Quality Of Service For Applications Research Articles

Penerapan Quality Function Deployment Untuk Meningkatkan EServequal (Implementation of Quality Function Deployment to Improve E-Servequal)

This study aims to identify the service attributes of the application needed by consumers of the Pegadaian Digital Service application. The research objective can be completed using the Quality Function Deployment method which is able to define consumer needs into the company's technical response in improving the quality of application services. The use of the Quality Function Deployment method in this study resulted in eight variables of consumer needs. The need variable with the highest weight value will be the priority. The product variables that are prioritized are Features, Applications, and Servers. The three highest priority consumer needs can be met by the company by carrying out 4 of the 18 technical responses that have the highest importance weight. The Technical Responses were "improvement of the database coding structure", "implementing the API system", "IT and Operational team training", and "adding Capacity Connecting Bandwidth".

Hotspot Mitigation for Mobile Edge Computing

Due to the long latency of accessing services from cloud datacenters, mobile edge computing (MEC) has been proposed to provide the capability of cloud computing in close proximity to mobile users. Mobile devices can offload computation-intensive applications to MEC servers to achieve better performance and reduce power consumption. Owing to user mobility, workloads of MEC servers would vary severely to cause imbalanced loads and incur hotspot servers. Previous studies of power saving and hotspot management apply mechanisms for cloud datacenters to MEC servers, but these studies may not be suitable for MEC servers due to the limited computation and communication capacity. We are motivated to propose a scheme, complementary offloading for hotspot mitigation (COHM), for MEC. COHM consists of mechanisms for MEC servers with different states, namely busy, idle, and available. These mechanisms jointly consider the computation tasks for MEC servers with different states to mitigate hotspots. The simulation results show that COHM can decrease the number of hotspot servers without degrading the quality of service for applications.

Analysing Service Quality of Mobile Health Platforms Using Text Analytics: A Case Study of Halodoc And Alodokter

As time passes by with the technological developments that affect people’s daily activities, the health sector has become one of the sectors undergoing digital transformation. Due to the emergence of the COVID-19 virus in early 2020 which became the source of a global pandemic outbreak, there has been a surge in the use of telemedicine applications in Indonesia. This study aims to determine users’ sentiment and main topics regarding the service quality of mobile health applications through content obtained from User-Generated Content (UGC) in form of Online Customer Reviews (OCR) on Google Play Store. The research method used in this study is text analytics, by analyzing and interpreting data in the form of texts to generate insights from customers regarding the service quality of two mobile health applications, Halodoc and Alodokter. The result of this study based on sentiment analysis and topic modeling points to positive insights for service quality of both applications. However, the privacy dimension on Halodoc and Alodokter leans towards a higher percentage of negative sentiment than positive sentiment. With knowledge of service quality based on insights that emerge from customer experience regarding Halodoc and Alodokter, the two platforms could evaluate and provide better service quality for users especially their privacy to maintain satisfaction and loyalty of the applications’ users.

Service Quality Analysis of Tokopedia Application Using Text Mining Method

Shopping transformation using online applications makes it easier for sellers and buyers to make transactions. In Indonesia, there are several shopping applications, Tokopedia is one of them. Tokopedia is one of the shopping applications developed in Indonesia. To ensure users’ loyalty to the Tokopedia application, quality analysis is done to determine how the application could be improved or developed. This study applied the Quality Measurement Theory to examine the mobile shopping application service quality with three variables: interaction quality, environmental quality, and outcome quality. The data used in this study are users’ reviews of the Tokopedia application on the Google Play Store website. Data retrieval is done by scrapping for the period June 2020 to December 2020 with a total data of 21,616 reviews. The review data is then analyzed using the text mining method. The sentiment analysis results indicated a value of 65% positive sentiment and 35% negative sentiment. In multi-class classification, the results were 16% interaction quality, 26% environment quality and 58% outcome quality. The text network analysis determined how the word network was formed and uncovered negative words mentioned in each indicator in the mobile apps service quality.

