IoT and Analytics in Renewable Energy Systems, Volume II: AI, ML and IoT Deployment in Sustainable Smart Cities

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Editors
Contributors
Chapter 1 Efficient Solutions From Smart Healthcare Ecosystem in the 21st Century – A Brief Study
	1.1 Introduction
	1.2 Healthcare Devices
	1.3 Influence of IoT, AI and Big Data
	1.4 Data Protection and Privacy
	1.5 Conclusion
	Bibliography
Chapter 2 IoT-Based Vehicle Monitoring System
	2.1 Introduction
	2.2 Related Work
	2.3 Proposed System
		2.3.1 App Creation Through MIT App Inventor
		2.3.2 Firebase Realtime Database Code for Storing Username and Password for the Application
	2.4 Results
		2.4.1 Thingspeak Output
		2.4.2 App Outputs
		2.4.3 Firebase Realtime Database Storage for Username and Password
	2.5 Conclusion and Future Works
	References
Chapter 3 G-GET: A Donation App to Reduce Poverty
	3.1 Introduction
	3.2 Literature Review
	3.3 Proposed Solution
	3.4 Working
		3.4.1 WANT HELP
		3.4.2 WANT TO HELP
		3.4.3 System Architecture
	3.5 Technologies Used
	3.6 Future Work
	3.7 Conclusion
Chapter 4 Enhanced K-Means with Automated k Estimation and Outlier Detection Using Auto-Encoder Neural Network
	4.1 Introduction: Background and Driving Forces
		4.1.1 Within-Cluster Sum of Squares
		4.1.2 The Traditional "Elbow" Method
		4.1.3 Auto-Encoders
		4.1.4 Silhouette Score
	4.2 Related Work
	4.3 Proposed Algorithm
		4.3.1 Naïve-Sharding Initialization
		4.3.2 Automating the Process of Detecting Optimal k-Value
		4.3.3 Noise Detection Using Auto-Encoders
			4.3.3.1 Auto-Encoders
			4.3.3.2 Anomaly Detection
	4.4 Experiment Results
	4.5 Conclusion
	References
Chapter 5 LPG Leakage Detector
	5.1 Introduction
	5.2 Motivation
	5.3 Description
	5.4 Applications
	5.5 Circuit Diagram
	5.6 Components Explanation
	5.7 Working Principle of Our Study
	5.8 Code
	5.9 Result and Inference
	5.10 Conclusion
	5.11 Future Scope
	Bibliography
Chapter 6 IoT-Based Intelligent Garbage Monitoring Management System to Catalyse Farming
	6.1 Introduction
	6.2 Need
	6.3 Background
	6.4 Literature Survey
	6.5 Objectives
	6.6 Motivation
	6.7 Goals
	6.8 Implementation
	6.9 Description
	6.10 Results and Discussions
	6.11 Summary
	6.12 Conclusion
	References
Chapter 7 IoT-Based Wildfire Detection and Monitoring System Using Predictive Analytics
	7.1 Introduction
	7.2 Related Work
	7.3 Proposed System
		7.3.1 Hardware Architecture
		7.3.2 Software Architecture
		7.3.3 Predictive Analysis
	7.4 Results and Discussion
		7.4.1 Hardware System
		7.4.2 Software System
		7.4.3 Predictive Analysis
	7.5 Future Work
	7.6 Conclusion
	References
Chapter 8 Rainfall Prediction Model Using Artificial Intelligence Techniques
	8.1 Introduction
	8.2 Taxonomy of Rainfall Prediction Terms
		8.2.1 Long-Term Prediction System
		8.2.2 Short-Term Prediction System
	8.3 Technologies Adopted for Rainfall Prediction Model
		8.3.1 Machine Learning-Based Models
			8.3.1.1 The ARIMA (Auto-Regressive Integrated Moving Rate) Model
			8.3.1.2 Support Vector Machines
			8.3.1.3 Support Vector Regression
			8.3.1.4 Random Forest
		8.3.2 Neural Networks and Deep Learning-Based Predictions Models
			8.3.2.1 Artificial Neural Network
			8.3.2.2 Back-Propagation Neural Network (BPNN)
