Research Articles (Electrical, Electronic and Computer Engineering)

Permanent URI for this collectionhttp://hdl.handle.net/2263/1693

Browse

Now showing 1 - 20 of 994

Enhancing solar irradiance estimation for pumped storage hydroelectric power plants using hybrid deep learning
(Springer, 2024-11) Konduru, Sudharshan; Naveen, C.; Bansal, Ramesh C.
This research article explores the potential of Pumped Storage Hydroelectric Power Plants across diverse locations, aiming to establish a sustainable electric grid system and reduce per-unit energy costs. A distinctive feature of the study involves forecasting solar irradiance on large-scale hydroelectric dam locations to identify optimal sites for a PV-integrated hydropower system. The research focuses on advancing the integration of floating solar power modules on water storage systems in eight selected regions across India, emphasizing precise solar irradiance estimation. The paper introduces a state-of-the-art hybrid intelligent deep learning model, combining time series analysis and deep learning through residual ensembling to address these challenges. The primary objective is to pinpoint the optimal location for a Power Storage System (PSS) with the highest solar irradiation for PV-integrated hydro system integration. A secondary goal involves minimizing errors within computational time constraints by the proposed model. The study also employs various optimization techniques to enhance its effectiveness and fine-tune the model’s performance, contributing to the advancement of sustainable energy solutions. The proposed model performs best with a Whale optimization algorithm with mean absolute error varying from 0.34 to 3.63 W/m2 and root mean square error from 0.75 to 9.51 W/m2 on PSS locations. The analysis also confirms average solar irradiance is high on PSS 7 with 221.0 W/ m2 followed by PSS 1 with 221.1 W/ m2 among the eight designated sites.
Reliability of using CBCT scans to derive the parameters of the facial canal
(Elsevier, 2025-09) Govender, Shavana; Hanekom, Tania; Human-Baron, Rene
PURPOSE : Studies have analysed the facial canal (FC) parameters using dissection or imaging technologies. To date, no studies have analysed the differences between these methods. The purpose of this study was to evaluate the reliability and accuracy of deriving the parameters from the FC on cone-beam computed tomography (CBCT) scans by comparing it to FC morphometric analyses of dissected heads. METHODS : Ten embalmed heads (n = 20) were CBCT scanned and analysed using ImageJ. Next, each FC segment was dissected. Measurements for both methods included the proximal and distal diameters and lengths for each segment, and angles of the first and second genua. RESULTS : The paired t-test indicated significant differences (p < 0.05) between the CBCT and dissected measurements for most FC segments measured. The respective dissection measurements were consistently higher than the CBCT measurements. However, the Bland-Altman plots showed agreement between the two modalities in measuring FC segments. Interobserver error values were 0.963 and 0.950 for the CBCT and dissection groups, respectively, indicating a high repeatability. CONCLUSIONS : Although the current study showed differences between the parameters of the FC derived from CBCT scans and dissected measurements, CBCT scans remain a valuable tool for non-invasive assessments. However, the differences have implications for modelling in that CBCT measurements underestimate the true size for the various segments of the FC. It is worth noting that a potential difference in segment sizes may exist between populations but will have no effect on using CBCT scans as a pre-operative assessment of the FC.
SPARCQ : enhancing scalability and adaptability of proactive edge caching through q-learning
(Institute of Electrical and Electronics Engineers, 2025-04) Lall, Shruti; De Clercq, Johan; Pillay, Nelishia; Maharaj, Bodhaswar Tikanath Jugpershad; shruti.lall@tuks.co.za
The exponential growth of network traffic and data-intensive applications demands innovative solutions to manage data efficiently and ensure high-quality user experiences. Proactive edge caching has become a crucial technique for enhancing network performance by predicting and pre-storing content closer to users before access. Accurate prediction models, such as Long Short-Term Memory (LSTM) networks, are crucial for effective proactive caching. However, these models rely on carefully tuned hyperparameters to maintain predictive accuracy, and manual tuning is impractical in dynamic and diverse network environments, limiting scalability and adaptability. To overcome these challenges, we propose a novel framework, SPARCQ, that leverages Q-learning, a reinforcement learning algorithm, to automate hyperparameter tuning for LSTM-based prediction models. By dynamically adjusting hyperparameters, our approach ensures accurate predictions, improving caching efficiency and adaptability. Using the MovieLens dataset, we achieve an average improvement of 8% in cache hit ratios compared to baseline models, including popularity-based and untuned models. Additionally, our framework demonstrates scalability and robustness across geographically distributed regions, consistently adapting to diverse and evolving data patterns.
