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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
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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Veröffentlichungsjahr: 2025
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Preface
List of Contributors
1 Efficient Energy Management in Hyperledger Fabric Blockchain Networks: A Proposed Optimized Solution
1.1 Introduction
1.2 Methodology
1.3 Experimental Analysis
1.4 Results and Discussion
1.5 Conclusion
References
2 Framework for UAV-Based Wireless Power Harvesting
2.1 Introduction
2.2 Literature Review
2.3 Results and Discussion
2.4 Conclusion
References
3 Future Generation Technology and Feasibility Assessment
3.1 Introduction
3.2 Next-Generation Electrical Technologies
3.3 Artificial Intelligence
3.4 Machine Learning
3.5 Conclusion
References
4 IoT-Enabled Weather Forecasting Systems in Future Networks: Constraints and Solutions
4.1 Introduction
4.2 Need of IoT-Based Weather Forecasting System
4.3 Methodology and Results
4.4 Conclusion
References
5 Cognitive Radio-Based NOMA Communication Networks
5.1 Introduction to Cognitive Radio and NOMA Networks
5.2 Fundamentals of Cognitive Radio Technology
5.3 Principles of Non-Orthogonal Multiple Access (NOMA)
5.4 Integration of Cognitive Radio with NOMA
5.5 Performance Evaluation and Analysis
5.6 Applications and Use Cases
5.7 Challenges and Future Directions
5.8 Conclusion
References
6 Cognitive Radio (CR) Based Non-Orthogonal Multiple Access (NOMA) Network
6.1 Introduction
6.2 Fundamentals of CR
6.3 Spectrum Management System
6.4 Noma Networks
6.5 Enabling Technologies
6.6 Conclusion
References
7 Artificial Intelligence and Machine Learning-Based Network Power Optimization Schemes
7.1 Introduction
7.2 Network
7.3 Decentralized Connection
7.4 Communication Network
7.5 Internet of Things (IoT)
7.6 5G and Future Technologies
7.7 Network Power and Unstable Power Supply of Computer Networks
7.8 Adaption of Optimization Schemes to Enhance Network Power
7.9 Related Work
7.10 Traditional Evaluation AI and ML-Based Network Energy Optimization Techniques
7.11 AI- and ML-Based Systems for Network Energy Optimization Techniques
7.12 Conclusion
References
8 Integration of PV Solar Rooftop Technology for Enhanced Performance and Sustainability of Electric Vehicles: A Techno-Analytical Approach
8.1 Introduction
8.2 Literature Review
8.3 Methods and Methodology
8.4 Result and Discussion
8.5 Conclusion
References
9 The Viability of Advanced Technology for Future Generations
9.1 Introduction
9.2 Communication Systems
9.3 Conclusion
References
10 Power Optimization and Scheduling for Multi-Layer, Multi-Dimensional 6G Communication Networks
10.1 Introduction
10.2 Literature Review
10.3 Multi-Layer, Multi-Dimensional 6G Communication Networks
10.4 Power Optimization in MLMD 6G Networks
10.5 Scheduling Strategies for MLMD 6G Networks
10.6 Proposed Framework
10.7 Challenges and Future Directions
10.8 Conclusion
References
11 Industry 4.0: Future Opportunities and Challenges
11.1 Introduction
11.2 Future Opportunities of Industrial 4.0
11.3 Increased Productivity and Efficiency
11.4 Innovation
11.5 Data-Driven Decision-Making
11.6 Supply Chain Optimization
11.7 Future Challenges of Industrial 4.0
11.8 Data Security and Privacy
11.9 Skills Gap and Workforce Training
11.10 Interoperability and Standardization
11.11 Ethical and Social Implications
11.12 Infrastructure Investment
11.13 Regulatory and Legal Challenges
11.14 Dependency on Technology
11.15 Conclusion
References
12 MIMO and Its Significance
12.1 Introduction
12.2 MIMO
12.3 Signal Model for MIMO
12.4 Standard MIMO Configurations
12.5 Why MIMO
12.6 Results
References
Index
Also of Interest
End User License Agreement
List of Illustrations
Chapter 1
Figure 1.1 The design process for energy-efficient Hyperledger Fabric blockcha...
Figure 1.2 Old energy strategy – wireless node energy levels during transmissi...
Figure 1.3 Improved energy-efficient strategy that involves multiple node ener...
Chapter 2
Figure 2.1 Proposed framework and circuit for energy harvesting and transmissi...
