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

2026-05-04

AI Summary

This work addresses the complexity of developing edge-to-cloud sensor applications, which typically requires cross-domain collaboration and hinders efficient transformation of raw data into actionable insights. To streamline this process, the authors propose an intent-driven, AI-assisted rapid development methodology that integrates reusable workflow patterns with intelligent configuration, enabling seamless edge adaptation and deployment without code rewriting. Built upon the Pegasus workflow system and deployed on the FABRIC testbed, the approach supports heterogeneous edge resources such as BlueField-3 DPUs and Raspberry Pi devices. Users can construct multi-stage sensing applications within 1–1.5 days, and the framework’s robustness and portability have been validated through real-world deployments in air quality, seismic activity, and soil moisture monitoring scenarios.

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.

Problem

• sensor-driven applications
• edge-to-cloud continuum
• workflow development
• AI-assisted development
• data-to-insight transformation

Innovation

• AI-assisted development
• pattern-based workflow
• edge-to-cloud continuum
• intent-first design
• sensor-driven applications