			8.3.2.3 Layer Recurrent Network
			8.3.2.4 Multilayer Perceptron (MLP)
			8.3.2.5 Convolutional Neural Networks (CNNs)
			8.3.2.6 Long Short-Term Memory (LSTM)
	8.4 Preprocessing Techniques in Rainfall Prediction System
		8.4.1 Downsampling
		8.4.2 Principal Component Analysis
		8.4.3 Inverse Distance Weighting
	8.5 Performance Measures in Rainfall Prediction Modules
		8.5.1 Root Mean Square Error (RMSE)
		8.5.2 Normal Standard Error (NSE)
		8.5.3 Threat Score (TS)
	8.6 Analysis of Existing Works
		8.6.1 Publication Statistics
		8.6.2 Country-Wise Systems
	8.7 Datasets for Rainfall Prediction Model
		8.7.1 Modules with Weather Numerical Dataset
		8.7.2 Modules with Fusion of GIS and Weather Numerical Dataset
	8.8 Conclusion
	Bibliography
Chapter 9 Intelligent Coconut Harvesting System
	9.1 Introduction
		9.1.1 Background
		9.1.2 Motivation
		9.1.3 Objectives
	9.2 Proposed Method
	9.3 Implementation Modules
		9.3.1 Coconut Harvesting Schedule
	9.4 Intelligent Coconut Harvesting System Working Model
		9.4.1 Classification of Ground Level Before Harvesting Coconuts
		9.4.2 The Process
		9.4.3 Measure Economic Feasibility of Harvesting
		9.4.4 Existing Harvesting Methods
		9.4.5 Method of Harvest Task to be Automated
	9.5 Algorithm
	9.6 Pseudo Code
	9.7 Conclusion
	9.8 Future Work
	Bibliography
Chapter 10 IoT-Based Live Ambulance: Management and Tracking System
	10.1 Introduction
	10.2 Related Works
	10.3 Proposed System
		10.3.1 Flow Adopted for Designing the System
	10.4 Result and Discussion
	10.5 Conclusion and Future Scope
	References
Chapter 11 Smart Robot Car for Industrial Internet of Things
	11.1 Introduction
	11.2 Background
		11.2.1 Literature Survey
		11.2.2 Problem Statement
		11.2.3 Scope of the Work
	11.3 Design and Implementation of the System
		11.3.1 Block Diagram Analysis
		11.3.2 Architecture Analysis
		11.3.3 Algorithm
	11.4 Software Implementation
		11.4.1 Arduino Implementation
		11.4.2 ThingSpeak Implementation
		11.4.3 Google Firebase and Node-RED Implementation
		11.4.4 Android Implementation
	11.5 Hardware Implementation
		11.5.1 Mechanism
	11.6 Results and Discussion
	11.7 Conclusion and Future Enhancement
	References
Chapter 12 IoT-Based Monitoring System with Machine Learning Analytics of Transformer: Mini Review
	12.1 Introduction
	12.2 Transformer Health Index Development and Threshold Values Setting with the Help of Machine Learning
	12.3 IoT-Based Transformer Monitoring
	12.4 Conclusion
	References
Chapter 13 Design of Earthquake Alarm
	13.1 Introduction
		13.1.1 Motivation
	13.2 Description
	13.3 Components Information
		13.3.1 Piezoelectric Sensor
		13.3.2 IC 555
		13.3.3 Transistor (BC547)
	13.4 Working Principle
	13.5 Results
	13.6 Future Scope
	13.7 Conclusion
	Bibliography
Chapter 14 Energy Demand and Flexibility of Energy Supply: A Case Study
	14.1 Introduction
	14.2 Literature Review
	14.3 Materials and Methods
	14.4 Thermotical Expression
	14.5 Considerations in Econometrics
		14.5.1 Data Overview
		14.5.2 Problems with Using an Aggregate Energy Measure
		14.5.3 Estimation and Outcomes Calculation
	14.6 Conclusion
	References
Chapter 15 Internet of Things-Based Toddler Security Monitoring and Management System
	15.1 Introduction
	15.2 Literature Survey
