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![Figure 2. Applications of WPT technologies in WPSNs [7,19].](profile/Muhammad-Yeasir-Arafat-2/publication/360015046/figure/fig2/AS:1146137805373440@1650271955042/Applications-of-WPT-technologies-in-WPSNs-7-19_Q320.jpg)
With the emergence of the Internet of Things (IoT), billions of wireless devices, including sensors and wearable devices, are evolving under the IoT technology. The limited battery life of the sensor nodes remains a crucial implementation challenge to enable such a revolution, primarily because traditional battery replacement requires enormous huma...
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... At the same time, emerging developments in several sectors of science and technology, such as the Internet of Things [32,33], machine learning [34,35], deep learning [36,37], big data [38][39][40], 5G [41][42][43], edge computing [44], energy harvesting [3,45,46], and wireless power transfer [3,46,47] seem to be promising to support and enhance the operation of WSNs, thus triggering corresponding research trends. ...
A typical wireless sensor network (WSN) contains wirelessly interconnected devices, called sensor nodes, which have sensing, processing, and communication abilities and are disseminated within an area of interest [...]
... Currently, there is an urgent demand to incorporate numerous renewable energy sources to address issues such as greenhouse gas emissions, voltage quality problems, power supply reliability, network extensions, underserved remote areas, and power losses prevalent in conventional power plants [1][2][3]. In Tunisia, the predominant electrical energy source is crude oil, except for certain countries relying on renewable sources [4]. ...
... we can storedhydrogen and use it after that in a fuel cell to produce electricity during periods when the sun is not shining. 3 The mutual inductance calculator facilitates the calculation of the mutual inductance between booth coils with the goal of obtaining the optimum M12 ratio for the maximum transfer point of the RWPT system. The adjustment impedance is represented by the optimized class-ϕ2 inverter's input and output impedance. ...
The design and integration of intelligent energy management systems in hybrid Electric Vehicle (EV) charging stations, leveraging Industry 4.0 and renewable energy sources, is crucial for advancing sustainability, efficiency, and technological development. This paper introduces a pioneering hybrid EV charging station that employs hybrid power sources, combining photovoltaic panels, a battery, and a fuel cell, each operating at 240V and 14A. Utilizing wireless power transfer technology, the system comprises five integral blocks: a power source, a boost converter, a High-Frequency (HF) inverter, transfer coils, and EV batteries. The optimization process involves two key stages: i) maximum power point tracking optimization of the boost converter, ensuring maximum power generation by varying the duty cycle between 10% and 90%, and ii) the HF phase employing a class-ϕ2 inverter at 30 MHz, synchronized with the resonant frequency of wireless power transfer coils. Control is executed through a digital signal processor card utilizing zero-voltage switching for efficient operations. This design enhances sustainability in EV charging solutions by optimizing power transfer through the use of hybrid sources.
... Wet-mate charging, which requires precise coordination with autonomous underwater vehicles (AUVs) and uses wet connectors that are unreliable, expensive, and have a limited lifespan, is the conventional method [4]. In contrast, underwater wireless charging offers a solution that eliminates the need for physical connections [5,6], significantly enhancing the safety, flexibility, and efficiency of underwater operations. ...
Autonomous underwater vehicles (AUVs) are now widely used in both civilian and military applications; however, wireless charging underwater often faces difficulties such as disturbances from ocean currents and errors in device positioning, making proper alignment of the charging devices challenging. Misalignment between the primary and secondary coils can significantly impact the efficiency and power of the wireless charging system energy transfer. To address the issue of misalignment in wireless charging systems, this paper proposes a multiple transfer coil wireless power transfer (MTCWPT) system based on backpropagation (BP) neural network control combined with nonsingular terminal sliding mode control (NTSMC) to enhance further the system robustness and efficiency. To achieve WPT in the ocean, a coil shielding case structure was equipped. In displacement experiments, the proposed multi-transmitting coil system could achieve stable power transfer of 40 W and efficiency of over 78.5% within a displacement range of 8 cm. The system robustness was also validated. This paper presents a new AUV energy supply solution based on MTCWPT. The proposed MTCWPT system can significantly improve the navigation performance of AUVs.
... The implementation of intelligent control algorithms that can dynamically adjust the frequency, phase, and amplitude of the RF signals to optimize the power transfer under varying conditions is another exciting advancement. Additionally, the synergy between RF-based WPT and emerging 5G or 6G technologies offers new opportunities for seamless connectivity and power supply in future smart cities and industries [25,[63][64][65]. ...
