Fault diagnosis and its prediction in wireless sensor networks using regressional learning to achieve fault tolerance

Swain, Rakesh Ranjan; Khilar, Pabitra Mohan; Dash, Tirtharaj

doi:10.1002/dac.3769

Cited by 24 publications

(10 citation statements)

References 27 publications

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“…Various authors have proposed numerous methods to diagnose the faults and classify them accordingly. The authors proposed a fault diagnosis with a fault tolerance approach using regressional learning in WSN 46 . The proposed approach diagnoses hard and soft permanent faults and also permanent and transient faults by comparing the values with the fault‐free neighboring nodes.…”

Section: Classification Of Node Faults In Wsnmentioning

confidence: 99%

Comprehensive analysis on sensor node fault management schemes in wireless sensor networks

Babu

Kumar

2022

Int J Communication

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Wireless sensor networks (WSNs) are ad hoc networks that consist of tiny sensor nodes deployed in the sensed environment to monitor the natural phenomenon and send the monitored data to the base station for further processing. Due to the unfriendly deployment of the sensor nodes and their resource-constrained nature, the sensor nodes are subjected to frequent node failure. Detecting sensor node failures and recovering them is a major challenge in WSN. Due to the failure of the nodes in WSN, there exist various problems such as a change in the network topology, communication link failure, a disjoint partition of the network, and failure to transmit the data to the sink. Various authors have proposed various techniques to detect and recover from node failure in the WSN. In this paper, a comprehensive analysis of the various node failure and node recovery techniques has been carried out to highlight the advantages and limitations along with the open research challenges for addressing the failure of the nodes in an effective manner.

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Section: Classification Of Node Faults In Wsnmentioning

confidence: 99%

Comprehensive analysis on sensor node fault management schemes in wireless sensor networks

Babu

Kumar

2022

Int J Communication

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“…Such an approach was introduced in [27] for detecting various kinds of faults in sensor networks. Recently, the authors have also attempted to use regressional machine learning to make a sensor network tolerant to occurrences of faults [28].…”

Section: Related Workmentioning

confidence: 99%

Lightweight approach to automated fault diagnosis in WSNs

2020

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Automated fault diagnosis in wireless sensor networks (WSNs) has always been an essential field for the networks research community. In resource-related constraints, the design of a lightweight method to automatically diagnose the fault in WSN is highly beneficial. In this study, the authors propose lightweight and less-overhead approaches to diagnose hard and soft faults in WSNs automatically. Correctly, a lightweight checksum method is implemented for hard or crash fault detection. This method is capable of detecting multiple hard faults within a single path with the help of a timeout mechanism. For diagnosis of soft faults such as permanent, intermittent, and transient faults, they implement the Anderson-Darling (AD) statistical method. The AD test analyses how the sensor readings are fitted in a specific distribution for a tested significance level. Several testbed experiments are conducted to validate their hypotheses and implementations. The performance parameters that are evaluated in this work include fault detection accuracy, false alarm rate, and false-positive rate. These parameters are evaluated about various fault probabilities in the network. They observe that the proposed lightweight schemes can diagnose both hard and soft faults in O(1) message complexity over the network, which makes it adaptable in practise.

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“…This is useful in industries as it ensures the prediction of a machine failure before it actually occurs . This is basically driven by the condition monitoring together with possible automatic maintenance utilizing advanced algorithms . This is a crucial challenge because the unexpected shutdown of machines or equipment lead to unscheduled downtime, which incurs huge losses.…”

Section: Iwsan Applications and Requirementsmentioning

confidence: 99%

“…70,71 This is basically driven by the condition monitoring together with possible automatic maintenance utilizing advanced algorithms. 72 This is a crucial challenge because the unexpected shutdown of machines or equipment lead to unscheduled downtime, which incurs huge losses. Therefore, a reliable sensor and actuator network is required to precisely predict the machine failure.…”

Section: Iwsan Applicationsmentioning

confidence: 99%

Industrial wireless sensor and actuator networks in industry 4.0: Exploring requirements, protocols, and challenges—A MAC survey

Raza

Faheem

Guenes

2019

Int J Communication

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Summary The vision to connect everyday physical objects to the Internet promises to create the Internet of Things (IoT), which is expected to integrate the diverse technologies such as sensors, actuators, radio frequency identification, communication technologies, and Internet protocols. Thus, IoT promises to transfer traditional industry to advance digital industry known as the Industry 4.0. At the core of the Industry 4.0 are the wireless sensor networks (WSNs) and wireless sensor and actuator networks (WSANs) that led to the development of industrial wireless sensor networks (IWSNs) and industrial wireless sensor and actuator networks (IWSANs). These networks play a central role of connecting machines, parts, products, and humans and create a diverse set of new applications to support intelligent and autonomous decision making. The IWSAN is a promising technology for numerous industrial applications because of their several potential benefits such as simple deployment, low cost, less complexity, and mobility support. However, despite such benefits, they impose several unique challenges at different layers of the protocol stack when deploying them for various monitoring and control applications in the Industry 4.0. In this article, we explore IWSAN, its applications, requirements, challenges, and solutions in the context of industrial control applications. Our main focus is on the medium access control (MAC) layer that can be exploited to satisfy such requirements. Our discussion presents extensive background study of the MAC schemes and it reviews the MAC protocols of the existing wireless standards and technologies. A number of application‐specific MAC protocols developed to support industrial applications, which are not part of these standards, are also elaborated. We rationalize to what extent the existing standards and protocols help in solving such requirements as laid down by the Industry 4.0. In the end, we emphasize on existing challenges and present important future directions.

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Fault diagnosis and its prediction in wireless sensor networks using regressional learning to achieve fault tolerance

Cited by 24 publications

References 27 publications

Comprehensive analysis on sensor node fault management schemes in wireless sensor networks

Comprehensive analysis on sensor node fault management schemes in wireless sensor networks

Lightweight approach to automated fault diagnosis in WSNs

Industrial wireless sensor and actuator networks in industry 4.0: Exploring requirements, protocols, and challenges—A MAC survey

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