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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
SELF-POWERED CYBER PHYSICAL SYSTEMS This cutting-edge new volume provides a comprehensive exploration of emerging technologies and trends in energy management, self-powered devices, and cyber-physical systems, offering valuable insights into the future of autonomous systems and addressing the urgent need for energy-efficient solutions in a world that is increasingly data-driven and sensor-rich. This book is an attempt to aim at a very futuristic vision of achieving self-powered cyber-physical systems by applying a multitude of current technologies such as ULP electronics, thin film electronics, ULP transducers, autonomous wireless sensor networks using energy harvesters at the component level and energy efficient clean energy for powering data centers and machines at the system level. This is the need of the hour for cyber-physical systems since data requires energy when it is stored, transmitted, or converted to other forms. Cyber-physical systems will become energy hungry since the industry trend is towards ubiquitous computing with massive deployment of sensors and actuators. This is evident in using blockchain technologies such as Bitcoin or running epochs for artificial intelligence (AI) applications. Hence, there is a need for research to understand energy patterns and distribution in cyber-physical systems and adopt new technologies to transcend to self-powered cyber-physical systems. This book explores the recent trends in energy management, self-powered devices, and methods in the cyber-physical world. Written and edited by a team of experts in the field, this book tackles a multitude of subjects related to cyber physical systems (CPSs), including self-powered sensory transducers, ambient energy harvesting for wireless sensor networks, actuator methods and non-contact sensing equipment for soft robots, alternative optimization strategies for DGDCs to improve task distribution and provider profits, wireless power transfer methods, machine learning algorithms for CPS and IoT applications, integration of renewables, electric vehicles (EVs), smart grids, RES micro-grid and EV systems for effective load matching, self-powered car cyber-physical systems, anonymous routing and intrusion detection systems for VANET security, data-driven pavement distress prediction methods, the impact of autonomous vehicles on industries and the auto insurance market, Intelligent transportation systems and associated security concerns, digital twin prototypes and their automotive applications, farming robotics for CPS farming, self-powered CPS in smart cities, self-powered CPS in healthcare and biomedical devices, cyber-security considerations, societal impact and ethical concerns, and advances in human-machine interfaces and explore the integration of self-powered CPS in industrial automation. Whether for the veteran engineer or student, this volume is a must-have for any library.
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
Preface
Acknowledgements
1 Self-Powered Sensory Transducers: A Way Toward Green Internet of Things
1.1 Introduction
1.2 Need of the Work
1.3 Energy Scavenging Schemes in WSAN
1.4 Self Powered Systems and Green IoT (G-IoT)
1.5 Application Area and Scope of Self-Powered System in G-IoT
1.6 Challenges and Future Scope of the Self-Powered G-IoT
1.7 Conclusion
References
2 Self-Powered Wireless Sensor Networks in Cyber Physical System
2.1 Introduction
2.2 Wireless Sensor Networks in CPS
2.3 Architecture of WSNs with Energy Harvesting
2.4 Energy Harvesting for WSN
2.5 Energy Harvesting Due to Mechanical Vibrations
2.6 Piezoelectric Generators
2.7 Piezoelectric Materials
2.8 Types of Piezoelectric Structures
2.9 Hybridized Nanogenerators for Energy Harvesting
2.10 Conclusion
References
3 The Emergence of Cyber-Physical System in the Context of Self-Powered Soft Robotics
3.1 Introduction
3.2 Actuators and Its Types
3.3 Soft Actuator Electrodes
3.4 Sensors
3.5 Soft Robotic Structures and Control Methods
3.6 Soft Robot Applications
3.7 Future Scope
3.8 Conclusion
References
4 Dynamic Butterfly Optimization Algorithm-Based Task Scheduling for Minimizing Energy Consumption in Distributed Green Data Centers
4.1 Introduction
4.2 Related Work
4.3 Improved Dynamic Butterfly Optimization Algorithm (IDBOA)-Based Task Scheduling (IDBOATS)
4.4 Results and Discussion
4.5 Conclusion
References
5 Wireless Power Transfer for IoT Applications—A Review
5.1 Introduction
5.2 Sensors
5.3 Actuators
5.4 Energy Requirement in Wireless Sensor Networks (WSNs)
5.5 Wireless Sensor Network and Green IoT (G-IoT)
5.6 Purpose of G-IoT
5.7 Motivation
5.8 Contribution
5.9 Need of the Work
5.10 Energy Transferring Schemes in WSAN
5.11 Electromagnetic Induction
5.12 Inductive Coupling
5.13 Resonance Inductive Coupling
5.14 Wireless Power Transmission Using Microwaves
5.15 Electromagnetic Radiations
5.16 Conclusion
References
6 Adaptive Energy Intelligence Using AI/ML Techniques
6.1 Introduction
6.2 Evolution of Cyber Physical System
6.3 Relationship With Internet of Things
6.4 Challenges in Design and Integration of Cyber Physical Systems
6.5 Future Challenges and Promises
6.6 Machine Learning Models
6.7 Estimation of Building Energy Consumption
6.8 Development of Artificial Intelligence
6.9 Usage of AI/ML in Adaptive Energy Management
6.10 Use of Hybrid/Ensemble Machine Learning Algorithm for Better Prediction
6.11 Conclusion
References
7 Renewable Energy Smart Grids for Electric Vehicles
7.1 Introduction
7.2 Integration of Electric Vehicles (EVs) into the Power Grid
7.3 EV Charging and Electric Grid Interaction
7.4 EVs with V2G System Architecture
7.5 EVs and Smart Grid Infrastructure
7.6 Renewable Energy Sources Integration With EVs
7.7 Application in Transport Sector
7.8 Application in Micro-Grid
7.9 State-of-the-Art Review
7.10 Future Trends
References
8 Recent Advances in Integrating Renewable Energy Micro-Grid Systems With Electric Vehicles
8.1 Introduction
8.2 Electric Vehicles and Renewable Energy Sources: A General Overview
8.3 Microgrid
8.4 Interactions Between Cost-Conscious EVs and RESs