Scalable Δ-AGC logic for enhanced electromechanical oscillation damping in modern power systems with utility-scale photovoltaic generators
(Institute of Electrical and Electronics Engineers, 2025-05) Ratnakumar, Rajan, Rajan; Venayagamoorthy, Ganesh Kumar
Power systems with utility-scale solar photovoltaic (PV) can significantly influence the operating points (OPs) of synchronous generators, particularly during periods of high solar PV generation. A sudden drop in solar PV output due to cloud cover or other transient conditions will alter the generation of synchronous generators shifting their OPs. These shifted OPs can become a challenge for stability as the system may operate closer to its stability limits. If a disturbance occurs while the system is operating at the shifted OP, with reduced stability margins, it will be more vulnerable to increased oscillations, loss of synchronism of its generator(s) and system instability. This study introduces a scalable Δ -automatic generation control ( Δ -AGC) logic method designed to address stability challenges arising from shifts in the OPs of synchronous generators during abrupt drops in PV generation. By temporarily adjusting the OPs of synchronous generators through modification of their participation factors (PFs) in the AGC logic dispatch, the proposed method enhances power system stability. The proposed Δ -AGC logic method focuses on the optimal determination of ΔPFs in power systems with large number of generators, using the concept of coherency and employing a hierarchical optimization strategy that includes both inter-coherent and intra-coherent group optimization. Additionally, a new electromechanical oscillation index (EMOI), integrating both time response analysis (TRA) and frequency response analysis (FRA), is utilized as an online situational awareness tool (SAT) for optimizing the system’s stability under various conditions. This online SAT has been implemented in a decentralized manner at the area level, limiting wide-area communication overheads and any cybersecurity concerns. The Δ -AGC logic method is illustrated on a modified IEEE 68 bus system, incorporating large utility-scale solar PV plants, and is validated through real-time simulation. Various cases, including high-loading conditions with and without power system stabilizers, conventional AGC logic, and Δ -AGC logic, are carried out to evaluate the effectiveness of the proposed Δ -AGC logic method. The results illustrate the performance and benefits of the Δ -AGC logic method, highlighting its potential to significantly enhance power system stability.
Blockchain-enhanced attribute-based encryption architecture with feasibility analysis
(Institute of Electrical and Electronics Engineers, 2025-03) Ferrer-Rojas, Agustin; Maharaj, Bodhaswar Tikanath Jugpershad; Hlophe, Mduduzi Comfort
In today’s digital landscape, data security is critical, particularly in the Internet of Things (IoT), where large volumes of sensitive data are exchanged. Traditional encryption methods like RSA and AES face challenges in balancing security and performance, exposing systems to advanced cyber threats. To address these issues, blockchain technology offers decentralized, tamper-resistant data protection that enhances trust and transparency. Attribute-Based Encryption (ABE) schemes have been developed, often combining asymmetric and symmetric encryption for efficiency and security. However, gaps remain in practical deployment due to underexplored network architectures and limited feasibility simulations. This study proposes an end-to-end security architecture integrating ABE with Linear Secret Sharing Scheme (LSSS) access policies and blockchain-based distributed key management. The system’s feasibility was evaluated using Network Simulator 3 (NS3) within a simulated IoT network. Results demonstrate a lightweight and scalable solution suitable for constrained environments. Numerical simulations showed consensus times as low as 0.25 seconds for key agreement and 0.7 seconds for message consensus, even in resource-constrained settings. For large networks, consensus times reached as low as 0.75 seconds. The system also achieved an average throughput of 0.3 transactions per second in low-resource environments. These outcomes highlight the system’s potential for secure, efficient data transmission in IoT and other distributed systems.
Alcohol degradation of anhydride-cured epoxy resin insulating materials containing SiO2 filler
(American Institute of Physics, 2025-06) Zhang, Xu; Li, Chengjie; Ye, Xianming; Zhang, Xiaoxing; Maluta, Eric; Wu, Yunjian
Please read abstract in the article.
