Energy Harvesting in Wireless Sensor Networks and Internet of Things
2: College of Communication and Information, University of Kentucky, USA
The energy efficiency paradigm associated with Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) is a major bottleneck for the development of related technologies. To overcome this limitation, the design and development of efficient and high-performance energy harvesting systems for WSN and IoT environments are being explored. This edited book comprehensively covers energy harvesting sources and techniques that can be used for WSN and IoT systems. The authors cover energy harvesting, energy management and energy prediction models to maximize the energy harvested. They also identify major architecture advances to develop cost-effective, efficient, and reliable energy harvesting systems. This is a useful reference for researchers, engineers, practitioners, designers, and R&D staff involved in the development of energy harvesting models, architectures and technologies for practical deployments in WSN and IoT environments. The book will be of interest to professionals involved in developing energy harvesting systems, industry practitioners, and manufacturers in IoT, sensing, and energy harvesting technologies. Finally, it will also be a useful reference for graduate, PhD and postdoctoral students following courses in WSNs, IoT and energy harvesting technologies.
Inspec keywords: energy harvesting; energy conservation; wireless sensor networks; Internet of Things; Internet
Other keywords: mobile computing; Big Data; data handling; energy conservation; public domain software; Internet of Things; energy harvesting; wireless sensor networks; parallel processing; Internet
Subjects: Wireless sensor networks; Energy harvesting; General electrical engineering topics; Energy harvesting; Energy conservation; Computer communications; General and management topics; Computer networks and techniques
- Book DOI: 10.1049/PBCE124E
- Chapter DOI: 10.1049/PBCE124E
- ISBN: 9781785617362
- e-ISBN: 9781785617379
- Page count: 328
- Format: PDF
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Front Matter
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Part I. Energy harvesting from ambient environments
1 Thermal energy harvesting for wireless sensor networks
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Development of a self-powered electronic module that does not need to replace power supply has been considered in the context of energy harvesting. Energy harvesting, which refers to the process of storing derived energy from ambient or external sources, is widely investigated, particularly for providing a small amount of power for low-energy electronics. The sources might be kinetic, solar power, or thermal, also known as ambient energy. The interest in wireless sensor networks (WSNs) which are basically a network of smart nodes that are connected wirelessly led to rapid progress in this area for various applications, more specifically in industrial and medical sections. With progressing the industrial internet of things (IIoT), the need for energy harvesting in smart cities is increased in recent years. With respect to the importance of energy harvesting in WSNs, this chapter provides an overview of energy harvesting sources, especially thermoelectric harvesters in smart cities with more emphasis on electric vehicles (EVs), smart grids, and medical and environmental aspects. Also, challenges regarding the application of WSNs have been investigated.
2 Auxetic designs to improve ambient strain energy harvesting for WSN/IoT
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Using an auxetic (material possessing an effective negative Poisson's ratio, such that it expands laterally under tension and vice versa) substrate under a piezoelectric layer can greatly increase the power output of a vibration energy harvester. This chapter discusses the testing and optimisation simulations for a selection of auxetic designs: re-entrant honeycomb, and rotating triangle, parallelogram and hexagram. The four best auxetic designs from this process were taken forward to the experimental stage to compare them empirically. All the auxetic samples had outputs more than triple the baseline, with the honeycomb having the highest gain of 5.7 times (570 versus 101 μW from an input excitation of 10 Hz, 100 με). These auxetic energy harvesters open up new avenues for wireless self-powered structural health monitoring sensor nodes in infrastructure, buildings and vehicles, where the ambient vibration energy might otherwise be too diffuse to harvest from.
