Wireless Sensor Network Modeling Using Modified Recurrent Neural Networks: Application to Fault Detection

Moustapha, A.I.; Šelmić, Rastko R.

doi:10.1109/tim.2007.913803

Cited by 132 publications

(43 citation statements)

References 20 publications

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“…In this subsection, the major supervised learning algorithms are discussed in the context of WSNs. In fact, supervised learning algorithms are extensively used to solve several challenges in WSNs such as localization and objects targeting (e.g., [21], [22], [23]), event detection and query processing (e.g., [24], [25], [26], [27]), media access control (e.g., [28], [29], [30]), security and intrusion detection (e.g., [31], [32], [33], [34]), and quality of service (QoS), data integrity and fault detection (e.g., [35], [36], [37]).…”

Section: A Supervised Learningmentioning

confidence: 99%

See 1 more Smart Citation

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Alsheikh

Lin

Niyato

et al. 2014

IEEE Commun. Surv. Tutorials

783

406

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Abstract-Wireless sensor networks monitor dynamic environments that change rapidly over time. This dynamic behavior is either caused by external factors or initiated by the system designers themselves. To adapt to such conditions, sensor networks often adopt machine learning techniques to eliminate the need for unnecessary redesign. Machine learning also inspires many practical solutions that maximize resource utilization and prolong the lifespan of the network. In this paper, we present an extensive literature review over the period 2002-2013 of machine learning methods that were used to address common issues in wireless sensor networks (WSNs). The advantages and disadvantages of each proposed algorithm are evaluated against the corresponding problem. We also provide a comparative guide to aid WSN designers in developing suitable machine learning solutions for their specific application challenges.

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Section: A Supervised Learningmentioning

confidence: 99%

“…Moustapha and Selmic [36] introduced a dynamic fault detection model for WSNs. This model captures the nodes' dynamic behavior and their effects on other nodes.…”

Section: B Quality Of Service Data Integrity and Fault Detectionmentioning

confidence: 99%

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Alsheikh

Lin

Niyato

et al. 2014

IEEE Commun. Surv. Tutorials

783

406

View full text Add to dashboard Cite

show abstract

“…Moustapha et al [230] RNN Applied RNN for fault detection. RNN, which is deployed in each sensor node, takes inputs from neighboring nodes, and generates outputs for comparison with the generated data; if the difference exceeds a certain threshold, the node is regarded as anomalous.…”

Section: Cellular Networkmentioning

confidence: 99%

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

et al. 2019

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While machine learning and artificial intelligence have long been applied in networking research, the bulk of such works has focused on supervised learning. Recently there has been a rising trend of employing unsupervised machine learning using unstructured raw network data to improve network performance and provide services such as traffic engineering, anomaly detection, Internet traffic classification, and quality of service optimization. The interest in applying unsupervised learning techniques in networking emerges from their great success in other fields such as computer vision, natural language processing, speech recognition, and optimal control (e.g., for developing autonomous self-driving cars). Unsupervised learning is interesting since it can unconstrain us from the need of labeled data and manual handcrafted feature engineering thereby facilitating flexible, general, and automated methods of machine learning. The focus of this survey paper is to provide an overview of the applications of unsupervised learning in the domain of networking. We provide a comprehensive survey highlighting the recent advancements in unsupervised learning techniques and describe their applications for various learning tasks in the context of networking. We also provide a discussion on future directions and open research issues, while also identifying potential pitfalls. While a few survey papers focusing on the applications of machine learning in networking have previously been published, a survey of similar scope and breadth is missing in literature. Through this paper, we advance the state of knowledge by carefully synthesizing the insights from these survey papers while also providing contemporary coverage of recent advances.

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“…Elhadef and Nayak 28 proposed a neural network based system level fault diagnosis using simple and generalized comparison models for wireless interconnected networks. Azzam et al 29 proposed a fault detection for WSNs by dynamic modeling of sensor nodes using modified recurrent neural network model. Swain and Khilar 9 proposed fault diagnosis protocols for permanent, intermittent, and transient faults (composite faults) in WSNs using neural network approaches.…”

Section: Types and Effects Of Sensor Node Faultsmentioning

confidence: 99%

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

Swain

Khilar

Dash

2018

Int J Communication

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Due to the wide range of critical applications and resource constraints, sensor node gives unexpected responses, which leads to various kind of faults in sensor node and failure in wireless sensor networks. Many research studies focus only on fault diagnosis, and comparatively limited studies have been conducted on fault diagnosis along with fault tolerance in sensor networks. This paper reports a complete study on both 2 aspects and presents a fault tolerance approach using regressional learning with fault diagnosis in wireless sensor networks. The proposed method diagnose the different types of faulty nodes such as hard permanent, soft permanent, intermittent, and transient faults with better detection accuracy. The proposed method follows a fault tolerance phase where faulty sensor node values would be predicted by using the data sensed by the fault free neighbors. The experimental evaluation of the fault tolerance module shows promising results with R 2 of more than 0.99. For the periodic fault such as intermittent fault, the proposed method also predict the possible occurrence time and its duration of the faulty node, so that fault tolerance can be achieved at that particular time period for better performance of the network.

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Wireless Sensor Network Modeling Using Modified Recurrent Neural Networks: Application to Fault Detection

Cited by 132 publications

References 20 publications

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Machine Learning in Wireless Sensor Networks: Algorithms, Strategies, and Applications

Unsupervised Machine Learning for Networking: Techniques, Applications and Research Challenges

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

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