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This paper investigates an indoor hybrid visible light communication (VLC) and radio frequency (RF) scenario with two-hop downlink transmission. A light emitting diode (LED) transmits both data and energy via VLC to an energy-harvesting relay node, which then uses the harvested energy to retransmit the decoded information to an RF user in the second phase. The design parameters include the direct current (DC) bias and the time allocation for VLC transmission. We formulate an optimization problem to maximize the data rate under decode-and-forward relaying with fixed receiver orientation. The non-convex problem is decomposed into two sub-problems, solved iteratively by fixing one parameter while optimizing the other. Additionally, we analyze the impact of random receiver orientation on the data rate, deriving closed-form expressions for both VLC and RF rates. An exhaustive search approach is employed to solve the optimization, demonstrating that joint optimization of DC bias and time allocation significantly enhances the data rate compared to optimizing DC bias alone.

Energy harvesting from human motions can extend battery lifespan and enhance mobile device convenience, with promising applications in military and medical fields. Knee motion is an excellent candidate for energy harvesting due to its significant recoverable negative work. This paper aims to design an Electro-Magnetic Energy Harvester (EMEH) that captures power from knee muscle work during walking. We used 2D motion capture techniques to obtain input data, including knee angular rotation and muscle work during walking. Position vectors were extracted with a motion tracker, angular positions were formulated, and the data were imported into MATLAB® Simscape MultibodyTM to develop a walking-leg model. The model obtained angular velocity, torque, and power, revealing an angular displacement within −60-0°, a maximum angular velocity of 5.42 rad/s, a maximum torque of 30 Nm, and an average power of 15.9 W. These results indicated both positive and negative work phases in one gait cycle, consistent with reference data, helping determine the energy harvester’s operating range. The EMEH model in MATLAB/Simulink® predicted the system’s output voltage and power, guiding prototype construction. Performance tests of the prototype showed a peak power output of about 8 W, with cumulative energy harvested at 5.5 J per gait cycle. With a prototype mass of 1.35 kg, the design achieved an average power density of 3.12 W/kg. Thus, our compact and lightweight EMEH prototype successfully converted the motion of the human knee into electricity while walking, achieving a power output suitable for practical applications.

In Cognitive Radio (CR) networks combined with Energy Harvesting (EH) technology, Secondary Users (SUs) are vulnerable to jamming attacks when sensing idle channels. At the same time, they may encounter numerous jamming and eavesdropping attacks during the data transmission phase. This paper examines the scenario in which SUs are susceptible to malicious attacks and energy constraints in both the sensing and transmission phases. We propose a utility function applicable to a single time slot. The blockchain uses Smart Contract (SC) technology to set rewards and punishments for users’ channel selection behavior and adjust mining difficulty. This method combines blockchain with spectrum sensing data fusion, abandons the decision-making mechanism of the traditional Cooperative Spectrum Sensing (CSS) Fusion Center (FC), and adopts a distributed structure to ensure the security and reliability of sensing data fusion. In addition, this paper uses the potential game and the Stackelberg game to study the optimal transmission channel and optimal time slot allocation strategy for SUs under malicious attacks. Considering the possible interference caused by channel switching and the greedy principle of Malicious User (MU), the proposed two-layer game method gradually optimizes the sensing detection probability and secure communication rate with time slot iteration. In order to further improve the secure communication rate, an iterative update formula for transmission power is given to make reasonable use of the remaining energy of each SU at the end of each time slot. Simulation results show that the proposed method is superior to traditional methods in both sensing performance and secure communication rate.

Energy Harvesting Wireless Sensor Networks (EH-WSNs) main goal is to increase efficiency in settings where Energy Harvesting (EH) is restricted by environmental resources. To solve the drawbacks of conventional Wireless Sensor Networks (WSNs) routing methods that usually ignore EH, this work presents a multi-hop clustering and renewable energy-based routing protocol designed for EH-WSNs. The suggested method performs clustering both centralized and decentralized using energy circumstances and the quantity of captured energy. The protocol operates in three phases: cluster formation, data transmission, and centralized management. To evaluate the effectiveness of the proposed approach, we analyze three distinct scenarios with different settings. The findings show that the suggested approach greatly lowers the total network energy usage while allowing a higher number of nodes to stay operational. We find that our approach outperforms AEHAC, CRBS, HUCL, and EADUC in terms of average energy levels, overall efficiency, network stability, and number of live nodes during the simulation. The results taken together show that the suggested method continuously improves network efficiency and stability in all assessed situations.

With the Internet of Things (IoT) rapidly increasing in popularity, it is necessary to progress Beyond fifth-generation (B5G) applications to support access to a large number of devices under the strict Ultra-Reliability and Low-Latency Communication (URLLC) requirements, which are intimately tied to strict delay requirements. This article focuses on minimizing co-channel interference, implementing efficient transmitter power, satisfying the Quality-of-Service (QoS), and assigning the energy harvesting requirement of radio frequencies using a Power Splitting Ratio (PSR) to maximize energy efficiency. This article proposes an adaptive approach algorithm for power allocation and PSR to reduce energy consumption caused by a nonconvex optimization problem, to achieve a high strict level of URLLC, QoS aware immediate reward, and huge connection. In addition, the proposed Deep Reinforcement Learning-Based Reduction Action Space (DRL-RAS) allows achieving a high degree of massive access to a huge number of devices by tackling the rate requirement of IoT devices with high intelligence to increase learning efficiency by maximizing the immediate reward function and ensuring strict URLLC. The simulation’s results demonstrate how DRL-RAS decreases co-channel interference based on decision selection to provide strict URLLC. The performance of energy efficiency and QoS are based on achieving a highly strict level of URLLC. The suggested DRL-RAS training strategy performs better, gaining QoS satisfaction levels of 17.21% and 8.67% when the needed QoS rises from 0.88 bits/sec/Hz to 1 bit/sec/Hz. Moreover, the proposed DRL-RAS eliminates error prediction during the training process by updating the weight of DRL-RAS to minimize the loss function and maximize energy efficiency.

The widespread adoption of fifth-generation (5G) wireless technology in Internet of Things (IoT) networks introduces a battery replacement challenge for countless IoT sensors. To address this issue, researchers are exploring energy harvesting techniques that involve extracting energy from radio frequency (RF) signals. The dense deployment of antennas in 5G networks, compared to prior technologies, ensures that an RF energy source is available in close proximity to IoT sensors which can be utilized as reliable power sources for these sensors. This study focuses on the design of an efficient energy harvester for low-power IoT sensors. Specifically, the investigation centers around a scenario where dedicated directional transmitters emit continuous waves to power up IoT sensors. An optimized high-efficiency Dickson rectifier circuit is designed to convert the RF signal to DC at a high-frequency band over a wide dynamic range. The Dickson rectifier is superior for low-power, high-frequency scenarios due to its voltage multiplication and reduced losses. To minimize the effect of parasitic capacitance associated with conventional capacitors, interdigital capacitors (IDC) are designed on an FR-4 substrate with a thickness of 1.57 mm. An impedance-matching circuit utilized as a passive voltage amplifier was also employed to enhance the RF-to-DC conversion efficiency. A prototype was built and tested at the 5.2 GHz frequency band. The measurement results demonstrate the peak efficiency of the proposed energy harvesting circuit which was determined to be 73.46% when subjected to an input power of -5 dBm at $500~\Omega $ load and 49.12% with -10 dBm input at 1 M $\Omega $ . The proposed energy harvesting solution can operate efficiently across a broad dynamic range, outperforming the solutions reported in the literature.

