Quality Of Service For Users Research Articles

EDLIoT: A method for decreasing energy consumption and latency using scheduling algorithm in Internet of Things

Decreasing energy consumption in networks with limited resources, such as the Internet of Things, has always been one of the main challenges in guaranteeing network performance. In this article, cooperative game theory is employed to improve the cooperation patterns of fog computing resources. The EDLIoT method consists of two main steps: “Topology Construction” and “Determining Optimal Fog Computing Resources to Process IoT Object Tasks”. In the first step of the proposed method, the set of reliable communications in the network is identified to establish connections between IoT objects and fog computing resources in the form of a tree structure. Then, in the second step, a model based on cooperative game theory and the cost function is used to determine the optimal computing resources in the fog layer for outsourcing the processing tasks of IoT objects. In EDLIoT, active IoT objects perform computation in the fog layer instead of locally, to conserve energy. This is done so that IoT objects, if possible, discover the most suitable processing resources in the fog based on characteristics such as energy consumption, delay, and processing power of the computing resource. The efficiency of the proposed method has been evaluated in a simulated environment, and the results have been compared with those of previous algorithms. The results demonstrate that using the EDLIoT method, in addition to decreasing energy consumption and delay, more computing tasks can be processed through fog resources, thereby increasing the quality of service for IoT users.

Empowering sports facilities and sports physiotherapy performance with Integrated Management Information System (IMIS): Enhanced user experience and service quality

This study investigates the development of Integrated Management Information Systems (IMIS) to improve user experience and service quality in sports facilities. This research conducts a systematic review (2018-2023) of 7 articles from Scopus, Google Scholar, and Semantic Scholar to explore how IMIS can address evolving industry needs. The system facilitates user access to information, online transactions, and feedback mechanisms while enabling personalization and recommendations. However, the review also highlights the potential of IMIS to revolutionize sports physiotherapy through enhanced athlete care, data-driven treatment plans, and streamlined operations. Successful IMIS implementation requires careful planning, stakeholder involvement, and continuous adaptation to emerging technologies like AI and IoT. By overcoming challenges and embracing opportunities, IMIS can usher in a new era of personalized, effective, and efficient athlete care. IMIS implementation involves evaluating facility needs, staff training, and introducing the system to users. Data security and privacy are paramount. Future integration of AI, IoT, and mobile applications holds promise for further enhancing IMIS effectiveness. Overall, IMIS implementation has demonstrably improved user experience and service quality. By prioritizing user experience and adapting to technological advancements, sports facilities can remain competitive in a digital age.

Design and implementation of an integrated OWC and RF network slicing-based architecture over hybrid LiFi and 5G networks

AbstractRadio frequency (RF) systems tend to become congested and overused due to the increasing number of users, devices and the multiple technologies involved in their deployment. This leads to the downgrading of quality of service (QoS) further caused by interference with different signals. Optical Wireless communications (OWC) are emerging as a feasible alternative as they offer unlicensed, interference-free spectrum by using the frequency range located in the visible and invisible light spectrum. Its applications can be found in various fields such as healthcare, education, finance and industry 4.0. Moreover, it enhances the security and privacy of communications. Nevertheless, the limited spectrum in OWC also requires optimised resource allocation to support the QoS of different applications or users whilst lacking established infrastructure to manage this. To address these challenges, this paper proposes a novel 5G-LiFi framework able to ensure QoS requirements by introducing network slicing in Light Fidelity (LiFi) networks integrated with 5G infrastructure. This paper has developed and deployed a 5G-LiFi architecture capable of providing network slicing capabilities over the LiFi segment of the hybrid network. It allows a full control over the network traffic and tailored, improved QoS capabilities. The proposed solution has been empirically validated and evaluated in a realistic testbed employing real-world LiFi and 5G network equipment, and yielded promising results in terms of bandwidth, delay, jitter and packet loss. This work concludes that the use of heterogeneous networks integrating OWC with RF is a suitable solution and it can lead to a better use and exploitation of the different spectrums, improving the QoS offered to end-users.

Resource optimization of multi-UAV assisted communication system based on user scheduling

In order to improve the transmission rate of the multi-user mobile communication downlink wireless transmission system, a resource optimization method for multi-unmanned aerial vehicle (UAV) assisted communication system based on user scheduling and trajectory optimization is proposed. The proposed method establishes an optimization problem with the maximization of the total multi-user throughput as the criterion under the constraints of user scheduling, total energy consumption of UAVs and user service quality requirements. In order to solve this non-convex problem, the original non-convex problem is decomposed into three easy-to-handle non-convex sub-problems by using the block coordinate descent method (BCD). The sub-problems are transformed and solved by introducing slack variables, first-order Taylor expressions, continuous convex approximation (SCA) and other methods, and then alternately iteratively optimized to obtain an approximate suboptimal solution to the original non-convex problem. Simulation results show that the proposed algorithm can effectively improve the total system throughput and has good convergence in both single and multi-UAV communication systems.