	15.3 Proposed System
		15.3.1 Face Detection Module (FDM)
		15.3.2 Face Detection Module: Implementation
		15.3.3 Face Recognition Module (FRM)
		15.3.4 FRM Implementation
		15.3.5 Door Detection Module (DDM)
		15.3.6 Mobile Application
	15.4 Conclusion
	References
Chapter 16 Adaptive Traffic Control
	16.1 Introduction
	16.2 Literature Survey
	16.3 Proposed Methodology
		16.3.1 System Design
		16.3.2 Implementation
		16.3.3 Results and Discussion
	16.4 Conclusion
	Bibliography
Chapter 17 Genuine Investments for Economic Energy Outputs
	17.1 Introduction
	17.2 Literature and Background
		17.2.1 Resource Curse
		17.2.2 Sustainability
	17.3 Model and Methodology
		17.3.1 Theoretical Model
		17.3.2 Empirical Strategy & Empirical Model
		17.3.3 Data
	17.4 Relationships to Be Expected
	17.5 Data Limitations
		17.5.1 Estimation and Results
	17.6 Potential Weakness
	17.7 Conclusion
	References
Chapter 18 IoT-Based Power Theft Detection: Mini Review
	18.1 A Comparative Study of Multiple Machine Learning Techniques for Detecting Electricity Theft
	18.2 Power Theft Identification and Alert System Using IoT
	18.3 Energy Monitoring and Theft System Using IoT
	18.4 Hardware Implementation of Power Theft Detection System and Disconnection Using Smart Meter
	18.5 Theft Detection Using Other Technologies
	18.6 Conclusion
	References
Chapter 19 Design and Implementation of Bluetooth-Enabled Home Automation System
	Abbreviations
	19.1 Introduction
	19.2 Home Automation Using Different Modules
	19.3 Advanced Home Automation Systems
	19.4 Home Automation Using Bluetooth Module HC-05
	19.5 Power Supply
	19.6 Main Circuit
	19.7 Output
	19.8 Conclusion
	References
Chapter 20 IoT-Based Smart Electricity Management
	20.1 Introduction
	20.2 Related Work
	20.3 Proposed System
		20.3.1 App Creation Through MIT App Inventor Blocks
		20.3.2 App Development Screen
		20.3.3 Firebase Realtime Database Code for Storing Username and Password for the Application
	20.4 Results
		20.4.1 Arduino Output in Serial Monitor
		20.4.2 NodeMCU Output in Serial Monitor
		20.4.3 ThingSpeak Output
		20.4.4 App Outputs
		20.4.5 Firebase Realtime Database Storage for Username and Password
	20.5 Conclusion and Future Works
	References
Chapter 21 IoT-Based COVID-19 Patient Monitoring System
	21.1 Introduction
	21.2 Literature Review
	21.3 Proposed Model
	21.4 Block Diagram
	21.5 Circuit Diagram
	21.6 Implementation
	21.7 Conclusion
	References
Chapter 22 Interleaved Cubic Boost Converter
	22.1 Introduction
	22.2 Circuit Description
	22.3 Modes of Operation
		22.3.1 Mode 1 (S1-ON; S2-ON)
		22.3.2 Mode 2 (S1-ON; S2-OFF)
		22.3.3 Mode 3 (S1-ON; S2-ON)
		22.3.4 Mode 4 (S1-OFF; S2-ON)
	22.4 Voltage Gain and Design Details
	22.5 Switch Ratings
	22.6 Diode Ratings
	22.7 Design Expressions
	22.8 Simulation and Inference
	22.9 Conclusion
	References
Chapter 23 Emerging Role of AI, ML and IoT in Modern Sustainable Energy Management
	23.1 Introduction
	23.2 Role of Artificial Intelligence (AI) in the Renewable Energy Sector
		23.2.1 Merits
		23.2.2 Demerits
		23.2.3 Applications of Artificial Intelligence
			23.2.3.1 Improved Integration of Microgrids
			23.2.3.2 Intelligent Centralization of Control Centres
			23.2.3.3 Smart Grid with Intelligent Energy Storage (IES)