8.5 Interaction Between Efficiency-Conscious EVs and RESs
8.6 Open Problems
8.7 Conclusion
References
9 Overview of Fast Charging Technologies of Electric Vehicles
9.1 Introduction
9.2 Different Levels of Charging Electric Vehicles
9.3 State-of-the-Art Fast-Charging Implementation
9.4 DC Fast-Charging Structure
9.5 Fast Chargers
9.6 Today’s Situation and Future Needs
9.7 Fast-Charging Point Power Requirements
9.8 Recent Technologies in Fast Charging, Machine Learning, and Artificial Intelligence
9.9 Effect of Fast Charging on EV Powertrain Systems
9.10 Grid Impacts Caused by EV Charging
9.11 Fast-Charging Technologies on the Self-Powered Automotive Cyber-Physical Systems
9.12 Conclusions
References
10 A Survey of VANET Routing Attacks and Defense
10.1 Introduction
10.2 Attacks in VANET
10.3 Impacts of Attacks on VANET Routing
10.4 Nonintentional Misbehavior
10.5 Intentional Misbehavior
10.6 Defence Mechanism of Routing Attacks in VANET Routing
10.7 Intrusion Detection Techniques in VANETs
10.8 Anonymous Routing in VANETs
10.9 Challenges and Future Directions
10.10 Conclusion
References
11 ANN-Based Cracking Model for Flexible Pavement in the Urban Roads
11.1 Introduction
11.2 Literature Review
11.3 Methodology
11.4 Structural Number
11.5 Modeling Methodology
11.6 Model Validation
11.7 Sensitivity Analysis
11.8 Conclusions
11.9 Limitations
11.10 Future Scope of Study
References
12 A Review of Autonomous Vehicles
12.1 Introduction
12.2 History
12.3 Degrees in Automation
12.4 Benefits and Drawbacks
12.5 Working Principle of Autonomous Vehicles
12.6 Mechanics Involved
12.7 Conclusion
References
13 Meeting Privacy Concerns in Intelligent Transportation Systems
13.1 Introduction
13.2 Synopsis of ITS
13.3 Future Research Direction
13.4 Contributions to this Research
13.5 Conclusions
References
14 Feasibility Study of Digital Twin in Automotive Industry—Trends and Challenges
14.1 Introduction
14.2 Industrial Evolution
14.3 Influence of IoT on Digital Twin
14.4 Digital Twin in CPS Applications
14.5 Digital Twin Types
14.6 Levels of Digital Twin
14.7 Digital Thread
14.8 State-of-the-Art Digital Twin Deployment
14.9 Benefits of Digital Twin
14.10 Digital Twin Life Cycle
14.11 Digital Twin in Automotive Industry
14.12 Applications of Digital Twinning Technology in the Automotive Industry
14.13 Role of Digital Twins in Addressing Current Automotive Challenges
14.14 Challenges for Implementing Digital Twin in Automotive Industry
14.15 Bridging the Gap
References
15 State-of-the-Art and Future Applications of Farming Robotics
15.1 Introduction
15.2 Components of Agricultural Robots
15.3 Types of Agricultural Robots
15.4 Implementation of Robotics in the Agricultural Process
15.5 Challenges
15.6 Conclusions
References
16 Review on Robot Operating System
16.1 Introduction
16.2 Nomenclature
16.3 ROS Implementation
16.4 Conclusion
References
17 An Overview of Collaborative Robots and Their Applications
17.1 Introduction
17.2 Art of Study
17.3 Implementation of Collaborative Robots
17.4 Conclusion
References
18 State-of-the-Art and Future Applications of Powered Exoskeleton
18.1 Introduction
18.2 Powered Exoskeleton
18.3 State of the Art
18.4 Design Parameters to be Considered
18.5 Challenges to Tackle
18.6 Applications of Powered Exoskeleton
18.7 Conclusion
References
19 An Overview of Recent Trends in Consumer Robotics
19.1 Introduction
19.2 Entertainment Robot
19.3 Educational Robot
19.4 Social Robot
19.5 Toy Robot
19.6 Conclusion
References
20 Soft Robotics in Waste Management
20.1 Introduction
20.2 Soft Robotics Insights
20.3 Soft Robots in Waste Management
20.4 Are Soft Robots the First Step for a Sustainable Future?
20.5 Conclusions
References
21 State-of-the-Art Review of Robotics in Crop Agriculture
21.1 Introduction
21.2 Scope
21.3 Advantages
21.4 Disadvantages
21.5 Applications
21.6 Automation in Agriculture
21.7 Precision Agriculture
21.8 Conclusion
References
Index
Also of Interest
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Energy harvesting by PV cell.
Table 1.2 Self-powered system in agricultural applications.
Table 1.3 Self-powered system in industrial applications.
Table 1.4 Challenges and future of G-IoT.
Chapter 2
Table 2.1 Piezoelectric materials under various categories [6].
Table 2.2 Comparison of pyrolectric and thermoelectric nanogenerator.
Chapter 3
Table 3.1 Actuator types and their merits and challenges.
Table 3.2 Nature of soft robots.
Chapter 4
Table 4.1 Knee solution with ATLP and profit of the proposed IDBOATS scheme ...
Table 4.2 Knee solution with ATLP and profit of the proposed IDBOATS scheme ...
Chapter 9
Table 9.1 DC charging power levels.
Chapter 10
Table 10.1 Defense mechanisms in VANET.
Chapter 11
Table 11.1 Chennai road network.
Table 11.2 Drainage coefficients.
Table 11.3 Strength coefficient of different layers.
Table 11.4 Input layer and hidden layer weighted matrix.
Table 11.5 Hidden layer and output layer weighted matrix.
Table 11.6 Range of independent variables.
List of Illustrations
Chapter 1
Figure 1.1 Constraints and challenges in WSAN.
Figure 1.2 Solar/PV cell.
Figure 1.3 Thermoelectric cell [24].
Figure 1.4 Piezoelectric cell [25].
Figure 1.5 Plant microbial fuel [27].
Figure 1.6 Triboelectric effect [30].
Chapter 2
Figure 2.1 Architecture of WSN with energy harvester [3].
Figure 2.2 Sources of energy harvesting.
Figure 2.3 Piezoelectric material structures (a) Wurtzite structure and (b) Pe...
Figure 2.4 Unimorph and Bimorph structures of the cantilever.
Figure 2.5 Cymbal transducer.
Figure 2.6 TENG modes of working.
Figure 2.7 π type TE device.
Chapter 3
Figure 3.1 Various actuation processes and its merits.
Figure 3.2 Octopus arm. (a) Grasping action. (b) Base to tip curvature during ...
Figure 3.3 (a) Pull moment of anchor of inchworm. (b) Inchworm anchor push and...
Figure 3.4 Pyramid with self-folding SMP.
Figure 3.5 (a) Floater to assure that the robot is in an afloat state. (b) Fin...
Figure 3.6 Soft tail fish actuator based on hydraulic elastomer.
Figure 3.7 Wing motion based on flapping schematic underneath the sunlight.
Figure 3.8 Deformation of combustion chamber.
Figure 3.9 Schematic of HASEL actuator.
Figure 3.10 SMA spring deformation starfish robot.