Optimizing fleet, staff configuration and operational strategies in one-way mixed fleet carsharing systems: a Lagrangian relaxation-based approach

ABSTRACT This study explores enhancing carsharing services by integrating gasoline and electric vehicles into a one-way mixed fleet carsharing system (OMFCS). The focus is on optimizing configurations (fleet and staff size, initial deployment) and operational strategies (vehicle relocation and staff rebalancing) while considering carbon emission costs. Employing a space-time-electricity network modeling approach, we developed an integer linear programming model to tackle the configurations and operational strategies optimization problem. For solving this model, we introduce a Lagrangian relaxation-branch bound approach, which integrates subgradient, dynamic programming and greedy-based heuristics algorithm. An illustrative case and a real-world case are conducted to demonstrate the efficiency of the proposed solution method and the analysis sheds light on the configurations and operational strategies of OMFCS. The sensitive analysis results suggest that OMFCS is more profitable and balances user service quality and carbon emissions better than carsharing systems using only one type of vehicle.

Transmission scheme for collaborative‐RIS‐aided NOMA system with multiple channels

AbstractThis paper investigates the combination of collaborative reconfigurable intelligent surface (RIS) with non‐orthogonal multiple access (NOMA) to enhance the quality of service (QoS) in communication systems. By introducing collaborative RIS, a virtual line‐of‐sight (LoS) link can be established between the base station (BS) and the users. In this article, the aim is to minimize the total transmit power under the constraint of each user's QoS by optimizing the phase shifts and the power allocation coefficients. To proceed, two approaches are proposed with different complexities to solve the formulated power minimization problem. Specifically, in the first method, a sub‐channel allocation scheme based on the user locations is developed, and the phase shifts are optimized to obtain the minimum transmit power for fixed channel assignment, which is further addressed by using the alternating optimization and the sequence rotation methods. In the second approach, a joint optimization algorithm that achieves the channel assignment vectors and the phase shift coefficients in an alternative manner, and a sub‐channel assignment algorithm based on the equivalent channel gain is developed. The simulation results show that the proposed transmission scheme has the capability of improving system performance significantly compared with the conventional schemes.

A Resource Allocation Algorithm for Cloud-Network Collaborative Satellite Networks with Differentiated QoS Requirements

With the continuous advancement of cloud computing and satellite communication technology, the cloud-network-integrated satellite network has emerged as a novel network architecture. This architecture harnesses the benefits of cloud computing and satellite communication to achieve global coverage, high reliability, and flexible information services. However, as business types and user demands grow, addressing differentiated Quality of Service (QoS) requirements has become a crucial challenge for cloud-network-integrated satellite networks. Effective resource allocation algorithms are essential to meet these differentiated QoS requirements. Currently, research on resource allocation algorithms for differentiated QoS requirements in cloud-network-integrated satellite networks is still in its early stages. While some research results have been achieved, there persist issues such as high algorithm complexity, limited practicality, and a lack of effective evaluation and adjustment mechanisms. The first part of this study examines the state of research on network virtual mapping methods that are currently in use. A reinforcement-learning-based virtual network mapping approach that considers quality of service is then suggested. This algorithm aims to improve user QoS and request acceptance ratio by introducing QoS satisfaction parameters. With the same computational complexity, QoS is significantly improved. Additionally, there has been a noticeable improvement in the request acceptance ratio and resource utilization efficiency. The proposed algorithm solves existing challenges and takes a step towards more practical and efficient resource allocation in cloud-network-integrated satellite networks. Experiments have proven the practicality of the proposed virtual network embedding algorithm of Satellite Network (SN-VNE) based on Reinforcement Learning (RL) in meeting QoS and improving utilization of limited heterogeneous resources. We contrast the performance of the SN-VNE algorithm with DDRL-VNE, CDRL, and DSCD-VNE. Our algorithm improve the acceptance ratio of VNEs, long-term average revenue and delay by an average of 7.9%, 15.87%, and 63.21%, respectively.

Jointly Optimization of Delay and Energy Consumption for Multi-Device FDMA in WPT-MEC System.

