Time Series Forecasting in Marine Environment Monitoring Sensor Networks Based on Edge Computing
Year: 2025
Type: article
Abstract: Marine sensor networks, essential for obtaining marine environmental data, face new challenges as marine environment monitoring extends into the deep sea and the demand for long-term in-situ time series analysis increases. Traditional centralized computing mode is inadequate for deep-sea monitoring and intelligent marine observation due to its limitations in communication and real-time processing. Moreover, long-term time series analysis models are typically complex and unable to provide in-situ real-time services. To overcome these challenges, this paper integrates edge computing into marine sensor networks and combines it with a lightweight time series forecasting model based on the Transformer structure, MarineEdgeformer. This presented model utilizes a linear attention mechanism and an optimized decoder structure, significantly reducing the model’s computational costs and improving inference speed while maintaining forecasting accuracy. Experimental evaluations using a real-world dataset with a Raspberry Pi as the edge computing node demonstrate that MarineEdgeformer effectively reduces computational costs and achieves reliable long-term forecasting accuracy. These results not only confirm the potential of edge computing in enhancing the real-time processing capabilities, reliability, and intelligence of marine sensor networks but also provide a technical solution for the development of future marine environment monitoring.
Source: IEEE Access
Authors: Jiajia Teng, Rufu Qin, Changwei Xu
Institutions: Tongji University, State Key Laboratory of Marine Geology
Language: English
Topic: IoT and Edge/Fog Computing
Subfield: Computer Networks and Communications
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,075
Awards: G1108058101, G2458880353, G7258269878
OpenAlex ID: W4408792124