Figure 3.11 Rolling robot with ring-type oscillator.
Figure 3.12 Self-powered tribo skin working principle.
Chapter 4
Figure 4.1 Convergence time of the proposed IDBOATS for increasing scaling fac...
Figure 4.2 Energy consumption of the proposed IDBOATS for increasing scaling f...
Figure 4.3 Mean execution time of the proposed IDBOATS for increasing scaling ...
Figure 4.4 Dollars of one day of the proposed IDBOATS representing revenue, co...
Chapter 5
Figure 5.1 Classification of wireless transfer techniques [52, 53].
Figure 5.2 Basic structure of a TET system [55].
Figure 5.3 Functional block diagram of wireless power transmission [54].
Chapter 6
Figure 6.1 Transition of cyber physical system to Internet of Things.
Figure 6.2 Relationship with IoT.
Figure 6.3 Integration with discipline and CPS.
Figure 6.4 Applications of AI in self-power CPS.
Figure 6.5 Framework of using ML/AI in energy management.
Chapter 7
Figure 7.1 Configuration of EV charging at AC L1 and L2 setup [33].
Figure 7.2 Configuration of EV charging at DC L1 and L2 setup [33].
Figure 7.3 VPP realization and control in V2G context [33].
Figure 7.4 (a) Schematic layout of ACS [34]. (b) AGV and ACS [34].
Chapter 8
Figure 8.1 The construction of BEVs.
Figure 8.2 The construction of parallel hybrid EVs.
Figure 8.3 Typical microgrid layout.
Chapter 9
Figure 9.1 Different charging levels of EV.
Figure 9.2 DC fast charging structure.
Figure 9.3 Charging system configuration for electric vehicle.
Figure 9.4 Charging station selection scenario.
Figure 9.5 Lithium plating.
Chapter 11
Figure 11.1 Flowchart showing methodology.
Figure 11.2 Zonal map of Chennai Municipal Corporation.
Figure 11.3 Modeling methodology.
Figure 11.4 Network architecture.
Figure 11.5 Training of data.
Figure 11.6 Predicted vs observed cracking.
Figure 11.7 Variation of cracking with ESAL.
Figure 11.8 Variation of cracking with drainage coefficient.
Figure 11.9 Variation of cracking with MSN.
Chapter 14
Figure 14.1 Digital twin concept.
Figure 14.2 Digital twin life cycle.
Figure 14.3 Product life cycle data (automotive).
Chapter 15
Figure 15.1 Schematic of a typical agricultural robot.
Chapter 18
Figure 18.1 Classification of exoskeleton.
Figure 18.2 Components of a powered exoskeleton.
Guide
Cover
Series Page
Title Page
Copyright Page
Preface
Acknowledgements
Table of Contents
Begin Reading
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])
Self-Powered Cyber Physical Systems
Edited by
Rathishchandra R. Gatti
Chandra Singh
Rajeev Agrawal
and
Felcy Jyothi Serrao
This edition first published 2023 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 © 2023 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 9781119841883
Cover image: Engineer controlling Robotic Arm, BiancoBlue | Dreamstime.comCover design by Kris Hackerott
Preface
This book is an attempt at a very futuristic vision of realizing self- powered Cyber-Physical Systems (CPS) by applying a multitude of current technologies such as ULP electronics, thin film electronics, ULP transducers, autonomous wireless sensor networks using energy harvesters at the component level, and efficient, clean energy for powering data centers and machines at the system level.
This is the need of the hour for cyber-physical systems, since data requires energy when it is stored, transmitted or converted to other forms. Since the verge is towards ubiquitous computing with massive deployment of sensors and actuators, cyber-physical systems will become energy hungry. Hence, there is a need for research to understand energy patterns and distribution in cyber-physical systems and to adopt new technologies to transcend to self-powered cyber-physical systems.
This book will explore the recent trends in energy management and self-powered devices and methods in the cyber-physical world. Self-powered devices include autonomous sensors, self-powered actuators, robots, and renewable-powered data centers. This book will also explore several advances in cyber physical systems, which may not be directly linked to self-powered autonomous systems but are enabling technologies that trend towards self–powered autonomy in the design and deployment of CPS.
Chapter 1 summarizes the low-cost, self-powered sensory transducers used in different application areas. The research aims at the different ways to generate/recharge power sources and their uses in different applications. It also focuses on recent challenges and the future scope of the device. Moreover, the work enlightens on the inter-relation of self-powered devices to the evolution of Green IoT.
Chapter 2 focuses on harvesting ambient energy sources to power wireless sensor networks. The two popular energy-harvesting devices that transform mechanical energy into electrical energy are piezoelectric nanogenerators and triboelectric nanogenerators. Utilizing these devices, self-powered sensors can be built. This chapter gives an overview of how the utilization of nanogenerators can lead to the development of self-powered sensors and their applications.
Chapter 3 focuses on various conventional actuator methodologies with the recent non-contact sensing equipment suitable for soft robots due to the low modulus factor. The inadequate nature of control over the soft robots in terms of conventional methods is overcome with the present changes in material fabrication, and design structure is taken into account to make flexible and reliable soft robots.
Chapter 4 focuses on various IDBOATS schemes, a multi-objective optimization method designed for DGDCs targeting reducing the likelihood of mean task loss and improving the profit of DGDC providers by partitioning the tasks among different ISPs and rate of task services associated with each GDC. It adopts the merits of a Mutation-based Local Search Algorithm (MLSA) for improving the diversity of solutions and preventing the issue of local optima by intelligently scheduling tasks of diversified applications and allocating the available resources within the bounds of response time.
Chapter 5 focuses on an overview of wireless power transmission techniques & also focuses on current issues and potential growth in the field.
Chapter 6 elaborates on work on computing machine learning algorithms that have to be proposed for CPS to be carried out, and application areas in the Internet of Things are ascertained.
Chapter 7 focuses on predominant concepts such as Grid to vehicles (G2V), Vehicle to Grid (V2G), and Virtual Power plant (VPP) will be discussed to evaluate the feasibility of a super system of smart Grid where renewables, EVs and the Grid are seamlessly integrated. During such integration, a smart grid will have multiple stakeholders, such as EV aggregators, Utility Service Providers, electrical transmission system operators (ETSOs) and electrical distribution system operators (EDSO) discussed in this chapter.
Chapter 8 discusses the recent technological trends in systems and subsystems of RES microgrids and EVs that aid in seamless integration, considering the optimal load matching of power demand and supply.
Chapter 9 focuses on different challenges that are discussed along with the state-of-art solutions and possible future solutions. Finally, it also focuses on the impact and leverages of fast charging technologies on self-powered automotive cyber-physical systems.
