Management Platforms and Protocols for Internet of Things: A Survey
Bibliographic record
Abstract
Internet of Things (IoT) management systems require scalability, standardized communication, and context-awareness to achieve the management of connected devices with security and accuracy in real environments. Interoperability and heterogeneity between hardware and application layers are also critical issues. To attend to the network requirements and different functionalities, a dynamic and context-sensitive configuration management system is required. Thus, reference architectures (RAs) represent a basic architecture and the definition of key characteristics for the construction of IoT environments. Therefore, choosing the best technologies of the IoT management platforms and protocols through comparison and evaluation is a hard task, since they are difficult to compare due to their lack of standardization. However, in the literature, there are no management platforms focused on addressing all IoT issues. For this purpose, this paper surveys the available policies and solutions for IoT Network Management and devices. Among the available technologies, an evaluation was performed using features such as heterogeneity, scalability, supported technologies, and security. Based on this evaluation, the most promising technologies were chosen for a detailed performance evaluation study (through simulation and deployment in real environments). In terms of contributions, these protocols and platforms were studied in detail, the main features of each approach are highlighted and discussed, open research issues are identified as well as the lessons learned on the topic.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.007 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".