Sensor Intelligence through Neurocomputing-Based Data Compression and/or Robust Time-Series Forecasting

in Special Issue   Posted on December 31, 2020 

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Submission Deadline: Sat 31 Jul 2021
Journal Impact Factor : 3.275
Journal Name : Sensors
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Special Issue Call for Papers:

Dear colleagues,

Sensor intelligence is a key enabler of better-performing, more energy-efficient, more robust (and thus more adaptive to environmental changes), much faster (and better fitting to any real-time constraints), and very data-rate-efficient sensor systems and/or sensor networks. Various highly innovative technical concepts of the future crucially need intelligent sensors. Examples of such contexts include cyber-physical systems, digital twins, IoT (Internet of Things), IIoT (Industrial Internet of Things), smart factories of the future, autonomous driving systems, smart online health systems of the future, etc.

However, sensor intelligence does face a series of difficult research challenges which need to be addressed by the relevant research community. Amongst the core scientific and technical challenges, efficient data compression, compressive sensing, and robust prediction or forecasting capability are some of the most prominent.

Selected keywords (not an exhaustive list):

Neurocomputing-based compressed sensing
Data compression schemes in sensors or in sensor networks
Data compression schemes for power reduction
Joint compressing and caching of data within sensor networks
On-board lossy compression schemes
Image-based compression of sensor data
Spatio-temporal data compression for sensor networks
Data compression and optimization schemes in cloud storage
Compressive sensing in the context of sensor data fusion
Edge machine learning w.r.t. to data compression and/or data forecasting
Combined compression of multiple data streams
Autoencoder techniques for robust and efficient data compression
Quantum data compression
Relational behavior forecasting from sensor data
Deep-learning-based forecasting of sensor data
Forecasting concepts w.r.t. or combined with tracking and state estimation
Data prediction in clustered sensor networks
Data-driven anomaly detection supported by prediction models
Traffic data forecasting (e.g., in transportation or in communication systems)
Sensor intelligence support of predictive maintenance
Sensor events prediction

Prof. Dr. Kyandoghere Kyamakya
Dr. Fadi Al-Machot
Dr. Ahmad Haj Mosa
Prof. Dr. Jean Chamberlain Chedjou
Prof. Dr. Zhong Li
Prof. Dr. Antoine Bagula
Guest Editors

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