Chapter 10 covers the anonymous routing schemes, which help to hide the vehicular identity from others. Various intrusion detection systems are also discussed. Briefly, the survey covers most information about VANET and its security systems.
Chapter 11 discussed the different novel techniques presented in this chapter aimed at predicting the distress on pavement using a data-driven methodology. The main focus of this study is to manage the difficulties and ensure safe and comfortable roadway use. Diagnosing deterioration types and using proper maintenance techniques is critical, especially in the early phases.
Chapter 12 portrays the fundamental ideas of an autonomous vehicle. The car business is one of the main ventures in the globe today. The ascent of driverless vehicles will significantly affect organizations and experts. Driverless vehicles could trade corporate task forces for transport, or moving delegates and employees would obtain valuable time in the day by functioning rather than driving during everyday drives. Advancements in the present field are additionally guaranteed to modify the vehicle insurance market by diminishing mishaps. The link and synchronization of radar and ultrasonic sensors and optical cameras permit driverless driving.
Chapter 13 presents the basics of Intelligent Transportation Systems and their protection worries throughout their applications. Intelligent Transportation Systems (ITS) focus on incorporating detecting, investigating, and executing correspondence into movement, well-being, and comfort. All vehicle producers continuously make more brilliant and futuristic vehicles, changing the feel of movement.
Chapter 14 discusses different types of digital twins, such as digital twin prototypes, product digital twin, production digital twins, and performance digital twins and their applications in the automotive domain. It also explains the different levels of digital twins, such as descriptive twin, informative twin, predictive twin, comprehensive twin, and autonomous twin, which are essential during the product’s lifecycle. State-of-the-art digital twin applications in the automotive domain are discussed, highlighting the benefits and challenges in adoption.
Chapter 15 discusses the state-of-the-art and the future applications of farming robotics that will be deployed to farming CPS. In the present and future era of automation being implemented into industrial technologies, the capabilities of the Robot Operating System are advancing swiftly. Even though this technology is relatively new to the robotics world, it is available openly to any early adopters. The fundamentals of ROS are presented in Chapter 16.
Chapter 17 dicusses the novel techniques of industry 5.0 of dividing work between human workers and collaborative robots, as well as safety technology, which were the most pressing development needs. There needed to be a better response from representatives from various industries.
Chapter 18 elaborates about the powered exoskeleton and how it impacts our current technologies and associated applications across different industries, such as the military, healthcare, and consumer electronics. Several state-of-the-art developed exoskeletons for critical applications such as the rehabilitation of a patient or boosting the physical attributes of a soldier are discussed.
Chapter 19 discusses the state-of-the-art development of consumer robots, different types of consumer robots that are commercialized and the future trends of consumer robotics.
Chapter 20 discusses soft robotics as a novel technique. Soft robots are made of natural polymers known for their flexibility and durability, allowing for new and innovative implementations. Soft robotics is now at the forefront of its field. Soft robotics’ progress in waste management might point the way to a more sustainable future.
Chapter 21 mainly focuses on the scope of the use of robots in the agricultural industry. We also look into the current methods for preparing the ground before growing, seeding, reaping, and measuring yield.
Acknowledgements
First of all, we would like to thank the authors for their valuable contributions to the book chapters and their patient cooperation. We would also like to thank the publisher Scrivener-Wiley for providing us with the opportunity to publish this book and being very friendly and supportive during our publishing process. We would also like to thank our reviewers for reviewing the chapters. Lastly, we would like to thank our respective organisations for their continued support of our publications.
Editors –SPCPS
Dr. Rathishchandra R. Gatti
Prof. Chandra Singh
Dr. Rajeev Agrawal
Dr. Jyothi F. Serrao
1Self-Powered Sensory Transducers: A Way Toward Green Internet of Things
Rajeev Ranjan
School of Computer Science and Applications, REVA University, Bangalore, Karnataka, India
Abstract
A sensory transducer is a device that can convert environmental energy or event into a signal/energy that can be stored for further processing. For these conversions, small and low-cost devices are preferred by many engineers. Most of the devices used in such ubiquitous applications work in a hostile environment, where changing or replacing the power module is almost impossible. To overcome these problems, energy scavenging may be the solution. Researchers focus more on such devices because they regenerate power from the environment and can be used for a more extended period with less maintenance monitoring systems with the Internet of Things (IoT).
This work summarizes an overview of the low-cost, self-powered sensory transducers used in different areas of applications. The research aims at the different ways to generate/recharge the power sources and their uses on different applications. It also focuses on recent challenges and the future scope of the device. Moreover, the work enlightens on the interrelation of self-powered devices to the evolution of Green IoT.
Keywords: Self-powered transducers, sensors, WSAN, Internet of Things, green computing, energy harvesting, wireless sensor networks
1.1 Introduction
On May 19, 2021, Cyclone Tauktae struck the coast of India with a speed of 80 kmph. Likewise, two cyclones hit Indian states in the era of COVID-19 pandemic. However, the early detection of such kinds of natural calamities, for example, could reduce damage and life causalities. This early detection and monitoring of events are possible with advanced devices like sensors and actuators. A sensor works on the principle of transduction, which can convert environmental parameters in a readable signal form [1, 2]. The actuator performs necessary actions to the environment based on the sensed data [3].
The invention of sensors and actuators makes human life more manageable. The goal of the invention is to monitor objects and events in the area where human intervention is almost impossible [4]. In the present era, sensors can be used in almost every part of life. These sensors and actuators form Wireless Sensors and Actuator Networks (WSANs) to communicate wirelessly in a single-hop or multi-hop manner [5], and hence, they experience all the challenges faced by any wireless device. Furthermore, the device needs to work in a hostile environment with constrained resources [6–8] (Figure 1.1).
The sensory transducers require power to operate. A typical sensor required 50 microwatts (mW) to 150 mW of power in an active state [19]. As a result of the hostile working environment and complex applications, changing the power module is a bit tedious. Therefore, to overcome the problem, there are two ways: (i) to minimize the energy demand by the sensor devices and network, (ii) self-powered devices by generating energy from the environment. In the early days of sensor research, scientists proposed many techniques to optimize energy consumption. However, it lowers the Quality-of-Service (QoS) and affects the environment. Therefore, scientists have shown the path for the second option. Self-powered devices will help the system run for a more extended period with high QoS [9].
Figure 1.1 Constraints and challenges in WSAN.
Sensor is the primary element of a typical IoT application because it creates data in the form of signals [10–12]. The data created by sensor networks are saved, processed, and analyzed to gain information. The term IoT, coined by Kevin Ashton in 1999 [13], is associated with a heterogeneous set of such sensors, communicating devices, and networks working together with the Internet to achieve the goal(s).
