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1.
Math Biosci Eng ; 21(4): 5207-5226, 2024 Mar 06.
Article in English | MEDLINE | ID: mdl-38872533

ABSTRACT

Hepatitis B is one of the global health issues caused by the hepatitis B virus (HBV), producing 1.1 million deaths yearly. The acute and chronic phases of HBV are significant because worldwide, approximately 250 million people are infected by chronic hepatitis B. The chronic stage is a long-term, persistent infection that can cause liver damage and increase the risk of liver cancer. In the case of multiple phases of infection, a generalized saturated incidence rate model is more reasonable than a simply saturated incidence because it captures the complex dynamics of the different infection phases. In contrast, a simple saturated incidence rate model assumes a fixed shape for the incidence rate curve, which may not accurately reflect the dynamics of multiple infection phases. Considering HBV and its various phases, we constructed a model to present the dynamics and control strategies using the generalized saturated incidence. First, we proved that the model is well-posed. We then found the reproduction quantity and model equilibria to discuss the time dynamics of the model and investigate the conditions for stabilities. We also examined a control mechanism by introducing various controls to the model with the aim to increase the population of those recovered and minimize the infected people. We performed numerical experiments to check the biological significance and control implementation.


Subject(s)
Computer Simulation , Hepatitis B virus , Hepatitis B , Humans , Incidence , Hepatitis B/epidemiology , Hepatitis B, Chronic/epidemiology , Basic Reproduction Number/statistics & numerical data , Liver Neoplasms/epidemiology , Models, Biological , Algorithms
2.
Sci Rep ; 14(1): 12047, 2024 May 27.
Article in English | MEDLINE | ID: mdl-38802447

ABSTRACT

In recent years, there has been a growing interest in incorporating fractional calculus into stochastic delay systems due to its ability to model complex phenomena with uncertainties and memory effects. The fractional stochastic delay differential equations are conventional in modeling such complex dynamical systems around various applied fields. The present study addresses a novel spectral approach to demonstrate the stability behavior and numerical solution of the systems characterized by stochasticity along with fractional derivatives and time delay. By bridging the gap between fractional calculus, stochastic processes, and spectral analysis, this work contributes to the field of fractional dynamics and enriches the toolbox of analytical tools available for investigating the stability of systems with delays and uncertainties. To illustrate the practical implications and validate the theoretical findings of our approach, some numerical simulations are presented.

3.
Sci Rep ; 14(1): 7902, 2024 04 04.
Article in English | MEDLINE | ID: mdl-38570524

ABSTRACT

The spatial movement of the human population from one region to another and the existence of super-spreaders are the main factors that enhanced the disease incidence. Super-spreaders refer to the individuals having transmitting ability to multiple pathogens. In this article, an epidemic model with spatial and temporal effects is formulated to analyze the impact of some preventing measures of COVID-19. The model is developed using six nonlinear partial differential equations. The infectious individuals are sub-divided into symptomatic, asymptomatic and super-spreader classes. In this study, we focused on the rigorous qualitative analysis of the reaction-diffusion model. The fundamental mathematical properties of the proposed COVID-19 epidemic model such as boundedness, positivity, and invariant region of the problem solution are derived, which ensure the validity of the proposed model. The model equilibria and its stability analysis for both local and global cases have been presented. The normalized sensitivity analysis of the model is carried out in order to observe the crucial factors in the transmission of infection. Furthermore, an efficient numerical scheme is applied to solve the proposed model and detailed simulation are performed. Based on the graphical observation, diffusion in the context of confined public gatherings is observed to significantly inhibit the spread of infection when compared to the absence of diffusion. This is especially important in scenarios where super-spreaders may play a major role in transmission. The impact of some non-pharmaceutical interventions are illustrated graphically with and without diffusion. We believe that the present investigation will be beneficial in understanding the complex dynamics and control of COVID-19 under various non-pharmaceutical interventions.


