Aχon: IoT Attribute-based dynamic connector For Emergency Applications
Bibliographic record
Abstract
Internet of Things (IoT) paradigm-based applications are becoming essential in our daily lives in smart houses, medical equipment, agriculture, industries, smart cities, and other fields. While building an IoT system, architects and developers use distributed architectures where the system is a set of interoperated components by connectors. Different limitations incur the development of a connector in IoT devices, i.e., connectivity and internal resources. The reason is that each service needs an exclusive allocation of the device’s storage, memory, and processing power which makes running multiple services on the same device cumbersome. In this research, we design a generic "Software Connector" that aims to optimize the use of the device’s resources. The proposed connector will dynamically allocate and deallocate necessary resources at runtime when receiving a message, and it will adapt its behavior based on a set of attributes obtained from the local database and the incoming messages. The connector improves messages’ reliability by following the broadcast-default function, especially in case of network failure. We applied the proposed approach to an "Emergency Response System" with different IoT device types, describing an emergency scenario to validate the proposed approach. More ever, to make the proposed connector more accessible for developers to extend its functionalities and increase the number of supported platforms, we will commit it to an online repository.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".