Accelerating the Delivery of Data Services Over Uncertain Mobile Crowdsensing Networks
Bibliographic record
Abstract
Efficient exchange and processing of big data in wireless mobile crowdsensing (MCS) networks require responsive data service provisioning. Traditional onsite spot trading of resources, which relies on real-time network conditions, can facilitate data sharing but often suffers from prohibitive delays and trading failures due to the need for timely analysis of dynamic network environments. These limitations motivate us to investigate an integrated forward and spot trading mechanism (iFAST), which is a stage-wise data sharing protocol designed for uncertain MCS ecosystems. In iFAST, sellers (i.e., mobile devices) can offer long-term or temporary data services to buyers (i.e., sensing tasks). Specifically, iFAST enables the signing of long-term contracts ahead of future transactions through a forward trading mode, leveraging historical network and market statistics. It also promotes the notion of overbooking. Additionally, it allows buyers with unsatisfactory data quality to recruit temporary sellers through a spot trading mode based on current network/market conditions. We analyze the crucial components of iFAST and provide a case study to demonstrate its performance. We also summarize insights for next-generation wireless sensing and communication.
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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 | 0.001 | 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 | 0.001 | 0.001 |
| Open science | 0.005 | 0.002 |
| 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".