Joint Annotator-and-Spectrum Allocation in Wireless Networks for Crowd Labeling
Bibliographic record
Abstract
The massive sensing data generated by Internet-of-Things will provide fuel for ubiquitous artificial intelligence (AI), automating the operations of our society ranging from transportation to healthcare. The implementation of ubiquitous AI, however, entails labelling of an enormous amount of data prior to the training of AI models via supervised learning. To tackle this challenge, we explore a new direction called wireless crowd labelling, which involves downloading data to many imperfect mobile annotators for repetition labelling with an aim of exploiting multicasting in wireless networks. In this cross-disciplinary area, the rate-distortion theory and the principle of repetition labelling for accuracy improvement together give rise to a new tradeoff between radio-and-annotator resources under a constraint on labelling accuracy. Building on the tradeoff and aiming at maximizing the labelling throughput, this work focuses on the joint optimization of encoding rate, annotator clustering, and sub-channel allocation, which results in an NP-hard integer programming problem. To devise an efficient solution approach, we establish an optimal sequential annotator-clustering scheme based on the order of decreasing signal-to-noise ratios, thereby allowing the optimal solution to be found by an efficient tree search. This solution can be further simplified when the channels are symmetric. Alternatively, the optimization problem can be recognized as a knapsack problem, which can be efficiently solved in pseudo-polynomial time by means of dynamic programming. In addition, the optimal polices are derived for the annotator constrained and spectrum constrained cases. Last, simulation results are presented to demonstrate the significant throughput gains based on the optimal solution compared with decoupled allocation of the two types of resources.
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 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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".