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Illustration of the Internet of Things (IoT) system architecture and node design: (a) Proposed layered IoT architecture for museum control; (b) block diagram of the Radio Frequency (RF) energy harvesting sensor node.
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Museum contents are vulnerable to bad ambience conditions and human vandalization. Preserving the contents of museums is a duty towards humanity. In this paper, we develop an Internet of Things (IoT)-based system for museum monitoring and control. The developed system does not only autonomously set the museum ambience to levels that preserve the he...
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... collected data is passed to the Internet to exploit its advantages with ubiquitous connectivity and deep data analysis. Figure 1a illustrates the IoT layered architecture developed in this paper. ...
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... does not only remove the bounds on the system's lifetime, but also makes the system greener. Figure 1b depicts a block diagram of the developed RF energy-harvesting wireless sensor node. The node is composed of three systems: RF energy harvesting system, power system, and sensor node. ...
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... (b) Figure 1. Illustration of the Internet of Things (IoT) system architecture and node design: (a) Proposed layered IoT architecture for museum control; (b) block diagram of the Radio Frequency (RF) energy harvesting sensor node. ...
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... proposed IoT system design is based on a four-layer architecture driven from [41]. It has four layers: physical interface layer, network connectivity layer, fog processing layer, and cloud processing layer, as shown in Figure 1a. ...
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... proposed IoT system design is based on a four-layer architecture driven from [41]. It has four layers: physical interface layer, network connectivity layer, fog processing layer, and cloud processing layer, as shown in Figure 1a. ...
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... horizontally polarized (H-pol) and vertically polarized (V-pol) gain of the DLPAA in a broadside direction are shown in Figure 10 The average gain is around 5.5 dBi for both V-pol and H-pol. The gain values of the antenna array at the frequencies correspond to the different wireless communication standards (GSM 1800, digital TV, Wi-Fi, and LTE) listed in Table 2. ...
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... horizontally polarized (H-pol) and vertically polarized (V-pol) gain of the DLPAA in a broadside direction are shown in Figure 10 The average gain is around 5.5 dBi for both V-pol and H-pol. The gain values of the antenna array at the frequencies correspond to the different wireless communication standards (GSM 1800, digital TV, Wi-Fi, and LTE) listed in Table 2. ...
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... 2019, 19, x;doi: www.mdpi.com/journal/sensors broadside direction are shown in Figure 10 The average gain is around 5.5 dBi for both V-pol and H-pol. The gain values of the antenna array at the frequencies correspond to the different wireless communication standards (GSM 1800, digital TV, Wi-Fi, and LTE) listed in Table 2. ...
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... received power for V-pol and H-pol of the antenna array are almost the same in the frequency bands of interest. The 3-D radiation pattern for the DLP array rectenna in all directions is shown in Figure 11a. The output voltage and efficiency of the system rectenna at different operating frequencies is shown in Figure 11b. ...
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... 3-D radiation pattern for the DLP array rectenna in all directions is shown in Figure 11a. The output voltage and efficiency of the system rectenna at different operating frequencies is shown in Figure 11b. The DLPAA achieves the maximum harvested voltage for the proposed system, which is 1000 mV, as shown in Figure 11b. ...
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... output voltage and efficiency of the system rectenna at different operating frequencies is shown in Figure 11b. The DLPAA achieves the maximum harvested voltage for the proposed system, which is 1000 mV, as shown in Figure 11b. It is achieved at 1.8 GHz with −9.1 dBm RF received power from a dedicated horn antenna source. ...
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... is achieved at 1.8 GHz with −9.1 dBm RF received power from a dedicated horn antenna source. A reflection coefficient of -18 dB has been achieved at 2.45 GHz, as shown in Figure 11c. Table 4 gives a comparison between the RF energy harvesting system proposed in this paper and the literature [51][52][53][54][55]. Table 4 shows that our proposed RF energy harvesting system has a larger Table 4 gives a comparison between the RF energy harvesting system proposed in this paper and the literature [51][52][53][54][55]. Table 4 shows that our proposed RF energy harvesting system has a larger number of covered frequency bands, reduced size, higher rectenna efficiency, higher RF sensitivity, and superior operation, as compared to different types of polarization techniques. ...