The Internet of Things (IoT) is a communication scheme which allows various objects to exchange several types of information, enabling functions such as home automation, production management, healthcare, etc. In addition, energy-harvesting (EH) technology is considered for IoT environment in order to reduce the need for management and enhance maintainability. Moreover, since environments considering outdoor elements such as pedestrians, vehicles and drones have been on the rise recently, it is important to consider mobility when designing an IoT network management scheme. However, calculating the optimal relaying topology is considered as an NP-hard problem, and finishing computation for mobility environment before the channel status changes is important to prevent delayed calculation results. In this article, our objective is to calculate a sub-optimal relaying topology for stationary and mobile system within reasonable computation time. To achieve our objective, we validate an iterative balancing time slot allocation algorithm introduced in the previous study, and propose a guided-mutation genetic algorithm (GMGA) that modulates the mutation rate based on the channel status for rational exploration. Additionally, we propose a mobility-aware iterative relaying topology algorithm, which calculates relaying topology in a mobility environment using an inheritance of the sub-optimal relaying topology calculations. Simulation results verify that our proposed scheme effectively solves formulated IoT network problems compared to other conventional schemes, and also effectively handles IoT environments including mobility in terms of minimum rate budget and computation time.

The magnetic spring placed on vibration generator consists of top and bottom fixed magnets with a third magnet levitated between them. Nonlinear dynamic analysis appears in the magnetic flux density of the magnetic spring activated by the vibration generator. This paper is focused on fast data-driven and accurate model for the prediction of magnetic flux density using a deep neural network (DNN) in the form of a Long Short-Term Memory (LSTM). As the input and training data for LSTM are used: supply voltage of the vibration generator, its frequency and measured magnetic flux density. The magnetic flux density measurements of the magnetic spring have been recorded by three Hall effect sensors. The prediction of magnetic flux density in magnetic spring has given accurate results and good applicability for better characterization of the device in energy harvesting system.

Nonorthogonal multiple access (NOMA) enhances spectral efficiency by enabling manifold devices to utilize the same frequency-time resource through nonorthogonal signal superposition. In addition, full-duplex (FD) transmission allows concurrent reception and transmission on the same frequency range, further improving spectral efficiency. Moreover, nearby wireless energy sources are utilized to enhance energy efficiency. Nonetheless, several challenges are inherent in NOMA FD transmission with energy harvesting (NOFE), including information security concerns and practical imperfections such as channel state information imperfection (CSIi), hardware impairment (HWi), and successive interference cancellation imperfection (SICi). The paper first proposes a solution to address these challenges by leveraging an energy source both as an energy supplier and a jammer to secure transmission under imperfections. The performance of the proposed solution is then evaluated using three key metrics (total throughput, outage probability, energy efficiency). The study highlights the significant impact of various factors such as energy harvesting, HWi, CSIi, NOMA, SICi, and FD on the performance metrics. The proposed solution also aims to prevent complete outage by optimizing required spectral efficiency, HWi, CSIi, SICi and power splitting parameter. Additionally, optimal configuration of system parameters is crucial for achieving optimum performance metrics. Furthermore, the proposed NOFE is demonstrated to be more secured than two benchmark techniques (orthogonal multiple access FD transmission and NOMA half-duplex transmission) across numerous parameter configurations, emphasizing the superiority of utilizing concurrently both NOMA and FD to only either NOMA or FD.

This paper proposes a scheme to integrate data communication, wireless energy harvesting and anti-eavesdropping functions into a hybrid beamforming downlink system to satisfy the demand for expanded functionalities in a communication system. To solve this difficult nonconvex problem, the Semidefinite Relaxation (SDR) method is employed to obtain the digital transmitter, the SDR method and penalty convex-concave procedure (CCP) method are employed to obtain the analog transmitter. Simulation results shows four conclusions about the system. Firstly, the larger the SINR target or the energy harvest target, the more transmit power is required. Secondly, the lower the data rate obtained by the eavesdropper, the more transmit power is required. Thirdly, the SDR method and the penalty CCP method complement each other. Finally, the transmit power can be reduced by increasing the number of RF chains or transmit antennas.

The temperature difference between soil and air holds the potential to generate energy to power many low-power IoT devices. However, there is a lack of studies in the literature that explore the nuances of soil-air thermal energy harvesting. This paper offers a comprehensive discussion on soil-air thermal energy harvesting. We engineer a custom Soil-air Thermoelectric Generator (SoTEG) that incorporates an off-the-shelf TEG and an efficient heat transfer network. A detailed discussion of the design and analysis of SoTEG is presented along with a simulation model which can be used to simulate the performance of the harvester under different ambient conditions. Investigations using the model and results gathered from experiments demonstrate that the SoTEG has a heat transfer efficiency of 34.5% with room for improvement and can power a load from temperature differences as low as 3 °C between soil and air, or 1 °C across the TEG. Power generated by SoTEG at 3 °C difference amounts to $\mathrm {110~\mu { W} }$ or a power density of 11.58 mW/m2. When connected to a Power Management Unit (PMU), the combined system generates around $\mathrm {30~\mu { W} }$ at 3 °C. During a 14-day outdoor deployment in a winter month, the maximum power generated by the combined system is $\mathrm {337~\mu { W} }$ when the temperature difference across the TEG is 2.75 °C. Additionally, the model analysis reveals that the weather conditions have an impact on the harvester. While Solar radiation enhances power generation, wind can either improve or diminish the harvested energy depending on whether it is day or night.

In the rapidly developing field of wireless communication, the control of beams in Reconfigurable Intelligent Surfaces (RISs) has emerged as a promising element beyond 5G wireless communication systems. Due to their distinctive reflecting elements, Reconfigurable Intelligent Surface (RIS) is essential in several operations, including beamforming and beam steering. However, the optimization of these functions necessitates complex solutions. In this study, the authors introduce the Feedback DNN strategy, which combines the Feedback Neural Network and Deep Neural Network techniques specifically designed for channel estimation. This methodology utilizes deep neural networks to provide the RIS and user equipment communication path, enabling improved beamforming and steering capabilities. This study highlights the incorporation of machine learning (ML) within the field of communication engineering, intending to enhance the reliability and effectiveness of wireless communication systems. The contributions encompass a novel methodology for managing RIS beams, sophisticated approaches for channel estimates, optimization of beam operations, and the potential to enhance the performance of wireless systems by utilizing RISs via a Feedback DNN (called DeepRISBeam). The proposed approach is compared against other state-of-the-art ML approaches regarding their training accuracy. At the same time, it evaluated Bit Error Rate performance in high- and low-mobility vehicular communication scenarios.

