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Poster abstract: towards distributed machine learning for data acquisition in wireless sensor networks
Publikationstyp
Conference Paper
Date Issued
2023-05-09
Sprache
English
Author(s)
Start Page
440
End Page
442
Citation
Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation: 440-442 (2023)
Contribution to Conference
Publisher DOI
Scopus ID
Publisher
Association for Computing Machinery
ISBN
979-8-4007-0037-8
Wireless sensor networks (WSNs) use low-cost sensors to monitor various environments, offering accurate and continuous surveillance. WSNs face a significant challenge in managing their limited energy resources due to communication overhead. To address this issue, we present a novel approach that leverages Neural Network (NN) models to predict data and reduce communication in WSNs. Our solution incorporates NN models on both the sensor and the cloud, enabling predictions to be made at the local level. The sensor sends data to the cloud only when the model is no longer able to predict accurately, cloud then fine-tunes the model based on the received data and sends updated weights of the NN to the sensor, reducing the need for communicating each sensed value to the cloud.
Subjects
Low-Power | Neural Networks | On-device data prediction | spatial-temporal data | Time-series data | Wireless sensor network
MLE@TUHH
DDC Class
600: Technology