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... PMU circuit is simulated using TINA-TI, as shown in Figure 12, where transient analysis results show that the input voltage is kept less than 500 mV. The battery voltage (VBAT) reaches the typical value of 3.15 V after 400 msec, while the input current is limited to 400 µA. ...
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... PMU circuit is simulated using TINA-TI, as shown in Figure 12, where transient analysis results show that the input voltage is kept less than 500 mV. The battery voltage (VBAT) reaches the typical value of 3.15 V after 400 msec, while the input current is limited to 400 µA. ...
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... 1200 mAh battery is charged using a DC voltage source with different values. The DC output voltage is expected to be constant around 3.15 V, as shown in Figure 13a. However, the measured results vary with a few millivolts around the required value, as shown in Figure 13b. ...
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... DC output voltage is expected to be constant around 3.15 V, as shown in Figure 13a. However, the measured results vary with a few millivolts around the required value, as shown in Figure 13b. ...
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... 1200 mAh battery is charged using a DC voltage source with different values. The DC output voltage is expected to be constant around 3.15 V, as shown in Figure 13a. However, the measured results vary with a few millivolts around the required value, as shown in Figure 13b. ...
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... DC output voltage is expected to be constant around 3.15 V, as shown in Figure 13a. However, the measured results vary with a few millivolts around the required value, as shown in Figure 13b. Sensors 2019, 19, x 18 of 27 ...
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... PMU circuit is simulated using TINA-TI, as shown in Figure 12, where transient analysis results show that the input voltage is kept less than 500 mV. The battery voltage (VBAT) reaches the typical value of 3.15 V after 400 msec, while the input current is limited to 400 µA. ...
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... 1200 mAh battery is charged using a DC voltage source with different values. The DC output voltage is expected to be constant around 3.15 V, as shown in Figure 13a. However, the measured results vary with a few millivolts around the required value, as shown in Figure 13b. ...
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... DC output voltage is expected to be constant around 3.15 V, as shown in Figure 13a. However, the measured results vary with a few millivolts around the required value, as shown in Figure 13b. ...
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... the first experiment, we show how light is autonomously controlled based on the current visitor occupancy of a museum section. The results of the experiment are shown in Figure 14. At 6:14 pm, a visitor entered the section, causing light intensity to be changed to 380 Lux; he stood still for about two minutes. ...
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... the first experiment, we show how light is autonomously controlled based on the current visitor occupancy of a museum section. The results of the experiment are shown in Figure 14. At 6:14 pm, a visitor entered the section, causing light intensity to be changed to 380 Lux; he stood still for about two minutes. ...
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... is worth mentioning that Tmean is configurable, and it is set to 2 min only for demonstration purposes. Another experiment was performed to demonstrate the IoT system's ability to keep the temperature in the museum at a desired range, as shown in Figure 15. Note that rapid change in temperature is harmful to artifacts [1]. ...
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... the temperature passed 20 ℃ at 1:00 pm, the air cooler was turned on again. Another experiment was performed to demonstrate the IoT system's ability to keep the temperature in the museum at a desired range, as shown in Figure 15. Note that rapid change in temperature is harmful to artifacts [1]. ...
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... that rapid change in temperature is harmful to artifacts [1]. Sensors 2019, 19, x 22 of 27 Figure 15. Automatic temperature control. ...
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... the depicted experimental results in Table 6, it is concluded that using the data augmentation approach improves the performance of LSTM and GRU deep learning models by decreasing the RMSE by approximately 2.54% for CO 2 , 23.82% for temperature, and 2.59% for humidity, compared to [13]. Figure 16 shows that the LSTM deep learning model outperforms GRU for CO 2 , while the GRU model outperforms for temperature and humidity. This is due to the nature of the time series pattern for CO 2 , which is seriously affected by the number of occupants on weekends. ...
