Impact of Network QoS factors on QoE of IoT Services

Khamosh, Abdurauf; Anwer, Mohammad Anwer; Nasrat, Nasratullah; Hamdard, Javid; Gawhari, Gawhar Shah; Ahmadi, Abdul Rahim

doi:10.1109/incit50588.2020.9310969

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…Shaikh [27] argued that research on QoE and QoS has been fragmentary and their relation remains blurry with the recent emerging technologies such as IoT. Khamosh et al [28] emphasized the fact that the connection between the IoT devices is basically depends on a wireless network due to their geographically location distribution. Often, wireless networks are unreliable and have their distortion rates are high.…”

Section: A) Quality Of Service Qos Metricsmentioning

confidence: 99%

“…In order to investigate the correlation between three QoS parameters: packet loss, latency and jitter and the QoE for IoT services, Khamosh et al [28] conducted a subjective experiment and a real time IoT service controlling testbed. The selected QoS parameters were investigated individually to discover their impact on the QoE.…”

Section: A) Quality Of Service Qos Metricsmentioning

confidence: 99%

See 1 more Smart Citation

Quality of Experience in Internet of Things: A Systematic Literature Review

Sanyour¹,

Abdullah²,

Abdullah³

2022

Preprint

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Section: A) Quality Of Service Qos Metricsmentioning

confidence: 99%

Section: A) Quality Of Service Qos Metricsmentioning

confidence: 99%

Quality of Experience in Internet of Things: A Systematic Literature Review

Sanyour¹,

Abdullah²,

Abdullah³

2022

Preprint

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“…This study determined various methods to enhance QoE and presented a mathematical model to meet the desired QoS. As QoS factors have a strong influence on QoE, IoT services were examined by equating three fundamental metrics of QoS: small loss, latency, and jitter [12]. These studies show that there is room for further improvement in the QoS of IoT.…”

Section: Related Workmentioning

confidence: 99%

An Enhanced Software Framework for Improving QoS in IoT

Tomer

Gandhi²

2022

Eng. Technol. Appl. Sci. Res.

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Internet of Things (IoT) and Artificial Intelligence (AI) with its subcomponents are the latest emerging technologies that make our daily lives easier. Quality of Service (QoS) plays a very important role in IoT due to the large number of interconnected nodes. QoS is inversely dependent on the node count, i.e. the increment of nodes causes hampering to QoS, as increasing the number of nodes increases the number of requests to the IoT server. An enhanced framework is strongly needed to control QoS in IoT applications. This study proposes and implements an enhanced framework using Matlab, to control the number of requests. The proposed model can improve QoS parameters like throughput, latency, and packet loss by reducing the number of requests generated by the end nodes without compromising the services to the end user. The results showed that QoS parameters improved in terms of throughput by 5-10%, packet loss by up to 6%, and packet latency by 4%. This model can also be tested in hardware and may provide a better QoS solution.

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“…Authors of [26] showed that three analysed QoS metrics (i.e., packet loss, latency, and jitter) have a very significant relationship with the QoE of IoT services. The authors [34] have stated that the QoS and QoE concepts are often studied separately, and making the comprehensive quality analysis is problematic and not trivial.…”

Section: Introductionmentioning

confidence: 99%

An Effect of User Experience on A Data-Driven Fuzzy Inference of Web Service Quality

Miliauskaitė

Kalibatiene

2023

INT J COMPUT COMMUN, Int. J. Comput. Commun. Control

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Today, various stakeholders provide a large number of functionally similarWeb Services (WS) to meet increasingly complex business needs. Therefore, to distinguish similar WS, some researchers have proposed using the non-functional characteristics, named Quality of Service (QoS), and user’s needs, expressed through Quality of Experience (QoE). Thus, all those QoS and QoE attributes should be taken into account in predicting WS quality jointly, called WS QoSE (Quality of Service and Experience). However, these attributes are different in nature, i.e., QoS is data-driven and numerical, while QoE is expert-based and linguistic. Consequently, to predict WS QoSE, in this paper, we propose a hybrid fuzzy inference approach, composing both quantitative and qualitative data inputs into WS QoSE output by applying the adaptive neuro-fuzzy inference system (ANFIS). The developed prototype allows us to implement the proposed approach, investigate its performance, and study the effect of QoE attributes on WS QoSE. The results of the two experiments show good performance and suitability of the proposed hybrid fuzzy inference approach for predicting WS QoSE based on combining QoS and QoE attributes. We expect that those results inspire researchers and practitioners to understand the WS QoSE better and develop user needs matching WS.

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Impact of Network QoS factors on QoE of IoT Services

Cited by 6 publications

References 13 publications

Quality of Experience in Internet of Things: A Systematic Literature Review

Quality of Experience in Internet of Things: A Systematic Literature Review

An Enhanced Software Framework for Improving QoS in IoT

An Effect of User Experience on A Data-Driven Fuzzy Inference of Web Service Quality

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