A Comprehensive Review of IoT Edge Gateways: Models, Methods, and Emerging Applications
Bibliographic record
Abstract
The rapid proliferation of Internet of Things (IoT) ecosystems has intensified the demand for efficient, scalable, and secure data processing architectures, positioning IoT edge gateways as a critical component in modern distributed systems. These gateways act as intermediaries between edge devices and cloud infrastructures, enabling real-time data processing, protocol translation, and localized decision-making. This paper presents a comprehensive review of IoT edge gateway models, methods, and emerging applications, with a strong emphasis on intelligent processing, security integration, and software engineering perspectives. The study systematically analyzes recent advancements in edge gateway architectures, including virtualization-based models, containerized microservices, AI-enabled gateways, and software-defined edge frameworks. Key findings reveal a shift from traditional rule-based processing toward adaptive, AI-driven edge intelligence, enhancing latency reduction, bandwidth optimization, and security enforcement. The review also identifies critical challenges such as resource constraints, interoperability issues, and security vulnerabilities in distributed edge environments. The primary contribution of this work lies in synthesizing recent research trends, identifying methodological gaps, and proposing future research directions that integrate edge intelligence with secure software engineering practices and DevSecOps pipelines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".