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... the depicted experimental results in Tables 6, it is concluded that using the data augmentation approach improves the performance of LSTM and GRU deep learning models by decreasing the RMSE by approximately 2.54% for CO2, 23.82% for temperature, and 2.59% for humidity, compared to [13]. Figure 16 shows that the LSTM deep learning model outperforms GRU for CO2, while the GRU model outperforms for temperature and humidity. This is due to the nature of the time series pattern for CO2, which is seriously affected by the number of occupants on weekends. ...
Citations
... The key challenge in using AI and ML for ZED is to ensure that the algorithms used are accurate and reliable (Chu et al., 2018;Eltresy et al., 2019), which requires extensive data collection and testing, as well as an understanding of the device's usage and environment. In addition, the algorithms must adapt to changing conditions and detect new energy consumption patterns. ...
The Cellular Internet of Things (CIoT) has seen significant growth in recent years. With the deployment of 5G, it has become essential to reduce the power consumption of these devices for long-term sustainability. The upcoming 6G cellular network introduces the concept of zero-energy CIoT devices, which do not require batteries or manual charging. This paper focuses on these devices, providing insight into their feasibility and practical implementation. The paper examines how CIoT devices use simultaneous wireless information and power transfer, beamforming, and backscatter communication techniques. It also analyzes the potential use of energy harvesting and power management in zero-energy CIoT devices. Furthermore, the paper explores how low-power transceivers can lower energy usage while maintaining dependable communication functions.
... To summarize, linearly polarized rectennas exhibit sensitivity to polarization orientation; therefore, signal intensity may be substantially reduced if the polarization of the incoming signal is not in alignment with the polarization of the rectenna [92]. Although dual linear polarized antennas provide some sort of diversity by capturing signals with both horizontal and vertical polarizations, they may still have certain drawbacks when confronted with situations involving unknown or rapidly changing polarization [93]. Generally, it is challenging to anticipate the polarization of the incoming electromagnetic waves, particularly in the context of ambient RF energy harvesting. ...
Radio frequency energy harvesting (RFEH) and wireless power transmission (WPT) are emerging alternative energy technologies that have the potential to provide wireless energy delivery in the future. A key component of RFEH or WPT systems is the receiving antenna, which significantly impacts the power delivery capability of the system. This survey extensively examines rectennas designed for multi-directional reception (or wide-angle coverage) of radio frequency (RF) signals with high gain. These rectennas perform better than other types of rectennas when the exact positions of RF sources are unknown or when the sources change location over time. This paper classifies rectennas into three categories based on their power combining approach: (i) DC power combining, (ii) RF power combining, and (iii) hybrid power combining. These rectennas will also be analysed in terms of angular coverage, size and profile, and gain, as well as polarization, broadband, and multiband performance. The various approaches adopted in the literature to address these challenges are critically analysed. To this end, based on the gaps in the literature and the lessons learnt, we propose the potential open research questions that the researcher can investigate in their research.
... The authors in [13] claimed that harvesting energy from RF sources is optimal for lowpower IoT sensors. The authors in [14] designed an energy harvesting model for low-power-consuming IoT sensors to investigate environmental monitoring systems. Another novel idea to power IoT sensors was proposed in [15], where the sensors harvested energy from nearby smart devices, such as smartphones. ...
