/pdf/artificial-intelligence-for-the-metaverse-a-survey-3tfftt9g.pdf

Artificial Intelligence for the Metaverse: A Survey

Sensor technologies empower Industry 4.0 by enabling integration of in-field and real-time raw data into digital twins. However, sensors might be unreliable due to inherent issues and/or environmental conditions. This article aims at detecting anomalies in measurements from sensors, identifying the faulty ones and accommodating them with appropriate estimated data, thus paving the way to reliable digital twins. More specifically, we propose a general machine-learning-based architecture for sensor validation built upon a series of neural-network estimators and a classifier. Estimators correspond to virtual sensors of all unreliable sensors (to reconstruct normal behaviour and replace the isolated faulty sensor within the system), whereas the classifier is used for detection and isolation tasks. A comprehensive statistical analysis on three different real-world data-sets is conducted and the performance of the proposed architecture validated under hard and soft synthetically-generated faults.

Sensor-Fault Detection, Isolation and Accommodation for Digital Twins via Modular Data-Driven Architecture

Artificial intelligence for industry 4.0: Systematic review of applications, challenges, and opportunities

In this article, we present a comprehensive study with an experimental analysis of federated deep learning approaches for cyber security in the Internet of Things (IoT) applications. Specifically, we first provide a review of the federated learning-based security and privacy systems for several types of IoT applications, including, Industrial IoT, Edge Computing, Internet of Drones, Internet of Healthcare Things, Internet of Vehicles, etc. Second, the use of federated learning with blockchain and malware/intrusion detection systems for IoT applications is discussed. Then, we review the vulnerabilities in federated learning-based security and privacy systems. Finally, we provide an experimental analysis of federated deep learning with three deep learning approaches, namely, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Deep Neural Network (DNN). For each deep learning model, we study the performance of centralized and federated learning under three new real IoT traffic datasets, namely, the Bot-IoT dataset, the MQTTset dataset, and the TON_IoT dataset. The goal of this article is to provide important information on federated deep learning approaches with emerging technologies for cyber security. In addition, it demonstrates that federated deep learning approaches outperform the classic/centralized versions of machine learning (non-federated learning) in assuring the privacy of IoT device data and provide the higher accuracy in detecting attacks.

/pdf/federated-deep-learning-for-cyber-security-in-the-internet-1bzlkr7brz.pdf

Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis

Multi-sensor filtering fusion (MSFF) is a fascinating subject in the realm of networked filtering due to its advantage of effectively integrating sensor outputs from multiple sources. Owing to the ...

Multi-sensor filtering fusion meets censored measurements under a constrained network environment: advances, challenges and prospects

In this paper, we propose a novel sensor validation architecture, which performs sensor fault detection, isolation and accommodation (SFDIA). More specifically, a machine-learning based architecture is presented to detect faults in sensors measurements within the system, identify the faulty ones and replace them with estimated values. In our proposed architecture, sensor estimators based on neural networks are constructed for each sensor node in order to accommodate faulty measurements along with a classifier to determine the failure detection and isolation. Finally, numerical results are presented to confirm the effectiveness of the proposed architecture on a publicly-available air quality (AQ) chemical multi-sensor data-set.

/pdf/a-data-driven-architecture-for-sensor-validation-based-on-2z35tx6xf4.pdf

A Data-Driven Architecture for Sensor Validation Based on Neural Networks

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Sensor-Fault Detection, Isolation and Accommodation for Digital Twins via Modular Data-Driven Architecture

A Data-Driven Architecture for Sensor Validation Based on Neural Networks