Subject(s)
COVID-19 , Epidemics , Humans , COVID-19/epidemiology , Nonlinear Dynamics , Computer Simulation , Diffusion
4.
Heliyon ; 10(7): e28352, 2024 Apr 15.
Article in English | MEDLINE | ID: mdl-38571657

ABSTRACT

The use of natural ester oils as electrically insulating fluids has gained significant attention from industries and electrical utilities as they aim to replace traditional mineral oils. However, most natural ester oils are derived from edible products, which has the potential to contribute to the food crisis. Therefore, nonedible green nanofluids made from cottonseed oil (CSO) have been targeted as a keen solution to this issue. However, Al2O3, TiO2, Fe2O3, SiO2, and graphene nanoparticles at (0.025, 0.05, and 0.075 wt/vol%) were used as additives, along with surfactant Olic Ac-id and Ethanol (1:5) due to their promising impact on the dielectric and thermal properties of the nanofluid. The nanofluid synthesis process was practically conducted in HV & Chemical Laboratories using one-step and two-step methods, and their breakdown voltage results and chemical properties (e.g., fire point, flash point, cloud point, pour point, viscosity, acidity, moisture content, resistivity, and dissipation factor) were compared. The physical mechanisms underlying these properties were also analyzed and tested. For the validation of the proposed vegetable oil the results have been compared with traditional mineral oil for high-voltage equipment's. The findings suggest that the proposed nonedible green nanofluids-based cottonseed oil (CSO) has a high potential to be used as electrically insulating fluids, providing a sustainable alternative to conventional mineral oils. Overall, this study provides insights into the use of non-edible green nanofluids as a solution to the potential contribution of natural ester oils to the food crisis. The findings highlight the importance of sustainable solutions in the energy industry and the need for further research in this area.

5.
Sci Rep ; 14(1): 6930, 2024 Mar 23.
Article in English | MEDLINE | ID: mdl-38521792

ABSTRACT

The fractional stochastic delay differential equation (FSDDE) is a powerful mathematical tool for modeling complex systems that exhibit both fractional order dynamics and stochasticity with time delays. The purpose of this study is to explore the stability analysis of a system of FSDDEs. Our study emphasizes the interaction between fractional calculus, stochasticity, and time delays in understanding the stability of such systems. Analyzing the moments of the system's solutions, we investigate stochasticity's influence on FSDDS. The article provides practical insight into solving FSDDS efficiently using various numerical techniques. Additionally, this research focuses both on asymptotic as well as Lyapunov stability of FSDDS. The local stability conditions are clearly presented and also the effects of a fractional orders with delay on the stability properties are examine. Through a comprehensive test of a stability criteria, practical examples and numerical simulations we demonstrate the complexity and challenges concern with the analyzing FSDDEs.

6.
Heliyon ; 10(6): e28073, 2024 Mar 30.
Article in English | MEDLINE | ID: mdl-38524527

ABSTRACT

Recent widespread connections of renewable energy resource (RESs) in place of fossil fuel supplies and the adoption of electrical vehicles in place of gasoline-powered vehicles have given birth to a number of new concerns. The control architecture of linked power networks now faces an increasingly pressing challenge: tie-line power fluctuations and reducing frequency deviations. Because of their nature and dependence on external circumstances, RESs are analogous to continually fluctuating power generators. Using a fractional order-based frequency regulator, this work presents a new method for improving the frequency regulation in a two-area interconnected power system. In order to deal with the frequency regulation difficulties of the hybrid system integrated with RES, the suggested controller utilizes the modified form of fractional order proportional integral derivative (FOPID) controller known as FOI-PDN controller. The new proposed controllers are designed using the white shark optimizer (WSO), a current powerful bioinspired meta heuristic algorithm which has been motivated by the learning abilities of white sharks when actively hunting in the environment. The suggested FOI-PDN controller's performance was compared to that of various control methodologies such as FOPID, and PID. Furthermore, the WSO findings are compared to those of other techniques such as the salp swarm algorithm, sine cosine algorithm and fitness dependent optimizer. The recommended controller and design approach have been tested and validated at different loading conditions and different circumstances, as well as their robustness against system parameter suspicions. The simulation outcomes demonstrate that the WSO-based tuned FOI-PDN controller successfully reduces peak overshoot by 73.33%, 91.03%, and 77.21% for region-2, region-1, and link power variation respectively, and delivers minimum undershoot of 89.12%, 83.11%, and 78.10% for both regions and tie-line. The obtained findings demonstrate the new proposed controller's stable function and frequency controlling performance with optimal controller parameters and without the requirement for a sophisticated design process.