Figure 2.2 Energy optimization in UAV-based cellular networks and approaches....
Figure 2.3 Illustration of the UAV-enabled wireless powered MEC.
Figure 2.4 Achieved energy efficiency of the whole IoT systems by the proposed...
Figure 2.5 Achieved energy efficiency of the whole IoT systems by the proposed...
Figure 2.6 Achieved energy efficiency of the whole IoT systems by the proposed...
Figure 2.7 Achieved energy efficiency of the whole IoT systems by the proposed...
Figure 2.8 The comparison between the asymptotically optimal result and the gl...
Chapter 3
Figure 3.1 Classification of factors contributing to the technological effecti...
Figure 3.2 Types of artificial intelligence.
Figure 3.3 Structure of artificial intelligence.
Figure 3.4 Working of machine learning.
Figure 3.5 Types of machine learning.
Chapter 4
Figure 4.1 Application of IoT in green computing [36].
Figure 4.2 Interconnection of DHT-22, ESP-32, and OLED display.
Figure 4.3 Data collection using Thing-Speak.
Figure 4.4 Data collection using Thing-Speak.
Chapter 5
Figure 5.1 Integration of conventional cognitive radio network with NOMA.
Figure 5.2 Basic NOMA architecture.
Chapter 6
Figure 6.1 System model of the generalized cognitive radio network [19].
Figure 6.2 Visualization of spectrum hole [28].
Figure 6.3 Physical structure of CR [28].
Figure 6.4 Cognitive cycle [28].
Figure 6.5 The concept of multiple access scheme [28].
Figure 6.6 CR networks’ multiple access scheme [28].
Figure 6.7 Spectrum management system [28].
Figure 6.8 Operating principle of NOMA scheme [28].
Figure 6.9 Non-orthogonal multiple access [78].
Figure 6.10 Combination of cognitive radio and NOMA [78].
Figure 6.11 5G emerging and existing technologies [78].
Chapter 7
Figure 7.1 Artificial intelligence versus machine learning versus deep learnin...
Figure 7.2 Components linked with network.
Figure 7.3 Categories of network.
Figure 7.4 Types of communication networks.
Figure 7.5 Traditional method of network energy optimization techniques.
Figure 7.6 Different phases for designing a system to evaluate AI- and ML-base...
Figure 7.7 Optimal solutions for designing a system to evaluate AI and ML-base...
Chapter 8
Figure 8.1 Electric motor and types.
Figure 8.2 Solar-powered induction motor-driven electric vehicle [23].
Figure 8.3 Simulation model of rotation speed controller [24].
Figure 8.4 Graph showing curve between torque and time [24].
Chapter 9
Figure 9.1 Technologies being used.
Figure 9.2 Comparison between 5G and 6G.
Figure 9.3 Satellite communications.
Figure 9.4 Signal processing in BCI.
Figure 9.5 Wearable IoT.
Chapter 10
Figure 10.1 Evolution of network communication.
Figure 10.2 6G Communication mobile technology (Banafaa
et al
., 2023).
Figure 10.3 Challenges and future directions for MLMD in 6G communication.
Chapter 11
Figure 11.1 Flow diagram of industrial revolutions.
Figure 11.2 Technologies for modern industry.
Figure 11.3 Industry 4.0 optimize workers.
Figure 11.4 System integration.
Figure 11.5 CPPS platform in Industry 4.0.
Figure 11.6 Industry 4.0 impact on the worker.
Figure 11.7 Different challenges of Industry 4.0.
Figure 11.8 Increase or decrease expected outcome in industry/occupations.
Chapter 12
Figure 12.1 MIMO technology.
Figure 12.2 MIMO transmitter.
Figure 12.3 MIMO receiver.
Figure 12.4 Spectral efficiency as a function of BS antennas for MR and ZF.
Figure 12.5 Two stage UE scheduling and feedback.
Figure 12.6 Achievable rate at I,j=4 for MF, ZF, RZF and HRZF.
Figure 12.7 Achievable rate at I,j=16 for MF, ZF, RZF and HRZF.
