A Systematic Literature Review of IoT System Architectural Styles and Their Quality Requirements
Bibliographic record
Abstract
The Internet of Things (IoT) is increasingly prevalent, with systems developed across various domains. Choosing the right IoT architectural style is challenging due to the diversity of devices, dynamic environments, and real-time data needs. This choice significantly impacts system quality, requiring a careful balance of quality requirements and tradeoffs. Previous studies have not adequately identified the most suitable architectural styles for specific IoT quality needs. This study presents a systematic literature review of 103 primary studies (PSs) on IoT system quality requirements and architectural styles, assessing how each architectural style satisfies specific requirements. We followed the preferred reporting items for systematic review and meta-analysis (PRISMA) protocol to report our findings and answer three research questions (RQs). We selected PSs by applying inclusion and exclusion criteria to relevant papers published until the end of 2023. We analyzed data from PSs to understand IoT system quality requirements and architectural styles, assessing their alignment. The research revealed ten essential quality requirements for IoT systems and identified ten distinct architectural styles. Notably, each architectural style varies in its capacity to fulfill specific quality requirements, particularly regarding security, scalability, and performance. SOA, client-server, and REST architectural styles best fulfill many quality requirements. However, various architectural styles, such as Layered, Microservices, and Peer-to-Peer, show limited support for privacy requirements. Our findings can guide IoT systems practitioners in selecting an architectural style that aligns with their desired quality standards. Additionally, we recommend new research opportunities to deepen understanding of key architectural styles based on specific quality requirements.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".