7.
PLoS One ; 19(3): e0298403, 2024.
Article in English | MEDLINE | ID: mdl-38527039

ABSTRACT

The anatomy of the edentulous posterior maxilla and maxillary sinus possess unique challenges in implant dentistry. The purpose of this study was to assess maxillary sinus membrane thickness (MT) and lateral wall thickness (LWT) in different facial index profiles and to describe the clinical implications. A retrospective image analysis of 75 CBCT scans was done, which yielded a total of 150 sinus images. The facial index was calculated as per the formula given in the text and grouped as euryprosopic, mesoprosopic and leptoprosopic. The images obtained were of 36 women (48%) and 39 men (52%), with maximum subjects in 30-39 years age group. MT and LWT were measured at three different points on the radiograph at every 3mm from the base of the sinus floor in premolar and molar regions of each image. Results showed females had significant differences from males in LWT in both premolar and molar regions (p = 0.018 and 0.032 respectively). Subjects in 40-49 years of age had significant differences (p = 0.021) in MT in premolar region only. Also, difference in MT in premolar and molar regions were also statistically significant. Lastly, the present study did not find any statistically significant difference in MT and LWT in all three facial indices groups. It can be concluded that different facial indices have no positive correlation with maxillary sinus membrane thickness and lateral wall thickness. Hence, surgical complications are avoidable with proper detailed knowledge and appropriate identification of the anatomic structures characteristic to the maxillary sinus.


Subject(s)
Maxillary Sinus , Sinus Floor Augmentation , Male , Humans , Female , Maxillary Sinus/diagnostic imaging , Retrospective Studies , Saudi Arabia , Cone-Beam Computed Tomography/methods , Maxilla
8.
Article in English | MEDLINE | ID: mdl-38396364

ABSTRACT

This research article presents a comprehensive analysis aimed at enhancing the stochastic modeling of COVID-19 dynamics by incorporating vaccination classes through a higher-order spectral scheme. The ongoing COVID-19 pandemic has underscored the critical need for accurate and adaptable modeling techniques to inform public health interventions. In this study, we introduce a novel approach that integrates various vaccination classes into a stochastic model to provide a more nuanced understanding of disease transmission dynamics. We employ a higher-order spectral scheme to capture complex interactions between different population groups, vaccination statuses, and disease parameters. Our analysis not only enhances the predictive accuracy of COVID-19 modeling but also facilitates the exploration of various vaccination strategies and their impact on disease control. The findings of this study hold significant implications for optimizing vaccination campaigns and guiding policy decisions in the ongoing battle against the COVID-19 pandemic.

9.
Sci Rep ; 13(1): 21224, 2023 Dec 01.
Article in English | MEDLINE | ID: mdl-38040862

ABSTRACT

In this article, we introduce a novel Bayesian Max-EWMA control chart under various loss functions to concurrently monitor the mean and variance of a normally distributed process. The Bayesian Max-EWMA control chart exhibit strong overall performance in detecting shifts in both mean and dispersion across various magnitudes. To evaluate the performance of the proposed control chart, we employ Monte Carlo simulation methods to compute their run length characteristics. We conduct an extensive comparative analysis, contrasting the run length performance of our proposed charts with that of existing ones. Our findings highlight the heightened sensitivity of Bayesian Max-EWMA control chart to shifts of diverse magnitudes. Finally, to illustrate the efficacy of our Bayesian Max-EWMA control chart using various loss functions, we present a practical case study involving the hard-bake process in semiconductor manufacturing. Our results underscore the superior performance of the Bayesian Max-EWMA control chart in detecting out-of-control signals.

10.
Sci Rep ; 13(1): 22703, 2023 Dec 19.
Article in English | MEDLINE | ID: mdl-38123625

ABSTRACT

Quality control often employs memory-type control charts, including the exponentially weighted moving average (EWMA) and Shewhart control charts, to identify shifts in the location parameter of a process. This article pioneers a new Bayesian Adaptive EWMA (AEWMA) control chart, built on diverse loss functions (LFs) such as the square error loss function (SELF) and the Linex loss function (LLF). The proposed chart aims to enhance the process of identifying small to moderate as well as significant shifts in the mean, signifying a notable advancement in the field of quality control. These are implemented utilizing an informative prior for both posterior and posterior predictive distributions, employing various paired ranked set sampling (PRSS) schemes. The effectiveness of the suggested chart is appraised using average run length (ARL) and the standard deviation of run length (SDRL). Monte Carlo simulations are employed to contrast the recommended approach against other control charts. The outcomes demonstrate the dignitary performance of the recommended chart in identifying out-of-control signals, especially applying PRSS designs, in comparison to simple random sampling (SRS). Finally, a practical application was conducted in the semiconductor manufacturing context to appraise the efficacy of the offered chart using various paired ranked set sampling strategies. The results reveal that the suggested control chart performed well in capturing the out-of-control signals far better than the already in use control charts. Overall, this study interposes a new technique with diverse LFs and PRSS designs, improving the precision and effectiveness in detecting process mean shifts, thereby contributing to advancements in quality control and process monitoring.

