Machine-to-infrastructure middleware platform for data management in IoT
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The emergent usage of network-based consumer devices has created an ecosystem for heterogeneous 'aware' and interconnected devices with unique IDs interacting with other machines/objects, infrastructure, and nature. This is called the internet of things (IoT), and it is inspired by smart devices with sensing and connectivity capability that can aid with data collection. While the data from sensors can give insightful enterprise information through analytics, it is needful to first and foremost create the IoT framework with automation support for machine-to-infrastructure (M2I) communication. However, there are only few research works that focus on enabling M2I communication though many studies are dedicated to machine-to-machine (M2M) communication. Key challenges in the IoT infrastructure design are multiple device semantics and protocol variations which can limit interoperability. This work proposes a middleware with both M2I and M2M capabilities which addresses these problems based on mapping techniques between the heterogeneous device semantics and providing a common interface for data exchanges via varied protocols. When a device is discoverable, our middleware uses enhanced environment-context ontology to match the appropriate communication protocol. This aids with pushing data from within-range sensors to a cloud-hosted infrastructure. The extensive experiments conducted on the proposed system show superiority over similar services.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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 it