Enhancing Internet of Things (IoT) communications through Reconfigurable Intelligent Surfaces (RIS) necessitates novel approaches that go beyond the conventional deployment of passive elements. This paper introduces an efficient method to enhance IoT system performance by combining active and passive functionality in RISs: an active and passive integrated approach. One identified enhancement area is efficiently managing signal throughput and exchanges in IoT systems, which is essential for supporting numerous devices. For this challenge, we propose a thorough process model that describes channel gains, develops a nonlinear energy harvesting, and identifies device-to-device communication schemes without violating information causality. The innovative component of our work is the utilization and strategic application of RIS panels that benefit from the advantages of active and passive components synergism to solve thermal noise issues and optimize signal reflection and transmission. An advanced optimization mechanism is developed based on mixed-integer nonlinear programming: an enabling approach between performance efficiency and maximum service utilization. Our simulation analyses show that developed RIS panels optimize IoT system performance and surpass existing performance indicators in conventional RIS-less systems.
... Sensors obtain information from their surroundings, while actuators or sensors provide the processed data to their surroundings [7,8]. The WSN is used in road traffic management and environmental concerns in IoT [9]. ...
Energy harvesting and Internet of Things (IoT) technology is being integrated with home appliances. IoT devices could harvest energy from alternative energy sources to convert the available energy from home appliances into electrical energy. Smart home energy management systems face isolated scheduling horizons and unreliable inputs. In this research, an IoT Heterogeneous Energy Harvesting (IHEH) technique has been proposed for residential home energy management to operate the smart home appliances which consists of four layers, namely, the Energy Harvesting Layer (EHL), the Control and Sensing Layer (CSL), the Application Layer (AL), and the Information Processing Layer (IPL). The proposed technique keeps track of the distribution of several types of energy, including thermal, piezoelectric, and light energy. The overall efficiency of the proposed method is 90% per day. The proposed IHEH technique yields more power and increases the lifetime of batteries.
... An Internet of Things (IoT)-based network for monitoring and controlling a museum's interior environment was developed where always-on and power-hungry sensor devices were energized through RFEH [34]. ...
Several practical engineering optimization problems are computationally demanding, requiring a large amount of computer time, processing power, and memory. These challenges can be mitigated by human-engineered systems exhibiting intelligent behavior. With the evolution of high-speed digital computers, the use of computational intelligence (CI) techniques has increased rapidly. According to Bezdek
[1]
, “A system is called computationally intelligent if it deals with low-level data such as numerical data, has a pattern-recognition component and does not use knowledge in the artificial intelligence (AI) sense, and additionally when it begins to exhibit computational adaptivity, fault tolerance, speed approaching human-like turnaround and error rates that approximate human performance.” Another definition, by Engelbrecht
[2]
, states that “CI is the study of adaptive mechanisms that enable or facilitate intelligent behavior in complex and changing environments. These mechanisms include those Artificial Intelligence paradigms that exhibit an ability to learn or adapt to new situations, to generalize, abstract, discover and associate.” Thus, CI is the general term used to classify all such nature-inspired methodologies and their associated theories and applications. The five important paradigms of the CI technique are artificial neural networks (ANNs), swarm intelligence (SI), evolutionary computation (EC), and fuzzy systems (FSs). The origin of each technique can be connected to a natural system; for example, an ANN imitates the biological neural system. SI models the behavior of organisms living in swarms, whereas EC models the natural evolution system. Similarly, an FS originates from human thinking processes. Many of the problems involved in designing next-generation systems can be resolved using these CI techniques or their combinations.
... The UWB monopole antenna is given in [9,10], consisting of a radiator based on a spline optimizer and a defected ground structure (DGS) [11]. Due to waste portions of radiated energy in undesirable directions, it is very efficient to use broadband dual interaction antennas to achieve powerful detection and sensing systems [12][13][14][15][16][17][18]. It is very important that wireless communication applications such as Worldwide Interoperability for Microwave Access (WiMAX), wireless local-area network (WLAN), lower band of 5G, and short-range communications (SRC) can be integrated easily into smart vehicles, where antenna size and placement are vital [19]. ...