... Recently, self-sustainable wireless powered sensor networks (WPSNs), which enable a permanent operation of sensor nodes without battery replacement or interruption by using wireless power transfer (WPT) technology, have been extensively studied [1][2][3][4]. Unlike existing wireless sensor networks (WSNs), WPSNs consist of multiple sensor nodes as well as hybrid access points (HAPs) that supply power to sensor nodes with radio frequency (RF) signals and collect data from the sensor nodes then transmit it towards the root. Accordingly, two types of traffic (i.e., data traffic and power traffic) with different characteristics coexist in WPSN. ...
This paper presents a Q-learning-based trafficaware parent selection (QTaPS) for wireless powered sensor networks (WPSNs). The QTaPS separately selects power and data parents using Q-learning, thereby improving the efficiency of both energy harvesting and data transmission in WPSNs. To this end, QTaPS uses two Q-learning agents with different reward functions to select power and data parents, respectively. In order to take into account the characteristics for each traffic, different metrics are used in each reward function. The simulation results showed that QTaPS obtained better performance than the existing scheme with respect to the aggregate throughput and average end-to-end delay.
... In situations when natural water pipelines have leaks, having accurate information about the location of the leak is very necessary to undertake successful remediation methods. The use of wireless sensor networks (WSNs) may be advantageous in some circumstances, particularly those in which conventional surveying techniques are either difficult to implement or unavailable to workers [4]. These networks are able to quickly monitor a wide range of environmental characteristics, such as temperature, infrared radiation, ultraviolet radiation, sonar signals, vital signs, and seismic activity in the area. ...
The process of acquiring location information is of utmost significance in the world of wireless sensor networks (WSNs). WSNs have a place of use in a wide variety of 3D settings, including but not limited to the depths of the ocean, mountainous landscapes, woodlands, and even the sky. Because of the complexities of three-dimensional environments, these 3D deployments need to make use of sophisticated localization methods. This results in an increase in the computing complexity of the process. It is vital to note that immediately extending 2D localization techniques to handle 3D circumstances is not practicable. This is something that should be kept in mind. WSNs have seen substantial improvements in both their accuracy and their ability to be used in real-world settings with the advent of three-dimensional localization techniques. Conventional localization methods focus exclusively on two-dimensional planes. As a direct result of this, the need for algorithms that can localize in three dimensions has skyrocketed. Both range-based and range-free localization approaches are commonplace in this sector of the computer science industry that deals with three-dimensional positioning. However, the currently available 3D localization algorithms have a number of drawbacks, the most notable of which are their increased complexity, reduced positional precision, and significant energy consumption. In this research, an improved version of a 3D localization method known as "3D-DV-CD" is presented as a solution to the issues provided by the many current 3D localization algorithms. This innovative strategy aims to improve the positioning accuracy as well as coverage. Notably, it adds the idea of coplanarity degree (CD) into the traditional 3D-distance vector hop (3D-DV-Hop) localization technique. The positioning inaccuracies brought on by coplanar anchor nodes are reduced as a result of this action. Additionally, this technique promotes nodes that were unknown in the past to the position of helper anchor nodes, hence increasing placement coverage as the fraction of anchor nodes grows. The results of the simulation demonstrate that the approach that was presented is successful, confirming that it is precise in positioning and has the ability to extend positioning coverage.
... However, the combination of site and battery power presents a challenge. While batteries require periodic replacement, placing sensors in specific locations can make this task difficult [4,5]. ...
Everyday tasks use sensors to monitor and provide information about processes in different scenarios, such as monitoring devices in manufacturing or homes. Sensors need to communicate, with or without wires, while providing secure information. Power can be derived from various energy sources, such as batteries, electrical power grids, and energy harvesting. Energy harvesting is a promising way to provide a sustainable and renewable source to power sensors by scavenging and converting energy from ambient energy sources. However, low energy is harvested through these methods. Therefore, it is becoming a challenge to design and deploy wireless sensor networks while ensuring the sensors have enough power to perform their tasks and communicate with each other through careful management and optimization, matching energy supply with demand. For this reason, data cryptography and authentication are needed to protect sensor communication. This paper studies how energy harvested with microbial fuel cells can be employed in algorithms used in data protection during sensor communication.
... There have been continuing interest in wireless transmission systems with energy harvesting (EH) relays [16]- [21]. [16] reviewed the recent progress of wireless power transfer applications in WSNs and discussed several important performanceenhancing techniques. ...
