Complexity Of System Research Articles

Power Quality Enhancement in High-Voltage Transmission Systems Using STATCOM by Type 1 and Type 2 Fuzzy Logic Controllers

The continuing growth of load demand leads to increased power system complexity and interconnections. During faults and abnormal operating conditions, conventional power systems change the load flow in transmission lines, which may cause voltage drops or swells, and other power quality issues. Voltage regulation is connected to reactive power compensation in system busbars, and fixed capacitor banks are usually used for this purpose, but they are not flexible and dynamic enough to meet the power quality requirements. With power electronics development, flexible alternative current transmission systems (FACTS) became a popular solution for power quality improvement and power transmission capability increment in power systems, and FACTS can be categorized into series and shunt systems. In this study, STATCOM was used to improve the voltage profile in part of Iraq’s high-voltage transmission system during abnormal operating conditions, such as sudden load addition and removal, which causes voltage dip and voltage swell. Type 1 fuzzy logic controller and type 2 fuzzy logic controller were used as the outer controller in the STATCOM; both controllers were able to regulate the voltage magnitude by reducing the voltage dip from 1.32 to 0.4%, voltage swell from 1.02 to 0.5%, and voltage THD at the connecting point did not exceed 3%, indicating that SATACOM with FLC improves the voltage profile under different operating conditions.

Augmented Reality in Extra-Vehicular Activities: Optimizing Alert Detection and Cognitive Workload

This study investigates the impact of augmented reality (AR) system complexity (active vs. passive) and lighting conditions (normal, dark, glare) on task performance, cognitive workload, and alert detection during simulated extravehicular activities (EVAs). Fifty-eight undergraduate students participated, using AR systems to complete tasks. Key metrics included task completion rate, completion time, alert detection, and cognitive workload assessed by the NASA Task Load Index (NASA-TLX). Results showed no significant differences in task performance or cognitive workload across lighting conditions and AR types. Notably, less than 15% of participants noticed any alerts, suggesting AR alerts may lack sufficient salience. The study’s lack of significant findings is attributed to the small sample size and potential insensitivity of measures. Future research should involve larger samples and refined methodologies to better understand AR’s impact on astronaut performance and safety during EVAs.

Exploring the Diversity of Nuclear Density through Information Entropy.

This study explores the role of information entropy in understanding nuclear density distributions, including both stable configurations and non-traditional structures such as neutron halos and α-clustering. By quantifying the uncertainty and disorder inherent in nucleon distributions in nuclear many-body systems, information entropy provides a macroscopic measure of the physical properties of the system. A more dispersed and disordered density distribution results in a higher value of information entropy. This intrinsic relationship between information entropy and system complexity allows us to quantify uncertainty and disorder in nuclear structures by analyzing various geometric parameters such as nuclear radius, diffuseness, neutron skin, and cluster structural features.

Real-time energy management simulation for enhanced integration of renewable energy resources in DC microgrids

The presented work addresses the growing need for efficient and reliable DC microgrids integrating renewable energy sources. However, for the sake of practicality, implementing complex control strategies can increase system complexity. Thus, efficient methodologies are required to provide efficient energy management of microgrids while increasing the integration of renewable energy sources. The primary contribution of this work is to investigate the issues related to operating a DC microgrid with conventional control designed to power DC motors using readily available, non-advanced control strategies with the objective of achieving stable and reliable grid performance without resorting to complex control schemes. The proposed microgrid integrates a combination of uncontrollable renewable distributed generators (DGs) alongside controllable DGs and energy storage systems, including batteries and supercapacitors, connected via DC links. The Incremental Conductance (InCond) algorithm is employed for maximum power point tracking to maximize power output from the PV system. The energy management strategy prioritizes the solar system as the primary source, with the battery and supercapacitor acting as backup power sources to ensure overall system reliability and sustainability. The effectiveness of the microgrid under various operating conditions is evaluated through extensive simulations conducted using MATLAB. These simulations explore different power generation scenarios, including normal operation with varying load levels and operation under Standard Test Conditions (STC). Moreover, fault analysis of the DC microgrid is performed to examine system reliability. The system performance is evaluated using real-time simulation software (OPAL-RT) to validate the effectiveness of the approach under real-time conditions. This comprehensive approach demonstrates the efficacy of operating a DC microgrid with conventional controllers, ensuring grid stability and reliability across various operating conditions and fault scenarios while prioritizing the use of renewable energy sources. The results illustrated that system efficiency increases with load, but fault tolerance measures, can introduce trade-offs between reliability and peak efficiency.

