Bibliographic record
Abstract
This paper considers energy management on LTE-enabled Internet of Things (IoT) devices. A characteristic feature of IoT applications is the periodic generation of small messages, whose transmission over LTE is highly energy inefficient. In this paper, we consider application message bundling to alleviate the effect of short message transmissions on energy consumption. Specifically, we model the interplay between energy consumption and the extended DRX mechanism introduced in LTE to deal with IoT traffic. We formulate bundling as a cost minimization problem and develop an online algorithm to solve the problem. Detailed analysis shows that, depending on DRX and application parameters, our algorithm is 1, 2, or 4-competitive with respect to the optimal offline algorithm that knows the entire sequence of application messages a priori. We evaluate the performance of the proposed algorithm and the accuracy of our analysis in a range of realistic scenarios using both model-driven simulations and real experiments on an IoT testbed. Our results show that, i) depending on application requirements, energy savings ranging from zero to about 100% can be achieved using our algorithm, and ii) ignoring DRX could significantly overestimate or underestimate energy consumption.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".