IoT plays a significant role in information and communication technology (ICT) to automate and monitor remote events. Various hardware devices and software modules are working together in the ICT sector to achieve a goal in almost all human life fields. The hardware module includes electronic chips, antenna, processing units, and I/O devices. These devices need to work with different communication protocols, scheduling algorithms, and data processing in a few cases. In this process, most 2% of total greenhouse emissions are caused by ICT and related areas [14]. To minimize these emissions, self-powered sensors and actuators are one of the technologies leading to Green IoT [15]. Green IoT portraits the concept of reduced energy consumption of IoT devices and WSNs to make the environment safe.
In this work, the need and development of self-powered devices have been studied. The significant contribution of the work focuses on the following:
The necessity of self-powered systems in IoT
Finding a connection of the devices with green computation
Parameter requirement and scope for development of such devices in different applications
Future scope and challenges of the devices in the current scenario
1.2 Need of the Work
Self-powered systems play an important role in IoT applications because of high energy demand. However, developing and work with these systems is challenging in nature. A few reasons are:
The sensors or actuators used in IoT are smaller in size and cost. These networks are always resource-constrained networks.
These devices have to work in a hostile environment with different sets of hardware and software.
The IoT applications use different transmission media, for example, RF, infrared, etc., for communication.
The survivability and maintenance of the sensor mote working for ICT sectors are very challenging. However, applicability and users are enormous in numbers.
Moreover, generating power from the environment also requires advanced techniques and processes, which require more resources. Therefore, extensive research is the need of the hour, which concentrates on the process, techniques, requirements, applicability, challenges, and future scope of the work. It is also required exploring its ways toward green ICT Technology.
1.3 Energy Scavenging Schemes in WSAN
Energy scavenging from the environment is required to fill the gap between energy demand and supply in WSAN. As nonrechargeable primary batteries cannot increase the network lifetime and thus lower the QoS, researchers have proposed techniques for power generation. As a result of this, self-powered systems and devices become possible [16]. In this section, a few of such techniques and methods have been discussed.
1.3.1 Photovoltaic or Solar Cell
Sun is a significant source of energy in our universe. The average solar power radiation arrives at the earth’s atmosphere annually is approximately 1361 W/m2 [17]. This gives a huge potential for the power-hungry device to work with a longer lifetime. The use of PV cell in WSN gives more advantages over another energy-generating mechanism.
Solar is a continuous and huge source of energy. It provides highest power density among other energy scavenging techniques [19]. Table 1.1 highlights the energy generated by PV cell.
Table 1.1 Energy harvesting by PV cell.
Outdoors
direct sun
15 mW/cm
2
cloudy day
0.15 mW/cm
2
Indoor
standard office desk
0.006 mW/cm
2
<60 W desk lamp
0.57 mW/cm
2
It can be used for power constrained, low cost, small size devices, as well as for heavy machines in an industry.
A typical solar cell consists of a semiconductor of NP junction [18] (Figure 1.2). When the light energy emits the semiconductor’s surface, it converts the light energy into electrical energy. This ability of the conversion is called energy conversion efficiency. The energy conversion efficiency is calculated as the ratio of energy output from the solar cell to input energy from the sun. The typical conversion efficiency of a solar cell is approximately 10% to 15%. Some researchers have proposed a maximum power point tracker (MPPT) algorithm based photovoltaic power supply for WSN [20–23]. It is a prevalent technique used in PV cells to maximize power extraction. It has been proven that the algorithm improves the utilization by 85-90% with little effect from environmental disturbance and noise. Much earlier research focuses on solar power-based energy harvesting.
In the one hand, many researchers have focused on a solar-powered cell in many applications; on the other hand, there are certain limitations, which restrict the use of PV cell. The PV cell can be used only for terrestrial applications; however, many underwater and underground applications exist where PV cell usage is restricted. As the sensor and actuator network is a highly densely deployed and low-cost network, equipping each sensor with a solar panel will be a tedious task, increasing the deployment cost. The charging rate of a PV cell depends on the size of the solar surface and the application area. There is always a need to periodically clean the solar cell surface to ensure the required efficiency of the cell. As per the knowledge, the energy generation prediction is very general and prone to substantial prediction error.
Figure 1.2 Solar/PV cell.
1.3.2 Temperature Gradient
Microthermoelectric generator works on the principle of the Seebeck effect [24], where the temperature difference between two thermocouples can be converted into electric voltage (Figure 1.3). The generator comprises two heat-conducting ceramic plates and a series of p-type and n-type semiconductor material-made thermocouples. The generator can deliver power in the range of mW. For example, as per the empirical study, it can generate the power of 2.7 mW and 20°C with a load of 10Ω. Therefore, it can be used for applications and devices where energy demand is less or nonperiodic based.
The use of a microthermoelectric generator has unique advantages. It uses only heat as input to convert into electric voltage [115]. It can generate power for more than one hundred thousand hours continuously, and it can be applied the heat as a source of any size. The thermocouple materials and other modules consist entirely of solid, fixed materials, and hence are more durable. It is also an environment-friendly option for self-powered devices [117–120].
However, the uses of thermoelectric generators in the WSN may lead to a few limitations. It cannot be used for applications with high energy demand. Therefore, it can hardly be used for periodic monitoring of events or objects. These generators only have an efficiency rate between 5% and 10%. The setup cost of these microgenerators for each sensor and actuator nodes are very costly, and it is not feasible. It may have poor thermal characteristics and high output resistance.
Figure 1.3 Thermoelectric cell [24].
1.3.3 Pressure Variations
The piezoelectric generator works on the pressure variations and converts mechanical stresses in the material to electrical energy [24]. This piezoelectric transducer produces very high direct current output impedance, and it is advantageous in WSN applications. The well-known examples of these generators are in the heel of shoes to recharge the pacemaker inside the heart. The piezoelectric generators are made of a piezoelectric crystal, placed between two metal plates (Figure 1.4). Mechanical strain is applied on the material using the plates, which results in the excess negative and positive charges appear on opposite sides of the crystal faces. The metal plates collect these charges to produce a voltage. A microgenerator can produce 13 to 40 V of voltage.
The use of a pressure variation based energy generator has many advantages [25]. The communication between sensors may be affected by temperature variations, but the piezoelectric generator can work in a range of temperature conditions. The carbon emissions in the generator are significantly less, and that is why it is environmentally friendly. There is very little or no effect of external electromagnetic fields on the generators.
