Smart Grid in IoT-Enabled Spaces: The Road to Intelligence in Power

✍ Scribed by Fadi Al-Turjman

• Publisher: CRC Press
• Year: 2020
• Tongue: English
• Leaves: 297
• Edition: 1
• Category: Library

✦ Synopsis

Internet of Things (IoT)-enabled spaces have made revolutionary advances in the utility grid. Among these advances, intelligent and energy-efficient services are gaining considerable interest. The use of the smart grid is increasing day after day around us and is not only used in saving energy but also in our daily life for intelligent health, traffic, and even farming systems. The grid enabled with IoT features is also expected to communicate with cellular networks smoothly in the next-generation networks (6G and beyond). This will open the door for other interesting research areas.

In this book, we consider the most significant and emergent research topics in this domain, addressing major issues and challenges in IoT-based solutions proposed for the smart grid. The chapters provide insight on comprehensive topics in IoT-based smart grids, combining technical aspects with the most up-to-date theory. It investigates the grid under varying and potential emerging paradigms such as edge/fog computing, in addition to big data aspects considerations in the IoT era. With comprehensive surveys and case studies, this book explores basic and high-level grid aspects in the emerging smart city paradigm, which makes it especially attractive to researchers, academics, and higher-level students. This authored book can be used by computer science undergraduate and postgraduate students, researchers and practitioners, city administrators, policymakers, and government regulators.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Preface
Author
List of Contributors
Chapter 1 IoT-Enabled Smart Grid: An Overview
1.1 Introduction
1.2 Overview of Related Surveys
1.3 Advanced Metering Infrastructure (AMI) Technology
1.3.1 Smart Meter Internal Structure
1.3.1.1 Microcontroller
1.3.1.2 Power Supply Unit
1.3.1.3 Energy Measurement Unit
1.3.2 Machine Learning in AMI
1.3.2.1 Supervised Learning Algorithms
1.3.2.2 Unsupervised Learning Algorithms
1.3.2.3 Semi-Supervised Learning Algorithms
1.3.3 Wireless Communication in AMI
1.3.3.1 Local Area Network (LAN)
1.3.3.2 Neighborhood Area Network (NAN) and Wide Area Networks (WAN)
1.3.4 Routing Algorithms
1.3.4.1 Delay
1.3.4.2 Security
1.3.4.3 Coverage
1.3.4.4 Scalability
1.3.4.5 Firmware Updates
1.3.4.6 Lifetime
1.3.4.7 Cost
1.3.4.8 Reliability
1.4 Smart Meters and Power Quality
1.4.1 Assessment Parameters
1.4.2 Techniques Embedded in SM for PQ Analysis
1.4.2.1 Wavelet Transform (WT)
1.4.2.2 Fast Fourier Transform (FFT)
1.5 Smart Meters and Power Reliability
1.6 Open Research Issues
1.7 Conclusion
References
Chapter 2 Energy Monitoring in IoT-Based Grid
2.1 Introduction
2.2 Energy Monitoring
2.1.1 Indirect Feedback Systems
2.1.2 Direct Feedback Systems
2.3 Energy-Monitoring Devices
2.3.1 Direct Sensing
2.3.1.1 ILD Sensors
2.3.1.2 RFID
2.3.1.3 Infrared Sensors
2.3.2 Indirect Sensing
2.3.2.1 Magnetic Sensors
2.3.2.2 Acoustic Sensors
2.3.2.3 Light and Piezoelectric Sensors
