Unlocking DtS-IoT Medium Access Through the Massively Scalable Distributed Queuing Protocol
Bibliographic record
Abstract
Direct-to-satellite Internet of Things networks (DtSIoT) offer a cost-effective way to establish global communication between IoT devices and low Earth orbit satellites. However, budget-conscious DtS-IoT systems face resource constraints in user, space, and ground segments. An increasing limitation in the performance of satellite IoT networks is observed in the link layer. This stems from the prevalent use of ALOHA-based MAC protocols, leading to constraints in managing larger networks. The requirements of DtS-IoT deployments with extensive network sizes amplify the growing demands for energy-efficient and scalable MAC protocols. In this paper, we formulate a new energy-efficient MAC protocol, Massively Scalable Distributed Queuing (MSDQ), inspired by components of the Distributed Queuing (DQ) protocol and the RESS-IoT protocol. Utilizing FLoRaSat, a realistic LoRa-based DtS-IoT network simulator, we showcase the performance of MSDQ, DQ, and RESS-IoT. Results indicate that MSDQ outperforms DQ by 2.2 times the throughput and 19 times the node energy efficiency of RESSIoT. These findings highlight the performance gap of existing MAC protocols for large and geographically distributed DtSIoT systems.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".