<scp>LoRa</scp> Meets Artificial Intelligence to Optimize Indoor Networks for Static <scp>EDs</scp>
Bibliographic record
Abstract
ABSTRACT The architectural design of the Indoor Internet of Things (IIoT) network targeting static end devices (EDs) and gateways (GWs) has been innovatively formulated in this paper, integrating LoRa technology to mitigate losses and ensure seamless information reception through meticulous ED allocation. The arrangement of simultaneously transmitted data within the network server (NS) employs a deep neural network (DNN) with distributed machine learning (DML) to adjust transmission parameters, ensuring frequent uninterrupted bidirectional communication. This augmentation is obtained by strategically deploying EDs within distinct clusters determined by K‐means and density‐based spatial clustering with noise (DBSCAN), thus optimizing spreading factor (SF) and data rate (DR) allocation to prevent data congestion and improve signal‐to‐interference noise ratio (SINR). The proposed hybrid model (DR|SF) for pure and slotted ALOHA amplifies the network's performance metrics for indoor scenarios. A unified framework utilizing a one‐slope model estimates path losses (PL) while exploring various bandwidths (BW), bidirectional interrogations, and duty cycles (DC) to lower the saturation and prolong the active lifespan of the EDs. The results manifest a packet rejection rate (PRR) of 0% for the DBSCAN, contrasting a 4.7% estimate for the K‐means. The network saturation is minimized to 9.5% and 10.1%, correspondingly, significantly increasing the efficiency of slotted ALOHA (91%) and pure ALOHA (90.6%), thereby prolonging the longevity of EDs.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".