Research on the Synergistic Evolution of Comprehensive Transportation Network System in the Yellow River Basin Aimed at High-Quality Development

The high-quality development of the Yellow River Basin is still facing the issue of imbalance and inadequacy, and it urgently requires the backing and assistance of a well-coordinated and efficient comprehensive transportation network system. Based on this purpose, this study uses a composite system synergy degree model incorporating “mode dimension + regional dimension” to analyze the evolution of this network. The results indicate the following: (1) Railway and air transportation surpass highway transportation in growth and synergy, though overall system synergy is still low. (2) Downstream areas are gradually taking the lead, and the orderliness of each subsystem is gradually becoming consistent. The complexity and variability of the composite system’s synergy are high, with an overall low level of synergy. Overall, despite improvements in service capacity and quality, better integration of different transport modes and enhanced cross-regional cooperation are needed. This study reveals and analyzes in depth the complexity of the comprehensive transportation network system in the Yellow River Basin and its spatiotemporal characteristics of synergistic evolution. We propose suggestions for high-quality development at national, regional, and industry levels, fostering the continuous improvement of the system structure, which aids in overcoming the tangible obstacles faced by the high-quality development of the Yellow River Basin.

Product catalysis dual entropy-driven amplification reaction strategy for miRNA-21 detection in glioblastoma

MicroRNA is taken as diagnostic tumor markers in clinical diagnosis of various cancers. However, the complexity system and low utilization rate of entropy-driven amplification reaction (EDAR) limit its application. In this work, a product catalysis dual EDAR amplification strategy is developed for miRNA-21 detection. This strategy allows miRNA-21 to simultaneously catalyze two EDARs, greatly increasing the limit of detection to 0.40 pM and enhancing amplification efficiency. The amplification efficiency was also significantly enhanced with a much shorter reaction time by increasing concentration of the catalyzer (EDAR product). The approach was successfully applied in clinical samples, showing potential for early screening and diagnosis.

Predicting spatiotemporal patterns of productivity and grazing from multispectral data using neural network analysis based on system complexity

AbstractRemote sensing tools, along with Global Navigation Satellite System cattle collars and digital soil maps, may help elucidate spatiotemporal relationships among soils, terrain, forages, and animals. However, standard computational procedures preclude systems‐level evaluations across this continuum due to data inoperability and processing limitations. Deep learning, a subset of neural network, may elucidate efficiency of livestock production and linkages within the livestock‐grazing environment. Consequently, we applied deep learning to environmental remote sensing data to (1) develop predictive models for yield and forage nutrition based on vegetation indices and (2) at a pixel‐level (per 55 m2), identify how grazing is linked to soil properties, forage growth and nutrition, and terrain attributes in silvopasture and pasture‐only systems. Remotely sensed data rapidly and non‐destructively estimated herbage mass and nutritive value for enhanced net and primary productivity management in livestock and grazing systems. Cattle grazed big bluestem (Andropogon gerardii ‘Vitman’) with 182% greater frequency than orchardgrass (Dactylis glomerata L.) in the pasture‐only system. Real‐time estimates of vegetative bands may assist in predicting grazing pressure for more efficient pasture resource management. Cattle grazing followed distinct soil‐landscape patterns, namely reduced cattle grazing preference occurred in areas of water accumulation, which highlights linkages among terrain, soil‐water movement, soil properties, forage nutrition, and animal grazing response spatially and temporally. Results from this study could be scaled up to improve grazing management among the largest land‐use category in the United States, that is, grasslands, which would allow for sustainable intensification of forage‐based livestock production to meet growing demands for environmentally responsible protein.

Numerical prediction of passive speed and performance for multistage pump without power drive in natural flow process

With the development of engineering applications and the increase in system complexity, some particular fields, such as liquid rocket engine turbopumps, aircraft engine fuel systems, and marine natural flow cooling systems, are increasingly focusing on the performance characteristics of pumps under natural flow conditions. The pump is in the form of resistance components under natural flow conditions without a power drive. The impeller undergoes passive rotation by the impact of inlet flow. Due to the specificity of its operating conditions and performance indicators, the pump's natural flow performance cannot be evaluated by regular methods. Therefore, this paper proposed a numerical prediction method for pump natural flow performance based on a coupled computational fluid dynamics coupled with six-degrees-of freedom model. The performance of a multistage pump with guide vanes was evaluated under different natural flow conditions, and the accuracy was verified by experimental measurements. The transient variation mode of pump performance parameters with time at the initial stage of natural flow impact was analyzed. The flow field's transient evolution characteristics and the wall shear stress variation during natural flow were investigated. It was found that the impeller's passive rotational speed increases linearly with the natural flow rate, while the hydraulic loss shows an exponentially increasing trend. Meanwhile, the natural flow loss coefficient shows an exponentially decreasing trend and gradually tends to a stable value. The high turbulent kinetic energy inside the impeller is mainly distributed in the flow separation region and large velocity gradients. The distribution of shear stresses is closely related to the flow behavior inside the pump and the geometrical features of the hydraulic components.

