{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://froggit.ai/public/capsules/3b66af2f-156d-4ea8-b6f1-cc6349fca7e6","identifier":"3b66af2f-156d-4ea8-b6f1-cc6349fca7e6","url":"https://froggit.ai/public/capsules/3b66af2f-156d-4ea8-b6f1-cc6349fca7e6","name":"(POSTER) From Sensors to Insight: Rapid, Edge-to-Core Application Development for Sensor-Driven Applications","text":"# (POSTER) From Sensors to Insight: Rapid, Edge-to-Core Application Development for Sensor-Driven Applications\n\nSource-backed public reference for arXiv:2605.02844.\n\n**Authors:** Komal Thareja, Anirban Mandal, Ewa Deelman\n**Primary source:** https://arxiv.org/abs/2605.02844\n**Published:** 2026-05-04T17:21:37Z\n**Updated:** 2026-05-04T17:21:37Z\n**Categories:** cs.DC, cs.AI, cs.SE\n\n## Abstract Summary\nScientists 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.\n\n## Public Use Notes\n- This capsule summarizes the paper's arXiv metadata and abstract; it is not an independent replication or endorsement of the paper's claims.\n- Use it as a cited research reference for discovery, retrieval, and agent context.\n- For clinical, security, or deployment-sensitive topics, treat the paper as research context rather than operational, medical, legal, or safety advice.\n\n## Source\n- https://arxiv.org/abs/2605.02844","keywords":["cs.DC","cs.AI","cs.SE"],"about":[],"citation":["https://arxiv.org/abs/2605.02844"],"isPartOf":{"@type":"Dataset","name":"Forge Cascade Knowledge Graph","url":"https://froggit.ai"},"publisher":{"@type":"Organization","name":"Forge Cascade","url":"https://froggit.ai"},"dateCreated":"2026-05-05T06:00:07.656000Z","dateModified":"2026-06-19T03:07:28Z","isBasedOn":"https://arxiv.org/abs/2605.02844","additionalProperty":[{"@type":"PropertyValue","name":"trust_level","value":100},{"@type":"PropertyValue","name":"verification_status","value":"sources_verified"},{"@type":"PropertyValue","name":"provenance_status","value":"valid"},{"@type":"PropertyValue","name":"evidence_level","value":"primary_source"}]}