			23.2.3.4 Supply Chain Management in Biogas Plants
			23.2.3.5 Improved Safety and Reliability
			23.2.3.6 Market Expansion
	23.3 Role of Machine Learning (ML) in the Renewable Energy Sector
		23.3.1 Merits
			23.3.1.1 Solar Energy
			23.3.1.2 Wind Energy
		23.3.2 Demerits
		23.3.3 Applications of Machine Learning
			23.3.3.1 Accurately Predict Energy Demand
			23.3.3.2 Optimize Energy Consumption
			23.3.3.3 Prediction of Accurate Prices of Energy
			23.3.3.4 Predict Merit Order of Energy Prices
			23.3.3.5 Price Optimization Through Better Trading
			23.3.3.6 Modelling of Bioenergy Plants
			23.3.3.7 Predicting Malfunction of Wind Turbines
			23.3.3.8 Increase Power Plant Profitability with Optimized Scheduling and Pricing
			23.3.3.9 Reduce the Potential for Human Error
			23.3.3.10 Predict Customer Lifetime Value (CLV)
			23.3.3.11 Predict the Probability of Winning a Customer
			23.3.3.12 Make Incredibly Specific Offers to Customers
			23.3.3.13 Reduction in Consumer Turndown
	23.4 Role of the Internet of Things in the Renewable Energy Sector
		23.4.1 Types of Internet of Things
			23.4.1.1 Actuators
			23.4.1.2 Sensors
			23.4.1.3 Communication Technologies
		23.4.2 Merits
			23.4.2.1 Intelligent Grid
			23.4.2.2 Sustainability
			23.4.2.3 Cost-Cutting and Data Management
			23.4.2.4 New Business Prospects
			23.4.2.5 Advanced Analytics
		23.4.3 Demerits
		23.4.4 Applications of Internet of Things
			23.4.4.1 Smart Grid Management
			23.4.4.2 Efficient Smart Meters
			23.4.4.3 Balanced Distribution Systems
			23.4.4.4 Higher Cost-Efficiency
			23.4.4.5 Better Transparency in the Way People Use Electricity
			23.4.4.6 Supreme Residential Solutions
			23.4.4.7 Monitoring Biogas Production
			23.4.4.8 Better Control, Automation and Futuristic Possibilities
	23.5 Future Scopes in AI, ML and IoT in the Renewable Energy Sector
	23.6 Conclusion
	Bibliography
Chapter 24 Automated Water Dispenser – A Hygiene Solution for Pandemic
	24.1 Introduction
	24.2 Methodology
	24.3 Simulation Model: Automated Filling of Water in Tanks
	24.4 Circuit Modelling in TinkerCAD
	24.5 Experimental Setup
	24.6 Results and Discussion
	24.7 Conclusion and Future Scope
	References
Chapter 25 Review of IoT-Based Smart Waste Management Systems
	25.1 Introduction
	25.2 Smart Segregation of Waste Using Embedded System Solutions
	25.3 Cloud Integration and IoT-Based Solutions
	25.4 Conclusion
	References
Chapter 26 Cyber Security in Smart Energy Networks
	26.1 Introduction
	26.2 Communication Network Architecture in Smart Grids
		26.2.1 Producing Section
		26.2.2 Transfer Section
		26.2.3 Distribution Section
		26.2.4 Consumer Section
		26.2.5 Market Section
		26.2.6 Service Providers Section
		26.2.7 Operation Section
	26.3 Wireless Sensor Network
		26.3.1 Zigbee
		26.3.2 Low-Power Wi-Fi
	26.4 Standards and Protocols of Communication in Smart Networks
		26.4.1 Protocol DNP3
		26.4.2 Protocol IEC61850
	26.5 Smart Network Security Goals
		26.5.1 Availability
		26.5.2 Integrity
		26.5.3 Confidentiality
	26.6 The Most Important Cyber-Attacks in Smart Networks
	26.7 Intelligent Measuring Equipment and Home Networks
	26.8 Security and Privacy of WSN-Based Consumer Applications
	26.9 Conclusion
	References
Index