(POSTER) From Sensors to Insight: Rapid, Edge-to-Core Application Development for Sensor-Driven Applications
2026-05-04 • Distributed, Parallel, and Cluster Computing
Distributed, Parallel, and Cluster ComputingArtificial IntelligenceSoftware Engineering
AI summaryⓘ
The authors present a new way to build sensor data applications more quickly using AI to guide the process. They show how to start from a template workflow for underwater sound monitoring and adapt it to other uses like air quality and earthquake sensing without rewriting code. Their method also works on different devices, from cloud servers to small gadgets like Raspberry Pis. They found that a beginner user could create these workflows much faster while keeping them reliable and easy to use on various systems.
sensor dataworkflowPegasusFABRIC testbedAI-assisted designedge computingOrcasound hydrophoneBlueField-3 DPURaspberry Piworkflow portability
Authors
Komal Thareja, Anirban Mandal, Ewa Deelman
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.