All Outputs (4)

Fault diagnosis based on extremely randomized trees in wireless sensor networks (2020)
Journal Article
Saeed, U., Jan, S. U., Lee, Y., & Koo, I. (2021). Fault diagnosis based on extremely randomized trees in wireless sensor networks. Reliability Engineering and System Safety, 205, Article 107284. https://doi.org/10.1016/j.ress.2020.107284

Wireless Sensor Network (WSN) being highly diversified cyber–physical system makes it vulnerable to numerous failures, which can cause devastation towards safety, economy, and systems’ reliability. Precise detection and diagnosis of failures or fault... Read More about Fault diagnosis based on extremely randomized trees in wireless sensor networks.

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, January). Machine Learning-based Real-Time Sensor Drift Fault Detection using Raspberry Pi. Presented at 2020 International Conference on Electronics, Information, and Communication (ICEIC), Barcelona, Spain

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, January). Machine Learning for Detecting Drift Fault of Sensors in Cyber-Physical Systems. Presented at 2020 17th International Bhurban Conference on Applied Sciences and Technology (IBCAST), Islamabad, Pakistan

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.