# Enhancing Sustainable IoT Systems Through a Goal-Oriented Requirements Analysis Framework
## 4 Goal-Oriented Requirements Analysis Approach for IoT Software Systems
### Application Example: IoT System for Monitoring Air Quality
#### 4.2.5 QVT for Model-to-Model Transformations
The results presented in this article indicate that the proposed framework simplifies the modeling of software and hardware components, as well as their inter-relationships, within a unified environment. This simplification contributes to reducing overall system complexity while ensuring a comprehensive analysis model that aligns with stakeholder expectations, making it suitable for both initial development and RE phases. Effective IoT system development requires a stronger connection between stakeholder goals and system artifacts, both during development and at runtime, to minimize effort and information inconsistencies. Furthermore, the proposed approach can be seamlessly integrated into a code generation tool, which forms a key component of a robust framework for defining and constructing IoT systems. This aligns with the principles of MDD and GORE, both of which have been recognized as effective methodologies for modeling and code generation [20; 24]. Although the present study validates the effectiveness of the GORA methodology through a smart home air quality monitoring scenario, we recognize that this case study does not encompass the full complexity and diversity of IoT ecosystems. The selected use case was intentionally scoped to allow controlled experimentation and to demonstrate the traceability of sustainability goals from requirement analysis to executable code. However, the underlying framework--including its goal modeling, model transformation, and code generation components--is domain-agnostic and designed for extensibility. As part of our future work, we plan to evaluate GORA in a broader range of IoT domains, such as industrial process monitoring, precision agriculture, and healthcare systems. These domains introduce additional requirements, such as real-time coordination, distributed decision making, and stringent reliability constraints, which will further validate and potentially extend the capabilities of the proposed methodology. In conclusion, the exploration of methodologies and tools that guide the systematic development of IoT systems remains a critical research area, particularly given the exponential growth of IoT technologies. Future research will focus on extending this approach within the MDD paradigm, covering all stages of RE. This includes the development of a code generation tool capable of configuring IoT devices and automatically generating software for end user interaction, thus fostering a more sustainable and adaptive IoT ecosystem. Conceptualization, J.A.A.-C.; methodology, J.A.A.-C. and C.T.-B. ; formal analysis, J.A.A.-C.; investigation, J.A.A.-C., P.A.A.-C. ([PERSON]), C.T.-B. and A.Z.-C.; writing--original draft preparation, J.A.A.-C. and C.T.-B. ; writing--review and editing, J.A.A.-C., P.A.A.-C. ([PERSON]), C.T.-B. and A.Z.-C.; supervision, J.A.A.-C. and C.T.-B. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors. Special thanks to Universidad Autonoma de Sinaloa (UAS, Mexico) and the research groups Tecnologia Educativa I + D + I (UAS-CA-303) and Sistemas Innovadores Aplicados al Contexto Educativo (UAS-CA-295) for their guidance to the members of the group. The publication has been partially supported by Coordinacion General para el Fomento a la Investigacion Cientifica e Innovacion del Estado de Sinaloa (CONFIE). The authors declare no conflicts of interest. ## References

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