A Quality Model for Evaluating IoT Applications

Mi, Kim Young

doi:10.17706/ijcee.2016.8.1.66-76

Cited by 41 publications

(23 citation statements)

References 5 publications

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“…From the works aimed at quantifying device reliability, there are several different contributions made. Some works, such as Mavrogiorgou et al (2018), Zin et al (2016) and Kim (2016) use standard reliability metrics to quantify the state of reliability in IoT devices. These standard metrics include MTTF, MTTR, Availability, Maintainability and Failure Rate (Fries 2006).…”

Section: Discussion Of Surveyed Workmentioning

confidence: 99%

“…Using this mechanism, the authors were able to build a ranking of connected fitness devices based upon their reliability results from known reliability metrics. Lastly, Kim (2016) used reliability, failure rate and recoverability in their study which proposed a weighted model to quantifying reliability in the IoT. The model consisted of four quality criteria; functionality, reliability, efficiency and portability.…”

Section: Device Reliabilitymentioning

confidence: 99%

“…. (2016),Sicari et al (2014) Accuracy, precision, timeliness, completenessKim (2016) Reliability, maturity, failure rate, recoverability Network layer reliabilityMaalel et al (2013) Link quality indicator (LQI) These approaches do not consider the possibility that hardware failures can occur at the device layer in the IoT paradigm. If a failure were to occur at the device level, then the information would not reach the intended target regardless of the network availability.…”

mentioning

confidence: 99%

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IoT reliability: a review leading to 5 key research directions

Moore¹,

Nugent²,

Zhang³

et al. 2020

CCF Trans. Pervasive Comp. Interact.

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The Internet of Things (IoT) is rapidly changing the way in which we engage with technology on a daily basis. The IoT paradigm enables low-resource devices to intercommunicate in a fully flexible and pervasive manner, and the data from these devices is used for decision-making in critical applications such as; traffic infrastructure, health-care and home security, to name but a few. Due to the scarce resources available in these IoT devices, being able to quantify the reliability of them is a critical function. This report presents a detailed evolution of the area of reliability measurement, followed by an in-depth review of the state-of-the-art for quantification of reliability in the IoT, revealing the many challenges associated with this task. From this in-depth review, a set of key research directions for IoT reliability is determined. Despite the critical nature of the research area, at this current moment, this study is the first detailed review available in the area of assessing IoT reliability.

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Section: Discussion Of Surveyed Workmentioning

confidence: 99%

Section: Device Reliabilitymentioning

confidence: 99%

mentioning

confidence: 99%

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IoT reliability: a review leading to 5 key research directions

Moore¹,

Nugent²,

Zhang³

et al. 2020

CCF Trans. Pervasive Comp. Interact.

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show abstract

“…The evaluation process is a crucial step permitting a relevant assessment of an achievement against various criteria and it follows different methods [30]. In this context, in order to identify the robustness and performance level of the previous detailed works, we try to evaluate and extract their main characteristics.…”

Section: Evaluation Of Existing Applicationsmentioning

confidence: 99%

Internet of Things in Industry 4.0 Case Study : Fluid Distribution Monitoring System

Abdelhafidh¹,

Fourati²,

Fourati³

et al. 2017

Computer Science &Amp; Information Technology (CS &Amp; IT)

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“…The concept has been used with variations, such as in our previous work on measuring the QoE of 4G mobile technological implementation in Indonesia [4]. Previous QoE measurement research by [5] has developed a quality model to evaluate IoT applications by mapping the characteristics of IoT to quality attributes under the scope of ISO 9126.…”

Section: Introductionmentioning

confidence: 99%

A 5-Step Framework for Measuring the Quality of Experience (QoE) of Internet of Things (IoT) Services

Suryanegara

Prasetyo

Andriyanto³

et al. 2019

IEEE Access

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The ultimate benefit of the Internet of Things (IoT) is to improve quality of life by supporting the automation and interconnection of various services; this interconnection includes public facilities in platforms such as smart cities. IoT implementation is believed to improve services and yield additional benefits. However, assessment of how the IoT brings such an experience to users is still a challenge that is not yet standardized. Given that little research has discussed this issue, this paper aims to formulate a framework for measuring the quality of experience (QoE) regarding IoT services on the basis of the Absolute Category Rating with Hidden Reference (ACR-HR) scale, which measures the perception of experience by having the IoT users provide scores for their experiences before and after IoT implementation. The proposed framework consists of 5 steps: setting up the focus of the IoT services and the QoE parameters being assessed, defining users of IoT services, conducting a mean opinion score (MOS) survey of the users, calculating the DMOS (differential MOS) on the basis of the ACR-HR quantitative scale, and finally providing the strategic implications to those who implement the service. To test the proposed framework, we have conducted a proof of concept by measuring the QoE for the IoT services in the Jakarta Smart City platform. Six institutional users operate the IoT for their public services, ranging from fire and rescue brigades to public bus transportation. The results imply that the IoT-based services provide a good experience to institutional users when compared to the state of services without IoT. A strategic implication is that some features of Jakarta Smart City must be evaluated and improved to increase the level of satisfaction of users. To conclude, our framework is sufficiently reliable in measuring the QoE for IoT-based services and can be recommended for use for any other IoT-type service.

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A Quality Model for Evaluating IoT Applications

Cited by 41 publications

References 5 publications

IoT reliability: a review leading to 5 key research directions

IoT reliability: a review leading to 5 key research directions

Internet of Things in Industry 4.0 Case Study : Fluid Distribution Monitoring System

A 5-Step Framework for Measuring the Quality of Experience (QoE) of Internet of Things (IoT) Services

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