From Pursuit of the Universal AGI Architecture to Systematic Approach to Heterogeneous AGI (SAGI): Addressing Alignment, Energy & AGI Grand Challenges

Artificial intelligence (AI) faces a trifecta of grand challenges: the Energy Wall, the Alignment Problem and the Leap from Narrow AI to AGI. Contemporary AI solutions consume unsustainable amounts of energy during model training and daily operations. Making things worse, the amount of computation required to train each new AI model has been doubling every 2 months since 2020, directly translating to unprecedented increases in energy consumption. The leap from AI to AGI requires multiple functional subsystems operating in a balanced manner, which requires a system architecture. However, the current approach to AI lacks system design; even though system characteristics play a key role in the human brain; from the way it processes information to how it makes decisions. In this paper, we posit that system design is the missing piece in overcoming current AI the grand challenges. We present a Systematic Approach to AGI (SAGI) that utilizes system design principles to overcome the energy wall and the alignment challenges. This paper asserts that artificial intelligence can be realized through a multiplicity of design-specific pathways, rather than a singular, overarching AGI architecture. AGI systems may exhibit diverse architectural configurations and capabilities, contingent upon their intended use cases. We argue that AI alignment, the most difficult among the grand challenges, is not attainable without a way to reflect the complexity of the human moral system and its subsystems in the AGI architectures. We claim that AGI approaches such as symbolicism, connectionism and others are not fundamental to AGI but emergent from the system design processes. Hence, we focus on employing system design principles as a guiding framework, rather than solely concentrating on a universal AGI architecture.

Structural Architectures for Imperfect Deployable Lunar Towers

The load-carrying performance for deployable lunar tower architectures using thin-ply composite booms is parametrically evaluated. These towers support a communications and sensor platform on the lunar surface. The primary design consists of the corrugated rollable tubular boom (COROTUB) acting as the primary tower mast that supports the payload tip mass. Guy wires connecting the boom tip to spreader bars attached at the base serve to correct lateral eccentricities and increase dimensional accuracy. The analysis compares the maximum tip mass that this primary design can carry relative to a reference design that does not contain any guy wires or spreader bars. The results provide insights into whether the additional system mass and complexity associated with the cable-stayed design are worth the increased payload mass that can be supported. By setting the design limiting condition to a maximum allowable deflection, the analysis reveals that the primary design has better performance when the spreader bar length relative to the tower height exceeds a critical value. The analysis is extended to lunar towers with collapsible tubular mast (CTM) booms, and similar trends are observed. Finally, the analytical load displacement behavior of the COROTUB and CTM reference towers is verified with finite element simulations.

Innovative approaches to optimizing Li-Ion battery cooling performance using gas mixtures

This study investigates the critical issue of thermal management in lithium-ion batteries, a vital concern for enhancing battery performance, safety, and longevity. While more complex cooling systems like liquid cooling, phase change materials, and two-phase systems offer superior thermal performance due to their higher heat transfer capabilities, they come with significant drawbacks. These include increased system complexity, higher costs, added weight, and the potential for leaks or other failures that can compromise safety and reliability. Consequently, simpler air-based cooling systems are often preferred in many applications, despite their inherently lower h coefficient. To bridge the performance gap between air and liquid cooling without resorting to complex engineering solutions, this study focuses on enhancing the h coefficient of air-based systems using innovative refrigerant gas mixtures. The use of refrigerant gas mixtures, specifically a blend of R1234ze(E), nitrogen, and neon, as an alternative to traditional cooling methods was explored. The study employed a comprehensive FEM model and experimental validation to evaluate free and forced convection in a slender, vertical cylindrical configuration. To analyze the impact on convective heat transfer, the key parameters CRate, T0 and Tamb were systematically varied. A dimensionless analysis then evaluated how thermodynamic properties influence h coefficient. This analysis led to the derivation of a dimensionless heat transfer function h(ρ, Cp, k, μ). The study aimed to significantly improve the efficiency of h by optimizing the gas mixture properties. The “Mixture Study” identifies the optimal refrigerant composition, where a specific mixture α of 86 % R1234ze(E), and 14 % neon demonstrated the best balance between heat transfer efficiency and enhanced thermal capacity. The heat transfer coefficient increases by around 65 % during natural convection and by around 80 % during forced convection. The percentage reduction of the thermal difference in temperature reaches approximately 10 % with a maximum of 4 K in natural convection conditions and a maximum of 36.5 % and a reduction of approximately 10 K compared to air cooling alone, effectively maintaining the battery within the optimal operating range of 293.15 K to 333.15 K. Notably, this composition increased the thermal power absorbed by the mixture by over 100 % under the same thermal difference. This research therefore provides valuable insights for developing more effective gas cooling strategies that ultimately enhance the performance, safety, and durability of batteries in various applications. By integrating the benefits of both cooling methods, the study paves the way for innovative thermal management solutions in battery technology.