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
Preface
List of Contributors
Begin Reading
Index
Also of Interest
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541J Beverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected]) Phillip Carmical ([email protected])
Energy-Efficient Communication Networks
Edited by
Shakti Raj Chopra
Krishan Arora
Suman Lata Tripathi
and
Vikram Kumar
This edition first published 2025 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA © 2025 Scrivener Publishing LLC For more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 9781394271658
Front cover images supplied by Adobe Firefly Cover design by Russell Richardson
Preface
The rapid expansion of communications networks has transformed how we interact, work, and share information. Expansion, though, has also greatly increased energy usage, with sustainability, operating expense, and environmental factors. With more and more demand for higher speeds, more reliability, and more pervasive connectivity, the need for energy-efficient communication networks has never been greater.
This book, Energy-Efficient Communication Networks, explores the basics, issues, and future directions toward reducing the energy requirements of modern networking infrastructure. The book offers a comprehensive view of energy-conserving approaches in network layers of architecture, ranging from the physical hardware to the transmission protocols and cloud infrastructure. The book further illustrates new technologies such as green networking, energy-efficient routing, software-defined networks (SDN), and artificial intelligence-based optimizations that enable communication infrastructure sustainability.
Intended for researchers, engineers, and students in the fields of networking and telecommunications, this book is an extremely helpful one on which to gain knowledge of new innovations in energy-efficient communication. Based on theoretical frameworks, actual case studies, and research agendas, the book equips readers with the knowledge and tools to design and roll out greener and more sustainable networks.
We would like to inspire further innovation in energy efficiency, towards a time when high-performance communication networks and environmental responsibility may coexist.
Shakti Raj Chopra Krishan Arora Suman Lata Tripathi Vikram Kumar
19th March 2025
List of Contributors
Kamurthi Ravi TejaDept. of Computer Science and Information Engineering, National Taipei University of Technology, Taipei, Taiwan
Shakti Raj ChopraSchool of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India
Tanishk SinghalSchool of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India
Harpreet Singh BediSchool of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India
Pradeep SinghABVGIET-Pragatinagar, Shimla, Himachal Pradesh, India
Krishan AroraSchool of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India
Umesh C. RathoreDirector-cum-Principal, ABVGIET-Pragtinagar, Shimla, Himachal Pradesh, India
Yogesh Kumar VermaSchool of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India
Archana KanwarSchool of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India
Manoj Kumar ShuklaDepartment of Robotics and Automation, Symbiosis Institute of Technology, Pune, India
Indu BalaSchool of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India
Raja GunasekaranDepartment of Mechanical Engineering, Velalar College of Engineering and Technology, Erode, Tamil Nadu, India
Ragavi BoopathiDepartment of Biomedical Engineering, Dr. N.G.P Institute of Technology, Coimbatore, Tamil Nadu, India
Gobinath Velu KaliyannanDepartment of Mechatronics Engineering, Kongu Engineering College, Erode, Tamil Nadu, India
Dinesh DhanabalanDepartment of Mechanical Engineering, Bannari Amman Institute of Technology, Sathyamangalam, Erode, Tamil Nadu, India
Kesavan DuraisamyDepartment of Mechanical Engineering, Velalar College of Engineering and Technology, Erode, Tamil Nadu, India
JyotiSchool of Computer Science & Engineering, Lovely Professional University, India
Aarti SharSchool of Computer Science & Engineering, Lovely Professional University, India
Ramandeep SandhuSchool of Computer Science & Engineering, Lovely Professional University, India
Manish Kumar SharmaSchool of Computer Science & Engineering, Lovely Professional University, India
Deepika GhaiSchool of Electronics and Electrical Engineering, Lovely Professional University, Jalandhar, India
Vinay AnandSchool of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, (India)
Himanshu SharmaSchool of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, (India)
Manjushree NayakDepartment of Computer Science and Engineering, NIST Institute of Science and Technology (Autonomous), Berhampur Odisha
Ashutosh PattnaikDepartment of Computer Science and Engineering, NIST Institute of Science and Technology (Autonomous), Berhampur Odisha
Harpreet Kaur ChanniDepartment of Electrical Engineering, Chandigarh University, Gharuan, Mohali, Punjab, India
Pulkit KumarDepartment of Electrical Engineering, Chandigarh University, Gharuan, Mohali, Punjab, India
Ramandeep SandhuSchool of Computer Science and Engineering, Lovely Professional University, Jalandhar, Punjab, India
Manoj Singh AdhikariSchool of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, Punjab, India
Raju PatelSchool of Electronics Engineering, Vellore Institute of Technology, Chennai, India
Manoj SindhwaniSchool of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, Punjab, India
Shippu SachdevaSchool of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, Punjab, India
Suman Lata TripathiDepartment of Electronics and Communication Engineering, Symbiosis Institute of Technology (SIT), Symbiosis International Deemed University (SIU), Pune, India
Shahid HamidPhysics Wallah Private Limited, Noida (UP), Uttar Pradesh, India
Shakti Raj ChopraSchool of Electronics and Electrical Engineering, Lovely Professional University, Punjab, India
1Efficient 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 to 5 have an average energy consumption of 16.38, 19.84, 18.92, 16.28, and 14.66 units, respectively, which accounts for 100% of the total energy consumption. Nodes 1 through 5 consumed 35.6, 37.6, 39.8, 31.8, and 30.8 units of energy, respectively, accounting for 100% of the total energy consumed across all nodes. These metrics provide a clear picture of the energy consumption patterns, which can be useful for optimizing energy usage and reducing waste. These metrics offer insights into individual node energy consumption, enabling efficiency analysis of your wireless communication simulation. In this simulation, lower average and total energy consumption is deemed more energy efficient.