This review explores the various strategies used to widen the input power (P $_{\mathrm {IN}}$ ) dynamic range of an RF-DC rectifier and analyzes the recent developments reported in the literature. This development is insightful as the PIN dynamic range is crucial for systems adopting wireless power transfer (WPT) or wireless energy harvesting (EH) for both near- and far-field applications to mitigate the reliance on the battery as its primary power source. However, the RF-DC rectifier exhibits non-linear characteristics, resulting in a narrow PIN band for reasonable power conversion efficiency (PCE). The different strategies reported in the literature are discussed and classified into three main concepts: reconfigurable, multi-path, and self-biased rectifiers. Despite having different design constraints and considerations during the inception of the reported work in the literature, this article analyzes and provides the general development trend, merits, and demerits.

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.

The integration of Edge Computing (EC) and Energy Harvesting (EH) technologies has facilitated the growth of the Internet of Things (IoT), allowing for the interconnectivity of a wide range of devices. The integration of this technology has not only enhanced energy sustainability but also significantly extended the battery life of these devices. Adopting Renewable Energy (RE) sources has become more widespread in energy systems as a strategy to reduce carbon emissions. Low energy consumption and constrained battery capacity for IoT devices are concerns related to offloading. The unpredictability of RE quality makes it difficult for edge servers to maintain high quality of service in EH-EC systems, which impedes effective energy conservation for IoT. To solve an optimization problem, RE Predictions with a Deep Reinforcement Learning algorithm named (REP-DRL) are proposed. Accurately, REP-DRL used the actor-critic technique to identify the best approach for predicting RE and optimal offloading decisions. The approach improves IoT device processing and expands the system state to offload experiences per time slot. To store excess energy during periods of abundance and use it during times of higher demand, the service offloading process is modelled based on the predicted amount of RE, to find the best service offloading technique and improve energy sustainability for IoT. By determining the most efficient service offloading approach using the predicted RE amount, this solution increases the energy sustainability of the IoT ecosystem. Finally, the simulation results show that the REP-DRL system utilizes local computing to conserve power when both the battery level and projected EH are low, showcasing its capacity to adapt to varying operating conditions and optimize the utilization of resources.

There is a growing demand for low-power network devices; therefore, enabling technologies for the Internet of Things (IoT) is significantly important. This paper proposed resource allocation by maximizing the harvested energy to substantially improve Energy Efficiency (EE) and regulate transmission power for the scheduled IoT devices. Energy Harvesting (EH) is a viable technology that enables long-term and self-sustainable operations for IoT devices. The Simultaneous Wireless Information and Power Transfer (SWIPT) has been proposed as a promising solution for maximizing EE while ensuring the quality of service of all IoT devices, where the ultra-low power devices harvest energy in Power Splitting (PS) mode. This paper applied the proposed Optimal Transmit Power and PS Ratio (OTPR) algorithm to maximize the EE for SWIPT based on the partial derivative of Lagrange dual decomposition methods. The algorithm jointly optimized the allocation of the channel, PS, and power control to solve the distributed non-convex and NP-hardness caused by co-channel interference. A novel training was proposed for Deep Neural Network (DNN) algorithms chain rules to minimize the loss function based on updating the parameters of the weights hidden layer and convergence training to achieve near-optimal performance and minimize unneeded label data. The simulation results showed that the DNN training for the chain rule provided a near-optimal performance EE with the shortest training time. This observation indicated that decreasing the loss function at every training optimizes the co-channel conditions for IoT devices by assigning the EH requirement to meet the minimum harvesting need.

This article deals with the design and implementation of a characterization system for thermoelectric modules in thermal energy collection systems in solar concentration systems. The design procedure involves the development of a modular system that includes a robotic manipulator for the solar tracking in the concentration task, an instrumentation system that considers the thermal conditions to avoid leakages or incorrect measurements, and a central processing system connected to a user interface. Each system was designed and validated computationally and the system integration was performed according to the concurrent engineering concepts. Finally, the system was tested in different modules providing adequate working conditions (collecting appropriate differentials of temperature) for five different thermoelectric modules in controlled conditions and in solar collector applications.

In this study, we explore a hybrid non-orthogonal multiple access and time division multiple access (NOMA-TDMA) approach designed to maximize sum throughput in a wireless powered Internet of Things (IoT) network (WPIN). Hybrid access points send energy signals to users on downlink, and users in various groups utilize that harvested energy to transmit information on uplink. This process is facilitated by the NOMA-TDMA scheme wherein users of the same group use NOMA for simultaneous transmissions, and separate time slots are assigned to each group through TDMA. Under this hybrid NOMA-TDMA scheme, the main objective is to enhance the network’s sum throughput by jointly optimizing both the allocation of time for downlink and uplink and the downlink beamforming vectors. Given the complex interdependence of variables, the problem is inherently non-convex, making it difficult to solve numerically. Therefore, we reformulate the problem as a bi-level programming problem—the outer-level sub-problem addresses beamforming vectors using a genetic algorithm while the inner-level sub-problem deals with the allocation of downlink and uplink time through the Lagrange multiplier method. Numerical results show that the proposed hybrid NOMA-TDMA scheme outperforms baseline schemes like orthogonal multiple access and equal time allocation, in terms of the sum throughput of the WPIN.

This paper proposes a novel backscatter-enabled partial non-orthogonal multiple access (part-NOMA) system design with imperfect successive interference cancellation. By exploiting part-NOMA combined with backscatter deployments, the considered system can flexibly control the overlap signal of two vehicles via either NOMA and orthogonal multiple access (OMA) to deal with the variation of channel conditions and their priorities while achieving spectral efficiency improvement and efficient interference reduction. On that basis, we first derive closed-form approximations for the outage probability of vehicles and then carry out the respective asymptotic in high signal-to-noise regions. This enables us to gain the diversity and coding gain of vehicles on the one hand and directly show how the considered system effectiveness compared to conventional NOMA and OMA counterparts by mathematical perspectives on the other hand. Moreover, we also provide guidance on how to achieve the approximate and asymptotic ergodic capacity in closed-form expressions, which is beneficial for pointing out the vehicles’ ergodic slope and demonstrating the effectiveness of part-NOMA over classical NOMA. Finally, we provide some illustrative simulations to verify our developed theoretical analyses.