With the rapid development of mobile edge computing (MEC) and wireless power transfer (WPT) technologies, the MEC-WPT system makes it possible to provide high-quality data processing services for end users. However, in a real-world WPT-MEC system, the channel gain decreases with the transmission distance, leading to "double near and far effect" in the joint transmission of wireless energy and data, which affects the quality of the data processing service for end users. Consequently, it is essential to design a reasonable system model to overcome the "double near and far effect" and make reasonable scheduling of multi-dimensional resources such as energy, communication and computing to guarantee high-quality data processing services. First, this paper designs a relay collaboration WPT-MEC resource scheduling model to improve wireless energy utilization efficiency. The optimization goal is to minimize the normalization of the total communication delay and total energy consumption while meeting multiple resource constraints. Second, this paper imports a BK-means algorithm to complete the end terminals cluster to guarantee effective energy reception and adapts the whale optimization algorithm with adaptive mechanism (AWOA) for mobile vehicle path-planning to reduce energy waste. Third, this paper proposes an immune differential enhanced deep deterministic policy gradient (IDDPG) algorithm to realize efficient resource scheduling of multiple resources and minimize the optimization goal. Finally, simulation experiments are carried out on different data, and the simulation results prove the validity of the designed scheduling model and proposed IDDPG.

An integrated analysis of consumer purchase decision-making on second-hand trading platforms: evidence from china

ABSTRACT In the context of circular economy and sustainable development, second-hand trading platforms are in the preliminary stages of development, revealing the consumer behavior of second-hand trading platform consumers has become a challenging topic. Previous studies have often used technology acceptance models to analyze consumers’ acceptance of electronic commerce platform technologies. However, these studies have not analyzed consumer purchase decision-making well, lacked affective explanations of the logic of consumer decisions, and lacked discussions of multiple stakeholders. Therefore, this study proposes a comprehensive framework model for consumer decision making on second-hand trading platforms. We analyzed consumer purchase decision process, identified three key dimensions eight factors. Through a combination of the ABC attitude model and the technology acceptance model, we sorted out a hypothetical model consisting of cognitive, affective, and behavioral. Then, we validated and analyzed the empirical results in China using the partial least squares structural equation technique. The findings underscore the significant impact of product and platform dimensions on consumer sentiment and behavior. Theoretically, this study advances the understanding of consumer decision-making in the context of second-hand platforms, particularly within the framework of the circular economy. Practically, it offers actionable insights for merchants and platform operators to enhance user experience and service quality, thereby supporting the sustainable growth of e-commerce.

Service placement strategies in mobile edge computing based on an improved genetic algorithm

In mobile edge computing (MEC), quality of service (QoS) is closely related to optimizing service placement strategies, which is crucial to providing efficient services that meet user needs. However, due to the mobility of users and the energy consumption limit of edge servers, the existing policies make it difficult to ensure the QoS level of users. In this paper, a novel genetic algorithm based on a simulated annealing algorithm is proposed to balance the QoS of users and the energy consumption of edge servers. Finally, the effectiveness of the algorithm is verified by experiments. The results show that the QoS value obtained by the proposed algorithm is closer to the maximum value, which has significant advantages in improving QoS value and resource utilization. In addition, in software development related to mobile edge computing, our algorithm helps improve the program’s running speed.

Optimal Sizing of Electric Vehicle Charging Stacks Considering a Multiscenario Strategy and User Satisfaction

The rapid growth of EVs relies on the development of supporting infrastructure, e.g., charging stations (CSs). The sizing problem of a CS typically involves minimizing the investment costs. Therefore, a flexible and precise sizing strategy is crucial. However, the existing methods suffer from the following issues: (1) they do not consider charging station deployments based on the charging stack; (2) existing sizing strategies based on smart charging technology consider a single scenario and fail to meet the demand for flexible operation under multiple scenarios in real-life situations. This paper proposes a novel CS sizing framework specific for charging stacks to overcome these challenges. Specifically, it first addresses the charging-stack-based CS sizing problem, and then it proposes the corresponding multiscenario constraints, i.e., exclusive and shared, for capacity-setting optimization. In addition, a novel quality of service (QoS) formulation is also proposed to better relate the user QoS levels to the CS sizing problem. Finally, it also explores the relationship between the investment costs and the total power of the needed charging stack under three business models. Extensive experiments show that the proposed framework can offer valuable guidance to CS operators in competitive environments.

