# 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
Nevertheless, it is important to note that sensor values were generated through pseudorandom distributions rather than acquired from physical sensor hardware. As such, the experiment may not fully capture the variability, noise, and anomalies that are characteristic of real-world sensing devices. Furthermore, while Docker-based statistics [36] were used to estimate energy consumption and resource usage, these measurements are indirect and may not correspond precisely to actual energy usage profiles on embedded hardware platforms. The evaluation was carried out within a virtualized testbed using Docker containers, which, despite offering a controlled and replicable environment, may limit the generalizability of the findings to physical smart home deployments and resource-constrained IoT hardware platforms, such as microcontrollers. Although the use of MQTT messaging [37] and logic processing components reflects the architecture of real-world IoT systems, external factors, such as variable network conditions, battery constraints, and environmental noise, were not fully emulated. Furthermore, the evaluation was limited to a single application domain--indoor air quality monitoring--which, while representative, may not be sufficiently generalized to other IoT contexts, such as industrial automation, precision agriculture, or digital health. The selected evaluation metrics, namely energy consumption, latency, memory usage, code development effort, and error rate, are aligned with the core objectives of the GORA methodology, particularly in terms of sustainability, system responsiveness, and development efficiency. However, certain metrics were derived using indirect proxies. For example, energy consumption was estimated through CPU usage rather than direct power measurements, and developer productivity was inferred from the number of lines of code rather than more nuanced measures, such as cognitive load or task completion time. Although these proxies are commonly adopted in empirical software engineering, they may not fully encapsulate the end user development experience or the complexity involved in system implementation. Implementation Limitations

Although commercial IoT simulation platforms, such as IoTIFY [38], offer streamlined mechanisms for MQTT traffic emulation, they lack the extensibility required for full-stack evaluations, as conducted in this study. Specifically, IoTIFY does not support the execution of model-generated code, Docker orchestration, or integration with essential back-end components, such as time-series databases and real-time dashboards. To address these limitations, a custom virtualized environment was developed using Docker Compose (v2.26.1), Python (v3.12.3, Node-RED (v3.1.5) [39], InfluxDB (v2.7.5) [40], and Grafana (v11.2.0) [41]. Although this infrastructure provides a high-fidelity simulation of the GORA workflow, it introduces additional dependencies and execution overhead that may not reflect the constraints typically encountered in edge computing environments. ## 6 Comparative Analysis with Existing Approaches

To contextualize the contribution of the GORA methodology, a comparative analysis was conducted against established RE and MDD approaches for IoT systems. This analysis is structured around five key dimensions that are particularly relevant to sustainability-aware smart home applications: tool support, integration of sustainability concerns, degree of automation, goal traceability, and methodological limitations. As detailed in the review of the literature section (Section 3), there is a growing body of research advocating goal-oriented approaches in the engineering of IoT systems. Notable methodologies, such as SysMLKaos [24] and e-CORE [27], contribute meaningful mechanisms to improve the adaptability and traceability of the system, while others emphasize architectural modeling [30] or agent-based system design [42].