Reliability of Sensor Nodes in Wireless Sensor Networks of Cyber Physical Systems

Kabashkin, Igor; Kundler, Jrg

doi:10.1016/j.procs.2017.01.149

Cited by 30 publications

(14 citation statements)

References 14 publications

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“…Scheduling policies formulated as a risk-sensitive Markov Decision Process were considered in an asymptotic approach to reliability [49]. Likewise, the Markov model for the reliability analysis of sensor nodes in wireless sensor networks was proposed in [3]. Similarly, the use of the stochastic optimization problem to maximize estimation reliability in water distribution networks was suggested in [50].…”

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

confidence: 99%

“…Sensors are driving the rapid growth of Cyber-Physical Systems (CPSs) and the Internet of Things (IoT) [2]. Both paradigms are pushing towards the next generation of sensor networks and unpredictable future applications, meaning that sensor reliability has become one of the most important and desirable performance indicators in the design, implementation, and deployment of future sensor networks [3,4].…”

Section: Introductionmentioning

confidence: 99%

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

confidence: 99%

Section: Introductionmentioning

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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“…Wireless sensor networks (WSNs) have been extensively used as devices for information collection and decision making. These capabilities have expanded the scope of applications of WSNs in many fields including Internet of Things (IoT), healthcare, search and rescue where sensor nodes are deployed at remote locations and under extreme conditions [1]. Power-consumption is one of the important challenges that restrict the efficiency of WSN since sensor nodes are battery operated.…”

Section: Introductionmentioning

confidence: 99%

Design and Development of BTI Model and 3D InGaAs HEMT-Based SRAM for Reliable and Secure Internet of Things Application

2020

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It is broadly accepted that the silicon-based CMOS has touched its scaling limits and alternative substrate materials are needed for future technology nodes. An Indium-Gallium-Arsenide ( I n G a A s )-based device is well situated for further technology nodes. This material also has better mobility of the electrons and holes for the high performance and real-time system design. The improved mobility helps to increase the operating frequency of the device which is useful for Internet of Things (IoT) applications. However, I n G a A s -based High Electron Mobility Transistors (HEMT) limits the reliability of the device due to the presence of dangling bonds at the channel–gate insulator interfaces. Weak dangling-bonds get broken under electric stress, and positive hydrogen atoms are trapped into the oxide. This charge trapping depends on the material parameters and device geometry. In this paper, the existing Bias-Temperature-Instability (BTI) model is modified based on the material parameters and device geometry. Charge trapping and annealing constants are the most critical BTI model parameters that are modeled and evaluated based on different HEMT material parameters. The proposed model was compared to experimental and TCAD simulation results. The proposed model has been used for lifetime prediction of the InGaAs HEMT-based Static Random-Access Memory (SRAM) cell because it is used to store and process the information in the IoT applications.

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“…The reliability of CPS sensors is influenced by many aspects such as environmental factors, component failure, task change, and network update. Igor et al [5] addressed the reliability of sensor nodes in wireless sensor networks of CPS. They used the Markov chain state transition diagram to model the behavior of sensors.…”

Section: Introductionmentioning

confidence: 99%

Failure Detection and Prevention for Cyber-Physical Systems Using Ontology-Based Knowledge Base

Ali

Hong

2018

Computers

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Cyber-physical systems have emerged as a new engineering paradigm, which combine the cyber and physical world with comprehensive computational and analytical tools to solve complex tasks. In cyber-physical systems, components are developed to detect failures, prevent failures, or mitigate the failures of a system. Sensors gather real-time data as an input to the system for further processing. Therefore, the whole cyber-physical system depends on sensors to accomplish their tasks and the failure of one sensor may lead to the failure of the whole system. To address this issue, we present an approach that utilizes the Failure Modes, Effects, and Criticality Analysis, which is a prominent hazard analysis technique to increase the understanding of risk and failure prevention. In our approach, we transform the Failure Modes, Effects, and Criticality Analysis model into a UML(Unified Modeling Language) class diagram, and then a knowledge base is constructed based on the derived UML class diagram. Finally, the UML class diagram is used to build an ontology. The proposed approach employs a 5C architecture for smart industries for its systematic application. Lastly, we use a smart home case study to validate our approach.

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Reliability of Sensor Nodes in Wireless Sensor Networks of Cyber Physical Systems

Cited by 30 publications

References 14 publications

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

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

Design and Development of BTI Model and 3D InGaAs HEMT-Based SRAM for Reliable and Secure Internet of Things Application

Failure Detection and Prevention for Cyber-Physical Systems Using Ontology-Based Knowledge Base

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