Zainab, TayyabaTayyabaZainabKarstens, JensJensKarstensLandsiedel, OlafOlafLandsiedel2025-02-052025-02-052023-05-09Proceedings of the 8th ACM/IEEE Conference on Internet of Things Design and Implementation: 440-442 (2023)979-8-4007-0037-8https://hdl.handle.net/11420/53855Wireless 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.enLow-Power | Neural Networks | On-device data prediction | spatial-temporal data | Time-series data | Wireless sensor networkMLE@TUHHTechnology::600: TechnologyConference Paper10.1145/3576842.3589158Conference Paper