Energy-Efficient Communication Networks
Description
Energy-Efficient Communication Networks is essential for anyone looking to understand and implement cutting-edge energy optimization strategies for communication systems, ensuring they meet growing energy demands while seamlessly integrating renewable energy sources and enhancing battery life in embedded applications.
Renewable energy, including solar, wind, and geothermal energy, for communication networks is a key area of exploration for meeting the demands of their increasing energy requirements. Scheduling and power cycle optimization are instrumental in deciding the effectiveness of these networks. Apart from communication, embedded systems running on batteries designed for data processing applications also face restrictions in terms of battery life-targeting low-energy consumption-based systems is particularly important here. The increased usage of sensor networks for personal and commercial applications has resulted in a surge of development to create energy-aware protocols and algorithms.
This book introduces energy optimization concepts for current and future communication networks and explains how to optimize electricity for wireless sensor networks and incorporate renewable energy sources into conventional communication networks. It gives readers a better understanding of the difficulties, limitations, and possible bottlenecks that may occur while developing a communication system under power constraints, as well as insights into the traditional and recently developed communication systems from an energy optimization point of view.
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Persons
Shakti Raj Chopra, PhD is an associate professor at Lovely Professional University with over 18 years of academic experience. He has published over 45 research papers in international journals and conferences. Additionally, he has worked on several consultancy projects and participated in over 20 national and international webinars. His areas of interest include cognitive radio, blockchain, artificial intelligence, and machine learning.
Krishan Arora, PhD is an associate professor and Head of the Department of Power Systems in the School of Electronics and Engineering at Lovely Professional University with over 16 years of academic experience. He has published over 70 research papers in international journals and conferences, organized several workshops, internships, and lectures, and participated in over 20 national and international webinars. His areas of research include electrical machines, non-conventional energy sources, load frequency control, and automatic generation control.
Suman Lata Tripathi, PhD is a professor at Lovely Professional University with more than 20 years of academic experience. She has published over 74 research papers in international journals, 13 patents, two copyrights, and has authored and edited over 17 books. She also serves as a session chair, conference-steering committee member, editorial board member, and peer reviewer for international journals. Her areas of interest include microelectronics device modeling and characterization, low-power VLSI circuit design, and embedded system design.
Vikram Kumar, PhD is a post-doctoral researcher and guest lecturer at the University of Calgary, Canada. He has published 82 research papers in international journals, three patents, and four copyrights. Additionally, he has presented research work in over two dozen national and international conferences and serves as an editorial board member of several journals. His areas of interest include multidisciplinary design and optimization, artificial intelligence and machine learning for numerical and engineering optimization, and information and communication technology for smart grid applications.
Content
Preface xv
List of Contributors xvii
1 Efficient Energy Management in Hyperledger Fabric Blockchain Networks: A Proposed Optimized Solution 1
Kamurthi Ravi Teja and Shakti Raj Chopra
1.1 Introduction 2
1.2 Methodology 3
1.3 Experimental Analysis 4
1.4 Results and Discussion 5
1.5 Conclusion 7
2 Framework for UAV-Based Wireless Power Harvesting 9
Tanishk Singhal and Harpreet Singh Bedi
2.1 Introduction 9
2.2 Literature Review 10
2.3 Results and Discussion 15
2.4 Conclusion 19
3 Future Generation Technology and Feasibility Assessment 23
Pradeep Singh, Krishan Arora and Umesh C. Rathore
3.1 Introduction 24
3.2 Next-Generation Electrical Technologies 26
3.3 Artificial Intelligence 37
3.4 Machine Learning 41
3.5 Conclusion 46
4 IoT-Enabled Weather Forecasting Systems in Future Networks: Constraints and Solutions 51
Yogesh Kumar Verma, Archana Kanwar and Manoj Kumar Shukla
4.1 Introduction 52
4.2 Need of IoT-Based Weather Forecasting System 53
4.3 Methodology and Results 58
4.4 Conclusion 61
5 Cognitive Radio-Based NOMA Communication Networks 65
Indu Bala
5.1 Introduction to Cognitive Radio and NOMA Networks 66
5.2 Fundamentals of Cognitive Radio Technology 67
5.3 Principles of Non-Orthogonal Multiple Access (NOMA) 70
5.4 Integration of Cognitive Radio with NOMA 73
5.5 Performance Evaluation and Analysis 77
5.6 Applications and Use Cases 78
5.7 Challenges and Future Directions 80
5.8 Conclusion 82
6 Cognitive Radio (CR) Based Non-Orthogonal Multiple Access (NOMA) Network 87
Raja Gunasekaran, Ragavi Boopathi, Gobinath Velu Kaliyannan, Dinesh Dhanabalan and Kesavan Duraisamy
6.1 Introduction 88
6.2 Fundamentals of CR 90
6.3 Spectrum Management System 96
6.4 Noma Networks 98
6.5 Enabling Technologies 104
6.6 Conclusion 107
7 Artificial Intelligence and Machine Learning-Based Network Power Optimization Schemes 115
