12/10/2026
๐ก๐๐ช ๐๐ข๐ก๐๐๐ฅ๐๐ก๐๐ ๐ฃ๐๐ฃ๐๐ฅ ๐ฃ๐จ๐๐๐๐ฆ๐๐๐
engineering proceedings
Q3 Scopus Indexed Jounal
๐๐๐๐ต๐ผ๐ฟ๐: ๐ญ๐ผ๐ฟ๐ฒ๐ป ๐ ๐ฎ๐ฏ๐๐ป๐ด๐ฎ, Jennifer Dela Cruz, Reggie Cobarrubia Gustilo
College of Engineering, Southern Luzon State University
๐๐ฏ๐๐๐ฟ๐ฎ๐ฐ๐:
WSN node energy forecasting contributes to improving network efficiency, extending network lifespan, and providing energy management strategies. In this study, a deep-learning-based wireless sensor network (WSN) node energy forecasting model based on Long Short-Term Memory (LSTM) and stacked-LSTM was developed across different wireless communication technologies in both static and dynamic WSN setups. The performance of the deep-learning-based models was compared with traditional forecasting techniques such as Exponential Smoothing and Prophet. The results showed the superiority of LSTM and stacked-LSTM in terms of root mean square error and mean absolute error, with consistently lower values compared with the traditional forecasting techniques. The results also show that the models perform best with Long Range technology. The deep learning-based model also demonstrates its ability to perform better in both static and dynamic WSN scenarios. These results demonstrate the potential of deep-learning-based models in WSN node energy management, which can result in an optimal energy efficiency and prolong the network lifetime. Future research is needed to explore hybrid approaches to further improve the prediction performance of deep learning-based models by combining their strengths with statistical or traditional forecasting techniques.
Keywords: wireless sensor network; energy forecasting; LSTM; deep learning; LoRa; Zigbee; BLE; Wi-Fi
Visit Article here: https://doi.org/10.3390/engproc2026134033
SDG 9: Industry, Innovation and Infrastructure
SDG 7: Affordable and Clean Energy