2.4 Smart Spaces
2.4.1 Online Spaces
2.4.2 Offline Spaces
2.5 Use Cases in Practice
2.5.1 Pilot Projects Using IHD
2.5.1.1. Hydro-One Project
2.5.1.2 CEATI Project
2.5.1.3 Cost Monitoring Project
2.5.1.4 SDG&E Project
2.5.1.5 ECOIS Project
2.5.2 Projects with IHD and Prepay Method
2.5.2.1 M-Power Project
2.5.2.2 Woodstock Hydro
2.5.3 Projects with Integrated IHD/TOU
2.5.3.1 Hydro-One TOU Project
2.5.3.2 Information Display Project
2.5.3.3 Home Energy Efficiency Trial
2.6 Observations and Statistics
2.7 Concluding Remarks
Acknowledgments
References
Chapter 3 Energy Harvesting in IoT-Based Grid
3.1 Introduction
3.2 Literature Review
3.2.1 Smart Grid
3.3 Advancements in the Smart Grid
3.3.1 Benefits of the Smart Grid to Energy Sector
3.4 Metering Technologies
3.4.1 Gain of Smart Meters vs. the Conventional Meter
3.5 Communications Used in Smart Grid
3.5.1 Zigbee
3.5.2 Wireless Mesh
3.5.3 GSM
3.5.4 Cellular Network
3.6 Billing Methods
3.6.1 Net Metering
3.6.2 Feed-In Tariff
3.6.3 Time of Use
3.7 Solar Energy
3.7.1 Solar Panels (Photovoltaic Modules, PV)
3.7.2 Solar Thermal (Concentrated Solar Power, CSP)
3.7.2.1 Dish Engine Technology
3.7.2.2 Parabolic Trough
3.7.2.3 Tower Focal Point-Concentrated Solar Power
3.8 Storage Facilities
3.8.1 Sodium Sulfur (NaS) Batteries
3.8.2 Flywheel Storage Device
3.9 Optimization of Storage Devices
3.10 Connecting Renewable (SOLAR) Energy to the Smart Grid
3.11 Grid Topology
3.11.1 Radial Distribution
3.11.2 Meshed Distribution
3.11.3 Looped Distribution
3.12 Conclusion
References
Chapter 4 Grid Energy Scenario and Storage Systems for the Vehicle-to-Grid Technology: An Overview
4.1 Introduction
4.2 Energy Scenarios in V2G Technology
4.2.1 Storage System in V2G Technology
4.2.1.1 Preferable Batteries for EVs
4.2.1.2 Mechanical Strike
4.2.1.3 Temperature-Stability
4.2.1.4 Storage Control Systems
4.2.1.5 Batteries Permanence
4.2.2 Charging Systems
4.2.2.1 Building Charging Station for Renewable Energies
4.3 System Infrastructure
4.3.1 Strategies
4.3.2 Energy Pricing
4.3.3 System Control and Communication
4.3.4 Smart Grid Modeling
4.3.4.1 Frequency Controller
4.3.4.2 Power System Integration
4.4 Conclusion
List of Abbreviations
References
Chapter 5 Data Traffic in IoT-Based Grid
5.1 Introduction
5.2 Related Work
5.2.1 Static Modeling
5.2.2 Dynamic Modeling
5.3 System Models
5.3.1 Queuing Model
5.3.2 Communication Model
5.3.3 Energy Consumption Model
5.4 The Green FBS Model for Smart Grids
5.5 A Typical Case Study in Smart Grids: e-Mobility
5.5.1 Simulation Setups
5.5.2 Results and Discussions
5.5.2.1 The Impact of Velocity on FBS Performance
5.5.2.2 The Impact of Traffic Load on Energy Consumption
5.6 Conclusion
References
Chapter 6 Mobile Couriers and the Grid
6.1 Introduction
6.2 Related Work
6.3 System Models
6.3.1 Notations
6.3.2 Network Model
6.3.3 Energy Model and Lifetime
6.3.4 Communication Model
6.4 Hybrid Collaborative Path Finder (HCPF)
6.4.1 Chromosome Representation
6.4.2 Generating Initial Population Strategy
6.4.3 Path Planning in HCPF
6.5 Use Case
6.6 Performance Evaluation
6.6.1 Simulation Setup
6.6.2 Experimental Setup and Baseline Approaches
