Geo-Distributed Big Data Analytics in Sensor Networks

Dear Colleagues,

The rising availability of geodistributed data streams from sensor networks presents great opportunities for the adoption of data analytics methods to extract valuable knowledge in a variety of real-world domains and applications. However, this type of data presents multiple challenges, including: i) missing values, due to possible sensor faults and communication issues; ii) outliers and anomalies, due to measurement errors; iii) temporal and spatial correlation and autocorrelation phenomena; iv) different (and asynchronous) time granularities. Specifically, the spatial proximity of geodistributed nodes may require the adoption of specific techniques (e.g., feature extraction and embedding methods, graph-based modeling techniques) to leverage the spatial autocorrelation induced by their proximity and to obtain high-quality models, for both descriptive and predictive tasks. Finally, data generated in sensor networks present a time-evolving nature, which requires methods and models that are capable of detecting and handling concept drift phenomena, and to dynamically adapt to changes in the observed data distribution.

This Special Issue will publish original research, reviews, and applications of methods for Geo-Distributed Big Data Analytics in Sensor Networks. The Special Issue areas of interest include but are not limited to the following topics:

• Clustering and summarization for streams of sensor data;
• Spatiotemporal data mining and knowledge discovery from streams of sensor data;
• Preprocessing techniques for streams of sensor data;
• Predictive modeling and forecasting on time series data;
• Anomaly detection and data repair methods for streams of sensor data;
• Adaptive models and algorithms for evolving data streams;
• Edge computing methods;
• High-performance computing for big data analytics in sensor networks;
• Active learning approaches for sensor data;
• Metalearning and transfer learning approaches for streams of sensor data;
• Change point detection and concept drift detection in sensor data;
• Feature selection for high-dimensional data domains;
• Feature extraction for complex data streams;
• Deep learning architectures and methods for sensor data analysis;
• Explainable AI techniques for sensor data;
• Data fusion approaches for feature-rich data analytics;
• Data monitoring and tracking in sensor networks;
• Correlation extraction and modeling with sensor data;
• Smart cities and smart grid applications;
• Cybersecurity applications, including intrusion detection in network data;
• Blockchain technologies for sensor network data.

Prof. Michelangelo Ceci
Dr. Gianvito Pio
Dr. Roberto Corizzo
Guest Editors

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