On the other hand, a piezoelectric generator possesses a few limitations. It is not suitable for large-scale energy scavenging because they are expensive. It is used for dynamic measurement only, not suitable for static conditions. Some piezoelectric crystals are soluble in water and therefore it is not suitable for a moist environment. If the temperature of the crystal increases above a particular threshold value, it stops working.
Figure 1.4 Piezoelectric cell [25].
1.3.4 Plant Microbial Fuel
Plant microbial fuel helps indoor and outdoor applications in WSN and IoT [26, 27]. It generates bioelectric current through the plant-microbe metabolisms at the rhizodiposits part of the plants. It generates a very low current of 100 µW/cm2 and thus, it can be used in a selected set of WSN & IoT applications. For example, Schamphelaire et al. [28] have generated biofuel from the rhizodeposition of rice. Typical plant-microbe fuel systems consist of an anode and cathode, separated by the plant membrane. Plant bacteria grow at the cathode and convert the bio species into carbon dioxide, protons, and electrons.
There are many advantages of microbial fuel in WSN and IoT applications. Biofuels are beneficial to the environment as it reduces the pollution in the environment. A wide range of different renewable substrates can generate energy, i.e., pee power, plant-microbe, desalination cells, and many more. The fuel cell performance is based on the size of the plant.
However, plant microbial fuel has a few disadvantages. It can generate very low electric current and the efficiency of the cell will not be based on plant height. The cost of biofuel is very high. It emits sulphur dioxide which causes acid rain.
1.3.5 Wind/Liquid Flow
Wind and liquid flow cause the generation of nontraditional energy [29]. Wind or liquid turbine rotates propeller-like blades around a rotor. The rotor turns the shaft to generate an electric current (Figure 1.5). Thus, the kinetic energy will be converted into mechanical energy and further, the mechanical energy is converted into electric energy. It can generate unlimited huge power, and thus, the applicability of the energy is very high. The main advantages of these power sources are it is highly inexpensive power generators and can produce high power. The land used for wind for liquid turbines can often also be used for other purposes, such as farming and thus it saves space. Wind turbines can be easily implementable on sensor devices working in the terrestrial environment whereas fluid turbines can be implemented in underwater applications. However, the main disadvantage of these types of electric sources is the cost of hydroelectricity is high and is always not suitable for low-cost network devices.
Figure 1.5 Plant microbial fuel [27].
1.3.6 Vibrations
The vibration based electric generators work on the principle of Faraday’s law of converting kinetic energy into electric energy. A small scale vibration-electricity generator generates 10 µW to 100 mW of power [30], which is enough for the sensor nodes and small power constraint devices working for monitoring purpose. Vibration based electric generators use a type of resonator which will vibrate with a certain frequency. A transducer mechanism is used to convert the kinetic energy to electric energy.
The major advantages of these generators are in industrial applications where the source of vibrations continuously exists as long as the machines are in operation. There are various types of vibration sources available in the environment, like, heartbeat and sinusoidal, which can be used as the input to the generators. However, theoretically, all types of vibrations can be converted into electric current, but practically, it is not always possible. There are certain types of vibrations are preferred in sensor applications. Scientists are working to identify the alternate sources of energy generation to reduce the load on primary nonrechargeable batteries so that there is a continuous supply to power-hungry devices. The energy scavenging techniques can increase the network lifetime of the WSN and IoT applications.
1.3.7 Friction
Friction-based electric generation is called as triboelectric effect where electrification can be done by the triboelectric materials like polymers, metals, and inorganic materials (Figure 1.6). The major advantage of such nanogenerators is there is no or less interference of the electrostatic scanning and thus it provides high efficiency. Energy density produced by the generator is proportional to pressing frequency. It requires low cost and maintenance.
Figure 1.6 Triboelectric effect [30].
However, it has high internal resistance between layers. The energy density of triboelectric generators is low compared to pyroelectric generators. Adverse environmental conditions may reduce its electrical output value.
1.4 Self Powered Systems and Green IoT (G-IoT)
It is estimated that nearly 50 billion wireless and wired devices have been connected to IoT by 2050 [31, 32]. These huge numbers of connected devices are working with the Internet and other related networks for many applications. This became possible due to the technological advancement of low-cost, low-size miniature, and portable devices. As per the statistics, it is estimated that IoT devices generate 79.4 Zettabytes by 2025 and 50,000 Zettabytes by 2050. Storage and processing of this vast data would require high bandwidth and energy. In the process, it emits a large number of greenhouse gases, which may reach 10-12% in five years, according to the study by U.S. Environmental Protection Agency. In this direction, scientists have focused on technologies to reduce greenhouse emission. Green IoT (G-IoT) is an energy-efficient approach to IoT to reduce the greenhouse effect caused by existing applications.
G-IoT aims at three types of IoT approaches:
To develop smart low power hardware.
To reduce the energy consumption in the devices and
To harvest the power from the environment.
Several earlier kinds of research focus on the first two options of saving or reducing energy consumption. The first option targets smart scheduling of tasks on cores [33], minimized processing power on devices [34], follows the layers of devices [35], and the use of passive low power sensors [36]. Researchers have also proposed many “energy–efficient” protocols and techniques to achieve the goals. The works include an optimized power model [37], dynamic modulation techniques [38], scheduling schemes [39], sleep-awake techniques [40], finding the least congested optimized route [41], topology control [42], and many more [43–46]. However, these works require thinking over the “trade-off” between resource optimization and QoS requirements. Therefore, in recent work, scientists explored the third option. The generations of new energy solutions have reduced the problem of “trade-offs” and aim to reduce the greenhouse effects.
G-IoT focuses on every design process of both hardware and software to reduce the greenhouse effect. The evolution of such power generating and wireless charging capabilities has shown how energy constrained devices work for a longer period in a more controlled and reliable way. Various Researchers have surveys and proposed energy charging to the sensor nodes deployed in the area [47–50]. This is a new and unique way to explore more, as it creates an environment in which sensor nodes can share energy between neighbours.
1.5 Application Area and Scope of Self-Powered System in G-IoT
The applicability of sensor-assisted networks has gained much focus in the recent era. In this section, different applications running on the self-powered system have been discussed briefly. Today, we have various self-powered systems and techniques ranging from agriculture to medical applications and from underground mineral exploration to Unmanned Arial Vehicles. Different WSN and IoT applications can be categorized into three parts based on the area of deployment.
