{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://froggit.ai/public/capsules/b3fe741a-f25c-476d-bf6d-d56950e58745","identifier":"b3fe741a-f25c-476d-bf6d-d56950e58745","url":"https://froggit.ai/public/capsules/b3fe741a-f25c-476d-bf6d-d56950e58745","name":"From Sensors to Insight: Rapid, Edge-to-Core Application Development for Sensor-Driven Applications","text":"# From Sensors to Insight: Rapid, Edge-to-Core Application Development for Sensor-Driven Applications\n\nSource-backed public reference for arXiv:2605.02859.\n\n**Authors:** Komal Thareja, Anirban Mandal, Ewa Deelman\n**Primary source:** https://arxiv.org/abs/2605.02859\n**Published:** 2026-05-04T17:36:42Z\n**Updated:** 2026-05-04T17:36:42Z\n**Categories:** cs.DC, cs.AI, cs.SE\n\n## Abstract Summary\nScientists increasingly rely on sensor-based data, yet transforming raw streams into insights across the edge-to-cloud continuum remains difficult. Provisioning heterogeneous infrastructure and managing execution on emerging platforms like Data Processing Units typically requires cross-domain expertise, creating significant barriers to rapid prototyping. This paper introduces an experience-driven methodology for the rapid development of sensor-driven applications. By combining pattern-based workflow engineering with AI-assisted development-implemented via Pegasus on the FABRIC testbed - we utilize an existing Orcasound hydrophone workflow as a reusable template. We introduce a pattern-based engineering methodology to generate and refine workflows for air quality, earthquake, and soil moisture monitoring. Furthermore, we show how these abstract structures are extended to edge resources through modular configuration and placement. Our evaluation focuses on user productivity and practical lessons rather than peak performance. Through these case studies, we illustrate how AI-assisted, pattern-based development lowers the entry barrier for non-experts and enables iterative exploration of sensor-driven applications across distributed infrastructures.\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, operational, or deployment-sensitive topics, treat the paper as resea","keywords":["cs.DC","cs.AI","cs.SE"],"about":[],"citation":["https://arxiv.org/abs/2605.02859"],"isPartOf":{"@type":"Dataset","name":"Forge Cascade Knowledge Graph","url":"https://froggit.ai"},"publisher":{"@type":"Organization","name":"Forge Cascade","url":"https://froggit.ai"},"dateCreated":"2026-05-05T06:00:07.636000Z","dateModified":"2026-06-19T03:22:45Z","isBasedOn":"https://arxiv.org/abs/2605.02859","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"}]}