... The proposed rectenna has a reasonable DC output voltage at different loads, makes it a good candidate for energy harvesting applications. The moderate efficiency and measured DC output voltage are due to the distance between the transmitter and rectenna (about 15 cm) determined by the measurement setup compared with 2.5 cm for Ref. [13]. The measured values are suitable for portable electronics and IoT applications. ...
This paper presents a single-substrate microstrip rectenna for dedicated radio frequency energy harvesting applications. The proposed configuration of the rectenna circuit is composed of a clipart moon-shaped cut in order to improve the antenna impedance bandwidth. The curvature of the ground plane is modified with a simple U-shaped slot etched into it to improve the antenna bandwidth by changing the current distribution; therefore, this affects the inductance and capacitance embedded into the ground plane. The linear polarized ultra-wide bandwidth (UWB) antenna is achieved by using 50 Ω microstrip line and build on Roger 3003 substrate with an area of 32 × 31 mm2. The operating bandwidth of the proposed UWB antenna extended from 3 GHz to 25 GHz at −6 dB reflection coefficient (VSWR ≤ 3) and extended from both 3.5 to 12 GHz, from 16 up to 22 GHz at −10 dB impedance bandwidth (VSWR ≤ 2). This was used to harvest RF energy from most of the wireless communication bands. In addition, the proposed antenna integrates with the rectifier circuit to create the rectenna system. Moreover, to implement the shunt half-wave rectifier (SHWR) circuit, a planar Ag/ZnO Schottky diode uses a diode area of 1 × 1 mm2. The proposed diode is investigated and designed, and its S-parameter is measured for use in the circuit rectifier design. The proposed rectifier has a total area of 40 × 9 mm2 and operates at different resonant frequencies, namely 3.5 GHz, 6 GHz, 8 GHz, 10 GHz and 18 GHz, with a good agreement between simulation and measurement. The maximum measured output DC voltage of the rectenna circuit is 600 mV with a maximum measured efficiency of 25% at 3.5 GHz, with an input power level of 0 dBm at a rectifier load of 300 Ω.
... They found that ML plays an essential role in modeling the relationships between occupant needs and environmental factor changes, concluding that its application to intelligent systems is required. Eltresy et al. [23] and Haruehansapong et al. [24] employed ML models to predict occupant requirements and provide support in dealing with dynamic environments. They argued that the ML model allows systems to uncover hidden knowledge from dynamic backgrounds, which can help occupants formulate decisions. ...
The primary objective of personal thermal comfort (PTC) is to enhance overall quality of life, encompassing well-being, productivity, and health. PTC necessitates the measurement of physiological responses and occupant preferences to generate intricate and dynamic comfort-related knowledge. This study introduces a comprehensive comfort-related processing framework that integrates physiological, environmental, and individual factors, examining physiological signals through occupant preference measurements within interventional chambers. Physiological signals, including skin temperature, heart rate, electrodermal activity, and airflow, are employed to portray an occupant’s physiological response to essential feature parameters. Additionally, variables such as age, sex, and body mass index are utilized to represent occupant preferences. The results reveal a highly significant relationship (p < 0.01) between physiological responses, taste, and satisfaction. This information serves as inputs to assist standard machine learning (ML) algorithms, categorized into probability, geometry, and logical expression, in encoding PTC and effectively predicting occupant satisfaction. The outcomes demonstrate that the logical decision tree, representing logical expression, along with k-nearest neighbors and artificial neural networks, representing geometry, achieved approximately 90%, 89%, and 80% of the average F-measure, respectively. These models exhibit superior accuracy in predicting individual occupant satisfaction compared to traditional approaches. This suggests their natural suitability for PTC-requiring intelligent systems.
... They discussed that the ML techniques could identify thermal comfort events in the building with excellent performance. C. Xu et al. [4] and N. A. Eltresy et al. [5] proposed IoT-based ML approaches for predicting events in thermal comfort, focusing on public buildings. They designed and developed IoT devices for sensing data and employing ML to predict indoor thermal comfort conditions on time. ...