Performance evaluation of wireless local area network with congested fading channels

The IEEE 802.11ay wireless communication standard consents gadgets to link in the spectrum of millimeter wave (mm-Wave) 60 Giga Hertz band through 100 Gbps bandwidth. The development of promising high bandwidth in communication networks is a must as QoS, throughput and error rates of bandwidth-intensive applications like merged reality (MR), artificial intelligence (AI) related apps or wireless communication boggling exceed the extent of the chronic 802.11 standard established in 2012. Thus, the IEEE 802.11ay task group committee has newly amended recent physical (PHY) and medium access control (MAC) blueprints to guarantee a technical achievement especially in link delay on multipath fading channels (MPFC). However, due to the congestion of super bandwidth intensive apps such as IoT and big data, we propose to diversify a propagation delay to practical extension. This article then focuses on a real-world situation and how the IEEE 802.11ay design is affected by the performance of mm-Wave propagation. In specific, we randomize the unstable MPFC link capacity by taking the divergence of congested network parameters into account. The efficiency of congested MPFC-based wireless network is simulated and confirmed by advancements described in the standard.

National Health Insurance (JKN) Mobile Application Use Towards Satisfaction of Participants of the Health Social Security Implementing Agency (BPJS) in Madani Hospital in Medan City

The National Health Insurance (JKN) mobile is an application launched by the Social Health Insurance Administration (BPJS) to make it easier for the public to get optimal health services online. The presence of this application is expected to increase the satisfaction of BPJS health participants as users. Consumer satisfaction is a representation of service quality (service quality). Online service quality (e-SERVQUAL) serves to measure customer perceptions of the quality of online mobile application services. This study aimed to analyze the use of the JKN mobile application on the satisfaction of BPJS health participants at the Madani Hospital in Medan City 2021. This study uses a quantitative approach. This type of research is an analytic survey with a cross-sectional design. Sampling is done by using the proportion estimation formula. The research sample was 92 BPJS Health participants who visited Madani Hospital Medan with the criteria of being at least 18 years old and had downloaded and used the JKN mobile application. The data collected was then analyzed using statistical tests including univariate, bivariate and multivariate. The data analysis process uses the help of Statistical Product and Service Solutions (SPSS) software. The results showed that the use of the JKN mobile application was based on the efficiency factor with a p-value of 0.000 and χ2 calculated 30.728, the reliability factor with a p-value of 0.023 and χ2 calculated 7.188, the fulfilment factor with a p-value of 0.002 and χ2 counts 12,841, the privacy factor with a p-value value 0.004 and χ2 count 11.897 affect the satisfaction of BPJS health participants. The most dominant factor influencing the satisfaction of BPJS health participants in using the JKN mobile application is efficiency. The efficiency variable has a significant effect and has a positive relationship to satisfaction. The efficiency variable increases the possibility as much as 62.801 times. The suggestion of this research for BPJS Kesehatan should continue to innovate to develop service features to suit the era of national digital transformation. Be more active and more creative in promoting and socializing the JKN mobile application and must be able to give confidence to the public that the JKN mobile application is well protected. The Medan City Madani Hospital should be more aggressive in socializing the use of the JKN mobile application to patients who use the JKN-KIS card, especially the online queue feature. The utilization of administrative services, especially during the Covid-19 pandemic, can avoid crowds in the waiting room. BPJS health participants should download and install the JKN mobile application on mobile devices to get easy access to health services and provide services online and offline so that access to health services can be done as much as possible.

Об одном методе интеллектуального планирования мониторинга компьютерных сетей

To ensure the efficiency of management, the security of computer networks (CN), as well as to ensure the required level of quality of service for network applications, accurate and up-to-date information on the state of the CN is required. This information can be obtained through continuous active monitoring of the quantitative characteristics of the CN. Thus, active monitoring becomes an important tool for ensuring the efficiency of management and security of the CN. However, continuous active monitoring, especially of large networks, can lead to congestion of network channels, which can reduce the effectiveness of monitoring the CN. Consequently, with active monitoring of the CN, it is necessary to manage the use of resources (channel and computational) of the network and reduce the load on the network. To solve this problem, this paper proposes a method for intelligent planning of monitoring of the CN. Using machine learning algorithms, can be analyzed the state and performance of the CN and acquire knowledge that can be used to determine the most appropriate rules for monitoring the CN. Thus, it is necessary to find such monitoring rules that will ensure the effectiveness of monitoring the CN. The proposed method can reduce the impact of monitoring on network performance, and on the operation of network applications.

