DtS-IoT Resource Allocation Analysis Framework: Assessing DQ and RESS-IoT
Bibliographic record
Abstract
The demand for expanding connectivity to remote and isolated areas has intensified the need for global Internet of Things (IoT) solutions. Direct-to-Satellite IoT (DtS-IoT), enabled by advances in nanosatellite technology, allows cost-effective global and direct connectivity between IoT devices and low Earth orbit satellites. However, the most cost-effective DtSIoT networks face resource limitations in user, ground, and space segments, demanding scalable solutions to fully realize their scalability potential. While several resource allocation protocols have been proposed for DtS-IoT, they have mainly been evaluated using static topologies or simplified simulations, overlooking the time-evolving orbital and channel dynamics crucial to DtS-IoT performance. This work bridges this gap by introducing the DtS-IoT Resource Allocation Analysis Framework (DRAAF), a comparison methodology and toolchain for evaluating resource allocation mechanisms in realistic DtS-IoT scenarios. Our method is framed in scalability, energy efficiency, throughput, delay, fairness, and overhead metrics. Utilizing FLoRaSat, a realistic LoRa-based DtS-IoT network simulator, we showcase the methodology with a use case investigating two energy-efficient MAC protocols for DtS-IoT: RESS-IoT and Distributed Queuing (DQ). Results indicate that DQ excels in throughput and node energy efficiency, whereas RESS-IoT reduces delay. Both protocols maintain satellite energy efficiency and enhance fairness in uplink transmissions. These findings underscore the value of our approach in evaluating resource allocation within complex DtS-IoT 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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".