From Sensors to Insight: Rapid, Edge-to-Core Application Development for Sensor-Driven Applications
Komal Thareja, Anirban Mandal, Ewa Deelman
TLDR
This paper presents an AI-assisted, pattern-based methodology for rapid, edge-to-core development of sensor-driven applications, lowering barriers for non-experts.
Key contributions
- Introduces an experience-driven methodology for rapid development of sensor-driven applications.
- Combines pattern-based workflow engineering with AI-assisted development using Pegasus on FABRIC.
- Extends abstract workflows to edge resources through modular configuration and placement.
- Demonstrates reduced entry barriers for non-experts via case studies (air quality, earthquake, soil moisture).
Why it matters
Scientists struggle to transform raw sensor data into insights across distributed infrastructures. This paper simplifies rapid prototyping by introducing an AI-assisted, pattern-based methodology, making complex sensor application development accessible to non-experts and fostering iterative exploration.
Original Abstract
Scientists 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.
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