Jyoti, Aarti Shar, Ramandeep Sandhu, Manish Kumar Sharma and Deepika Ghai
7.1 Introduction 116
7.2 Network 117
7.3 Decentralized Connection 120
7.4 Communication Network 121
7.5 Internet of Things (IoT) 123
7.6 5G and Future Technologies 123
7.7 Network Power and Unstable Power Supply of Computer Networks 123
7.8 Adaption of Optimization Schemes to Enhance Network Power 124
7.9 Related Work 127
7.10 Traditional Evaluation AI and ML-Based Network Energy Optimization Techniques 129
7.11 AI- and ML-Based Systems for Network Energy Optimization Techniques 132
7.12 Conclusion 135
8 Integration of PV Solar Rooftop Technology for Enhanced Performance and Sustainability of Electric Vehicles: A Techno-Analytical Approach 139
Vinay Anand and Himanshu Sharma
8.1 Introduction 140
8.2 Literature Review 142
8.3 Methods and Methodology 144
8.4 Result and Discussion 147
8.5 Conclusion 153
9 The Viability of Advanced Technology for Future Generations 157
Manjushree Nayak and Ashutosh Pattnaik
9.1 Introduction 158
9.2 Communication Systems 159
9.3 Conclusion 171
10 Power Optimization and Scheduling for Multi-Layer, Multi-Dimensional 6G Communication Networks 175
Harpreet Kaur Channi, Pulkit Kumar and Ramandeep Sandhu
10.1 Introduction 176
10.2 Literature Review 177
10.3 Multi-Layer, Multi-Dimensional 6G Communication Networks 180
10.4 Power Optimization in MLMD 6G Networks 183
10.5 Scheduling Strategies for MLMD 6G Networks 184
10.6 Proposed Framework 187
10.7 Challenges and Future Directions 190
10.8 Conclusion 193
11 Industry 4.0: Future Opportunities and Challenges 199
Manoj Singh Adhikari, Raju Patel, Manoj Sindhwani, Shippu Sachdeva and Suman Lata Tripathi
11.1 Introduction 200
11.2 Future Opportunities of Industrial 4.0 201
11.3 Increased Productivity and Efficiency 202
11.4 Innovation 203
11.5 Data-Driven Decision-Making 205
11.6 Supply Chain Optimization 205
11.7 Future Challenges of Industrial 4.0 206
11.8 Data Security and Privacy 207
11.9 Skills Gap and Workforce Training 208
11.10 Interoperability and Standardization 210
11.11 Ethical and Social Implications 211
11.12 Infrastructure Investment 211
11.13 Regulatory and Legal Challenges 212
11.14 Dependency on Technology 213
11.15 Conclusion 213
12 MIMO and Its Significance 217
Shahid Hamid and Shakti Raj Chopra
12.1 Introduction 218
12.2 MIMO 219
12.3 Signal Model for MIMO 221
12.4 Standard MIMO Configurations 223
12.5 Why MIMO 224
12.6 Results 225
References 227
Index 231
1
Efficient Energy Management in Hyperledger Fabric Blockchain Networks: A Proposed Optimized Solution
Kamurthi Ravi Teja1 and Shakti Raj Chopra2*
1Dept. of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan
2School of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India
Abstract
This study aims to address the energy-efficiency challenge in Hyperledger Fabric networks, focusing on the energy consumption of network nodes during communication. A simulation model was developed to evaluate energy consumption patterns among nodes during simulated data transmissions. The simulation considers data transmissions, random data sizes, and energy consumption associated with these interactions. The results of this study provide insights into optimizing energy transmission and reception among multiple nodes, leading to a reduction in energy waste and an improvement in energy utilization. The study evaluates energy efficiency by calculating average and total energy consumption metrics for each node and visualizing energy consumption patterns. The experimental analysis involves adjusting parameters, including transmission times, data sizes, and communication protocols, to provide a comprehensive understanding of energy-efficient communication in blockchain networks, with a focus on Hyperledger Fabric. The proposed Hyperledger Fabric network strategy targets reducing energy consumption in wireless communication involving multiple nodes by refining the transmit data function and associated methods to incorporate energy-saving measures, sleep modes, or communication protocol optimizations.
Keywords: Blockchain, networks, energy, hyperledger, communication
1.1 Introduction
Blockchain technology, particularly Hyperledger Fabric networks, has enabled decentralized and secure data management. Hyperledger Fabric, a key player in enterprise-grade blockchain solutions, is widely used in finance and supply chain. However, concerns about energy efficiency arise with the growing reliance on these distributed networks [1-3].
This study aims to address the critical challenge of elevated energy consumption during communication within Hyperledger Fabric networks by examining the energy efficiency of network nodes, which is essential for sustainability and operational costs. Employing a simulation model, this research delves into the intricacies of wireless communication within a Hyperledger Fabric network, specifically focusing on node 1 to node 5 transmission network. The primary objectives of this research include evaluating energy consumption patterns among nodes during simulated data transmissions and also to develop a model for wireless communication between network nodes while simultaneously highlighting the energy consumption associated with this process. By analyzing the dynamics of energy consumption, the study aims to uncover variations in efficiency levels among nodes, which can inform subsequent optimization strategies [4-6]. The findings from this study contribute to the ongoing discourse on enhancing the sustainability of Hyperledger Fabric networks, and advancing our understanding of energy-efficient blockchain communication [4-10].
