Anomaly Detection and Fault Diagnosis in Sensor Networks

Dear Colleagues,

Sensor networks play a crucial role in monitoring and collecting data across various applications, from industrial processes to environmental monitoring. Ensuring the reliability and efficiency of sensor networks is crucial, as anomalies or faults can lead to erroneous data, system failures, or compromised performances. At the same time, sensor networks can, in turn, become tools for the efficient and effective detection of anomalies in other industrial systems.

Innovative methodologies leverage advanced machine learning algorithms, statistical analysis and distributed computing to identify abnormal patterns and diagnose faults in both real-time and off-line applications. The focus is on developing robust models capable of adapting to dynamic environmental conditions, varying network topologies and diverse sensor types.

This Special Issue aims to collect high-quality research papers and review articles focusing on recent advances, technologies, solutions, applications and new challenges in the field of anomaly detection and fault diagnosis techniques tailored for sensor networks.

Potential topics include, but are not limited to, the following:

• Anomaly detection algorithms in sensor networks;
• Fault diagnosis in sensor networks;
• Sensor networks as a tool for anomaly detection and fault diagnosis;
• Machine learning algorithms for anomaly detection in sensor networks;
• Artificial intelligence methods for improving accuracy in anomaly detection;
• Bayesian methods, statistical analysis and machine learning for identifying abnormal patterns in sensor data;
• Distributed algorithms and protocols for real-time anomaly detection in large-scale sensor networks;
• Adaptive fault diagnosis;
• Energy-efficient anomaly detection and fault diagnosis in wireless sensor networks;
• Uncertainty evaluation in anomaly detection algorithms and models;
• Diagnostic and prognostic models using sensor networks;
• Fault-tolerant sensor networks;
• In-sensor machine learning computing;
• Security aspects in anomaly detection.

Dr. Gabriele Patrizi
Dr. Marco Carratù
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sensors is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• anomaly detection
• fault diagnosis
• sensor networks
• artificial intelligence
• machine learning
• instrument fault detection
• statistical analysis and Bayesian methods