Simple ARQ Protocol for Reliable Transport in LowPANs
Bibliographic record
Abstract
Due to the surge in IoT devices, numerous protocols have been proposed to meet their needs. UDP is commonly used for IoT because of its simplicity, low latency, minimal overhead, and low energy consumption. In contrast, TCP is less suitable for IoT due to its higher resource demands, complexity, and greater energy consumption, which are challenging for small devices with limited resources. Therefore, often, reliability is provided by upper-layer protocols, mainly by the applications themselves. In fact, classical IoT applications such as sensing, identification and actuating generate multiple copies of data due to the hardware redundancy and periodic updates. Examples of such applications include, agricultural, environmental, traffic, and healthcare monitoring. UDP and TCP protocols may be inadequate for IoT applications requiring reliable, real-time communication. In critical situations like battlefields or disasters, sensors might only send a few messages before being destroyed. Therefore, the network itself must ensure reliability in these scenarios. Moreover, smart textiles are more and more integrating sensory devices and require reliable transmissions. In fact, with the very stringent resource constraints on one hand, and the requirements to respect the Specific Absorption Rate (SAR) of human bodies, on the other hand, re-transmissions and power must be kept at their lowest levels. In this paper, we propose a simple Automatic Request (SARQ) transport protocol that uses acknowledgments and a retransmission mechanism. Through a realistic simulation setup using Contiki motes in cooja simulator, we show that our protocol exhibits slightly higher energy consumption and resource requirements than UDP, but far less than TCP. Conversely, we show that our protocol exhibits 99% Packet Delivery Ratio (PDR), while UDP and TCP exhibit 74% and 99% PDR, respectively.
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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 | 0.004 |
| 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 | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".