An intelligent and privacy-enhanced data sharing strategy for blockchain-empowered Internet of Things

With the development of the Internet of Things (IoT), the massive data sharing between IoT devices improves the Quality of Service (QoS) and user experience in various IoT applications. However, data sharing may cause serious privacy leakages to data providers. To address this problem, in this study, data sharing is realized through model sharing, based on which a secure data sharing mechanism, called BP2P-FL, is proposed using peer-to-peer federated learning with the privacy protection of data providers. In addition, by introducing the blockchain to the data sharing, every training process is recorded to ensure that data providers offer high-quality data. For further privacy protection, the differential privacy technology is used to disturb the global data sharing model. The experimental results show that BP2P-FL has high accuracy and feasibility in the data sharing of various IoT applications.

THE EFFECT OF PEDULILINDUNGI APPLICATION SERVICE QUALITY ON FULFILLMENT INFORMATION NEEDS FOR USERS

This article was prepared based on the results of research on the effect of pedulilindungi application service quality on meeting information needs for users. The study aimed to measure the effect of service quality of Pedulilindungi application on the fulfillment of information needs for users. The method used is a survey with a quantitative approach with a simple Linear regression. The results showed a simple linear regression test of the influence of application service quality to meet the needs of Information R 0.836 can be interpreted that the contribution of application service quality to meet the needs of information 83.6%. R Square of 0.700 means 70%.

6TiSCH Low Latency Autonomous Scheduling for Industrial Internet of Things

With the advancements in the Internet of Things (IoT), machine-to-machine communication, big-data, and the associated environment, a new model of the Industrial Internet of Things (IIoT) has emerged. The IIoT brings sensors, intelligent machines and tools, instruments, and analytics together for applications like manufacturing, robotics, and many others. Most of these applications require adaptive and autonomous behavior, Quality of Service (QoS), efficient resource allocation, and reservation. One of these crucial challenges is to build and maintain a data communication schedule. Time Slotted Channel Hopping (TSCH) based network operation promises required QoS for low-power applications and enables high reliability. 6TiSCH layer is being developed by standardizing the protocol stack to achieve industrial performance requirements by using IPv6 over IEEE802.15.4e TSCH MAC. 6TiSCH aims to manage the schedule and configure it with the topology and traffic requirements in the industrial environment. This paper proposes a novel low latency autonomous scheduling scheme for the 6TiSCH networks. It generates a segmented schedule for the network where all source nodes can send application data packets to the root node in a single slotframe. The performance of the proposed technique is compared with existing scheduling techniques. Our scheme outperforms the other techniques. The result shows that the latency is reduced up to 41% in comparison with the other scheduling schemes. The proposed scheme has a lower radio duty cycle as the node’s ON time is reduced, making it more energy efficient and reliable.

Empirical Study on the Effect of Birds on Commercial Microwave Links

With emerging technologies such as the 5G and IoT, wireless communication has become more and more dominant in human life. Many applications require high quality of service (QoS) and even momentarily interruption may cause irreversible damage. The signal level in wireless point-to-point communication links is affected by environmental phenomena, including objects blocking the propagating electromagnetic wave. While the relation between signal attenuation and weather phenomena such as rain has been well studied, in this paper we empirically show, for the first time, the relation between the presence of birds and attenuation in Commercial Microwave Links ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CMLs</i> ). Using real <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CML</i> s data collected in Israel that was intersected with GPS data from tagged birds, we empirically associate measured attenuation in the signal in a given link with the presence of birds in its vicinity.We quantified this relation by evaluating the Receiver Characteristics Operating ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ROC</i> ) curve describing the false positive vs. false negative decision on the presence of birds by setting a threshold on the measure attenuation in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">CML</i> s. The results show encouraging performance and establish the potential of the proposed approach for opportunistic automatic monitoring of migrating birds, and for understanding the hazards to QoS in sensitive applications.

