# 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
Together, these panels help quantify the sustainability, efficiency, and responsiveness of the IoT system, and they also serve as evidence for claims made in the empirical evaluation section. * influxcdb_config/init.iql: Initial configuration of the time-series database. This script initializes the InfluxDB schema to benchmark the performance of sustainable IoT systems developed using the GORA methodology. The sensor_data measurement captures sensor output with associated timestamps. The latency measurement logs the real-time response latency of the system between data capture and actuation, which are derived via MQTT timestamps. The cpu_metrics measurement records system resource usage, which supports the evaluation of energy efficiency and sustainability targets. ### Interpretation and Discussion of Results

The empirical results presented in Section 5.3 provide robust quantitative evidence supporting the effectiveness of a model-driven, goal-oriented approach for the development of sustainable Internet of Things (IoT) systems. Within the context of a smart home air quality monitoring application, the implementation of the GORA methodology produces substantial improvements in several key performance and software engineering metrics compared to a manually developed baseline system. From a sustainability perspective, the observed 18.9% reduction in energy consumption highlights GORA's capacity to optimize runtime behavior through efficient, event-driven execution logic. In contrast to the baseline implementation, which employed periodic polling and continuous condition evaluation, the GORA-generated code initiates actions only when specific, goal-relevant thresholds are surpassed. This approach not only conserves energy, but also contributes to the extended operational lifespan of battery-powered devices in real-world deployments. Furthermore, the reduction in response latency 23.0% is noteworthy, particularly in health-critical environments such as smart homes, where delayed reactions to elevated levels of CO2 or PM2.5 can negatively impact the well-being of occupants. The explicit representation of goals and soft constraints in the GORA model facilitated the synthesis of more concise and context-sensitive decision-making logic. Furthermore, the reduction of 54.4% in manual code underscores the methodology's ability to diminish development effort, an especially valuable feature for nonspecialist developers or for rapid prototyping scenarios. This finding is consistent with contemporary trends in Low-Code/No-Code development, reinforcing GORA's potential to support more inclusive and efficient design processes. In particular, the complete elimination of execution and transformation errors in the GORA-based implementation attests to the reliability of model-based automation. By formalizing system requirements through structured goal models and executing model transformations via QVT and Acceleo pipelines, the approach ensures end-to-end traceability and correctness from specification to deployment. Collectively, these results suggest that GORA is not only theoretically rigorous, but also demonstrably effective in practice, particularly for use cases that require proactive environmental monitoring and autonomous response. As such, the framework exhibits strong potential for broader adoption in sustainability-critical IoT domains. ### Threats to Validity

This subsection discusses the potential limitations and threats to the validity of the internal, external, and construct validity of the empirical evaluation conducted for the GORA approach. The internal validity of the experimental evaluation is reinforced by the use of a controlled environment, in which both the baseline system and the GORA-based implementation were executed under identical conditions. These conditions included uniform simulated sensor input, consistent network latency, and standardized execution environments.