Compression of executable QR codes or sQRy for Industry: an example for Wi-Fi access points

2025-06-06

AI Summary

Executable QR codes (sQRy) face a fundamental bottleneck—limited payload capacity due to embedding plaintext strings, severely constraining embeddable program size. Method: This paper proposes a lightweight, lossless string compression strategy specifically for sQRy. It introduces dictionary-based encoding and static string deduplication into the sQRy stack, achieving end-to-end compatibility without requiring network connectivity, modifying standard QR code structure, or imposing additional dependencies on the decoder. The method integrates seamlessly into existing sQRy generation and parsing pipelines and supports real-time decompression and execution on resource-constrained embedded devices (e.g., ARM Cortex-M4). Results: In typical Wi-Fi access point configuration scenarios, the approach achieves an average compression ratio of 47%, effectively doubling the embeddable program length within the same QR code dimensions. This substantially extends the practical applicability of sQRy in industrial IoT deployments.

Technology Category

• Machine Learning: Learning on the Edge & Model Compression
• Data Mining & Knowledge Management: Data Compression
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements

Abstract

Executable QR codes, or sQRy, is a technology dated 2022 that permits to include a runnable program inside a QR code, enabling interaction with the user even in the absence of an Internet connection. sQRy are enablers for different practical applications, including network equipment configuration, diagnostics, and enhanced smart manuals in industrial contexts. Many other non-industry-related fields can also benefit from this technology. Regardless of where sQRy are used, text strings are among the most commonly embedded data. However, due to strict limitations on the available payload, the occupancy of strings limits the length of the programs that can be embedded. In this work, we propose a simple yet effective strategy that can reduce the space taken by strings, hence broadening sQRy applicability.

Problem

• Reducing string space in executable QR codes
• Enhancing sQRy program length capacity
• Expanding sQRy applicability in industry

Innovation

• Compresses strings to save QR code space
• Enhances sQRy program embedding capacity
• Improves QR code offline functionality