Energy Aware Scheme for Underwater Wireless Sensor Networks

The development of wireless sensor networks (WSNs) in the underwater environment leads to underwater WSN (UWSN). It has severe impact over the research field due to its extensive and real-time applications. However effective execution of underwater WSNs undergoes several problems. The main concern in the UWSN is sensor nodes’ energy depletion issue. Energy saving and maintaining quality of service (QoS) becomes highly essential for UWASN because of necessity of QoS application and confined sensor nodes (SNs). To overcome this problem, numerous prevailing methods like adaptive data forwarding techniques, QoS-based congestion control approaches, and various methods have been devised with maximum throughput and minimum network lifespan. This study introduces a novel Seeker Optimization based Energy Aware Clustering Scheme for Underwater Wireless Sensor Networks (SOEACS-UWN). The presented SOEACS-UWN model follows the operation on a collection of solutions named search population (i.e., human team) and considered optimization procedure as a searching process of optimum solutions via human teams. The SOEACS-UWN model constructs a fitness function for effectual CH choices using diverse variables namely distance, residual energy, node degree, centrality, and link quality. The simulation analysis of the SOEACS-UWN model is tested and the outcomes were investigated under diverse aspects. The experimental outcomes demonstrated the supremacy of the SOEACS-UWN model over other approaches.

МЕТОД ВЫЯВЛЕНИЯ АНОМАЛИЙ В СЕТЕВОМ ТРАФИКЕ

Introduction. Computer networks (CN) are highly developed systems with a multi-level hierarchical structure. The use of information and communication technologies in the CN to collect information allows an attacker to influence networks through cyber-attacks. This is facilitated by the massive use of outdated operating systems, ineffective protection mechanisms and the presence of multiple vulnerabilities in unsecured network protocols. Such vulnerabilities help a potential attacker to change the settings of network devices, listen and redirect traffic, block network interaction and gain unauthorized access to the internal components of the CN. The impact of cyber-attacks leads to the appearance of abnormal traffic activity in the CN. For its constant monitoring and detection in the CN, it is necessary to take into account the presence of a large number of network routes, on which sharp fluctuations in data transmission delays and large packet losses periodically occur, new properties of network traffic appear, which requires ensuring high quality of application service. All this served as an incentive to search for new methods of detecting and predicting cyber-attacks fractal analysis can also be attributed to them. The aim of the work is to develop a conceptual method for detecting anomalies caused by cyber-attacks in network traffic through the use of fractal analysis. Methods used. The main provisions of the fractal theory and the use of self-similarity assessment methods proposed by this theory, such as the extended Dickey-Fuller test, R/S analysis and the DFA method, are applied. When testing fractal methods that allow conducting studies of long-term dependencies in network traffic. The scientific novelty lies in the fact that the proposed method correctly identifies anomalies caused by the impact of cyber-attacks, and also allows you to predict and detect both known and unknown computer attacks at an early stage of their manifestation. Practical significance. The presented methodology can be used as an early detection system for cyber-attacks, based on the detection of anomalies in network traffic and the adoption of effective measures to protect the network.

Machine and Deep Learning for Resource Allocation in Multi-Access Edge Computing: A Survey

With the rapid development of Internet-of-Things (IoT) devices and mobile communication technologies, Multi-access Edge Computing (MEC) has emerged as a promising paradigm to extend cloud computing and storage capabilities to the edge of cellular networks, near to IoT devices. MEC enables IoT devices with limited battery capacity and computation/storage capabilities to execute their computation-intensive and latency-sensitive applications at the edge of the networks. However, to efficiently execute these applications in MEC systems, each task must be properly offloaded and scheduled onto the MEC servers. Additionally, the MEC servers may intelligently balance and share their computing resources to satisfy the application QoS and QoE. Therefore, effective resource allocation (RA) mechanisms in MEC are vital for ensuring its foreseen advantages. Recently, Machine Learning (ML) and Deep Learning (DL) have emerged as key methods for many challenging aspects of MEC. Particularly, ML and DL play a crucial role in addressing the challenges of RA in MEC. This paper presents a comprehensive survey of ML/DL-based RA mechanisms in MEC. We first present tutorials that demonstrate the advantages of applying ML and DL in MEC. Then, we present enabling technologies for quickly running ML/DL training and inference in MEC. Afterward, we provide an in-depth survey of recent works that used ML/DL methods for RA in MEC from three aspects: (1) ML/DL-based methods for task offloading; (2) ML/DL-based methods for task scheduling; and (3) ML/DL-based methods for joint resource allocation. Finally, we discuss key challenges and future research directions of applying ML/DL for resource allocation in MEC networks.

