Delta compression optimisation for UAV‐enabled mobile edge caching
Bibliographic record
Abstract
Unmanned aerial vehicles (UAVs)‐based sensor network is an effective mechanism for recognising and tracking of manoeuvring targets as well as expanding the monitoring coverage in the battlefield. However, there exists redundancy among the spectrum data collected by a UAV monitor within a data collection period, which may waste storage space and reduce the speed of data uploaded to the control centre. The authors assume that each UAV is equipped with an edge computing server and propose a delta compression method, which can save cache space and transfer time. First, they present a cost model and evaluation model for delta compression of the COPY/ADD class. Then an optimisation problem is formulated aiming to obtain the optimal delta encoding. Additionally, a maximal total length of copied fragments (MTLC) algorithm is proposed to find more mutually separated L ‐grams common fragments between the data collected at two adjacent moments. Theoretical analysis proves that the MTLC algorithm can generate a good delta encoding with the maximum total length and then the minimum total number of the COPYs. Moreover, numerical results show that MTLC has better performance in constructing a good delta encoding than the simple greedy and hash suffix array delta algorithms.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".