3 Energy harvesting from human body
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This chapter reviews the concepts used to harvest energy inside the human body and compare the harvesters' output in each concept with each other. Also, the chapter introduces a solution that avoids drawbacks in previous work done in this area. Implantable energy harvesters target to harvest energy from internal human body energy (mostly) or external energy sources. Examples of internal body energy are expanding and contracting arteries, blood flow and pressure in veins and arteries, vibrating organs, body heat, and chemical reactions. The chapter starts with current trends and challenges in the field of harvesting energy inside the human body. It categorizes the sources of energy inside the human body. Section 3.2 reviewed the previous work done in this field and classified it according to the harvesting concept (electromagnetic, piezoelectric, fuel cell, and electrostatic). Section 3.3 introduces the proposed solution, which utilizes the blood flow and pressure energies inside a large vein to deform piezoelectric beams and generate electricity. Future challenges and research opportunities are discussed in Section 3.4, and finally, Section 3.5 ends with the conclusion.
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Part II. RF energy harvesting
4 Cognitive and energy harvesting-based D2D communication in wireless multimedia sensor networks underlying multi-tier cellular networks
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In this chapter, we use a statistical approach based on stochastic geometry to model and evaluate the performance of a cognitive D2D-enabled wireless multimedia sensor network (WMSN) in the presence of a multi-tier multi-channel cellular network. It will be shown that energy harvesting can efficiently power D2D communications in the WMSN. We will also observe that cognitive channel access can help improve the quality of service of D2D connections in the WMSN.
5 A systematic study on the metamaterial microstrip antenna design for self-powered wireless systems
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This chapter discusses comprehensively the possibility of desigining a low-profile metamaterial (MTM) and electromagnetic bandgap structures for self-powered wireless systems. The designed antenna would be mounted on a flexible solar panel substrate for wearable applications. For this, an antenna based on a Hilbert-shaped MTM is exemplified to performance over the frequency ranging from a few MHz up to 6 GHz to satisfy the frequency bands of modern 5G networks and wireless sensor networks/Internet of Things applications.
6 On the trade-off of RF energy harvesting and transmission intervals in cognitive IoT network using fuzzy logic
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This study explores an optimal mode selection strategy for radio frequency (RF)-powered Internet of Things (IoT) devices based on cognitive radios (CRs). The RF energy harvesting allows the IoT devices to operate for a possibly much longer lifespan. It is assumed that the IoT devices harvest RF energy obtained from the primary user. However, IoT devices cannot carry out RF energy harvesting and opportunistic spectrum access at the same time. The IoT system can then determine whether to access the spectrum or harvest RF energy at each time slot to optimize the expected total throughput. This chapter canvasses over methods for selecting the suitable mode for IoT devices using a fuzzy logic-based decision-making process. Numerical results have also been discussed, showing that the developed policy balances obtaining the immediate throughput and harvesting the RF energy for future use. The simulation results show the improved performance of cognitive IoT networks using an optimal model selection strategy.
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Part III. Emerging trends in energy harvesting
7 UAV-assisted energy harvesting for WSNs/IoT networks
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The stringent energy constraint imposed by the small size of sensing nodes is a critical issue in realizing the perpetual operation of wireless sensor networks/ Internet of Things (WSNs/IoT) networks. The unmanned aerial vehicle (UAV) network is a promising technology that is already being used for several applications. They can be a substantial and convenient source of energy harvesting for WSNs/IoT applications, especially in far-flung areas. However, due to mobile nature and constraints such as budget and airspace congestion, and different capabilities of different UAVs, many factors need to be considered in designing UAV-assisted energy harvesting schemes. In this chapter, we present a comprehensive review of various schemes that are proposed to improve energy harvesting directly or indirectly by improving some dependent performance objectives in UAV-assisted WSNs/IoT networks.
8 A review on resonant beam communication with simultaneous wireless data transmission and energy harvesting techniques
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Limited power sources in wireless sensor network and Internet of Things (IoT) devices are major obstacles that prevent extensive growth. Thereafter, wireless energy harvesting methods were developed to provide energy for these devices and prolong their lifetime. In this chapter, an in-depth review of the essential and resonant beam charging (RBC) energy harvesting method is described. Besides, a detailed explanation of the system model, working mechanism, and mathematical analysis of the RBC system is furnished and discussed. In addition, improving the overall system efficiency using an adaptive control system is introduced. The recent RBC-simultaneous wireless information and power transfer (SWIPT) system, which is implemented to support data and power transfer, is studied and named resonant beam communication (RBCom). An analytical model and mathematical analysis of RBCom are provided. Finally, the scheduling algorithm for multiple users charging is presented while considering other latent alternatives.
