Efficient Image Transmission Using LoRa Technology In Agricultural Monitoring IoT Systems
Bibliographic record
Abstract
Reliable image transmission using LoRa in IoT monitoring systems is considered to be challenging due to insufficient LoRa data rate and payload size. Existing approaches transmit an image in a sequence of packets each of which is individually acknowledged. This approach results in a long image transmission time due to the time spent waiting for the many individual acknowledgements. The acknowledgement traffic also inflates network load. To facilitate LoRa-based image transmission in agricultural monitoring IoT systems, this paper proposes a new reliable delivery protocol, Multi-Packet LoRa (MPLR), for transmission of large messages, such as images, in LoRa networks. The proposed protocol is implemented and evaluated using a LoRa testbed network. In point-to-point experiments with a single sender/receiver pair, MPLR reduced image transmission time by an average of 24% in scenarios with no packet loss, and by averages of 30%, 42%, and 49% in scenarios with 2%, 5%, and 10% loss rate, respectively. When multiple LoRa nodes send images to a single gateway, high channel utilization and an unacceptable collision probability can be experienced with the standard LoRa MAC ALOHA protocol. In experiments with between 5 and 20 nodes, MPLR in conjunction with a channel reservation protocol can successfully send more images and reduce the maximum successful image transmission time between 2 and 7 times, compared to stop-and-wait packet transmission with ALOHA.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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".