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

Problem
This paper addresses the challenge of transforming raw sensor data into actionable insights across the edge-to-cloud continuum, a task that is often hindered by the need for specialized cross-domain expertise in provisioning heterogeneous infrastructures. The authors highlight the difficulties faced by non-experts in rapidly prototyping sensor-driven applications, particularly when utilizing emerging platforms like Data Processing Units (DPUs). This work is presented as a preprint, indicating it has not yet undergone peer review.

Method
The authors propose an experience-driven methodology that integrates pattern-based workflow engineering with AI-assisted development, implemented using Pegasus on the FABRIC testbed. They leverage an existing hydrophone workflow from Orcasound as a reusable template and introduce a pattern-based engineering methodology to facilitate the generation and refinement of workflows for various sensor applications, including air quality, earthquake, and soil moisture monitoring. The methodology emphasizes modular configuration and placement to extend these abstract structures to edge resources. The approach is designed to enhance user productivity and enable iterative exploration, focusing on practical lessons rather than optimizing for peak performance.

Results
The evaluation of the proposed methodology is centered on user productivity metrics rather than traditional performance benchmarks. The authors report significant improvements in the speed and ease of application development for non-experts, although specific quantitative results (e.g., time savings, user satisfaction scores) are not detailed in the abstract. The case studies demonstrate that the AI-assisted, pattern-based approach effectively lowers barriers to entry for users without extensive technical backgrounds, facilitating the rapid development of sensor-driven applications across distributed infrastructures.

Limitations
The authors acknowledge that their focus on user productivity may overlook peak performance metrics, which could be critical for certain applications. Additionally, the reliance on a specific testbed (FABRIC) may limit the generalizability of the findings to other environments or infrastructures. The paper does not address potential scalability issues when deploying these workflows in larger, more complex sensor networks or the implications of varying data quality from different sensor types.

Why it matters
This work has significant implications for the field of sensor-driven applications, particularly in democratizing access to advanced data processing techniques for non-experts. By lowering the entry barriers and enabling rapid prototyping, the proposed methodology could accelerate the development of innovative applications in various domains, such as environmental monitoring and disaster response. Furthermore, the integration of AI-assisted development with pattern-based engineering could inspire future research into automated workflow generation and optimization, potentially leading to more robust and scalable solutions in sensor data processing.

Authors: Komal Thareja, Anirban Mandal, Ewa Deelman
Source: arXiv:2605.02859
URL: https://arxiv.org/abs/2605.02859v1