Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Electromagnetic Energy Harvesting <s> This paper discusses far-field wireless powering for low-power wireless sensors, with applications to sensing in environments where it is difficult or impossible to change batteries and where the exact position of the sensors might not be known. With expected radio-frequency (RF) power densities in the 20-200- μW/cm2 range, and desired small sensor overall size, low-power nondirective wireless powering is appropriate for sensors that transmit data at low duty cycles. The sensor platform is powered through an antenna which receives incident electromagnetic waves in the gigahertz frequency range, couples the energy to a rectifier circuit which charges a storage device (e.g., thin-film battery) through an efficient power management circuit, and the entire platform, including sensors and a low-power wireless transmitter, and is controlled through a low-power microcontroller. For low incident power density levels, codesign of the RF powering and the power management circuits is required for optimal performance. Results for hybrid and monolithic implementations of the power management circuitry are presented with integrated antenna rectifiers operating in the 1.96-GHz cellular and in 2.4-GHz industrial-scientific-medical (ISM) bands. <s> BIB001 </s> Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Electromagnetic Energy Harvesting <s> Radio frequency (RF) energy transfer and harvesting techniques have recently become alternative methods to power the next generation wireless networks. As this emerging technology enables proactive energy replenishment of wireless devices, it is advantageous in supporting applications with quality of service (QoS) requirement. In this paper, we present an extensive literature review on the research progresses in wireless networks with RF energy harvesting capability, referred to as RF energy harvesting networks (RF-EHNs). First, we present an overview of the RF-EHNs including system architecture, RF energy harvesting techniques and existing applications. Then, we present the background in circuit design as well as the state-of-the-art circuitry implementations, and review the communication protocols specially designed for RF-EHNs. We also explore various key design issues in the development of RF-EHNs according to the network types, i.e., single-hop network, multi-antenna network, relay network and cognitive radio network. Finally, we envision some open research directions. <s> BIB002 </s> Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Electromagnetic Energy Harvesting <s> RF energy harvesting (RFH) is emerging as a potential method for the proactive energy replenishment of next generation wireless networks. Unlike other harvesting techniques that depend on the environment, RFH can be predictable or on demand, and as such it is better suited for supporting quality-of-service-based applications. However, RFH efficiency is scarce due to low RF-to-DC conversion efficiency and receiver sensitivity. In this article, we identify the novel communication techniques that enable and enhance the usefulness of RFH. Backed by some experimental observations on RFH and the current state of the art, we discuss the challenges in the actual feasibility of RFH communications, new research directions, and the obstacles to their practical implementation. <s> BIB003 </s> Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Electromagnetic Energy Harvesting <s> This paper proposes a novel cooperative composite energy harvesting system that consists in the association of a traditional passive UHF Radio Frequency Identification (RFID) chip with an Electromagnetic Energy Harvesting Circuit (EEH-C). The objective is to exploit the i-v nonlinearity of the rectifier by applying a signal with time-varying envelope in order to improve the RF-to-dc conversion efficiency. Thanks to a multisource configuration, i.e., an RFID reader at 0.868 GHz and an external source at 2.45 GHz, the EEH-C is able to rectify the $3^{\rm rd}$ harmonic product of the RFID chip, in addition to the 2.45 GHz signal, without compromising the RFID communication. Additionally, digitally modulated signals are used at 2.45 GHz to further enhance the harvesting efficiency of the EEH-C. From theory, simulations and measurement it is demonstrated that the exploitation of the three nonlinear effects of rectifying circuits, i.e., (i) impedance power dependency, (ii) harmonic signals production, and (iii) waveform dependency can greatly improve the conversion efficiency of the EEH-C. <s> BIB004 </s> Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Electromagnetic Energy Harvesting <s> Radiative wireless power transfer (WPT) is a promising technology to provide cost-effective and real-time power supplies to wireless devices. Although radiative WPT shares many similar characteristics with the extensively studied