A comprehensive wireless sensor network reliability metric for critical Internet of Things applications

Deif, Dina S.; Gadallah, Yasser

doi:10.1186/s13638-017-0930-3

Cited by 38 publications

(20 citation statements)

References 20 publications

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“…The authors assessed reliability for different WSN routing protocols while considering the battery level as the key reliability metric. The authors of [ 20 ] used a combinatorial approach to compute the reliability of a WSN for more realistic scenarios of failures. In their work, they used more reliability metrics: processor, sensor, transceiver and battery failures.…”

Section: Related Workmentioning

confidence: 99%

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An End-to-End Reliability Framework of the Internet of Things

Azghiou

Mouhib

Koulali

et al. 2020

Sensors

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The Internet of Things (IoT) paradigm feeds from many scientific and engineering fields. This involves a diversity and heterogeneity of its underlying systems. When considering End-to-End IoT systems, we can identify the emergence of new classes of problems. The best-known ones are those associated to standardization for better interoperability and compatibility of those systems, and those who gave birth of new paradigms like that of Fog Computing. Predicting the reliability of an End-to-End IoT system is a problem belonging to this category. On one hand, predicting reliability can be mandatory, most times, before the deployment stage. On another hand, it may help engineers at the design and the operational stages to establish effective maintenance policies and may provide the various stakeholders and decision-makers a means to take the relevant actions. We can find in the literature works which consider only fragments of End-to-End IoT systems such as those assessing reliability for Wireless Sensors Networks (WSN) or Cloud subsystems, to cite just a few. Some other works are specific to well-defined industries, like those targeting reliability study of E-health and Smart-Grid infrastructures. Works that aims to assess reliability for an End-to-End IoT system are remarkably rare and particularly restrained in terms of expressiveness, flexibility, and in their implementation time complexity. In this paper, we apply the Reliability Block Diagram (RBD) paradigm to set up a framework for End-to-End IoT system reliability modeling and analysis. Our contribution is four-fold: we propose an IoT network-based layered architecture, we model in depth each layer of the proposed architecture, we suggest a flow chart to deploy the proposed framework, and we perform a numerical investigation of simplified scenarios. We affirm that the proposed framework is expressive, flexible, and scalable. The numerical study reveals mission time intervals which characterize the behavior of an IoT system from the point of view of its reliability.

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Section: Related Workmentioning

confidence: 99%

“…The authors in [ 19 , 20 ] investigated an aspect of the perception layer according to our suggested architecture. Although in [ 20 ] we can observe more realistic assumptions, both of the works provided solutions that are context dependent.…”

Section: Related Workmentioning

confidence: 99%

An End-to-End Reliability Framework of the Internet of Things

Azghiou

Mouhib

Koulali

et al. 2020

Sensors

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“…Certainly, the evaluation of sensor reliability is a highly important task, especially for Internet of Things (IoT) applications, and new performance indices need to be proposed [54]. Some recommendations for the standardized evaluation of sensor reliability are given in [55].…”

Section: Reliability Of Sensors In Cyber-physicals Systems a Brief Smentioning

confidence: 99%

Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study

et al. 2019

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Nowadays, reliability of sensors is one of the most important challenges for widespread application of Internet-of-things data in key emerging fields such as the automotive and manufacturing sectors. This paper presents a brief review of the main research and innovation actions at the European level, as well as some on-going research related to sensor reliability in cyber-physical systems (CPS). The research reported in this paper is also focused on the design of a procedure for evaluating the reliability of Internet-of-Things sensors in a cyber-physical system. The results of a case study of sensor reliability assessment in an autonomous driving scenario for the automotive sector are also shown. A co-simulation framework is designed in order to enable real-time interaction between virtual and real sensors. The case study consists of an IoT LiDAR-based collaborative map in order to assess the CPS-based co-simulation framework. Specifically, the sensor chosen is the Ibeo Lux 4-layer LiDAR sensor with IoT added capabilities. The modeling library for predicting error with machine learning methods is implemented at a local level, and a self-learning-procedure for decision-making based on Q-learning runs at a global level. The study supporting the experimental evaluation of the co-simulation framework is presented using simulated and real data. The results demonstrate the effectiveness of the proposed method for increasing sensor reliability in cyber-physical systems using Internet-of-Things data.

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“…The camera networks, as a kind of wireless sensor networks, have received considerable attention due to the potential value for the practical applications [1][2][3][4]. One of the most important applications of camera networks is person re-identification (re-ID) which is an issue of searching the same person from one camera sensor across different camera sensors with a probe image.…”

Section: Introductionmentioning

confidence: 99%

“…On one hand, a lot of efforts expect to develop discriminative feature representations that are robust to the changes in views, pose, and illumination. Gray and Tao [5] proposed the Ensemble of Localized Features (ELF), each of *Correspondence: zhong.zhang8848@gmail.com 1 Tianjin Key Laboratory of Wireless Mobile Communications and Power Transmission, Tianjin Normal University, Tianjin, China 2 College of Electronic and Communication Engineering, Tianjin Normal University, Tianjin, China which consisting of a feature channel, location, and binning information, to overcome the viewpoint changes. Bazzani et al [6] presented the Symmetry-Driven Accumulation of Local Features (SDALFs) which considers symmetry and asymmetry perceptual principles to handle the environmental variances.…”

Section: Introductionmentioning

confidence: 99%

Learning deep features from body and parts for person re-identification in camera networks

Zhang

2018

J Wireless Com Network

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In this paper, we propose to learn deep features from body and parts (DFBP) in camera networks which combine the advantages of part-based and body-based features. Specifically, we utilize subregion pairs to train the part-based feature learning model and predict whether they belong to positive subregion pairs. Meanwhile, we utilize holistic pedestrian images to train body-based feature learning model and predict the identities of the input images. In order to further improve the discrimination of features, we concatenate the part-based and body-based features to form the final pedestrian representation. We evaluate the proposed DFBP on two large-scale databases, i.e., Market1501 database and CUHK03 database. The results demonstrate that the proposed DFBP outperforms the state-of-the-art methods.

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A comprehensive wireless sensor network reliability metric for critical Internet of Things applications

Cited by 38 publications

References 20 publications

An End-to-End Reliability Framework of the Internet of Things

An End-to-End Reliability Framework of the Internet of Things

Sensor Reliability in Cyber-Physical Systems Using Internet-of-Things Data: A Review and Case Study

Learning deep features from body and parts for person re-identification in camera networks

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