This study introduces a highly efficient harmonic reflection (HR) rectenna designed for precise wireless power transfer (WPT) antenna alignment. The proposed HR rectenna not only converts RF signals to DC power but also transmits a second-harmonic signal to determine the beam alignment between the device and transmitter in diverse environments, including free space and within a medium such as the human body. The proposed rectenna integrates a dual-band slot patch antenna and a HR rectifier. The HR rectifier is configured using a matched voltage doubler and independent second-harmonic impedance control network (ISHICN). To reduce the complexity of the dual-band matching network and mitigate the coupling between the fundamental and harmonic impedances of the HR rectifier, an independent matching technique is applied to a matched voltage doubler and ISHICN, to ensure matching at both the fundamental and harmonic frequencies. Measurement results of the proposed HR rectifier demonstrate impressive performance metrics, including a peak power conversion efficiency (PCE) of 71.6 % at 28 dBm of input power, as well as a peak conversion gain (CG) of -10.4 dB at 8.4 dBm of input power. Notably, the frequency range for PCE >50 % and CG $\gt -22$ dB spans over 0.8 GHz. The antenna adopts a microstrip-fed slot patch design for wide beamwidth at 2.45 GHz and 4.9 GHz, achieving peak gains of 4.9 dBi and 3.8 dBi, respectively. The HR rectenna is integrated with a dual-band microstrip-fed slot patch antenna with sized 60 mm $\times 72.5$ mm. The implemented rectenna is verified to be an efficient WPT receiver through an antenna beam alignment experiment in free space and minced-pork environment.

This research presents a novel microstrip configuration for a lowpass-bandpass triplexer. To design this triplexer, first a lowpass filter (LPF) with some empty spaces is designed. Then, two microstrip sections are embedded inside the empty spaces to create bandpass (BP) channels. Therefore, this triplexer occupies a very small size of $0.003~\lambda \text{g}^{2}$ . A rigorous mathematical analysis and an optimization process are conducted to enhance the triplexer performance. The proposed triplexer features a lowpass (LP) channel cut-off frequency at 870 MHz with bandpass (BP) channels operating at 1.335 GHz and 2.055 GHz. The achieved insertion losses are 0.2 dB, 0.09 dB, and 0.04 dB for all LP and BP channels that are remarkably low, making our triplexer ideal for energy harvesting applications. All bands are flat with suitable group delays (GDs). Meanwhile, the BP channels are wide with 14.1% and 25.5% fractional bandwidths (FBWs) for the middle and upper channels respectively. Our triplexer suppresses the harmonics after the LP channel from the $1^{st}$ harmonic up to $3^{rd}$ harmonic. Fabrication and measurement of the proposed triplexer validate our designing method and the simulation results.

Monitoring water flow helps to identify leaks and wastage, leading to better management of water resources and conservation of this precious resource. To address this challenge, there is a need for an efficient and sustainable water management system. This paper presents an Internet of Things (IoT) based solution that involves retrofitting existing analog water meters using readily available off-the-shelf electronic components. Real-time data collection and analysis are performed through edge computation, which locally processes water meter images captured by the camera and extracts water meter readings. These readings are transmitted to the cloud for storage and further analysis. Various strategies have been implemented to optimize supply-current usage, preserving charge-discharge cycles of solar-powered batteries even in adverse environmental conditions. To streamline the firmware update process for multiple connected devices, a broadcasting technique is employed, offering the benefits of reduced manual labor and time savings. To assess the reliability and performance of developed solution, field deployment is conducted over several months, enabling the characterization of water usage patterns across different locations. Integrating energy harvesting capabilities into system reduces maintenance costs and promotes eco-friendly energy practices. Overall, this solution offers an effective and comprehensive approach towards achieving efficient and sustainable water management.

The use of RF harvesting as a power source for low-power systems, such as wearables, wireless sensor nodes, and Internet of Things, has gained attention. The primary obstacle to develop an effective RF-DC system has been the RF rectifier. The method most frequently employed in the design of RF-DC converters is called Cross-Coupled Differential Drive (CCDD). To enhance the DC output voltage, multistage systems are typically employed. In these designs, DC output will reach saturation as the number of stages rises. This is because the NMOS threshold voltage will rise if the bulk of the NMOS is connected to the lowest potential. An alternative is to employ the twin-well CMOS process, which is more expensive than the regular n-well process. A novel CMOS RF-DC converter is presented in this brief. The design is hybrid in the sense the first stage is designed using a standard CCDD, while the second and subsequent stages are designed exclusively with PMOS transistors. This allows the use of the n-well process during fabrication. With CADENCE Virtuoso in $0.18~\mu \text{m}$ TSMC CMOS technology, the suggested design’s functionality is verified. The design is functional and achieves 45% efficiency and a power dynamic range of 23dB for power conversion efficiency (PCE) >20%, according to simulation findings.

In this paper, we consider and analyze the performance of exploiting simultaneously wireless information and power transfer (SWIPT) for coordinated-non-orthogonal multiple access (NOMA) systems under Nakagami- ${m}$ fading channels. Specifically, to deal with limited energy issues at the relay unit while taking into reusing resource efficiency, we propose to use SWIPT-based power splitting mechanisms in conjunction with NOMA approaches by means of proposing two separate scenarios, the first focus is on forward-based device-to-device (D2D) transmissions, called SWIPT-NOMA-D2D, and the second focus is on forwarding signal only, called SWIPT-NOMA-noD2D. Evaluating case by case, we derive exact and approximate closed-form expressions for the outage probabilities of cell-center and cell-edge users, respectively. Besides, we also obtain an exact closed-form solution for the ergodic rate of message transmission, which enables us to effectively characterize the system spectrum utilization. The correctness of our analyses and the tightness of the approximation developed are validated through Monte Carlo simulations and numerical techniques. Finally, we show that the two proposed schemes achieve the near ergodic sum rate in two cases of unconstrained power designs at the relay unit and their effectiveness in improving the outage performance of centre-cell users.

In this paper, a novel structure of multilayer organic photovoltaic cell has been designed and simulated. The integration of Poly(3-hexylthiophene-2,5-diyl) (P3HT) buffer layer and Poly(9,9-bis(3’-(N,N-dimethyl) N- ethylammoinium propyl-2,7-fluorene)-alt-2,7-(9,9 dioctyl fluorene)) dibromide (PFN:BR) electron transport layer (ETL) in the proposed solar cell has improved the performance significantly. The various performance measuring parameters like power conversion efficiency (PCE), short circuit current (Jsc), open circuit voltage (Voc), fill factor (FF), quantum efficiency (QE) have improved significantly. Furthermore, the effect of different layer thickness, the density of traps $N_{t}$ and temperature on the proposed solar cell has been studied and the optimum value has been obtained. It has been observed that after optimizing the different parameters of the proposed structure, the performance measuring parameters shows an improvement of 14%, 33.3%, 200% and 300% in Voc FF, Jsc and PCE respectively over the reported organic solar cells. Further, a QE of about 90% is achieved in the proposed structure.

