LEMONS: Leveraging Model-Based Techniques to Enable Non-Intrusive Semantic Enrichment in Wireless Sensor Networks

2026-08-25

AI Summary

该论文提出一种基于模型的方法，利用语义网技术在无线传感器网络中实现非侵入式语义增强，提高数据互操作性和简化网络配置。

Abstract

The paper presents an efficient approach to the semantic enrichment of measured sensor data in Wireless Sensor Networks (WSNs), by bridging techniques from Model-driven Software Development (MDSD) and Semantic Web Technology (SWT). Our approach reinforces data interoperability, fostering data sharing and reuse, by utilizing SWT. Model-based and type-agnostic configuration reduces the overall effort for WSN setup and maintenance, which are traditionally complex and time-consuming tasks. The presented approach addresses the problem of large-scale WSN management through the application of SWT in WSN configuration and management without requiring expert knowledge. Additionally, we present a generic architecture and an implementation which is also supplemented by hands-on descriptions of an illustrative use case. Our experimental results demonstrate that our model-based approach provides non-intrusive semantic enrichment with sub-millisecond computational overhead, as well as partially automated configuration of WSNs.

Problem

• Wireless Sensor Networks
• Semantic Enrichment
• Data Interoperability
• Model-driven Software Development
• Semantic Web Technology

Innovation

• Model-driven Software Development
• Semantic Web Technology
• Non-intrusive Semantic Enrichment
• Wireless Sensor Networks
• Automated Configuration