Figure 1.1 The design process for energy-efficient Hyperledger Fabric blockchain transmission networks
This chapter examines the use of blockchain technology in wireless networks to enhance energy efficiency in communication systems. Through simulations and experiments, the proposed method is shown to improve energy efficiency in wireless networks. The design process for energy-efficient Hyperledger Fabric blockchain transmission networks is illustrated in Figure 1.1.
1.2 Methodology
A simulation model was developed to capture the complex dynamics of wireless communication within a Hyperledger Fabric network by instantiating nodes (node 1 to node 5) to mimic the communication process. The simulation takes into account data transmissions, random data sizes, and the energy consumption associated with these interactions. This study seeks to address the energy-efficiency concerns that arise from the widespread use of blockchain technologies, particularly in Hyperledger Fabric networks. By simulating data transmissions and calculating the resulting energy consumption, the study evaluates the energy-efficiency levels of the nodes. The findings of this research suggest that there is potential for optimizing energy transmission and reception among multiple nodes, which could lead to a reduction in energy waste and an improvement in energy utilization. The implications and insights derived from this study are discussed in detail in the chapter. These results have significant implications for the development of effective strategies aimed at enhancing the sustainability and efficiency of blockchain technologies in enterprise environments. The simulation collects data on node energy levels during multiple transmissions using randomized data sizes and target node selections. Recorded energy levels provide a comprehensive dataset for subsequent analysis.
The study evaluates energy efficiency by calculating average and total energy consumption metrics for each node and visualizing energy con-sumption patterns. The experimental analysis involves adjusting parameters including transmission times, data sizes, and communication protocols to provide a comprehensive understanding of energy-efficient communication in blockchain networks, with a focus on Hyperledger Fabric.
1.3 Experimental Analysis
1.3.1 Existing Problem in the Network
The old system's network simulation considers wireless data transmission's time and energy, incorporating a basic model of communication overhead that introduces delay. It also includes a simplified energy consumption model for nodes during transmission. In a two-node example, consider these key points.
- In this simulation, node 1 sends data to node 2 using the transmit_data method. Before sending, node 1 checks its energy level and updates it after transmission. Node 2 receives the data using the receive_data method.
- The energy levels of both nodes are continuously monitored and recorded as they engage in simulated data transmissions. This information is then used to visualize the changes in their energy levels over time.
The impact of data transmissions on energy levels can be observed in Figure 1.2, which displays the fluctuating energy levels of node 1 and node 2 after each transmission. While node 2's energy consumption remained constant at 100%, the energy levels of node 1 decreased as the number of transmissions increased. Moreover, node 2 consumed more energy with each additional transmission. This problem is exacerbated by the outdated energy system, which does not optimize energy consumption for both nodes.
Figure 1.2 Old energy strategy - wireless node energy levels during transmissions.
1.3.2 Proposed Hyperledger Fabric Network Approach
The Hyperledger Fabric network strategy targets reducing energy consumption in wireless communication involving multiple nodes. The approach begins with a simulation environment to analyze energy consumption patterns during data transmission. The proposed network simulation models data transmission between five nodes, with modifications and optimizations to enhance energy efficiency, such as refining the transmit_data function and associated methods to incorporate energy-saving measures, sleep modes, or communication protocol optimizations. To reduce energy consumption during data transmission in wireless communication within a Hyperledger Fabric network, consider the following steps.
Establish a network with multiple nodes (node 1 to node 5) to represent entities within a Hyperledger Fabric network. Simulate data transmissions between nodes using the "simulate_data_transmissions" function, con-sidering varying data sizes and arbitrary target nodes. Model energy consumption of nodes during data transmission using the WirelessNode class with methods such as "transmit_data", "calculate_transmission_time", and "calculate_energy_consumption". Record and visualize fluctuating energy levels of individual nodes over time. Incorporate random data sizes and target node selection for a dynamic environment. Analyze energy efficiency based on observed behavior and provide insights into the energy efficiency of the network.
1.4 Results and Discussion
The graph in Figure 1.2 illustrates the energy levels of node 1 and node 2 after each transmission. Node 2 consistently consumed 100% of its energy in every data transmission, while node 1's energy levels decreased with increased transmissions. Additionally, more energy was consumed on node 2 with greater data transmitted. This limitation of the traditional approach is addressed by the proposed Hyperledger Fabric network, which focuses on the energy levels of nodes (node 1 to node 5) in multiple transmissions, providing insights into the effectiveness and dynamics of the data transmission process. As the primary objective of this research is to offer an optimized solution for network energy consumption, it is evident from Figure 1.3 that as the transmission number is increased, the level of energy consumption experienced between transmissions is reduced.
From Table 1.1 nodes 1...