11.
Sci Rep ; 13(1): 17787, 2023 10 18.
Article in English | MEDLINE | ID: mdl-37853028

ABSTRACT

An efficient finite difference approach is adopted to analyze the solution of a novel fractional-order mathematical model to control the co-circulation of double strains of dengue and COVID-19. The model is primarily built on a non-integer Caputo fractional derivative. The famous fixed-point theorem developed by Banach is employed to ensure that the solution of the formulated model exists and is ultimately unique. The model is examined for stability around the infection-free equilibrium point analysis, and it was observed that it is stable (asymptotically) when the maximum reproduction number is strictly below unity. Furthermore, global stability analysis of the disease-present equilibrium is conducted via the direct Lyapunov method. The non-standard finite difference (NSFD) approach is adopted to solve the formulated model. Furthermore, numerical experiments on the model reveal that the trajectories of the infected compartments converge to the disease-present equilibrium when the basic reproduction number ([Formula: see text]) is greater than one and disease-free equilibrium when the basic reproduction number is less than one respectively. This convergence is independent of the fractional orders and assumed initial conditions. The paper equally emphasized the outcome of altering the fractional orders, infection and recovery rates on the disease patterns. Similarly, we also remarked the importance of some key control measures to curtail the co-spread of double strains of dengue and COVID-19.


Subject(s)
COVID-19 , Dengue , Manipulation, Osteopathic , Humans , Basic Reproduction Number , Models, Theoretical , Dengue/epidemiology
12.
Sci Rep ; 13(1): 17528, 2023 Oct 16.
Article in English | MEDLINE | ID: mdl-37845300

ABSTRACT

This paper aims to analyze the coupled nonlinear fractional Drinfel'd-Sokolov-Wilson (FDSW) model with beta derivative. The nonlinear FDSW equation plays an important role in describing dispersive water wave structures in mathematical physics and engineering, which is used to describe nonlinear surface gravity waves propagating over horizontal sea bed. We have applied the travelling wave transformation that converts the FDSW model to nonlinear ordinary differential equations. After that, we applied the generalized rational exponential function method (GERFM). Diverse types of soliton solution structures in the form of singular bright, periodic, dark, bell-shaped and trigonometric functions are attained via the proposed method. By selecting a suitable parametric value, the 3D, 2D and contour plots for some solutions are also displayed to visualize their nature in a better way. The modulation instability for the model is also discussed. The results show that the presented method is simple and powerful to get a novel soliton solution for nonlinear PDEs.

13.
Sensors (Basel) ; 23(16)2023 Aug 18.
Article in English | MEDLINE | ID: mdl-37631793

ABSTRACT

Predicting attacks in Android malware devices using machine learning for recommender systems-based IoT can be a challenging task. However, it is possible to use various machine-learning techniques to achieve this goal. An internet-based framework is used to predict and recommend Android malware on IoT devices. As the prevalence of Android devices grows, the malware creates new viruses on a regular basis, posing a threat to the central system's security and the privacy of the users. The suggested system uses static analysis to predict the malware in Android apps used by consumer devices. The training of the presented system is used to predict and recommend malicious devices to block them from transmitting the data to the cloud server. By taking into account various machine-learning methods, feature selection is performed and the K-Nearest Neighbor (KNN) machine-learning model is proposed. Testing was carried out on more than 10,000 Android applications to check malicious nodes and recommend that the cloud server block them. The developed model contemplated all four machine-learning algorithms in parallel, i.e., naive Bayes, decision tree, support vector machine, and the K-Nearest Neighbor approach and static analysis as a feature subset selection algorithm, and it achieved the highest prediction rate of 93% to predict the malware in real-world applications of consumer devices to minimize the utilization of energy. The experimental results show that KNN achieves 93%, 95%, 90%, and 92% accuracy, precision, recall and f1 measures, respectively.