Quality assessment of the contact center while implementation the IP IVR system by using teletraffic theory

The paper considers the form of taking into account the specialization of information needs. An analysis of the work of modern call centers has been carried out. The authors noted the effectiveness of using IVR devices, operators, and consultants for differentiated customer service and the need to take feedback into account when forming the revenue stream of applications. The models make it possible to determine the leading indicators of the quality of service for applications arriving at the call center. Formal expressions for descriptions are derived from the input parameters' values and the model's stationary probability. The relationships between the characteristics of the call center that regulate the intensity of incoming and outgoing calls, call processing through 3CXPhone, corporate mail, and social networks were obtained using Global Statistic. The developed methodology for organizing information and reference systems makes it possible to consider modern trends in the development of call centers. The paper presents the results of research using the IP IVR system. The results of calculating service characteristics are given for two different types of calls with mixed order ω=(0.5; 0.7; 0.9). The presented results were obtained by using experimental data of the JSC Kazakhtelecom's call center. For the calculations, the authors used the formulas of the teletraffic theory for a mixed service system. It also assesses the extent of combined service model effects for the contact center's call quality. It is shown that the probability of lost calls depends on the incoming load. The obtained results show that the mixed order for incoming calls servicing affects the probability of service failure.

An Optimization-Based Orchestrator for Resource Access and Operation Management in Sliced 5G Core Networks.

Network slicing is a promising technology that network operators can deploy the services by slices with heterogeneous quality of service (QoS) requirements. However, an orchestrator for network operation with efficient slice resource provisioning algorithms is essential. This work stands on Internet service provider (ISP) to design an orchestrator analyzing the critical influencing factors, namely access control, scheduling, and resource migration, to systematically evolve a sustainable network. The scalability and flexibility of resources are jointly considered. The resource management problem is formulated as a mixed-integer programming (MIP) problem. A solution approach based on Lagrangian relaxation (LR) is proposed for the orchestrator to make decisions to satisfy the high QoS applications. It can investigate the resources required for access control within a cost-efficient resource pool and consider allocating or migrating resources efficiently in each network slice. For high system utilization, the proposed mechanisms are modeled in a pay-as-you-go manner. Furthermore, the experiment results show that the proposed strategies perform the near-optimal system revenue to meet the QoS requirement by making decisions.

A QoS-Aware Machine Learning-Based Framework for AMI Applications in Smart Grids

The Internet of things (IoT) enables a diverse set of applications such as distribution automation, smart cities, wireless sensor networks, and advanced metering infrastructure (AMI). In smart grids (SGs), quality of service (QoS) and AMI traffic management need to be considered in the design of efficient AMI architectures. In this article, we propose a QoS-aware machine-learning-based framework for AMI applications in smart grids. Our proposed framework comprises a three-tier hierarchical architecture for AMI applications, a machine-learning-based hierarchical clustering approach, and a priority-based scheduling technique to ensure QoS in AMI applications in smart grids. We introduce a three-tier hierarchical architecture for AMI applications in smart grids to take advantage of IoT communication technologies and the cloud infrastructure. In this architecture, smart meters are deployed over a georeferenced area where the control center has remote access over the Internet to these network devices. More specifically, these devices can be digitally controlled and monitored using simple web interfaces such as REST APIs. We modify the existing K-means algorithm to construct a hierarchical clustering topology that employs Wi-SUN technology for bi-directional communication between smart meters and data concentrators. Further, we develop a queuing model in which different priorities are assigned to each item of the critical and normal AMI traffic based on its latency and packet size. The critical AMI traffic is scheduled first using priority-based scheduling while the normal traffic is scheduled with a first-in–first-out scheduling scheme to ensure the QoS requirements of both traffic classes in the smart grid network. The numerical results demonstrate that the target coverage and connectivity requirements of all smart meters are fulfilled with the least number of data concentrators in the design. Additionally, the numerical results show that the architectural cost is reduced, and the bottleneck problem of the data concentrator is eliminated. Furthermore, the performance of the proposed framework is evaluated and validated on the CloudSim simulator. The simulation results of our proposed framework show efficient performance in terms of CPU utilization compared to a traditional framework that uses single-hop communication from smart meters to data concentrators with a first-in–first-out scheduling scheme.