1.5.1 Terrestrial Applications
The applications above the earth’s surface are listed under terrestrial applications. The deployment of the nodes in this category will be random or fixed deployment. In this, the major focused areas of applications are:
1.5.1.1 Agriculture
WSN and IoT applications in agriculture are huge. Considerable research work has been conducted to increase the agricultural yield in various parts of the world [51–60]. Among these, solar powered systems gain more mileage than other wireless charging systems [51–54]. Udutalapally et al. [51] have proposed a machine learning model to monitor the health of rice, tomato, potato, and corn plants. Results indicate that the PV cells can generate power 8.33 V to 16.3 V when the solar cell has inserted up to 30 cm in the ground. The generated power was enough to work micro controlled water motor and soil sensors. Sharma et al. [52] have proposed a solar powered system to monitor the plant growth. The result shows that the lifetime of the solar powered system increased by approximately 20 times with network throughput increased by 33%. Na & Isaac [53] implemented a solar powered system with the dimension of 170 x 170 x 2 mm, the solar panel was providing an output of 6 V at 3.65 W. Similarly, Heble et al. [54] installed the solar panel for maize crop monitoring and claimed the network lifetime increased by 83%. The model has been developed with Zigbee with LoRa module.
Another area of deployment of IoT and WSNs is in monitoring the agricultural yield. Ikeda et al. [55] have developed a microthermoelectric generator powered soil sensor. Researchers claim that the proposed method can harvest 100 μW–370 μW on average, which is enough for the micro-controller to perform. Researchers in [56, 57] have developed a biofuel powered WSN system for land and flora health monitoring. The biofuel system [56] generates average output power in the range from 200 μW to 300 μW. Reference [57] proposed the plant microbial fuel powered system for land monitoring. Keswani et al. [58], Nigussie et al. [59] and Benyezza et al. [60] suggested the energy charging of the sensor nodes using a fluid turbine. The fluid turbine helps in smart irrigation based on weather conditions in different zones and terrains. Akhund et al. [61] used the fluid turbine to develop an intelligent poultry farm. Terteil et al. [62] have proposed a real-time, low-cost precision agriculture monitoring system which consumes less power and emits less greenhouse gas. It is a LoRa based security system that would also detect the chemical composition of the soil [115]. Table 1.2 features the self energy systems in agricultural applications.
1.5.1.2 Smart Home and Cities
Development and design of smart cities focus more on self-powered G-IoT to optimize the power need. Researchers have proposed various models to fulfill the needs. Kaur et al. [63] have designed a five-layered architecture for G-IoT to reduce energy consumption. Researchers in [64] proposed G-IoT–assisted intelligent parking system. It is well known that the boundaries of smart cities and home applications are expanding very fast and widespread and large amount of techniques/algorithms have been proposed in various areas. Solar-powered self-charging points for network devices gain mileage in these applications also [65–68]. Researchers in [69] have deployed the PV-equipped WSN in the forest area to evaluate the environmental effects of a forest fire. The study proves that the network is reliable even two months after the fire. However, the network lifetime decreases in a harsh environment where fire propagation speed is higher. Research in [70–72] proposed the PV-enabled WSN for data-centric applications. The researchers have proposed secure data access control using encryption methods to maintain the balance among security, energy efficiency, and interoperability. Similarly, PV-equipped WSNs have been proposed for time synchronization [73–76], urban computing [77, 78], and so on. Solar power-based energy generation and distribution has attracted much attention from Industry and academics.
Table 1.2 Self-powered system in agricultural applications.
Work
Energy harvester
Parameter sensed
Power generated
[
51
]
Solar
Soil moisture, humidity, temperature
8.33 V to 16.3 V (Daytime)
[
52
]
Solar
Humidity, temperature, pressure
0.75V/cm
2
[
53
]
Solar
Soil moisture, humidity, temperature
6 V
[
54
]
Solar
Soil moisture, soil temp, humidity, light intensity, temperature
-
[
55
]
Thermoeleclric
Soil Temp
100 μW–370 μW
[
56
]
Bio - Fuel
Biosensor
200 μW to 300 μW
[
57
]
Bio - Fuel
pH value, temperature, humidity, type of soil
200 mV
[
58
]
Fluid Turbine & Solar
Soil moisture, soil Temperature, humidity, CO
2
sensor, light intensity
-
[
59
]
Fluid Turbine
Temperature, moisture, pH, microclimate
15 mW/cm
2
[
60
]
Fluid Turbine
Soil humidity and temperature
-
Furthermore, these areas have been conducted to generate power from sources other than solar plants. Yun et al. [79] have investigated the use of thermoelectricity to charge the nanosensors used in home applications. Imperial study shows that the temperature difference across an insulation panel harvests ~100 μW of energy. Likewise, researchers in [80–82] have studied extensively the use and scope of piezoelectric generators to charge and supply power to the devices in smart city and smart home applications. Thyagaraj Naidu [83] proposed to generate the vibration power from human movement.
Wind and fluid turbine is a hot topic in the area of the self-powered system for smart applications. Smart grid-based energy generation and distribution is the need of the hour for every big city. Various works, for example, generation and storage of such energy [84–86], forecast of wind energy [87, 88], distribution [89], have been proposed. Carmo et al. [90] have used the energy to develop vapor-compression heat pumps in residential use. They also proposed the technique to maintain and monitor the health of such turbines to work longer.
1.5.1.3 Industry
Sensor-enabled Industrial monitoring was started during the fourth Industrial revolution [91], initiated in the 1990s with increased connectivity and information exchange. This era is advanced in product development and monitoring automation. The exchange of information which makes the revolution was possible due to WSAN and IoT. Due to many such industrial applications, starting from raw material to final product quality, quantity, and process monitoring, it has gained much interest among scientists, engineers, and policymakers [92]. Energy consumption in any Industrial Internet of Things (IIoT) system depends on the device usages, the complexity of the algorithm or protocols, the size of the packet, the size of the data, total transmissions and retransmissions, and retransmissions mobility of the nodes. Moreover, most of the WSAN-IoT system uses a web interface to monitor and control the components. These web interfaces increase carbon emission.
In order to provide the G-IoT solutions in Industrial applications, pressure, vibration, heat, and motion are the primary sources of renewable energy [14]. Table 1.3 underlines the self energy systems in industrial applications. Firmansah et al. [93] have used piezoelectric power to monitor the Induction motor. The results obtained showed that the vibration generated by the sensor was 0.53V at a frequency of 50Hz. Plessis et al. [94] have proved that WSAN can monitor the machine with piezoelectric generated power and cantilever architecture. Similarly, works with references [95–98] generated power with piezoelectric generators to monitor and diagnose machines. In 2018, Hou et al. [99] have used the temperature gradient-based self-powered WSN and monitored the temperature generated by the machines. The paper concludes that the energy conversion rate is ~27%, and it can recharge the WSN nodes with an active period of 0.9s. McNinch et al. [100] have discussed the use of thermocouples in the mining industry. Williams et al. [101] have proposed a hybrid approach of the solar and radiofrequency powered system in data centers monitoring. They have applied the technique with a sleep-wake scheduling scheme to optimize the power usages. Pozo et al. [102] have suggested using another hybrid method with wind turbine and PV cell in IIoT. The requirement of these hybrid methods balances the considerable energy requirement in Industry to work continuously with different devices.