Figure 1.3 Improved energy-efficient strategy that involves multiple node energy levels during transmissions.
Table 1.1 Energy consumption metrics.
Average energy consumption in units (on average out of 100)
Total energy consumption in units (accumulated over all transmissions out of 100)
Node 1
16.38
Node 1
35.60
Node 2
19.84
Node 2
37.60
Node 3
18.92
Node 3
39.80
Node 4
16.28
Node 4
31.80
Node 5
14.66
Node 5
30.80
Simulation model development for wireless communication energy consumption reduction has been successful. By studying node energy consumption patterns in node 1 to node 5 wireless transmission, we gained insights into data transmission efficiency. Benchmarking against real-world data or industry standards validates our findings for accurate Hyperledger Fabric network results. Examining energy consumption among network nodes offers valuable insights into data transmission efficiency. Our results contribute to ongoing discussions on enhancing the sustainability and performance of enterprise blockchain networks. However, further optimization is needed, including evaluating and improving simulations, incorporating additional elements like consensus mechanisms and smart contract execution, and conducting additional experiments with various simulation parameters, node behaviors, and communication protocols to enhance energy efficiency.
1.5 Conclusion
This study presents a comprehensive analysis of energy consumption in wireless communication for Hyperledger Fabric networks, providing a solid foundation for sustainable and efficient blockchain technology development in enterprise environments. The findings have implications for enhancing sustainability and efficiency, but the scope is limited to energy consumption analysis. Further research is needed to explore other data transmission optimization aspects, and advanced consensus mechanisms within the Hyperledger Fabric network are a potential area for future research.
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.
Note
*
Corresponding author
:
2Framework for UAV-Based Wireless Power Harvesting
Tanishk Singhal and Harpreet Singh Bedi*
School of Electronics and Electrical Engineering, LPU, Punjab, India
Abstract
This chapter navigates the evolution of Unmanned Aerial Vehicles (UAVs) and the challenges posed by traditional power sources, leading to the exploration of wireless power harvesting technologies. We introduce a comprehensive framework that synergizes solar, radio frequency harvesting, and energy scavenging, addressing the unique energy needs of UAVs. Through literature review, methodology, and results, we validate the theoretical foundations, interpret data insights, and set the stage for future advancements. The conclusion envisions a transformative era in UAV-based wireless power harvesting, promising sustainability, efficiency, and autonomy. Subsequent chapters delve into the framework’s inner workings, providing practical insights and exploring its implications across technical, economic, and societal dimensions.
Keywords: Unmanned aerial vehicles (UAVs), wireless power harvesting, UAV power systems, radio frequency harvesting, comprehensive framework
2.1 Introduction
This chapter navigates the evolutionary trajectory of Unmanned Aerial Vehicles (UAVs), shedding light on the limitations inherent in traditional power sources. As UAVs become increasingly pervasive across diverse applications, the demand for sustainable and efficient power solutions grows. This chapter introduces a pioneering framework that amalgamates solar power, radio frequency harvesting, and energy scavenging, offering a holistic response to the distinctive energy requirements of UAVs [1]. Through a synthesis of literature review, methodology, and results, the chapter sets the stage for an exploration into the theoretical foundations and practical implications of UAV-based wireless power harvesting [2]. The ensuing discussion envisions a transformative era in which this framework reshapes the capabilities of unmanned aerial systems, promising advancements in sustainability, efficiency, and autonomy.