9 Simultaneous wireless information and power transfer in Internet of Things
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Recently, we have seen massive integration of devices with the Internet which helped in envisioning the concept of Internet of Things (IoT). IoT comprises a distributed network of low powered, low storage, low processing, and low wireless communication range devices. Typically, these devices are battery operated with limited lifespan and replacing the batteries is a cumbersome process. Therefore, effective energy management and energy harvesting schemes need to be exploited. Generally, the major source of power drain is associated with the wireless communication between the devices. On the contrary, information gathering and transfer are attributed as the main functions of the IoT network. Thus, simultaneous wireless information and power transfer (SWIPT) is a promising choice for IoT. We describe various SWIPT architectures and enabling technologies in IoT. We review the various approaches and techniques proposed in the literature for SWIPT-enabled IoT. Finally, we highlight future design challenges of SWIPT that must be addressed to deliver information and energy continuously and reliably to IoT devices.
10 Energy-efficient computing for future IoT applications
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Energy management is a key challenge for future Internet of Things (IoT) applications. As the IoT nodes are energy-constrained devices, energy management for IoT networks is of paramount importance. A major component of future 6G-empowered IoT networks will be massive computing enabled by fog nodes. In this chapter, we present recent work carried out related to energy-efficient computing at the fog nodes and energy-efficient transmission between IoT devices and fog nodes. This chapter covers techniques and algorithms related to energy efficient task offloading, device processing parameters control, content caching, and intelligent-surface-based transmissions. Finally, we present future opportunities and open challenges to improve energy efficiency for IoT applications.
11 Intelligent MapReduce technique for energy harvesting through IoT devices
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MapReduce is a programming model that processes large-scale datasets in the distributed computing environment. It manages enormous data (big data) using system software such as Apache Hadoop. We recently found that MapReduce does not restrict itself for execution on data repositories only; devices are also using it for edge-node based runtime efficient data computing. Thus, it also enables MapReduce effective on Internet of Things (IoT) devices for various reasons, such as producing data analytics within the device and preparing a complex analytics dataset. Therefore, we find it applicable that it could be programmed on an IoT device to regulate energy consumption and perform an energy harvesting technique based on the data's production pattern. This chapter presents a novel technique for energy harvesting through the IoT enabled MapReduce paradigm. The Amazon Web Services platform's evaluation shows how a simple MapReduce paradigm harvests energy through an IoT device.
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Part IV. Security and energy harvesting
12 Hide-and-detect: forwarding misbehaviors, attacks, and countermeasures in energy harvesting-motivated networks
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Multi-scale, heterogeneous, and battery-powered Internet-of-Things (IoT) sensors and devices (later in short, nodes) have been widely deployed in diverse applications and networks. Due to the limited amount of battery energy, energy harvesting motivated networks (EHNets) powered by immediate environmental resources are increasingly popular and rapidly emerging as the next generation of ubiquitous communication infrastructure. However, EHNets are admittedly vulnerable to a denial-of-service (DoS) attack because of the shared medium, centralized coordination, and limited computing and communicating capabilities. Because of inherent resource constraints, EHNets seldom deploying conventional heavy-weight cryptographic techniques and secure algorithms and protocols. In light of these, we first investigate energy harvesting-based networking operations and applications. Second, we analyze the different types of forwarding misbehavior and attack caused by malicious nodes and their corresponding detection strategies. We introduce a set of adversarial scenarios and visualize its communication activities to capture vulnerable scenarios and potential malicious nodes. Here, single and multiple malicious nodes colluding together are considered. Lastly, we comprehensively compare the detection strategies of forwarding misbehavior by considering six perspectives and provide future research directions with interdisciplinary points of view.
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Back Matter
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