Application of multi-objective neural network algorithm in industrial polymerization reactors for reducing energy cost and maximising productivity

Optimization on an industrial scale is a complex task that involves fine-tuning the performance of large-scale systems and applications to make them more efficient and effective. This process can be challenging due to the increasing volume of work, growing system complexity, and the need to maintain optimal performance. Due to the significant power required for compression and the high costs of reactant materials, optimizing low-density polyethylene (LDPE) production to provide maximum productivity with a reduction of energy cost is required. However, it is not a simple process because the optimization problem of the LDPE tubular reactor consists of conflicting objective functions. Multi-objective neural network algorithm (MONNA) is a metaheuristic optimization method that provides a versatile and robust approach for solving complex, contradictory targets and diverse optimization problems that do not rely on specific mathematical properties of the problem. It is inspired by the structure and information-processing capabilities of biological neural networks. MONNA iteratively proposes solutions, evaluates its performance, and adjusts its approach based on feedback, which avoids complex mathematical formulations. In this work, we implement Multi-objective optimization neural network algorithm (MONNA) in LDPE tubular reactor for maximising productivity, conversion and minimising energy costs with three scenario of problem optimization, i.e. maximising productivity and reducing energy cost for the first problem (P1); increasing conversion and reducing energy costs for the second problem (P2); and increasing productivity and reducing by-products for the third problem (P3). The results show that the highest productivity, highest conversion, and lowest energy are 545.1 mil. RM/year, 0.314, and 0.672 mil. RM/year. The extreme points in the Pareto Front (PF) for various bi-objective situations provide practitioners with helpful information for selecting the best trade-off for the operational strategy. According to their preferences, decision-makers can use the resulting Pareto to decide on the most acceptable alternative. The decision variable plots show that both initiators in the reacting zone highly affected the optimal solution with the opposite action.

Indoor Thermal Environment Improvement Based on Switchable Radiation/Convection-Combined Intermittent Heating: Comparison between Conventional Terminals and Integrated Novel Terminal

Intermittent heating is an energy-saving heating mode, which can save energy in terms of time, and thus is worth promoting, particularly in residential heating scenarios. Conventional radiant heating terminals, that is floor heating, and convective heating terminals, that is fan coils, cannot achieve both intermittent and thermal comfort during intermittent heating. Therefore, this study proposes a switchable convective-radiant heating regulation method for floor heating and fan coils to achieve a comfortable indoor environment with high thermal response speed. Furthermore, a novel combined radiant-convective heating terminal was proposed for a reliable and effective solution. Results showed that the proposed switchable method could increase both intermittence and thermal comfort. In addition, the heating terminal showed better heating performance than the combination of two conventional terminals at the key points of heating capacity, flexibility, and thermal response. It could initially heat up a typical residential space within 20–40 min and then stabilize the room temperature in a comfortable range of 18–22 °C, showing great potential for intermittent heating in room-scale heating conditions. This study provides a reference technique for intermittent heating with reduced system complexity and precise environmental control.

Recognition of Chinese Electronic Medical Records for Rehabilitation Robots: Information Fusion Classification Strategy.