Table 1.3 Self-powered system in industrial applications.
Work
Energy harvester
Parameter sensed
Power generated
[
93
]
Piezoelectric
Vibration of motor
0.53V at 50Hz
[
94
]
Piezoelectric
Temperature, voltage, vibration
2.8 mW at 100 Hz
[
95
]
Piezoelectric
Accelerometer, temperature, voltage
0.04W at 1 Khz
[
96
]
Piezoelectric & Electromagnetic
Temperature, vibration
0.003 to 0.015 mw at 100hz
[
97
]
Magneto Piezoelectric
Temperature, movement, accelerometer, magnetometer, gyroscope, light, humidity, barometer
39.2 mw (max)
[
98
]
Piezoelectric And Electromagnetic
Temperature, humidity, light, motion
25.45 mw at 60 Hz
[
99
]
Temperature Gradient
Temperature
32.7 mw and 34.7 mw (Two Thermoelectric Generator)
[
100
]
Temperature Gradient
-
-
[
101
]
Solar And Radio Frequency
Temperature
-
Moreover, various IoT architectures and layered approaches have also been proposed to reduce the greenhouse effects. Shekhar Rao et al. [103] have designed architecture with a 5G network to manage large data generation by IIoT devices. Ghader et al. [104] have proposed a data management layer to cache data called proxy nodes to optimize network energy consumption. Chiarotti et al. [121] have proposed a thermoelectric generator for industrial plant.
1.5.1.4 Medicines
Wearable sensory transducers are one of the critical technologies in personalized applications. With other communicating devices, biosensors form Body Area Networks (BAN), which can measure blood glucose, temperature, heartbeat, oxygen level and help doctors and health personnel monitor the patient remotely and effectively. The low-powered sensors help in early-stage disease detection, diagnosis, and management. It can be used to monitor quarantined people and severe patients during the COVID-19 pandemic [105]. Numerous solutions have been proposed with low power biosensors; however, energy consumption and optimization are significant hurdles in timely reporting and identifying disease. Lou et al. [106] have done a detailed survey on self–powered biosensors for medical applications. As per the literature, biosensors can be charged by the electric current generated by mechanical forces, such as vibration and friction, temperature gradient, and solar energy. Similarly, researchers in [107, 108] have also studied various self-powered sensors for medicine and health applications.
Different researchers have also implemented self-powered biosensors in different health applications. Mao et al. [109] have devised an auto-powered sensor system to monitor the Maximal Lactate Steady State (MLSS) during the exercise of sportsmen. The biosensors were getting charged by piezoelectric signals generated by the motion of the sportsman. The researchers in [110] proposed biosensors attached with the diaper to measure the glucose level in the urine of a diabetic patient. The system was powered by an enzymatic biofuel cell, which used the glucose in urine as fuel to generate electricity. Zhang et al. [111] has used motion powered system for respiratory monitoring.
1.5.1.5 Environment Monitoring
Environment monitoring is a way to keep track and predict climate change across the globe. It includes (i) air quality monitoring [116, 131–134], (ii) water quality monitoring [149, 150], (iii) waste monitoring, and (iv) disaster monitoring [122–124]. As per UNICEF reports, the UNECE Ministers endorsed recommendations for strengthening environmental monitoring and information systems in European and Central Asian countries. It was prepared by the UNECE Working Group on Environmental Monitoring at the Fifth Ministerial Conference on Environment Europe held at Kyiv in 2003 [112]. Moreover, G-IoT focuses on environmental and climate clean initiatives. However, the traditional power supply through battery affects the climate mainly because it has a limited lifetime and increased e-waste [113, 114]. In this work [113], researchers have proposed a wind–hybrid generated power to charge the wireless node for climate monitoring (temperature, humidity, and pressure). The system uses three nanogenerators, namely, triboelectric piezoelectric nanogenerator and electromagnetic generator, to produce power.
Water quality and contamination will be discussed in next section in details.
1.5.1.6 Structural Monitoring
Structural monitoring includes damage and load over a structure and prediction of the lifetime of the same [125]. In the recent era, it can be monitored and analyzed remotely by WSN and IoT. Aono et al. [126] has proposed a pressure powered system to detect cracks in the steel frame. Zheng et al. has generated power through windmill to recharge the sensor cell used in subway tunnel monitoring. In reference [126], researchers have generated energy using the magnetic levitation, which can be used to charge a lithium battery. The battery gives power to the rail side Zigbee module. The researchers claim that the device can produce up to 2.3V with the rail vehicle running speed of 130 kmph. Researchers in [127] have powered the wireless sensor system using piezoelectric material to monitor large-scale bridges remotely. Based on the empirical studies conducted, it was found that the energy harvested were enough to detect the lifetime of the bridge by finding the cracks, damage and load of the bridge. Salehi et al. [127] recently studied in details about the self-powered system used in structural monitoring. The literature highlighted that majority of the structural monitoring and civil engineering applications use strain and triboelectric voltage for energy harvesting.
1.5.1.7 Indoor Applications
Applications related to smart homes and indoor applications are interlinked to each other. In this era of smart devices, advanced IoT and sensor devices can be used in indoor applications to make our homes smart where different devices communicate. The indoor applications include intruder detection, appliances monitoring and control, indoor air quality, floor sterilization, dust removal and many more. Major advantage of such applications is the deployment of the nodes may be deterministic rather than random deployment [128]. Therefore, the energy consumption required for deploying the node is negligible. However, self-sustainable sensor nodes and other communicating devices are required to gain the QoS in the device to device communication. Researchers in [129, 130] have proposed different methods of self-sustainable sensor nodes applications to detect intruders. Dong et al. [129] have used four Electromagnetic harvesters in series to recharge a 470-μF storage capacitor through a rectifier. Sultana et al. [130] have used a piezoelectric generator to provide charge to the microcontroller. References [131–133] have discussed self-powered systems to monitor the air quality in smart homes. Tran et al. [131] have generated power from radio frequency operated at 915 MHz to operate 0.5 mW microcontroller units. The power generated by RF can be used to monitor volatile organic compounds, temperature, humidity, and atmospheric pressure inside the building. Researchers in [132] have tried indoor PV cells to provide power for the same application. Jiang et al. [133
