Saltus-"A Sudden Transition" Empowered by Federated Learning for Efficient Big Data Handling in Multimedia Sensor Networks

In the realm of sensor networks, the substantial rise in multimedia data production, covering audio, video, and acoustic measurements, has expanded the scale of big data. Multimedia Sensor Networks (MSN) excel in managing diverse sensor outputs, representations, and encoding across domains. Existing...

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Bibliographic Details
Published inIEEE access Vol. 12; pp. 88620 - 88633
Main Authors Remya, S., Pillai, Manu J., Sha, Akhbar, Rajan, Ginu, Rama Subbareddy, Somula, Cho, Yongyun
Format Journal Article
LanguageEnglish
Published Piscataway IEEE 2024
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects
Acoustic measurement
Audio data
Big Data
Data analysis
Data models
Data privacy
Data processing
Federated learning
Machine learning
Multimedia
Multimedia communication
Multimedia sensors
Networks
Privacy
Reviews
Sensors
Wireless communication
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Summary:In the realm of sensor networks, the substantial rise in multimedia data production, covering audio, video, and acoustic measurements, has expanded the scale of big data. Multimedia Sensor Networks (MSN) excel in managing diverse sensor outputs, representations, and encoding across domains. Existing models for event detection in sensor networks fall short in handling the sheer volume and speed of these measurements from a Big Data perspective. This research work introduces "Saltus," a model that aligns multimedia data from sensor networks to a standardized feature space. Saltus employs a machine learning-centric architecture to enhance data analysis possibilities. Crucially, the model integrates federated learning to address the evolving landscape of sensor networks. This approach optimizes the collaborative learning capabilities by allowing distributed nodes to train machine learning models locally, preserving data privacy. Saltus emerges as a solution that not only streamlines multimedia data processing but also establishes a more secure and privacy-preserving analytics framework in large-scale sensor networks. The model signifies a step forward in integrating multimedia data into an easily analyzable format, leveraging the advantages of federated learning in big data analytics.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2024.3418629