wireless information transfer or communication, they also differ significantly in terms of design objectives, transmitter/receiver architectures and hardware constraints, and so on. In this paper, we first give an overview on the various WPT technologies, the historical development of the radiative WPT technology and the main challenges in designing contemporary radiative WPT systems. Then, we focus on the state-of-the-art communication and signal processing techniques that can be applied to tackle these challenges. Topics discussed include energy harvester modeling, energy beamforming for WPT, channel acquisition, power region characterization in multi-user WPT, waveform design with linear and non-linear energy receiver model, safety and health issues of WPT, massive multiple-input multiple-output and millimeter wave enabled WPT, wireless charging control, and wireless power and communication systems co-design. We also point out directions that are promising for future research. <s> BIB005 </s> Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Electromagnetic Energy Harvesting <s> This paper addresses two of the most critical challenges in today's wireless sensor network (WSN)-based Internet of Things (IoT) applications: the self-sustainable operability and the matching between harvested energy and data transmission demands from sensor nodes. To this end, we propose a self-sustainable radio frequency (RF) energy harvesting algorithm that optimizes the energy harvesting period in order for the sensor nodes to harvest energy from the downlink physical channels of the LTE eNodeB (i.e., PBCH and PDCCH). The proposed algorithm adapts the energy harvesting period in accordance to the stochastic characteristics of the traffic load from the IoT applications and the incoming RF energy at sensor nodes. Simulation results confirm that the developed algorithm allows the sensor node to harvest sufficient energy to operate in a self-sustainable manner. It is also shown that the proposal significantly improves the energy-efficiency while satisfying the quality-of-service requirements of the WSN-based IoT applications. <s> BIB006 </s> Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Electromagnetic Energy Harvesting <s> This paper presents a state-of-the-art review on a hot topic in the literature, i.e., vibration based energy harvesting techniques, including theory, modelling methods and the realizations of the piezoelectric, electromagnetic and electrostatic approaches. To minimize the requirement of external power source and maintenance for electric devices such as wireless sensor networks, the energy harvesting technique based on vibrations has been a dynamic field of studying interest over past years. One important limitation of existing energy harvesting techniques is that the power output performance is seriously subject to the resonant frequencies of ambient vibrations, which are often random and broadband. To solve this problem, researchers have concentrated on developing efficient energy harvesters by adopting new materials and optimising the harvesting devices. Particularly, among these approaches, different types of energy harvesters have been designed with consideration of nonlinear characteristics so that the frequency bandwidth for effective energy harvesting of energy harvesters can be broadened. This paper reviews three main and important vibration-to-electricity conversion mechanisms, their design theory or methods and potential applications in the literature. As one of important factors to estimate the power output performance, the energy conversion efficiency of different conversion mechanisms is also summarised. Finally, the challenging issues based on the existing methods and future requirement of energy harvesting are discussed. <s> BIB007 </s> Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Electromagnetic Energy Harvesting <s> Abstract This study investigates the vibration and dynamic response of a system of coupled electromagnetic vibration energy harvesting devices that each consist of a proof mass, elastic structure, electromagnetic generator, and energy harvesting circuit with inductance, resistance, and capacitance. The governing equations for the coupled electromechanical system are derived using Newtonian mechanics and Kirchhoff circuit laws for an arbitrary number of these subsystems. The equations are cast in matrix operator form to expose the device’s vibration properties. The device’s complex-valued eigenvalues and eigenvectors are related to physical characteristics of its vibration. Because the electrical circuit has dynamics, these devices have more natural frequencies than typical electromagnetic vibration energy harvesters that have purely resistive circuits. Closed-form expressions for the steady state dynamic response and average power harvested are derived for devices with a single subsystem. Example numerical results for