Cognitive radio is a technology that allows Secondary Users (SUs) to access vacant spectrum areas allocated to Primary Users (PUs) by dynamically adjusting their settings. However, the spectrum detection subsystem of SUs consumes battery power that could be used for transmission. This work aims to address the energy availability issue for cognitive radio devices by two methods: energy harvesting from the ambient environment and deep learning prediction of future energy levels. We compare three deep learning models: Long-Short Term Memory (LSTM), Convolutional Neural Network (CNN), and Convolutional Long-Short Term Memory (ConvLSTM) with three classic machine learning models: Artificial Neural Networks (ANN), Support Vector Regressor (SVR), and Extreme Gradient Boost (XGBoost). The results show that deep learning models outperform machine learning models across all datasets, with ConvLSTM being the best model with a Normalized Root Mean Squared Error (nRMSE) of 0.0632 and Mean Absolute Error (MAE) of 1.479, which are 8.80% and 9.04% better than the best machine learning model, ANN, with nRMSE of 0.0693 and MAE of 1.626.

In this paper, the design and experimental validation of an active rectifier for ultrasound-based implanted biomedical devices are presented. One of the main contributions is the adoption of an optimized design procedure based on the $g_{m}/I_{D}$ of the transistors. Implemented in a 28-nm CMOS technology and powered by a 0.5-mm thickness and 1-mm2 surface area piezoelectric transducer, the rectifier delivers 1-mW of power to the output load. The adoption of a square wave to drive the transmitting power transducer is experimentally demonstrated to be more power effective. The rectifier exhibits a measured peak power conversion efficiency and a power density equal to 95% and 222mW/ $mm^{2}$ , respectively, revealing itself as the best trade-off between measured power conversion efficiency and power density within the literature of US-powered AC/DC converters.

Power transmission lines are equipped with various electronic devices, including power line inspection robots, safety sensors, and monitoring systems. However, providing power to devices installed on these lines often requires extensive construction and poses many challenges. Therefore, to solve such problems, active research is being conducted on Magnetic Energy Harvesters (MEHs) that harness the magnetic field generated by the current in the power lines to harvest electricity. A crucial design objective of the MEH is to maximize harvested power even if the saturated region is partially included, unlike a current transformer (CT), which must maintain an unsaturated state throughout the operating region. In this paper, design considerations of an MEH are discussed in comparison to the CT. Specifically, this paper formulates the relationship of the airgap between core cutting surfaces, the timing of power harvesting, and the amount of harvested power. It was observed that the MEH core should be designed to have a B-H curve close to the step function by minimizing the airgap and selecting magnetic material with high saturation magnetic flux density to achieve high harvesting power. An optimal MEH model for harvesting the maximum power for each given input current condition is presented. Furthermore, experiments were conducted to verify that 28 W of power was harvested under the input current condition of 150 Arms.

This study is focused on energy harvesting. The goal was to develop a new energy-harvesting system using a pressure regulator and establish an efficient energy flow model within it. The harvested electrical energy can be used as a power source for devices around the regulator, such as primary battery-powered gas meters, thereby enabling battery-free operation. Here, we focus on pressure regulators for LP and city gases, which are used by a vast number of consumers. We systematically modeled complicated energy-conversion processes using the transfer matrix method to maximize the recovered electrical energy. Under normal gas usage conditions, the gas flow is low, and the harvested energy is limited. However, it surpasses the energy supplied by the primary batteries. The results obtained under the actual operational conditions were compared with those calculated using a theoretical model. Within the system, a DC motor and an AC motor were used as generators, and the results were compared. When a DC motor was used as the generator under 0.05 MPa, 0.8 L/m, and maximum power-harvesting load conditions, the harvested power was 0.237 W, which is sufficient to drive the meters. Additionally, the error between the experimental and theoretical results ranged from 2.6% to 4.6%. When an AC motor was used as a generator, the harvested energy was 0.015 W and the error was −14.7%. Utilizing this theoretical model allows for the calculation of the optimal conditions for energy harvesting from various pressure regulators, thereby offering design guidelines for energy-harvesting systems.

A multi-band and polarization-insensitive metamaterial is proposed to harvest electromagnetic energy efficiently in three frequency bands. Based on a square resonant ring and cross resonant structure, a metamaterial is designed to achieve high energy harvesting efficiency of 81.2%, 93.3% and 93.5% at 2.45 GHz, 4.0 GHz and 5.8 GHz, respectively. The overall structure consists of an $8\times 8$ periodic array of meta-cells with two vias loaded by two $50~\Omega $ resistors, an impedance matching network and a rectifier circuit. Results show that the simulated rectification efficiency of rectifier circuit reaches 69.2%, 58.8% and 52.1% at 2.45 GHz, 4.0 GHz and 5.8 GHz, respectively, when the incident power is 5 dBm. The measured harvesting efficiency (RF-DC) is found to be 51.6%, 54.2% and 58.7% with the TE polarization and 50.6%, 53.7% and 58.6% with the TM polarization incidence at the three operating frequency bands, respectively. Although the incident power is reduced to 0 dBm, the energy harvesting efficiency can still reach more than 42% in both TE and TM polarizations. Therefore, it is shown that the energy harvesting system can maintain high efficiency under different polarizations in a wide power range.

In this paper, a novel design of dual-band (DB) inverse Doherty rectifier is presented. Specifically, the proposed structure consists of two Schottky diodes, a DB quarter-wave impedance inverter (DBII) for tolerance of input power and dc load fluctuation, a DB second harmonic termination network (DBHTN) for power conversion efficiency (PCE) enhancement, and a DB impedance transformer (DBIT) for fundamental matching to the source of $50~\Omega $ . Each sub-network is investigated in closed-form equations. For validation, a DB inverse Doherty rectifier is simulated, fabricated, and measured at two target separate frequencies of 0.915 GHz and 2.45 GHz. The experimental results show that peak PCE is achieved at 79.95 % and 77.67 % for 0.91 GHz and 2.4 GHz with $500~\Omega $ load resistance, respectively. Moreover, the 50 % over PCE is remained within a wide dynamic range (DR) of input power, i.e., 20 dB at 0.91 GHz, and 18 dB at 2.4 GHz with $500~\Omega $ load resistance. In addition, the 50 % over PCE within a wide DR of dc load resistance is also recorded with over $1850~\Omega $ at 0.91 GHz, and over $1800~\Omega $ at 2.4 GHz for 10 dBm input power. Compared to other works, the proposed DB rectifier exhibits advantages in terms of high PCE, compact size and wide DR of input power and dc load resistance.