14.
Sci Rep ; 13(1): 13547, 2023 Aug 20.
Article in English | MEDLINE | ID: mdl-37599295

ABSTRACT

The extended exponential weighted moving average (EEWMA) statistic is a memory type statistic that uses past observations along with the current information for the estimation of a population parameter to improve the efficiency of the estimators. This study utilized the EEWMA statistic to estimate the population mean with a suitable auxiliary variable. The ratio and product estimators are proposed for the surveys that are time-based by using current information along with that information. The approximate mean square errors are computed for the proposed memory type estimators and mathematical comparison is discussed to demonstrate the efficiency of the estimator. The simulation study was carried out to evaluate the performance of the proposed memory type estimators. It can be seen from the results that the efficiency of the estimator enhances by utilizing the current sample as well as past information. A real-life example is presented to illustrate the usage of proposed estimators.

15.
Entropy (Basel) ; 25(7)2023 Jul 06.
Article in English | MEDLINE | ID: mdl-37509974

ABSTRACT

In this paper, we design constant modulus waveforms for dual-function radar-communication (DFRC) systems based on a multi-input multi-output (MIMO) configuration of sensors for a far-field scenario. At first, we formulate a non-convex optimization problem subject to waveform synthesis for minimizing the interference power while maintaining a constant modulus constraint. Next, we solve this non-convex problem, iteratively, using the alternating direction method of multipliers (ADMM) algorithm. Importantly, the designed waveforms approximate a desired beampattern in terms of a high-gain radar beam and a slightly high gain communication beam while maintaining a desired low sidelobe level. The designed waveforms ensure an improved detection probability and an improved bit error rate (BER) for radar and communications parts, respectively. Finally, we demonstrate the effectiveness of the proposed method through simulation results.

16.
Sensors (Basel) ; 23(12)2023 Jun 07.
Article in English | MEDLINE | ID: mdl-37420558

ABSTRACT

Retinal optical coherence tomography (OCT) imaging is a valuable tool for assessing the condition of the back part of the eye. The condition has a great effect on the specificity of diagnosis, the monitoring of many physiological and pathological procedures, and the response and evaluation of therapeutic effectiveness in various fields of clinical practices, including primary eye diseases and systemic diseases such as diabetes. Therefore, precise diagnosis, classification, and automated image analysis models are crucial. In this paper, we propose an enhanced optical coherence tomography (EOCT) model to classify retinal OCT based on modified ResNet (50) and random forest algorithms, which are used in the proposed study's training strategy to enhance performance. The Adam optimizer is applied during the training process to increase the efficiency of the ResNet (50) model compared with the common pre-trained models, such as spatial separable convolutions and visual geometry group (VGG) (16). The experimentation results show that the sensitivity, specificity, precision, negative predictive value, false discovery rate, false negative rate accuracy, and Matthew's correlation coefficient are 0.9836, 0.9615, 0.9740, 0.9756, 0.0385, 0.0260, 0.0164, 0.9747, 0.9788, and 0.9474, respectively.


Subject(s)
Deep Learning , Neural Networks, Computer , Tomography, Optical Coherence/methods , Retina/diagnostic imaging , Predictive Value of Tests
17.
Sensors (Basel) ; 23(12)2023 Jun 08.
Article in English | MEDLINE | ID: mdl-37420607

ABSTRACT

In this paper, we present a hybrid frequency shift keying and frequency division multiplexing (i.e., FSK-FDM) approach for information embedding in dual-function radar and communication (DFRC) design to achieve an improved communication data rate. Since most of the existing works focus on merely two-bit transmission in each pulse repetition interval (PRI) using different amplitude modulation (AM)- and phased modulation (PM)-based techniques, this paper proposes a new technique that doubles the data rate by using a hybrid FSK-FDM technique. Note that the AM-based techniques are used when the communication receiver resides in the side lobe region of the radar. In contrast, the PM-based techniques perform better if the communication receiver is in the main lobe region. However, the proposed design facilitates the delivery of information bits to the communication receivers with an improved bit rate (BR) and bit error rate (BER) regardless of their locations in the radar's main lobe or side lobe regions. That is, the proposed scheme enables information encoding according to the transmitted waveforms and frequencies using FSK modulation. Next, the modulated symbols are added together to achieve a double data rate using the FDM technique. Finally, each transmitted composite symbol contains multiple FSK-modulated symbols, resulting in an increased data rate for the communication receiver. Numerous simulation results are presented to validate the effectiveness of the proposed technique.


