Sensor-Driven Anomaly Detection Using Deep Learning Techniques
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: 25 September 2026 | Viewed by 7
Special Issue Editor
Interests: convolutional neural network; generative adversarial networks; sensing; imaging; 3D point cloud; machine/deep learning; computer vision; medical image analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The integration of sensor technologies with deep learning has revolutionized intelligent monitoring and automated decision-making across diverse domains such as healthcare, industrial automation, smart cities, and environmental systems. Sensors, including inertial measurement units (IMUs) and wearables, radar and mmWave devices, LiDAR and 3D scanners, hyperspectral imagers, acoustics arrays, and biosensors continuously generate large volumes of complex, real-time data. These sensing modalities are often deployed on edge or IoT platforms that impose constraints related to latency, energy consumption, and computational resources.
Despite these advances, accurately detecting anomalies remains challenging due to sensor-specific issues such as inherent noise, high dimensionality, label scarcity for abnormal events, streaming data dynamics, and concept drift over time. Additionally, robustness to sensor faults, out-of-distribution (OOD) scenarios, and the need for uncertainty quantification and explainability are critical, especially in safety-critical applications.
This Special Issue highlights cutting-edge deep learning approaches that automatically extract meaningful patterns, model complex temporal and spatial dependencies, and detect deviations in sensor data without relying heavily on labeled datasets. We welcome high-quality submissions presenting novel methods spanning supervised, unsupervised, and semi-supervised learning, multimodal sensor fusion, synthetic data generation, and real-time deployment strategies. Emphasis will be placed on experimental rigor, including validation on real sensor data or physical testbeds, comparison against strong baselines, use of public datasets when applicable, and promotion of reproducibility through code and data availability.
Applications of interest include, but are not limited to, biomedical sensing, industrial IoT, environmental monitoring, smart infrastructure, and autonomous systems. By addressing these challenges, the Special Issue aims to advance sensor-driven anomaly detection methods that are robust, interpretable, and practical for deployment in real-world, dynamic environments.
Dr. Naveed Ilyas
Guest Editor
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Keywords
- sensors
- anomaly detection
- deep learning
- sensor fusion
- unsupervised learning
- industrial iot
- biomedical sensors
- real-time monitoring
- temporal modeling
- healthcare
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