... There have been continuing interest in wireless transmission systems with energy harvesting (EH) relays [16]- [21]. [16] reviewed the recent progress of wireless power transfer applications in WSNs and discussed several important performanceenhancing techniques. [17] and [18] applied power and packet size adaptation to optimize the energy efficiency of wireless transmission systems with EH relays. ...
... We now optimize the charging power P SC , information transmission power P S , and time switching factor α to minimize the total energy consumption of a specific transmission session, given in Eq. (16). Note that the above objective function is not convex. ...
Reliable and energy-efficient wireless transmission is of critical importance to the success of future advanced Internet of Things (IoT). Due to the sporadic nature of IoT transmissions, the energy consumption of the individual IoT transmission session varies dramatically with the instantaneous operating environment as well as the quality of service (QoS) requirements. In this paper, we analyze and design the energyefficient relay transmission systems from an individual data transmission session perspective. Specifically, we consider a dualhop transmission system with a decode-and-forward relay that is solely powered by wireless power transfer from source node. For both time switching and power splitting modes of simultaneous power and information transmission, we analyze and minimize the total energy consumption of the system when transmitting a fixed amount of data, under a piecewise linear energy harvesting (EH) model. Closed-form expressions for optimal transmission parameters are obtained with and without the consideration of latency constraint. Through selected numerical results, we illustrate various design tradeoffs between energy consumption and latency constraint. We show that with optimal transmission parameters, relay transmission with energy transfer can achieve considerable energy saving compared to direct transmission when the direct link quality is poor and the latency constraint is not stringent. We also show that with optimized parameters, power splitting mode leads to lower energy consumption and smaller transmission duration than time switching mode, at the cost of higher implementation complexity.
... Moreover, sensor nodes demand an uninterrupted power supply [4]. Therefore, the energy harvesting system has become essential in obtaining energy from the environment and supplying this energy to sensing nodes, batteries, and low-energy electronic devices. ...
... The conversion efficiency of the RF to direct current (RF-DC) is a key indicator for evaluating the EH circuit, which indicates the ratio of the output power of the circuit to the power of the input RF signal. Many research works have shown that with the increase in the input RF signal power, the conversion efficiency of the RF-DC gradually decreases in the saturation region [9][10][11]. Therefore, in order to reduce the energy loss, designing new EH receivers and energy receiving mechanisms is important to improve the conversion efficiency of the RF-DC. ...
... Part of the conversion efficiency mapping is shown in Table 1, and the method of obtaining Table 1 is summarized as Algorithm 1. Algorithm 1 starts from the actual characteristics of the EH circuit; when determining the number of power shunts, the number of comparisons is small, which is effective for determining the o L from small to large, and the mapping table required for EH can be constructed efficiently and completely. In [24], the author adopted the SMO method (with a computational complexity of (^2 ) O M L ), and the proposed Algorithm 1 has a lower computational complexity ( (10 ) O NT ). In addition, building the mapping table offline does not add computational complexity to the inline beamforming design. ...
Simultaneous wireless information and power transfer (SWIPT) technology can effectively extend the lifecycle of energy-constrained networks. In order to improve the energy harvesting (EH) efficiency and network performance in secure SWIPT networks, this paper studies the resource allocation problem based on the quantitative EH mechanism in the secure SWIPT network. Based on a quantitative EH mechanism and nonlinear EH model, a quantified power-splitting (QPS) receiver architecture is designed. This architecture is applied in the multiuser multi-input single-output secure SWIPT network. With the goal of maximizing the network throughput, the optimization problem model is established under the conditions of meeting the legal user’s signal-to-interference-plus-noise ratio (SINR), EH requirements, the total transmit power of the base station, and the security SINR threshold constraints. Due to the coupling of variables, the problem is a nonconvex optimization problem. To deal with the nonconvex optimization problem, a hierarchical optimization method is adopted. Firstly, an optimization algorithm based on the optimal received power of EH circuit is proposed, and a power mapping table is constructed through the optimization algorithm, from which the optimal power ratio to meet the user’s EH requirements is obtained; then, the nonconvex problem is transformed into a convex problem by using variable substitution, semidefinite relaxation, dichotomous optimization, etc. The simulation results show that compared with the power splitting receiver architecture, the input power threshold range of the QPS receiver architecture is larger, which can avoid the EH circuit falling into the saturated working area and maintain high network throughput.
