Capturing the Reliability of Unknown Devices in the IoT World

Mavrogiorgou, Argyro; Kiourtis, Athanasios; Symvoulidis, Chrysostomos; Kyriazis, Dimosthenis

doi:10.1109/iotsms.2018.8554720

Cited by 21 publications

(9 citation statements)

References 23 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%

“…Fries (2006) defines these requirements of reliability by stating that devices must be able to; perform a required function, perform without failure, perform under stated conditions and operate for a specific period of time. Therefore, the specification for reliability requires that we fully identify the expected conditions of use, what constitutes proper function and what constitutes a failure (Mavrogiorgou et al 2018). The remainder of this section will cover the key areas in establishing reliability engineering.…”

Section: The Science Of Reliabilitymentioning

confidence: 99%

“…These components are prone to wear and tear, and therefore we would expect the reliability to reduce over time for these components. Software components, on the other hand, should not be subject to physical wear and tear, therefore we would not expect to see a decay in reliability over time (Mavrogiorgou et al 2018). Network reliability concerns the network performance over a given period of time, which is determined by a blend of hardware and software.…”

Section: Reliability In Computingmentioning

confidence: 99%

“…This probabilistic function allows estimation of the expected time to failure, availability and reliability for a given IoT device. Meanwhile, Mavrogiorgou et al (2018), included Mean Time to Repair (MTTR), MTTF, MTBF, and availability metrics in their work, which proposed a mechanism for capturing the reliability of heterogeneous IoT devices. This mechanism considered both known and unknown device types and sought to differentiate between which devices were reliable and which were not, with the goal of collecting data from the reliable ones and discarding data from unreliable devices.…”

Section: Device Reliabilitymentioning

confidence: 99%

See 3 more Smart Citations

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: The Science Of Reliabilitymentioning

confidence: 99%

Section: Reliability In Computingmentioning

confidence: 99%

Section: Device Reliabilitymentioning

confidence: 99%

See 2 more Smart Citations

IoT reliability: a review leading to 5 key research directions

Moore¹,

Nugent²,

Zhang³

et al. 2020

CCF Trans. Pervasive Comp. Interact.

View full text Add to dashboard Cite

show abstract

“…For that reason, it is necessary to measure and evaluate the quality of all the produced data, so as to finally only keep the reliable data that comes from only reliable devices. In order to achieve that, as stated in our previous research in [28] for capturing the quality levels of data, it is more effective to estimate both the devices’ quality themselves, and the quality of their produced data. Thus, in this stage of the mechanism, three (3) different steps are followed, as they are illustrated in Figure 4.…”

Section: Methodsmentioning

confidence: 99%

IoT in Healthcare: Achieving Interoperability of High-Quality Data Acquired by IoT Medical Devices

Mavrogiorgou

Kiourtis

Perakis

et al. 2019

Sensors

Self Cite

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It is an undeniable fact that Internet of Things (IoT) technologies have become a milestone advancement in the digital healthcare domain, since the number of IoT medical devices is grown exponentially, and it is now anticipated that by 2020 there will be over 161 million of them connected worldwide. Therefore, in an era of continuous growth, IoT healthcare faces various challenges, such as the collection, the quality estimation, as well as the interpretation and the harmonization of the data that derive from the existing huge amounts of heterogeneous IoT medical devices. Even though various approaches have been developed so far for solving each one of these challenges, none of these proposes a holistic approach for successfully achieving data interoperability between high-quality data that derive from heterogeneous devices. For that reason, in this manuscript a mechanism is produced for effectively addressing the intersection of these challenges. Through this mechanism, initially, the collection of the different devices’ datasets occurs, followed by the cleaning of them. In sequel, the produced cleaning results are used in order to capture the levels of the overall data quality of each dataset, in combination with the measurements of the availability of each device that produced each dataset, and the reliability of it. Consequently, only the high-quality data is kept and translated into a common format, being able to be used for further utilization. The proposed mechanism is evaluated through a specific scenario, producing reliable results, achieving data interoperability of 100% accuracy, and data quality of more than 90% accuracy.

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Reliability Evaluation and Reliability Prediction for Internet of Things (IOT) Appliance

Fuqing

2024

Risk, Reliability and Safety Engineering

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Capturing the Reliability of Unknown Devices in the IoT World

Cited by 21 publications

References 23 publications

IoT reliability: a review leading to 5 key research directions

IoT reliability: a review leading to 5 key research directions

IoT in Healthcare: Achieving Interoperability of High-Quality Data Acquired by IoT Medical Devices

Reliability Evaluation and Reliability Prediction for Internet of Things (IOT) Appliance

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