Mental disorder assessment in IoT-enabled WBAN systems with dimensionality reduction and deep learning
(MDPI, 2025-06) Olatinwo, Damilola D.; Abu-Mahfouz, Adnan Mohammed; Myburgh, Hermanus Carel
Mental health is an important aspect of an individual’s overall well-being. Positive mental health is correlated with enhanced cognitive function, emotional regulation, and motivation, which, in turn, foster increased productivity and personal growth. Accurate and interpretable predictions of mental disorders are crucial for effective intervention. This study develops a hybrid deep learning model, integrating CNN and BiLSTM applied to EEG data, to address this need. To conduct a comprehensive analysis of mental disorders, we propose a two-tiered classification strategy. The first tier classifies the main disorder categories, while the second tier classifies the specific disorders within each main disorder category to provide detailed insights into classifying mental disorder. The methodology incorporates techniques to handle missing data (kNN imputation), class imbalance (SMOTE), and high dimensionality (PCA). To enhance clinical trust and understanding, the model’s predictions are explained using local interpretable model-agnostic explanations (LIME). Baseline methods and the proposed CNN–BiLSTM model were implemented and evaluated at both classification tiers using PSD and FC features. On unseen test data, our proposed model demonstrated a 3–9% improvement in prediction accuracy for main disorders and a 4–6% improvement for specific disorders, compared to existing methods. This approach offers the potential for more reliable and explainable diagnostic tools for mental disorder prediction.
Refurbishment and commissioning of a dual-band 23/31 GHz tipping radiometer at potential radio astronomical sites
(American Institute of Physics, 2025-04) Cuazoson, J.; Hiriart, D.; Stander, Tinus; Botha, R.; Contreras, J.; Ferrusca, D.; Ibarra-Medel, E.; Kurtz, S.; Neate, Reuben; Rojas, D.; Velázquez, M.
This paper reports on the refurbishment of a dual-band 23/31 GHz tipping radiometer previously used to measure atmospheric water vapor content at the Hartebeesthoek Radio Astronomy Observatory. We describe the motivation for the refurbishment, provide technical details of the modifications made to the instrument, and present preliminary test results of field measurements at the site of the High Energy Stereoscopic System Observatory in Namibia. Together with other radiometric measurements, this instrument will contribute to the site characterization of potential radio astronomical observatories in southern Africa.
Application of back propagation neural network in complex diagnostics and forecasting loss of life of cellulose paper insulation in oil-immersed transformers
(Nature Research, 2024-03-13) Ngwenyama, M.K.; Gitau, Michael Njoroge; u11265702@tuks.co.za
Oil-immersed transformers are expensive equipment in the electrical system, and their failure would lead to widespread blackouts and catastrophic economic losses. In this work, an elaborate diagnostic approach is proposed to evaluate twenty-six different transformers in-service to determine their operative status as per the IEC 60599:2022 standard and CIGRE brochure. The approach integrates dissolved gas analysis (DGA), transformer oil integrity analysis, visual inspections, and two Back Propagation Neural Network (BPNN) algorithms to predict the loss of life (LOL) of the transformers through condition monitoring of the cellulose paper. The first BPNN algorithm proposed is based on forecasting the degree of polymerization (DP) using 2-Furaldehyde (2FAL) concentration measured from oil samples using DGA, and the second BPNN algorithm proposed is based on forecasting transformer LOL using the 2FAL and DP data obtained from the first BPNN algorithm. The first algorithm produced a correlation coefficient of 0.970 when the DP was predicted using the 2FAL measured in oil and the second algorithm produced a correlation coefficient of 0.999 when the LOL was predicted using the 2FAL and DP output data obtained from the first algorithm. The results show that the BPNN can be utilized to forecast the DP and LOL of transformers in-service. Lastly, the results are used for hazard analysis and lifespan prediction based on the health index (HI) for each transformer to predict the expected years of service.
