Advances in Sensor-Based Lightweight and Adaptive Time Series Anomaly Detection
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Physical Sensors".
Deadline for manuscript submissions: 25 July 2024 | Viewed by 191
Special Issue Editors
Interests: time series analysis; Internet of Things; parallel and distributed computing; cloud computing; machine learning; security; privacy
Interests: time series; anomaly detection; machine learning; artificial intelligence; cloud computing; big data analytics; and high-performance computing
Special Issue Information
Dear Colleagues,
In an era marked by the exponential growth of time series data across diverse domains, the need for robust, efficient, and adaptable anomaly detection methods has never been greater. This Special Issue, titled "Advances in Sensor-Based Lightweight and Adaptive Time Series Anomaly Detection", seeks to showcase cutting-edge research and innovations in the realm of time series data analysis. With a specific focus on the twin pillars of "lightweight" techniques that minimize computational overhead and "adaptive" approaches capable of real-time adjustment, this Special Issue invites contributions that explore novel solutions to the challenges posed by time series anomaly detection.
This Special Issue serves as a platform for the dissemination of research that pushes the boundaries of time series anomaly detection, bridging the gap between theoretical advancements and practical implementation. As we navigate an ever-evolving data landscape, the pursuit of lightweight, adaptive solutions is paramount to meeting the demands of the digital age, especially in the domains of IoT, electronic sensors, and robotics. We welcome submissions that contribute to this endeavor and anticipate publishing a collection of articles that will significantly impact the field of time series anomaly detection, enhancing the effectiveness and reliability of IoT wearable sensors, electronics, and robotics in real-world applications.
Dr. Jia-Chun Lin
Dr. Ming-Chang Lee
Guest Editors
Manuscript Submission Information
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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.
Keywords
- time seies data
- anomaly detection
- lightweight algorithms
- adaptive models
- real-time detection
- resource-efficient methods
- comparative evaluation
- IoT
- sensors technology
