Data-Driven Methods for Smart and Adaptive Sensor Systems
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Intelligent Sensors".
Deadline for manuscript submissions: 15 August 2026 | Viewed by 300
Special Issue Editor
Interests: machine learning; NLP; knowledge discovery; data nining; blockchain
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advances in sensing technologies have led to an unprecedented growth in the volume, variety and velocity of sensor data generated across diverse application domains, including smart cities, healthcare, environmental monitoring, industrial automation and cyber–physical systems. Traditional model-based and rule-driven approaches are increasingly insufficient to handle the complexity, heterogeneity and dynamic nature of modern sensor systems. As a result, data-driven methods, particularly those based on artificial intelligence and machine learning, have emerged as key enablers for building smart, adaptive and autonomous sensor systems.
This Special Issue aims to provide a comprehensive forum for novel research on data-driven techniques for sensor data acquisition, processing, interpretation and decision-making. Topics of interest include machine-learning and deep-learning models for sensor data analytics, adaptive sensing strategies, sensor fusion, edge and distributed intelligence, real-time processing and self-learning sensor systems. Emphasis is placed on methods that enhance adaptability, robustness, efficiency and intelligence in sensor-based systems, as well as on practical implementations validated with real-world sensor data.
By bringing together contributions from both academia and industry, this Special Issue seeks to highlight recent methodological advances, system architecture and application-driven studies that demonstrate how data-driven approaches can transform conventional sensors into intelligent sensing systems capable of learning from data, adapting to changing environments and supporting reliable decision-making.
Dr. Zeinab Shahbazi
Guest Editor
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Keywords
- data-driven sensor systems
- machine learning for sensors
- adaptive sensing and control
- intelligent sensor data analytics
- deep learning for sensor applications
- sensor data fusion
- edge intelligence for sensors
- real-time sensor data processing
- self-learning and adaptive sensor systems
- smart sensors and IoT Analytics
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