Unexplainable indoor thermal comfort events from black-box models influence people to distrust suggestions from decision-support systems and ask for help from engineers and practitioners that are labor-intensive and time-consuming. These problems come from unknown cause-and-effect in the environments that cause the system not to produce explainable outcomes. This study proposes the cause-and-effect discovery for indoor thermal comfort events that help systems make human-like explanations to overcome these issues. The research contributions consist of three essential points. The first is perceptions based on the Internet of Things technologies that imitate human perception organs, which could sense signals as a system input component. The second is qualitative knowledge representation using random variable systems and graphs as the ground truth-the representation stores in the manner of human-like intelligence that people and systems can understand. The third is causal discovery algorithms that automatically determine cause-and-effect in Machine Learning (ML) models from observational data. The results showed that models could discover cause-and-effect relationships close to the human-like intelligent-based model blueprint given observational data. They produce reasonable explanations for indoor thermal comfort events that help people trust such information and utilize it to make decisions.
... Wireless power transfer offers another solution to deliver energy to the IoT devices. Batteries and complex power lines could be avoided [14]. However, the power conversion efficiency of the wireless power transfer systems is affected by the linkage between power sources. ...
... We also apply the maximum power tracking in the AC energy domain in this work. The matching network is conventionally designed with a time-consuming recursive process [14]. A simple and efficient process is adopted from [17]. ...
This work proposes a dual-domain maximum power tracking (MPPT) technique for multiple-input RF energy harvesting systems. A differential rectifier array is used to implement 4-channel reconfigurable RF to DC power conversion, and an adjustable 4-bit capacitor array is designed to improve the impedance matching between the antennas and the rectifiers. Using the perturbation and observation (P&O) method, both arrays are adaptively configured in the background with the variations of the input energy and output loading. Experimental results show that the proposed circuit successfully tracks the maximum power points while harvesting RF energy, with the peak conversion efficiency of 49.06% when the input energy is −6 dBm. With the proposed dual-domain MPPT, the high efficiency range of the energy harvesting system is greatly extended to 21 dB (−21–0 dBm).
... 28,91 However, from the various reviews, 1,23,28-34 we state that RF-based EH remains the best solution for various application scenarios related to low-energy IoT devices. Eltresy et al., 92 Caselli et al., 93 and Lin et al. 94 highlight the RF-based EH potentials for IoT-based environmental and healthcare monitoring applications. There are other approaches exploiting RF-based EH techniques and include opportunistic charging from nearby smartphones. ...
Distributed sensor networks have emerged as part of the advancements in sensing and wireless technologies and currently support several applications, including continuous environmental monitoring, surveillance, tracking, and so on which are running in wireless sensor network environments, and large-scale wireless sensor network multimedia applications that require large amounts of data transmission to an access point. However, these applications are often hampered because sensor nodes are energy-constrained, low-powered, with limited operational lifetime and low processing and limited power-storage capabilities. Current research shows that sensors deployed for distributed sensor network applications are low-power and low-cost devices characterized with multifunctional abilities. This contributes to their quick battery drainage, if they are to operate for long time durations. Owing to the associated cost implications and mode of deployments of the sensor nodes, battery recharging/replacements have significant disadvantages. Energy harvesting and wireless power transfer have therefore become very critical for applications running for longer time durations. This survey focuses on presenting a comprehensive review of the current literature on several wireless power transfer and energy harvesting technologies and highlights their opportunities and challenges in distributed sensor networks. This review highlights updated studies which are specific to wireless power transfer and energy harvesting technologies, including their opportunities, potential applications, limitations and challenges, classifications and comparisons. The final section presents some practical considerations and real-time implementation of a radio frequency–based energy harvesting wireless power transfer technique using Powercast™ power harvesters, and performance analysis of the two radio frequency–based power harvesters is discussed. Experimental results show both short-range and long-range applications of the two radio frequency–based energy harvesters with high power transfer efficiency.
