(POSTER) From Sensors to Insight: Rapid, Edge-to-Core Application Development for Sensor-Driven Applications

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 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.