Time Series Forecasting in Marine Environment Monitoring Sensor Networks Based on Edge Computing

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.