6.6.3 Performance Metrics and Parameters
6.6.4 Experimental Results
6.6.5 Simulation Results
6.7 Conclusion
Note
References
Chapter 7 Combination of GIS and SHM in Prognosis and Diagnosis of Bridges in Earthquake-Prone Locations
7.1 Introduction
7.2 Overview of the Tools
7.1.1 Structural Health Monitoring (SHM)
7.1.2 Geographical Information System (GIS)
7.3 Bridges Performance Assessment
7.3.1 Bridges Performance Assessment Using SHM
7.3.2 Bridges Performance Assessment Using GIS
7.3.3 Bridges Performance Assessment Using SHM-GIS
7.4 Intelligent SHM-GIS Cloud-Based Bridge Monitoring System
7.5 Machine Learning in SHM Application: A Complementary Addition
7.6 Drone-Assisted SHM: A Synergistic Medium
7.7 Conclusion
Notes
References
Chapter 8 Smart Medium Access in Mobile IoT
8.1 Introduction
8.2 Related Work
8.3 Framework Description
8.3.1 Functioning of the Vehicular-Cloud Framework
8.3.2 Registration Phase
8.3.3 Channel Coding Phase
8.4 Results and Discussions
8.5 Concluding Remarks
References
Chapter 9 Smart Parking and the Grid
9.1 Introduction
9.1.1 The Scope of This Chapter
9.1.2 Comparison of Other Surveys
9.2 Smart Parking Systems and Classifications
9.2.1 Centralized-Assisted Smart Parking Systems
9.2.1.1 Parking Guidance and Information System (PGIS)
9.2.1.2 Centralized-Assisted Parking Search (CAPS)
9.2.1.3 Car Park Occupancy Information System (COINS)
9.2.1.4 Agent-Based Guiding System (ABGS)
9.2.1.5 Automated Parking
9.2.2 Distributed-Assisted Smart Parking Systems
9.2.2.1 Transit-Based Information System (TBIS)
9.2.2.2 Opportunistically Assisted Parking Search (OAPS)
9.2.2.3 Mobile Storage Node-Opportunistically Assisted Parking Search (MSN-OAPS)
9.2.3 Non-Assisted Parking Search (NAPS)
9.2.4 Use Cases in Practice
9.2.4.1 Smart Payment System (SPayS)
9.2.4.2 Parking Reservation System (PRS)
9.2.4.3 E-Parking System
9.3 Sensors Overview
9.3.1 Active Sensors
9.3.1.1 Active Infrared Sensor
9.3.1.2 Ultrasonic Sensors
9.3.1.3 CCTV and Image Processing
9.3.1.4 Vehicle License Recognition
9.3.2 Passive Sensors
9.3.2.1 Passive Infrared Sensor
9.3.2.2 LDR Sensor
9.3.2.3 Inductive Loop Detector
9.3.2.4 Piezoelectric Sensor
9.3.2.5 Pneumatic Road Tube
9.3.2.6 Magnetometer
9.3.2.7 Vehicle-In-Motion Sensors
9.3.2.8 Microwave Radar
9.3.2.9 RFID
9.3.2.10 Acoustic Sensor
9.4 Design Factors
9.4.1 Soft Design Factors
9.4.1.1 Software Systems in Smart Parking
9.4.1.2 Privacy and Security in Smart Parking
9.4.2 Hard Design Factors
9.4.2.1 Communication Networks
9.4.2.2 Errors in Data Collection and System Reliability
9.4.3 Interoperability and Data Exchange
9.4.3.1 Interoperability with Privacy and Security Aspect
9.5 Solutions in Practice
9.5.1 Implementation of Smart Parking Systems
9.5.1.1 Single-Vehicle Detection Sensors
9.5.1.2 Smart Parking in Smart City Ecosystem
9.5.2 New Applications in Smart Parking System
9.5.2.1 Vehicle to Everything (V2X)
9.5.2.2 Unmanned Aerial Vehicles (UAVs)
9.5.3 Hybrid System
9.6 Open Research Issues
9.7 Concluding Remarks
Notes
References
Chapter 10 Correctness of an Authentication Scheme for Managing Demand Response in Smart Grid
