In-Network Data Aggregation for Information-Centric WSNs using Unsupervised Machine Learning Techniques

IoT applications are changing our daily lives. These innovative applications are supported by new communication technologies and protocols. Particularly, the information-centric network (ICN) paradigm is well suited for many IoT application scenarios that involve large-scale wireless sensor networks...

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Bibliographic Details
Main Authors: Pellenz, Marcelo E., Lachowski, Rosana, Jamhour, Edgard, Brante, Glauber, Moritz, Guilherme Luiz, Souza, Richard Demo
Format: Conference Proceeding
Language:English
Subjects:
Data Aggregation
Information Centric Networks
Information-centric networking
IoT
Machine learning
Machine learning algorithms
Protocols
Spread spectrum communication
Telecommunication traffic
Wireless sensor networks
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Summary:IoT applications are changing our daily lives. These innovative applications are supported by new communication technologies and protocols. Particularly, the information-centric network (ICN) paradigm is well suited for many IoT application scenarios that involve large-scale wireless sensor networks (WSNs). Even though the ICN approach can significantly reduce the network traffic by optimizing the process of information recovery from network nodes, it is also possible to apply data aggregation strategies. This paper proposes an unsupervised machine learning-based data aggregation strategy for multi-hop information-centric WSNs. The results show that the proposed algorithm can significantly reduce the ICN data traffic while having reduced information degradation.
ISSN:2642-7389
DOI:10.1109/ISCC53001.2021.9631416