Outputs (23)

A distributed sensor-fault detection and diagnosis framework using machine learning (2020)
Journal Article
Jan, S. U., Lee, Y. D., & Koo, I. S. (2021). A distributed sensor-fault detection and diagnosis framework using machine learning. Information Sciences, 547, 777-796. https://doi.org/10.1016/j.ins.2020.08.068

The objective of this work is to design a sensor-fault detection and diagnosis system for the Internet of Things and Cyber-Physical Systems. The challenge is, however, achieving this objective within the limited computation, memory, and energy resour... Read More about A distributed sensor-fault detection and diagnosis framework using machine learning.

Machine Learning-based Real-Time Sensor Drift Fault Detection using Raspberry Pi (2020)
Presentation / Conference Contribution
Saeed, U., Ullah Jan, S., Lee, Y., & Koo, I. (2020). Machine Learning-based Real-Time Sensor Drift Fault Detection using Raspberry Pi. In 2020 International Conference on Electronics, Information, and Communication (ICEIC). https://doi.org/10.1109/iceic4

From smart industries to smart cities, sensors in the modern world plays an important role by covering a large number of applications. However, sensors get faulty sometimes leading to serious outcomes in terms of safety, economic cost and reliability... Read More about Machine Learning-based Real-Time Sensor Drift Fault Detection using Raspberry Pi.

Machine Learning for Detecting Drift Fault of Sensors in Cyber-Physical Systems (2020)
Presentation / Conference Contribution
Jan, S. U., Saeed, U., & Koo, I. (2020). Machine Learning for Detecting Drift Fault of Sensors in Cyber-Physical Systems. In 2020 17th International Bhurban Conference on Applied Sciences and Technology (IBCAST). https://doi.org/10.1109/ibcast47879.2020.

Cyber-Physical System (CPS) emerges as a potential direction to improve the applications relating to object-to-object, human-to-human and human-to-object communications in both the real world and virtual world. The examples of CPSs include Smart Gird... Read More about Machine Learning for Detecting Drift Fault of Sensors in Cyber-Physical Systems.

On Lightweight Method for Intrusions Detection in the Internet of Things (2019)
Presentation / Conference Contribution
Shakhov, V., Jan, S. U., Ahmed, S., & Koo, I. (2019). On Lightweight Method for Intrusions Detection in the Internet of Things. In 2019 IEEE International Black Sea Conference on Communications and Networking (BlackSeaCom). https://doi.org/10.1109/blacks

Integration of the internet into the entities of the different domains of human society is emerging as a new paradigm called the Internet of Things. At the same time, the ubiquitous and wide-range systems make them prone to attacks. Security experts... Read More about On Lightweight Method for Intrusions Detection in the Internet of Things.

Toward a Lightweight Intrusion Detection System for the Internet of Things (2019)
Journal Article
Jan, S. U., Ahmed, S., Shakhov, V., & Koo, I. (2019). Toward a Lightweight Intrusion Detection System for the Internet of Things. IEEE Access, 7, 42450-42471. https://doi.org/10.1109/access.2019.2907965

Integration of the Internet into the entities of the different domains of human society (such as smart homes, health care, smart grids, manufacturing processes, product supply chains, and environmental monitoring) is emerging as a new paradigm called... Read More about Toward a Lightweight Intrusion Detection System for the Internet of Things.

Performance Analysis of Support Vector Machine-Based Classifier for Spectrum Sensing in Cognitive Radio Networks (2018)
Presentation / Conference Contribution
Jan, S. U., Vu, V. H., & Koo, I. S. (2018). Performance Analysis of Support Vector Machine-Based Classifier for Spectrum Sensing in Cognitive Radio Networks. In 2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery

In this work, the performance of support vector machine (SVM)-based classifier, applied for spectrum sensing in cognitive radio (CR) networks, is analyzed. A single observation given input to classifier is composed of three statistical features extra... Read More about Performance Analysis of Support Vector Machine-Based Classifier for Spectrum Sensing in Cognitive Radio Networks.

A Novel Feature Selection Scheme and a Diversified-Input SVM-Based Classifier for Sensor Fault Classification (2018)
Journal Article
Jan, S. U., & Koo, I. (2018). A Novel Feature Selection Scheme and a Diversified-Input SVM-Based Classifier for Sensor Fault Classification. Journal of Sensors, 2018, Article 7467418. https://doi.org/10.1155/2018/7467418

The efficiency of a binary support vector machine- (SVM-) based classifier depends on the combination and the number of input features extracted from raw signals. Sometimes, a combination of individual good features does not perform well in discrimin... Read More about A Novel Feature Selection Scheme and a Diversified-Input SVM-Based Classifier for Sensor Fault Classification.

Throughput Maximization Using an SVM for Multi-Class Hypothesis-Based Spectrum Sensing in Cognitive Radio (2018)
Journal Article
Jan, S. U., Vu, V., & Koo, I. (2018). Throughput Maximization Using an SVM for Multi-Class Hypothesis-Based Spectrum Sensing in Cognitive Radio. Applied Sciences, 8(3), Article 421. https://doi.org/10.3390/app8030421

A framework of spectrum sensing with a multi-class hypothesis is proposed to maximize the achievable throughput in cognitive radio networks. The energy range of a sensing signal under the hypothesis that the primary user is absent (in a conventional... Read More about Throughput Maximization Using an SVM for Multi-Class Hypothesis-Based Spectrum Sensing in Cognitive Radio.

Sensor faults detection and classification using SVM with diverse features (2017)
Presentation / Conference Contribution
Jan, S. U., & Koo, I. S. (2017). Sensor faults detection and classification using SVM with diverse features. In 2017 International Conference on Information and Communication Technology Convergence (ICTC). https://doi.org/10.1109/ictc.2017.8191044

Sensors in industrial systems fault frequently leading to serious consequences regarding cost and safety. The authors propose support vector machine-based classifier with diverse time- and frequency-domain feature models to detect and classify these... Read More about Sensor faults detection and classification using SVM with diverse features.

Sensor Fault Classification Based on Support Vector Machine and Statistical Time-Domain Features (2017)
Journal Article
Jan, S. U., Lee, Y., Shin, J., & Koo, I. (2017). Sensor Fault Classification Based on Support Vector Machine and Statistical Time-Domain Features. IEEE Access, 5, 8682-8690. https://doi.org/10.1109/access.2017.2705644

This paper deals with the problem of fault detection and diagnosis in sensors considering erratic, drift, hard-over, spike, and stuck faults. The data set containing samples of the above mentioned fault signals was acquired as follows: normal data si... Read More about Sensor Fault Classification Based on Support Vector Machine and Statistical Time-Domain Features.