Dynamic resource provisioning in containerized edge systems with reconfigurable edge servers
(SpringerOpen, 2025-04) Awoyemi, Babatunde Seun; Hlophe, Mduduzi Comfort; Maharaj, Bodhaswar Tikanath Jugpershad
Recent technological advancements have seen powerful computational resource-enriched virtual machines (VMs) being used for processing data in edge servers. However, the high energy demands and excessive overhead associated with launching VMs are major obstacles to achieving energy-efficient operations in multi-access edge computing environments. As a result, there has been a relentless acceleration toward container virtualization to provide containerized services at the edge. The lightweight nature of containers compared to VMs makes them a popular technology for edge computing platforms. However, two significant challenges have been identified. The first is the problem of providing real-time support for containerized edge systems (to combat issues of high latency, anomaly detection, and automated monitoring and control, among others). The other problem is that, although containers help reduce application deployment time, considerable network bandwidth is expended and longer download queues are experienced on each node in the network. We propose a dynamic resource provisioning scheme for containerized edge systems to address these challenges. The proposed scheme employs containerized reconfigurable edge servers, which enable computational task operations to be moved to the data source for easier and quicker completion. Then, a novel adaptive power management technique based on predictive control through finite system observations is used to effectively estimate and regulate the energy consumption in the edge-based network. The adaptive controller schedules computational resources on a time slot basis in an adaptive manner, while continuing to receive updates to plan future resource provisioning. The proposed technique is evaluated using welfare gain, server response rate, and energy consumption metrics and is shown to outperform recent comparative models significantly.
Optimal parking lot retrofit planning for electric vehicle charging station during prolonged load shedding
(Elsevier, 2025-05) Yu, Gang; Ye, Xianming; Xia, Xiaohua; xianming.ye@up.ac.za
The rapidly increasing demand for electric vehicle (EV) charging drives the transition from conventional parking lots into charging stations. This transition, however, faces challenges in countries of Africa, Asia, and South America, where prolonged load shedding results in an unreliable power supply. This study addresses the optimal parking lot retrofit planning for EV charging stations, aiming to determine an ideal number of charging poles to be deployed within parking lots under prolonged load shedding. A multi-objective optimization approach is introduced to balance financial return and user satisfaction, generating Pareto-optimal solutions for charging stations. The impact of load shedding on the optimal retrofit planning is analyzed. Post-outage demand peaks substantially increase the maximum demand costs. The proposed charging scheduling method achieves a 14% reduction in maximum demand costs. The proposed parking lot retrofit planning approach improves weekly profit by 19% and user satisfaction by 14% compared to the existing planning approach. Additionally, this study investigates the implications of load shedding uncertainty, EV penetration rate, charging pole type, and time-of-use pricing on the optimal retrofit planning.
LCL filter design of STATCOM using genetic algorithm scheme for SCIG based microgrid operation
(Taylor and Francis, 2024) Saxena, Nitin Kumar; Gupta, Anmol; Jalil, Mohd Faisal; Gupta, Varun; Bansal, Ramesh C.
In a microgrid static Compensator (STATCOM) is the most prominent inverter circuit for stabilizing the bidirectional power flow requirements of the system. This inverter circuit is the primary source of harmonics when the supply current feeds from the microgrid to the main grid. Improved control strategy and proper filter design may give solution to these issues and so, there is a huge scope of research in the field of the converter control techniques and filter designing for such microgrid based power system. The key objectives of this paper are (i) to develop an adequate current control scheme for adjusting real and reactive power fluctuations produced by load time to time, and (ii) to reduce the harmonic level of output characteristics in terms of real and reactive power flow and current frequency. For this, an approach is presented to estimate the filter design parameters for current controlled STATCOM connected to squirrel cage induction generator (SCIG) based microgrid. A nature-inspired optimization namely, genetic algorithm (GA), is implemented to estimate the most suitable parameters for the LCL filter. Results obtained through GA are validated with a conventional mathematical method in terms of real and reactive power flow through microgrid along with harmonic-based studies.
Electric vehicle charging infrastructure, standards, types, and its impact on grid : a review
(Taylor and Francis, 2024) Bhosale, P.; Sujil, A.; Kumar, Rajesh; Bansal, Ramesh C.
The growth of EV penetration brings numerous benefits in economic and environmental aspects, but it also presents deployment opportunities and challenges of EV charging stations. The EV owners benefit from lower fuel and operating expenses compared to ICE vehicles because of higher efficiency of electric motors reaching it as high as 60–70%. The electric vehicles are intermittent load to the grid since the number of users charging the electric vehicle at different charging station at different time. Moreover, the increasing EV penetration leads to the increase in load requirement on charging stations and will place a heavier load on the grid, necessitating the exploration of alternative resources. So, it significantly effects on power quality of the distribution grid. This charging requirement needs to be effectively managed to ensure uninterrupted energy supply for charging EVs batteries. By employing a basic charging plan, the estimated system cost per vehicle per year in Denmark is $263. Implementation of smart charging, the system cost decreases to $36 per vehicle per year, resulting in substantial savings of $227 per vehicle per year. Controlled charging methods also effectively reduce system costs by 50% and decrease peak demand. An EV fleet has the potential for cost savings in the power system, amounting to $200–$300 per year per vehicle. The aim of the review is to address the impact on power quality of the distribution grid and study the nature of EV unbalanced loads in order to minimize impact on grid efficiently by managing the resources.