Subject(s)
Communication , Radar , Computer Simulation
18.
Sensors (Basel) ; 23(13)2023 Jul 06.
Article in English | MEDLINE | ID: mdl-37448038

ABSTRACT

By definition, the aggregating methodology ensures that transmitted data remain visible in clear text in the aggregated units or nodes. Data transmission without encryption is vulnerable to security issues such as data confidentiality, integrity, authentication and attacks by adversaries. On the other hand, encryption at each hop requires extra computation for decrypting, aggregating, and then re-encrypting the data, which results in increased complexity, not only in terms of computation but also due to the required sharing of keys. Sharing the same key across various nodes makes the security more vulnerable. An alternative solution to secure the aggregation process is to provide an end-to-end security protocol, wherein intermediary nodes combine the data without decoding the acquired data. As a consequence, the intermediary aggregating nodes do not have to maintain confidential key values, enabling end-to-end security across sensor devices and base stations. This research presents End-to-End Homomorphic Encryption (EEHE)-based safe and secure data gathering in IoT-based Wireless Sensor Networks (WSNs), whereby it protects end-to-end security and enables the use of aggregator functions such as COUNT, SUM and AVERAGE upon encrypted messages. Such an approach could also employ message authentication codes (MAC) to validate data integrity throughout data aggregation and transmission activities, allowing fraudulent content to also be identified as soon as feasible. Additionally, if data are communicated across a WSN, then there is a higher likelihood of a wormhole attack within the data aggregation process. The proposed solution also ensures the early detection of wormhole attacks during data aggregation.


Subject(s)
Computer Security , Data Aggregation , Computer Communication Networks , Algorithms , Confidentiality
19.
Sensors (Basel) ; 23(11)2023 May 23.
Article in English | MEDLINE | ID: mdl-37299731

ABSTRACT

In recent years, Internet of Things (IoT) advancements have led to the development of vastly improved remote healthcare services. Scalability, high bandwidth, low latency, and low power consumption are all essential features of the applications that make these services possible. An upcoming healthcare system and wireless sensor network that can fulfil these needs is based on fifth-generation network slicing. For better resource management, organizations can implement network slicing, which partitions the physical network into distinct logical slices according to quality of service (QoS) needs. Based on the findings of this research, an IoT-fog-cloud architecture is proposed for use in e-Health services. The framework is made up of three different but interconnected systems: a cloud radio access network, a fog computing system, and a cloud computing system. A queuing network serves as a model for the proposed system. The model's constituent parts are then subjected to analysis. To assess the system's performance, we run a numerical example simulation using Java modelling tools and then analyze the results to identify the key performance parameters. The analytical formulas that were derived ensure the precision of the results. Finally, the results show that the proposed model improves eHealth services' quality of service in an efficient way by selecting the right slice compared to the traditional systems.


Subject(s)
Telemedicine , Telemedicine/instrumentation , Telemedicine/methods , Cloud Computing , Internet of Things , Neural Networks, Computer , Computer Simulation
20.
Sensors (Basel) ; 23(11)2023 May 27.
Article in English | MEDLINE | ID: mdl-37299838

ABSTRACT

The 5G network is designed to serve three main use cases: enhanced mobile broadband (eMBB), massive machine-type communications (mMTC), and ultra-reliable and low-latency communications (uRLLC). There are many new technological enablers, including the cloud radio access network (C-RAN) and network slicing, that can support 5G and meet its requirements. The C-RAN combines both network virtualization and based band unit (BBU) centralization. Using the network slicing concept, the C-RAN BBU pool can be virtually sliced into three different slices. 5G slices require a number of Quality of service (QoS) metrics, such as average response time and resource utilization. In order to enhance the C-RAN BBUs utilization while protecting the minimum QoS of the coexisting three slices, a priority-based resource allocation with queuing model is proposed. The uRLLC is given the highest priority, while eMBB has a higher priority than mMTC services. The proposed model allows the eMBB and mMTC to be queued and the interrupted mMTC to be restored in its queue to increase its chance to reattempt the service later. The proposed model's performance measures are defined and derived using a continuous-time Markov chain (CTMC) model and evaluated and compared using different methodologies. Based on the results, the proposed scheme can increase C-RAN resource utilization without degrading the QoS of the highest-priority uRLLC slice. Additionally, it can reduce the forced termination priority of the interrupted mMTC slice by allowing it to re-join its queue. Therefore, the comparison of the results shows that the proposed scheme outperforms the other states of the art in terms of improving the C-RAN utilization and enhancing the QoS of eMBB and mMTC slices without degrading the QoS of the highest priority use case.


Subject(s)
Awareness , Communication , Benchmarking , Markov Chains , Resource Allocation
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