Towards an optimal 3-D design and deployment of 6G UAVs for interference mitigation under terrestrial networks

Unmanned aerial vehicles (UAVs) have opened new communication possibilities by being able to access remote areas. Their ability to serve a large number of users based on demand and adaptability is a key strength. In Sixth Generation (6G) networks, UAVs are highly valued for their cost-efficiency and versatile deployment. However, the mobility of UAVs introduces different types of interference issues, resulting in a decrease in network performance and quality of service (QoS) for edge users. To address these challenges, this paper introduces a clustering-based solution involving three main steps. Firstly, UAVs are deployed in three-dimension (3D) space based on user requests using mini-batch K-mean clustering Subsequently, re-clustering is explored to tackle load balancing within clusters. Finally, outliers and boundary users are classified to enhance QoS for edge users. This model effectively reduces interference and boosts UAV reliability in terrestrial networks. Also, a case study is presented to show how UAVs can mitigate interference in maritime communication within terrestrial networks. Numerical results demonstrated that the proposed scheme increases throughput by 33.06% and reduces energy consumption and time delay by 73.15% and 9.15%, respectively, as compared to the existing baseline schemes.

Delighting customers: Evaluating service quality and customer satisfaction of self-checkout users in sports retail

Digitalization has transformed dynamics across all fields, and technology has completely changed the customer experience. One prominently utilized technology in offline retail is self-checkout services. The present study intends to investigate the attributes that influence people to use self-checkout services and assess their impact on service quality and customer satisfaction. Drawn from Dabholkar’s attribute-based model, the study employs a positivist approach to test the conceptual framework. After the preliminary survey of 330 respondents, it identified ninety-nine consumers who had used the self-service check-out facility. The data collected were analyzed using a multi-variate technique – Partial Least Squares Structural Equation Modeling (PLS-SEM) – owing to the small sample size requirement. All independent variables taken for the study positively affect the service quality. Customer perception of control, ease of use, reliability, enjoyment, speed, adventure, and openness positively affect service quality. It was noted that ease of use, with a variance value of 2.451, and openness to experience, with a variance value of 2.437, show the importance of determining independent variables with service quality. The study findings reported that service quality is primarily influenced by ease of use, enjoyment, and openness to experience. It underlines that some retail customers will likely feel frustrated rather than enjoy the self-service technology, perceiving it as less reliable. The study suggests incorporating openness to experience and adventure shopping in retail outlets that can enhance consumer satisfaction and loyalty. Adopting an immersive and interactive shopping experience will ultimately improve the perception of service quality and customer happiness.

The Influence of Mobile Application User Experience, Service Quality, and Social Interaction on Customer Satisfaction Quantitative Research in the Online Service Industry

The Influence of Mobile Application User Experience, Service Quality, and Social Interaction on Customer Satisfaction Quantitative Research in the Online Service Industry

Exploring the Impact of User Interface and Service Quality in Enhancing Repurchase Intentions

Exploring the Impact of User Interface and Service Quality in Enhancing Repurchase Intentions

Development and validation of the mobile adherence satisfaction scale (MASS) for medication adherence apps

ObjectiveTo develop and validate the Mobile Adherence Satisfaction Scale (MASS) for assessing user satisfaction with mobile health applications aimed to improve medication adherence. MethodsThe study involved patients over 18 with asthma, hypertension, heart failure, or diabetes, who used the CareAide® app for six months. Scale development included a literature review, expert consultations, and patient interviews, initially identifying 129 items. These were refined to 27 using a two-round Delphi technique and grouped into six dimensions: user interface, perceived usability, system quality, service quality, feature satisfaction, and general satisfaction. A pilot study with 30 participants further refined the model, which was then validated with 135 participants using exploratory and confirmatory factor analyses in SPSS 29 and SmartPLS 4. Data were collected via self-administered questionnaires. ResultsA total of 135 complete questionnaires were analysed. Respondents had an average age of 66.7 years (SD = 11.6) with 42.2 % male (n = 57) and 57.8 % female (n = 78). After removal of an item due to cross loading, exploratory factor analysis resulted six dimensions and 26 items with Kaiser-Meyer-Olkin measure of 0.837 and Bartlett's Test of Sphericity (χ2(n = 325) = 2085.673, P < 0.001). The confirmatory factor analysis confirmed high reliability and validity: Cronbach's alpha values > 0.70 for each dimension and an overall alpha of 0.89, with Composite Reliability and Average Variance Extracted both >0.70 and >0.50, respectively, for each dimension. Structural model indicated a significant positive impact of user interface (β = 0.226, P = 0.006) and feature satisfaction (β = 0.230, P = 0.002) on general satisfaction, explaining 23.1 % of the variance (R2 = 0.231). ConclusionThe study developed and validated the MASS, a reliable tool for assessing user satisfaction with mHealth apps. User interface design and feature satisfaction are key for long-term engagement and consistent medication adherence.