This paper presents a novel three-time slot transmission protocol designed for energy-constrained two-way relay systems. In a departure from traditional energy sources, our approach serves a dual purpose by not only providing power to the system but also significantly enhancing its security. We leverage both physical-layer network coding (PNC) and bit-level Exclusive OR (XOR) technology to amplify system capacity and bolster security. Given the unavailability of exact closed-form solutions, we utilize tight upper bounds for the signal-to-noise ratio (SNR) to derive approximate closed-form results, specifically focusing on outage and intercept performance under Rayleigh channel conditions. We also provide performance bounds for outage and intercept probabilities in the high SNR regime. Our simulation results validate the accuracy of our derived probabilities, demonstrating consistency with Monte Carlo simulations. Furthermore, our investigation reveals that the time allocation factor has minimal impact on the security and reliability of the proposed protocol, particularly under high SNR conditions. In addition, we conduct a comparative analysis between the XOR scheme introduced in this article and non-XOR (NXOR) systems. This analysis underscores the superior security-reliability tradeoff of our approach, particularly in scenarios characterized by poor channel conditions between the energy source user and potential eavesdroppers.

This paper presents a comprehensive overview and analysis of the state-of-the-art (SoA) in semi-passive or Battery-Assisted (BAP) Ultra-High Frequency (UHF) Radio Frequency Identification (RFID) sensor tags compliant with EPC Global G2/ISO-18000C. These tags operate on the same communication principle as fully passive sensor tags but incorporate a battery or an energy harvesting module. This additional power source extends communication ranges and enables power demanding applications using low-power microcontrollers (MCUs) and higher-end sensors. This article also analyzes various key features, including tag integrated circuit (IC) architecture, types of energy harvesting modules, and communication range. The main conclusions are threefold. Firstly, selecting the appropriate tag IC requires a careful analysis of its features such as sensitivity, sensor interfaces, or data logging capabilities. For instance, among the solutions examined in the SoA, half of them opted for a tag IC capable of MCU communication via SPI or I2C buses. Secondly, it is essential to assess both the forward and backward communication links to leverage the sensitivity of the tag IC in BAP mode. Interestingly, only one-third of the SoA solutions achieved the theoretical communication range anticipated by the sensitivity of the tag IC. Finally, an energy budget analysis is required to ensure that the energy generation suffices to meet the energy requirements of the tag. While most solutions rely on batteries as the energy source and analyze battery lifespan, only a few studies employing energy harvesters conduct an energy budget analysis due to the additional complexity involved.

Reliable energy supply remains a crucial challenge in the Internet of Things (IoT). Although relying on batteries is cost-effective for a few devices, it is neither a scalable nor a sustainable charging solution as the network grows massive. Besides, current energy-saving technologies alone cannot cope, for instance, with the vision of zero-energy devices and the deploy-and-forget paradigm which can unlock a myriad of new use cases. In this context, sustainable radio frequency wireless energy transfer emerges as an attractive solution for efficiently charging the next generation of ultra low power IoT devices. Herein, we highlight that sustainable charging is broader than conventional green charging, as it focuses on balancing economy prosperity and social equity in addition to environmental health. We discuss the economic implications of powering energy transmitters with ambient energy sources, and reveal insights on their optimal deployment. Moreover, we overview different methods for modeling the energy arrival process of ambient energy sources and discuss their application in different use cases. We highlight the potential of integrating sustainable WET with energy harvesting from nearby transmitters and discuss enhancements in energy receiver design. We also illustrate the role of different technologies in enabling sustainable WET and exemplify various use cases. Besides, we reveal insights into low-complexity architectures designed at the energy transmitters. We highlight relevant research challenges and candidate solutions.

This paper investigates a system for unmanned aerial vehicle (UAV)-enabled wireless power transfer (WPT) and non-orthogonal multiple access (NOMA) mobile edge computing (MEC) in the Internet of Things (IoT) - UWNMI, wherein a UAV equipped with an energy transmitter (ET) acts as a relay. In particular, we consider a situation involving two clusters of IoT devices (IDs) that have limited resources and therefore are unable to compute their own tasks and must instead offload them to a base station (BS) via the UAV relay (UR) in the presence of a passive eavesdropper. In the UWNMI system, the effects of imperfect channel state information (ICSI) and imperfect successive interference cancellation (ISIC) are considered, along with the usage of artificial noise (AN) to improve the physical layer security (PLS) of the system. We derive closed-form expressions for the successful computation probability (SCP) and secrecy outage probability (SOP) under the Nakagami- $\mathfrak {m}$ fading channel model in order to evaluate the performance of the system. Moreover, we present optimization problems with the objectives of optimizing the position and height of the UR, the time switching ratio (TSR), and the power allocation for the AN in order to maximize the SCP and minimize the SOP. These problems are solved using a particle swarm optimization (PSO)-based algorithm. In addition, Monte Carlo simulation results are presented to validate the accuracy of our analysis based on simulations of the system performance under different values of the system parameters, including the number of antennas at the BS, the number of IDs in each cluster, the TSR, and the position and height of the UR.

The emergence of Wireless Body Area Networks (WBANs) with health monitoring capabilities has revolutionized health care. Implementing fully independent WBAN nodes is important to the long-term viability of this initiative. Regularly recharged and depletable batteries remain a significant impediment in such systems. Energy harvesting (EH) from environmentally clean sources has thus been receiving increasing attention. Nevertheless, the autonomy and optimization of existing WBAN sensor nodes have remained questionable because methods that integrate realistic usage conditions into the design process have been lacking. A plausible method is proposed to establish a framework for designing a sustainable health monitoring node in this work. A Health Monitoring Energy System (HeMeS) tool prototype is consequently developed using comprehensive analytical models and utilized to demonstrate system design space exploration for various patient types, incorporating environmental factors, electronic load activity levels, and system cost/size constraints. It is concluded that the patient-centered system design approach incorporating interactions across transducers, electronics, sensors, user environment and data duty-cycling profiles, is viable, and is in fact appealing in safeguarding truly autonomous and cost-optimal WBANs that are compatible with climate-neutral society.

This paper presents a novel performance evaluation framework for energy harvesting communications. As the harvested energy may not always be at the required levels in the transmitter’s battery, possible energy outage may hinder the transmission, especially in weak channel conditions. Herein, we analyze the performance of an energy harvesting communication link by allowing a certain level of energy outage to occur. Such operation is challenging, given that the energy coming into the battery from an uncontrollable source, e.g., solar energy, does not relate to the channel conditions and quality-of-service (QoS) requirement, whereas energy going out of the battery is directly dependent on both. Hence, the incoming energy and outgoing energy become independent of each other. Knowing the exact level of energy that is accumulated in the battery is therefore challenging. To deal with these challenges, a probabilistic energy-outage approach and a virtual battery queuing model are proposed and used to develop the target performance evaluation framework while leveraging the large deviation principle theorem. The derived energy-outage probability of the communication system relates the system parameters, namely, QoS component, channel conditions, and harvested energy. Numerical results are presented to confirm the analytical findings and discuss the performance of energy harvesting based communication with tolerable energy-outage as a function of the system parameters.

