| Title | In-Network Data Aggregation for Information-Centric WSNs using Unsupervised Machine Learning Techniques |
| Publication Type | Conference Paper |
| Year of Publication | 2021 |
| Authors | Pellenz, Marcelo E., Lachowski, Rosana, Jamhour, Edgard, Brante, Glauber, Moritz, Guilherme Luiz, Souza, Richard Demo |
| Conference Name | 2021 IEEE Symposium on Computers and Communications (ISCC) |
| Keywords | Collaboration, composability, compositionality, data aggregation, Human Behavior, Information Centric Networks, information-centric networking, IoT, machine learning, machine learning algorithms, Metrics, Protocols, pubcrawl, resilience, Resiliency, Scalability, spread spectrum communication, telecommunication traffic, Wireless sensor networks |
| Abstract | 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. |
| DOI | 10.1109/ISCC53001.2021.9631416 |
| Citation Key | pellenz_-network_2021 |
In-Network Data Aggregation for Information-Centric WSNs using Unsupervised Machine Learning Techniques