10.1 Introduction
10.1.1 Motivations and Contributions
10.2 Review of the Scheme of Kumar et al
10.2.1 System Setup
10.2.2 SG Device Registration
10.2.3 UC Registration
10.2.4 Authentication
10.2.5 SG Device Dynamic Addition
10.2.6 UC Dynamic Addition
10.3 Disadvantages of Kumar et al.’s Scheme
10.3.1 Incorrectness
10.3.2 No Initial Verification on UC[sub(b)] Side
10.4 Conclusion
References
Chapter 11 An Overview about the Cyberattacks in Grid and Like Systems
11.1 Introduction
11.2 Threats to Cybersecurity
11.2.1 Phishing
11.2.1.1 Phishing Tips
11.2.1.2 How Does Phishing Work?
11.2.1.3 What Are the Dangers of Phishing Attacks?
11.2.1.4 How Do I Protect against Phishing Attacks?
11.2.1.5 Examples of Phishing Attacks
11.2.1.6 Advanced Email Security Protection
11.2.2 Ransomware
11.2.2.1 How Does Ransomware Work?
11.2.2.2 Ten Ways to Protect Yourself from Ransomware
11.2.3 Malware
11.2.3.1 How Do I Protect My Network Against Malware?
11.2.3.2 How Do I Detect and Respond to Malware?
11.2.3.3 Types of Malware
11.3 Evolutions in the Threat Landscape
11.3.1 The Top Fifteen Threats in 2018
11.3.1.1 Threat Agents
11.3.1.2 Attack Vectors
11.4 Top Cyberthreats
11.4.1 Threat 1: Malware
11.4.2 Threat 2: Web-Based Attacks
11.4.3 Threat 3: Web Application Attacks
11.4.4 Threat 4: Phishing
11.4.5 Threat 5: Denial of Service Attacks
11.4.6 Threat 6: Spam
11.4.7 Threat 7: Botnets
11.4.8 Threat 8: Data Breach
11.4.9 Threat 9: Insider Threat
11.4.10 Threat 10: Physical Damage and Loss
11.4.11 Threat 11: Information Leakage
11.4.12 Threat 12: Identity Theft
11.4.13 Threat 13: Cryptojacking
11.4.14 Threat 14: Ransomware
11.4.15 Threat 15: Cyber Espionage
11.4.16 Threat 16: Exploit Kits
11.5 Concluding Remarks
References
Chapter 12 Security in Grid and IoT-Enabled Cities
12.1 Introduction
12.1.1 Comparison of Similar Surveys
12.2 Architecture of Smart Cities
12.2.1 Physical Layer
12.2.2 Network Layer
12.2.3 Database Layer
12.2.4 Virtualization Layer
12.2.5 Data Analytics and Mining Layer
12.2.6 Application Layer
12.3 Applications of Smart Cities
12.3.1 Smart Grid
12.3.2 Smart Transportation
12.3.3 Smart Environment
12.3.4 Smart Living
12.3.5 Smart Health
12.2.6 Smart Energy
12.4 Security and Privacy Issues in Smart Cities’ Applications
12.4.1 Cyber-Security
12.4.2 Botnet Activities in IoT-Based Smart Cities
12.4.3 Threats of Unmanned Autonomous Vehicles in Smart Cities
12.4.4 Privacy Leakage
12.5 Security and Privacy Solutions for Smart Cities’ Environment
12.5.1 Blockchain
12.5.2 Cryptography
12.5.3 Biometrics
12.5.4 Machine Learning (ML) and Data Mining
12.5.5 IoT Regulations
12.6 Security and Privacy Requirements for Smart Cities’ Services
12.6.1 Privacy by Design
12.6.2 Testing and Verification
12.6.3 Privacy Architecture
12.6.4 Data Minimization
12.6.5 Secret Sharing
12.6.6 System Security and Access Control
12.6.7 Secure Multi-Party Computation
12.7 Recommended Secure Architecture
12.7.1 Black Networks (BNs)
12.7.2 Trusted SDN Controller
12.7.3 Unified Registry (UR)
12.7.4 Key Management System (KMS)
12.8 Open Issues and Future Research Directions
12.8.1 Mobile Crowd Sensing
12.8.2 Big Data
12.8.3 IoT-Based Network Security
12.8.4 Lightweight Security Solutions
12.8.5 Authentication and Confidentiality
12.8.6 Availability and Integrity
12.8.7 The Application of Cloud/Fog Technology in Smart Cities
12.9 Conclusion
References
Index