In this paper, we analyze an unmanned aerial vehicle (UAV) that harvests energy from a power beacon to support the transmissions of finite blocklength packets from a source to two destinations using non-orthogonal multiple access (NOMA) scheme. The impacts of line-of-sight (LoS) and non-LoS communications on the block error rate (BLER) and throughput of the system are presented via the Rician factor. For the evaluation of system performance, we derive the closed-form and approximate expressions of the BLERs of each hop and the end-to-end communication path between source and destination. Based on these derived BLER expressions, we calculate the throughput of the considered UAV-NOMA-short packet communication (SPC) system. Analytical results are verified by Monte-Carlo simulations. Numerical results show that the best system performance can be achieved by selecting a reasonable time switching duration for energy harvesting, blocklength, and the number of training bits. The minimal BLERs can be obtained by controlling the trajectory of the UAV, i.e., its altitude and location on the horizontal plane. The BLER performance of the UAV-NOMA-SPC system is compared with that of the UAV-orthogonal multiple access (OMA)-SPC system, showing that the UAV-NOMA-SPC gives a lower average BLERs than the UAV-OMA-SPC system.

Battery technology revolutionized power storage for various purposes, including uninterrupted IoT-based Wireless Sensor Networks (WSNs) operations. However, limited energy sources pose a challenge for sensor nodes. To overcome this, researchers introduced Energy Harvesting (EH) in IoT-based WSNs, enabling them to overcome energy limitations and recharge IoT devices. The shift towards energy harvesting-aware routing is necessary to achieve a near-perpetual network environment. In light of the energy consumption of IoT-based WSN nodes, a simple energy-efficient routing technique is proposed to consume less energy. The protocol incorporates efficient link selection based on the closest angle to the destination node and is divided into two main parts: distributed neighbor discovery and routing processes. By incorporating EH techniques, the proposed model aims to improve network longevity. Experimental results demonstrate successful routing and promising outcomes in terms of energy and utilization techniques.

An energy-independent wearable electronic system is being researched, where the energy required for its operation is obtained from human body movements and heat flow. The system power source consists of an electromagnetic motion energy harvester (mass up to 70 g, peak power reaching 38 mW depending on the intensity of movement), a thermoelectric harvester (mass 112 g, peak power up to 3 mW), power converters and a storage capacitor. The functional part of the system consists of a temperature and humidity sensors and a wireless data transmission module, while the energy-aware power management and the main function control is accomplished by a microcontroller, maintaining a safe input voltage range by adjusting the average consumption of the device. Only off-the-shelf components that provide greater configurability and could utilize a broader harvested voltage range were used. This study demonstrates that using a common energy storage element provides an energy flow stabilization effect that reduces the overall power instability, which can be a limiting factor for a single energy harvester system. The prototype was tested in controlled environment by walking on a treadmill and provided the necessary amount of energy for a sustained system operation even during motion pauses. Electrical energy storage efficiency of the system averaged at 17 %, with a peak efficiency reaching 21 %.

To increase spectral and power efficiencies, network coverage, and reduce outage probability, this study investigated the end-to-end performance of a coded dual-hop RF/FSO wireless communication system that operates across both Nakagami- $m$ fading and Malaga- $\mathcal {M}$ atmospheric turbulence channels and is supplied with an amplify-and-forward semi-blind energy harvesting fixed-gain relay in order to increase spectral and power efficiencies, network coverage, and outage probability. We examine the use of CSOC codes at the source and a majority logic decoding method (MLGD) at the destination. Interestingly, our goal is to obtain an average bit error probability analytically by accounting for various interferers in the first hop as well as residual hardware impairments at the relay and destination receivers. As a consequence, the decoding method used in this investigation was shown to be adequate for the SWIPT/TS-assisted RF/FSO-coded AF cooperative communication system, requiring excellent accuracy while having a low computational cost.

This review article examines the crucial role of energy harvesting and energy recovery in the design of battery electric vehicles (BEVs) and fuel cell hybrid electric vehicles (FCHEVs) as these vehicles have limited onboard power sources. Harvesting energy and recovering energy from onboard systems can significantly improve energy efficiency, increase range, and reduce fuel consumption. The latest advances in vehicular energy recovery and harvesting, including regenerative braking, regenerative suspension, solar and wind energy harvesting, and other recovery methods are studied and the impact of the energy storage system and powertrain architecture on energy harvesting is investigated. Regenerative brake control strategies and driver behaviour’s effect on energy recovery are reviewed, and the potential of energy harvesting in electric vehicles is discussed, including experimental and low-power harvesting methods. The importance of using perception and navigation technologies in autonomous vehicles to enhance energy efficiency is highlighted. The article identifies critical research gaps, challenges, and future directions for research in this field. This review stands out from previous papers by covering overlooked subjects such as driver behaviour and deceleration planning in autonomous vehicles, low-power harvesting methods, and experimental techniques applicable to electric vehicles.

Energy harvesting technologies are contributing to the development of the Internet of Things. These techniques can provide continuous, energy-efficient and environmentally friendly power supplies and reduce manual maintenance requirements, and thus have strong prospects for sensing and monitoring applications, particularly in certain harsh working environments. The proposed electromagnetic–piezoelectric hybrid harvester contains both electromagnetic generator (EMG) and piezoelectric generator (PEG). Through vibration of the cantilever beam, the magnetic circuit in the soft-magnetic material is both connected and disconnected, and this can cause the magnetic flux in the coil to change dramatically, resulting in induction of a large voltage in the coil. This paper illustrates the feasibility and the optimal characteristics of the proposed hybrid harvester using theoretical verification and simulations, and demonstrates the factors that affect the power generation effect through testing. The study found that the maximum open-circuit voltages of the EMG and the PEG are 16 V and 42 V and the maximum peak powers of the EMG and the PEG reach 20 mW and 35 mW, respectively. The proposed energy harvester offers advantages in terms of both peak voltage and peak power, and provides a new method and concept for the vibration energy harvesting field.