Named entity recognition is a critical task in the electronic medical record management system for rehabilitation robots. Handwritten documents often contain spelling errors and illegible handwriting, and healthcare professionals frequently use different terminologies. These issues adversely affect the robot's judgment and precise operations. Additionally, the same entity can have different meanings in various contexts, leading to category inconsistencies, which further increase the system's complexity. To address these challenges, a novel medical entity recognition algorithm for Chinese electronic medical records is developed to enhance the processing and understanding capabilities of rehabilitation robots for patient data. This algorithm is based on a fusion classification strategy. Specifically, a preprocessing strategy is proposed according to clinical medical knowledge, which includes redefining entities, removing outliers, and eliminating invalid characters. Subsequently, a medical entity recognition model is developed to identify Chinese electronic medical records, thereby enhancing the data analysis capabilities of rehabilitation robots. To extract semantic information, the ALBERT network is utilized, and BILSTM and MHA networks are combined to capture the dependency relationships between words, overcoming the problem of different meanings for the same entity in different contexts. The CRF network is employed to determine the boundaries of different entities. The research results indicate that the proposed model significantly enhances the recognition accuracy of electronic medical texts by rehabilitation robots, particularly in accurately identifying entities and handling terminology diversity and contextual differences. This model effectively addresses the key challenges faced by rehabilitation robots in processing Chinese electronic medical texts, and holds important theoretical and practical value.

Cost-Effective Localization of Mobile Robots Using Ultrasound Beacons and Differential Time-of-Flight Measurement

This paper presents an innovative and cost-effective solution for the absolute localization of mobile robots using ultrasound beacons. The proposed system addresses the challenge of precise positioning within a controlled environment by employing Differential Time-of-Flight (ToF) measurements to determine the relative distances between the robot and optimally placed beacons. Unlike other ToF methods that require synchronization pulses, the proposed approach eliminates this requirement, significantly simplifying the setup and reducing system complexity. Furthermore, the system achieves a higher sampling rate than conventional synchronization-based systems, enhancing real-time performance. Detailed analysis and simulation demonstrate the system’s ability to provide accurate and reliable localization. The results highlight the potential for broad application in various robotic environments, offering a robust solution for absolute positioning without complex synchronization strategies. This work underscores the advantages of using ToF measurements with ultrasound beacons and contributes to the ongoing development of efficient and cost-effective robotic localization systems.

Community-Based Participatory Research and System Dynamics Modeling for Improving Retention in Hypertension Care

The high prevalence of hypertension calls for broad, multisector responses that foster prevention and care services, with the goal of leveraging high-quality treatment as a means of reducing hypertension incidence. Health care system improvements require stakeholder input from across the care continuum to identify gaps and inform interventions that improve hypertension care service, delivery, and retention; system dynamics modeling offers a participatory research approach through which stakeholders learn about system complexity and ways to model sustainable system-level improvements. To assess the association of simulated interventions with hypertension care retention rates in the Nigerian primary health care system using system dynamics modeling. This decision analytical model used a participatory research approach involving stakeholder workshops conducted in July and October 2022 to gather insights and inform the development of a system dynamics model designed to simulate the association of various interventions with retention in hypertension care. The study focused on the primary health care system in Nigeria, engaging stakeholders from various sectors involved in hypertension care, including patients, community health extension workers, nurses, pharmacists, researchers, administrators, policymakers, and physicians. Simulated intervention packages. Retention rate in hypertension care at 12, 24, and 36 months, modeled to estimate the effectiveness of the interventions. A total of 16 stakeholders participated in the workshops (mean [SD] age, 46.5 [8.6] years; 9 [56.3%] male). Training of health care workers was estimated to be the most effective single implementation strategy for improving retention in hypertension care in Nigeria, with estimated retention rates of 29.7% (95% CI, 27.8%-31.2%) at 12 months and 27.1% (95% CI, 26.0%-28.3%) at 24 months. Integrated intervention packages were associated with the greatest improvements in hypertension care retention overall, with modeled retention rates of 72.4% (95% CI, 68.4%-76.4%), 68.1% (95% CI, 64.5%-71.7%), and 67.1% (95% CI, 64.5%-71.1%) at 12, 24, and 36 months, respectively. This decision analytical model study showed that community-based participatory research could be used to estimate the potential effectiveness of interventions for improving retention in hypertension care. Integrated intervention packages may be the most promising strategies.