A Tutorial on Next Generation Heterogeneous IoT Networks and Node Authentication

The extreme heterogeneity in Internet of Things (IoT) networks impacts the network performance, application service quality, and user experience, and has opened many vistas for adversaries to compromise and eventually knock down the networks. The concern for network security and network reliability will further increase in next generation networks (NGNs) such as 5G-IoT by many folds and requires a new level of security approaches. The first line of defense to secure network and its resources is node authentication. However, we note that without alleviating heterogeneity, NGNs cannot fully provide network security or meet the network performance demands. Further, a granular level of autonomy cannot be provided in NGNs, which impacts the IoT node authentication. This makes the authentication issue in heterogeneous networks a more challenging problem. In this article, we first highlight the heterogeneous NGN concept, its impact (particularly on IoT node authentication), and commonly used authentication protocols and industry standards. We also emphasize the need for enhancing NGNs&#x0027; security by a novel multi-factor approach that also addresses heterogeneity. Unique research challenges and potential opportunities are highlighted.

Impact of Social Media Application Qualities on Using Them for Daily News

A model is introduced to investigate the effect of social media application qualities on the using of these applications for daily news. A standard questionnaire was designed and distributed among randomly selected social media users in the city of Neyshabur in Iran. The content of the questionnaire was validated by experts and its reliability verified using Cronbach's alpha. Random sampling was used identify participants. The SmartPLS software was used to investigate the research findings. Structural equation modelling is used for data analysis. The results show that the factors of system quality, information quality, service quality and personalization of applications affect their perceived usefulness. System quality and service quality affect the perceived ease of use of applications. However, information quality does not affect perceived ease of use. Also, the results show that perceived usefulness has a greater effect (with a path coefficient of 45%) on the attitude in respect of perceived ease of use (path coefficient of 26%). Personalization has the most positive impact on perceived usefulness and service quality has a great impact on perceived ease of use. Facilitating conditions have a positive impact on the use of social media and their use for news. Furthermore, the results show that the factors that affect the use of applications in Telegram are better than those in Viber. These findings justify why Iranian people migrate from Viber to Telegram as their social media application.

Fog Computing Service Provision Using Bargaining Solutions

To meet the needs of many IoT applications with low-latency requirement, fog computing has been proposed for next-generation mobile networks to migrate the computing from the cloud to the edge of the network. In this paper, we study the fog computing service deployment problem, where the operator allocates and deploys the required computing and network resources on the edge of the network to accommodate the requests of various applications operated by the application service providers (ASPs). The operator negotiates with the ASPs to determine serving QoS of applications and how much to pay. A queuing-based latency performance model with bulk arrival is proposed for the problem to estimate the resources needed for the fog network to achieve the QoS requirements of applications. We then model and analyze the interactions between the operator and multiple ASPs as sequential one-to-many bargaining using Nash bargaining. Next, to find the optimal bargaining sequence, we propose an improved optimal algorithm, along with fast heuristic algorithms, to find the optimal sequence with low complexity. Through extensive simulations, we show that the fog service can benefit all parties, and the proposed optimal and heuristic algorithms can improve the OP's payoff by averages of 21.24 and 14.16 percent respectively.