single and double subsystem devices show that the natural frequencies and vibration modes obtained from the eigenvalue problem agree with the resonance locations and response amplitudes obtained independently from forced response calculations. This agreement demonstrates the usefulness of solving eigenvalue problems for these devices. The average power harvested by the device differs substantially at each resonance. Devices with multiple subsystems have multiple modes where large amounts of power are harvested. <s> BIB008 </s> Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Electromagnetic Energy Harvesting <s> Abstract The main challenge in the design of vibration energy harvesters is the optimization of energy outcome relative to the applied excitation. This depends strongly on the used energy harvesting principle. Possible principles to generate energy from common existing vibration sources are electromagnetic, magnetoelectric, electrostatic and piezoelectric. At low vibration frequencies, electromagnetic and magnetoelectric energy harvesting techniques deliver energy with the highest efficiencies and are therefore the most promising principles. In this paper, an overview of the recent developments in the state of the art of vibration energy harvesters is presented. Mainly, electromagnetic and magnetoelectric principle are discussed and they have been classified relative to design aspects. Considering delivered energy and depending on the type of vibration excitation, reported converters are compared. <s> BIB009 </s> Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Electromagnetic Energy Harvesting <s> Energy Harvesting techniques are increasingly seen as the solution for freeing the wireless sensor nodes from their battery dependency. However, it remains evident that network performance features, such as network size, packet length, and duty cycle, are influenced by the sum of recovered energy. This paper proposes a new approach to defining the specifications of a stand-alone wireless node based on a Radio-frequency Energy Harvesting System (REHS). To achieve adequate performance regarding the range of the Wireless Sensor Network (WSN), techniques for minimizing the energy consumed by the sensor node are combined with methods for optimizing the performance of the REHS. For more rigor in the design of the autonomous node, a comprehensive energy model of the node in a wireless network is established. For an equitable distribution of network charges between the different nodes that compose it, the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol is used for this purpose. The model considers five energy-consumption sources, most of which are ignored in recently used models. By using the hardware parameters of commercial off-the-shelf components (Mica2 Motes and CC2520 of Texas Instruments), the energy requirement of a sensor node is quantified. A miniature REHS based on a judicious choice of rectifying diodes is then designed and developed to achieve optimal performance in the Industrial Scientific and Medical (ISM) band centralized at 2.45 GHz . Due to the mismatch between the REHS and the antenna, a band pass filter is designed to reduce reflection losses. A gradient method search is used to optimize the output characteristics of the adapted REHS. At 1 mW of input RF power, the REHS provides an output DC power of 0.57 mW and a comparison with the energy requirement of the node allows the Base Station (BS) to be located at 310 m from the wireless nodes when the Wireless Sensor Network (WSN) has 100 nodes evenly spread over an area of 300 × 300 m 2 and when each round lasts 10 min . The result shows that the range of the autonomous WSN increases when the controlled physical phenomenon varies very slowly. Having taken into account all the dissipation sources coexisting in a sensor node and using actual measurements of an REHS, this work provides the guidelines for the design of autonomous nodes based on REHS. <s> BIB010 </s> Energy Harvesting Technologies for Achieving Self-Powered Wireless Sensor Networks in Machine Condition Monitoring: A Review <s> Electromagnetic Energy Harvesting <s> In this paper, a new RF energy harvesting (RF-EH) policy is proposed for Internet of Things (IoT) networks using cooperative communication approach. The proposed policy is evaluated in three devices network which are source, relay, and destination. Such a network model can be adopted in most IoT networks. Three scenarios are studied, where the relay has RF-EH capability with limited battery in all scenarios. However, the source has unlimited energy in the first scenario, but the limited energy in the second and third ones. In addition, in the third scenario source has RF-EH capability. New analyses based on discrete phase-type distribution (D-PHD) model as well as a simple analytical method (SAM) are presented to derive performance metrics which are network effective lifetime, average delay of source/relay packets, and source packets overflow. The effect of energy harvesting on such metrics are investigated to find the optimum relay policy. Results show that the proposed policy can maximize network effective lifetime in comparison with time switching (TS) and power splitting (PS)-based protocols. <s> BIB011