2.2 Literature Review
To delve into the intricacies of powering Unmanned Aerial Vehicles (UAVs), it is imperative to scrutinize the existing landscape of power sources. Conventional batteries, while reliable, grapple with the challenge of energy density and weight, directly impacting the endurance and range of UAVs. The quest for efficient alternatives has led to the exploration of fuel-based systems, presenting their own set of challenges in terms of environmental impact and logistical complexities [3, 4]. As UAVs continue to diversify in their applications, from surveillance to delivery services, the inadequacies of these traditional power solutions become increasingly apparent.
Wireless Power Harvesting Technologies: Amid these challenges, wireless power harvesting technologies emerge as a promising frontier. Solar power, a stalwart in renewable energy, finds application in UAVs through the integration of solar panels on their surfaces [5]. This technology capitalizes on sunlight, converting it into electrical power and extending operational windows, particularly in regions with abundant sunshine. Radio Frequency (RF) harvesting, another avenue, taps into ambient radio frequency signals to generate electrical energy. While offering a continuous power source, RF harvesting operates at lower power levels, necessitating careful consideration of its trade-offs [6]. The nascent field of energy scavenging explores harnessing ambient environmental energy, such as vibrations or airflow, to power UAV systems.
Relevance to UAVs: The application of wireless power harvesting technologies to UAVs introduces a transformative dimension to their power dynamics. Solar-powered UAVs, equipped with efficient solar panels, boast the potential for prolonged flight durations, making them well-suited for missions in sun-drenched areas [7]. RF harvesting, with its continuous but relatively low-power output, presents opportunities for on-the-go recharging, potentially reducing dependence on conventional batteries during flight. Energy scavenging, though in early stages, holds promise in capturing untapped ambient energy, thereby augmenting overall energy efficiency. As UAVs evolve in complexity and mission profiles, the adaptability and versatility of these wireless power harvesting technologies become increasingly relevant.
2.2.1 Proposed Framework
Components of the Framework: In the pursuit of addressing the shortcomings of conventional power sources for UAVs and harnessing the potential of wireless power harvesting technologies, our proposed framework stands as a holistic solution [8, 9]. The components intricately woven into the framework are not just mere elements but rather synergistic building blocks meticulously designed to optimize energy acquisition, distribution, and utilization in a UAV’s dynamic operational context.
Energy Harvesting Devices: At the core of our framework lies a sophisticated selection of energy harvesting devices, each tailored to the specific energy sources prevalent in the UAV’s operational environment. High-efficiency solar panels are integrated for regions basking in sunlight, tuned RF harvesting antennas for continuous on-the-go recharging, and cutting-edge energy scavenging modules designed to capture and convert ambient environmental energy during flight. The synergy of these devices ensures a diversified and efficient energy acquisition strategy.
Communication Systems: The effectiveness of any framework hinges on seamless communication. Our proposed framework incorporates robust communication systems, enabling real-time data transfer between the energy harvesting devices, the UAV’s power management system, and ground control [10]. This not only ensures efficient monitoring of energy levels but also facilitates dynamic adaptation to changing environmental conditions [11]. The framework’s communication capabilities create a symbiotic relationship between the UAV and its energy sources, allowing for optimized performance in varied scenarios. Figure 2.1 shows the proposed framework and circuit for energy harvesting and transmission using UAV technology.
Power Management: Efficient energy utilization is a linchpin in extending UAV operational capabilities. To address this, the framework integrates advanced power management algorithms. These algorithms operate with surgical precision, optimizing the distribution of harvested energy to various on-board systems [12]. Prioritization of essential functions during critical mission phases, dynamic adjustment of power distribution based on real-time demand, and intelligent management of surplus energy for storage or transmission are integral aspects of this component, ensuring a judicious use of available power.
Figure 2.1 Proposed framework and circuit for energy harvesting and transmission using UAV technology.
2.2.2 Integration with UAV Systems
A distinguishing feature of our proposed framework lies in its seamless integration with existing UAV systems. Recognizing the diverse array of UAV types and sizes, the framework is engineered for adaptability. Whether applied to compact surveillance drones or large-scale cargo UAVs, the components harmonize to augment the UAV’s energy efficiency without compromising its core functionalities. This integration is not a mere overlay but a transformative enhancement, ensuring that the framework becomes an intrinsic and indispensable part of the UAV’s architecture [13, 14]. This symbiosis enhances overall mission success and operational sustainability, making the UAV not just a flying device but a sophisticated, self-sustaining system. Figure 2.2 shows the energy optimization techniques in UAV based cellular networks and different approaches towards it.
