsters
3.2.2. Sensor Usage
3.2.3. Wi-Fi Module
3.2.4. Arduino-Uno Controller
3.2.5. Management and Control System
3.2.6. Transport System
3.2.7. Webpage of Garbage Management System
4. NOVELTY OF THIS PROPOSED WORK
CONCLUSION
REFERENCES
Power Generation Prediction in Solar PV system by Machine Learning Approach
Rajesh Kumar Patnaik1,*, Chandra Sekhar Kolli2, N. Mohan3, S. Kirubakaran4 and Ranjan Walia5
1. INTRODUCTION
2. RELATED WORKS
3. ISSUES IN ARTIFICIAL NEURAL TRAINING
3.1. Weights Initial Value
3.2. Rate of Learning
3.3. Oversampling or Overfitting
3.4. Scaling of the Input
4. IMPORTANT ELEMENTS IN ARTIFICIAL NEURAL NETWORK (ANN) FOR PV
4.1. Feed-forward ANN Network
4.2. Feed-backward ANN Network
The Following Algorithm 1 Depicts the Simplified Operation of an ANN.
Algorithm 2: Proposed Functionality of PV Prediction System
Machine Learning Algorithm 3
5. PROPOSED METHODOLOGY
5.1. Current Sensing Unit
5.2. Voltage Sensing Unit
5.3. The PV Generation Prediction Process and Implementation with Sensor Outputs
6. VARIABILITY IN DATA
6.1. Data Processing
7. RESULTS
7.1. Temperature Values
7.2. Power Generated Values
8. THE OUTCOME OF THIS METHODOLOGY
CONCLUSION
REFERENCES
An Efficient Framework and Implementation of a Weather Prediction System
Smitha Shekar1,*, G. Harish1, K. N. Asha1 and K. P. Asha Rani1
1. INTRODUCTION
2. RELATED WORKS
3. PROPOSED SYSTEM
3.1. System Architecture
3.1.1. Node-MCU
3.1.2. DHT22 Humidity Sensors
3.1.3. BMP Sensors
3.1.4. Rain Intrusion Sensors (FC37)
3.2. Artificial Neural Network (ANN) and Components
3.2.1. Variations in ANN
3.2.2. ANN Methodology and its Background
3.2.3. Analytical Eorking of ANN
3.2.4. Types of Manipulation in Intermediate Layers
4. DATA PROCUREMENT AND ANALYSIS
Algorithm 2: Proposed Functionality of Smart Weather Prediction System
5. HARDWARE IMPLEMENTATION AND SENSOR OUTPUTS
6. NOVELTY OF THIS RESEARCH WORK
CONCLUSION
REFERENCES
Hybrid Machine Learning Techniques for Secure IoT Applications
Udayabalan Balasingam1,*, S. B. Prathibha2, K. R. Swetha3, C. Muruganandam4 and Urmila R. Pol5
1. INTRODUCTION
2. ML ALGORITHM
2.1. Supervised and Unsupervised Learning
3. INTRODUCTION TO IOT
4. METHODS OF ML FOR THE LEARNING
4.1. Improved Understanding Techniques
4.2. Acquiring Implicit Data
4.3. ML and Repetition
5. MODELS AND METHODS FOR HYBRID ML
6. HYBRID ALGORITHMS IN IOT APPLICATIONS
6.1. Energy
6.2. Routing
6.3. In Living
6.4. Industry
7. ML TECHNIQUES IN IOT SECURITY
8. DISCUSSION
CONCLUSION
REFERENCES.
Subject Index.