Electromagnetic energy is usually captured from the ambient RF sources which generate high electromagnetic fields, like TV broadcast stations, radar stations, Wi-Fi routers, Bluetooth, global system for mobile communications (GSM) and other communication networks BIB002 BIB003 BIB010 . RF energy harvesting devices can convert electromagnetic energy into a useful direct current (DC) voltage to power low-power consumer electronics and WSNs. The radio signals have a wide frequency range from 300 GHz to as low as 3 kHz, which are used as a medium to carry energy in a form of electromagnetic radiation. A basic RF energy harvesting system consists of a receiving antenna, matching circuit, rectifier, and power management as shown in Figure 7  . The antenna aims to capture electromagnetic waves, and then the matching circuit is used to achieve maximum power by using coils and capacitors. The rectifier circuit can convert the alternating current (AC) signal to a DC one, and finally the voltage output is adjusted using the voltage elevator [73] . Due to the wide coverage of RF, RF energy harvesting is suitable for powering a larger number of devices distributed in a wide area however the energy carried in the far-field RF waveform is limited BIB002  . According to the reference BIB002 , the harvested power from different RF sources is between 1 μW to 189 μW at a frequency of around 900 MHz and a distance of 5 m to 4.1 km. The energy harvesting rate varies significantly depending on the source power and distance. The relationship between received power and distance in the environment of 900 MHz and 2.4 GHz is described in [73] as shown in Figure 8 . Zeng et al. BIB005 summarizes and concludes the radiative wireless power transfer techniques and the novel methods to tackle the challenges (such as more compact antenna equipment, increasing conversion efficiency, minimizing impact on WSNs) in the period of the development of wireless energy harvesting. Popovic et al. BIB001 has successfully designed and manufactured a RF energy harvester integrated with all functions to harvested the 1.96 GHz and 2.4 GHz radiation energy. As with the development of WSNs, WSN-based Internet of Things (IoT) has improved dramatically, resulting in extended lifespans and a wider range of applications utilizing RF energy harvesting. Nguyen et al. BIB006 has designed a self-sustainable RF EH algorithm for IoT applications through adapting the EH period according to stochastic characteristics of the Due to the wide coverage of RF, RF energy harvesting is suitable for powering a larger number of devices distributed in a wide area however the energy carried in the far-field RF waveform is limited BIB002  . According to the reference BIB002 , the harvested power from different RF sources is between 1 µW to 189 µW at a frequency of around 900 MHz and a distance of 5 m to 4.1 km. The energy harvesting rate varies significantly depending on the source power and distance. The relationship between received power and distance in the environment of 900 MHz and 2.4 GHz is described in [73] as shown in Figure 8 . Zeng et al. BIB005 summarizes and concludes the radiative wireless power transfer techniques and the novel methods to tackle the challenges (such as more compact antenna equipment, increasing conversion efficiency, minimizing impact on WSNs) in the period of the development of wireless energy harvesting. Popovic et al. BIB001 has successfully designed and manufactured a RF energy harvester integrated with all functions to harvested the 1.96 GHz and 2.4 GHz radiation energy. As with the development of WSNs, WSN-based Internet of Things (IoT) has improved dramatically, resulting in extended lifespans and a wider range of applications utilizing RF energy harvesting. Nguyen et al. BIB006 has designed a self-sustainable RF EH algorithm for IoT applications through adapting the EH period according to stochastic characteristics of the traffic load from IoT applications and the incoming RF energy at sensor nodes. Notably, a new relay policy in RF EH for IoT networks has been proposed by Behdad et al. to maximize the network effective lifetime in BIB011 . Moreover, RF energy harvesting has attracted the investigations on radio-frequency identification (RFID) which consumes low power to identify and track objects' position with passive tags scavenging energy from nearby radio waves  BIB004 . energy harvesting rate varies significantly depending on the source power and distance. The relationship between received power and distance in the environment of 900 MHz and 2.4 GHz is described