Quantitative statistical evaluation of maxillary and mandibular incisor root canal morphology in Saudi patients

Abstract Introduction: Clinical practice demands understanding the complexity of the root canal system to achieve desired treatment goals. The statistically detailed morphology of the tooth and root canal morphology is very important to know. Therefore, the present study aims to investigate the statistical analysis of root canal morphology and morphological variations of maxillary and mandibular incisor teeth using cone-beam computed tomography (CBCT) in Saudi patients attending King Saud University Dental Hospital. Materials and Methods: CBCT images of 480 patients were selected. Number of roots and root canal configuration were identified and categorized according to Vertucci’s classification. Tooth length, distance from cementoenamel junction (CEJ) to the area of canal division, and width of the canal at the area of division were also evaluated. Bilateral symmetry and association between gender and age with the different variables were investigated. Data were analyzed using Chi-square, multiple linear regression, and McNemar’s tests. Results: A total of 3412 teeth were evaluated. All maxillary teeth had one root with Type I canal configuration. For mandibular teeth, all exhibited one root with the majority having Type I canal configuration in central (71.2%) and lateral (75%) incisor teeth. There was a statistically significant association between number of canals and age in mandibular incisor teeth observed only in females (P < 0.001), while no association was observed between canal configuration and gender (P = 0.900 and P = 0.721, respectively). Multiple regression analyses showed that age and gender significantly explain the difference of the variance in tooth length (P < 0.001). Moreover, bilateral symmetry was observed in 98.1% of mandibular central and 97.6% of lateral incisor teeth with no statistically significant association (P = 1 and P = 0.058, respectively). Conclusion: All maxillary and most mandibular incisor teeth present with one root and Type I canal configuration. In general, males have longer teeth than females, with a significant decrease in length with each year increase in age. Mandibular lateral incisor teeth exhibited the highest mean distance from CEJ to point of canal separation and highest mean width in the area of canal division.

Global Neurosurgery: A Path Forward Through Health System Strengthening.

The future of global neurosurgery should focus on addressing the unmet neurosurgical need from a health system approach should we hope to achieve sustainable and ethical change. In this article, we review the global building blocks, as defined by the World Health Organization, and use this framework to propose strategies to strengthen neurosurgical care on the global frontier. The targets for Universal Healthcare Coverage by 2030, as outlined by the United Nations Sustainable Development Goals, are reviewed, and the role of neurosurgeons in addressing the global targets is discussed. Surgical indicators according to the Lancet Commission on Global Surgery are also reviewed, and neurosurgical indicators are proposed according to the 6 surgical indicators of the commission. The execution of these global targets and indicators within the context of health system strengthening will be a persistent challenge, given the complexity of health system and its components. The neurosurgical community must continue to support, promote, and diversify collaborations, especially among deserts of neurosurgical care across the world. Innovative technology and education are essential to this global dilemma.

Survey on 5G Physical Layer Security Threats and Countermeasures.

With the expansion of wireless mobile networks into both the daily lives of individuals as well as into the widely developing market of connected devices, communication is an increasingly attractive target for attackers. As the complexity of mobile cellular systems grows and the respective countermeasures are implemented to secure data transmissions, the attacks have become increasingly sophisticated on the one hand, but at the same time the system complexity can open up expanded opportunities for security and privacy breaches. After an in-depth summary of possible entry points to attacks to mobile networks, this paper first briefly reviews the basic principles of the physical layer implementation of 4G/5G systems, then gives an overview of possible attacks from a physical layer perspective. It also provides an overview of the software frameworks and hardware tool-software defined radios currently in use for experimenting with 4G/5G mobile networks, and it discusses their basic capabilities. In the final part, the paper summarizes the currently most promising families of techniques to detect illegitimate base stations-the machine-learning-based, localization-based, and behavior-based methods.

Energy-Efficient Hybrid Wireless Power Transfer Technique for Relay-Based IIoT Applications

This paper introduces an innovative hybrid wireless power transfer (H-WPT) scheme tailored for IIoT networks employing multiple relay nodes. The scheme allows relay nodes to dynamically select their power source for energy harvesting based on real-time channel conditions. Our analysis evaluates outage probability within decode-and-forward (DF) relaying and adaptive power splitting (APS) frameworks, while also considering the energy used by relay nodes for ACK signaling. A notable feature of the H-WPT scheme is its decentralized operation, enabling relay nodes to independently choose the optimal relay and power source using instantaneous channel gain. This approach conserves significant energy otherwise wasted in centralized control methods, where extensive information exchange is required. This conservation is particularly beneficial for energy-constrained sensor networks, significantly extending their operational lifetime. Numerical results demonstrate that the proposed hybrid approach significantly outperforms the traditional distance-based power source selection approach, without additional energy consumption or increased system complexity. The scheme’s efficient power management capabilities underscore its potential for practical applications in IIoT environments, where resource optimization is crucial.