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

TL;DR

This paper presents a pattern-based, AI-assisted methodology for rapid development of sensor-driven applications across edge-to-cloud platforms, lowering barriers for non-experts.

Contribution

It introduces a reusable, pattern-based engineering approach combined with AI assistance to streamline sensor application development on heterogeneous, distributed infrastructures.

Findings

Workflow engineering with patterns accelerates development.

AI-assisted tools improve user productivity.

Case studies demonstrate lower entry barriers for non-experts.

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.