IoT Gateway Middleware for SDN Managed IoT
Bibliographic record
Abstract
Internet of Things (IoT) refers to interconnection of a significant number of “things” which include objects, services and living beings. The realization of IoT systems will fundamentally change how we interact with the world; a key technology in that direction is Middleware. Middleware is an intermediary software system between IoT devices and application services. The objective of this paper is to propose and evaluate a lightweight Middleware solution which can be deployed either on the Cloud (remote data centers) for deep analytics or on the Edge Network (nearby IoT Gateways) for local analytics to support near real-time applications. Middleware supports interoperability between heterogeneous devices and applications, which is one of the most important system requirements, by providing multiple protocol bindings as plug and play services. Experiments have been conducted on a SDN (Software-defined Networking) managed IoT network testbed and the results show that the proposed Middleware solution is suitable for both Cloud (resourceful) and Edge network devices (IoT Gateway, designed on a resource constrained single board computer such as Raspberry Pi 3) and provides interoperability between IoT devices and applications.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".