A systematic approach to improving consumers' comfort through on-grid renewable energy integration and battery storage
(Taylor and Francis, 2024) Sharma, Ankit Kumar; Doda, Devendra Kumar; Soni, Bhanu Pratap; Bansal, Ramesh C.; Palwalia, Dheeraj Kumar
This article explores the integration of on-grid renewable energy with battery storage to improve consumers’ comfort. Demand response (DR) programs are utilized to balance power supply and demand, offering consumers three response options: reducing consumption, shifting consumption, or utilizing on-site generation. However, these options may temporarily affect comfort. To address this, on-site generation through renewable energy integration has gained attention for its environmental and economic advantages. The study aims to demonstrate an environmentally friendly renewable integration system that resolves electrical power problems, ensures consumer comfort, and provides pollution-free energy. The proposed system primarily relies on solar panels with batteries as backup. Optimization is conducted using the HOMER software, and the system design represents a novel approach for the selected site. Simulation results indicate that the proposed approach significantly enhances consumer satisfaction and lowers energy costs in the absence of DR programs. This research presents a comprehensive analysis of the integration approach, emphasizing its benefits for consumers and the environment. By combining renewable energy integration and battery storage, it contributes to sustainable and comfortable energy solutions for consumers.
18-45-GHz sideband-separating downconverter with RF image rejection calibration
(Institute of Electrical and Electronics Engineers, 2025-02) Mundia, Sitwala; Stander, Tinus
We present a sideband separating downconverter for radio astronomy applications, featuring radio frequency image rejection calibration for frequencies between 18 and 45 GHz. The multichip module optimizes image rejection for specific target observation frequencies by injecting a modulated portion of the Q-branch signal into the I-branch with independent upper and lower sideband injection control. Measurements demonstrate an average image rejection ratio improvement of 9 dB over a 7-GHz band of interest compared with a baseline uncalibrated operation, with improvement of over 40 dB in targeted subbands.
Vessel classification using AIS data
(Elsevier, 2025-03) Meyer, Rory George Vincent; Kleynhans, Waldo; waldo.kleynhans@up.ac.za
Maritime Domain Awareness (MDA) relies heavily on Automated Identification System (AIS) data for vessel tracking. This research focuses on developing a novel vessel classification framework that uses AIS derived features. The algorithm effectively classifies ocean-going vessels into behavioural categories, providing valuable insights for MDA. RESULTS : demonstrate the effectiveness of the classification framework in achieving high accuracy (F1 score of 0.88–0.9) in vessel classification. The choice of class labels and data pre-filtering significantly impacts performance. The algorithm's feature importance analysis highlights the relevance of self-reported vessel dimensions, location, and behaviour. While cargo and tanker vessels exhibit some overlap, fishing vessels are accurately classified. However, recreational and passenger vessels, due to limited samples, require further refinement. Future research could explore time series methods and tailored algorithms for specific vessel classes to enhance classification accuracy. Overall, this study contributes to improving MDA by providing a robust vessel classification tool. Further investigation is needed to address the high proportion of unlabeled vessels classified as fishing vessels.
Segment reduction-based SVPWM applied three-level F-type inverter for power quality conditioning in an EV proliferated distributed system
(Wiley, 2025-02) Madhavan, Meenakshi; N., Chellammal; Bansal, Ramesh C.
The objective of this paper lies in the realization of a three-level F-type inverter (3L-FTI) as a shunt active filter in an EV-proliferated environment. The switches are triggered using segment reduced space vector pulse width modulation (SVPWM). This modulation technique provides a lower number of switching transitions than existing PWM strategies. Consequently, the inverter switches experience a decrease in both switching stress and switching losses. A 3L-FTI is a diode-free structure that reduces the harmonics in the source current with a high power factor (PF), where instantaneous reactive power (IRPT) theory is employed to generate the reference currents from the utility grid. In contrast to traditional three-level inverters, two-thirds of switches in 3L-FTI can tolerate a voltage stress equal to half of the DC input voltage. While studying the behaviour of this shunt active filter, with three different nonlinear loading conditions, the current total harmonic distortion (THD) is reduced from 28.43% to 2.13% after compensation, which is under 5% of IEEE standard 519-2014. Therefore, the 3L-FTI controlled by segment reduction SVPWM can be considered as better candidate for active filter in an EV proliferated distribution system.
