Dear Colleagues,

Emerging applications, such as the smart meters, smart city and smart grids, are based on wireless  sensor networks, where a large number of sensors and Internet-connected devices generate a huge quantity and complex datasets. Data complexity arises from several factors, such as the conditions under which the sensors are deployed, procurement with different sensors at different periods, frequencies or resolutions. These factors often render the collected dataset to be uncertain and imprecise. As robust knowledge extraction and information fusion are indispensable to the success of these emerging applications, issues associated with automatically extracting useful information from large, uncertain and imprecise sensor-generated datasets must be addressed before the full benefits of the smart applications can be achieved. It is also important to create more reliable, efficient, stable and flexible smart sensor-driven systems based on various machine learning techniques. Therefore, there is a need for advanced data analysis and fusion techniques, systems, algorithms, mechanisms and methodologies to extract useful information from large, uncertain and imprecise sensor generated datasets.

This Special Issue solicits original contributions dealing with intelligent and learning-based data analysis and fusion techniques. Previously unpublished surveys, and practical and theoretical papers related to learning-based data analysis and fusion techniques in WSNs are welcome.

The potential topics appropriate for this Special Issue include, but are not necessarily limited to:

• AI-based sensor information fusion techniques
• Learning models for sensor information fusion
• Intelligent and learning-based fusion techniques for multi-sensor system
• Intelligent data analysis for sensor information fusion
• Learning models for uncertain information integration
• Intelligent techniques for data processing in wireless sensor networks
• Big data modeling and analytics in wireless sensor networks
• An anomaly detection based on data fusion algorithm in WSN
• Deep learning and machine learning for sensor message control
• Evolutionary approaches for sensor information fusion techniques
• Data fusion using data mining and artificial intelligence
• Machine learning techniques for sensor information fusion
• Computational intelligence techniques for sensor information analysis and fusion

Prof. Dr. Jemal H. Abawajy
Guest Editor

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