用AI辅助模板快速构建传感器应用，从代码转向意图设计。
(POSTER) From Sensors to Insight: Rapid, Edge-to-Core Application Development for Sensor-Driven Applications
- 以模式复用和AI辅助实现从意图到工作流的快速构建。
- 1-1.5天完成一个工作流开发，支持跨边缘到云端部署。
- 适合初学者快速搭建传感器应用，提升开发效率。
科学家日益依赖传感器数据，但将原始数据流转化为跨边缘到云的洞察仍面临挑战，因需协调多种数据与计算流程。本文提出一种基于模式的AI辅助方法，实现传感器驱动应用的快速开发。通过在FABRIC测试平台上运行的Pegasus工作流，演示了五步开发循环，将工作流构建从代码优先转向意图优先。以已有的Orcasound水听器工作流为可复用模板，生成并优化了空气质量、地震和土壤湿度监测的应用工作流。进一步展示这些工作流可通过配置和部署而非重写，扩展至边缘资源（如BlueField-3 DPU和Raspberry Pi）。对新手Pegasus用户的评估表明，该方法使多阶段工作流开发压缩至每工作流1-1.5天，同时保持执行的严谨性与可移植性。
原文摘要 · Abstract (English)
Scientists increasingly rely on sensor-based data; however transforming raw streams into insights across the edge-to-cloud continuum remains difficult due to the breadth of expertise required to coordinate the necessary data and computation flow. This paper introduces a pattern-based, AI-assisted methodology for rapid development of sensor-driven applications. Using Pegasus workflows executing on the FABRIC testbed, we demonstrate a 5-step development loop that shifts workflow construction and deployment from code-first to intent-first design. Starting from an existing Orcasound hydrophone workflow as a reusable template, we generate and refine workflows for air quality, earthquake, and soil moisture monitoring applications. We further show how these workflows extend to edge resources-including BlueField-3 DPUs and Raspberry Pis-through configuration and placement rather than workflow redesign. Our evaluation, from the perspective of a novice Pegasus user, shows that AI-assisted pattern reuse compresses multi-stage workflow development to 1-1.5 days per workflow while preserving the rigor and portability of workflow-based execution.
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