Sentiment Analysis of Alfagift Application User Reviews Using Long Short-Term Memory (LSTM) and Support Vector Machine (SVM) Methods

The rapid advancement of mobile apps has emerged as an important aspect of the routine of internet-connected users. In Indonesia, many companies are introducing their apps to improve the quality of service for users, and Alfamart is one of them. However, users have identified many shortcomings in these apps. This feedback is provided by users on the review feature of the Alfagift app on the Google Play Store. This research aims to apply a sentiment analysis approach to identify the application's shortcomings so that developers can understand the aspects that need to be improved to improve the quality of application services. The research stages include data collection, preprocessing, labeling, weighting, classification of LSTM and SVM methods, and performance evaluation using a confusion matrix. The dataset consists of 1000 reviews obtained through web scraping techniques. This research uses the Lexicon-based method to classify the dataset into positive, negative, and neutral categories. The analysis results show that 801 data are classified as positive sentiment, 77 as negative sentiment, and 122 as neutral sentiment. Based on testing, both SVM and LSTM methods show good performance. The best accuracy results were obtained using the SVM method, which amounted to 83.5%. Meanwhile, the LSTM method achieved an accuracy of 82%.

Enhancing public transport accessibility: Exploring hybrid car sharing systems for improved connectivity

Transportation networks within a region are vital. They link individuals from their starting points to their destinations. Nonetheless, even highly effective transportation systems may not ensure optimal performance, service quality, or fairness for users unless they are extensively interconnected and accessible to as many people as possible. In this context, we present a planning framework for complementing public transit (PT) systems with other cost-effective mobility systems, such as car-sharing (CS) systems, to improve performance in terms of connectivity and accessibility. These metrics are evaluated by using the suitably introduced Connectivity and Accessibility Index (CAI). Specifically, in the present research, we first introduce a novel methodology for assessing the connectivity and accessibility values of existing PT systems. Then, the proposed approach provides an optimization-based design model for a hybrid CS system comprising both one-way (OW) and two-way (TW) models. To evaluate the capabilities of the proposed approach, a real-world case study of the PT system of the city of Trento (Italy) is evaluated.

Asynchronous modeling workflows in CyberWater with on-demand HPC/Cloud access

Workflow management systems (WfMSs) are commonly used to organize/automate sequences of tasks as workflows to accelerate scientific discoveries. During complex workflow modeling, a local interactive workflow environment is desirable, as users usually rely on their rich, local environments for fast prototyping and refinements before they consider using more powerful computing resources. However, existing WfMSs do not simultaneously support local interactive workflow environments and HPC resources. In this paper, we present a mechanism for on-demand access to remote HPC resources from desktop/laptop-based workflow management software to compose, monitor, and analyze scientific workflows in the CyberWater project. CyberWater is an open-data and open-modeling software framework for environmental and water communities. In this work, we extend the open-model, open-data design of CyberWater with on-demand HPC accessing capacity. In particular, we design and implement the LaunchAgent and JobManager, which can be integrated into a local desktop/Laptop environment to allow on-demand usage of remote HPC resources for computational modeling workflows effectively and efficiently. LaunchAgent manages user authentication to remote resources, prepares computation-intensive or data-intensive tasks as batch jobs, submits jobs to remote resources, and monitors quality of services for users. LaunchAgent interacts seamlessly with other components in CyberWater, providing advantages of user-friendly feature-rich desktop software experience and increased computing power through on-demand HPC/Cloud usage. In our evaluations, we demonstrate how a hydrological modeling workflow that consists of both local and remote tasks can be constructed and show that our new on-demand HPC/Cloud usage helps speed up hydrology workflows while allowing asynchronous HPC/Cloud access from workflows using a desktop graphical user interface.

Simulation of association rule mining based on sensor networks in Chinese language learning recommendation system for college students

Based on the increasing learning needs of students that cannot be met by traditional education in schools, the use of online learning platforms has become a way for many college students to learn Chinese, thereby improving their academic performance and literary literacy. However, it is difficult to guarantee the service quality of online platforms for Chinese learning at present, so this paper proposes an association rule mining algorithm to strengthen Chinese online learning. We using user service quality as a constraint to improve spectrum utilization and energy efficiency, and defining the user state space, borrowing the obtained resource allocation optimization function to reward a small portion of users' communication costs, ultimately obtaining user state space information and one-dimensional state space data. This algorithm performs grouping operations on the data estimated by a large amount of computation, and divides the data into balanced categories to reduce the amount of input data in the network. The performance test results show that this paper has made a great breakthrough in the personalization of Chinese learning, and has outstanding performance in the processing and classification of Big data. There are also some solutions to the problem of too much existing data. In a period of use experience analysis report, we found that the online platform for Chinese learning can give consideration to students' personalized needs and experience.