Adaptive Latency Reduction in LoRa for Mission Critical Communications in Mines
Bibliographic record
Abstract
Reliable communication is essential to alleviate incidents and escalate rescue operations. However, wireless communication is very challenging in underground mine due to irregular confined shapes and rough environments. A recent wireless standard LoRa (Long Range) is promising in mine environments because of its ultralow power consumption, long range, and deep penetration capabilities. In underground mine, sensors are deployed for continuous monitoring the working environments as well as tracking objects and miners. Therefore, different types of traffic are generated with different QoS requirements. LoRaWAN, the standardized medium access control (MAC) protocol is based on pure ALOHA that can not meet the requirements of mission-critical communications. The mission-critical applications require very low latency and high reliability. In this paper, we evaluate the performance of LoRa and LoRaWAN technologies in an underground mine in the presence of different kinds of traffic; and subsequently we propose redundant retransmission aided adaptive latency reduction protocol for low latency communication. In this protocol the ACK-TIMEOUT is adjusted based on the air time of the previous uplink transmission and the contention stage. Simulation results demonstrate that the proposed protocol significantly improves the performance of the system and outperforms LoRaWAN in terms of data extraction ratio (DER) and average transmission delay.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".