Pragmatic Internet of Everything (IOE) for Smart Cities : 360-Degree Perspective / Satya Prakash Yadav, Sansar Singh Chauhan, and Sanjeev Kumar Pippal.

Format:

Book

Author/Creator:

Yadav, Satya Prakash, author.

Chauhan, Sansar Singh, author.

Pippal, Sanjeev Kumar, author.

Language:

English

Subjects (All):

Internet--Social aspects.

Internet.

Problem solving--Data processing.

Problem solving.

Smart cities.

Physical Description:

1 online resource (211 pages)

Edition:

First edition.

Place of Publication:

Singapore : Bentham Science Publishers Ltd., [2023]

Summary:

Pragmatic Internet of Everything (IOE) has emerged as a powerful paradigm for representing and solving complex problems. This reference demonstrates how to coordinate behaviour among a collection of semi-autonomous problem-solving agents: how they can coordinate their knowledge, goals and plans to act together, to solve joint problems, or to make individually or globally rational decisions in the face of uncertainty and multiple, conflicting perspectives. The book presents a collection of articles surveying several major recent developments in Pragmatic Internet of Everything (IOE). The book focuses on issues and challenges that arise in building IOE systems for smart cities in real-world settings. It also presents solutions to the issues faced by system architects. The synthesis of recent thinking, both theoretical and applied, on major IOE problems makes this essential reading for anyone involved in the design and planning of IOT systems for smart cities. Key Features - Summarizes available literature and practical ventures with references - Merges different perspectives on IoT technology thereby giving a 360-degree perspective to the reader - Gives some tips for implementation of practical ventures in this space - Includes an analysis of information gathered from citizens of smart cities.

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 &amp

Comprehensive Review of Practical and Literal Models

Debajit Mishra1,*, Muskan Jindal2 and Dimitrios A. Karras3

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

Categorizing Obstacles in the Implementation of Smart Cities with Probable Solution Models

Debajit Mishra1,*, Sumedha Jain2, Muskan Jindal3 and Satya Prakash Yadav4

1.1. Cloud Computing

1.2. Fog Computing

1.3. Edge Computing

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

Understanding the Future of Smart Cities from Technological and Commercial Point of View

Arushi Kapoor1,*, Vartika Agarwal2, Muskan Jindal3 and Shashank Awasthi4

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

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

IoT Enabled Energy Optimization Through an Intelligent Home Automation

N. Chitra Kiran1,*, J. Viswanatha Rao2, Sagaya Aurelia3, M. G. Skanda4 and M. Lakshminarayana5

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

Garbage Management and Monitoring System Using IOT Applications

A. Kumaraswamy1,*, Chandra Sekhar Kolli2, Sagaya Aurelia3, P. Vasantha Kumar4 and M. Lakshminarayana5

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

Power Generation Prediction in Solar PV system by Machine Learning Approach

Rajesh Kumar Patnaik1,*, Chandra Sekhar Kolli2, N. Mohan3, S. Kirubakaran4 and Ranjan Walia5

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

An Efficient Framework and Implementation of a Weather Prediction System

Smitha Shekar1,*, G. Harish1, K. N. Asha1 and K. P. Asha Rani1

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

Hybrid Machine Learning Techniques for Secure IoT Applications

Udayabalan Balasingam1,*, S. B. Prathibha2, K. R. Swetha3, C. Muruganandam4 and Urmila R. Pol5

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

REFERENCES.

Subject Index.

Notes:

Includes bibliographical references.

Description based on publisher supplied metadata and other sources.

Description based on print version record.

ISBN:

9789815136173

9815136178

OCLC:

1405899216