This article aims to contribute for the improvement of low-power Radio Frequency Energy Harvesting (RFEH) designs based on Schottky Barrier Diode (SBD) rectifiers, presenting a study of the relationship between RF-DC Power Conversion Efficiency (PCE) and circuit temperature, which is directly related to the non-linear behavior of the metal-semiconductor junction of SBDs. For this purpose, SPICE diode models were revisited, evaluating the temperature dependence and its effects on forward and reverse conduction modes. The SBDs SMS7621 and SMS7630 temperature dependent characteristics are evaluated according the proposal, and their use is explored through analytical modeling and simulation analyzes of an RFEH system for temperatures ranging from 240 to 360 K. Hence, after an initial formulation of the optimum operation in terms of PCE, two series RF rectifiers are designed based on the mentioned diodes, aiming at an efficient operation over different temperature ranges according to each component optimum PCE, at 300 K for the SMS7630 and over 340 K for the SMS7621. For the SMS7630 prototype, the maximum measured PCE is 25.33% around 293 K, but decreases to 3.65% around 353 K, and for the SMS7621 the measured PCE goes from 11.56% to 16.34% in the same temperature range, considering −20 dBm as Input Power ( $P_{in}$ ) due to the low-power RFEH premise. The results leads to a higher PCE stability for the SMS7621 through the whole analyzed range, despite the overall PCE that is limited by the matching network losses intrinsic to the design.

This paper investigates the performance analysis of cognitive-inspired wireless-powered non-orthogonal multiple access (NOMA) systems, where two collaborative schemes are jointly proposed to enhance the quality-of-signal at the primary cell-edge (CE) user in dead zones. In particular, the first scheme, namely internal collaborative schemes, is developed based on the availability of primary cell-near users (CU), while the rest, called external collaborative schemes, comes from the help of a secondary source (SS). For the second scheme, SS’ operation is considered with two relaying-encoding NOMA protocols, namely decode-and-forward (DF) and amplifier-and-forward (AF). In order to enable CE’s communications in short execution times, the power-splitting mechanism is considered at both CU and SS. To capture the system’s performance trend accurately, closed-form approximate expressions for the outage probability of users are derived. Moreover, we also carry out the respective asymptotic expression at high transmit power regimes to show some useful insight, such as diversity order and coding gain. Finally, extensive Monte-Carlo simulations are presented to collaborate the accuracy of the theoretical analysis and numerical results show that the proposed collaborative scheme provides CE’s performance enhancement better than using only one collaborative scheme.

In this paper, we examine Internet of Things (IoT) systems related to smart cities, smart factories, connected cars, etc. To support such systems in a wide area with low power consumption, energy harvesting technology utilizing wireless charging infrastructure is necessary for the longevity of networks. Considering that the position and amount of energy charged for each device could be unbalanced according to the distribution of nodes and energy sources, maximizing the minimum throughput among all nodes has become an NP-hard challenging issue. To overcome this challenge, we propose a machine learning based relaying topology algorithm with a novel backward-pass rate assessment method to present proper learning direction and an iterative balancing time slot allocation algorithm which can utilize a node with sufficient energy as the relay. To validate our proposed scheme, we conducted simulations on our established system model; thus, we confirm that the proposed scheme is stable and superior to conventional schemes.

We explore the potential for predicting indoor photovoltaic energy on a forecasting horizon of up to 24 hours. The objective is to enable energy management approaches that exploit harvesting opportunities more strategically, for which they require more accurate energy intake predictions. Our study is based on a data set covering over 3 years, for which we simulate online machine learning algorithms with different amounts of training data and input features. Our results show that relatively simple machine learning methods can outperform a persistent predictor considerably, and we observed a reduction of errors of up to 56%. When devices obtain a significant amount of sunlight, adding the weather forecast improves the prediction accuracy. We discuss prediction features, the amount of training data and analyze the sources of errors to understand the potential of indoor photovoltaic energy harvesting predictions.

The exponential growth of data traffic from mobile devices requires the implementation of heterogeneous networks (HetNets), which densely deploy multiple radio access technologies to match such demands. The deployment of many small base stations (SBSs) leads to backhauling problems where wired backhauling is neither available nor efficient. millimeter waves (mmWaves) can potentially mitigate the backhauling problem by providing high throughput and low capital expenditure (CAPEX). However, due to the high attenuation rate in mmWaves, the increase in the distance between SBS and macro base station (MBS) can severely degrade the system’s overall performance. On the other hand, densely deployed SBSs with wireless backhauling can cause high energy consumption in the system. Therefore, in this work, a novel network model is presented in which a combination of SBS, active antenna units (AAUs), and edge computing units (ECUs) are deployed to minimize the overall energy consumption of the network while maintaining the sufficient quality of service (QoS). A mathematical model based on optimization modeling is introduced to solve user equipments (UEs) association, dynamic sleeping, backhauling and fronthauling, and power transmission. Due to the complexity of the formulated problem, a heuristic algorithm is introduced. Namely, integrated access and backhauling (IAB) with Edge Computing and Dynamic Sleeping Algorithm (IEDS) algorithm is introduced to decompose the formulated problem into two parts and solve them iteratively. Finally, computer simulation results that demonstrate the model’s performance are presented for comparison between optimal solution, IEDS, and HBDS, which shows that IEDS outperformed HBDS in performance with negligible computation difference.

The existing clearance in the mechanical joints plays a crucial role in the assembly of mechanical systems, allowing the mobility of its components. However, few studies have explored models that consider joint clearance in the case of electromechanical energy harvesting systems. This paper examines the effect of existing clearance in an electromagnetic energy harvester attached to the human lower limb. The dynamic response of the system and an estimate of its generated power are obtained by developing a lumped model, in which clearance is included by adding a dead band and assigning a stiffness coefficient during contact between elements. The natural motion of the lower limb is the input to the formulated model, which takes into account the nonlinear interaction of the electromagnetic device and the power conditioning circuit. Central composite design is used to study the influence of two selected factors on the dynamics of the system; joint clearance size and contact stiffness. The results suggest that the presence of clearance between the clamping mechanism and the human body positively affects the performance of the analyzed electromagnetic energy harvesting system. It was revealed that an increase of around 27% of output power could be intentionally achieved by adding larger clearance sizes.

Energy Harvesting technology contributes significantly to green cellular networking by ensuring self-sustainability and extinguishing environmental hazards. Due to the imbalance between the harvested energy and traffic load of the base stations (BSs), energy cooperation has become a crucial requirement. However, the decision of optimal energy cooperation among the BSs in a multi-operator cellular network is a challenging task due to the consideration of various factors, such as cost, loss of energy, future information of traffic load, and harvested energy of the BSs, etc. The two conflicting objectives are minimizing the energy buying cost and the loss of energy while transferring through the power links. In this work, we present an optimal energy cooperation framework, formulated as a multi-objective linear programming (MOLP) problem which brings a trade-off between the two above-mentioned conflicting objectives considering the harvested energy and load of the BSs at future time slots. For the prediction of harvested energy of the BSs, we develop a Deep Q-Learning-based prediction method that intelligently increases measurement accuracy through continuous exploration and exploitation. The results of simulation experiments carried out in MATLAB depict that the proposed multi-operator energy cooperation framework outperforms state-of-the-art works in terms of cost, performance, and energy-loss reduction.