Addressing Context Awareness Requirements of IoT Systems: A Model-Driven Approach
Bibliographic record
Abstract
Context awareness (CA) plays a crucial role in Internet of Things (IoT) systems, enabling them to dynamically adapt based on environmental, user, and system states. The elicitation, specification, and management of these CA requirements is challenging due to their inherent complexity, dependencies, and potential conflicts. Existing approaches primarily focus on runtime adaptations but lack structured methodologies for early-stage modelling and integration of CA requirements. This paper introduces a model-driven approach that systematically incorporates context awareness into the requirements engineering process. It leverages large language models (LLMs) for automated elicitation and classification of CA requirements, enabling the discovery of new adaptive services that emerge from system context awareness. Our work builds on the UCM4IoT requirements modelling language tailored for IoT systems. We also propose a domain-specific language, CARDML4IoT, for modelling and analysis of dependencies in CA requirements. Our proposed approach is demonstrated with a smart store system application.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".