Fault Diagnosis in Sensor Network-Based Systems
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensor Networks".
Deadline for manuscript submissions: 31 December 2024 | Viewed by 508
Special Issue Editors
Interests: fault diagnosis; safety analysis; data-driven models; cyber-physical system safety; risk assessment; machine learning
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning applications in process safety and security; AI safety; human–AI interaction
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The rapid growth in sensor networks has significantly impacted various industries, including healthcare, transportation, agriculture, chemical plants, and manufacturing. These systems rely heavily on the accuracy and reliability of sensor data to make informed decisions and optimize processes. However, the presence of faults and anomalies in a sensor network can compromise the overall performance of a system and lead to undesirable consequences. As such, the ability to diagnose and handle faults effectively becomes crucial for the reliable operation of such systems.
This Special Issue, titled "Fault Diagnosis in Sensor Network-Based Systems", aims to bring together cutting-edge research, methodologies, and applications related to fault diagnosis, detection, and isolation in sensor networks. We invite high-quality, original research articles that address the challenges, limitations, and advancements in this crucial area of study.
Topics of interest include, but are not limited to, the following:
- Advanced fault diagnosis techniques for sensor networks;
- Fault-tolerant sensor network design and optimization;
- Anomaly detection and isolation in industrial systems;
- Cybersecurity and protection of sensor networks from malicious attacks.
Through this Special Issue, we aim to foster interdisciplinary collaboration and highlight the latest developments in fault diagnosis for sensor network-based systems. The insights gained will contribute to improvements in the reliability of sensor networks, ultimately benefiting a wide range of applications and industries.
This Special Issue aims to collect both original reviews and technical research articles that take theoretical or application-based approaches. It will showcase works from a variety of engineering disciplines, including machine learning-model-based works using sensor data.
Dr. Md Tanjin Amin
Dr. Rajeevan Arunthavanathan
Guest Editors
Manuscript Submission Information
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Keywords
- sensor networks
- abnormal situation management
- fault diagnosis
- condition monitoring
- artificial intelligence
- industrial Internet of Things
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