Adaptive power management for multiaccess edge computing-based 6G-inspired massive Internet of Things
(Wiley, 2025-01) Awoyemi, Babatunde Seun; Maharaj, Bodhaswar T. Sunil
Multiaccess edge computing (MEC) is a dynamic approach for addressing the capacity and ultra-latency demands caused by the pervasive growth of real-time applications in next-generation (xG) wireless communication networks. Powerful computational resource-enriched virtual machines (VMs) are used in MEC to provide outstanding solutions. However, a major challenge with using VMs in xG networks is the high overhead caused by the excessive energy demands of VMs. To address this challenge, containers, which are generally more energy-efficient and less computationally demanding, are being advocated. This paper proposes a containerised edge computing model for power optimisation in 6G-inspired massive Internet-of-Things applications. The problem is formulated as a central processing unit energy consumption cost function based on quasi-finite system observations. To achieve practicable computational complexity, an approach that uses a search heuristic based on Lyapunov techniques is employed to obtain near-optimal solutions. Important performance metrics are successfully predicted using the online look-ahead technique. The predictive model used achieves an accuracy of 97% prediction compared to actual data. To further improve resource demand, an adaptive controller is used to schedule computational resources on a time slot basis in an adaptive manner while continuing to receive workload levels to plan future resource provisioning. The proposed technique is shown to perform better compared to a competitive baseline algorithm.
Specific emitter identification with different transmission codes and multiple receivers
(Institute of Electrical and Electronics Engineers Inc., 2025-04) Diedericks, Lodewicus Johannes; Du Plessis, Warren Paul
A specific emitter identification (SEI) system that expands previously published results by identifying remote keyless-entry (RKE) remotes with an accuracy of over 95% even when different digital transmission codes are used is described. This system successfully rejects replay attacks with no replay attacks being incorrectly identified as known remotes. The effect of using multiple receivers is then evaluated using this SEI system. It was found that poor accuracy of under 33% was obtained when attempting to identify transmitters using an SEI system trained on data recorded by other receivers. However, including recordings from all receivers among the receivers used to provide the training data was found to increase the accuracy to over 91%. Increasing the number of receivers used to record the training data was found to slightly reduce the identification accuracy.
Priority-based data flow control for long-range wide area networks in Internet of Military Things
(MDPI, 2025-04) Kufakunesu, Rachel; Myburgh, Hermanus Carel; De Freitas, Allan; rachel.kufakunesu@tuks.co.za
The Internet of Military Things (IoMT) is transforming defense operations by enabling the seamless integration of sensors and actuators for the real-time transmission of critical data in diverse military environments. End devices (EDs) collect essential information, including troop locations, health metrics, equipment status, and environmental conditions, which are processed to enhance situational awareness and operational efficiency. In scenarios involving large-scale deployments across remote or austere regions, wired communication systems are often impractical and cost-prohibitive. Wireless sensor networks (WSNs) provide a cost-effective alternative, with Long-Range Wide Area Network (LoRaWAN) emerging as a leading protocol due to its extensive coverage, low energy consumption, and reliability. Existing LoRaWAN network simulation modules, such as those in ns-3, primarily support uniform periodic data transmissions, limiting their applicability in critical military and healthcare contexts that demand adaptive transmission rates, resource optimization, and prioritized data delivery. These limitations are particularly pronounced in healthcare monitoring, where frequent, high-rate data transmission is vital but can strain the network’s capacity. To address these challenges, we developed an enhanced sensor data sender application capable of simulating priority-based traffic within LoRaWAN, specifically targeting use cases like border security and healthcare monitoring. This study presents a priority-based data flow control protocol designed to optimize network performance under high-rate healthcare data conditions while maintaining overall system reliability. Simulation results demonstrate that the proposed protocol effectively mitigates performance bottlenecks, ensuring robust and energy-efficient communication in critical IoMT applications within austere environments.

Browse

Recent Submissions