in [73] as shown in Figure 8 . Zeng et al. BIB005 summarizes and concludes the radiative wireless power transfer techniques and the novel methods to tackle the challenges (such as more compact antenna equipment, increasing conversion efficiency, minimizing impact on WSNs) in the period of the development of wireless energy harvesting. Popovic et al. BIB001 has successfully designed and manufactured a RF energy harvester integrated with all functions to harvested the 1.96 GHz and 2.4 GHz radiation energy. As with the development of WSNs, WSN-based Internet of Things (IoT) has improved dramatically, resulting in extended lifespans and a wider range of applications utilizing RF energy harvesting. Nguyen et al. BIB006 has designed a self-sustainable RF EH algorithm for IoT applications through adapting the EH period according to stochastic characteristics of the traffic load from IoT applications and the incoming RF energy at sensor nodes. Notably, a new relay policy in RF EH for IoT networks has been proposed by Behdad et al. to maximize the network effective lifetime in BIB011 . Moreover, RF energy harvesting has attracted the investigations on radiofrequency identification (RFID) which consumes low power to identify and track objects' position with passive tags scavenging energy from nearby radio waves  BIB004 . However, such RF signals are rarely used in the mechanical systems. The RF sources around machines are usually undesired and thus the potential energy harvested from these RF signals is limited. It is insufficient as a primary power source, but RF energy harvesters can assist other energy However, such RF signals are rarely used in the mechanical systems. The RF sources around machines are usually undesired and thus the potential energy harvested from these RF signals is limited. It is insufficient as a primary power source, but RF energy harvesters can assist other energy generators. Additionally, the RF energy harvesting technology still has a bright prospect due to its rapid development and wide application in wireless networks. Electromagnetic induction refers to a part of the conductor in a closed-circuit which cuts magnetic flux in a non-parallel direction to generate an induced current in the circuit. In other words, the wasted kinetic energy can be converted into electricity based on the conversion mechanisms with the assistance of magnetic fields for small-scale autonomous powered devices. The disadvantages of electromagnetic generators are the mismatching of electromagnetic generators' resonance frequencies with external vibration sources and the narrow response frequency band. As the external vibration source is random and non-stationary, the output power will be dramatically decreased if the matching frequency has a slight offset BIB007 . A mathematical model is built by Bernal and García  to study the interaction between electromagnetic induction and damping, and also the relationship between dimensions and energy output. Meanwhile, Cooley has analysed and verified the mechanisms and dynamics of vibrational electromagnetic energy harvesters by means of matrix operator form representing the eigenvalue problem of harvesters BIB008 . The results show that eigenvectors with strong electromechanical coupling usually generate large average power output. During the circuit design process, it should be paid attention that the inductance, resistance, and capacitance have effects on the eigenvalues and dynamic response of harvesters. For further understanding of the electromagnetic energy harvesting, Naifar et al. BIB009 summarizes a substantial amount of literature published during recent years and gives details of the principle behind electromagnetic generators. They present many electromagnetic prototypes and classify them by the different harvester architectures (like the cantilever, magnetic spring and rotary pendulum). Table 5 lists several electromagnetic generators designed in these years and compares their performance based on power density and features. It is reported that most of the generators work in a narrow and low-frequency band. Moreover, the magnitude of the power density varies greatly, even for those that approximate the response frequencies and accelerations of the energy source. From the references, most of electromagnetic generators can convert kinetic energy into a considerable amount of energy (generally at milliwatt level) at a low-frequency band of vibration with relatively low fabrication cost. However, the increase of the internal resistance caused by the micro-coil leads to inevitable power consumption, which may affect the output power of the electromagnetic generators. In addition, the size of the coils makes the microscale electromagnetic generator fabrication difficult to implement at present. There are also obstacles to integrate them with MEMS, which restricts the widespread applications of electromagnetic generators.