Adaptability and Scalability: Anticipating the continuous evolution of UAV technology, our proposed framework is built with adaptability and scalability at its core [15]. As technological advancements in energy harvesting devices emerge, and as UAV missions diversify, the framework can gracefully evolve to incorporate new technologies. Its modular design is not just a feature but a strategic advantage, allowing for scalability to accommodate UAVs of varying sizes and capabilities, from micro-drones to high-altitude, long-endurance vehicles [16, 17]. This adaptability is a testa-ment to the framework’s future-proof nature, ensuring it remains relevant and effective amidst the ever-changing landscape of UAV technologies.
In the subsequent sections, we will embark on a comprehensive exploration of each component, dissecting their synergies, testing methodologies, and real-world applications. The proposed framework, rather than being a static set of principles, stands as a living blueprint that can dynamically adapt and grow with the technological landscape. It serves as a beacon, guiding UAV technology toward a future where wireless power harvesting empowers unmanned aerial systems to transcend current limitations and redefine the possibilities of sustained, efficient, and autonomous operation [18]. Join us as we unravel the intricate workings of this groundbreaking framework and explore its potential to reshape the future of UAV energy sustainability.
Figure 2.2 Energy optimization in UAV-based cellular networks and approaches.
2.2.3 Methodology
Simulation and Testing: The journey toward realizing the proposed framework is a meticulous blend of conceptualization and practical validation [19]. Simulation and testing methodologies form the linchpin of this process, ensuring that the envisioned framework translates seamlessly into a robust and adaptable solution for UAVs.
In the realm of simulation, we employ cutting-edge computer-aided tools to subject the framework to a vast array of virtual scenarios. These simulations meticulously replicate diverse environmental conditions, mission profiles, and energy harvesting challenges that UAVs may encounter [20]. This iterative process allows us to fine-tune the algorithms governing the framework, optimizing them for real-world applications. The simulations act as a proving ground, where the framework’s responses are scrutinized under varying parameters, providing critical insights for further refinement.
Real-world testing follows suit, taking the framework from the digital domain to the dynamic complexities of the physical world [21]. Field tests involve the deployment of UAVs equipped with the framework in controlled and dynamic environments, mirroring the unpredictable conditions of real missions [22, 23]. The data collected during these tests, including energy harvesting rates, power distribution efficiency, and adaptability to unforeseen challenges, serves as a crucial feedback loop. This iterative process of simulation and testing ensures that the framework evolves not in isolation but through a continuous dialogue with the practical demands of UAV operations. Figure 2.3 shows the illustration of the UAV-enables wireless powered MEC server.
Data Collection: The methodology’s robustness lies in its commitment to comprehensive data collection. As the framework undergoes simulation and real-world testing, a wealth of information is gathered, spanning energy consumption patterns, environmental variables, communication system performance, and the overall health of the UAV’s power management system [24].
Quantitative metrics, such as energy efficiency ratios, power transmission rates, and adaptive response times, are rigorously measured. Qualitative data, derived from user feedback and operational observations, provides a nuanced understanding of the framework’s real-world implications [25]. This amalgamation of quantitative and qualitative data forms a comprehensive dataset that not only validates the theoretical underpinnings of the framework but also serves as a foundation for iterative optimization.
Figure 2.3 Illustration of the UAV-enabled wireless powered MEC.
2.3 Results and Discussion
Performance Metrics: The culmination of simulation, testing, and data collection yields a rich tapestry of performance metrics that quantifies the framework’s efficacy. Figure 2.4 shows the achieved energy efficiency of the whole system by the proposed scheme at the power splitting factor ρ=0.5. Energy efficiency emerges as a pivotal metric, measured in terms of the energy harvested per unit time. This metric serves as a litmus test for the framework’s ability to sustain UAV operations over extended periods. Power transmission rates and communication system reliability metrics offer insights into the real-time adaptability and responsiveness of the framework to the dynamic operational conditions often encountered by UAVs. Figure 2.5 shows the achieved energy efficiency of the whole system by the proposed scheme at the power splitting factor ρ=0.1.
The granularity of these metrics provides a nuanced understanding of the framework’s strengths and potential areas for improvement. Figure 2.6
