(POSTER) From Sensors to Insight: Rapid, Edge-to-Core Application Development for Sensor-Driven Applications
Komal Thareja, Anirban Mandal, Ewa Deelman

TL;DR
This paper presents an AI-assisted, pattern-based methodology for rapid development of sensor-driven applications across edge and cloud, demonstrated on Pegasus workflows with significant time savings.
Contribution
It introduces a novel intent-first, pattern-based approach for developing and deploying sensor workflows on edge-to-cloud platforms, reducing development time.
Findings
Workflow development time reduced to 1-1.5 days.
AI-assisted pattern reuse maintains workflow rigor and portability.
Demonstrated on multiple sensor applications and edge resources.
Abstract
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…
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